Podcasts about gpt

Claude's Fable 5 just got yanked, and the story why keeps shifting by the hour. A contested jailbreak, an export-control, crackdown, and a lot of fingers pointing. This week on AI For Humans, Anthropic's Claude Fable 5 is still unavailable and the explanations keep changing. We dig into the contested jailbreak report, the export-control directive that pulled it, and the reporting that Amazon raised concerns before the crackdown. Then we get into why this matters far beyond one model: what happens when the government steps into the AI world, why Fable 5 was such a leap, and what it signals for whatever comes next. Plus, Epic's game designers are using AI tools alongside artists and the internet is furious, Disney Imagineering is testing Adobe Firefly in the parks, ChatGPT's market share slips under 50 percent for the first time, a fake Mistral model called Le Chaton Fat takes over the internet, and PJ Accetturo breaks down exactly how he made his viral AI short film with prompts. THE BEST AI WE EVER USED IS BEHIND BARS. AND NOW WE WAIT. SHOW LINKS Original Anthropic statement on Fable and Mythos access https://www.anthropic.com/news/fable-mythos-access Full timeline of the Anthropic, Amazon, and White House story https://www.axios.com/2026/06/13/anthropic-amazon-white-house Amazon CEO reportedly raised Anthropic model concerns before the government crackdown https://techcrunch.com/2026/06/13/amazon-ceo-reportedly-raised-anthropic-model-concerns-before-government-crackdown/ Simon Willison on the contested Fable jailbreak report https://x.com/simonw/status/2066722034491789720 ChatGPT market share slips below 50 percent for the first time https://techcrunch.com/2026/06/16/chatgpts-market-share-slips-below-50-for-first-time/ GPT-5.6 next week? Polymarket odds https://x.com/Polymarket/status/2066644087340495081 Possible new ChatGPT voice mode leak https://x.com/testingcatalog/status/2066919098236146167 Space X Buys Cursor https://www.cnbc.com/2026/06/16/spacex-spcx-cursor-acquisition-ipo.html Epic explores using NanoBanana and GPT-Image-2 in workflows with humans https://x.com/UnrealEngine/status/2066686216779509850 SEGA's Crazy Taxi AI statement https://x.com/SEGAInforment/status/2063990392085766622 PJ Accetturo breaks down how he made his three minute short film with prompts https://x.com/PJaccetturo/status/2066582776934289438 Le Chaton Fat, the fake Mistral model that took over the internet https://x.com/AlexanderKnigge/status/2066267845546442762  

A pronounced infrastructure dependence on third-party AI models has emerged across the MSP ecosystem, largely due to the rapid adoption and integration of AI-powered features within vendor products. This structural shift is increasingly opaque, as providers are sold features rather than transparent access to underlying models, leaving MSPs exposed to changes in technologies and policies enacted upstream by vendors or regulators. The episode highlights how this dependency extends to delivery teams and end clients, with operational continuity tightly linked to decisions and actions outside the MSP's direct control. The most consequential development referenced is Anthropic's release and rapid withdrawal of its Fable 5 AI model following a directive from the U.S. Commerce Department, which ordered a cutoff of model access to foreign nationals within 72 hours of public launch. According to published benchmarks, Fable 5 surpassed GPT 5.5 in performance, but the government-mandated suspension exposed how quickly model access can be rescinded. The policy move immediately impacted any MSP or client with offshore or nearshore staff relying on AI features invisibly powered by that model. Further supporting the central theme, companies such as PAX8, Enforcer, and CloudRadio are embedding AI capabilities into platforms used by MSPs to manage Microsoft 365 environments, automate ticketing, and support scalable client operations. In parallel, vendors like Proofpoint are integrating compliance solutions directly with AI model APIs, further entwining risk management tools with the same core AI infrastructures. A Netrio survey cited in the episode found that while 82% of mid-market IT leaders have AI in production, only 26% report organization-wide governance, highlighting an accountability and visibility gap. Operationally, MSPs face heightened contract and vendor risk. Most lack an accurate inventory of which AI models underpin their services and how rapidly these dependencies can be affected by regulatory directives or vendor shifts. The discussion underscores the need for explicit procurement protocols, delivery mapping, and outage runbooks that account for opaque model dependencies. As clients seek greater transparency and contractual assurances regarding model use and continuity, MSPs who anticipate and document these dependencies may be positioned to reduce exposure and establish clearer accountability. 00:00 Switched Off  03:19 Painted Over 05:20 Govern or Absorb 08:41 Why Do We Care?  Supported by: Pax8 Sign up for the SMB Online Conference: www.smbonlineconference.com

Terry has been REVITALISED after a gig in the rain, Andy talks about his abusive relationship with chat GPT while Terry swears off all new tech! We also have an update to the Spidey & Friends pop punk saga, and Terry hits Andy with a NEW pop punk kids show banger!!Enjoy!

Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ https://youtu.be/j0TuosYDQe4?si=7mzUwBe4PrQ-eB2E In this insightful session from the Ultimate Partner Live event in Bellevue, Washington, Vince Menzione sits down with Stephen Boyle, Corporate Vice President for Enterprise Partners at Microsoft, to pull back the curtain on the tectonic shifts redefining the tech ecosystem. Boyle details Microsoft's massive organizational pivot into enterprise and SME/channel divisions , explaining how artificial intelligence acts as the foundational thread unifying systems integrators, software vendors, and digital natives. Moving past market noise surrounding competing foundational models , he highlights Microsoft's strategy to become the ultimate “platform of platforms” by prioritizing user choice, security, and trust. Emphasizing a shift away from infrastructure technicalities and toward practical business outcomes , Boyle delivers an urgent mandate for partners to scale technical talent, eliminate traditional operational silos, and brace for the incoming consumption-driven, agent-based future of enterprise computing. Key Takeaways Microsoft has restructured its global sales divisions into distinct Enterprise and SME/Channel organizations to better target its massive total addressable markets. Artificial intelligence is fundamentally altering the partner ecosystem by dismantling traditional software and systems integrator silos to build interconnected, multi-party solutions. Rather than forcing alignment to a singular model, Microsoft aims to be the definitive platform of platforms by offering extensive choice across over 1,100 language models. The enterprise landscape is rapidly moving past experimental AI pilot phases and entering production setups completely focused on transforming core business outcomes. Tomorrow's service organizations are aggressively evolving into software-minded operations that deploy repeatable, highly specialized internal autonomous agents. Managing tokens and monitoring usage metrics represents the emerging operational baseline for balancing efficiency against the scaling expenses of large language models. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags AI frontier, platform of platforms, enterprise partners, global systems integrators, digital natives, language models, token consumption, agent sprawl, citizen developers, shadow IT, business outcomes, technical enablement, marketplace growth, hyper-scalers, processing fluency, sovereign AI, industry ecosystems, data governance. Transcript [00:00:00] Stephen Boyle: This is the biggest, most transformative, iterative change in technology we’ve ever seen, where, if you wanna call it a paradigm shift or whatever word comes after paradigm shift. [00:00:12] Vince Menzione: We just came back from Ultimate Partner live in Bellevue, Washington, where we hosted incredible leaders for two amazing days. Come join us for this next session where we explore the tectonic shifts we’ve all been seeing. Uh, I am thrilled to invite our next guest up on stage. I’ve known this gentleman for several years back in my days at Microsoft, and, um, we’ve been friends, actually Microsoft, and then we both went and did different things, came he’s come back to Microsoft in a big way. [00:00:46] Vince Menzione: Uh, Steven Boyle, for those of you don’t know, is recently a named the C. We will talk about it in a second, but I, I need to announce you properly. Is the corporate vice president, which by the way in Microsoft is a big deal for enterprise partners. He and Nicole De and I would say are the two Microsoft leaders in the organization. [00:01:06] Vince Menzione: Nicole is the channel chief. Steven has a, a big remit and we’ll talk about that up on stage. But I’m just so delightful for his support and for making the time in a very busy week at Microsoft ’cause this is CEO summit this week to make some time to come with us and be on stage with me. Please welcome my good friend Steven Boyle. [00:01:29] Vince Menzione: Good to see you, sir. To see. So I’m gonna put you on this side. [00:01:33] Stephen Boyle: Okay. [00:01:35] Vince Menzione: The hot seat. So I’m gonna, I, I didn’t do a justice and I, I wanted you to explain your role. I, I think I know, but I think for the, for the people in the room, uh, talk to us what Enterprise Partners means at Microsoft and what that role remit and remit looks like. [00:01:50] Stephen Boyle: Um, CVPs may or may not be important, but one thing they don’t do is get invites to the CEO summit. So I’m super pleased to be here with you guys. No, no, it’s totally cool. It’s totally cool if that phone rings. No, I’m kidding. Doesn’t. So what does it mean? So I’d like quickly, um. January last year, uh, we split the sales organization into enterprise and small to medium enterprise and channel. [00:02:15] Stephen Boyle: You guys probably familiar with that? Nicole is the, uh, chief partner officer lives in the SMA and C world and drives the channel, um, drives our marketplace business and, and a lot of other things. Um, for that 60 billion, um, you know, total addressable market that we have. Down there in SME and C. Um, at the same time, we established enterprise partner as part of Nick Parker’s overall organization. [00:02:40] Stephen Boyle: Um, but for most of 2025 we ran it as global systems integrators and advisories, ISVs and digital natives. So three separate footprints all focused entirely on, on, on enterprise. Um, in December, January, we talked about establishing an enterprise partner leader that would. You know, aggregate all of this stuff. [00:03:00] Stephen Boyle: Um, I was fortunate to come through, um, some frankly, pretty hairy, uh, experiences, I bet with some of our senior leaders. Um, I, I’ve loved to [00:03:08] Vince Menzione: been in the room for that [00:03:09] Stephen Boyle: questions like, why Steven Boyle and things like that, right? And really have to dig deep to, uh, to justify. Anyway, uh, I’m blessed and honored, uh, to run that entire portfolio of partners, uh, for the entirety of the enterprise partner world, which now from a chief revenue officer perspective, belongs to Deb. [00:03:25] Stephen Boyle: Deb Co. So Deb is the enterprise leader for all of our sales that we do into that space. Awesome. Um, I have three regional leaders, Nina Harding here in the United States, Ehab Ra in in Europe, and Heather Gordon in Asia that mirror and replicate and flow down the things that we decide to do from a strategy perspective for the, uh, for the core. [00:03:45] Vince Menzione: And we love Nina. She’s been, she was at our last event, [00:03:47] Stephen Boyle: super, super lady. And, uh, you know, the US is still 50% of our overall business. [00:03:53] Vince Menzione: Yeah. [00:03:53] Stephen Boyle: Too big to fabric. Every time I talk to Nina, I’m like, Nina, you’re too big to fail. We can’t cover you anywhere else. So you know, you’ve gotta be successful here in the Americas. [00:04:01] Vince Menzione: So I think just for breaking it up, I, ’cause I do want to like, it’ll lead to the next question, right? So you have the global systems integrators, all these systems integrators. Essentially you have all of the software companies we used to call ISVs, we now call SDCs or software development corporations. [00:04:17] Vince Menzione: And then you also have the AI stack, I’ll call it. Right? So under Jason Grafe. Yeah. Many, many might know. Jason’s been a guest on the podcast and was Satya’s chief of staff at one time, eight years. Eight years. Wow. I didn’t realize there was that many. [00:04:31] Stephen Boyle: Carry carried a lot of bags for Satya over the years. [00:04:34] Vince Menzione: Unbelievable. Well, let’s, I mean, so AI is an important component, right? And you saw Jay’s, Jay talking, just talking about AI and all these things. I would love to start here, right? Because, uh, you’re, you’re, I wanna get your perspective as Microsoft, your perspective as Microsoft on the biggest shifts you’re seeing in defining this we’ll call AI Frontier. [00:04:54] Vince Menzione: We’re seeing right now, how should partners translate that into how they position and go to market externally? How, how do we need to think about this time? [00:05:02] Stephen Boyle: Yeah, that is, uh, that is a huge question and I’m not sure we’ve got enough time to go into the, into all of the detail. Um, so let me sort of up level it a little bit for you. [00:05:10] Stephen Boyle: And I think, look, the move that we meet at made a couple of months ago and pulling together those three aspects. Nicole had already done it in SME and C. Right. One partner organization across the world with a very common set of goals. We were working closely together, Sandy Gupta, on ISV, Jason on ai, and myself on on si. [00:05:29] Stephen Boyle: But we were still working closely together across silos. So the opportunity for me, 60 days into this role is AI just allows you to wire the partner ecosystem together differently. Right? And even if you look at how we’re going to market an AI today, um. You know, with, with, with chat GPT, with Claude, with Anthropic, um, I think there’s something like 1100 different, you know, language models on Microsoft today. [00:05:55] Stephen Boyle: So the way I think about AI is we are absolutely gonna be the ultimate platform of platforms. Yeah, choice is incredibly important. Um. It’s, it’s, you know, turn the clock back 12 months, everybody was chat gpt five point x, you know, and then six months ago it was Gemini and now it seems to be clawed. And honestly I don’t know what it’s gonna be next quarter. [00:06:15] Stephen Boyle: So the only thing I can do is offer you choice. [00:06:18] Vince Menzione: Yeah. [00:06:18] Stephen Boyle: And from a partner perspective, I think that minimizes or reduces the risk that you have betting on the Microsoft platform because you can go in a multitude of different directions. I know we’re not in Europe, but if you were in Europe and you were worried about G-G-D-P-R and Jay mentioned sovereignty, you’d probably be like lining up really closely to Misra. [00:06:37] Stephen Boyle: Yeah. And a bunch of other Europe, European partners. So wherever you are in the globe, I wanna be that platform choice. Um, and we will lead with our own first party solutions. I hope they’re not coming for me. Um. I parked safely in the hotel. It can’t be me. Um, but you weren’t vibe coding in the room. Um, but you know, wherever you are in the world, in whichever industry you are in, um, it is our intent to, to offer that platform of platforms and to give the broadest set of partners the opportunity to engage with us. [00:07:07] Vince Menzione: I think that’s really important because I, I have found, especially in the last month or two, people are, it’s almost like a knee jerk. Don’t you feel like people don’t know what to do? There’s been so much noise in the press and the media and, and the markets around open AI and anthropic especially. Where do I go? [00:07:26] Vince Menzione: Seems to be like when I, when I sit, I watch everybody in the room here. I think they’re, they’ve all been thinking that as well. So you can, [00:07:31] Stephen Boyle: there’s a, a little bit of a deer in the headlights moment. Yes. And even I like, I get that. Yeah. Um, you know, I saw, uh, Jay slides. Jay, love the presentation. Love the slides, man. [00:07:40] Stephen Boyle: I’m gonna steal several of them. Um, we’ll talk about that later. We, we [00:07:43] Vince Menzione: have the deck, [00:07:45] Stephen Boyle: but, but in all seriousness, you know, this, this is like. It’s a new paradigm. I will date myself a little bit. Some of you might heard me say this. I sold many computers in the 1980s. Mini computers. Some of you in the room are going, what’s a mini computer? [00:07:59] Stephen Boyle: Um, I sold client server for Sun Microsystems in the nineties. I sold an awful lot of Oracle databases in the Auts, I think they’re called, and I’ve done two stints with Microsoft. This is the biggest, most transformative. Iterative change in technology we’ve ever seen. What, if you wanna call it a paradigm shift or whatever word comes after paradigm shift. [00:08:18] Stephen Boyle: Um, and we are building intelligent systems at scale faster than we’ve ever seen. Scalable, mission critical solutions being implemented today inside of Microsoft and with our most important customers. So, and we can’t do it without partners, right? There is absolutely nothing we can do in this industry. I will, I will put the, you know, the elephant in the room out there. [00:08:40] Stephen Boyle: Our ISD organization has between five and 7,000 people. Our forward deployed engineering organization is about a thousand people. [00:08:47] Vince Menzione: Yeah. [00:08:48] Stephen Boyle: So when you look at the scale of the total addressable market that Jay just talked about. We are gonna service directly like this much [00:08:55] Vince Menzione: used to be 5%. Was it even, is it even that high? [00:08:58] Stephen Boyle: I doubt it’s, I doubt it’s even that. And the billions of dollars that we spend every year helping our customers transform to what we’re now calling frontier firms is gonna be, have to be driven with every single person in this room in some way, shape, or form. Judson is not asking Marla to significantly increase ISD. [00:09:15] Stephen Boyle: Not asking John to significantly increase FDE, although we probably will hire in that area just because of the, the newness and the, you know, bright shiny object that everybody’s like, oh, FDE, I’ve gotta have those. We’ve got a thousand already today that have been around in John’s organization for 10 plus years doing the things that we are doing today. [00:09:32] Stephen Boyle: But we are gonna build out that muscle. But the real way we’re gonna build out that muscle is with all of you in this room. That’s like categorical. That is my like, probably number one goal for the next one to three years is make sure that, that story that Jay just told about Microsoft not being involved in AstraZeneca. [00:09:48] Stephen Boyle: I probably won’t tell Judson that Jay, but I love the story. Um, like if you could all do that for me, like win, um, that is so, you know, from our worldwide learning, through our skilling enablement through our cloud solution architects that I personally own. We are pivoting aggressively towards making sure that the partners understand our platforms better than any other job, number one for me right now, if you don’t understand what I’m selling, like I’m kind of dead in the water obviously. [00:10:15] Stephen Boyle: Well, [00:10:15] Vince Menzione: I was gonna ask you why now? Why Microsoft? Why now? Right? Because there is a lot of noise. You know, Google just announced, you all announced your results on the same day, which was astounding. That was freaky, wasn’t it? It was. It was the first time. And the, the total commitment, customer commitment is over a trillion dollars now, I think 1.2 trillion is what I counted up. [00:10:33] Stephen Boyle: Yeah. [00:10:34] Vince Menzione: But it’s saying a lot about like, what do I do now, like as these partners in the room. Um, how, I think you kind of already, and you’ve talked about this, about differentiating where Microsoft is, I think J Slide does a lot of justice there. It says how, uh, Microsoft Partners came into the room, surrounded the customer. [00:10:52] Vince Menzione: It feels like Microsoft has always leaned in big time on partners. Uh, more so I would say than any other organization out there. What would [00:10:59] Stephen Boyle: you say Joe Roses, my chief of staff, business manager and so many other things was telling me last night that, you know, we used to say 500,000 partners. [00:11:05] Vince Menzione: Yeah, [00:11:06] Stephen Boyle: it’s a, it’s a significantly higher number than that as well. [00:11:09] Stephen Boyle: So there’s an element of, you know, back to the deer in the headlights, which partners are, are more important. One of my other phrases that I say on a regular basis, the winners and losers are yet to be decided in this next wave. Like, I want all of us to on the right side of that argument. Right? But, but it’s gonna be a challenge and, and companies are going through shifts. [00:11:28] Stephen Boyle: You know, Accenture, maybe, possibly doesn’t need 750,000 employees in the not too distant future. Maybe TCS at 600,000 doesn’t need 600,000 human employees. So we’re going through this dramatic shift of, you know, what’s the right balance going forward. What I would say about Microsoft is notwithstanding the fact that we’ve figured this out for 51 years, which is a little bit mind blowing, um, that you know, all the way back in the seventies we’ve gone through so many iterative changes. [00:11:56] Stephen Boyle: People have questioned just like they’ve questions. A lot of other technology companies, are you gonna be around for the long haul? I think we’ve proven time and time again, and I love Jay’s story. I’ve used that myself about how many companies disappear on a, on a decade to decade, you know, business. 10 years ago I had the opportunity to listen to Craig Clayton Christensen, who’s sadly no longer with us. [00:12:15] Stephen Boyle: Yeah. But you know, the books that he wrote and the story that he told to Microsoft 2014, we were nowhere in cloud. [00:12:21] Vince Menzione: Yeah. [00:12:22] Stephen Boyle: AWS was so far ahead of us, it was crazy. And he came in and he’s like. You know what? You guys need to be successful. You need to figure out how to cross this chasm again, and we’ve done it time and time again. [00:12:32] Stephen Boyle: You can go back. You know, Microsoft used to be known as a fast follower in ai. I don’t think we’re a fast follower. I think we’re right up there. We’re right at the front, but that race is still being run and the winners are losers are yet to be decided. [00:12:44] Vince Menzione: I was in that room with Clayton Christensen with you, by the way. [00:12:46] Vince Menzione: I remember, I remember that. That was at a Prism conference. [00:12:49] Stephen Boyle: Yeah. Yeah. [00:12:50] Vince Menzione: You men, you touched on this with the GSIs a little bit. How do you see the roles evolving? You know, we, we, we bucketed all, we’ve always been. Fantastic about bucketing ISVs or SDCs and sis and digital natives. Yeah. How does it, how does that all come together? [00:13:06] Vince Menzione: Does it come together any differently in this new AI platform era, or is it the same? [00:13:11] Stephen Boyle: I look, I, I’ve said this for a long time, like if you go into AstraZeneca, the six plus, you know, frontline partners, there’s probably a whole board of second, third tier that, that we don’t know about doing, you know, things across the AstraZeneca group. [00:13:25] Stephen Boyle: It takes several villages and sometimes a small town, especially in my world, in the enterprise world, strategic five hundreds. Yeah. Um, you know, we, we ran some reports a few years ago and it is shocking how many global systems integrators have a footprint in Shell or Exxon or, you know, bank of America or whatever else. [00:13:44] Stephen Boyle: So I’ve always believed that partner to partner is critical. Yeah. I think it became even more critical in the, in the AI world, and I’ll take my new friends at Anthropic. So I went to the first Anthropic partner Summit. Some of you might have been down there in, in San Diego, um, just a couple of months ago. [00:13:59] Stephen Boyle: Same partners, same people from the same partners. In the room, you know, talking about what they’re gonna do together with Anthropic. Um, and I’m looking out across this audience going, okay, well I know him and I know her and I know those guys, and like, I need to figure out how I’m gonna weave this together. [00:14:14] Stephen Boyle: So it’s not just an Accenture and Anthropic or an NTT data and anthropic, but it’s an NTT data plus anthropic plus Microsoft. Story going forward. And then who’s best at delivering those services capabilities? So it’s it at every juncture that I see in the, in the partner community, and this is the, the reason why I argued vehemently with Nick, that it has to be one organization I’m gonna create maybe given a little bit away. [00:14:40] Stephen Boyle: So if you’re recording, stop now. Um, I’m gonna create an enablement organization that is partner agnostic. I don’t necessarily care. I do care about the digital natives, but I don’t care about how I train them. Right. What I’m more important of is how do I train the digital natives in what the sis are doing, and how do I train the sis and what the ISVs Plus digital Natives are doing. [00:15:01] Vince Menzione: Yeah. [00:15:01] Stephen Boyle: That is my, that’s my game plan. If I fail there, then I think we fail to raise the bar and be differentiated in an AI world, and I’m not set up like that today. [00:15:12] Vince Menzione: I wanna, I wanna ask you, uh, uh, because I was looking at Jay’s slide and the, the managed piece is. And we have a lot of managed service providers in this room today. [00:15:20] Vince Menzione: A lot of them, by the way, come from the old school of managed services. The managed piece seems to be like, if I’m doing something today with ai, we’re gonna talk about security next, uh, up on stage here. It seems like there’s a new set of skills or a different approach to the customer, don’t you? Don’t you agree? [00:15:37] Stephen Boyle: I I [00:15:37] Vince Menzione: think you need to keep your hands on the steering wheel at all [00:15:39] Stephen Boyle: times. I think what it boils down to is you can’t do AI unless you do certain other things. [00:15:44] Vince Menzione: Yeah. [00:15:44] Stephen Boyle: Right. You could be a modern work specialist and you could make a lot of money being a modern work specialist, or you could be a, a dynamic specialist. [00:15:52] Stephen Boyle: We just held our, uh, inner A in a circle conference last last week, which I was disappointed to miss for the first time in a few years. Those, those days are, are, are fast becoming over. [00:16:03] Vince Menzione: Yeah. [00:16:04] Stephen Boyle: Um, why? Because everything that I’ve just said is tied together by ai. Yes. And in order to do good ai, you need good data. [00:16:12] Stephen Boyle: And in order to trust everything that you’re getting, as Judson talks about trust and intelligence, you need to wrap that in a really secure [00:16:19] Vince Menzione: Yes. [00:16:19] Stephen Boyle: You know, en en environment. Now we will do our best to provide levels of security into how we deliver ai. But that’s not the end of the game, right? You have to take it all, all the way to the edge. [00:16:30] Stephen Boyle: So that’s why a siloed partner or a singular commercial solution area partner in Microsoft’s terms, has got to transform its business. ’cause if you’re gonna do ai, you’ve gotta do those other things as well. [00:16:41] Vince Menzione: Agreed. I must see the model changing, and in fact, I see like bigger organizations becoming managed service providers in many respects. [00:16:48] Stephen Boyle: Yeah. Yeah. I mean, look, there’s still, there’s still a role for all the old terminology you mentioned is SV to sdc. Yeah. I’m like, I’m been around long enough. Look, it’s ANB still anv, it’s still an isv. Thank you. Independent software vendor. Um, and it’s, you know, where, where AI is allowing software to be, you know, frankly developed in a number of different places. [00:17:07] Stephen Boyle: We are all citizen developers. Um, you know, I was on a call with our internal leadership yesterday, um, and you guys might have heard this story ’cause I think it came out at Ignite. When we turn the agent 365, around and on ourselves. We found 130,000 agents running across Microsoft that had been developed and deployed internally with, I mean, you could call it shadow it. [00:17:28] Stephen Boyle: I guess that would be one phrase that you would use for it, but the reality is if you, if you haven’t got something to do your job today, you have the tools. To build it really, really fast. Um, and that, you know, that’s, that’s a great opportunity for people to be able to do their work, you know, in a better and in a different way. [00:17:45] Stephen Boyle: But it’s also a huge opportunity to make sure that data governance and security and all the other things that we need to deliver are there out of, out of the gate and out of the platform that we deliver. So security’s absolutely critical. Not saying that managed services won’t grow, um, at, at some level as well, but only if they transform into this multifaceted way. [00:18:04] Stephen Boyle: Yeah. Thinking [00:18:05] Vince Menzione: about, well, that’s what I was, I was gonna lead to here with innovating. It’s happening across, I mean, we’re talking about chips, we’re talking about foundational models, LLMs, we’re talking about applications, we’re talking about agents. How should we think about where to play and how to differentiate as partners in this room? [00:18:22] Stephen Boyle: I think. [00:18:25] Stephen Boyle: So look, I mean, one, one of the ways that Judson talks about it is I think silicon’s gonna change over time. Yes. NVIDIA’s definitely the 800 pound gorilla, maybe the 8,000 pound gorilla. Yeah. Uh, but you know, if you read the press, there’s, there’s things happening in, in different places as first party silicon, which we clearly are, are developing, um, in a quantum direction for sure. [00:18:45] Stephen Boyle: Um, there’s lots of different language models that haven’t even been launched on, on, on the marketplace yet, so. You know, Judson’s trying to uplevel our conversations. You’ll hear us talking about conversations more and more as we go into FY 27, um, that obviate all of those layers. Just like even when I was selling Sun Microsystems, it was about the business outcome and the business solution that we were solving for not necessarily the fastest piece of hardware or the best client service solution on, on the market. [00:19:17] Stephen Boyle: So I think what’s gonna happen over the next 12 to 24 months is we’ll have so many different models to choose from. We’ll have more silicon to choose from, but those won’t be the real buying decisions. The real buying decisions of what? How am I trying to transform my finance organization, my HR organization, and my supply chain? [00:19:36] Stephen Boyle: Because the underlying technology, Judson says commodity I, I guess I can go with that. It will be commoditized and we’ll really start to focus back on what the important things are. We’re moving a lot from pilot to production. You guys have probably seen that. The numbers that Jay just showed about how many. [00:19:52] Stephen Boyle: Projects are failing, is getting less and less because we’re getting smarter and smarter about what it takes to actually drive the business outcome. And I need all of us to be talking that same language. Yeah. Having conversations with head of HR about how we’re gonna transform human capital management in the, in the age of agents, if you like, like the underlying platform. [00:20:14] Stephen Boyle: It’s not, don’t worry about it. You wanna be on a secure platform. Don’t get me wrong. But at the same time, I don’t think we, we spent too much time worrying about that. [00:20:21] Vince Menzione: Yeah. We’re not, what you’re saying is we’re not spending enough time on outcomes. On the business outcomes. Right. And that’s where we need to focus. [00:20:27] Vince Menzione: We’re, we’re focusing on, I, I feel like we’re, it’s a signal to, to noise ratio that we’re living through right now. There’s too much noise. [00:20:33] Stephen Boyle: Yeah. [00:20:34] Vince Menzione: And we’re not focusing on the signal. I think that’s what you’re saying. [00:20:36] Stephen Boyle: I, it’s got to be, I mean, to be honest with you, it’s always been, you know, even when I sold what I would perceive, you know, sun in the nineties was a rockman ship to the stars and, you know, kind of sad what happened to that company. [00:20:47] Stephen Boyle: Um, but we, we were, we were fixated on, we had the best client server. But, but nobody was buying, you know, a piece of Sun hardware as a room heater, which is all it did, you know, like for the longest. But if you had SAP, if you had Cybase, if you had Bond, remember Bond, I mean all of those applications that drove the business outcomes, we’ve gotta get back to that kind of mentality. [00:21:09] Stephen Boyle: Yes. And worrying a little bit less about the underlying architecture. Yeah. It needs to be, it needs to be part of the conversation. ’cause it needs to deliver trust and security and intelligence and everything else. Then you need to rapidly move to what are you trying to achieve and how can we ensure the, the, the success of, of your business outcome. [00:21:27] Stephen Boyle: And look, I mean, Palantir pri you know, sort of came out and said, well, the way we do that is through forward deployed engineering. Um, and they stole the show. And, and, you know, they’re, they’re doing very well as a result of doing that. Uh, but if you go and talk to, um, Tom Siebel’s organization at C3 ai. [00:21:43] Stephen Boyle: They’ve had FDS for quite a while. You know, I told you about John Chuchu 10 years ago. John Chu, Chuck’s job was to go and get all the applications that we needed on the Microsoft phone. Remember that? [00:21:54] Vince Menzione: Yes. Um, [00:21:55] Stephen Boyle: you know, so we’ve pivoted John o over the years to doing what he’s doing now, which is to go sometimes in partnership with, with partners into the customer and say, what is it you’re trying to achieve? [00:22:05] Stephen Boyle: Let me show you how I can build that for you in three weeks or three months. That might have taken you three years. We literally just did a hackathon with one partner last, last, last week with, uh, with our ISE organization, the, the, the forward deployed, uh, group that John runs. Um, and one of the big customers said, I’ve just done in three days what would’ve taken me three months. [00:22:26] Stephen Boyle: Now he hasn’t productized it and rolled it out and blah, blah, blah. But the reality is that is how fast things are changing. And this was not a small company. This was a very, very large oil company, and they were like blown away by how much we can achieve. We’ve gotta do that at scale. [00:22:41] Vince Menzione: Yeah. [00:22:42] Stephen Boyle: You know, we, we have a commitment to scale our FDE community through partnerships to touch all of the S 500 in a very personalized way. [00:22:51] Stephen Boyle: And then, you know, at a slightly, you know, lower ratios down through the, through the majors and into, into Nicole’s SME and C world as well. [00:22:59] Vince Menzione: Jay talks about the decade of the ecosystem. He coined that term back, back on a podcast way back in nine, in, uh, in 2020. Microsoft has been at the, for, we used to call partner to partner back, back in the day. [00:23:10] Vince Menzione: Mm-hmm. Do you remember those days? How do you think about this ecosystem evolving and what steps are you taking to help bring these organizations together? Because I, I, again, we look at the seven seats or 6.3 seats at the table. The customer has the power now that they didn’t have before. ’cause they have the commitment with like with Microsoft and they can buy off of the marketplace and pull together multiple organizations to go, go do that. [00:23:34] Vince Menzione: How do you think about helping to orchestrate that as the leader of the enterprise partner business? [00:23:39] Stephen Boyle: So I’ll start with a really big example, and I’ll try and sort of scale it down a little bit. But my friends at Accenture, with the Accenture, Microsoft Business Group, we spend an awful lot of time, you know, in, in each other’s pockets, in each other’s deals. [00:23:51] Stephen Boyle: We know everything that’s going on in the Accenture, Microsoft Business Group. And a couple of weeks, or maybe a month or so ago, I was told that the Microsoft Business Group is now larger than the SAP Business group. It probably flip flops. [00:24:03] Vince Menzione: Yeah, [00:24:04] Stephen Boyle: it won’t be too long before the Anthropic Business Group is bigger than both of those. [00:24:08] Stephen Boyle: So what I need my Microsoft team to do is to not spend all of their lives in the. A MBG, the Azure, the Accenture, Microsoft Business group, but to go make friends in the Anthropic Accenture Business group and frankly still to make friends in the SAP business group and maybe in the Oracle Business Group and the list goes on. [00:24:27] Stephen Boyle: So at a macro 11, in the very largest accounts where we haven multiple practices, where we haven’t spent time before, I’m gonna. Push my people into uncomfortable zones and I’m gonna push them to go into those other areas and I’m gonna load them up with technical talent and cloud solution architects and ai, you know, forward deployed engineers. [00:24:45] Stephen Boyle: And I’m gonna force different people to talk together that haven’t talked together. So I can do that in TCS. I can do that, Capgemini, I can do that. Um, you know, in Europe with Capgemini and Misra is a classic example. Um, with the, with the Indian sis, Indian based sis, they’re all big enough where I know all the practices exist. [00:25:04] Stephen Boyle: I just need to do a better job of, of talking to them. Now, when you downsize that into, you know, into a, a company that doesn’t have all of that scale, this the same truth still holds. I need to talk to people who aren’t necessarily motivated every single day to do something with Microsoft. I need to talk to people who are motivated to do something with an AI partner or even a traditional SaaS partner. [00:25:27] Stephen Boyle: I noticed yesterday, actually no, this morning I got a notification that we just passed, um, a billion dollars in revenue on the marketplace with ServiceNow. [00:25:35] Vince Menzione: Nice. [00:25:36] Stephen Boyle: Um, and I think AWS announced the same thing, by the way this month as well. Um, so thank you to the ServiceNow people. Yeah. Um, you know, that is that there’s a tremendous demonstration of how far we’ve come in marketplace. [00:25:48] Stephen Boyle: ’cause that’s another one where we trailed AWS quite significantly. But with the right partnerships. And driving the right motions, we can, you know, we can definitely catch up and we will continue to pass, uh, some of, some of the other hyperscalers in, in, in that way. So really the bottom line to your question is partner to partner is still real. [00:26:08] Vince Menzione: Yeah, [00:26:08] Stephen Boyle: how we do it and what we use to tie things together. And I know that compensation drives behavior and we’re not gonna get into a compensation about like how we get compensated and everything else, but the reality is I’ve gotta break down those barriers and those silos and I’ve gotta deliver real meaningful enablement and practice development so that, so that the people who sit in the Anthropic business group and the people who sit in the Microsoft Business Group are spending as much time together as they are with me. [00:26:34] Stephen Boyle: That makes sense. Simply put, that’s what I, I need to achieve at scale rapidly. [00:26:40] Vince Menzione: So to, we’re getting close to time here, but as you look forward, what would define the most successful partnerships in this ecosystem? Is it, is it what you described, the opening up the aperture or for the, for the leaders in the room here today, what should they go do better and differently? [00:26:58] Stephen Boyle: Um, so obviously we’re closing out this fiscal, we’ve got Microsoft start and Microsoft start for partners coming up in July. Um, I mentioned the fact that we’re, we’re driving. Cu customer engagement through the lens of conversations and how do we achieve business outcomes? I would encourage you to, to gravitate, if you like, above the commercial solution areas where you might have understood, this is how I interact with Microsoft today. [00:27:23] Stephen Boyle: Um, and abstract it up to that AI layer. You know, think about trust, think about intelligence, think about business outcomes, and how do I potentially weave together a story? If I’m in the dynamic space, how do I get better in data? If I’m in the data space, how do I get better in. In that modern work environment, but really use AI as the overlay to, to help tie that together. [00:27:44] Stephen Boyle: That’s one thing. The second thing is if we’re not training you in the right direction, it’s stevenBoyle@microsoft.com. Let me know. Awesome. Um, we’ve got programmatic stuff, um, you know, and we’ve got high touch stuff as well. So I think this is, this is another time where Microsoft is gonna over pivot on all of the training and enablement that we need to do to make sure that you’re, you know, you’re grounded in our platform. [00:28:07] Stephen Boyle: Um, I think there’s a huge opportunity with this agenda future to become more of a software partner. You know, even the deepest services organizations are going to need agents, and the more successful ones will be the ones that can turn on those agents in a repeatable way. So. Our agents, the new SaaS. I’m not exactly saying that, but I think that the agen future is one where even the more services oriented companies will, will have teams of agents that they’re deploying. [00:28:35] Stephen Boyle: In fact, I had a very, very large systems integrator, um, in, in the EBC just about a month ago, three weeks ago. Um, and I was sat next to their head of consulting and he showed me what he called his God dashboard. Uh, and right in the middle of his God dashboard there are like 450 accounts. All of whom I recognized, ’cause they were all in the enterprise, right in the middle of his dashboard was, how many tokens am I spending? [00:29:00] Vince Menzione: Yeah. [00:29:01] Stephen Boyle: Like, not like what’s my daily runway? You know, not am I making a profit on that account or anything else like that is like, how many tokens have I consumed? Yeah. Because there is an awful lot of, that is the new juice, if you like. That’s, that’s driving the success. You can have the smartest people on the planet, but you’ve got to still arm them with all the best tools that are available out there. [00:29:22] Stephen Boyle: So it’s fascinating to listen to him, how he had gone through that thing of, you know, agent sprawl, how many are really working, how many are not working? How can we prove that? You can prove it through, you know, managing your tokens. There’s a new version of. Finops for tokens, for want of a better phrase, that’s gonna be critical for us all to understand. [00:29:40] Stephen Boyle: ’cause they’re not cheap, they’re not free, that’s for sure. And, and they might not be cheap if you’re not, if you’re not managing them and using them effectively. Yeah. So that’s the other thing that I would really get on top of. And, you know, we’re gonna make some announcements in the not too distant future about the consumption driven future. [00:29:56] Stephen Boyle: Um, that, that we will, that we will deliver with our first party and third party platforms going forward. So that’s another. Another critical thing [00:30:03] Vince Menzione: sounds like some exciting announcements. Pretty soon. [00:30:06] Stephen Boyle: Yeah, could look close. Quarter four, help me close. Quarter four. Yes. That’s priority number one, two, and three right now. [00:30:12] Stephen Boyle: Uh, but get ready for some, you know, for some new announcements in July. Um, look, the future is incredibly bright with Microsoft. It’s incredibly bright in the industry as a whole, right? I mean, let, let’s be honest, the, the growth targets that we will have for ne next year are astronomical, and we will not make them without the partner community that we have, without training and enabling the partner community that we need for tomorrow. [00:30:34] Stephen Boyle: So like, stay close, you know, stay engaged. Talk to your partner development managers, talk to the talk to field reps, talk to the accounts that that, that you are in, and stay as close as you possibly can to our emerging strategy. And, um, you know, look, I, I think if I had fivefold or tenfold the people I have today, I still wouldn’t be able to touch everybody that I would like to touch in the partner community. [00:30:58] Stephen Boyle: So I’ll apologize in advance. Um, but we’re gonna have some, you know, some really cool ways of learning. Um, and we’re gonna make sure that they’re available to the widest possible audience. [00:31:07] Vince Menzione: Well, we bring the practitioners and the experts in the room to help with that as well. Right? Yeah. Because you can’t always have a partner development manager tied to everybody in the room. [00:31:14] Stephen Boyle: I, I would do hackathons on AI every week with every partner and every part of the world, but I can’t. [00:31:19] Vince Menzione: Yeah, exactly. Well, so good to have you today. Thank you. So good to see you again. I don’t know what your schedule is like. I, we didn’t, we don’t have enough time for questions. [00:31:28] Stephen Boyle: That’s cool. [00:31:28] Vince Menzione: From the audience. [00:31:29] Stephen Boyle: I’m gonna stay around for a little [00:31:30] Vince Menzione: while this [00:31:30] Stephen Boyle: morning and I’m coming back [00:31:31] Vince Menzione: for cocktails. Alright, terrific. So. Stephen Boyle will be here for cocktail hour. Thank you. Four 30 and uh, I wanna thank you, sir. So good to have you. Thank you. Good to see you. Absolutely. [00:31:42] Stephen Boyle: So much. Absolutely. Hey, thanks everybody. [00:31:43] Stephen Boyle: Thanks for what you do today, and hopefully thank you for what you do tomorrow as well. [00:31:46] Vince Menzione: Thank you. An incredible leader. [00:31:49] Stephen Boyle: Don’t forget, ultimate [00:31:51] Vince Menzione: partner Alive is coming soon, June 18th at our executive breakfast in New York. I hope to see you there.Description The Future of Tech is Here. Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ I

Six months after their last roundup, Jacob sits down with Ari Morcos (Datology AI CEO, former Meta AI researcher) and Rob Toews (Radical Ventures partner, Forbes AI columnist) to take stock of an AI landscape that has shifted dramatically: coding agents crossing the long-time-horizon threshold has turned engineers into managers of agents, near-frontier open weight AI looks like it may be disappearing as Meta and the Chinese labs pull back, and Anthropic's restrictions on its newly released Fable model have its biggest supporters questioning whether safety framing is masking competitive positioning. The conversation runs through the full state of the lab wars, including Rob doubling down on his Sam Altman ouster prediction and the Bret Taylor succession theory, why Google's structural advantages remain intact despite falling behind on coding, what xAI's Cursor acquisition is really for, and Ari's claim that compute constraints could push labs to suspend their APIs entirely. The back half digs into the physical bottlenecks underneath it all, from atom and x-ray lithography startups challenging ASML to H100 prices reversing their decline, before closing with predictions: recursive self-improvement is closer than it was six months ago but slower than the takeoff narratives suggest, robotics is nearing its GPT-3 moment, and Anthropic's next chapter may be life sciences.   (0:00) Intro (1:40) Coding Agents Cross a Threshold (3:29) Is Open-Weight AI in Retreat? (7:37) Cost Crunch & Scaffolding (12:13) The "Apps Are Cooked" Debate (16:37) Sam Altman Under Scrutiny (19:44) Anthropic's Fable Backlash (23:24) How Big a Step Change Is Fable? (26:50) What's Going On at Google? (33:20) Could the APIs Go Away? (34:11) Breaking the Semiconductor Bottleneck (35:42) Beyond EUV: Atom & X-Ray Lithography (37:23) Implications of a Compute Shortage (40:20) Do Alt Chips Actually Help? (43:43) SpaceX, xAI & the Cursor Acquisition (48:50) How Close Are We to RSI? (52:21) Quickfire With your host: @jacobeffron - Managing Director at Redpoint

In this CEO Edition of the Events Demystified Podcast, the host interviews Johan Wadenholt, CEO of Voxo, about transforming live events from fleeting moments into measurable, reusable systems by turning stage conversations into structured, real-time outputs attendees and speakers can use immediately. Johan shares Voxo's origins in 2016 financial-services note-taking and compliance, early struggles with Nordic speech-to-text accuracy, partnerships to train models, and the shift to event reporting before and after GPT. They discuss solving FOMO in multi-track events, the forgetting curve, and why trust, consent, reliability, and humans-in-the-loop are essential to avoid hallucinations and protect speakers. They outline who benefits—attendees, organizers, sponsors, speakers, and content teams—plus analytics for leads and agenda decisions, AI's role in managing cognitive overload, and leadership lessons from scaling a startup amid rapid AI change.

Hey folks, Alex here, and welcome to a BIG MODEL week! We finally got Mythos (well almost)! Let me catch you up! This week started with WWDC26 from Apple, and Max Weinbach, who was in the room at Apple Park and actually has access to some of the new features including an all new SIRI AI, joined us to break down what could be the most used AI in the world very soon. At first I was skeptical, but he convinced me that the new Siri is actually good! Then, we saw the ultimate model drop: Anthropic finally shipped Mythos (X, my system card thread, benchmarks). Same weights, two names: Mythos 5 is the unrestricted version that only Project Glasswing partners get, Fable 5 is what the rest of us get, wrapped in the heaviest guardrails I've ever seen ship on a frontier model. It's state of the art on nearly every benchmarkThe model that was “too dangerous to release” is now... well, released, but with the heaviest guardrails we've seen. More on this later. Peter Gostev from Arena.ai joined us to break down the new model. Last but definitely not least, Google released a real-time translation model, that our friend Thor Schaeff from DeepMind demoed live, while we all spoke in different languages and it translated us in REAL TIME. It was really cool, definitely check that out. There's quite a few more things, like Loop Engineering Alpha, Swyx came by to talk about FrontierCode, OpenAI confirmed our suspicions that the anti-datacenter social media posts could be a concerted effort by groupds links to the Chinese government and much more. Let's dive in! ThursdAI - Let me catch you up, every week!

איזה ז'אנר מוזיקלי הכי הפתיע אתכם כשיצרתם שיר בסונו:https://itayverchik.co.il/suno-ai/אחת המהפכות הגדולות ביותר של תקופת הבינה המלאכותית היא היכולת לייצר מוזיקה ברמה אולפנית, גם אם מעולם לא ניגנתם על כלי נגינה או למדתם הפקה. מערכת Suno AI מאפשרת לכל אחד להקליד כמה משפטים ולקבל תוך שניות שיר מלא, עם זמר או זמרת, לחן מקורי, וכלים חיים בהתאמה אישית לכל ז'אנר שתבחרו.בסרטון הזה אני מראה לכם צעד אחר צעד איך להשתמש בממשק של Suno, איך לכתוב את הפרומפט (פקודה) המדויק ביותר כדי לקבל את הסגנון המוזיקלי שאתם מחפשים, ואיך להפוך טקסט פשוט ללהיט של ממש.מה נראה במדריךממשק רגיל מול מצב מתקדם (Custom Mode): ההבדל בין בקשה לשיר כללי לבין שליטה מלאה במילים, בסגנון ובכותרת של הטראק.כתיבת מילים אישית (Lyrics): איך להזין טקסט שכתבתם בעצמכם (או בעזרת צ'אט GPT) ולסדר אותו נכון עם בתים ופזמון כדי שהמערכת תשיר אותו בקצב הנכון.בחירת סגנון מוזיקלי (Style of Music): איך להשתמש במילות מפתח (Tags) כמו פופ אקוסטי, רוק כבד, או טראנס אלקטרוני כדי לכוון את הבינה המלאכותית לסאונד המדויק שאתם שומעים בראש.הארכת שירים (Extend): איך לקחת שיר שהסתיים באמצע הפזמון, או כזה שאתם רוצים להוסיף לו סולו מיוחד בסוף, ולהמשיך אותו בצורה חלקה מבלי לשבור את הקצב.הורדה ושימוש: איך לייצא את השיר כקובץ שמע (MP3) או כסרטון קצר (MP4), ומה חשוב לדעת על זכויות היוצרים בגרסה החינמית מול גרסת הפרו.יצירת מוזיקה בעזרת AI היא כלי פסיכי ליוצרי תוכן, בעלי עסקים שצריכים מוזיקת רקע מקורית לסרטונים, או סתם למי שרוצה להפתיע חבר עם שיר אישי.המדריך עזר לכם להפיק את הלהיט הראשון שלכם?אל תשכחו לעשות לייק לסרטון להירשם לערוץ וללחוץ על הפעמון כדי לקבל עדכונים על עוד מדריכי בינה מלאכותית, יצירת תוכן ושיווק דיגיטלי.

Anthropic's new Claude Fable 5 is both the best model in the world and potentially one of the most dangerous.

#thismorning | #Doctor #GPT? #AI Gets #Healthcare #Questions Right 76% of the Time | Amulya Yadav, Penn State University | #Tunein: broadcastretirementnetwork.com #Aging, #Finance, #Lifestyle, #Privacy, #Retirement, #wellness

Jeremy Packee and Emily Anderson break down April's biggest paid media updates, including Google's aggressive AI Max expansion across Search and Shopping campaigns, Microsoft launching AI Max for Search, and OpenAI officially entering the ad platform space with self-serve ChatGPT ads and CPC bidding. They also discuss Google's new AI-powered qualified call lead tracking, Meta opening AI connectors for advertisers, and the growing shift toward conversational and visual search experiences. The episode explores how AI-generated ad copy, automation-heavy campaign types, and intent-based targeting are changing the way advertisers think about performance media strategy. While these tools continue evolving rapidly, the hosts emphasize the importance of testing carefully and maintaining strong human oversight. Episode Highlights Biggest Shift Google officially replacing Dynamic Search Ads with AI Max marks another major step toward keywordless and AI-driven campaign management across Search and Shopping. Biggest Platform Signal OpenAI launching self-serve ChatGPT ads with CPC bidding signals that conversational AI platforms are rapidly becoming legitimate advertising channels. New Feature to Test Google's AI-powered qualified call lead tracking could provide advertisers with more meaningful phone call conversion data without relying entirely on third-party tools. Control Upgrade Google's new AI Brief controls for AI Max campaigns give advertisers more influence over messaging, audience direction, and search matching through natural language prompts. Creative Reality Check AI-generated ad copy and creative tools continue improving quickly, but Jeremy and Emily caution that brands still risk losing differentiation if everyone relies too heavily on the same automation systems. Other Platform Updates • Microsoft launched AI Max for Search campaigns • Google introduced real-time policy reviews for Responsive Search Ads • Reddit expanded Reminder Ads globally for all advertisers • TikTok added more Smart+ campaign controls and expanded Symphony AI creative tools • Demand Gen added view-through conversion optimization and Commerce Media Suite support • OpenAI released GPT-5.5 and ChatGPT Images 2.0 • Anthropic launched Claude Opus 4.7 and Claude Design • Meta expanded its AI business assistant and introduced Ads AI connectors in open beta • Google updated Ads data controls and added new experiment auto-apply settings • Microsoft added landing page reporting for Performance Max campaigns • eMarketer projects Meta could surpass Google in digital ad revenue by the end of 2026 Final Take AI is no longer just assisting campaign management, it's actively reshaping how advertising platforms operate. But as automation expands across search, creative, targeting, and reporting, the competitive advantage still comes from strategy, testing, and knowing when human judgment matters most.   Follow The Click Brief for fast, no-fluff performance marketing updates. Visit The Click Brief blog for more in-depth analysis and updates from April

Free Resource Mentioned in This Episode The AI Career Audit Coach The free tool Ann references throughout this episode. It is a custom GPT that asks you focused questions about your specific role, your practice area, and your goals, then builds you a personalized roadmap showing you exactly which tasks are at risk, which skills to develop now, and a simple one-hour-a-week plan to get there. Free. About 10 minutes. You will need a ChatGPT account to use it, but the free ChatGPT version works just fine. Get the AI Career Audit Coach here. Episode Summary Are you spending more time wondering if AI is going to come for your paralegal job than actually doing something about it? You are not alone, and this episode is going to change that. In Episode 179, Ann Pearson tackles the question every paralegal is thinking about but very few are addressing head-on. Which parts of your role are at risk of being absorbed by AI, and which skills are going to make you genuinely indispensable in the next chapter of this profession? Ann starts with the conversation that sparked this whole episode: her recent interview with Tony Castro, the Miami-based freelance paralegal whose courtroom-based business is one of the most AI-proof models in the industry right now. From there she gets real about why most freelance paralegal businesses are not the safety net people think they are, why the in-house paralegals who lean into this moment are the ones getting promoted and recruited, and three new paralegal job titles that did not exist three years ago. Then she hands you three strategies you can start working on this week. Loved This Episode? Share it with one paralegal in your network who needs to hear this conversation. This is the kind of episode that changes career trajectories, and you sharing it is what gets it into the right hands. And if you have not yet, take 10 minutes today and run your free AI Career Audit. Your future career will thank you.

Keith talks with data-driven investor Neal Bawa, the "mad scientist of multifamily," about why apartment values have dropped 20%–30% while single-family prices have stayed resilient.  They break down how interest rate shocks, the homeowner lock-in effect, and a wave of new multifamily supply are reshaping returns for today's investors.  Keith and Neal also dissect the build-to-rent model—who it really serves, how apartment oversupply is pressuring its rents, and why pending legislation could upend the space.  Neal closes with a specific, data-backed timeline for when multifamily rents and values may finally turn the corner, giving listeners a concrete roadmap instead of vague market guesses. Resources: Grocapitus Website - https://www.grocapitus.com Multifamily U's Free eBook: Location Magic - https://multifamilyu.com/lp/location-magic-ebook/ Multifamily U's Investor Club – https://multifamilyu.com/club Episode Page: GetRichEducation.com/609 For access to properties or free help with a GRE Investment Coach, start here: GREmarketplace.com GRE Free Investment Coaching: GREinvestmentcoach.com Get mortgage loans for investment property: RidgeLendingGroup.com or call 855-74-RIDGE  or e-mail: info@RidgeLendingGroup.com Invest with Freedom Family Investments.  For predictable 10-12% quarterly returns, visit FreedomFamilyInvestments.com/GRE or text  FAMILY to 66866  Unlock truly passive real estate income—visit flockhomes.com/GRE today to see if your properties qualify for a 721 exchange with Flock Homes. To get in the best physical, mental, and professional shape of your life, go to DanielThomasHind.com and apply for Daniel's intensive 1-on-1 coaching for burnt-out entrepreneurs and executives. Will you please leave a review for the show? I'd be grateful. Search "how to leave an Apple Podcasts review"  For advertising inquiries, visit: GetRichEducation.com/ad Best Financial Education: GetRichEducation.com Get our wealth-building newsletter free— GREletter.com  Our YouTube Channel: www.youtube.com/c/GetRichEducation Follow us on Instagram: @getricheducation Complete episode transcript:   Keith Weinhold  0:00   Keith, welcome to GRE. I'm your host, Keith Weinhold. The single-family real estate market is steady, but with apartment building values down 20 to 30% since 2022 when will the multifamily Armageddon end? We ask our qualified guest, and how will slowing birth rates in immigration affect real estate? And more today on Get Rich Education. You know, Mid South Home Buyers, that top Memphis turnkey provider. I learned that a secret weapon behind their explosive growth is more than just you buying their properties, it's an executive coach for nine years now, their CEO, Terry Kerr, and his COO, Pat Nix, have worked privately with a coach who I've now learned from too, and he doesn't market himself online anywhere. After 12 years behind the scenes, that coach is now making himself available exclusively for GRE listeners. His name is Daniel Thomas Hind. If you're a hard-charging business owner or investor who wants to get in the best shape of your life, physically, mentally, and professionally, you can fill out an application for a free consult. This is private one on one coaching for those willing to go to uncommon lengths to achieve uncommon results. Thanks to Daniel, we've all become better leaders, better operators, and better men. It started by showing up for ourselves. Now it's your turn. Go to Daniel Thomas hind.com H I N D, that's Daniel Thomas hind.com and sign up before Spotsville Flock homes helps multifamily owners exit the operator grind, whether it's your six plex or a 50 unit apartment, through a 721 exchange. This defers your capital gains tax. It's a strategy long used by institutions. Now you can swap tenants and toilets for passive income and zero management. Request your initial valuations. See if your property qualifies at flockhomes.com/gre That's F L O C K homes dot com slash G R E.   Neal Bawa  2:13   You're listening to the show that has created more financial freedom than nearly any show in the world. This is Get Rich Education.   Keith Weinhold  2:29   Welcome to GRE from Valencia, Spain to Valencia, California, and across 188 nations worldwide. America's favorite shaved mammal on a microphone is back with you for another wealth building week. I'm Keith Weinhold, and you're listening to Get Rich Education. The world's biggest problems are the world's biggest businesses. That's not a coincidence, and that's why we discuss housing here. And there's been a chronic shortage of affordable housing last month at a commencement speech, Harrison Ford, yes, the guy that played both Han Solo and Indiana Jones, talked about how a fulfilling life has both passion and purpose. Passion is what gets you out of bed in the morning, purpose is what helps you sleep at night, you and I. We can bring this mindset to our lifestyle, to the business we do, and to our investing. Treating tenants well is what helps real estate investors sleep well at night. While we're doing well, we can be doing good too. Multifamily syndicators keep failing, going out of business, and losing all of their investors' money due to mortgage rate resets. It just keeps happening. What this really means, that these groups that pooled together investor money to buy apartment buildings, largely that were set up in 2022 and earlier keep blowing up almost fully due to the fact that interest rates reset higher. Some of them had a fixed rate for five years. Well, rates spiked four years ago, and that's why a lot of them have yet to blow up, and these apartments have lost so much value that no one will refinance them, you know. Even if that apartment operator increased the net operating income over the years, even if rents went up, it doesn't matter. So, you still haven't heard the last of it. Do you remember a couple years ago, when a lot of people in the apartment space, they were saying just stay alive till 25 and that nonsense, like if you keep your head above water until 2025 oh well, then rates are certainly going to fall, and everyone's going to be okay. Well, 2025 is long gone.    Keith Weinhold  5:01   Mortgage rates haven't fallen in any significant way, so that survive until 25 thing or whatever mantra derivative people used that was a farce, like I've said on the show here for years. You cannot predict interest rates, so I didn't make the call that they were going to go up or down at all, because you can't predict them, but so many people said, oh, rates will fall substantially by now, no way, you just can't make that assumption, you've got to take history over hunches, and all of that, a lot of those multifamily deals 100% depended. depended on refinancing at favorable rates, and that's exactly why they failed. A surefire way to look foolish is to predict interest rates. We'll talk more about the multifamily Armageddon with today's guest. I also want to get into what's called the 21st century road to housing act, because that became one of the most hotly debated housing policy provisions this year. And what this is, is a Senate bill, and it would require certain large institutional investors that develop these bills to rent single family communities. It would force them to sell those homes to individual buyers within seven years. So, in other words, what a big firm could do is build a neighborhood of rental homes, lease them for up to seven years, but they couldn't hold on to them any longer than that. They couldn't hold them indefinitely as rentals, this bill is not aimed at you, the individual investor. It is aimed at big institutions, and what I mean by that is that's generally defined as owning 350 or more homes. That's what we're talking about here. Small landlords and mom and pop investors are not the target, it targets corporate portfolios, and this means groups whose names you've probably heard of, like Blackstone, First Key Homes, Progress Residential, and Invitation Homes. They are some of the heavyweights that the government is looking to clamp down on, so whenever you hear someone talk about big Wall Street landlords, that is who they're talking about. Now, some groups are pretty worried about the 21st Century Road to Housing Act, like the NHB, that's the National Association of Home Builders, and a lot of multifamily groups are concerned, and why is that? Well, the effect is it could dramatically reduce new housing production.   Keith Weinhold  7:44   See, a big institution like First Key Homes or Blackstone, they wouldn't want to even get into this business anymore. They wouldn't want to build big build to rent communities anymore if they have to sell them all within seven years. See, they want to buy and hold for the long term, kind of like what you and I are doing, because you and I know that owning a group of selective buy and hold single family rentals is a really profitable place to be, but so if they don't want to build, then that creates a reduction in supply, which could make prices go up, and then obviously hurt those trying to afford their own home. Well, that would defeat the purpose of this whole thing. I mean, my gosh, this always seems to happen when government gets involved. So, the 21st Century Road to Housing Act could limit supply, which is the exact opposite of its intent to get first-time home buyers into their first home, and if this passes, it does have bipartisan support. This lower supply, then yes, indeed puts upward pressure on prices. Just amazing. So then it could actually go on to help the everyday mom and pop investor, like you and I, that already owns property, the individual at last check, though they're looking to pass a version that still restricts some of these giant institutions from getting into build to rents, but yet it does not have that seven year sale requirement. What's really important to remember here is that Washington, they're looking to stifle big Wall Street players from the rental market, which could reduce supply. They're not targeting individual investors. The context that's important is that these groups, they own 10s of 1000s of homes, they don't own hundreds of 1000s, and they don't own a million, so it's a really small percentage of the housing market, whatever direction policy breaks, then the headlines that it creates are just greater in magnitude than the effect on the market is. It's an important frame of reference here. Let's meet this week's guest. This week we're welcoming back a guest that we haven't heard from in a year or two in real estate circles. He is popularly known as the mad scientist of multifamily. He's quite an in-demand speaker. He has a $500 million multifamily portfolio that he essentially shares with over 1300 investors. He's sharp, a good educator, and a straight shooter. That's why he's here. It's a warm welcome back to Neal Bawa.   Neal Bawa  10:32   Thanks for having me on the show again. It's delightful to be here, and so many interesting things to talk about in the world these days.   Keith Weinhold  10:38   There really are.. I don't know if we can get it all in, Bawa is spelled B A W A. Neal, I want to get to your future housing market outlook later. How you think the future looks, including when multi families quasi Armageddon might end. But first, you're known as a data driven real estate guy. Tell us about that, and how being data driven makes you profitable.   Neal Bawa  11:03   I see concern, and I'll tell you why. The single family and multifamily market have been atrociously incredibly divergent since the first quarter of 2022 They have not tracked yet each other at all, even though if you look at the last 50 years, they tend to track each other. So you know, 2008 was a Armageddon for single family, Armageddon for multifamily, and they both sort of came up in 2012 2013 and then they had a really good time until Covid.   Keith Weinhold  11:30   Yeah,   Neal Bawa  11:31   but the second quarter of 2022 is when Fed started raising rates, and since then we've sort of slid - multifamily has gone down in terms of pricing between 20 and 30% depending upon the metro, you know, and depending upon whether it's new construction, new construction assets have gone down more than 30% and existing assets that are filled up have gone down by 20 to 30% depending upon the metro. So, metros that have a large amount of supply, closer to 30% decline in value, the metros that have less supply probably closer to 20% decline in value, right.   Keith Weinhold  12:03   Demand demand has been pretty resilient. It's more of a supply story.   Neal Bawa  12:06   It's a huge supply story, right. So, if you look at, you know, occupancy, essentially what's happened is there was so much supply that came in that really people started on those projects in 2022 maybe they didn't start a construction until 2023 they didn't finish construction until 2025 so they started leasing up in 2025 They had to give offer concessions two months, sometimes three months free, and so that pushed down the rents in 2025. And they're not done, because you typically can't rent an apartment in six months. If it's brand new, it's going to take you about 18 months to rent it, and sometimes 24 months, and so it's affected our rents in 2025 it's affecting our rents in 2026. Now it's unlikely to affect it in 2027 but we'll go there, you know, at a later stage. But at the moment, we, what we've seen is negative rent growth in the United States for multifamily for the last 12 to 15 months, and what I think is going to be negative rent growth in Q of this year and Q2 of this year, so Q1 was negative, Q2, which we are in now, is likely to be negative or flat now. Single family, on the other hand, has gone in a different direction, which has been very difficult to understand, and I believe it's taken me a while to really understand this, but I think I've finally figured it out. Single family prices are not down since 2022 which makes no sense at all, because the average mortgage in the United States today is almost double, almost double, not quite double, but almost double of what it was in at the beginning of 2022 when interest rates were about 3.3 3.4% Right now we're sitting around, you know, six and a half percent interest rates, so not quite doubled interest rates, but they've obviously gone up a fair bit, and as a result, your average, you know, mortgage has almost doubled, but home prices haven't dropped, which makes no sense if you really think about it, because home prices are a factor of demand, and they're also a factor of people's ability to pay, so if all of a sudden within four years you're paying, the mortgage is doubled, then less people are going to be able to buy, but it stayed up, the market has stayed up, and the biggest reason it stayed up is because of what is known as the lock-in effect. So, the US market typically has a million new homes every year, and there's more than a million existing homes that are transacted, right? So, it's an open market, it's a perfect competition market, but it hasn't been perfect competition for the last four years, because so many people locked in ridiculously low interest rates.    Neal Bawa  14:28   Perfect example, in 2021 and 2022 I have a 15 year mortgage at 1.75% If I sell my house back to myself, my mortgage quadruples, quadruples, right, because it goes from 1.75% to six and a half percent, so I can't even imagine even think about leaving my home, right, because it's just such a perfect loan. Most people don't have anywhere near 1.75% but there's lots of people with more mortgages in the 3% three and a half percent, and 4% range that basically can't go anywhere, and because those homes are not coming into the market. The last three years the market has had this unusual not enough supply factor, and that's been keeping prices up. That is ending. That is ending, because what we've been tracking is the percentage of homes in the United States that have low mortgages. Low is simply defined as anything under four and a half percent, and that percentage is going down each quarter, because you know divorces happen, deaths happen, you know people move for jobs, and so every time that happens, that locked in rate goes away, because you sell your home and move on, and so for a while that lock in effect was predominant, it was controlling everything, but as time has gone on, interest rates were higher in 2324 2526 For also almost four years have passed since the rate started going up. So each quarter the percentage of homes in the US that have these low interest rates has slowly moved down, and we're almost back to a normal timeframe.   Neal Bawa  15:53   And this is causing the single family market to not have a conniption, but we're starting to see a balancing of the market, where it's not just a buyer's market anymore, in some places it's actually seller's market, some places it's a buyer's market. So we're now starting to see home prices drop in number of markets in the United States. I can't say that they've dropped in super majors, but we're seeing a flattening out effect of home prices in most metros in the US, and there should be a flattening effect. Just to be blunt, I mean, obviously I own a bunch of single-family homes, so I just wanted them to keep going up for selfish reasons. But if you think about it, we had huge home price growth in like 30 plus percent in number of years, 2021 22 and even 23 and during those years, salaries only went up by two to 3% a year. In one year, they went up by 4% and rents also went up like crazy. There was a 2021 was 15% rent growth year. So, at some point, there had to be an adjustment, and we are in that period of adjustment where single family prices are basically flat on a national basis. Yes, going up in the San Francisco Bay Area because of AI, and going up in a couple other technology-heavy metros because of AI, but otherwise fairly flat, and I don't expect that to change for the next year. So, my forecast is next 12 to 18 months, home prices in the US are going to be flat on a nominal basis, they're going to be down on an inflation-adjusted basis, but you know, because of the Iran, more inflation's three and a half percent, so home prices should go up three and a half percent. So, if they stay where they are, well, they're really dropping three and a half percent.   Keith Weinhold  17:29   Yeah, before this year began, I released our forecast, it was for 2% nominal home price appreciation in the one to four unit space for the US this year, and I still like how that looks. There's so much to unpack with what you just talked about. In my view, there's nothing unusual at all that when mortgage rates rose sharply a few years ago, that home prices rose as well. Why? Because actually, that's what usually happens, which is counterintuitive to most people. In all of our lifetimes, residential real estate prices have only fallen significantly one time, that was around 2008 due to a number of unusual circumstances. The only thing that's a bit different this time is, of course, how fast rates increased in 2022 and 2023 and people wondering if residential real estate prices could still keep up, and they certainly have, but yeah, you brought up this dichotomy, this bifurcation about how the apartment market and the one to four unit space kind of separated from each other in 2022 or 2023 That's what's so interesting.   Neal Bawa  18:36   I do want to point out a couple things, though, and I don't want to be a Pollyanna here and talk about negative stuff, but I think that there's big difference between 2008 and that timeframe and where we are today, and that difference is, and it has multiple parts. Not all of your audience is aware of this. Until about 2012 the United States had very reasonable birth rates. You know, we were one of those countries that had avoided the debacle that Japan, Korea, China, and a number of other countries are seeing South Korea being the absolute worst, where basically they were producing one baby per generation, where you need about 2.2 babies just to kind of keep your population where it is, right, and the US was unusually high in that, and that we were still above that threshold, which meant that our population would continue to grow and not fall. Now, there was two reasons our population was growing: One, we had more than 2.2 babies per household, and second, we had a very significant amount of legal and a very significant amount of illegal or undocumented immigration. Right, so we had both of those pipelines today. All three of those have flipped, so the United States now basically looks like Korea or China or Japan in that every household is producing about one and a half babies, which means that our population growth, which hasn't stopped yet, because it takes a while for these things to catch. Up is likely to stop, like it's, and at some point decline again. Luckily, we're not there yet. The US is a fairly young population, unlike Japan, which is one of the oldest populations in the world. So, it'll, we'll still continue to see population growth, but there is no doubt. And you can ask Chat GPT, right? How has population growth in the United States slowed over the last 20 years.    Neal Bawa  19:22   Make me a graph, and it will make you a very nice graph, and you'll very clearly see there's a slowdown in population growth. The second part is both documented and undocumented immigration. It's my estimate that since this administration took over, somewhere between half 1,000,001 million people have left the United States. Now it's very difficult to get an actual number, as you can imagine. A number of these people were undocumented, so we didn't really know how many there were to begin with. And a number of them, when they left, they also left by an undocumented rate, that you know, path. So we've lost a bunch of those people, and also the people that have stayed in the country, we've lost a number of them in the workforce. Here's a perfect anecdote, Keith. About 33% of the construction workforce in the United States was undocumented, one in three. In Texas, as much as 40%   Keith Weinhold  19:45   Yeah, that's huge.   Neal Bawa  19:45   It's very significant. Number of those people don't show up for work anymore. I don't think they've left the US, at least I don't think so. But they don't show up for work anymore, because that's how they get caught, right. So, what we've seen is that the construction workforce in the United States has become been decimated over the last 12 months, and the impact is much greater in the second half of 2025 than the first half. Why? Because even though they wanted to do ICE enforcement, they just simply didn't have enough agents, enough facilities, enough judges. When the second half of last year, they sort of started catching up on that, hiring more agents, getting more facilities, getting more judges, and so we started to see a real challenge there. I have properties in 10 markets in the US, and what I can say is about seven of those markets, mostly Southern markets, I am beginning to see dropping occupancy related to this phenomenon. I'm seeing a reduction, and so markets like Georgia and Texas, Florida are more hit than my northern markets like Idaho. I haven't seen any impact at all, but these southern markets, multiple properties, multiple metros, I'm seeing this - people, mostly of Spanish, Mexican origin, not renewing leases. I don't know what they're doing. I don't know if they're sleeping in their cars. I don't know if they're basically just, you know, staying with mom or staying with, you know, some other family. But I'm seeing a very, very big pullback in my leases tied to this, and occupancy is dropping in those markets that are heavily Hispanic. And so I'm seeing the impact of that on landlords, but I also know that there's an impact on the US at all, and overall demand on rentals, whether it's single family or multifamily. This is a significant impact, because I don't think that the Republicans are going to make a U-turn on this. I don't want to get political, but you know, stating the obvious.   Keith Weinhold  19:45   Yes, United States had its biggest birth year in 2007 when there were more than 4 million babies born. The average age of the first time homebuyer today is 40 years old. If that holds true, that peak would take place in 2047 And then, yes, to your point about changes in immigration, yes, it sounds like a potentially a reduction in demand with what you're talking about, with some vacancies, and also maybe a reduction in supply when you have fewer construction workers to build these places as well, we're talking about building properties. Neal, I want to talk to you about the build to rent space. Somewhat is build to rent better than traditional real estate? I think that's what we really want to know. And for those that don't know, build to rent means when you construct a property where from day one that construction project is built for a tenant, not an owner occupant. I see a lot of pros and cons there. Can you talk to us about the trade-offs between build to rent and traditional real estate?   Neal Bawa  19:52   Yeah, if you think about it, it's a really terrible word, built to rent, because if you think about the word built to rent should be apartments, right, but actually doesn't mean apartments, right? So, built to rent actually means single family or town homes that were built to rent out, right? And then you're like, why don't they just said built to rent apartments and town homes? Well, you know, was too long an acronym, and we suck at acronyms anyway. But BTR, or built to rent, is essentially building single family or town homes, but specifically building them to rent, and it doesn't include any apartments at all, right? And the reason why the BTR market was growing in the last five or six years is that roughly 18 million American families can no longer afford to buy starter single family homes, you know, and by starter I mean, small old single-family homes. That's how Americans usually started, you know, in their 20s and 30s. They would buy these homes, some of them, but they would fix up, and then they over time, in their 30s, late 30s and 40s and 50s, they would upgrade, and then at starting the 50s, it would flatten out, and then the 60s, they would start to downgrade, right? That's been a typical thing that's happened in America for 56 5070, years. Well, that is, cannot happen anymore. And it broke in 2022 until 2022 It was a normal cycle beyond 2022 because interest rates almost doubled, and the mortgages almost doubled, but the incomes only increased by 10 to 20% There became this orphaned generation of Americans, roughly 18 million families, that simply cannot afford to buy that starter home, and they are now forever renters. They don't know it. They think that they're going to catch up at some point, but five minutes with an Excel spreadsheet, I could prove it to them that they're not going to catch up.    Neal Bawa  25:35   Maybe one in 100 families would see a very large increase in income, and that would result in them catching up, but for the most part, as a group, these 18 million families, they're forever enters as a group that didn't exist before 2021 right. It's entirely because of this outrageous increase in mortgages, while not seeing a drop in home prices, that led to this, and so those orphan families, they actually earn pretty well, so these are families that make 70, 80, $90,000 in mid markets. They make over $100,000 if they're living on the coasts or in expensive markets, and they still can't buy that, you know, starter home. And so they don't want to live in apartments. I have lots of apartments, old ones, new ones, and I want these people to live there, but they don't want to live there, and so they've been looking for an option, and that option has been developers like me building communities of 200 300 townhomes or single family homes with a small little yard, and then basically from day one, instead of selling them, renting them out, and then once you're done renting out the whole community with 200 tenants, then you sell that to an apartment company. You know, there's lots of apartment companies in the US that have 100,000 units. Well, they want to buy these because the turnover is lower. So, what happens is most of these town homes and single-family homes for rent. Families come in, and they typically rent for three to five years before they move, whereas in on my apartments I lose 40% of my tenants each year. So, if I have 200 tenants, I lose 80 of them every year, and I have to basically go back, clean up those units, deal with the vacancy. But when I have townhome communities like my Idaho Falls townhome community. I lose a tenant at roughly every four years, and so, as you can imagine, profitability goes up when turnover goes down, right?   Neal Bawa  27:31   Because you don't have that cost of turnover and vacancy, and so eventually those large landlords that are holding 100,000 units figured out, I like this, what Neal Bawa is doing, he's building these 200 townhomes, I want to buy these from him when they're rented. I don't want to build them, I don't want to lease them up, I just want to buy them when they're stabilized. And so BTR became that name for that marketplace where developers would build townhomes and single families, rent them out, and then sell them to institutional, and it was some—   Keith Weinhold  27:56   People think of fabulous institutionalization of the starter home.   Neal Bawa  28:00   And in many ways it is, because what happened is, for a while, these institutional players, like Blackstone and BlackRock, they were like, we are just going to go out and buy 50,000 single-family homes, and that's going to be the institutionalized. Well, that worked really well if you bought in 2008 2009 2010 2011 because you got them bought them at a discount, but when they started buying them in 2015, 16, 17, 18 at ever higher prices, they didn't make any money. So the vast majority of these public funds that were created to buy large amounts of single family have failed if they've purchased anything in the last seven or eight years. If they bought before that, they made huge amounts of money. Family homes are so expensive that basically buying them for rental did not make sense, so these companies have now pivoted to saying we'll only buy communities that have 100 or 200 or 300 of these homes, because then we get the benefits of having centralized leasing, centralized property management, centralized maintenance, and I don't have homes spread all over the metro, they're all in one place, and I can make more profit from that. In theory, that's been good, and you might think that I'm bullish on BTR, but I'm actually today bearish on BTR for one single reason. About seven months ago, Republicans started talking about a bill - I don't know what the name of the bill is, but what this bill does is it forces builds to rent developers like me within seven years of building the property to sell all of the homes in that property to single family tenants, not to Blackstone, not to Blackrock, but to single family tenants. Hasn't passed yet, but it passed the Senate with an 8910 vote, which means that both Democrats and Republicans wanted to vote for this. If it passes the House, and because Donald Trump himself is very heavily opposed to it, he's made it very clear he doesn't like this. He's a developer, obviously. It hasn't passed the House yet, but if it passes the house, that will destroy the build to rent market. No one will ever build build to rent, because the worst possible thing is I build this, and within seven years I have to actually sell it to individual buyers. If I do that, my banks are going to hate me and not give me loans to build BTR anymore. Obviously, there's going to be some grandfathering to the communities that I'm building now, or maybe even build the ones that I'm building in 2027 maybe grandfathered. It usually is, because you know, Congress never does anything retroactively, and they give you a year or two, but if it passes, it's doomsday for BTR. I hope it doesn't happen, but that's the way it's looking, because it's bipartisan. Bipartisan bills are more likely to pass   Keith Weinhold  30:40   Now for the mom and pop investor, the individual investor build to rents have obvious appeal due to your point about the lower turnover, lower maintenance costs on a new build, lower insurance costs often on a new build, and then there's the tenant appeal to a new build as well, but of course there is that investor downside. I think a lot of investors are aware of their thin initial cash flow that they're going to have on build to rent, but you know, Neal, another downside with build to rent, I think a lot of investors don't look at is, hey, just how many of these things are they building? Are they building 500 of them? Do I have some overbuild risk if I buy into this community that could suppress occupancy and rents for a while.   Neal Bawa  31:21   What we've seen is that when Built to Rent started out in 2017-2018 it was its own asset class. It wasn't competing with apartments, it wasn't competing with single family rentals, it was just its own thing. However, in the last two or three years, as more and more apartments flooded the marketplace, we had a glut. It moved away from that. It basically started getting affected, and the rent started falling, just like any other portion of the market. You know, think of it as three portions of market. There's the built to rent, which I described, you know, brand new single family homes, town homes per rent. There's the apartments, both brand new and existing, and there's the single family rentals, right, which there are millions of. What we are seeing now is it's become one market, right? All of them are affecting each other, and the apartments, which have a huge amount of glut, there's a massive amount of new apartments that have come in in the last two years, are really pushing the rents down for single family, they're pushing that rents down for BTR. So, at this point, what I would say to people that have this concern, Keith, is simply look at incoming apartment supply, because if you're in a marketplace, and I'll give you examples of really good markets that are crushed right now. If you're in a market that has a lot of incoming supply, whether you buy a single family rental, a quadplex, a 50 plex that's an apartment, or 100 unit BTR, you're going to suffer for rent growth if you have a lot of incoming supply in 2026 and that is across the board in every market in the US. Huntsville, Alabama is, in my opinion, one of the most interesting markets in the US for 5 year, 10 year growth, right?    Neal Bawa  32:54   If I had to say you don't need a loan, it's just your own cash, no investors, where would you put money in? It would be at the top of my list, not at the very top. Idaho Falls is definitely the number one market in the US in my list, but Huntsville is up there. But right now, do you know what rent growth in Huntsville is? Minus 2% negative 2% Why? Because there's 6000 units coming into a market that's, you know, 1/5 or 1/10 the size of Phoenix, right. It's 1/10 the size of Dallas, but it has half the units of Dallas or Phoenix coming in, and so rent growth is negative there. So, what I would say is today absolutely everyone that is an investor should understand that we live in the magic world of AI, and you should be talking with Chat GPT about incoming supply for any market that you're interested in, and using that to make your decisions, because all of these markets merged, BTR, new apartments, old apartments, single family, everything has emerged in the last 24 months, where they're all affecting each other, and if there's too much supply of any one kind, it's affecting all of the other markets, and that's the message that I have. And none of this is like you have to go buy a $25,000 software like Costar today. Chat GPT is your costar.   Keith Weinhold  34:11   You're listening to Get Rich Education. We're talking with the mad scientist of multifamily, Neal Bawa, where we come back, including what he thinks about recovery for the beleaguered multifamily market. I'm your host, Keith Weinhold. What if you got your mortgage loans the same place I get mine? You sure can at Ridge Lending Group, NMLS 42056 They provided GRE listeners with more loans than anyone, because Ridge specializes in investment property. They'll help you build a long-term plan for growing your real estate empire with leverage. Start your prequal, and even chat directly with President Caeli Ridge. While it's on your mind, start at ridgelendinggroup.com that's ridgelendinggroup.com    Keith Weinhold  34:56   Let me ask you something: if you've worked hard to build wealth, is your money positioned to actually support your goals? A lot of accredited investors leave capital sitting in cash because it feels safe, but inflation and missed income opportunities can quietly erode its value. Freedom Family Investments offers freedom notes for investors seeking structured income backed by real estate. It's a straightforward approach built on real assets, not speculation. In full disclosure, I'm an investor myself. What I like is that their team walks you through how it all works, so you can decide if it aligns with your portfolio and income goals. Every investment carries risk, and nothing is guaranteed, but with a track record of consistent on-time investor payouts, they built real credibility. Go to freedomfamilyinvestments.com to book a clarity call, or text family 268 66 That's Family 266 866    Speaker 1  36:00   This is the star of the A E Show, The Real Estate Commission. Todd Rollette. Listen to Get Rich Education with my friend Keith Weinhold, and don't quit your daydream.   Keith Weinhold  36:20   Welcome back to Get Rised Education. We're talking with Neal Bawa, a really sharp multifamily syndicator who's also highly data driven. And Neal, tell us more about the beleaguered multifamily market that had those aforementioned problems really cropping up in 2022 and we had a lot of supply and spiking rates. What does it look like for the path to recovery for the US multifamily market?   Neal Bawa  36:45   Luckily, demand is strong, and even though occupancies have dropped, typically the multifamily market, the large multifamily market in the US, tends to be between 95 and 96% occupied. Okay, and right now we're on 93% so that all that incoming supply means that about 7% of our apartments in the US are empty at the moment, we're trying to fill them, and we are seeing that occupancy drop, not across just new apartments that are leasing up, but also drop in class B and class C. We've also seen a huge increase in concessions, so I studied this quite obsessively, and I can tell you that 2026 in some markets is the recovery year, but not across the board in the United States, and the reason for that is sentiment. Once renters get used to huge amounts of concessions, it's like a drug, it takes a little while before you wean those renters off of those drugs, and so there's that hit right now. Every renter program,   Keith Weinhold  37:44   Everyone wants their freebie for good.    Neal Bawa  37:46   Yeah, exactly. It's like, hey, what, you're not giving me two months free? Hey, what, you're not even offering me one month free? It takes a while for that expectation to happen, because there's such a huge amount of concessions in the US. So, to me, there are a few markets, usually the smaller markets or very fast growing markets, where there's a recovery in 2026 but otherwise 2027 The first half of 2027 is recovery. The second half of 2027 is fast rent growth in a lot of markets. Why? Because remember, interest rates have been high since 2023 A lot of projects were started in 2022 went into construction in 23 came to market in 25 and 26 Lease ups are happening in 25 and 26 By early mid 27 these are all leased up, right? The second half of 2027 there isn't a lot of delivery in any of these big markets, because to deliver in the second half of 27 you would have started construction in that second half of 2025 and I counted those permits market by market. There's just not a lot, because by that time everyone knew that projects were not getting funded, everyone knew that interest rates were high, so there wasn't a lot of supply of new starts in the apartment market in the second half of 25 so there's not going to be a lot of delivery in the second half of 27 and all of the existing stuff would have been leased by then. So 2026 is one of those years where we could still see more concessions in the second half of 2026 I still see rent growth for apartments to be flat. You mentioned single family might be a little bit higher. It tends to be a little bit higher than apartments in terms of rent growth, but I think flat rent growth for 2026 is what I'm projecting. I'm projecting small rent growth in the first half of 2027 for most markets, and then I'm projecting robust rent growth, call it 3% or greater on an annualized basis, in the second half of 2027 and I'm projecting that most markets in the US that are not seeing a population drop, so count out places like Detroit are going to see a very aggressive rent growth, four or 5% rent growth, that's aggressive in our world, in 2028 28 and 29 are shaping up to be. Supply deficit years, years where supply is well under demand.   Keith Weinhold  40:05   It's pretty easy to project completions when you just go ahead and look at starts, and really, what you're counting is the story of absorption.   Neal Bawa  40:14   Yep, and what's nice about apartments is you can actually build a single family home in about nine months, right, but you can't build apartments in less than 24 months. There's just so much permitting issues, there's so many delivery issues, fire code issues, and so we have a crystal ball on the multifamily side that we are now getting better at using. I don't think the industry was very good at this in 2022 but now we're really all obsessed with how many permits does my metro have, and how many permits does my state, and how many permits does the US have? And everyone that I know in the industry that's data driven knows that there's a massive glut now, maybe a little bit of a glutton that remaining portion of 2026 equilibrium in 27 and a huge, huge supply deficit in 28 and 29 So everything that I'm doing is based on this, and this crystal ball actually works because of that two year gap between shovels in the ground and delivery,   Keith Weinhold  41:10   and it sounds like you've recommended Chat GPT as a go-to source for investors to look into these things, that happens to be my favorite one as well, and you are well, maybe it's a bit too much to say, but it almost feels like to me pioneering with the way that you use AI. In fact, I know before our show today you were running some other things in the background that made me wonder, hey, am I talking to the real Neil or the clone Neil? I know I've got the real Neil here, but why don't you tell us about how you're using AI to make data-driven decisions in real estate?   Neal Bawa  41:40   Sure, so the first thing is that we've completed our journey with the low hanging fruit of AI. Every single person in our company is fully trained on how to use Chat GPT. Most of our research-related processes are automated. For example, 100% of our investor updates are now written by Chat GPT. What we do is we go into our property manager meetings on Mondays or Tuesdays sit down with them, beat them up, and the transcript is then taken by our team in the Philippines. They take that transcript and put it into a pre-trained Chat GPT string, it's called a custom GPT, and the string took a while to train, but now that it's trained, all it needs is a transcript. We just copy paste it in, we don't give it any instructions, and it outputs a really wonderful investor update, right. And so our updates for our investors are 99% written by AI. Of course, we'll go in and add our comments at the end of the process. So we've automated investor updates, rent comps, so you know if we are underwriting a new property today, what we do is we simply go into a Google file and copy paste the address and hit enter roughly once a minute. A software, which is written by AI - we're not coders, but the software knows how to write code - it checks the file, if it sees a new address, it goes in there, grabs the address, and then it basically goes to apartments.com rent.com realtor.com and all of these places, and checks the rents for this particular property in two mile radius. It eliminates all the ones that don't match, like you don't want to match the rents of a 1970 or 80s built property with a brand new 25 built property. Those are not comps, it's not comparable. So it basically is very careful, it keeps a radius range of two miles, and also basically is a property of the same kind, you know, like it never matches up a three story property with a 10 story property. Those don't match, one of them obviously is more of a central business district or downtown sort of thing, and so it basically grabs all of those rent comps and then puts them into a file and posts in a Slack channel. Usually it takes it about 1213 minutes to do that, and so whoever put that address in about 12 minutes later goes into the Slack channel and says, "Hmm, these are all my rent comps, right? And boom, now you're basically, you have all these ready rent comps. So, what we've done is, we've automated a significant portion of what we are doing with both our property managers and inside the company with acquisitions and things like that, we're also scraping massive amounts of data from the Bureau of Labor Statistics website, which we just couldn't deal with that data before, and building very beautiful, very interactive dashboards. We don't use Chat GPT for that. We find for dashboarding a tool called Claude, which is by a company called Anthropic, is much better, so we have currently over 150 interactive dashboards that Claude has created that update in real time and give us access to data. If anything, I find that we are in this incredible time where decision making has become much easier, as long as you spend time with these tools. So, in our company we have an absolute mandate that no one has broken for the last year. One year per day, people must program, and by programming we mean issuing common language instructions to tools and build dashboards and build software that automates our work. Have we laid off anyone because of this? I mean that. Be the next obvious question. The answer is no, because it's made it easier for us to serve a much larger audience, so it's easier to grow your company. We just are not hiring anyone, and we haven't hired anybody for the last 18 months, so we have a hiring freeze, but at the same time all of our people are employed because they're they're now much more valuable. So everyone in our company is now a programmer, and even though that sounds weird, it's completely true.   Neal Bawa  45:24   Every single person in our company writes code, and they write code by talking with Cloud Code or talking with Chat GPT, and then Chat GPT, of course, does the actual code writing, but people have become very, very good at answering questions and saying, "I want a dashboard like this, turn these radio buttons into drop boxes, and give me the last month, and last three months, and last 12 months, and do this, and do that, and connect this, and I also want to host this on a server, but I want to make sure that only I can see it. I need a password added. Imagine 1000 of these conversations happening in our company every day. Yeah, that's interesting. And what you just described   Keith Weinhold  46:00   there at Gro Capitas is somewhat of a microcosm for what's happening in the broader economy, where we've been in this low high or low fire environment for quite a while. Well, Neal, as we're winding down here, we recently had a new Fed chair come in. It seems incomprehensible to me that there could possibly be any rate cuts. I don't know how we could responsibly make a rate cut with all these inflationary layers. We had the pandemic, and then terrorists, and then the Iran war, and the energy shocks, and all these bottled up supply chains. What are your thoughts with regard to the Fed?   Neal Bawa  46:29   I still think that we'll get one rate cut, and that rate cut will be based on political pressure. So, for the first time ever, I have seen the Fed break into factions, so if you look at the latest Fed meeting, which happened, you know, there was dissent, there were two clear factions, so the Fed is becoming less data driven and more faction driven, and I think that one of the factions, which obviously wants rate cuts to go down, is going to triumph at some point later in the year, but until we get past the incredible increase in inflation because of the Iran war, I don't think that faction is going to win. Right, there's three or four people in that faction, that's not enough votes to get past the others. So I'm predicting no rate cuts until Q4 of this year. If the Fed was entirely logical, there should still not be a rate card in Q4, but I think it'll happen because there's political pressure.   Keith Weinhold  47:25   The preservation of independence is key. Neil Bhawa, this has been great, and a lot of people learn from you. You're a brilliant educator, as well as what you're doing in the multifamily space, and a lot of other places. So, if someone wants to connect with you, learn more about what you do. What's the best way for them to do that?   Neal Bawa  47:43   So we built a website called Multi Family University. It's completely free. There is no subscription. There's no upsell. We do not have an educational product, but what we do is each year we have 8-12 webinars that we create with their extraordinarily good looking thanks to the use of AI. Yay, and we share them with an audience, and usually between 5000 and 1000 people attend our webinars each year, of which roughly 1% become investors with us. The rest, the remaining 99% just continue to get free access to data, and we cover every imaginable real estate topic: Single family, multifamily, industrial hotels, self storage, Airbnb, and even controversial topics outside of real estate, like climate change or impact of climate change and impact of AI. So you know, multifamily university is the best place you can go to, multifamily you.com/club It's a free club, and it's free forever.   Keith Weinhold  48:42   Neal, it's been valuable to our audience. Thanks so much for coming back out of the show.   Neal Bawa  48:46   Thanks for having me.   Keith Weinhold  48:53   Oh, a terrific, wide-ranging chat with Neal. There, yes, this interesting 2022 divergence between single family and multifamily, the slowing birth rate, and how that won't really catch up with real estate in a big way for perhaps 20 plus more years. How single family rentals beat multifamily on the basis of tenant retention, and a lot more that we covered there, and he's got a good data driven timeline for apartments being back in favor by 2027 and 2028 After the interview, Neil and I chatted some more off Mike, and he would like to come back on the show next year. We're probably going to have him, because we have a lot more to talk about at that time. We can see if the multifamily market is really healing. Also, did you pick up on this? I wonder why, for his own home he would get a 15 year mortgage at 1.75% interest, so I'll have to ask him about that. That's surely a fantastic interest rate, but a 15 year loan rather than a 30 year that maybe he could have gotten at two and a half percent at the time. Well, 15 year probably. Is not the best use of capital, because it increases your equity position rapidly. When instead, those dollars could have been out in the market earning an actual return somewhere else. But he's a smart guy, he must have an answer. We can talk about that at that time. We've got a lot of terrific shows coming up here on the GRE podcast, specific learning episodes, where it's just me teaching you, as well as new guests and returning guests too. Until next week, I'm your host, Keith Weinhold. Don't quit your daydream.   Speaker 2  50:35   Nothing on this show should be considered specific personal or professional advice. Please consult an appropriate tax, legal, real estate, financial, or business professional for individualized advice. Opinions of guests are their own. Information is not guaranteed. All investment strategies have the potential for profit or loss. The host is operating on behalf of Get Rich Education LLC exclusively.    Speaker 2  51:03   The preceding program was brought to you by Your Home for Wealth Building, getricheducation.com.  

Description The Future of Tech is Here. Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ In this presentation from Ultimate Partner Live, industry analyst Jay McBain breaks down the monumental macroeconomic shifts rewriting the tech sector in 2026. https://youtu.be/r0qTDyw97Gs As the industry rapidly approaches a $6.07 trillion valuation, driven by massive AI infrastructure investments from Sam Altman and the “Magnificent Seven,” traditional sales and channel models are fundamentally collapsing. McBain reveals how buyer demographics have transformed to an integration-first millennial base, why marketplace ecosystems now command over half of all partner-funded deals, and how a tiny elite of just 1,000 tech service providers control two-thirds of global tech revenue. Learn the exact mechanics behind how Microsoft out-partnered AWS to win 26 straight quarters of dominant growth and how your business can deploy an algorithmic early warning system to capture massive wallet share before competitors even step into the boardroom. Key Takeaways Over half of the Fortune 500 companies vanish every 20 years because their leadership fails to anticipate macroeconomic technological cycles. The true opportunity in the $6.5 trillion AI boom lies not in single vendor products, but in the hardware, software, services, and telecom ecosystem surrounding them. Indirect tech sales are undergoing a structural shift toward direct cloud hyperscaler models driven heavily by Nvidia's core infrastructure client base. Modern business deals are won or lost months before the point of sale based on the average of 6.3 partners surrounding a customer’s environment. Over 51% of tech buyers are now millennials who prioritize software integration capabilities and digital marketplaces over traditional human sales interactions. Tech service economics are pivoting aggressively away from upfront margins toward point-based multi-partner funding across subscription cycles. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags Nvidia AI buildout, $7 trillion AI opportunity, cloud ecosystem decade, Microsoft vs AWS growth, multi-partner cloud deals, digital marketplace migration, millennial B2B buyers, B2B tech subscription economics, tokenized micro consumption, tech services wallet share, hybrid cloud infrastructure, 28 customer moments, IT services industry growth, telecom spend breakdown, channel chief strategy, managed service providers MSP, global systems integrators GSI, software integration first, point-based vendor incentives, automated co-selling workflows Transcript JAY McBAIN AUDIO PODCAST [00:00:00] Jay McBain: So to go back to that story about the 53% of companies who are gonna fail, one of us is gonna be asked to write the book, but chapter one is always you Blame the CEO. [00:00:13] Vince Menzione: We just came back from Ultimate Partner live in Bellevue, Washington, where we hosted incredible leaders for two amazing days. Come join us for this next session where we explore the tectonic shifts we’ve all been seeing. With that, I am incredibly blessed to invite a friend of mine to the stage. I have a quick little side note, like I found an old LinkedIn post from this gentleman from like many years ago, like 20 years ago. [00:00:39] Vince Menzione: And I wasn’t really that nice to you on that LinkedIn post. Like, oh, like this is before Jay became the Jay, that we all know Jay to be j. But he was in the space and I was at Microsoft doing something and he reached out about something. It was kind of rude, Jay. I was like, oh my gosh. I can’t believe. But Jay has been a great friend. [00:00:54] Vince Menzione: When we started the podcast back up, uh, during COVID we started doing podcasts together. When we moved to the studio, Jay was the first person in the studio. He’s always got a spot, uh, at our events. He’s s Spot Art, and, and he’s a great friend and supporter of Ultimate Partner Jay McBain. For those of you who don’t know him, Jay, welcome. [00:01:13] Vince Menzione: Thank you, sir. [00:01:22] Jay McBain: 31 days ago, we landed Artemis two. The furthest humans have ever been away from the planet Earth 57 years ago. We landed on the moon in the 56 years. Between those two moments, the tech industry has been the fastest growing industry in the world. Every single year we moved from the space race to the technology race, and we’re just getting started. [00:01:46] Jay McBain: If you’re old enough, you’ll recognize the mainframe and mini era for 20 years. You’ll recognize a young disheveled Bill Gates showing up in Boca Raton, Florida for, uh, August the 12th, 1981 launch, where Bill thought that every one of us would’ve a PC in our home, and IBM thought they were gonna sell 10,000 of them to hobbyists. [00:02:12] Jay McBain: 1999, a small startup from an executive who just left Oracle in San Francisco named Mark Benioff. A couple of years later, Jeff Bezos went into a boardroom and said, listen, we’ve spent a lot of money building infrastructure to our busiest day, Christmas, black Friday. You’re telling me this stuff sits idle 10 or 20% for the rest of the year. [00:02:35] Jay McBain: Why don’t we rent that out to others? Got laughed outta that boardroom and then got made of fun of on magazine covers. Maybe you should just tend the store, let the adults talk about technology. In March of 2023, our neighbors, our friends, our family saw DeepFakes. They saw poetry, they saw music, and they came to us as tech people and said, did we just light up Skynet? [00:03:03] Jay McBain: Now every one of these 20 year eras, this is the Taylor Swift version of our industry. Every single one of these eras triggers the fastest growing product in history. Today it’s actually Chacha bt first to a billion users. It triggers a new, richest person in the world, bill Gates, to Jeff Bezos. Now, Elon Musk is the first to sign a trillion dollar pay package, and it’s not for car. [00:03:27] Jay McBain: It’s not for cars. It also triggers a most valuable company in the world change. And today that’s nvidia. These are monumental changes in our industry and they’re monumental changes in partnering every single time. And it also links to our customers. If you take a 20 year view of business, one era, and, and think about the AI era, you know, at the start of it here, if you’re to grab the Fortune 500 magazine from 20 years ago and start to flip through it, 53% of the companies in there no longer exist. [00:04:06] Jay McBain: Every 20 year cycle, we lose over half of the biggest companies in the world. These are the companies that have very deep pockets to buy their way outta problems. If you’re not in the Fortune 571% of tech companies don’t make it 10 years. These are the changes that cost industries. There are changes that cost really big companies and the decisions we make, the trends we’re in right now, in 2026 will be written about in the future. [00:04:39] Jay McBain: This new era, a lot of big numbers being thrown around. Vince’s best friend talk about a six and a half trillion dollar AI opportunity, but it’s not Microsoft’s tam. Microsoft is chasing about a trillion dollars of this. And the ecosystem, the hardware, the software, the services, the telecom is gonna make up the rest. [00:05:04] Jay McBain: It is an ecosystem. Every time these big numbers are thrown, the word ecosystem is always thrown around it. Not to be outdone, Sam Altman’s talking about a $7 trillion build out. The world economy this year, the world GDP will be 126. These are material numbers to world GDP, but even better, they’re both larger than our entire industry is today. [00:05:27] Jay McBain: So what took 56 years of the fastest growing industry this year will be $6.07 trillion. Big numbers, but it’s easier to think about it in terms of a dollar that our customers spend in that dollar. They’re gonna spend 25 cents on hardware. They’re gonna spend 25 cents on software. So for anyone that read the memo 15 years ago, that software’s gonna eat the world, there’s still a dollar a hardware to run every dollar of that software. [00:05:57] Jay McBain: And whether you’re thinking humanoid robots or whichever future you’re envisioning, there’s going to be a dollar of hardware to run every dollar of software for the next 20 years. There’s over 25 cents now in IT services, and in many cases, these services are growing faster than the product categories and just under 25 cents in telecom, that’s how it breaks out today. [00:06:19] Jay McBain: And this industry, which took 56 years to get to this point, is gonna double in size in the next three to five years. We already have two and a half trillion of that seven raised and being spent. Part of the reason Nvidia is the most valuable company in the world. Now our industry, uh, you talk about ultimate partnerships. [00:06:40] Jay McBain: Our industry traditionally, and world trade by the way, is 75% indirect. The dealerships, the agencies, the brokers, the resellers, the retailers, the franchisees, the gas stations, the grocery stores, the pharmacies, all 27 industries sell indirect. You gotta think back the last time you bought something direct. [00:07:01] Jay McBain: Well, I bought a Dell from that dude in the nineties. Cool. Well, Dell Technologies is now 60% indirect. Well, I bought insurance. Direct is 15 minutes. Could save me 15%. Well, Geico last year sold more insurance through agencies and brokers than they did direct. This is the world now. We used to be 75% indirect four years ago. [00:07:26] Jay McBain: Then it went to 73.2, then it went to 70.1 and it then it went to 66.7. By the way, marketplace is in these numbers indirect. It’s not marketplace causing this change. It’s one company, Nvidia. Nvidia has seven customers. The magnificent seven, uh, half of them are in the room right now that every morning we wake up to a hundred billion dollars press release about this $7 trillion buildout. [00:07:56] Jay McBain: What’s interesting is indirect sales in our industry is growing by revenue. It increases every year, just not at the pace that this AI build out is happening direct with seven companies. But the reason we’re all here, and I think the core reason that Vince is building this community is this, you know, Microsoft forever has measured and been very vocal. [00:08:21] Jay McBain: About 96% of their deals have partners in them. Kind of who cares, who collects the money. We care about the moments, the 28 moments before the customer makes a purchase. We care about every 30 days forever, because two thirds of our industry, over $4 trillion now is subscription consumption based. Winning a customer today is only winning the first 30 days. [00:08:46] Jay McBain: We care about this cycle. We care about who surrounds our customer. So six years ago, I stood on a big stage and said, you know, we went through a decade of sales. You know, in 1999, you thought you were born to be a salesperson. You’re managing your territory with your gut. Well, a few years later, you were introduced to the science of selling. [00:09:07] Jay McBain: You know, 10 years later you thought as a marketer, you sit around a cocktail party joking with your friends, 50% of my marketing dollars are wasted. I just don’t know which 50%. Really funny. In 2009 until every 58-year-old CMO got replaced by a 38-year-old growth hacker. Coming in with Marketo and Eloqua and Pardot and HubSpot, and 15,505 as of yesterday, MarTech and iTech tools, ninjas in marketing, they wouldn’t let a nickel go through without measuring. [00:09:43] Jay McBain: Now we understand 96% of deals and partners that surround it. No deal is gonna be won or lost in this era without partnering effectively. So we had to have this decade of the ecosystem. One of the ways we’re tracking is by outsiders. You know, Salesforce every year publishes the state of sales and they’ve got, you know, the number one CRM in the world. [00:10:05] Jay McBain: So they get to go talk to all the CROs, all the salespeople in the world. And as of this year, a couple months ago, 94% of every salesperson in every industry in the world uses partners every single day. You wanna see what this number was six years ago. Also, 89% of salespeople around the world don’t think they’re going to club this year without partners. [00:10:29] Jay McBain: So this is a big moment for us, halfway through the decade ecosystem, but we’re only halfway through. We’re starting to understand now at a more granular level. What partnering means. It’s not theory, it’s not flywheels. It’s not really cute. McKinsey slides that we keep showing to our board saying how important partnering is. [00:10:51] Jay McBain: We’re trying to get to the very specific level of the 6.3 partners on average that surround the deal and what they’re doing. How their business model works, and that’s average if I’m working on a public sector deal. I was at a Red Hat conference yesterday talking sovereignty. If I’m in an enterprise or a large public sector deal, it’s north of 10 partners in the deal. [00:11:15] Jay McBain: So we’re starting to understand what used to be this, this, you know, you’ve been the fastest growing industry for 56 straight years. Every single professional services person in every industry has come in to join the fund. Over 90% of accountants are tech services firms. Over 90% of marketing agencies are tech services agencies. [00:11:36] Jay McBain: All of this 250,000 software companies, a million emerging comp tech companies, the half a million VAR that have been in that traditional channel. The managed service providers, all of these 20 different partner types, millions of companies, tens of millions of people competing for 6.3 spots. Around the customer. [00:11:58] Jay McBain: That’s it. Luckily, there’s 141 million global customers to compete for. There’s, there’s some open slots that you can go find, and that’s the point. Our industry never had our own Fortune 500. We always talk to, you know, these partners and GSIs are doing this and SI are doing that. And we never really had a view of capability and capacity or what our own TAM was inside of that partnering. [00:12:25] Jay McBain: And so we set out and we would’ve loved, you know, chat GPT or Gemini or Claude or any of those tools to do this. But there’s one problem in partnering with AI is that it doesn’t know one partner from the next. There’s a big digital sameness problem in our industry that every single partner, whether it’s Larry in the White van or Accenture, with 786,000 employees all say they do all things to all people all the time. [00:12:53] Jay McBain: 98% of them, 99% of them are private companies that don’t share their p and l. You can’t go into Microsoft’s LinkedIn system and find out how many employees, ’cause it’s a block system, it AI can’t see into it. So it just sees, and it’s a great pattern matching. Google, SEO can’t figure out who’s who, nor today can the large language models. [00:13:14] Jay McBain: ’cause all the things they’re trying to match, the transformers are trying to match. It all looks the same. Every tweet, every ebook, every website, every digital history looks the same. So this took us thousands of people hours across two years to do, to dig into every p and l to dig into every dollar of what they’re doing. [00:13:33] Jay McBain: But what was interesting is only a thousand partners in our industry do two thirds of all tech services. When you get into enterprise, it goes up to 80 to 90%. The partners in the middle, in Blue do more tech services. The 30 of them than the 970 partners in white on the outside, the 970 partners in White do more tech services than the next million combined. [00:14:03] Jay McBain: This is our industry in a nutshell. Every time we talk to a a vendor, every time we talk to a partner, every time we talk to a distributor, we’re now talking names, faces, and places. You you wanna talk sovereignty. Yesterday in Atlanta, 90% of sovereign conversations in public sector in the globe is handled by these companies here. [00:14:26] Jay McBain: Forget about how much you do with these partners today. You wanna chase the next column, which is the wallet share. And I was a channel chief for 17 years. I get the weekly report and I see a million dollar partner, another million dollar partner, sorted top to bottom. You don’t know which partners which, which of those million dollar partners is doing 1.2 million in your category. [00:14:46] Jay McBain: They deserve a baseball cap and a front row seat at your event as an MVP. The next partner right next to them is doing 10 million in your category. They’re only doing a million with you. ’cause customers are pulling them into it. Nine times outta 10. They’re leading with your competitor. So I don’t want that list anymore. [00:15:03] Jay McBain: I want the new list, which is showing me those $9 million opportunities. And I as a board member, as A CEO, as a CFO, as a CRO, I wanna see this list. And then I want to talk people, processes, programs, technology. What are we gonna do to go get our fair share of that 9 million? Where’s our lowest hanging fruit? [00:15:24] Jay McBain: How do we double our pipeline? How do we double the size of our company in three years? It’s all right here. Let’s have very specific conversations and move away from flywheels and move around from force multipliers and and things like that in partnering. Let’s figure out how this partner community is surrounded. [00:15:45] Jay McBain: What do 10 million people who have to be smart in front of their customers every single day, what do they read? Where do they go and who do they follow? It’s the law of a few. This is the old Malcolm Gladwell of tipping point 10 million people in the broader channel. A hundred percent of our TAM comes down to only a thousand watering holes. [00:16:08] Jay McBain: 12% of that entire audience. Doesn’t sound like a lot, but it’s over A million. People love podcasts. Number one way they learn the Joe Rogan effect. In our industry, there’s 121 podcasts. These are all public lists. You can go get on my LinkedIn newsletter on canals, oia. But there’s 121 podcasts that drive him forward. [00:16:28] Jay McBain: Really high up on that list, actually number one on the list is ultimate partner, Vince. That’s how I met. ’cause I asked people, 10 million people, you love this. You walk your dog, you drive to work, you listen to podcasts. I’m not the biggest podcast fan. It’s not number one on my list, but it’s number one on theirs. [00:16:44] Jay McBain: They say, you know, you gotta meet this guy, Vince. It’s unbelievable how great these podcasts are. They’re ultimate. [00:16:54] Jay McBain: Then I talked to Vince and said, but Vince, you know, 35% of your community, the 10 million people love to come to events like this one. The hallway conversations, the hotel lobby bar last night. This is what we love to do, especially post pandemic. It’s the number one way we learn. We learn from our peers, we learn from those around us, and, and the learn from the conversations we have here. [00:17:17] Jay McBain: We always remember these moments, you know, years and years later. There’s 352 choices. I’m going to five of them this week in five different cities. It’s a lot of coverage, but again, it’s a tighter li list of how people work. The magazine lists 106 of them associations like Conter. Now the GTIA peer groups, there’s 15 different spheres of influence, but only a thousand places. [00:17:43] Jay McBain: I could walk you through billionaire, after billionaire, after billionaire in this industry and show you how they did this. How did Arne Bellini at ConnectWise? How did Austin McCord at Datto, how did Nerdio become a unicorn? How did threat locker and huntress move away from 6,500 cyber companies and become unicorns over and over and over again? [00:18:05] Jay McBain: It’s only one slide. Unicorns and billionaires are made here, and a lot of people don’t get it. So walking away from Bellevue, a thousand partners, top down, a thousand watering holes, bottoms up. You’ve covered a hundred percent of your tam. You do it better than 10% of your competitor, 10% better than your competitors. [00:18:27] Jay McBain: You win. You carry that on your resume into the next company. You get a bigger job at a bigger pay scale. Let’s just walk through some examples. Cyber 91.7% of it goes through the channel. Huge channel audience. You know, if you’re in MarTech, it’s only 10%, but this one happens to be all channel, but that’s not the story. [00:18:48] Jay McBain: For every dollar that the 6,500 cyber companies are trying to close, there’s $2 in services. Plot twist, the products are grown at 11, the services are grown at 12.6. Your partners are growing faster than you are, and they will continue to for the next, at least five years, probably 10. So when I’m here, five years from now, you’ll hear in me talk about a three to one split in cyber and then a four to one split in cyber. [00:19:18] Jay McBain: Now, when we’re in Miami a couple days ago is CrowdStrike, they’re talking about a $7 and 5 cent multiplier, chasing that two to one up higher. You look at managed services. Here’s a fun story. Managed services. 82% of customers who are man, uh, outsourcing more this year than last year. 650 billion in size. [00:19:38] Jay McBain: This is bigger than the entire SaaS industry. Salesforce, ServiceNow, Workday, Marketo, NetSuite, HubSpot, 250,000. Others. This is bigger. It’s also bigger than all the Hyperscalers combined, not just AWS, Microsoft and Google, but Alibaba and Oracle and everybody down the list. This is a massive market also growing at double digits. [00:19:59] Jay McBain: So these are some big things and obviously we’re watching, you know, week in and week out, quarter in, quarter out, the Battle of Software and Battle of the Hyperscalers and things like that, and who’s growing at what pace and, and how partnering is connecting to all of this. You know, we watched a moment really early in the pandemic where Microsoft started growing faster than AWS and they haven’t stopped since 26 straight quarters. [00:20:27] Jay McBain: And you ask customers and say, you know, does Microsoft have a better product? And in most cases they say no. You know, AWS had a five year head start. Well, did they have a better price? Well, no, actually most cases Microsoft’s more expensive. Well, did did they have better promotion? Was their Super Bowl ad better? [00:20:44] Jay McBain: No, they’re both kind of crap. So you kind of ask the questions of what’s the only difference that could create growth above the leader in the market? Well, it’s place. More of the 6.3 partners are walking into those keyboard room meetings and drawing clouds up on the wall and labeling the Microsoft than they are AWS. [00:21:03] Jay McBain: Very simple. It’s never been about product. The best product in our industry has never won. And now the best way forward is that partnering moment, and this is the moment. So to go back to that story about the 53% of companies who are gonna fail, one of us is gonna be asked to write the book. And it could be the book like Kodak, they invented the product that ended up killing them. [00:21:26] Jay McBain: And it’s a woe is me story, but chapter one is always you blame the CEO. How could they not see those trends happening in 2026? How could they, you know, were they blind? Were they stuck in their own, you know, innovation chamber? Innovator’s dilemma, were they stuck in their own boardrooms? Why couldn’t they see? [00:21:46] Jay McBain: Well, chapter two, you, you blame the board. They have fiduciary responsibility, outsider view, and how could they not see it? But really, this is the future right here. If you take this slide and apply it 10 or 20 years from now to every failure and every success, these are the chapters of the book. Your buyer is now a millennial. [00:22:05] Jay McBain: As of last year, the 51% of our market is bought by people born after 1982. Different psychology, different behavior, different journey, different criteria, their integration. First buyers. The buy a product, 80% as good as the next one. If it works better in their environment. 94% of people won’t buy a car unless it has CarPlay or Android Auto. [00:22:26] Jay McBain: New Buyer. You have to be more integrated than your competitors. That’s a partnering story. The 6.3 partners. If you heard cyber, you need some great channel partnerships, but you need the other 5.3 partners as well, the consultants, the advisors, the designers, the architects, the implementers, the integrators, the manner service, all of the other partners. [00:22:44] Jay McBain: You need to know more of them than your competitors do, and have them label clouds with your name in them. You need better alliances. Even if you compete, you only compete in the morning. You’re best friends by the afternoon. You have to be tight with the hyperscalers, tight, with the big SaaS platforms, tight with cyber, tight with distribution, there are layers, seven layers to every deal. [00:23:04] Jay McBain: You gotta be tight in and have better alliances than your competitors. And then it all comes to the 28 moments, which I’m gonna end on, but the go to market of all of this, the co-selling, co-marketing, co-innovation, co-development, co keeping. This is it. Your product has to be good enough that somebody’s gonna renew it. [00:23:21] Jay McBain: Your Super Bowl has to be, you know, ad has to be good enough that people don’t, you know, shame you on social media. Your pricing has to be somewhere in a country mile of the bell curve of what the customer wants to pay. But successor failure is just here and platforms are synonymous with partnering. [00:23:40] Jay McBain: It’s our role now in the decade of the ecosystem to drive our companies forward. Marketplace. It’s probably the most predict, you know, great prediction we ever made. You know, growing at 82% compounded, it’s hard to predict ’cause it doubles almost every year. We were almost exact to the decimal point. Five years later now till 2030, we’re watching a second story, which is more interesting. [00:24:02] Jay McBain: If 96% of all deals have partners inside of them and there’s private offers and multi-partner offers and distributor sellers record all these funding mechanisms or services as a product. As of last week, over 50% of all deals in marketplaces now have partner funding. It means that while money changes hands differently, the respect and the recognition of what partners do is in the deal. [00:24:26] Jay McBain: We think that’s going to 59, but at some point, that’s gonna have to hit 96. ’cause to run the best programs, whether it’s an indirect sale, whether it’s a direct sale, whether it’s a marketplace deal, it doesn’t matter how money changes hands. What matters is we recognize the 6.3 partners. They’re not only making the deal happen bigger and faster, but renewing and enriching that every 30 days forever. [00:24:48] Jay McBain: When we watch, you know, billion dollar clubs and when we read all the press releases and all the hubbub about how fast this is growing and who, which companies are behind all this. When I’m quoted in some of these press releases, it’s because of this. You know, CrowdStrike, you know, brags are a billion dollars in a single year, but inside of that, they’re showing that 91% growth in marketplaces, which is pretty phenomenal for any company to almost double in size every single year. [00:25:17] Jay McBain: What’s more phenomenal is they’re growing the channel piece of it, 3548%. That green part of it is growing. Companies that understand platform and have people and processes and programs and technology to do it are winning. And they’re getting recognition and partners are starting to join the Billion Dollar Club who don’t sell a product, but are also winning at Extreme Scale. [00:25:44] Jay McBain: So talk about those partner 1000 and who are leaning in to win at this level. As well as everything changes, traditional billing moved into subscription models, moved into consumption models. Now we’re being tokenized to death multi it’s, it’s in this mode of micro consumption. There’s no chance there was little chance in subscription consumption that would be resold. [00:26:09] Jay McBain: You don’t buy Netflix from the cable guy in the white van. There’s zero chance when you’re buying tokens at a buck a piece that that’s going through any indirect sale. This continues to grow. Now the tectonic shifts is what happens when money changes hands differently. These old programs that we used to all write hundreds of different boxes, we checked every day on deal reg and trainings and all the other things are changing. [00:26:35] Jay McBain: To this, you’ll get these slides, by the way, in high res, inside of this now is the customer. For the first time ever, 45 years later, we have the customer in the middle of what we do, the 28 moments in green before they buy the seven layer stack and the partners inside it. The implementation. The integration, the managed services in a cycle that never ends, and two thirds of our industry. [00:26:55] Jay McBain: With the customer in the middle, we can now move money around to the different moments. It’s not all landing in front or backend margins or market development funds or new customer bonuses or spiffs. It’s landing where it needs to land. Over 400 companies now, pretty much led by Microsoft 400 companies are in a point system right now and 400 more. [00:27:18] Jay McBain: We’re working kind of behind the scenes to get that announced in the next 12 months. This is a total changeover in terms of how economics work and partners are yelling over half of us. I don’t care. Don’t call me a VAR anymore. Don’t call me an MSP. Don’t call me a regional system integrator. I do the consulting over half the time. [00:27:36] Jay McBain: I do the design, I do the implementations, I do the managed services, and 44% of us are vibe coding. On weekends. We’re not happy. Just on the services side. We wanna join the seven layer tech stack as well. These are partners growing faster than their vendors by understanding this cycle and where to show up and where the money is in ai. [00:27:56] Jay McBain: And the number one thing they’re asking for is not more leads, which they did for 45 years. The number one thing is now recognized for what I do. I’ve never just been a cash register. We’re completely now past this idea of a channel being a channel of distribution, and now a channel being this platform for the future. [00:28:16] Jay McBain: As we lay that on top of ai, the first couple of years of AI has really been consumer driven. The 95% failure rate that MIT reported last year is now 70%. That’s the failure to get from proof of concept to production. That 70 will be 50 by the summer we’re moving now in business, the maturity rates are going up at the end customer and in 88% of cases, that’s because of the channel. [00:28:43] Jay McBain: They’re working with partners. They’re not vibe coding themselves and working in little skunkwork groups. They’re working with partners to make it happen, and it now becomes the partner’s number one growth opportunity. I can grow at 11 or 12% in cyber every year. Compounded I can grow in 10% in managed services. [00:29:03] Jay McBain: You know, those are great double digit growth ’cause my customers are growing at 2.7% and I can go four x my customer, but I can go 10 x my customer if I have the right services built around ai. And this compounded growth rate and that big number in 2 20 32, 267 is what’s got those top 1000 partners obsessed. [00:29:25] Jay McBain: And your companies are leading with ai. Now you need to connect to those AI services. You need to get partners on this scale of growth. And they will be adding your name inside every cloud. They write on every whiteboard, but 82% of partners around the world, you know, we survey 25,000 of them aren’t ready, and they’re blaming vendors for not being ready, and they’re telling them exactly the workshops and the training that they need to get ready for this cycle. [00:29:53] Jay McBain: 82% of our entire partner, tens of millions of people, aren’t ready to grow at 35% and they need our help. Last thing I’ll say about AI is it’s the first time from client server to cloud, edge to cloud that it’s been segment driven. SMB alone has one, you know, six different segments, one to nine, 10 to 24, 25 to 49, et cetera. [00:30:18] Jay McBain: Mid-market into enterprise. No one that runs a restaurant is calling Jensen to buy a GPU to put next to the stove. No one’s calling Sam or Dario or anyone at Anthropic or OpenAI directly. They’re waiting. If you run a restaurant with all the people running around with tablets, you’ve invested in toast or square or clover or one of the platforms to run your business. [00:30:41] Jay McBain: A hundred different things. And you’re gonna wait for toast to work with a hyperscaler and build out the capabilities genetically. So when they see a spike in Uber Eats orders, they automatically place a food order and automatically change the staffing to deliver on it. That’s what the restaurant’s waiting for, and there’s no one calling and having a big a agent conversation. [00:31:03] Jay McBain: But even if you go into hundreds of people in medium sized business, every one of the vice presidents have their tech stack already built. I talked about the marketing person already, but the HR leader has one, and everybody’s got their seven layer stack. They’re not calling to buy a GPU and they’re not calling to, you know, bring in open AI directly or, or anthropic. [00:31:22] Jay McBain: They’re waiting for the platform they built to integrate together ag agenta capabilities. Everybody’s in wait mode up until enterprise and public, large public sector. So we are looking at this market and at 90% of that AI market is run by those thousand companies, and the rest of the millions of partners are helping in terms of how these businesses are gonna change at that level. [00:31:46] Jay McBain: Here’s where I end. You know, the 28 moments used to be a theory. It used to be a flywheel. How do we buy a car? [00:31:55] Vince Menzione: Well, we Google it, [00:31:57] Jay McBain: 81% of us now, 94% of us use large language models. We find out that there’s 365 brands of car. I’d have to test drive one every day of the year to get through them all. So we start narrowing these things down. [00:32:09] Jay McBain: We configure it. We put our rims on it, we color it. We download the invoice price. We download the backend rebates this month, whether I buy it in May or June, we find out what 5,000 people paid for our exact car within 50 miles of us. And then we don’t wanna go to the dealer because we know more than the salesperson, the manager ever will. [00:32:26] Jay McBain: We know what we’re gonna pay within, you know, dollars or cents. Just carvana the car. Hand me the keys. Let’s just forget the whole eight hour back and forth. I’ll get you a deal thing. I’m smarter than you in technology. Our customers are smarter than us, smarter than salespeople. That’s why 75% of millennials don’t wanna talk to a salesperson. [00:32:48] Jay McBain: They want to end digitally, and by the way, they’re not gonna send a fax after 28 digital moments. They’re gonna end on a digital marketplace. This is all demographics. It’s not hard to see where it’s going, but we’re getting into names, faces, places again. What if every dollar of your tam, the board, the CEO, runs around with their big multi-billion dollar number, they’re chasing? [00:33:09] Jay McBain: What if every single deal looks the exact same? This is a deal with AstraZeneca, A real deal, real customer spending millions of dollars. We know it starts in October, it ends in April. It’s a six month cycle. We see what they read, the MQ ls at the beginning. We see the sales demo moments. We see ISV, but we’ve never had the light blue boxes. [00:33:30] Jay McBain: What if we as a team could overlay the 6.3 partners in this deal? And when you find out a couple things. Here’s where I end. In December, five deals were one, three of them by NTT. The person at NTT probably coaches AstraZeneca’s, you know, kids’ soccer team. They probably have a cottage together at the lake. [00:33:50] Jay McBain: For the last 20 years, if the person at NTT worked at Deloitte, Deloitte would’ve run this deal. But Software One and Yash are both there, so we understand that when they were drawing clouds up on the wall in the boardroom in December, this deal was won and lost there. It was not won and lost at the point of sale. [00:34:09] Jay McBain: So what if you knew more about this and could see every dollar in your tam? You had an early warning system that this was happening. Two things jump out at this now that we’re in Bellevue. AWS was touched twice in this deal, directly in the marketing cycle and the sales cycle. AWS lost this deal. Here’s an example of Microsoft winning a deal with Microsoft never being touched. [00:34:34] Jay McBain: For some reason, NTT who won, who won AWS’s partner of the year a couple years ago led with Microsoft, so did Software one, Microsoft’s biggest reseller in Europe, and as did Yash, they all led with Microsoft and without Microsoft, knowing Microsoft took a multimillion dollar deal away from their competitors by winning in December. [00:34:53] Jay McBain: That’s one. Second. These partners didn’t just show up other than soccer and cottages. They didn’t show up in December. It went closed one in their CRM system. Back in the summer, August, September, we already knew AstraZeneca was in market, spending millions of dollars. We didn’t need them to read an ebook or go to an event to find that out. [00:35:17] Jay McBain: We knew it because it was closed one. They’re spending hundreds of thousands of dollars times five in December to know what to do at the end. This is an early warning system that’s better than any MQL, better than any SQL. And if you could give your company these level of view into their pipeline with an early warning system that I can work with those partners for months before they ever show up at the customer’s boardroom. [00:35:44] Jay McBain: This is it. Talk about 47% winners. This takes you from not only surviving the AI era to being a top five platform winner. Thank you very much. [00:36:01] Vince Menzione: Until next time, we’ll see you in person. Hopefully at our next event.

This episode contains several visual examples. For the best experience, head to our YouTube channel "Run a Profitable Gym."Get a free audit of your gym's Instagram with our custom GPT tool: GymIG.comIf you're a gym owner who wants to post more content but doesn't know where to start, this episode is for you. Many gym owners have the misconception that they simply need to post more. But getting the right mix of content actually matters more than how often you post.Today on “Run a Profitable Gym,” Two-Brain CEO John Franklin breaks down the three types of content every gym owner needs to be posting on Instagram.He shares real examples from gyms who are doing this well and walks through exactly how to replicate their posts. You'll learn how to use your gym's Instagram page to make people like you, trust you and move closer to doing business with you.  Tune in and walk away with a simple three-post-a-week plan you can start this week.LinksAI Gym Instagram AuditGym Owners UnitedBook a Call0:00 - Intro0:55 - Why variety beats volume on social media1:07 - Posts that get people to buy from you2:43 - Social proof that actually works7:30 - Building connection through Instagram10:10 - Becoming a trusted voice11:31 - Your exact weekly posting plan

Microsoft Build 2026 announced an end-to-end agentic AI stack. COMPUTEX Taipei confirmed heterogeneous AI infrastructure across ARM, Marvell, Intel, Qualcomm, and NVIDIA. Alphabet raised $80 billion. Cisco Live repositioned the network as the AI platform. Patrick Moorhead and Daniel Newman break it all down alongside earnings from Broadcom, HPE, Palo Alto Networks, and CrowdStrike, plus the token cost conversation, the edge AI push, and what Palantir and Oracle are saying about proprietary data as the real AI moat. The handpicked topics for this week are: Microsoft Build 2026 Announced an End-to-End Agentic AI Stack: Microsoft shipped MAI-Thinking-1, its first homegrown thinking model, alongside Scout, Microsoft IQ, Project Solara, and a Majorana 2 quantum update targeting a 2029 commercial timeline with claims of a 1,000x reliability gain. Pat describes MAI-Thinking-1 as likely better than Sonnet 4.6 in blind testing and delivering close to GPT 5.5 quality at a far lower cost. Scout is Microsoft's first autopilot agent, anchoring the M365 Agent Suite with Office Pilot Agent Mode and Agent 365. Microsoft IQ serves as the context layer, integrating M365, business data, boundary IQ, and web IQ with GitHub Copilot, Foundry, and Copilot Studio. Project Solara is a new Android-based platform built for agent-first devices across transportation, retail, and hospital settings. Microsoft also added 83 Unix commands to the Windows stack. Dan frames Microsoft's real play as distribution, not frontier model development, noting that the open model ecosystem being pulled into the platform will matter more to CFOs managing token costs at scale. (The Decode) The AI Stack Goes Multi-Silicon — COMPUTEX Taipei 2026 Confirms Heterogeneous AI Infrastructure: ARM's AGI CPU is in production with Google moving its TPU head node to ARM, and adding Oracle and ByteDance as new customers. ARM also introduced a new switch, the TT100, and put the 51T CPO switch on stage. Marvell received a trillion-dollar company endorsement from Jensen Huang, adding $90 billion in market cap on the comment alone. Intel announced disaggregated inference details and Xeon 6+ Clearwater Forest, its first 18A data center processor. Vista Equity and Cambium Capital announced a NeoCloud called Vector Core Compute, with Xeon 6 handling orchestration, Salmonova RUs handling decode, and Blackwell GPUs handling pre-fill. Qualcomm's Cristiano Amon announced the Dragonfly data center brand with Snapdragon C details coming at their June investor day. The WSTS raised the 2026 semiconductor TAM forecast by 90% to $1.51 trillion, with Pat noting the market could hit a trillion dollars if memory is excluded entirely. (The Decode) NVIDIA RTX Spark and the Edge AI Push: NVIDIA coordinated with ARM and Microsoft around the RTX Spark at COMPUTEX, with the shared message being that the future of Windows is here. Signal65's Ryan Shrout asked Jensen directly why NVIDIA wants to be in the PC business, given low margins and diminishing returns. Dan frames the answer in the context of devices increasingly becoming mobile data centers, capable of running models at much greater efficiency than cloud delivery. The edge AI conversation is also directly tied to token cost economics: as intelligence delivery moves closer to the device, the cost per token drops significantly. The jury is still out on whether NVIDIA will meaningfully disrupt the PC market, but its influence over OEMs like Lenovo and Dell that depend on it for data center gives it real leverage over SKUs. (The Decode) Token Economics and Frontier Model Cost Pressure: Dan and Pat discuss a substantive shift in how enterprises are thinking about AI consumption costs. Dan argues that "token maxing," the practice of defaulting to the most powerful frontier model for every task, has now effectively peaked, as bills have come due at scale. Companies paying for tokens in volume are starting to question whether they can afford the prices that frontier models actually cost to deliver. Pat pushes back, saying the dynamic is still present, but both analysts agree that the market is moving toward a model where token selection is matched to the job, with Microsoft's MOE approach and thinking models positioned to help CFOs manage that economics story. (The Decode) Continuum Goes Public at Highest Valuation for an AI Platform: Dan notes that Continuum, the Honeywell-spawned quantum company, went public this week at what he calls the highest valuation for an AI platform to date. He flags that IonQ will likely contest that characterization. The broader context is Microsoft entering the quantum conversation with Majorana 2 at Build, a name that has largely been absent from the quantum race, while IBM has received most of the attention. (The Decode) AI CapEx Has Outgrown Cash Flow — Alphabet's $80 Billion Equity Raise: On June 1, Alphabet announced an $80 billion equity capital raise, upsized to $85 billion, structured as $40 billion ATM, $30 billion underwritten, and a $10 billion private placement with Berkshire Hathaway anchoring. Pat frames the questions over CapEx returns as entirely dependent on whether you are an AI boomer or a doomer: if the payback comes, the raise is the right move. If it does not, the math doesn't close. Dan argues the investment is existential, drawing parallels to how infrastructure-first companies have always spent ahead of monetization, and notes that Google's equity is being used as a capital engine that may be more efficient than the debt markets right now. Both analysts flag the downstream implications for Broadcom, MediaTek, and Marvell given the TPU connection. (The Decode) The Network Becomes the AI Platform: Cisco Live 2026: Cisco launched Silicon One P200, the Secure AI Factory with NVIDIA and Spectrum X, AgenticOps, MCP-native automation, Cisco IQ, LiveProtect, and folded Astrix Security and Galileo into Splunk under one control plane. Pat identifies Cisco Cloud Control as the biggest announcement of the entire show, pulling together Catalyst, Meraki, Nexus, Firewall, and WebEx under agentic ops that run natively through MCP, with code running directly on smart switches that have x86 processors. Pat also credits Cisco for establishing Silicon One as a credible chip alternative for hyperscalers capable of taking on Tomahawk and Jericho. Dan frames the long-term opportunity as campus and branch enablement when industrial AI and robotics deployments accelerate, arguing that the numerator of AI's economic impact has barely started, as edge deployment spending has not yet begun. (The Decode) The Flip: Did Microsoft Build 2026 Effectively End the OpenAI Partnership? Pat argues the divorce decree has been filed. MAI-Thinking-1 was built with zero distillation from third-party models offering clean enterprise data lineage, with Maia 200 in production plus Anthropic chip supply, which signals vendor hedging. OpenAI is going all-in on AWS, which means you cannot be married to two people, and the full Build stack covering model, OS containment via MXC, agents via Scout and Agent 365, and context via Microsoft IQ removes every architectural dependency on OpenAI. Dan counters that Microsoft is hedging rather than leaving and predicts the partnership will run through the decade. Enterprise Copilot customers are explicitly showing in data that they demand GPT 5.5, internal benchmarks have not been independently validated, and Microsoft stands to make meaningful money from the OpenAI IPO. (The Flip) Broadcom Q2 FY26 Earnings: Broadcom posted revenue of $22.19 billion, a narrow miss depending on which consensus data set is used, with EPS of $2.44 beating estimates and AI semis at $10.8 billion. Hock Tan declined to raise the $100 billion full-year AI chip target, and the stock dropped 13% in premarket trading. Q3 guide came in at $29.4 billion. Pat calls the miss a timing issue driven by Google's multi-sourcing across Marvell, MediaTek, and Broadcom rather than a fundamental problem. Dan flags that Hock Tan opened the earnings call by accidentally reading from the 2025 print, calling it "not the best moment." Sell-side re-ratings held in the 500s across Jefferies, Mizuho, and Deutsche Bank despite the drop, with Futurum Equities having it at 600. (Bulls and Bears) Hewlett Packard Enterprise Q2 FY26 Earnings: HPE delivered revenue of $10.68 billion, up 40% year over year, and EPS of $0.79, up 100%. Juniper integration and AI servers both outperformed, and all FY26 guides were raised. The stock jumped 19% after hours before settling into a roughly 15% gain, with HPE up 68% over the last month. Pat frames HPE as a value play rather than a volume play, methodically targeting enterprise and sovereign cloud deals where it can maintain profitability, rather than competing for massive NeoCloud volume. Antonio Neri was clear on the call that the profitability pull-forward is a one-shot deal. Pat and Dan will both be at HPE Discover the week after next to interview Neri and the C-suite. (Bulls and Bears) Palo Alto Networks Q3 FY26 Earnings: Palo Alto posted revenue of $3.0 billion, up 31% year over year, beating the $2.94 billion estimate, with non-GAAP EPS of $0.85, beating the $0.79 to $0.81 range. NGS ARR reached $8.1 billion, up 60% year over year, including $1.6 billion from CyberArk and Chronosphere. RPO hit $18.4 billion, up 36%. Both FY26 revenue and EPS guides were raised. Adjusted FCF margin came in at 38.5% TTM, up 430 basis points. The stock jumped 11% immediately after hours, then drifted lower. Pat points to 2,200 platformized customers and 120% net retention as the most important metrics. Dan notes the SaaSpocalypse thesis continues to be wrong. (Bulls and Bears) CrowdStrike Q1 FY27 Earnings and the Proprietary Data Moat Argument: CrowdStrike posted revenue of $1.39 billion with EPS of $1.10 and ARR of $5.51 billion. Net new ARR of $255.8 million set a Q1 record, up 32% year over year. FY27 net new ARR guide was raised by $52 million to a $1.29 billion midpoint, and FY27 revenue was raised to $5.915 to $5.959 billion. A 4-for-1 stock split was announced effective July 2nd. The stock dropped 11% despite the beat after a 64% year-to-date run into earnings. Dan uses the results to make a broader argument against the software disruption thesis, referencing Palantir CEO Alex Karp daring customers to build without him using Anthropic or OpenAI, and Larry Ellison's argument that the real AI value unlock sits in proprietary enterprise data that is not accessible to frontier models. Enterprises with governed, secure, proprietary data will continue to need platforms like CrowdStrike regardless of what frontier models can do. (Bulls and Bears) Six Five Summit is coming. Salesforce CEO Mark Benioff will kick off the event. Register and stay current at sixfivemedia.com/summit. Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode.   The Decode Microsoft Declares Independence — Build 2026 Ships an End-to-End Agentic AI Stack (MAI-Thinking-1 + Scout + Microsoft IQ + Project Solara + Majorana 2) https://www.theverge.com/tech/941738/microsoft-build-2026-biggest-announcements The AI Stack Goes Multi-Silicon — Computex 2026 Confirms a Heterogeneous AI Infrastructure (ARM + Marvell + Intel ASIC + Qualcomm + RTX Spark); WSTS Raises 2026 Semi TAM Forecast 90% to $1.51T https://www.tomshardware.com/tag/computex AI Capex Has Outgrown Cash Flow — Alphabet's $80B Equity Raise Is the Largest in U.S. Corporate History; Berkshire Anchors $10B https://abc.xyz/investor/news/news-details/2026/Alphabet-Announces-Proposed-80-Billion-Equity-Capital-Raise-to-Expand-AI-Infrastructure-and-Compute-2026-b0myAMewCa/default.aspx The Network Becomes the AI Platform — Cisco Live 2026 Launches Silicon One P200, Secure AI Factory (with NVIDIA), AgenticOps, Astrix Security + Galileo https://www.cisco.com/site/us/en/about/whats-new/index.html The Flip Did Microsoft Build 2026 Effectively End the OpenAI Partnership? MAI-Thinking-1 Beats Sonnet 4.6 in Blind Testing, Microsoft Claims GPT-5.5 Parity at 10x Cost Efficiency — Will MS Quietly Wind Down OpenAI Exclusivity by FY28, or Is OpenAI Still the Frontier Anchor Microsoft Needs?   FOR:  MAI-Thinking-1 beating Sonnet 4.6 in blind preference + GPT-5.5 parity at 10x cost efficiency is a frontier-model independence proof point https://www.latent.space/p/ainews-microsoft-build-mai-thinking Build 2026: Accumulating Evidence of Microsoft's AI Independence — EDN (June 4) — https://www.edn.com/build-2026-accumulating-evidence-of-microsofts-ai-independence/ Maia 200 in production + Anthropic-Maia chip talks signal Microsoft is hedging its inference vendor stack https://blogs.microsoft.com/blog/2026/01/26/maia-200-the-ai-accelerator-built-for-inference/ Microsoft canceled Anthropic's internal software licenses + pivoted to chip-supply pursuit — customer-not-competitor positioning https://www.cnbc.com/2026/05/21/anthropic-microsoft-maia-200-ai-chip.html   AGAINST:  Enterprise Copilot customers explicitly demand GPT-5.5 — internal benchmarks don't replace the brand https://learn.microsoft.com/en-us/microsoft-365/copilot/release-notes?tabs=all MAI-Thinking-1 benchmarks haven't been third-party verified — Microsoft is the only source https://www.latent.space/p/ainews-microsoft-build-mai-thinking The MS-OpenAI partnership is contractual through 2030+ — unwinding it is impractical and expensive https://blogs.microsoft.com/blog/2026/04/27/the-next-phase-of-the-microsoft-openai-partnership/ Microsoft's actual strategic risk is OpenAI leaving, not MS leaving — Anthropic + OpenAI IPOs make OpenAI exit risk the real concern https://www.anthropic.com/news/confidential-draft-s1-sec Bulls & Bears Broadcom (AVGO) Q2 FY26 ACTUALS — Rev $22.19B (Narrow Miss) + EPS $2.44 (Beat); AI Semis $10.8B; Hock Tan Refuses to Raise the $100B Full-Year AI Chip Target — Stock −13% Premarket; Q3 Guide $29.4B https://www.cnbc.com/2026/06/03/broadcom-avgo-earnings-report-q2-2026.html Hewlett Packard Enterprise (HPE) Q2 FY26 ACTUALS — Blowout: Rev $10.68B (+40%), EPS $0.79 (+100%); Juniper Integration + AI Servers Both Outperform; FY26 Guides All Raised; Stock +19% AH https://www.businesswire.com/news/home/20260601866494/en/HPE-Reports-Fiscal-2026-Second-Quarter-Results Palo Alto Networks (PANW) Q3 FY26 ACTUALS — Beat-and-Raise: Rev $3.0B (+31% YoY, Beat $2.94B), Non-GAAP EPS $0.85 (Beat $0.79-0.81); NGS ARR $8.1B (+60% YoY, $1.6B from CyberArk + Chronosphere); RPO $18.4B (+36%); FY26 Revenue + EPS Guides BOTH RAISED; Adj FCF Margin 38.5% TTM (+430 bps); Stock +11% Immediate AH, Then Drifted Lower https://www.paloaltonetworks.com/company/press/2026/palo-alto-networks-reports-fiscal-third-quarter-2026-financial-results CrowdStrike narrowly beats estimates on AI tailwinds, but stock falls 9% — CNBC (June 3) — https://www.cnbc.com/2026/06/03/crowdstrike-crwd-q1-2027-earnings.html  

AI is quickly becoming the go-to tool for property research—but can it actually predict where the market is heading?In this episode, Luke Metcalfe returns to put that assumption to the test, analysing thousands of suburb predictions across multiple GPT models and real market data. The result is clear: AI doesn't just struggle to forecast property growth—it often underperforms the market entirely.We unpack why this happens, from the way large language models are trained to the dangerous feedback loop of consensus-driven data. If AI is simply reflecting what's already popular, are investors just paying a premium to follow the crowd? The conversation also explores the rise of AI-generated property reports, the risks of relying on median data, and why many “data-driven” strategies may not be as objective as they seem.Beyond the critique, this episode shifts the focus to what actually drives capital growth. From owner-occupier demand and supply constraints to hyper-local, street-level dynamics, Luke shares what the data reveals when you strip away the noise. We also touch on how recent policy changes and shifting incentives could reshape investor behaviour—and why thinking like a homeowner may be more important than ever.If you've been relying on AI, hotspot lists, or broad market trends to guide your decisions, this conversation will challenge your assumptions and give you a clearer framework for investing with confidence.Episode Highlights01:27 – Meet Luke Metcalfe: Data Meets Property Reality03:35 – AI vs Reality: Why Predictions Underperform05:35 – AI Reports & Buyer Agents: Risky Shortcut?13:52 – Selling at a Loss: A Hidden Growth Signal16:18 – Humans vs AI: Who Gets Forecasting Right?22:49 – Budget Shock: How Incentives Just Changed26:52 – Supply, Scarcity & Why Tranquillity Wins29:51 – Fast Food & Growth: The Signal No One Expects31:44 – Rezoning Risks: Predicting What's Coming35:14 – Melbourne vs Sydney: A Tale of Two Markets37:23 – Investor Incentives Are Shifting Fast39:35 – Holding Property: What the Data Reveals43:09 – Rental Crunch: What's Really Driving It49:33 – Airbnb & Second Homes: Supply Squeeze52:24 – Yield vs Growth: What Actually Matters55:15 – Model Accuracy: Where Data Goes Wrong57:54 – Key Takeaways: What Investors Must KnowAbout the GuestLuke Metcalfe is the founder of Microburbs, a property analytics platform focused on uncovering the underlying drivers of capital growth at a granular level. With a background in data science and machine learning, Luke has spent years analysing Australian property markets using large-scale, longitudinal datasets that go beyond traditional metrics like median prices.His work centres on understanding property performance at the street and individual asset level, incorporating factors such as resale data, supply dynamics, and behavioural patterns. Known for challenging conventional wisdom in property investing, Luke combines technical expertise with practical insight to help investors move beyond trends, hype, and surface-level data.Through his research and platform, he continues to push for a more evidence-based approach to property decision-making—one that prioritises real outcomes over popular narratives.Connect with LukeLinkedIn - Luke MetcalfeEmail - Luke MetcalfeWebsite - MicroburbsLinkedIn - Microburbs ResourcesVisit our website: https://www.theelephantintheroom.com.auIf you have any questions or would like to be featured on our show, contact us at:The Elephant in the Room Property Podcast - questions@theelephantintheroom.com.auLooking for a Sydney Buyers Agent? https://www.gooddeeds.com.auWork with Veronica: https://www.veronicamorgan.com.auLooking for a Mortgage Broker? alcove.com.auWork with Chris: chrisbates@alcove.com.auEnjoyed the podcast? Don't miss out on what's yet to come! Hit that subscription button, spread the word, and join us for more insightful discussions in real estate. Your journey starts now!Subscribe on YouTube: https://www.youtube.com/@theelephantintheroom-podcastSubscribe on Apple Podcasts: https://podcasts.apple.com/ph/podcast/the-elephant-in-the-room-property-podcast/id1384822719Subscribe on Spotify: https://open.spotify.com/show/3r0nnJrLUu3t1GpO7X3j6EIf you enjoyed today's podcast, don't forget to subscribe, rate, and share the show! There's more to come, so we hope to have you along with us on this journey!See you on the inside,Veronica & Chris

In Episode 70, Drew Brucker and Rory Flynn are joined by Tyler Bernabe, better known as jboogxcreative, a full-time generative AI creator, strategist, and social menace responsible for some of the wildest AI videos your algorithm has probably shoved into your face at 1:13 a.m.They get into how Tyler has gone viral across multiple generations of AI tools, why copying trends is creative quicksand, how shock value actually works when it is paired with taste, and why the best AI creators are building formats instead of chasing them.The conversation also goes deep into the unglamorous machinery behind creative internet magic: Instagram to Patreon funnels, ManyChat, six-hour livestreams, creator burnout, client work, taste, consistency, and why “just post more” is advice usually given by people who should post less.Then things get properly nerdy.Tyler breaks down his current AI creative stack, including Midjourney 8.1, Seedance, Claude, YAML-style video prompting, Nano Banana, GPT image editing, Kling, Artcraft, Venice AI, Magnific/Freepik Spaces, Weavy, Reeve 2.0, and the never-ending wait for a proper Midjourney editor.Along the way, they cover Chinese prompt translation for Seedance, 10,000-character prompt workflows, reference image construction, anime style development, mood board blending, why AI should sometimes pull you away from your own creative bias, and why the smallest edit in AI video still feels like defusing a tiny cursed bomb.⏱️ Fast Hour00:00 Fast Hours welcomes Tyler aka jboogxcreative01:51 Going viral through every AI era02:35 The anatomy of scroll-stopping AI03:07 The seductive food video origin story05:32 Running opposite the AI meta08:24 Is AI art? Tyler's best answer10:35 Why clients pay for your thing12:10 Stop copying other creators17:11 Viral views vs real conversion22:03 Building a creator business solo26:28 Burnout, longevity, and going through it33:05 Tyler's current AI tool stack38:15 Artcraft, Seedance, and Chinese prompts42:05 Claude, YAML, and 10K prompts50:44 Tyler's reference image hack58:15 Dream sketches and creative prototyping01:04:10 Reve 2.0 and layered editing01:10:38 Waiting for Midjourney's editor01:16:02 Closing thoughts#FastHours #jboogxcreative #AIVideo #AIArt #GenerativeAI#Midjourney #Seedance #ClaudeAI #AICreator #AIWorkflow #ContentCreation #AIPrompting #CreatorEconomy #AIAnimation#CreativeAI

https://novacut.ai/  https://genaimeetup.com/  Anthropic has officially closed a $65 billion Series H at a $965 billion valuation, nearly 2.5x its valuation from just 100 days ago. Meanwhile, funding is flowing across the ecosystem: Frameworks AI at $15B, Baseten at $11B, OpenRouter's $113M Series B, and Cognition AI's $1B Series D. NVIDIA went on an open-source super week with Nemotron 3 Ultra, Cosmos 3, and Nemotron 3.5 ASR. Microsoft dropped 5 new MAI models. Google released Gemma 4 12B, and Anthropic shipped Opus 4.8. On the benchmarks front, DeepSWE crowns GPT-5.5 as the leader in long-horizon coding tasks, while ITBench shows even frontier models struggle with real-world SRE incidents — Claude Opus 4.7 tops out at just 47%. Plus: Cloudflare acquires VoidZero to build the future of AI-native edge development, and Google is paying SpaceX $920M/month for compute. Topics covered: • Anthropic's $65B Series H and path to $1T • Fireworks AI, Baseten, OpenRouter & Cognition funding rounds • Microsoft's 5 new MAI models • NVIDIA's open-source super week (Nemotron, Cosmos 3) • MiniMax M3, Gemma 4 12B, JetBrains Mellum2, Opus 4.8 • DeepSWE benchmark: GPT-5.5 leads long-horizon coding • ITBench: Frontier models under 50% on real SRE tasks • Cloudflare + VoidZero for AI-native edge dev • Google's $920M/month SpaceX compute deal #AI #Anthropic #NVIDIA #OpenAI #AInews #TechNews #LLM     Funding rounds Anthropic formally confirmed the closure of its $65 billion Series H funding round at a post-money valuation of $965 billion. This represents a 2.5-fold increase over its $380 billion Series G valuation from February 2026, adding $585 billion in value in approximately 100 days https://www.anthropic.com/news/series-h  Frameworks AI raising at 15B valuation representing a near fourfold increase from its $4 billion Series C valuation recorded in October 2025 processing 15 trillion tokens daily for major production clients including Cursor, Notion, and Perplexity https://finance.yahoo.com/sectors/technology/articles/fireworks-ai-eyes-15-billion-174609357.html Baseten is raising 1B at 11B valuation annualized revenue, which skyrocketed from $200 million to $600 million over a single quarter https://techstartups.com/2026/05/26/ai-inference-startup-baseten-in-talks-to-raise-1-billion-at-11-billion-valuation/  OpenRouter has secured a $113 million Series B funding OpenRouter has experienced exponential traffic growth, with weekly production throughput expanding fivefold from 5 trillion to 25 trillion tokens over a six-month horizon https://www.businesswire.com/news/home/20260526953416/en/OpenRouter-Raises-%24113-Million-CapitalG-led-Series-B-as-Weekly-Volume-Explodes-to-25T-Tokens  Further up the stack: Cognition AI secured a $1 billion Series D round led by Lux Capital and 8VC https://cognition.ai/blog/series-d   Model Releases MAI models: MAI-Code-1-Flash: A 5-billion active parameter model optimized for ultra-low latency within GitHub Copilot and VS Code. MAI-Image-2.5: A high-fidelity image generation model ranking third on global image evaluation arenas, outperforming competing architectures like Nano Banana Pro. MAI-Transcribe-1.5: A multi-lingual speech processing engine offering fivefold speed improvements across 43 languages. MAI-Voice-2: Natural audio and voice generation across 15 languages, available at a highly competitive price point. Web IQ: A search-grounding API engineered to directly compete with Perplexity. https://microsoft.ai/models/    https://www.peoplematters.in/news/ai-and-emerging-tech/uber-imposes-dollar1500-monthly-ai-spending-limit-on-employees-amid-rising-costs-50073    Nvidia has executed an "Open-Source Super Week," positioning itself as a dominant software and model publisher: Nemotron 3 Ultra (best US open source open weights model but behind china): A massive 550-billion parameter MoE (55 billion active) designed with a 1-million token context window, optimized specifically for high-throughput, cyclical agent loops. It achieved peak throughput rates of 400 tokens per second on day-zero optimized clusters. Cosmos 3: A physical AI world-modeling framework comprising 16-billion Nano and 64-billion Super variants. Built on a Mixture-of-Transformers (MoT) architecture, Cosmos 3 natively binds textual, visual, auditory, and physical kinetic vectors. Nemotron 3.5 ASR: A highly compact 0.6-billion parameter streaming speech recognition model pushing sub-100 millisecond latencies across 40 language locales.   https://www.minimax.io/models/text/m3  MiniMax M3: A 1-million token context model hitting 59.0% on SWE-Bench Pro and 74.2% on MCP Atlas, though noted for high token consumption due to intensive internal self-validation loops.   https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/  Gemma 4 12B: Google's Apache 2.0 on-device model, which utilizes an encoder-free architecture that projects vision and audio vectors directly into the text-token space, bypassing separate CLIP-style encoders to minimize local memory footprints. https://www.jetbrains.com/mellum/  JetBrains Mellum2: A compact 12-billion parameter MoE (2.5 billion active) engineered for ultra-low latency routing and retrieval-augmented generation (RAG) sub-agents within developer IDEs. Opus 4.8 https://www.anthropic.com/news/claude-opus-4-8    https://www.cnbc.com/2026/06/05/google-to-pay-spacex-920-million-a-month-for-xai-compute-capacity.html      Benchmarks: https://deepswe.d atacurve.ai/blog https://venturebeat.com/technology/deepswe-blows-up-the-ai-coding-leaderboard-crowns-gpt-5-5-and-finds-claude-opus-exploiting-a-benchmark-loophole (GPT 5.5 the winner in long horizon tasks) a highly complex software engineering benchmark focused on original, long-horizon tasks across five distinct programming languages. Comprising 113 chaotic tasks across 91 live, production-grade repositories, DeepSWE forces agents to generate 5.5 times more code and modify an average of 7 separate files per task compared to standard evaluations. On this challenging leaderboard, GPT-5.5 leads with a score of 70%, establishing a significant 16-percentage-point lead over contemporary alternatives I think older benchmarks where models reach ~90% accuracy can be considered saturated. Few percentage points don't give us any good signal.  https://research.ibm.com/publications/developing-ai-agents-for-it-automation-tasks-with-itbench  ITBench-AA, an evaluation framework focusing on live Kubernetes incident response and Site Reliability Engineering (SRE) operations. Comprising 59 live, containerized SRE incident snapshots, the results are remarkably sobering: every frontier model scored under 50% on successful incident resolution, with Claude Opus 4.7 leading at 47% and GPT-5.5 following closely at 46%.   Edge AI announcements: https://www.cloudflare.com/press/press-releases/2026/cloudflare-acquires-voidzero-to-build-the-future-of-the-ai-native-web/  The consolidation of the AI-native developer stack has reached the runtime virtualization layer. Cloudflare recently completed the acquisition of VoidZero, the development group responsible for Vite, Vitest, Rolldown, and Oxc, backing the transaction with a $1 million open-source ecosystem fund. This acquisition is highly strategic; as autonomous agents write an increasing proportion of production software, local development environments, compilation pipelines, and bundlers must be optimized for execution speeds that match agent speeds. Cloudflare's goal is to construct a localized, full-stack edge playground. In this sandbox, AI agents can generate, test, bundle (utilizing the highly parallelized, Rust-based Oxc and Rolldown engines), and deploy entire web applications end-to-end within milliseconds. This architecture completely bypasses traditional local machine container bottlenecks, enabling high-velocity agent loops to execute in a fully sandboxed, web-scale edge runtime.

today we discuss a comprehensive evaluation of the artificial intelligence landscape in early 2026, highlighting a shift from simple generation to advanced agentic reasoning. While OpenAI's GPT-5.4 is recognized for its structured logic and superior production-grade coding, Google's Gemini 3.1 leads in massive context processing and native multimodal integration. The reports emphasize a narrowing performance gap, noting that open-source models like GLM-5 and DeepSeek V4 now rival proprietary systems at a fraction of the cost. Benchmark data from 2026 indicates that choosing a model now depends more on specific workflow needs and ecosystem compatibility than on raw intelligence. Additionally, some independent research suggests that high-profile releases like Meta's Llama 4 may struggle to meet expectations in specialized coding tasks compared to its predecessors. These sources collectively map the economic and technical divergence between high-cost professional tools and affordable, ubiquitous AI utilities.

Our 247th episode with a summary and discussion of last week's big AI news!Recorded on 06/03/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Anthropic released Claude Opus 4.8 with improved benchmark scores, discussed eval-awareness findings and welfare/corrigibility themes from its system card, and introduced Dynamic Workflows for long-running multi-agent tasks.Microsoft unveiled the always-on Microsoft Scout assistant built on OpenClaw plus new in-house MAI models (including MAI Thinking 1) and “frontier tuning,” emphasizing enterprise security architecture and model-from-scratch capability.Major business moves included Anthropic's $65B Series H at a $965B valuation alongside an IPO filing, a JPMorgan analysis arguing OpenAI needs major revenue growth to justify infrastructure spend, and Cognition raising $1B at a $25B valuation.Policy and security highlights covered Trump's voluntary pre-release government testing framework for powerful AI, Meta AI support being exploited to hijack Instagram accounts, tightened US Nvidia export controls and China's travel approvals for AI experts, plus expanded Glasswing/Mythos-style cyber and biodefense initiatives.Timestamps:(00:00:10) Intro / Banter(00:04:10) Sponsors(00:07:10) News PreviewTools & Apps(00:07:54) Anthropic releases Opus 4.8 with new 'dynamic workflow' tool | TechCrunch(00:22:37) Microsoft Scout is a new AI personal assistant built on OpenClaw | The Verge(00:26:55) Microsoft launches new MAI family of AI models at Microsoft Build | Mashable(00:37:43) Robinhood now lets your AI agents trade stocks | TechCrunch(00:40:49) OpenAI launches new Codex tools for white-collar work | TechCrunch(00:43:40) ElevenLabs' new music-generation model can switch genres mid-track | TechCrunchApplications & Business(00:44:35) Anthropic Hits $965 Billion Valuation, Surpassing OpenAI - WSJ(00:45:32) Anthropic Files to Go Public, Setting Stage for Huge I.P.O. - The New York Times(00:51:15) China's ByteDance Developing New AI Chips Like Those from Nvidia Partner Groq(00:55:00) Anthropic expands Mythos to 150 additional organizations(00:55:35) OpenAI needs a 26x revenue increase to justify its buildout(00:58:46) AI coding startup Cognition raises $1B at $25B pre-money valuation | TechCrunchProjects & Open Source(01:00:50) MiniMax-M3 debuts, eclipsing GPT-5.5 and Gemini 3.1 Pro on key benchmark performance for just 5-10% of the cost | VentureBeatPolicy & Safety(01:06:08) Trump Signs Executive Order Seeking Oversight of A.I. Models - The New York Times(01:11:45) Hackers Simply Asked Meta AI to Give Them Access to High-Profile Instagram Accounts. It Worked(01:13:058) Chinese AI experts in private firms now required to secure approval before international travel — Beijing enforces policy to secure top-tier talent, expands measures beyond government(01:17:53) U.S. Tightens Controls on Nvidia AI Chip Exports | Let's Data Science(01:21:47) OpenAI launches Rosalind Biodefense, offers federal agencies early access to its life-sciences model(01:24:00) Using LLMs to secure source code(01:26:19) Project Glasswing: An initial update(01:29:30) White House Approves $9 Billion for Spy Agencies to Catch Up on A.I.(01:32:11) US Law Enforcement Warns of ‘Anti-Tech Extremism' as AI Hatred GrowsSynthetic Media & Art(01:35:38) YouTube will now automatically label AI videos | TechCrunchResearch & Advancements(01:36:22) Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention(01:41:26) From Simulation to Enaction: Post-trained language models recognize and react to their own generationsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

✅ New autonomous agents. ✅ Canva designs made for you. ✅ Codex upgrades to make your business move. If you had your head down in spreadsheets this week, you missed some MAJOR AI upgrades that are available now. We track what's hot and what's not and break it all down on Fridays with our Friday Features. Autonomous Copilot agents, new Codex tools, Github CoPilot app and 7 more AI updates you should be using — An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:OpenAI Codex Role-Specific Plugins LaunchMicrosoft Build Conference AI Feature ReleasesChatGPT Memory and Business Account UpgradesMicrosoft Flash Image Model for PowerPointCanva Integrated with ChatGPT and CodexGitHub Copilot Standalone Desktop App PreviewMicrosoft Autopilot Always-On Work AgentsOpenAI Models Now Available on AWS BedrockCodex Sites: AI-Built Internal Web AppsTimestamps:00:00 OpenAI's big money moves03:47 Explaining role-specific plugins09:02 Microsoft's new image model release11:09 Microsoft's AI strategy and Canva update14:23 Canva integration with ChatGPT16:56 GitHub Copilot's new canvas feature20:46 AI token subscription changes24:42 AWS adds OpenAI models to Bedrock28:25 Introducing OpenAI's CodeX Sites Feature32:07 Launch of OpenAI's New Plug-in34:16 Overview of podcast structureKeywords: Autonomous copilot agents, Codex tools, GitHub Copilot app, OpenAI Codex, ChatGPT business accounts, OpenAI enterprise, Microsoft Build conference, Microsoft always-on agents, AWS AI updates, Canva plugin, ChatGPT memory upgrade, Windows Codex integration, Microsoft Flash model, Enterprise apps integration, Role-specific plugins, Sales data analytics, Product design AI, Creative production AI, Investment banking plugin, Public equity investing, Data analytics plugin, Workspace admins, App permissions, Role-aware work agent, Financial research automation, Microsoft image generation model, PowerPoint AI integration, OneDrive AI features, Visual design creation, Canva app for ChatGPT, Canva MCP server, Agentic context carry, Full screen design preview, GitHub Copilot desktop app, GitHub Copilot Canvas, Agent-native command center, Parallel agent work tree, Code app interface, Model options in GitHub, Token usage limits, Subscription token subsidizing, Anthropic token efficiency, Amazon Bedrock, GPT-4, GPT-4.5, Small language models, Token reckoning, Security governance, Inference engine, Code app sidebar, Codex Sites, Internal dashboards, Project trackers, Interactive web apps, Shareable AI apps, Enterprise data connectors, ChatGPT Canvas, Automated workflow, Workplace authentication, Creative briefs repository.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

Today on the AI Daily Brief, NLW breaks down new pieces from OpenAI and Anthropic that reveal how the leading AI labs think about recursive self-improvement, frontier AI governance, and what happens next as AI starts accelerating its own development. In the headlines: reports that the U.S. government is discussing taking equity stakes in major AI labs, OpenAI upgrades ChatGPT memory, and rumors swirl around GPT-5.6 and Anthropic's Mythos.Sign up for AI Executive Catchup: ⁠⁠https://aiexecutivecatchup.com/⁠⁠Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠kpmg.com/us/Sophisticated⁠⁠⁠⁠⁠⁠⁠⁠Bolt - Claim a free month of Bolt Pro - ⁠https://bolt.new/partner/aidb/⁠Outsystems - Stop wondering how AI will change your business and start building the agents that will lead it - http://outsystems.com/Scrunch - The AI customer experience platform - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://scrunch.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Zenflow Work - Agents for knowledge work - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://zenflow.free/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

Everyone is talking about Mercury-alpha, the mystery model that many believe could be GPT-5.6.In this live discussion, we're separating fact from speculation and unpacking what would actually matter if OpenAI releases a new flagship model this week.We'll cover:

Summary Jared Correia sits down with Sean McTigue, a partner at Bartko Pavia LLP and one of the more technically fluent attorneys in practice today. Sean unpacks how his firm navigated the leap from legal-specific AI tools to a direct enterprise deployment of Anthropic's models, and why he thinks that distinction matters a lot more than most firms realize. The conversation covers practical ground: how to use Westlaw's Quickcheck as a verification loop, why lawyers overestimate what AI will do for them on the first try, and how to find the early adopters inside a firm and turn their discoveries into firm-wide workflows. Sean also looks ahead at what AI means for the billable hour model and why the legal profession can't afford to stay in the way. About the Guest Sean McTigue is a partner at Bartko Pavia LLP in San Francisco, where he handles complex litigation with a particular focus on integrating AI into the practice of law. He has been following the development of large language models closely since GPT-4's launch and has led the firm's rollout of Anthropic for Enterprise. Sean studied philosophy at the University of Utah and earned his law degree at Berkeley Law. Key Takeaways Hallucination risk in AI outputs is a solved problem, using tools like Westlaw's Quickcheck as a verification flywheel alongside AI drafting, not a reason to avoid AI entirely. Legal-specific tools rarely add value beyond a general foundation model; the wrapper around the model matters less than most vendors claim. Direct enterprise deployment of a foundation model lets firms ride the frontier rather than being stuck on whatever model a SaaS vendor last tested. The billable hour model is under pressure, and firms that build internal AI capital now are better positioned to shift toward fixed-fee and alternative-fee arrangements. Adoption inside a firm starts with finding the heavy users, learning what they figured out, and distributing those workflows to everyone else. Links and Resources Red Cave Law Firm Consulting Bartko Pavia LLP Westlaw CoCounsel Westlaw Quickcheck - available inside your Westlaw subscription Anthropic for Enterprise Keywords AI adoption in law firms, legal AI tools, Westlaw Quickcheck, AI hallucinations legal, foundation models for lawyers, Anthropic for Enterprise, billable hours AI, legal tech vendor evaluation, Sean McTigue, Bartko Pavia, Jared Correia, Adventures in Legal Tech, CoCounsel Westlaw, AI verification legal, small firm AI, legal workflow automation, enterprise AI deployment, AI research tools lawyers, prompt engineering legal, alternative fee arrangements AI Episode Highlights [00:02:01 - 00:04:54] Sean introduces Westlaw Quickcheck as the underused verification tool that turns hallucination risk into a manageable step in the workflow. [00:05:00 - 00:07:57] Sean explains why lawyers who try AI once, find it imperfect, and dismiss it are missing the workflow question entirely. [00:08:13 - 00:09:28] The hammer-and-nail analogy: being handed a tool and told to use it without any guidance on what the full project actually looks like. [00:19:14 - 00:23:27] Sean describes the frustration of vetting legal AI vendors who can't tell you what model they're running, including an e-discovery platform using Haiku 3 on million-document reviews. [00:24:54 - 00:28:05] The case for direct foundation model deployment over legal-specific SaaS wrappers, and what you can do with a generalist model that a niche tool will never offer. [00:36:44 - 00:40:43] The future of legal billing: from the billable hour back toward fixed-fee engagements, and why firms that build AI capital now are better positioned. [00:41:50 - 00:47:23] Sean's starter recommendation: Westlaw citing references downloaded in bulk and fed to an LLM, plus why Google AI Overview is already AI whether lawyers know it or not.

Sign up for Practi, a new platform that helps law firms use subscription billing.Here are the top 5 takeaways from this episode:* AI native law firms are built on aligned incentives. The billable hour model actively disincentivizes efficiency. General Legal charges a flat $500 per contract negotiation, so their interests align with the client's: get the deal done quickly and at high quality.* The AI efficiency gap in law is now enormous. Five years ago, AI might have improved legal workflows by 10–20%. Today, the gap between firms barely using AI and those using frontier LLMs (Claude, GPT, Gemini) at every step is transformative. JP calls it a “massive, massive gap.”* Frontier AI models still need guardrails for legal work. Raw frontier models are overconfident and can produce legally reckless markups (e.g., zero liability caps, no indemnification). Using them “cold” without legal expertise and custom tooling can actually harm clients and kill deals.* The MSO structure unlocks outside investment for law firms. General Legal separates into a Delaware C Corp (the tech company, which takes VC investment from Y Combinator) and a California law firm (owned and overseen by barred attorneys). This structure lets non-lawyers invest in the technology layer while preserving ethical compliance on the legal side.* The billable hour has 3–5 years left as the dominant model. JP predicts it will die slowly due to law firms' structural resistance since profits are distributed annually rather than reinvested in efficiency. But AI-native firms and fixed-fee models will increasingly take market share, and the trend is irreversible.__________________________Want your question to be answered on a future show? Fill out this short survey.Have subscription model question? Check out this free resource to ask all of your questions at notebook.practi.ai.Check out General Legal.Sign up for Paxton, my all-in-one AI legal assistant, helping me with legal research, analysis, drafting, and enhancing existing legal work product.Get Connected with SixFifty⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, a business and employment legal document automation tool.Sign up for ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Gavel⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, an automation platform for law firms.Visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Law Subscribed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to subscribe to the weekly newsletter to listen from your web browser.Prefer monthly updates? Sign up for the Law Subscribed Monthly Digest on LinkedIn.Check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mathew Kerbis'⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ law firm ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscription Attorney LLC⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Want to use the subscription model for your law firm? Click here to sign up for a new platform that helps law firms use subscription billing. Get full access to Law Subscribed at www.lawsubscribed.com/subscribe

Jamie Metzl asked AI to distill thousands of years of human wisdom into 10 commandments. What it reflected back says more about us than the machine.Full show notes and resources can be found here: jordanharbinger.com/1338What We Discuss with Jamie Metzl:AI is a mirror, not a prophet. For his latest book, The AI Ten Commandments, Jamie Metzl worked with GPT-5 to mine humanity's scriptures, wars, myths, and philosophies for ten universal principles — not to worship AI or replace religion, but to hold up a mirror and stress-test the rules by which we're already living.Radical transparency about AI co-authorship cuts both ways. Putting GPT-5 on the cover felt honest to Jamie, but with public sentiment soured, the same disclosure that read as bold a year ago now reads to many as an admission of cheating.Pressing the button gets you "the total average of crap." Jamie cut 40% of the draft, rewrote the whole book, and hired two human editors — proof that good AI-assisted work comes from relentless human editing, not from outsourcing the thinking.Humans aren't on the verge of obsolescence. We represent nearly four billion years of embodied evolution, and the claim that machines will soon do everything better sells short the majesty of being human; the real frame is a Venn diagram of overlapping strengths.Stop building second-rate humans and second-rate machines. Don't fear replacement — ask how to help your humans be the best humans and your machines be the best machines, and use AI to stress-test the rules by which you're already living.And much more...And if you're still game to support us, please leave a review here — even one sentence helps! Sign up for Six-Minute Networking — our free networking and relationship development mini course — at jordanharbinger.com/course!Subscribe to our once-a-week Wee Bit Wiser newsletter today and start filling your Wednesdays with wisdom!Do you even Reddit, bro? Join us at r/JordanHarbinger!This Episode Is Brought To You By Our Fine Sponsors: Lufthansa Allegris: Go to Lufthansa.com and search for "Allegris" to learn moreEarnIn: Download EarnIn on the App Store or Google Play, type JordanHarbinger under PodcastDripDrop: 20% off: DripDrop.com, code JORDANBooking.com: Book your getaway now with booking.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of the Ecomm Breakthrough Podcast, host Josh Hadley shares seven practical ways his e-commerce business uses AI to optimize operations and scale growth. Drawing from his experience building an eight-figure brand across Amazon, TikTok Shop, and Shopify, Josh covers strategies including building custom GPTs, automating TikTok Shop listing optimization, streamlining hiring processes, leveraging Alexa data, analyzing meeting transcripts, scaling ad creative production, and cloning leadership decision-making into AI-powered SOPs. Josh emphasizes treating AI like a new team member requiring proper training, offering actionable, real-world insights over hype.Bullet Points:Practical applications of AI in e-commerce operationsOvercoming fears and misconceptions about AI adoptionCustom GPT development for task automationAI-driven optimization of product listings on TikTok ShopAutomating the hiring process with AI scoring systemsUtilizing AI for product insights through Amazon Alexa dataAnalyzing meeting transcripts for business insights and decision-makingScaling ad creative production using AI-generated video contentCloning leadership decision-making into AI Standard Operating Procedures (SOPs)Viewing AI as a team member requiring onboarding and trainingTimestamps:00:02:00 Weekly Custom GPT CreationThe speaker's 35-person team is required to create or enhance a custom GPT weekly to automate their specific tasks.00:04:05 AI Agent for TikTok Shop OptimizationAn AI agent integrated with the TikTok Shop API continuously tests and optimizes product titles, descriptions, and main images weekly.00:08:32 Automating Hiring Case Study ScoringAI is used to automatically score applicant case studies based on a predefined rubric, saving hours of manual review time.00:11:29 Custom GPTs Integrated with AlexaCreating custom GPTs that analyze customer questions on Amazon Alexa to optimize product listings and improve Alexa recommendation rankings.00:12:11 Analyzing Company Meeting RecordingsAI analyzes transcripts from all company meetings to identify business constraints, track team progress, and provide a leadership pulse.00:13:56 Scaling Ad Creative ProductionUsing AI video generation tools to quickly produce a high volume of ad creative for Meta and TikTok campaigns.00:14:48Cloning Leadership Judgment and Decision-MakingUsing AI to document processes and decision-making frameworks from leaders, creating an internal knowledge base to empower team members.Links and Mentions:AI Tools:"ChatGPT": "00:02:00""Claude AI": "00:02:00""Fireflies AI Notetaker": "00:11:25""Veo3": "00:14:27""Notion": "00:16:24"E-commerce Platforms:"TikTok Shop": "00:04:05"Videos and Resources:"30 60 90 Day Onboarding Framework": "00:07:52""Episode on Cloning Yourself Utilizing AI": "00:15:24"Transcript:Josh Hadley 00:00:00  Today, I'm going to be walking through seven different ways that we are implementing AI into our e-commerce business and practical steps that you can take to implement it in your business as well. Welcome to the Ecomm Breakthrough Podcast, I'm Josh Hadley. I've scaled my own ecommerce brand from 0 to 8 figures, and I'm actively building towards nine figures in sales. This podcast is where I document that journey and share the systems, the strategies, and the lessons learned in real time so that you can learn what actually matters and scale your own business. My name is Josh Hadley. First and foremost, I'm a man of faith. I'm a husband to a beautiful wife and also the father of four children. I've been selling in the e-commerce space for over a decade now, doing multi-million in revenue on Amazon, TikTok, shop and Shopify. And I am also the host of the number one business strategy podcast for ecommerce, and that is E-com breakthrough. Today, I want to dive into the practical use cases of how we're implementing AI into our business.Josh Hadley 00:00:58  Today. I hear a lot of noise going on in a lot of the e-commerce groups. There's a lot of like doom and gloom of, oh, you're getting left behind if you're not actually implementing AI in your business today, if you don't have an agent managing your PPC campaigns, you're late to the party, etc., etc. there's a lot of fear. And then what ultimately happens is there's a lot of entrepreneurs that because there's so much fear and anxiety around it and feel like they're already behind. They just stay stuck and they're just kind of like frozen because nobody's providing actionable content regarding like, here are the actual practical use cases of AI. Yes, there are some incredible features with Claude and integrating it to your email system, right. And being able to monitor your emails for you. Yes, there are some incredible ways to use ChatGPT and the new images that it's able to produce, right? Like, there's a lot of good things that are happening that way, but a lot of times the practical use cases where actually maximizes value in the business gets left to the side, or nobody's actually addressing them.Josh Hadley 00:02:00  So that's what I wanted to do today, is actually provide you with practical use cases that if you're an e-commerce brand, you can go replicate these exact same frameworks and implement AI in your own br...

Ever wonder how G2 employees manage to completely dominate your LinkedIn feed with high-value content?In this episode, Lewis Gray and Elliot Elsley sit down with Jenny Gardynski, Senior Director of Content and Communications at G2. With over 13 years of PR agency experience representing fast-growing B2B tech giants, Jenny knows exactly how to build authentic brand awareness from the inside out.Listeners will dive into the exact mechanics behind G2's manual advocacy program, learning how their communications team builds high-performing social kits, launches tactical Slack engagement loops, and embeds custom LinkedIn branding into new-hire onboarding.Jenny shares strategies on why workforce personalization outpaces corporate channels by 5x, how to execute strategic quarterly contests that prompt passive employees to start posting, and how to effectively build custom GPT structures to scale executive voices while completely eliminating robotic "AI-isms".If you're currently struggling to prove a proof of concept with a manual social pilot, or if your employees are hesitant to share content because they are afraid of sounding unauthentic, this episode provides a realistic blueprint.Resources:Want to know how your employee advocacy strategy really stacks up?Grab your FREE Employee Advocacy Health Check and see how you compare against your competitors.Book a call to discover how employee advocacy can benefit your team.Ready to elevate your employee advocacy? Get a free copy of Bradley Keenan's essential book, ‘Employee Advocacy: 101 Cheat Codes' for deeper insights and actionable strategies.Download The World's Biggest Employee Advocacy Study.Subscribe to the Employee Advocacy & Influence Podcast on Spotify or your favorite platform to never miss an episode.

Dan opens in the water off Koh Lanta, Thailand. The science: a 133-foot tsunami wave registers as zero in deep water. Depth neutralizes force before it ever arrives. The wave only becomes a wave when the ocean floor rises to meet it. That is the operating principle for this session: the operators who get hurt in an AI-disrupted industry are not the ones who were too far away from the change. They are the ones who were too shallow when it arrived. Dan then goes personal. The company collapse. Ten years of revenue, one email in Thailand, zero in January. A leadership meeting where he broke down in front of his team. A vision board that had to be rebuilt around actual DNA rather than borrowed aspiration. Kolbe 7-2-9-2: Quick Start 9, Fact Finder 7 -- the profile that explains why he was always ahead of the room and always restless inside frameworks built for Follow-Thru operators. The session is a product launch, but the argument for the product is rooted in scar tissue, not pitch mechanics. The second half is live demos. Personal Operating Diagnostic: a multi-instrument synthesis agent that reads your personality assessments and outputs a build order for your first three AI agents, sequenced to how you actually work. Robert Andjelic GPT: asked where to buy farmland in Western Canada, it answered with the specificity of a researcher and the posture of a trusted advisor. Melissa the nutrition coach GPT. Then: a full website built in 35 minutes with Claude Code -- the same work that cost $5K and months with a contractor. Dan's own fleet of 50 agents, all running on a markdown brain. Agents include a completion system, a voice-lock drafting agent, and a decision brief agent. The demos are not proof of concept. They are the product, already running. The offer: GYFOS Cohort 1. $1,997 USD one-time. 90 days. 12-13 live sessions. 8-12 operators per cohort. 30-day money-back guarantee. $29/month continuation access after the cohort closes. The pitch is precise: this is not a course. It is a guided build. You leave with an operating system that reflects how you think, not a certificate that something was completed. KEY TOPICS - Wave physics as positioning strategy: depth not distance, get in the sweet spot before the ocean floor rises - Dan's company collapse and rebuild arc -- scar tissue behind the GYFOS product design - Kolbe 7-2-9-2 and core values (Integrity, Growth, Wisdom) as the foundation for AI agent design - Personal Operating Diagnostic -- multi-instrument personality synthesis, AI build order output - Live demo: Robert Andjelic farmland GPT, Melissa nutrition coach GPT, 35-minute website build with Claude Code - Fleet of 50 agents on a markdown brain (completion system, voice-lock drafting, decision brief) - Adoption context: 1.8% of Western Canadian agribusiness using AI; 19% of all businesses; 41% of workers - Historical wave pattern: horses to tractors, zero-till (called "trash farming"), internet (2.6M users 1990 to 2B by 2010) - Three takeaways: You are not late. Start with yourself, not the AI. Learn to orchestrate, not operate. - GYFOS Cohort 1 offer: $1,997 USD, 90 days, 8-12 operators, 30-day guarantee   CONNECT - Dan Aberhart: growingthefuture.ca - GYFOS enrollment: growingthefuture.ca (GTF Mastermind) Register for the Convergence Conference at convergence.ag and stay updated by subscribing to the Growing the Future Podcast at growingthefuturepodcast.ca.

We've informally heard that Satya is a listener to LS for a couple years now, but it was still absolutely surreal to meet him and do a live pod at Build, together with our friends at No Priors, the leading VC AI Podcast that we also greatly admire!We covered the MAI model technical takeaways on yesterday's AINews, so I will focus our recap of Satya's main messages around three elements:* Satya's adaptation of the Bill Gates Line for positioning Microsoft as the Frontier Intelligence Platform — customers must gain much more value from the Microsoft ecosystem than Microsoft itself, by building on multi-model harnesses like OpenClaw and Scout, drawing on the full enterprise context exposed by context layers like Work IQ (heavily dogfooded by his C-suite), and building up private evals and traces as a new form of Token IP* AI ROI: On one hand, enterprises are having difficult conversations around Tokenmaxxing and Layoffs, and on the other hand, there are serious re-evaluations of the End of SaaS since the Build vs Buy equation has changed so much. Our previous SemiAnalysis guest had… interesting comments on Microsoft's position on this as the ur-SaaS titan, and Satya had great answers* Making the Impossible Possible: Kevin Scott's inspiring framing around what the most ambitious version of applying AI and technology at large to business and social problems, like education and social impact.Enjoy!Full VideoTranscriptVoiceover: Welcome swyx, Sarah Guo, Elad Gil,, and Chairman and Chief Executive Officer of Microsoft, Satya NadellaSarah Guo: Welcome to a crossover episode of No Priors and Lane Space with Satya Nadella. Um, congratulations on an amazing build. No, thank you so much, and it's great to be with both of you. I listen to both of you or b- both the podcasts all the time. It's great to be on it.Thank you so much. [00:01:00] So you're just talking about, um, these amazing, uh, announcements from across the Microsoft estate all morning for, I think, three hours. What is the, uh, what's the most important reflection or takeaway you have?AI as an Ecosystem PlatformSarah Guo: I, I'd say there are, uh, perhaps the, the biggest one for me is let's sort of conceptualize this more as an ecosystem play as opposed to a single model or even a single platform, right?Satya Nadella: I mean, you know, whatever I... At least for me, having grown up at Microsoft, having seen, whatever, four major platform shifts, uh, I sort of fall into that, um, uh, camp where a platform is defined by fundamentally its ability to create more value about the platform versus what's captured in the platform. And so if you, you view what's happening right now, I think this morning's keynote was how can any company, whether it's an AI native company or a traditional enterprise company, participate as a first-class participant where they can point to AI they created, [00:02:00] right?It's not that they don't use other people's AI. Of course they will. But to me, what's the path? What's the recipe? How do I do it? What does a stack look like? What does the tooling look like? What is valuable? How do you do that? That's it. That's sort of our job to do. Yeah. Ecosystem strategy is, uh, very complicated, right?Sarah Guo: Because you end up building certain components, partnering for certain components, supporting them. You just announced this big suite of models. Like, tell us a little bit about the, uh, training strategy for Microsoft now. Yeah.MAI Models & Training StrategySarah Guo: So, so the thing that we wanted to do with the MAI models was to build, and as Mustafa talked about, first of all, a great lineage, right?Satya Nadella: Starting with pre-training, uh, with very good data quality, uh, doing all the ablations, making sure because in, in some sense it's becoming even harder to build a clean lineage model just because there's so much stuff out there, uh, that you truly need to ablate out to be able to have a fantastic [00:03:00] pre-trained model.In fact, that's one of the challenges of a lot of the open weight models is they look great on one benchmark or two, but they're not great on practice. So that's why, in fact, even in the RFDEs are, they, they are pretty gone really excited about these MAI models because how the heck can a small five B model hill climb?Uh, and it goes back a little bit to what I think is ultimately the key thing to do, which is try to pursue finding that cognitive core. Uh, so to me, starting with a clean lineage- Then creating that ability for companies to be able to use this, right? Not just as a generalist, but to create their own specialist by building this hill climbing scaffold around it, right?So it's not just the model, but you have a hill climb scaffold around it, then you will start building your RLE. You will start collecting the traces. Most importantly, you'll have private evals because we know all the evals out there are good, interesting, [00:04:00] but they're not really that critical- They're work, yeahSwyx: at this point because they all can be maxed. And so the point is each company will have its own private eval. And so that end-to-end platform story around our models is sort of, uh, what I think is interesting. And then the one other thing, Sarah, since you brought that up, is I do feel there's a new frontier.Satya Nadella: Like people talk about the frontier and are you operating at the frontier. Um, interestingly enough, if you add a little temporality to it, you can use, let's say, in, in, in fact, the, the Lando Lakes demo we showed was pretty cool. We used, whatever, GPT-55, right? Then you collected a bunch of traces, and then you took a 5B reasoning model and achieved higher.Sarah Guo: Uh, so that is another aspect of what it means to appear... uh, you know, operate at the frontier Yeah. I, I think, uh, I first of all have to congratulate you on basically building a frontier neo lab inside of Microsoft in two years. Um, I'm wondering, you know, you have all this AI strategy that you're rolling out.Lessons from Two Years of AI DevelopmentSwyx: I'm wondering, what do you know now that you wish you would tell yourself two years ago where- or two or [00:05:00] three years ago? Three years for the Jensen partnership, two years for, uh, MEI. Yeah, I mean, I think the, the thing when, that I reflect quite a bit, right, which is sort of obviously I got into all this when I got excited by the, the scaling laws paper and, you know, when, you know, even the OpenAI partnership came about when those folks said, “Hey, we're gonna really throw a lot of computer transformers.”Satya Nadella: Uh, and they've helped. I- the thing that I always look back and say, “Wow, these things, uh, do have capability that they're climbing up.” W- I mean, this, you know, this crude way of saying it is intelligence is log of compute kind of works. Now what I think we underestimated perhaps is the real-world complexity of deploying these so that they actually deliver the value in the real world, right?So the outcomes as measured by any benchmark is interestingly important, but the true eval is when people out there are able to do unique things that they only can value, and it's very [00:06:00] measurable, right? That I wish we had sort of even, like, had more in our consciousness, right? Which is as an industry.Sarah Guo: Because right now I think when people say, “Wow, I don't want a token max,” it's an artifact of us not having thought ourselves as an industry that we are using tokens to create value every step of the way. So I think that's kind of what I wish we had gotten there, but I'm glad we are here.Real-World Value & Use CasesSarah Guo: What are some of the use cases that you've seen that have created the most value for your customers?Because I know that people talk a lot about code, and I think it's pretty clear that that's something that's having very large scale impact. Are there other areas that you find in common that your customers are really benefiting from? Yeah. I think, yeah, to your point, obviously coding is now got... But it's interesting, by the way, Elijah, to even talk about the coding, right?Satya Nadella: Which is coding has worked so well that we now have to rebuild the IDE, right? I mean, it's kind of nuts to see what we sh- launched is like, oh my God, I have these hundred agent sessions. I... The cognitive load it transfers back to me as a human is so [00:07:00] excessive that now I need a new UI. Uh, oh, by the way, I, like the, the chat as the only artifact was also impossible, so that's why we need a canvas.So it's kind of interesting for all the things about where is software needed or where is UI needed, uh, you kind of need that even for code, right? In a fully agentic world. But that said, one of the things that we are starting to see, we started seeing with co-work, but even some of the work we, we showed with auto com- uh, um, autopilot Right on what you see with claws is a good one because if you sort of think about a lot of human capital is doing the glue work, right?If you now can augment that with tokens/agents that are long-running, durable, right, then your ability to scale even what is still judgment and glue work gets amplified like coding does. Uh, so you can... Like, I'm positive that six months from now we'll all be saying, “Oh, wow,” like, all through ni- the night there was a bunch of stuff that [00:08:00] all these autopilots that I have working on my behalf with my delegated authority, so to speak, right?I can... Sort of given even my identity, did a bunch of work, then of course I'll need my new ADE to say, “Well, what did you do?” Like, I might... “Did I do this work?” And so on. So I think that that's where compressing of workflows, uh, completing of tasks, uh, that's where I think a lot of the value gets created. I think you raised a really interesting point, which is there's the actual agent that's doing the code, and then there's a harness around it, and that's the environment, that's the context, that's everything you're setting up as a developer around actually a coding agent.The Harness Concept for Enterprise AISarah Guo: What is the harness for the enterprise? Is there an equivalent concept for broader productivity work, or how do you think about that concept sort of generalized? That's right. So, so in some sense you kind of want the harness to define the models, the, the data, uh, and the tools, and so that you have a loop across those three.Satya Nadella: And so what we are trying to, first of all, make sure is each of our products that we build, right, whether it's GitHub Copilot or the security copi- the, the [00:09:00] stuff we showed with MDASH or even the discovery for science, it doesn't matter, all of them are multi-model harnesses, um, with tools access so that you can do this progressive, uh, disclosure of tools even so that they're token efficient.Uh, and then you're feeding it with very rich context because that's sort of the other hard lesson we have learned in the last two years is, oh my God, the amount of work you need to do to prep the context layer, uh, such that your plan can execute in the most efficient way is where the magic is. So we have, in our case, we have the GitHub harness, which essentially we're using across all our products.It's available in Foundry, and we are open, like you can use your Llama harness, whatever. Or you can use the, um, uh, you know, any open harness or any harness of yours and train with your tools and multiple models and your context. And so that's the pitch. Because right now a lot of dialogue is, um, “Hey, if I train the harness plus tools and the model together, you get [00:10:00] evals.”Elad Gil: And what we are proving out is... And the best example of that is what we did with MDASH, right? Because when it launched, uh, it found bugs or vulnerabilities that were not found by Mythos Uh, and so there is existence proof, I would claim, that you can have a multimodal harness, uh, that can in fact be more, uh, performant in the real world So a premise behind the, uh, training at the independent frontier labs is really, you know, we're gonna have these models, and we'll have an API business, and we'll support enterprises and startups.Sarah Guo: ButPlatform Strategy & Developer EcosystemSarah Guo: a first-party product, be it productivity or code or search, drives the majority of revenue. That's a different value equation than you're describing, I think, with the Microsoft ecosystem. Uh, if, if that's the case, tell me if it's the case, uh, ‘cause obviously you have first-party products and you have enablement products.Satya Nadella: Um, what is the role of the develop- Like what is gonna be hard and the set of skills and the value capture the developer has in that world? Yeah. So I think that there's always [00:11:00] gonna be the case that someone who is super successful in- as a platform builder can also have first-party products. It was true with Windows.It is true, uh, with, uh, the, the SaaS side and the cloud side as well with us and others and so on. But the thing that is, is it should not be a limiter to other people achieving that same success, right? That I think is the core difference, which is the, the network effects this time around, around intelligence are such because they learn from data, and not really lots of data.It's just a few samples that you have to see to understand what's novel about something. So that's why the game becomes how to protect. So that's why I would say every company, having private evals may be the biggest IP, right? Think about it, like what's that private eval that you can then use even a frontier model to hill climb on and not leak the traces may be one of the biggest [00:12:00] drivers, uh, of IP.Like, so in other words, another te- acid test is you have an eval that's private. You're using, uh, a g- a Model A. Can you switch it to Model B and e- you know, climb up? If you can, then you're in control. If you can't, you're not in control, and that's where even the harness decision becomes super important, right?swyx So therefore, having an open harness, letting all models come in, having your evals, your context, your tools help you hill climb, I think is the skills that an AI native startup needs, a SaaS company needs, or every enterprise needs. Yeah, I think in, in a very real way you are ... Microsoft historically is an operating systems company and th- then become a cloud company.Maybe like the third act is that you're a harness or evals company. Whatever w- ... whatever the, the sort of conglomerate of concepts that you wanna put together. Um, and, and I think like enabling every company to have like frontier intelligence or what- what- Yeah ... I forget the, the [00:13:00] exact term that you used, um, is the, is the mission, right?Satya Nadella: That's it. Like that is, that is the platform promise, that you build with us, you will get your intelligence, uh, for your data. That's it. That ... To, to me, that is the ... Like if there was one tagline, uh, for this entire developer conference is- Can everybody operate at the frontier with their frontier intelligence, right?To me, that is so important because otherwise it, I, I don't know how you achieve stable equilibrium, right? Which is how do I then go and say, “Well, my company is gonna have a terminal value because I now know how to continuously compound-” Yeah ... on top of what's a platform that gets better,” right? So when, like Windows obviously came out, Adobe built, Autodesk built, uh, or even like take what Jensen said.We built DX and he built, you know, CUDA on top of it. Um, right? I mean, I always say to Jensen, “God, I got the short end of that,” right? “I wish, uh, we had recognized it.” But nevertheless, but that, that idea that you can build a platform layer [00:14:00] that someone else can then extend out, um, and build their own intelligence layer in this case, I think is everything, right?Without it, why have a developer conference? I can just come and have you all sort of just worship at the altar of one model. Yeah. But that's not a developer conference. Uh,IP, Evals & Company Valueswyx: backstage we, we had a discussion about what is IP or what is the, the value in a company. It used to be the length of, uh, human experience at a company, and now it's this other thing which is the evals, the, uh, experience in sort of applying agents to the company. Can you... I just want you to like flesh that out a bit more ‘cause- Yeah ... it was very insightful.Satya Nadella: It's a great way to frame it, right? Because yeah, at the end of the day, every company is gonna have both the human capital that is still gonna be super valuable, uh, because humans, uh, and their ability to find the gaps that exist at all times is going to be the way we all will create value, right?I mean, so I'm definitely in the camp that this is going to be about expressing new forms of human agency and ambition even as token capital goes up, right? So let's say a cor- any corporation [00:15:00] has lots of tokens and lot of human capital. The question is how do you compound the two? So if you have a... Like if you take in Teams I have a bunch of agents doing work and a bunch of humans doing work, and the traces between those, that is really important context of how that enterprise is creating value.Then that goes back to train not a generalist model, but to train the company veteran agent, uh, right? That is super valuable again, right? Which is when a company goes says, “It should in fact go onto the balance sheet,” is how I think about it, right? That's so... In fact, there may be... Like human capital was never possible to go put on a balance sheet, uh, because you didn't know how to capture the tacit knowledge.swyx: Whereas now I think you can with the agents that have learned through the h- through, through time, through all the traces. Uh, so that's what at least we think will happen. I, I think the SEC is gonna have to have accounting standards- ... for token, uh, expertise Uh, y- y- you're talking about the equilibrium [00:16:00] state, um, and a stable equilibrium where companies have this compounding value and can see terminal value for themselves.Future of SaaS & Business ModelsSarah Guo: Another challenge to, you know, the considered equilibrium of, okay, there are applications and workflows that are sort of common to a vertical or a horizontal. Um, and this was, like, the generation of SaaS companies and, you know, Microsoft has lots of SaaS properties as well. And then there are things that are very specific to every enterprise that they're differentiated against.Elad Gil: Um, I'm sure you have heard much and participate in much of the debate about the end of software because all these workflows are, are cheap to generate now. Um, do you think the equilibrium looks different between what agents get built- Yeah ... in enterprises versus in their vendors in the future? Yeah. So I think what's happening there is, see, we, we had a particular way we captured, um, I would say workflow in apps, right?Satya Nadella: Because we built a, a data model, right? We schematized some part of some business process. Mm-hmm. We then built a bunch of business logic. Yep. And then we put a bunch of UI [00:17:00] on top of it, right? So that's kind of what every SaaS company- And a little configuration. For, like, 20, 20 years that was the plan.Right, that- Yeah ... and that was it. So interestingly enough, now you kind of get to re-litigate that vertical stacking, right? So I still think, for example, that data model that you built underneath every SaaS application is super good, right? Like, why reinvent it? Like, I, I, my general ledger better be a general ledger.I don't need new schema creation. No. Uh, in fact, that entity relationship, uh, is actually pretty good, robust thing that I want to feed. And you want it to be stable. That's right. Yeah. Then same thing with business logic, right? If, if you look at, uh... We have this product called Power BI, right? It is like dashboards galore people created.The beauty underneath that dashboard is a very rich semantic model, right? Someone took the pain to create a dashboard and do all the measures, and you want that. That's business logic, right? I want that to be available to me. So I think the [00:18:00] challenge of the SaaS business model is we packaged one way. We now have to learn how to unbundle these things and rebundle in new ways and discover new business models, right?I mean, if you look at it, d- what's happening today with Microsoft 365 is a great example, right? We have this thing called Work IQ. In fact, like, what we are realizing is, oh my God, like, you know, if you look at... In fact, there's a pa- historical parallel too, right? We sold first Exchange and SharePoint and, uh, you know, before Teams, we had a thing called Lync Server and what have you, and we thought, “Oh, that's all gonna move to the cloud.”But little did we realize that, um, the number of people who will use servers in the cloud is 10X, 100X, right? Because people were not buying servers, they were just buying a subscription. Mm-hmm. The same thing is now happening with M365 because with Work IQ, we have exposed what is perhaps the most important database in a company that never got used as a database because it was only captive to our apps.Mm-hmm. Right? It, it was all email operated on it, Teams operated [00:19:00] on it, Word, Excel, PowerPoint, SharePoint. But now, like this is one of the coo- coolest things I get to do with Work IQ. I go to a GitHub repo and I say, “Hey, I attended a bunch of design meetings last week related to this repo. Can you capture all that and tell me what changes I should make?”I mean, think about that, right? It literally can go look at all those transcripts, come back with a plan to change a code base, right? Previously, you could never have thought of using M365 for something like that. So the value creation opportunity now in the agent world is in fact 10X more, but it does require us to have...Sarah Guo: For example, there's going to be usage around M365, right? Which is going to be perhaps more than even the e- end users and we have to even re-architect. Like, in fact, like what I use to serve an inbox or a mailbox cannot be used to serve an agent. Uh, and so that's sort of what we are doing.Pricing Models: Per-User, Consumption & OutcomesSarah Guo: I don't believe in, like, permanent business models for any of these domains, but in the [00:20:00] near term, do you have a prediction between, uh, you know, outcomes-based pricing, token-based pricing?Elad Gil: Enterprise bundles Yeah. The way I- I think about this is always we've had... Like, let's even take the per-user pricing. Mm-hmm. The per-user pricing is really an artifact of someone creating a budget needing certainty, right? Because it's the most important thing. Like, somebody wants a budget- Mm-hmm ... they need a per user.Satya Nadella: And, and per user is just a set of entitlements to usage, right? That's kind of what it is. And so the way is, if the first bundling will be take some usage, bundle it into per user stacks and, you know, then sell subscriptions. So subscriptions I think are gonna be there, per user is gonna be there. Then the next big thing will be consumption.So people will say, “I want consumption.” And it's also possible that people will say, “I don't even want to pay for any of the subscriptions or the consumption's outcome.” Mm. But remember, most people love outcomes until they have an outcome, because once you have an outcome, it's like giving away royalty, [00:21:00] right?Mm. I mean, like I, I've talked to customers who love, you know, outcome-based pricing, and I say, “I'm all in,” until they, “Oh my God,” like, “what are you talking about? You're sharing in my outcome? No, no, no. I want you to go back to per-user pricing, and I want you to consumption price,” right? So I think that debate will go on.Uh, but and all, all, all of these business models have a particular time and a place versus one to rule them all. And if anything, if you're a SaaS vendor or you're a platform vendor, having that flexibility... And quite frankly, we face this with GitHub, right? We just recently announced a per-user pricing on GitHub because little, you know, we- GitHub Copilot was constructed at a per-user level before we understood even, uh, the intensity of usage of agents, right?It was an interactive way for a developer to use code complete, maybe tasks. It was not like, oh, I launched 10,000, you know, agents that are going on all day, right? So that is what the adjustment is about. So now that we really want, there will [00:22:00] always be a per user, but there will have to be a consumption meter.Durability of SaaS & Build vs BuySarah Guo: How do you think about the durability of SaaS more generally? One thing I've observed is in a lot of enterprises internally, there will be teams that almost have agent euphoria. They're so excited about the explosion of things they can build that they're trying to rebuild a lot of applications or going to their SaaS vendors and saying, “We're not gonna work with you anymore,” or, “We're considering an internal project.”And it seems like in six to nine months, maybe some of those people will come back and say, “Actually, we, we can't rebuild everything.” How do you think about what's durable in this world and what isn't? Yeah, it's a... It... I think we have to go through one full budget cycle on this to really see the, um- Uh, the sort of the emergence of the equilibrium, because at the end of the day, there's marginal cost to even generating the app, right?Elad Gil: In, in fact, there can be even a, a simple way to say it, like if you should always acquire something if the marginal cost of building and maintaining, uh, something on your own is higher. Uh, right? That should be like it's a quantifiable- Yeah. Right? A quantifiable thing. And [00:23:00] the maintenance part is important, right?Even, like you got to remember like, hey, you know, all the security stuff that now AI will find, you better fix them too fast. Uh, of course, there's a coding agent to help you with, but then that burns tokens, right? So whose responsibility is it? It's kind of like a, a cycle that you've got to think through.And I think we have gone through the excitement that I can generate a lot of software. I think the next thing would be what software do I really want to generate? Mm-hmm. What software do I want to use from others? How do I compose these two into some agentic workflow that I have agency over, right?Sarah Guo: Because I think there'll be very little tolerance for anybody who's inflexible, uh, at the vendor level. Uh, but at the same time, I think that anyone who has got that flexibility shows up, delivers the value, will be back at again, right? We're selling software, uh, but with just different business models, in fact Uh, speaking about building software, um, one of my favorite moments from, I think, a previous build maybe one or two years ago was they had a b- they, they...Swyx: There was a section of you building your [00:24:00] own software. I'm curious if you're building anything now. Yeah. So I, I think the... You know, first of all, let's face it, right? Building software has made it possible for even the incompetence of a CEO of a company- ... like ours, uh, you can build, so thank God. But that said, I, I, I, I do feel that, you know, something like, um, GitHub Copilot to me, and especially the new Sessions app or the new app, has just made it so much more possible for you to have agency over artifacts that you felt you couldn't touch before, right?Satya Nadella: So to, for me as a CEO, even to go to a code base, uh, to be able to learn about it, like I remember joining Microsoft long back, you know, first and then you say, man, everybody had to go in and look at, you know, whatever, Cutler's, Malik, or what have you to learn how to do good C, uh, C++ code. Um, so now that ability to be more full stack up and down is so good, but that doesn't mean every one of us should be doing the same thing.The question is: [00:25:00] how do you then have the ability to inspect things, learn things, see things, um, I think is just so much more. And so to me, what I'm building a lot of is these long-running Foundry agents. Uh, right? So there's autopilots. So the easiest thing is, to me, I think I just built one, uh, even last week, where the idea was, hey, can I have an agent that is continuously monitoring essentially my own chief of staff autopilot, right?We're gonna have that obviously in, uh, Scout. That's what, uh, uh, we showed. But it is so easy and trivial to build. I took Work IQ. I said, “Take Work IQ, go, uh, and build a Foundry long-running agent.” Uh, store all the memory in, um, uh, using Ray Fin, right? Basically at my backend as a service. And lo and behold, it built it, and not only built it, I could say publish to Teams, and it published the damn thing to Teams.Sarah Guo: So the ability, uh, to have a, you know, some end-to-end project like this complete is just pretty [00:26:00] miraculous. How do you think, uh,Future Engineering RolesSarah Guo: that impacts the different types of engineering roles that exist in the future? Because right now I think there's, you know, a dozen different types of engineers that you can be, from QA, front end, et cetera.You know, there's a big swath. I've heard some people argue that in four or five years we'll basically end up with four engineering roles. It'll be people who are managing agents, it'll be four deployed engineers or FDEs, it'll be security engineers, and then people working on large scale infrastructure for a small number of services, and then everything else just collapses into the agentic world.Satya Nadella: Yeah, I- Do you think that's a correct view of the world? Yeah, I mean, I think, I think we'll have to experiment our way through it. But what you said is what... There are some very at scale things. At LinkedIn, they did structurally change- Mm-hmm ... uh, and it, you know, basically built up a new discipline called full stack builder, right?So they went and said, “Hey, let's bring, uh, people from design and product management, front end engineering, all put them together.” Uh, but also have an edge, right? It's not like the design person still doesn't have the design edge, or the front end [00:27:00] person doesn't have the front end edge, but you can give yourself bigger scope in roles so that you're not confined to one role.Um, and then r- equally, infrastructure has become very critical, right? So in other words, like, I mean, RLEs, I mean, one thing we've realized is even for the Excel team, for example. Mm-hmm. Building the RLE in which a reward can be learned is actually one of the hardest sort of infrastructure problems.Mm-hmm. Uh, and so you kind of need even new talent, right? Distributed systems people even in what was considered an end user app team, uh, because it's a different skill set. So yes, infrastructure, science is the other one, obviously. Um, so I think we'll see how these evolve, right? Where's the s- real... I mean, always the world will have a bunch of specialists.Okay. Um, you know, I think the generalist role is going to be the most exciting, right? Because the leverage of a generalist- Mm-hmm ... um, is where we are going to see the maximum returns, right? When, when you said, “Hey, are you coding?” I'm now a gen- Like, what... I've basically translated [00:28:00] knowledge work Right?Which I did, where I created a Word document or a spreadsheet, or even, uh... And now I can build an app, right? It's in the same sentence. Uh, right? That idea that, “Oh, wow, my generalist skills have gotten higher leverage,” I think is what we're gonna see across the board. Music to the ears of CEOs and VCs that are, like, a little dangerous and a lot of- Golden age for idea peopleSarah Guo: idea people. Yeah. Uh- With a lot of agency. I- if you take that idea of personal agency and you just zoom it out to the organizational context, um, uh, my partner Mike Renall, who, uh, actually started his career at Microsoft, just wrote an essay where one of the big takeaways is i- it's an age where you can be much more ambitious, and you need to be, given the pace of the environment and how quickly, actually, users and companies are open to adopting new technologies.Satya Nadella: Um, how do you think about... I, I feel silly asking this of somebody running a, you know, trillion-dollar-plus company already, butAmbition & Making the Impossible PossibleSatya Nadella: how do you think about how Microsoft can be more ambitious now? It's a great question. Um, I [00:29:00] think, um- I think the, the thing in these type of transitions is to have a conceptual model of how work can change to go after outcomes that you could hardly imagine previously, right?In fact, Kevin Scott has this nice line, right, which is, um, when you can make the impossible... Like, when you're making hard things easier, that's sort of one point of leverage. But true ambition is about making the impossible possible. So now the thing that is missing a little bit in all of our organizations is what is that new conceptual model of what can we build?What was impossible and what can we build? And I'll give you one example of this, right, which is I take great inspiration from sort of the people who were managing the Azure net- network. And they came to the... This was from even last year. You know, we were scaling. You saw that I, I [00:30:00] talked about sort of how we built in the last 15 months more Azure capacity than we built in the first 15 years.I mean, it's crazy. Wild. Yeah. Right? It's pretty wild. And it's the same team. So they saw that and they said, “Bob, this just ain't gonna work if we don't reconceptualize our work.” So they built... Essentially they said, “Our job is not to do Azure networking. Our job is to build the agentic system does, that, that does Azure networking,” right?These are the folks managing the 500-plus fiber operators managing the VAN, right, all over. And fiber operations ultimately is a physical operation. Things get cut, things get, uh, you know, have to be repaired. You know, we have fancy words called DevOps and so on. Basically, emails are coming in and you gotta go respond to them, take care of it.So they built this agentic system. They even have a character for it. It's called Miles, and it sort of does all this stuff, right? They started sort of screaming for more tokens and so on. And so they were saying, “Look, uh, we don't need a headcount. We need tokens in order to be able to [00:31:00] manage, uh, our operation.”That reconceptualization- Mm-hmm ... of what their work is, right? They, they basically took their work and made it meta, right? That meta work is now their new work. Mm-hmm. Right? In the ‘80s, if somebody had come to us and said, “4 billion people are gonna get up in the morning and start typing,” my model would've been, we need 4 billion typists?But we're not doing typing, we're doing knowledge work. So that, to me, I think is it, right, which is whether it's Microsoft or whether it's any organization, is to give ourselves permission to do new types of metacognition, meta work, using these new tools to change the outputs that matter, uh, and then really make the impossible possible.Sarah Guo: So completing that dot or the, the connective tissue across those, I think, is where a lot of the enterprise value will get created.Data Center Build-Out & Community ImpactSarah Guo: Should we talk about data centers? Yeah, please ask. Oh, okay. Well, uh, uh, w- we-- this leads nicely into the data center build-up. I always think, I- I just-- I'm just impressed at the sheer scale of the [00:32:00] build-out from Microsoft, but also everyone else, that this is redefining what it means to be a hyperscaler.And I just feel like that, that, that is at unprecedented scale on finances, uh, on the way you run the company, but also the communities that are, that are impacted. Um, yeah, just talk a bit more about what you're seeing on the ground, like when you visit your- Yeah, I think there are two aspects of it.Satya Nadella: Obviously, the, the build-out is, uh, extraordinary. Um, you know, nothing like this has happened, and it's great to be, uh, one of the participants in it. Uh, but you brought up the other part, right? I think at this point it's clear that unless we as an industry, uh, are very principled about ensuring that the benefits of all the stuff we're talking about are felt in real ways, uh, at the community level, right?Because this is not just a, a campaign, um, right? It has to be real, where people are saying, “Look, this is not ch- changing the prices on energy for me.” In fact, if anything, it's bringing down prices because long term there's going to be a better [00:33:00] grid, there is going to be more energy. Water consumption is, in fact, not sort of, uh...In fact, water is being replenished, right? You gotta really, you know, educate folks on truly what's happening, the cl- uh, the closed loop systems we are building. We have to invest in the training, the jobs, the tax base. In fact, the least talked about stuff is the amount of jobs that get created during construction, after construction.What's the tax base that's there in the community? And, and all this has to be real. Um, and, and if that is the case, then we will have permission. If it is not, we won't have permission. It's as simple as that, right? Which is, uh, we, we... I think we have to take it as an industry pretty seriously. Uh, I think it's good for communities to be skeptical, ask the hard questions, for us to do the hard work, earn that.Um, but at the end of the day, if there's-- if we can really be the produ-- Wait. I've always felt like in human history, if you use a lot of energy but also create a lot of value for society- The story has been fantastic. If you don't [00:34:00] do that, it's not been that great. And this time around, I'm a firm believer that ultimately if you do have a token economy that drives productivity, that drives economic growth, that drives broad spread, um, you know, participation, better health outcomes, um, then I think we'll be in a great place.Sarah Guo: Uh, and that's at least what we all have to be focused on. Yeah. It, it makes me think actually that with all these initiatives that you're doing, might be e- easier to see ROI in the communities first before in enterprise. Yeah. I, I mean, I think both sides. Yeah. In fact, it comes back together. It has to be the people in the communities are going to be employed, are going to be participants, uh, in the real economy, right?Satya Nadella: That's I think the question is. Like, if we- if the broad economy is doing well and the communities are doing well, the dots get connected. It's sort of the market forces are such that we will connect the dots. And that I think is it. Like, you ought to be able to see the evidence. You can't be about o- any one company, uh, but it has to be broad economic growth and broad [00:35:00] ec- you know, community permission.Elad Gil: Yeah. I guess I wanna talk aboutSocietal Impact & Optimism About AIElad Gil: what you're most optimistic about currently or what have you most updated your personal models on regarding societal impact of AI? So you're saying what's the, the, the- What have you updated most on in terms of societal impact of AI? Yeah. I think the, um, the p- the most, um- Critical thing is the first question we even started with, which is we need to tell the story and make it real that everybody has a real shot to participate as a first-class participant in this new economy.Satya Nadella: Right? That's kind of, I think we- in the next 12 months, 18 months, we need a way for people to say, “Oh, wow, I get it.” Right? There's going to be tremendous capability, tremendous amount of infrastructure, but I can see what is going to happen, whether it's the benefits like health outcomes or my ability to create a startup or my ability to run my [00:36:00] local sort of, uh, store more efficiently.It's just happening, and I see that, uh, benefit myself, right? That to me, you know, earning that permission in a path-dependent way, we can't wait. See, the one thing, Eli, that I've now learned is I think the world is gonna be very skeptical of tech and tech companies that say, “Trust us, we've got it. The g- future is gonna be glorious.”Sarah Guo: Uh, you kind of have to deliver tangible benefits. Um, and quite frankly, politicians winning elections, uh, because they have advocated for that. That will be at least my adjustment because without it, um, thinking that somehow... Because it's too important this time around. It's too much of the economy for it not to be the case So one very simple framework I have for, you know, what are, what is gonna be the broad benefit of AI, um, beyond the communities just working in technology, are, are sort of wealth creation- Yepit's [00:37:00] gonna happen in a ton of different companies, startups and large companies. Then you have healthcare. Uh, you, you had amazing demos today. There are companies like Open Evidence. I think that is happening. Um,Education & Future of LearningSarah Guo: education seems like another one that's an- Yep ... obvious good where we haven't seen as much impact as I'd expect.Swyx: Do you have a hypothesis on why that might be, or if it'll come? Yeah, I mean, I think this is where, again, how we think about education, how... You know, recently I met with, uh, the founders of Alpha School and learnt a lot about what they were going and going about, and it's fascinating to listen, uh, to how to even rethink- MmSatya Nadella: uh, what does education really look like. Because I think it's actually very important. Mm. Uh, and I'm not saying anything traditionally being done is less important, right? I was even looking at the, uh... It's fascinating to see. I, I, I forget the which Stanford class it was, uh, the, the Asian guidelines for CS something.Mm. Uh, because you still need people to learn. Uh, like it was an interesting AI class that they were making sure people were learning how to apply softmax appropriately versus saying, “Hey, fix my training run.” Mm-hmm. Uh, so I think learning concepts is important. It's going to [00:38:00] be, uh, critical. But the way we create the incentives, what are the credentials, how we value those credentials, what is the employment opportunity for those credentials?So I think that there's a complete change that has to happen, uh, given the way to get to information, way to educate yourself, way to continuously keep yourself updated has changed so much. So I think interestingly enough, maybe the next big startup and success story could be someone who builds a new university, um, or a new, um, pedagogy even of how to get someone to go through a curriculum and find economic opportunity, uh, that's highly valuable.Well, that has felt, uh, perhaps impossible for a long time, but it's a great note to end on and something that might be possible. It's still possible. Yeah. Thank you, Satya. Thank you so much. Thank you. Yeah. I appreciate it. Thank you all. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

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In this episode of The Good Robot, Eleanor Drage sits down with David Griffiths, founding director of the nonprofit Then Try This. Inspired by a childhood spent coding pixels next to his mother's traditional floor loom, Dave unpacks the deep historical links between textiles and programming, arguing that technology is never neutral and that true innovation relies on grassroots, participatory design.They explore brilliant local projects like Sonic Kayaks, which use underwater soundscapes to map marine data for visually impaired paddlers, and Nurgle, an accessible game tracking public health trends using specialized audio cues. Finally, they reveal the hidden, feminist histories of computing, showing how modern microchips directly owe their legacy to the complex creativity of Navajo weavers. Tune in to discover why the future of tech belongs to frugal, community-led innovations rather than just the next iteration of GPT.Reading List:Then Try ThisIndigenous Circuits: Navajo Women and the Racialization of Early Electronic ManufactureQueer In AI: A Case Study in Community-Led Participatory AI Sensing Bodies: Engaging Postcolonial Histories through More-than-Human InteractionsEdited by: Meibel Dabodabo

Anthropic reicht das Börsenprospekt ein. Pip erklärt, warum Anthropic unbedingt vor OpenAI rausgehen muss. In Lenny Rachitskys Umfrage unter Tech-Profis ist Anthropic mit Abstand der Lieblings-Arbeitgeber. Google macht eine Kapitalerhöhung über $80 Mrd. statt wie üblich Aktien zurückzukaufen. Nvidia-CEO Jensen Huang sagt mit einer einzigen Aussage die SaaSocalypse ab und schickt Software-Aktien auf eine Rally. Nvidia greift mit RTX Spark Intel und AMD im PC-Markt an. Die Chicago Mercantile Exchange launcht AI-Token-Futures wie für Gold und Öl. Short-Seller Andrew Left wird wegen Marktmanipulation verurteilt. Bloomberg deckt auf, wie der SpaceX-IPO die S&P-500-Regeln zur Profitabilität aushebelt. Antonio Gracias wird durch den IPO zum Milliardär, sein Off-Balance-Sheet-Konstrukt mit SpaceX wird zum Streitthema. US Space Force vergibt $4,16 Mrd. an SpaceX für den Golden Dome. Instagram-Accounts werden gehackt, indem Hacker einfach Meta AI fragen. Salesforce kauft das Berliner Startup Contentful und hält selbst inzwischen einen $5-Mrd.-Anteil an Anthropic. Anthropic gibt der EU-Cybersecurity-Agentur ENISA Zugang zu Mythos. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Anthropic reicht IPO-Prospekt ein (00:18:14) Anthropic ist Lieblings-Arbeitgeber (00:20:17) Google macht $80-Mrd.-Kapitalerhöhung (00:27:04) Jensen Huang sagt SaaSocalypse ab (00:33:47) Nvidia RTX Spark gegen Intel/AMD (00:39:46) AI-Token-Futures an der CME (00:43:06) MiniMax M3: China-Modell für 5-10% des Preises (00:46:01) HPE (00:47:13) Peter Thiel zieht nach Argentinien (00:49:32) SpaceX-Skeptiker: Musk vs. eigenes IPO-Filing (00:55:25) SpaceX-IPO biegt S&P-500-Regeln (00:59:29) Antonio Gracias und SpaceX' Off-Balance-Sheet-Konstrukt (01:07:28) US Space Force vergibt $4,16 Mrd. an SpaceX (01:09:15) Instagram-Hack via Meta AI (01:13:24) Salesforce kauft Berliner Contentful (01:18:17) Anthropic gibt EU/ENISA Zugang zu Mythos (01:19:33) Salesforces Anthropic-Stake auf $5 Mrd. Shownotes Anthropic-Ankündigung - xcancel.com Lenny Rachitsky: Ergebnisse einer Umfrage zu AI-Tools - linkedin.com Alphabet - ft.com Nvidia RTX Spark N1/N1X: AI-CPU/GPU für Laptops und Desktops - theverge.com Nvidia-CEO Jensen Huang zerstreut SaaSocalypse-Sorgen - wsj.com AI-Token-Futures kommen wie Gold und Öl - techcrunch.com MiniMax M3: Schlägt GPT-5.5 und Gemini 3.1 Pro bei 5-10% der Kosten - venturebeat.com HPE shares soar 37% - ft.com NYT: Warum Peter Thiel sich auf ein Leben nach Amerika vorbereitet (Argentinien) - nytimes.com SpaceX-Skeptiker: Musks Aussagen weichen vom IPO-Filing ab - cnbc.com Short-Seller Andrew Left wegen Wertpapierbetrug verurteilt - bloomberg.com SpaceX-IPO zwingt Indexfonds und Retail, die Regeln zu ändern - bloomberg.com Hedgeye-Tweet zu SpaceX/Markt - xcancel.com Fortune: SpaceX-IPO macht Musks Freund Antonio Gracias zum Milliardär - fortune.com US Space Force vergibt $4,16-Mrd.-Vertrag an SpaceX - reuters.com Coinbase und Kalshi launchen regulierte Perpetual-Krypto-Futures - reuters.com Hacker bekommen Zugang zu High-Profile-Instagram-Accounts durch Meta AI - 404media.co Gergely Orosz Tweet - xcancel.com Jane Wong Tweet - xcancel.com Salesforce übernimmt Berliner Startup Contentful - manager-magazin.de Salesforce kauft Contentful: Headless CMS für Agentforce - thenextweb.com Anthropic gibt EU-Cybersecurity-Agentur Zugang zu Mythos - bloomberg.com Salesforces Anthropic-Investment auf rund $5 Mrd. bewertet - bloomberg.com

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

Patrick Moorhead and Daniel Newman cover Daniel's acquisition of Enterprise Technology Research, IBM's historic $15 billion single-day commitment spanning quantum and open-source security, Anthropic's Claude Opus 4.8, and the heaviest single earnings night of the season featuring Dell, Marvell, Salesforce, Synopsys, Snowflake, HP, and Micron crossing $1 trillion in market cap. The handpicked topics for this week are: Anthropic Releases Claude Opus 4.8: Six Weeks After 4.7 Anthropic dropped Opus 4.8 just six weeks after 4.7, claiming it surpasses GPT-5.5 and Gemini 3.1 Pro on agentic coding, knowledge work, and computer use. Benchmark improvements across the board: agentic coding up from 64.3% to 69.2%, knowledge work from 1753 to 1890, agentic computer use from 82.8% to 83.4%. Three new features ship alongside it: Dynamic Workflows for multi-subagent orchestration inside Claude Code, Effort Control for managing token spend, and mid-task system messages via the API. Fast mode is now 2.5x faster and 3x cheaper. Pat's honest take: what it says on paper is good, particularly on tool triggering and citation precision, but he has lost significant trust in the company and is watching closely. (The Decode)   IBM Commits $10 Billion to Quantum: The Largest Single Quantum Bet in History IBM announced a $10 billion commitment over five years targeting a large-scale fault-tolerant quantum computer by 2029, landing the same day as the $5 billion Project Lightwell announcement for a single-day IBM strategic commitment of $15 billion. Pat has been calling 2029 to 2031 as the realistic commercial quantum window and calls this the strongest single corporate financial signal yet that the timeline is real. Daniel's framing: IBM wants to be the NVIDIA of quantum, and with a $10 billion commitment, it's sending a flare to the entire industry that pure-play quantum companies cannot compete at this balance sheet level. (The Decode)   IBM and Red Hat Launch Project Lightwell: $5B to Secure Open-Source Software IBM and Red Hat committed $5 billion and a global force of 20,000 engineers to secure open-source software for enterprises through frontier agentic AI, anchored by 11 of the largest US and Canadian banks including Bank of America, Goldman Sachs, JPMorgan Chase, Mastercard, and Visa. Pat's read: this is the productization answer to Anthropic Mythos. Mythos found the vulnerabilities. Lightwell is the industrial-scale patching and validation layer enterprises can actually buy on a subscription. Daniel adds that IBM is flexing its engineering talent base as a premium strategic asset, a direct counter to the narrative that AI replaces engineers. (The Decode)   Anthropic Project Glasswing: 23,000 Vulnerabilities Found Across 1,000 OSS Projects Anthropic's Claude Mythos scanned more than 1,000 widely deployed open-source projects and surfaced approximately 23,000 candidate vulnerabilities, with 1,094 confirmed as critical severity. The Cyber Verification Program now gates the strongest cyber-capable Claude variant behind vetted defenders only. While the tool creates real value, the surface of attack will likely grow as fast as any tool built to defend it. (The Decode)   Anthropic in Talks to Run Claude on Microsoft Maia 200 CNBC and The Information reported Microsoft is in active negotiations to supply Anthropic with its custom Maia 200 inference chip, which would make Anthropic the only frontier lab simultaneously running production workloads on four distinct silicon stacks: NVIDIA, AWS Trainium, Google TPU, and Microsoft Maia. Pat's context: Maia 200 delivers 30% better tokens per dollar than the latest Azure fleet per Satya Nadella, and this deal would be Maia's first major external deployment. Daniel's read: what can be built will be sold right now, and Anthropic chasing every available compute source is simply the structural reality of growing at 80x when you planned for 10x. (The Decode)   The Flip: Is AI CapEx Too Expensive to Earn Its Return? Pat takes the affirmative. With $725 billion in hyperscaler CapEx tracking for 2026, likely $1 trillion next year, memory has become the choke point making it even more expensive, and open-source models have closed enough of the quality gap for most enterprise tasks that the premium of frontier APIs is increasingly hard to justify. A recent Signal65 white paper shows on-prem payback at 18 months. Daniel's counter: Dell just booked $24 billion in AI orders in a single quarter. Agentforce crossed $1 billion ARR at 169% growth. NVIDIA guided to $91 billion. Only 20% of enterprises are using AI and only 2% of consumers. Both hosts admitted off the flip their notes looked nearly identical. (The Flip)   Micron Crosses $1 Trillion Market Cap Micron became the 12th US company ever to cross $1 trillion in market cap, surging 19% on May 26th as UBS raised its price target to $1,625, implying a $1.8 trillion market cap. Samsung's Q1 memory ASP jumped 146% year over year. DRAM spot prices spiked 55 to 60% quarter over quarter. Daniel has been pounding this call since sub-$100 and calls it a cycle elongated beyond anything seen in the 27 prior memory cycles, driven by HBM capacity reallocation away from consumer DRAM creating structural shortage. (Bulls and Bears)   Dell Technologies Q1 FY27: The Biggest Enterprise AI Infrastructure Print of 2026 Record $43.8 billion revenue, up 88% year over year, crushing the $35.7 billion consensus by $8 billion. AI-optimized servers at $16.1 billion, up 757% year over year. $24.4 billion in AI orders booked in a single quarter. FY27 AI server revenue guide raised from $50 billion to $60 billion. Non-GAAP EPS of $4.86 beat the $2.96 consensus by 64%. Stock up 18% after hours. Pat's framing: Dell was very clear about what they were going to do. Rack engineering, sales, and service. The basics. And they executed the basics at an extraordinary level while building a special relationship with NVIDIA who views Dell as a market maker for both enterprise and NeoCloud. Daniel's add: play nice and win. Michael Dell navigated the political landscape brilliantly and pulled the entire Dell brand along with him. (Bulls and Bears)   Marvell Technology Q1 FY27: Record Revenue, Data Center at 76% of Mix Record $2.418 billion revenue, up 28% year over year. Data center at $1.833 billion, up 27% year over year, now 76% of total revenue. Q2 guide of $2.7 billion at midpoint accelerates growth to 35% year over year. Operating cash flow a record $638.8 million. Daniel went on TV and said it's "written in the stars," arguing the market had misunderstood this one for too long by conflating its custom AI ASIC story with the full breadth of its connectivity and networking portfolio. Pat's closing: the shorts are eating it now and the custom AI ASIC versus merchant GPU debate is finally settling into the right answer, which is both in lockstep. (Bulls and Bears)   Salesforce Q1 FY27: Agentforce Crosses $1 Billion ARR Revenue $11.13 billion, up 13% year over year. Non-GAAP EPS of $3.88 crushed the $3.12 consensus by 24%. Agentforce ARR crossed $1 billion, up 169% year over year, with 28.6 trillion tokens processed, up 152% quarter over quarter. 50% of Agentforce bookings came from existing customers expanding. Daniel flagged the $25 billion accelerated buyback funded by new debt as an interesting signal worth watching. Pat's bottom line: it's not perfect, but certainly no "SaaSpocalypse" in those numbers. (Bulls and Bears)   Synopsys Q2 FY26: First Full Quarter With Ansys Integrated Revenue $2.276 billion, up 42% year over year, beating consensus. Non-GAAP EPS of $3.35 beat $3.15. FY26 guide raised to $9.665 billion midpoint. Daniel's framing: every chip runs through Synopsys tools, and the Ansys addition makes it the full-stack co-design platform Jensen Huang keeps talking about. Synopsys is not just the pick and shovel of current AI silicon. It is the pick and shovel of quantum, robotics, and space as well. (Bulls and Bears)   Snowflake Q1 FY27: Strongest Sequential Dollar Growth in Company History Product revenue $1.33 billion, up 34% year over year, the strongest sequential dollar growth in Snowflake history. Net revenue retention 126%. FY27 product revenue guide raised to $5.84 billion. Natoma acquisition announced for secure agentic enterprise connectivity. New $6 billion multi-year AWS commitment. Daniel's closing: proprietary unique data is the real moat of the agentic era, and that data has to live somewhere. It is going to go to platforms like Snowflake. (Bulls and Bears)   HP Inc. Q2 FY26: Eight Straight Quarters of Growth With AI PCs at 44% of Shipments Revenue $14.4 billion, up 9% year over year, the company marks its eighth consecutive quarter of top-line growth. Non-GAAP EPS of $0.86 beat the prior guide. Personal Systems at $10.2 billion, up 13%, with 30% operating profit growth. AI PCs jumped from 35% to 44% of shipments quarter over quarter, with HP guiding to 60 to 70% next fiscal year. FY26 EPS guide raised. Pat's note: they still need a permanent CEO, which would help investors sleep better at night. Daniel's add: the real explosive moment for device companies comes when AI moves to the edge and enterprises shift from expensive frontier model consumption to on-device inference. (Bulls and Bears)   Everpure Q1 FY27: Record Revenue, Rebrand Complete Record revenue of $1.1 billion, up 35% year over year. Product revenue $577 million, up 55%. Subscription ARR at $2 billion. FY27 guide raised to $4.41 to $4.51 billion. Pure Storage officially completed its rebrand to Everpure. Daniel's emerging thesis: the agentic era has focused enormous attention on memory and compute, but after the inference runs, the data has to sit somewhere. Storage has not seen its full inflection yet and Everpure is well positioned when that wave arrives. (Bulls and Bears)   The Decode Anthropic Releases Claude Opus 4.8 May 28  https://techcrunch.com/2026/05/28/anthropic-releases-opus-4-8-with-new-dynamic-workflow-tool/ IBM Commits $10B Over Five Years to Quantum Computing the Same Day as $5B Project Lightwell, Bringing IBM's One-Day AI https://www.barrons.com/articles/ibm-stock-quantum-computing-aafbb1eb IBM + Red Hat Announce Project Lightwell  https://newsroom.ibm.com/2026-05-28-ibm-and-red-hat-commit-5-billion-to-redefine-the-future-of-open-source-in-the-ai-era Anthropic Project Glasswing / Claude Mythos Finds 23,000 Potential Vulnerabilities Across 1,000+ Open-Source Projects https://www.securityweek.com/anthropic-mythos-detected-23000-potential-vulnerabilities-across-1000-oss-projects/ Anthropic Negotiating to Run Claude on Microsoft's Maia 200 AI Chips  https://www.cnbc.com/2026/05/21/anthropic-microsoft-maia-200-ai-chip.html OpenAI + Anthropic Walk Back the AI Jobs Apocalypse Ahead of IPOs https://finance.yahoo.com/sectors/technology/articles/ai-chiefs-walk-back-job-193605798.html https://x.com/RiskCentre/status/2059397756016611668 The Flip Is AI Capex Becoming Too Expensive to Earn Its Return — and Will the Result Be a Forced Shift to Open-Source and Smaller Use-Case-Specific Models, or a Continued $725B+ Hyperscaler Buildout That Vindicates the Capex on Productivity Gains? FOR:  The shift is to open-source + smaller use-case-specific models with better token economics, not away from AI https://x.com/danielnewmanUV/status/2059822712122400975 DeepSeek 75% permanent price cut + Anthropic Claude Code restriction reversal https://www.buildfastwithai.com/blogs/ai-news-today-may-26-2026 $190B Microsoft capex + $725B+ aggregate hyperscaler capex with no analog ROI yet  https://www.buildfastwithai.com/blogs/ai-news-today-may-26-2026   AGAINST:  Salesforce Agentforce ARR crossed $1B this quarter on 28.6T tokens processed  https://www.stocktitan.net/sec-filings/CRM/8-k-salesforce-inc-reports-material-event-3b8ead2852bb.html Lenovo +105% AI revenue, +84% Q4; Dell $43B AI backlog: the AI infrastructure flywheel is converting capex to revenue today https://investor.marvell.com/news-events/press-releases/detail/1023/marvell-technology-inc-reports-first-quarter-of-fiscal-year-2027-financial-results NVIDIA $91B Q2 guide + $1T Blackwell+Vera Rubin CY25-CY27 reaffirmed  https://www.cnbc.com/2026/05/20/were-raising-our-price-target-on-nvidia-after-another-knockout-quarter-and-guide-.html DeepSeek + Chinese price war is a Chinese export-controls story, not a US economic ceiling story https://www.cnbc.com/2026/05/21/anthropic-microsoft-maia-200-ai-chip.html   Bulls & Bears Micron (NASDAQ: MU) Crosses $1 TRILLION Market Cap for the First Time https://www.cnbc.com/2026/05/26/micron-stock-trillion-market-cap.html Dell Technologies Q1 FY27 ACTUALS  https://www.cnbc.com/2026/05/28/dell-q1-earnings-report-2027.html Marvell Technology Q1 FY27 ACTUALS https://investor.marvell.com/news-events/press-releases/detail/1023/marvell-technology-inc-reports-first-quarter-of-fiscal-year-2027-financial-results Salesforce CRM Q1 FY27 ACTUALS  https://investor.salesforce.com/financials/quarterly-results/ Synopsys SNPS Q2 FY26 ACTUALS https://investor.synopsys.com/events-and-presentations/events/event-details/2026/Q2-Fiscal-Year-2026-Earnings/default.aspx Snowflake SNOW Q1 FY27 ACTUALS  https://www.businesswire.com/news/home/20260527027931/en/Snowflake-Reports-Financial-Results-for-the-First-Quarter-of-Fiscal-2027 HP Inc. HPQ Q2 FY26 ACTUALS https://finance.yahoo.com/markets/stocks/articles/hp-q2-earnings-call-highlights-230459161.html Everpure (NYSE: P, formerly Pure Storage) Q1 FY27 ACTUALS  https://investor.salesforce.com/financials/quarterly-results/ Synopsys SNPS Q2 FY26 ACTUALS https://investor.synopsys.com/events-and-presentations/events/event-details/2026/Q2-Fiscal-Year-2026-Earnings/default.aspx Snowflake SNOW Q1 FY27 ACTUALS  https://www.businesswire.com/news/home/20260527027931/en/Snowflake-Reports-Financial-Results-for-the-First-Quarter-of-Fiscal-2027 HP Inc. HPQ Q2 FY26 ACTUALS  https://finance.yahoo.com/markets/stocks/articles/hp-q2-earnings-call-highlights-230459161.html Everpure (NYSE: P, formerly Pure Storage) Q1 FY27 ACTUALS https://www.prnewswire.com/news-releases/everpure-announces-first-quarter-fiscal-2027-financial-results-302783502.html

(Presented by Ent.ai: Ent delivers intent-aware security that protects every action, adapts to every workflow, and works for every user. Enterprise threat detection, reimagined.) Three Buddy Problem - Episode 99: Microsoft is now threatening legal action against researchers who drop zero-days. We debate whether it's a fair line against extortion, or amateur-hour PR from a company that already torched its own research community? Costin plays reluctant defender, JAGS says the damage was done years ago, and Ryan reopens the long history of silent fixes and stolen bounties. Plus, on the 10th anniversary of the Shadow Brokers leak, we discuss some enduring mysteries, theories on attribution and an interesting trail that leads to Edward Snowden. We also unpack Rob Joyce's warning that China's cyber explosives are already planted in US infrastructure, and the Pope's warnings about around artificial intelligence. Cast: Juan Andres Guerrero-Saade, Ryan Naraine and Costin Raiu. Timestamps: 0:00 - Introductory banter 2:03 - The Pope's AI paper 3:35 - New sponsor: Brandon Dixon's Ent Security 9:34 - Costin's Chinese-model OSINT rabbit hole 13:34 - Codex, GPT-5.5, and the "American AI welfare state" 23:20 - Microsoft threatens vulnerability researchers 27:06 - Is it extortion or retribution? The disclosure fight 40:48 - How Microsoft's consultant class broke MSRC and MSTIC 48:42 - Silent fixes, stolen bounties, and the marketing machine 1:02:29 - Ten years of the Shadow Brokers 1:14:20 - The Snowden theory 1:32:34 - Rob Joyce: China's cyber explosives are in place 1:53:26 - Shout-outs

Sure, you didn't miss Anthropic's BIG Opus 4.8 drop.

Claude Opus 4.8 arrives as a modest but meaningful upgrade, with early users pointing to better judgment, less bluffing, stronger self-checking, and a greater willingness to push back. NLW breaks down first impressions, benchmark comparisons with GPT-5.5, Claude Code's new dynamic workflows, and why the model harness may matter as much as the model itself. In the headlines: Kirkland & Ellis bets big on internal AI, OpenAI updates GPT-5.5 Instant, Cognition raises at a $26B valuation, Meta considers AI cloud, and Microsoft prepares new models.Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at ⁠⁠kpmg.com/us/Sophisticated⁠⁠Scrunch - The AI customer experience platform - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://scrunch.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Zenflow Work - Agents for knowledge work - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://zenflow.free/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

K, grab your glass of red and come sit. We need to talk about AI.The discourse has been rampant the last few months: vibe-coding, Claude Cowork, agentic AI, OpenAI and partnering with the Department of War, AI psychosis, custom dashboards, apps, and GPTs...We are FLOODED with info right now, and instead of leading with curiosity, social media has become a 360 slam-dunk fest on who's right or wrong. When the internet is filled with black and white thinking, name-calling, and passive aggressive posts... I can't help but wonder: isn't this what led to the surge of AI in the first place? Did the AI boom happen due to a lack of communication, conflict resolution, and critical thinking skills?Why would people turn to a robot instead of a human to ask questions about business, life and love? Why aren't we hiring real people right now, even when we know it's the "right" thing to do? What's the point of moving at lightning speed? Where is this all taking us?These are all the questions I ask today, from both sides of the debate. I was a Power User not too long ago, and am currently on an "AI cleanse". I share how one long afternoon with a custom GPT and one too many prompts led me to this point...As usual, no final answers here. Just observations, thoughts, questions, and ideas for how we can stop pieing each other in the face about AI and instead, build a bridge with curiosity, imagination, stories and some good ole fashioned empathy.I can't wait to hear your thoughts!People mentioned in this ep:Ximena - everyone's favorite Mexican Eco-PhilosopherKP Pilley - Editorial Strategist & 9-Grid ExtraordinaireXanthe Appleyard & Social LifeSocial media is fickle AF, but your community doesn't have to be. Life of the Party is a content strategy program that helps creative leaders own their growth, engagement, and joy online.  Walk away with a sustainable strategy to skyrocket the visibility of your biz, become known for your unique POV, and grow an online community stacked with the caliber of clients you've always dreamed of collabing with. Use code NOTES for $100Let's connect!

Robinhood launched agentic stock trading, letting users link Claude or Cursor to dedicated accounts. Micron hit $1T market cap in record time on AI memory demand. YouTube now auto-labels AI content, a new coding benchmark crowns GPT-5.5 the clear leader, and Roku overhauls its homescreen. Robinhood launches a feature to let users link AI agents, such as Claude or Cursor, to separate, dedicated investment accounts for trading stocks autonomously (WSJ) Micron hit a $1T market value for the first time on May 26 after its stock closed up 19.29%, rising from $700B earlier in May, driven by high memory chip demand (CNBC) YouTube makes its AI content labels more prominent on desktop and mobile, and will apply them automatically if it detects "significant photorealistic AI use" (Variety) Roku launches its first major homescreen overhaul in over a decade, including a large "marquee" ad spot to tout apps or shows, in a bid to drive more engagement (Hollywood Reporter) Datacurve releases the DeepSWE coding benchmark, a 113-task test across 91 open-source repositories: GPT-5.5 leads at 70%, GPT-5.4 got 56%, and Opus 4.7 got 54% (VentureBeat) Learn more about your ad choices. Visit megaphone.fm/adchoices

How is AI transforming accessibility for indie authors — and why should you care even if you consider yourself able-bodied? What happens when the tools designed to help people with disabilities end up making everyone's creative business better? Jeff Adams, accessibility expert and romance author, explores how AI is opening doors that were previously closed. In the intro, Spotify Audiobook Innovations; The Economics of Convention Life [The Indy Author]; Friction in your Author Business [Self-Publishing with ALLi]. Today's show is sponsored by Draft2Digital, self-publishing with support, where you can get free formatting, free distribution to multiple stores, and a host of other benefits. Just go to www.draft2digital.com to get started. This show is also supported by my Patrons. Join my Community at Patreon.com/thecreativepenn Jeff Adams is the author of YA thrillers and gay romance, and the co-author of Content for Everyone, a practical guide for creative entrepreneurs to produce accessible and usable web content. You can listen above or on your favorite podcast app or read the notes and links below. Here are the highlights and the full transcript is below. Show Notes How ending a long-running podcast made space for more writing — and how to know when it's time to let go of a good thing What accessibility really means for indie authors and why your digital content might be excluding part of your audience How AI agents like Claude Cowork are removing physical and cognitive barriers for authors with disabilities, chronic pain, or limited energy The culture of shame around AI use in the writing community and why blanket anti-AI statements can be ableist Practical tools including NotebookLM, ElevenReader, and ChatGPT for marketing copy, metadata management, and multimodal research Exciting futures in personalised reading, real-time translation, and AI browser agents that could change how everyone interacts online You can find Jeff at JeffAdamsWrites.com. Jeff also now has a SubStack at contentforeveryone.substack.com Transcript of the interview with Jeff Adams Jo: Jeff Adams is the author of YA thrillers and gay romance, and the co-author of Content for Everyone, a practical guide for creative entrepreneurs to produce accessible and usable web content. Welcome back to the show, Jeff. Jeff: Thanks so much, Jo. It's good to be back. Jo: It is. You were last on the show in March 2023, so over three years ago now. Give us a bit of an update on your writing and publishing business and what it looks like at the moment. Jeff: Sure. I think the biggest thing that happened is that my husband Will, who is also a writer, we ended the Big Gay Fiction Podcast at the end of 2024, after 470-something episodes. It was basically time to do that. So we both focused on writing from that point. In 2025 we had some of our biggest successes in getting writing out into the world. I refound my groove—my difficulty in writing went away finally. We talked a little bit about that back in 2023 too. Will started a new pen name and started producing again, and it was really good to be able to move in that direction. Jo: Was this the hockey romance that really hit at the right time? Jeff: You know, I wish I could have capitalised more on Heated Rivalry when it came out, but I did get hockey books out, and I think I did get to ride that wave a little bit there too. Jo: Yes, and if people don't know about that, that was a super popular streaming series. Was that based on a book? Jeff: It was, yes. Rachel Reid was the author of that book and that series that then Jacob Tierney optioned and made into what fairly turned into a global phenomenon at the end of 2025. Jo: Yes, absolutely. Although I particularly liked Red, White and Royal Blue. That was the one I liked. Not so much into hockey. But anyway, I just wanted to ask you about the Big Gay Fiction Podcast. As you say, you did hundreds of episodes over many years. You and I met over podcasting. You've had lots of connections with people. You ended it, and I know you struggled with ending it, but it sounds like it went really well for you. So maybe you could talk a bit about— How do you know when it's time to end something—a good thing rather than something bad? Does that make more space for writing, essentially? Jeff: It absolutely did make more space for writing for both of us, in particular for me because I have a day job. I balance everything on the creative side with the day job. Will and I had been talking about it for over a year. It just was like, it's really time. After nine years, getting to that 470 mark, we thought about trying to get to 10 years and we thought about, if not 10, then getting to 500 and ending on a milestone. As we looked at everything in our creative business, it was like, this is fun, we enjoy it, but we're not getting as much out of it as we might be if we were actually also writing books, which we also really want to do. It became a time thing and what was the best use of the time. We absolutely miss it occasionally. The whole Heated Rivalry thing, I would've loved to have had episodes to talk about that on, but in the long run, it was worth it. Jo: I mean, one of the things with a podcast, particularly around fiction, was that it was a marketing angle for your fiction. This show is a marketing angle mainly for my nonfiction. So what did you replace the podcast with, in terms of book marketing? Jeff: It was really stepped-up email marketing. I'd always had a list. Will started a list, of course, as he started his new pen name. So it was really turning on that, focusing on that, getting some email marketing with a Bargain Booksy and a Fussy Librarian and a BookBub occasionally to do that work. To be honest, even though we covered things in our genre that if you like what we're talking about, you should like our books, there was never as much of a connection there as you'd want there to be. Even from that book marketing angle, these other things that we can do, it's also a better spend of the money to get those types of promos than it was to continue running the show. Jo: Yes, that is interesting. I mean, obviously I think about podcasting a lot since I have this one, and I put Books and Travel on a hiatus and that was meant to help my fiction and definitely didn't help my fiction sales. But I want to bring it back again because I love doing it. Do you have this hankering sometimes? Do you think you'd ever do the podcast again? Because you are also quite into all the technical stuff and all that. Jeff: It's possible. I've toyed with the idea of doing a short accessibility podcast geared towards creatives, tilting to the same audience that Content for Everyone does. Then I come back and look at the time—is my time better served writing new fiction or perhaps starting a Substack, which I also toy with the idea of, for accessibility stuff? So it bounces around in my head to do another show, but I haven't really decided to jump on that yet. Jo: Yes, and I think that waiting is really good. As you say, you quit a big thing and you don't have to rush to fill it again. I love that you guys are writing more books. So I wanted us to talk about that up front because I know people who listen to this show—I encourage people to start podcasts if you want to, but equally it can take a lot of time. So that's fantastic. Now, you mentioned accessibility, and I feel like the word can be quite difficult for people. So let's just start with a definition. What is accessibility? Why do you care and why should we care? Jeff: So accessibility is really about making sure that whatever the thing is, whether it's something out in the physical world or in the online world, that everybody has access to it. Access to the information, access to getting into a building or being able to cross the street appropriately, whatever that is—that the accessibility of the thing is high. So that regardless of who is approaching it, they can interact with whatever the thing is. If we put that into the digital world, it's about making sure that text on a screen can be perceived by anybody, whether they're trying to read it visually or if they're trying to read it through a screen reader or through a braille monitor. Whatever that is, they need to be able to interact with it, get the information they need, do all the functions of whatever it is on the screen. Check out on Amazon, check out at their favourite e-commerce place, be able to get the products in their cart, check out, et cetera. For creatives, it's about the things that we do: the websites that we build for ourselves, the e-commerce platforms that we use, our email marketing, our social media posts. Making all of that as accessible as we can so that we're not perhaps missing a part of our audience or our prospective audience from being able to engage with our work and in turn, hopefully, buy our books and enjoy our books and become a fan. This became important to me because of my day job. I hadn't really considered this—like, I think most people don't—until I started working at UsableNet. It's going to be 15 years I've been at that company come this autumn, and I really started to see the impacts because UsableNet is all about accessibility on the digital front. I really started to learn, being a project manager for them, what all of that meant and how it impacted people who couldn't buy something online, couldn't book a hotel room, couldn't book an airline ticket. It just really became something I got passionate about. I ended up writing the book because I realised that nobody talks to creatives about this. Nobody tells the independent author what they should do to help make their digital stuff accessible so that they don't miss people. I never expected my day job to interact with my creative side so much, but this certainly has over the last few years. Jo: I mean, has it got better? Like we said, you were on here three years ago. We did talk about some of the things around EPUB formats and taking off DRM and what we need to do on our websites—labelling images, for example, and that kind of thing. Do you think accessibility has gotten better? Jeff: I think the awareness of it has improved, both within the creative community and in the broader web ecosphere, that the awareness is better. There's so much knowledge that needs to go into creating something that is accessible. Sometimes there's so much that you have to think about with colours and alt tags on images and all the little bits and pieces, if it doesn't really come to muscle memory, it's easy for it to fall off. There's a survey that's done by WebAIM every year about the top one million homepages out in the universe, and they surveyed those for just the things that an automated scan can detect, which is a small portion of overall accessibility, and the number of errors across that top million actually ticked up this year. Even though there's all these laws around the world—people get sued all the time in the US—the number of errors ticked up for the first time in a few years. So I think the awareness is up, but I think being able to take action on it and make the time to take action on it isn't where it needs to be. Jo: So last time you gave us all those tips. I'll refer people back to that and also to your book Content for Everyone, which has got loads of great stuff in. I wanted to talk to you for this show because I was sitting watching Claude Cowork—now I use Claude Code a lot more—but updating 140 titles on IngramSpark, where me clicking things and there's like 15 clicks per record on IngramSpark updates for pricing, is an absolute nightmare. I was watching the AI do the work and I realised this isn't just saving me time, it's actually saving my wrist and my arm from repetitive strain injury. That's when I thought about this accessibility thing. As you mentioned, for example being physically accessible into a building, say someone's in a wheelchair, they can't necessarily get into a building if there's no ramp. I was thinking that for many years, being an indie author, being a writer online, there's also been these physical barriers because there's a lot of plumbing and clicking for us. So I wondered, starting with an attitude around a shift in who this is opening up to— How is AI starting to help people with these accessibility issues? Jeff: Yes, there's so much opportunity around this. We should note, just to timestamp this, that we're talking on 14th April 2026, because who knows what will change, even in an hour from now. I think Cowork was one of the first things that we saw, and that's only been out since the very top of this year. Being able to do actual agentic tasks. Other things have sort of gotten there, but Cowork really opened it up. You mentioned the repetitive stress that you would've had clicking all of those forms on IngramSpark across 140 books. But there's that type of stress, chronic pain, cognitive drain for somebody who may have some cognitive disability and trying to work through that form. The cognitive energy just might drain out and maybe knock them out for several days after trying to get through that, or the tasks take them multiple days to do. Someone who has lower vision, someone who's trying to work through that form with a screen reader—all of that draws energy, draws focus. Now we've got something where, with plain language, we could say something like: here's all my pricing information, I've logged into IngramSpark, go update these books. Obviously the prompt's going to be a little more than that, but in broad terms, that's what we're going to tell it. Jo: Hmm. Jeff: And being able to have it go through and do the thing. If it gets stuck, have it come back and say, “Hey, I've got trouble with this. Please help me.” That can just free up so much of the drains that people can have—the things that can take them out of doing the part of the work that they need to do for an author business. They can go write the book through whatever process you're going to use to do that, rather than getting caught up in something like having to update all those books on IngramSpark. Jo: You mentioned writing the book there. I have this real sense of being an able-bodied indie author in terms of my computer use and my ability to write a whole book, a 70,000-word thriller that I write regularly. We're all special in some way, but I do have a reasonably normal brain where I can do this work without too much strain. It's hard work, but I can do it. I meet people who are now using AI to help them write, to help them organise their work—maybe someone has dyslexia or ADHD or cognitive issues or pain—there's just so many things that I take for granted that don't affect me. I hear from people who, at this point in time in the community, are almost shamed for using AI to write. So I wanted to bring this up to discuss it under the terms of accessibility. Do you have any thoughts on that? Jeff: I have real difficulty with people who will say anything in the broad range of, “I don't need to use this thing, and therefore you should not either.” Which is adjacent to indie anti-AI speak that there is out there. Certainly we're living right now at probably the highest point that it's ever been, where more and more there's a sentiment towards not using AI for whatever the reason is. I totally respect that people can have concerns about the environment and about energy use and water use, et cetera. Not to mention all the other things that are on the more difficult side of AI. To shame someone who may not be able to put their story out there without the use of that AI, whichever one they're using, or to shame them because they're using AI to run part of their business—updating IngramSpark, doing other things like that—I think it can come down to there being some ableism there. Ther is some privilege behind that too, where they're just like, “I don't need this, and you shouldn't have it either.” I want to give people just a sliver of an idea of what this can mean for someone who is disabled and what AI can unlock for them. There is a person on LinkedIn that I follow whose name is Hannah Desmond. She's an ADHD coach and a former software developer, and very recently she posted this on LinkedIn. This is a paraphrase of what she said, but: having something that can meet you where you are and help you bridge that gap is what I think I have found so helpful about using AI. Here's what I keep coming back to. Without that support, I wasn't more motivated or more capable. I was just stuck. That's the bit that gets lost. We've been taught that struggling is how you know you're doing it properly. So when something reduces the struggle, it can feel wrong—even when it's the thing that actually makes the work possible. Because there's a difference between avoiding thinking and being able to think at all. I think that rounds it up. She's talking about her time as a software developer, but you can apply that to any realm of AI when we're thinking about trying to shame someone for why they may be using it. We may not know that they have a disability because we don't always share that part of ourselves. So I really feel strongly about that and how we are in this culture of shame. Jo: Yes. It drives me up the wall, actually. But I will also say: you don't have to have a disability or accessibility issues in order to use AI in whatever way you personally decide is okay—talking to the listeners now. I think Orna Ross from the Alliance of Independent Authors says it well, which is you should have your own AI policy. So you personally decide where your lines are, how it helps you, what you want to keep for you, and what you want help with. I was also thinking in terms of accessibility around money. Again, for many of us, professional cover design, professional editing, professional human-level translation, these are things that are pretty pricey for many people. So again, this makes it more accessible. One of the reasons we got into the indie way and being indie authors was to try and remove the barriers to entry to people who have been excluded from the environment of publishing. So, yes, it is really hard to talk about this, and yet that's why I wanted to talk about it, because— There's so many variables for each individual and there's no situation that's the same, really, is there? Jeff: No, not at all. The things that I may need to do my work in the most efficient way possible is different from the way that you're going to work, is different than the way my husband's going to work, is different than every other person and the way that they're going to work. Which is why any kind of blanket statement about “I don't need something and therefore you shouldn't need it either” can just be so problematic, because we have no idea what someone else is going through. Either it's a permanent part of their lives or maybe it's something that is happening temporarily with them where they might need to leverage other tools. Jo: Yes. Talking about that temporary, I think I really got the first sense of this when I had COVID the first time, which was really bad. I remember I was so sick, the only thing I could do was listen to an audiobook. I couldn't think, I couldn't read. It was really probably months of not having my brain back. Then the other thing that's happened as I age, as women age, is menopause kicks in and the brain fog is a real thing. I've heard from other people too who've said having Claude or whoever, an AI tool, to help with the brain fog is so important because otherwise I just wouldn't be able to gather my thoughts. Again, as you said— Even if we don't need these things now, it's quite likely we're going to need them at some point, given ageing, given the potential for injury and disease. I mean, we don't escape this alive, do we? Jeff: Yes, that's a great point because unless we're extremely lucky as individuals, we're all likely to have some sort of a disability in our lives at some point. I know for me, as I age and my eyes get more and more tired after being in front of a screen all day for work, and then whatever creative stuff I do in the afternoon on a book—when it comes near bedtime and I do want to read, I probably want to do that with an audiobook, much more audio, especially for any long reading project. That can also be like, if I have a long document or a long article to read, I am likely to give it to ElevenReader, let it load itself up, and then listen to it, because I take the information in better than trying to follow words across a screen. Jo: Yes. Jonathan, my husband, now also listens to a lot of academic papers on ElevenReader. Most of us will know it as where we publish some audiobooks from ElevenLabs, or you can also publish other things there. So it is super useful to think about what we can do with ElevenReader. Another thing that I found really useful recently is NotebookLM. On NotebookLM, there is a free tier. You can put various things in there and then create a custom audio. So this is something I've been doing as part of research. You can put in, say, 10 YouTube videos or some PDFs or your book or whatever, and then you can create a custom audio. Then I'll go for a walk and I'll listen to the custom audio, and then I'll go back and look at the detail of what it was. It gives me the framework of whatever I'm thinking about on a broader level, and then I can come back to the details. So again, it's this multimodal approach that can help us manage our energy, I guess. Jeff: And it's all about the managing of the energy, I think, too. That is a great way to think about the accessibility of it all. You mentioned a great use there for NotebookLM. That could also be putting your book in there and having it help you build a world bible or something like that. Or building marketing materials off of that. There's a lot of things now that NotebookLM can do in terms of helping you create FAQs maybe for a newsletter or for your website, and building video stuff off of the material that it has. So there's a lot of options there, and ever-growing options that can be useful for someone to manage any number of the things that they may need in their creative business. Jo: Yes. In fact, talking about Claude, there are a lot of Claude plugins now, skills and integrations. Shopify just released a Claude plugin and many of us now have Shopify stores. I have a lot of products with a lot of different variations and the metadata. There's so much metadata. And again, I'm just so pleased now that I can work with Cowork and get it to actually update directly into Shopify. In fact, coming back, you mentioned updating alt tags earlier. That's something again that AI could help you update—the back list of your alt tags on a website. I've now got my Cowork doing EPUBs so I could finally update all my EPUBs with back matter and all of this kind of thing. So I feel like perhaps we could go beyond accessibility to talk about amplification. All the things that we didn't do because it was too tiring and we just couldn't be bothered, or it would just be way too much work, that now it's opened up as a possibility because of these tools. Jeff: Absolutely. I mean, you look at a backlist as large as yours and the things that you're now able to do. I didn't know that Claude had a Shopify plugin. So the abilities that we have now to maybe do things in the business that we hadn't before. One of the things I've been working with Claude on is rewriting my website and creating a more proper website for Will. I'm really making sure that it is not only SEO prepared but also GEO prepared, with all the metadata and all the backend code schema that it needs so that LLMs can find me, can understand what I do, can understand the books, branch out to the other areas that it needs to. Doing that through WordPress would've been so much more difficult, even with Claude, that to be able to rewrite the site in a way that is going to let me manage it better so that I will do it on a more consistent basis. Whatever that thing is, we're now able to do these things. That could be updating keywords in Amazon or making sure we're aligned across all of the sales platforms that we might be on and things like that, that Claude can do and do well. Jo: Yes, I think marketing is just the killer app really for people, isn't it? I think most authors do not enjoy marketing. I find Claude better for creative work, for strategic work, for doing work through Cowork or Code, but— ChatGPT with marketing copy is very, very good. So I've actually been using that as we record this. I've got a Kickstarter launching next week, so I've been getting it to do ad copy and social media copy and all that kind of thing. This is stuff when you have to produce—give me 20 taglines, give me 20 hooks, give me another 20 and another 20. I mean, we just cannot do it as humans, right? Jeff: Yes, I have found GPT wildly helpful. I mentioned trying to get Bargain Booksy and Fussy Librarian promos. Jo: Mm. Jeff: And you have to give it the marketing hook, and it can't just be the blurb that's on Amazon—it's got to be something fresh, and they each have slightly different requirements. Having GPT—here's the blurb, give me a dozen different options—and then I may take pieces of all of them and create one of my own. But it reworks that much faster than my brain was ever going to try to find the right thing I want to give to Bargain Booksy. Jo: Yes, you are right. Or it says write this in 300 characters or less. Jeff: Yes. Jo: I do exactly the same. That kind of transformative work can be really good. In fact, there was somebody I know who has been rampantly anti-AI for years and then said, “Would this help me? I have to do a synopsis for an agent, so I've got this 100,000-word book and it needs to be a 10-page synopsis. How would I do that with AI?” So I was encouraging her to take each chapter and ask it to summarise the chapter, and of course read through it and everything. But I mean, doing a synopsis once you've actually written a book—that can be super useful. So I think what we're saying is— There are levels of need in terms of both the author and the audience. Then there are levels of your personal use from one end of the spectrum to the other in terms of how far you want to go in every area of the business. And in that way, it's just different for everyone. Jeff: Yes, and I think getting to that mindset shift that we were talking about a little bit—it can be so easy to dip your toes in. That one author came to you and said, “Do you think it could do this?” And I think that's the beginning exploratory area for perhaps anyone. People are going to hear us talk about this and it might inspire them to go try something that we've talked about. But these things, whether it's Claude or GPT or Gemini or whichever one it is, you can come to it and say, “I'm an author, I have X, Y, Z going on in my life”—whether that's a disability, whether that's a time constraint because you have a day job and maybe you have kids and a family that need your attention—”I have these time constraints, I want to do X, Y, and Z in my business. How can you help me with that?” It's going to tell you what it can do to help you with that. I would even say, if you have the ability to have multiples of these, you could ask the same question to GPT and Claude, and they're going to give you similar answers in some instances, but they may also have different ones because of the abilities that the different platforms have around these things as well. That can help you make that mindset shift of, “Well, now I see that it can do that. Could it also do this?” And then ask it if it could do that. Because I know for me, Jo, I've taken so much from you and your journey with Cowork that it's like, “Oh, she did that. I wonder if I could do this.” And all of that piles on top of itself. Then eventually I think your brain starts to think on its own, “Oh, I have to do this task. Can Claude maybe do this for me? Let's go find out.” Jo: Yes, and if it couldn't do it for you yesterday, you never know, it might be able to do it tomorrow. Jeff: Right? Because I haven't tested yet its new ability to actually use your computer. Jo: Mm. Jeff: And I'm curious what that might open up. Because one of the things that I've seen that I wish it would do is be able to take the EPUB that's on my drive and actually put it into a platform I'm trying to upload to. Cowork on its own hasn't been able to cross that barrier, but I wonder if with computer use added to that, if it could. Like, “here's the EPUB, upload that over there,” be able to pick it from the file picker, essentially. Jo: Yes. I think, well, a little tip for everyone: I wouldn't give access to your entire file system to the AI. Jeff: That's a good point too. Jo: Yes. I have a Claude folder in my drive and it only has access there. So if you put files in that drive, it might be able to do that. But I know what you mean. I have been using it to help me publish things in German on KDP. Now I can use the browser, so you can actually do that. In terms of uploading the actual file, I know what you mean. These things will change. As we record this, again middle of April, we are almost about to get the next models being Mythos, which might be Claude 4.7 Opus, or also ChatGPT has a new model coming, and these models are getting very powerful. With every shift they can do more things. So as you say, the very first thing to do is ask it, “I want to do this—what are my options?” And some of them, for example, doing an AI-narrated audiobook, ChatGPT and Claude don't do that. You want ElevenLabs or one of the other services for that, but they can tell you what your options are. So that's one thing, but I wondered if you have any thoughts on the gaps that you are seeing. You mentioned one there around file uploads, but— What do you hope might come and some of the things that might be exciting if they arrive? Because you never know, they might be here already. Jeff: There's certainly some movement in some areas. One of the things I'll share is, in March I was at the 2026 CSUN Assistive Technology Conference—CSUN is California State University, Northridge—and they've run this conference for some 40 years now. One of the sessions I went to was from Tara Maisel—I hope I'm pronouncing her last name right. She's a senior project manager in books accessibility at Amazon, and she was doing a session specifically on readability. She had all kinds of statistics and information about what goes into making something readable. One of the things she talked about with AI was the future of personalised reading. If you think about the Kindle app, for example, there's a lot of settings you can make there—font size, colours, brightness, text spacing. There's a lot of tools in there. She was pointing out that potentially readers don't even know what they actually need for the optimised visual reading experience. She sees a world where AI can perhaps do an analysis of your reading behaviour and then help you find the optimal settings. Maybe even multiple optimal settings for, say, if you were reading in a room that had daylight versus at bedtime, and the ways you might shift it. I was almost thinking of this like when you're at the optometrist and they're like, “Which lens is better—this one or that one?” Jo: Oh, sometimes that is very hard. Jeff: Yes. It's that AI could step you through that a little bit to help you find that optimal reading experience in that moment. And then it might even notice, potentially, if you're changing something in the way that you're moving through a page, that it might flag to say, “Hey, do we need to adjust something?” Some other areas that I think are really exciting, for everyone and perhaps particularly for people who are disabled and needing the support of some assistive technology, is what we're seeing in the browsers. OpenAI's Operator has been out for quite a while now, since sometime I think autumn of last year. Perplexity Comet has been around even longer. Then we've got browser extensions from Gemini and Claude that are available, that can let you just type natural language. You know, “Please go find for me jeans in this size that are on sale on this website. Find me the best price for blue jeans on this site and this size,” and it'll just go do it. Which can certainly speed things up for people in the disabled community to find things quickly, to spend time navigating less, and maybe ending up with the AI coming back and saying, “I found these five things. Which one would you like me to buy for you?” Or, “I found this one thing that you do need and it's waiting for you in your shopping cart.” The ability for that on the horizon is an amazing jump from an accessibility point of view. But really it's one of those things that accessibility will then help everyone because we can all just shop that way, if we choose to. These are early days for these browsers and these extensions. The other side of it comes back to basic web accessibility too, because I've seen these types of activities not work so well on a site that may not actually be accessible on its own. A great example is something I ran into with Claude Cowork about a month ago. I was testing to see if it could help me navigate and get things uploaded together for a site where I wanted to upload books, knowing again that it's not going to upload the actual file, but it could fill in the metadata from my master database of metadata stuff. There were areas on the site that it actually couldn't hit the button, because the site itself was also not functional to a screen reader. So there are gaps there. It's early days, but I really see that as an interesting future that'll really help people with disabilities—but again, help everybody too, just manage time better. Jo: I know exactly what you mean there. I've done some collaborative work with Claude Code when it's like, “I can't click the button,” and I'm like, well, I'll click the button—you fill in everything else. Jeff: Exactly. Jo: It's actually quite a funny situation. But goodness, coming back to IngramSpark again—these things need APIs. We need better functions. It's funny because I think a lot of traditional publishers have these APIs or backend upload things that you can do. I'm like, well, we need to get to that with these systems. But I think things will change. Another thing that I think has also shifted is the use of voice. Voice for dictation—it used to be with dictation that you would have to say “comma,” “open quote,” “new line,” and all of that. And you'd also have to make sense. Whereas now I feel like you can just dictate a whole load of things to these AIs and then say, “Tidy that up,” and they will do a lot more than the old situation. So I think voice will also help. Also automatic translation. I don't know if you know this about X, and if you're on X anymore, but just this week they've made it multi-language. So I can read tweets by people who've posted in another language in English. I can read something from Korean or read something that someone French has posted and it gets translated. It has made a huge difference to the content I'm seeing, which is fascinating because I don't think we've ever had this kind of automatic “everything is translated into your language” situation. It's really got me thinking about how [automatic translation] might work for eBooks or other things if the rights are there. I don't know. Have you seen stuff like that? Jeff: There's so much available now with voice and the ability to not have to speak all the other stuff that went with it—comma, full stop, next line. It was a little mind-bending sometimes, trying to think about quote marks and all that stuff. And now it's so good. Different platforms do it to different degrees of ability. Even being able to speak your prompts into the very platforms themselves without having to type all of it. Chronic pain comes to mind, any kind of mobility thing—all the typing would be a drain or maybe even impossible. So the voice ability is so powerful there and unlocks more things. At the same time, those translation abilities—I believe AirPods now have the ability, if you've got the right stuff on your phone, that you could be talking to somebody, they may speak back to you in a language you don't speak, but your AirPods will give it to you in your language. Jo: Hmm. Jeff: Google has, I believe, a live captioning app that you can use. I think there's even a split screen—I don't know if that's available now or something in their future—where you could put the phone on the table and tell it who's looking at what side of the screen, and it'll put the language that I need on my side and the language the other person needs on the other. So there continues to be such a shift in how we're being able to translate stuff that really opens up communication and can open up our books to so many more people. I'm very interested to see—I haven't pulled the trigger on this yet—but how Amazon's auto-translation rolls out and how that's received in terms of the accessibility around our books and being able to put it in someone's hands who doesn't speak—I think it's only English to other languages right now—but who doesn't speak the language it was written in but wants to read that book. We could never, as indies, or really even big five publishers, wouldn't have the money to create custom translations everywhere. But if the AI can help do that and spread those books around so that everybody could have the story they want to read, I think that's such a win for the reading audience. Jo: Yes, I think it's so exciting to think what might be coming, and that's what I want to stay on the side of on the AI discussion. There's enough negativity out there and you can get that information somewhere else, but for me I want us to stay on the positive side of how this helps both the author and the reader. And hopefully the community, to create more and read more and enjoy being human more. Right? Because I find that I do get out more and listen to stuff, or I'm out walking instead of at my desk, and I mean, that's what it's about. I'm pretty excited about the future. How about you? Jeff: I am. I think there are, quite honestly, some scary things that could be out there in the future. I mean, there's been a lot of talk about what Mythos is capable of. But on the other side of it, there are all these advances. I also look back at Google and AlphaFold and what DeepMind was able to do there for science. There's more of that stuff out there, and individually for each of us, spending a little bit of time—and I do have to say, I think you need to spend time on a paid plan because the free stuff doesn't give you the idea of what these platforms are actually capable of. So if you only drop in, even briefly, to experiment on one of the $20-a-month plans and give it your situation, ask it what it can do for you, I think you'll see where, on a personal level, AI will help you unlock some things. It can help you move some things to the next level in your business that for whatever reason you haven't been able to do. You don't have to use it for everything. You may decide that it's still not for you for whatever reason, and that's fine. But I think there's so much to explore here and to let your curiosity run for a little bit to see what's possible and what you might unlock with it. Jo: Brilliant. So where can people find you and your books and everything you do online? Jeff: So pretty much everything lives at JeffAdamsWrites.com. Jo: Well, thanks so much for your time, Jeff. That was great. Jeff: I loved it, Jo. Thanks for having me..The post Accessibility And AI: How New Tools Are Opening Doors For Indie Authors With Jeff Adams first appeared on The Creative Penn.

In this episode, the Moonshot mates discuss SpaceX's record-breaking IPO filing and its growing ties to Anthropic, OpenAI's AI model disproving a decades-old Erdős conjecture in mathematics, and GPT-5.5 beating prediction markets at forecasting. Get access to metatrends 10+ years before anyone else - ⁠https://qr.diamandis.com/metatrends⁠   Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360 Salim Ismail is the founder of OpenExO Dave Blundin is the founder & GP of Link Ventures Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified Apply for Salim's Pilot Program: https://openexo.com/organizational-singularity-pilot?podcast=23.5.26 Subscribe to Salim's channel: https://www.youtube.com/@salimismail – My companies: Apply to Dave's and my new fund: ⁠https://qr.diamandis.com/linkventures...⁠ Go to Blitzy to book a free demo and start building today: ⁠https://qr.diamandis.com/blitzy⁠ Your body is incredibly good at hiding disease. Schedule a call with Fountain Life to add healthy decades to your life, and to learn more about their Memberships: ⁠https://www.fountainlife.com/peter⁠ _ Connect with Peter: ⁠X⁠ ⁠Instagram⁠ ⁠Substack⁠ ⁠Website⁠ ⁠Xprize⁠ Connect with Dave: ⁠Web⁠ ⁠X⁠ ⁠LinkedIn⁠ ⁠Instagram⁠ ⁠TikTok⁠ Connect with Salim: ⁠X⁠ ⁠Join Salim's Workshop to build your ExO⁠  Connect with Alex ⁠Website⁠ ⁠LinkedIn⁠ ⁠X⁠ Email ⁠Substack⁠  ⁠Spotify⁠ ⁠Threads⁠ Listen to MOONSHOTS: ⁠Apple⁠ ⁠YouTube⁠ – *Recorded on May 21st, 2026 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

Google dropped like 197 new AI features this week.

PREVIEW for Later Today: Kevin Frazier examines how AI tools like Mythos and GPT 5.5 reveal critical vulnerabilities in national infrastructure. He highlights U.S. Navy cyber weaknesses and emphasizes the urgent need for a robust national cybersecurity apparatus.JUME 1957