Podcasts about Workday

This episode of Pearls On, Gloves Off is powered by Workday. Learn more at workday.com. In this episode, Mary sits down with Claire Hart, Chief Operating Officer, Chief Legal Officer, and Board Member at Groq, to talk about what legal leaders should expect in the AI era - from their law firms, their teams, and themselves. With senior leadership roles at Google, Blizzard, and Genies, Claire brings a sharp perspective from the intersection of law, business, and technology. The conversation starts with the LinkedIn comment that got people talking: Claire said she would be horrified to learn that some of the law firms she works with are not using AI. From there, she and Mary unpack why adoption is still so uneven, how the billable hour distorts incentives, what young lawyers need to stay relevant, and why judgment, curiosity, and team design matter more than ever as legal moves into an AI-driven future. In this episode: Claire's AI hot take: Why clients should be alarmed if their outside counsel aren't using AI The adoption problem: How risk concerns and the billable hour are slowing real change Efficiency vs. incentives: Why the tech clients want conflicts with how firms make money What young lawyers need now: Judgment, communication, and adaptability over pure technical skill The blurring of roles: How lawyers, legal ops, and contract managers are starting to overlap What law firms still miss: Why understanding how businesses actually operate is now a competitive edge Join Mary's Substack Community Follow Mary on LinkedIn Rate and review on Apple Podcasts  

Listen in as host Paul Spain sits down with Joe Goddard (One NZ) as they explore One NZ's satellite-to-mobile networks, introduction of One Wallet and Phone Dollars and what's next for One NZ.They also review tech news from the week including:InternetNZ's latest findings on Kiwis' online habits and AI caution Datagrid's South Island 'AI factory' QR-code food labels concerns Starlink's pricing backlash for pilots,150 years of the telephone NASA's Artemis II launch targetA big thank you to our show partners One NZ, Spark, Workday, 2degrees, Fortinet and Gorilla Technology.

Ephesians 4: Putting Off the Old, Putting On the New (Truth, Anger, Words & Forgiveness) + Easter AnnouncementsJeremy shares upcoming Easter plans, including a free citywide egg hunt in Shepherdsville with two hunts (11:00 and 1:00), 40,000 eggs, registration via Facebook/website, and volunteer needs, plus school egg hunts the following week and a March 26 workday to prepare the flood-recovering building. Because Bullet Central won't have electricity, four on-campus Easter services are planned: Thursday at 7:00 PM (with a kids glow-in-the-dark egg hunt afterward) and Sunday at 8:30, 10:00, and 11:15, with requests to attend Thursday or 8:30 if flexible. The sermon continues Ephesians, emphasizing daily “put off/put on” transformation through renewed minds: reject falsehood by speaking truth in love, handle anger without sin or letting it linger, avoid “decaying” speech and use words to build others up, and replace bitterness, rage, and malice with kindness, compassion, and forgiveness modeled after God's forgiveness in Christ.00:00 Easter Announcements00:30 Serving the Egg Hunt01:12 Workday and Easter Services02:53 Stay Connected and Give03:05 Ephesians Series Setup04:56 Put Off and Put On09:39 Renewing the Mind12:17 Truth Over Falsehood17:59 Honesty in Practice19:33 Anger Without Sin22:06 What Anger Reveals23:39 When Anger Mutates24:28 Don't Let It Linger25:16 Footholds and Spillover28:00 Pause and Get Honest29:10 Words That Corrode32:42 Build Up With Speech34:49 Replace Rage With Kindness37:21 The Cost of Forgiveness40:11 Daily Put Off Put On42:47 Prayer and Sending

This week I discuss the AI, HR Tech, and consumer AI market in front of announcements next week at the Unleash Conference in Vegas. I discuss how HR Tech is now becoming “Life Tech” (not just Work Tech) and the dynamics of big players like Microsoft, Oracle, Workday, SAP, ServiceNow, Anthropic, OpenAI, Google, and smaller vendors like Cornerstone, Findem, Lightcast, Maki People, Eightfold, WorkHuman and others who are vying for attention with their AI offerings. Next week I'll detail many of these announcements in my keynote and I hope to see many of you in Vegas at Unleash and the following week at Transform. So much to absorb and understand: we are here to help you sort it all out. Additional Information Layoffs at Atlassian, Block, Amazon are Misleading. AI Alone Is Not The Story. The World of Corporate Training Lurches Toward Enablement Oracle's Earnings Prove That AI Infrastructure Is Eating Enterprise Software Enterprise AI Architecture: Imperatives for 2026 Webinar: Watch a replay of Josh's walkthrough of the 11 essential imperatives HR & business leaders need to know for success and progress in 2026. Galileo Learn: Complete The Superworker Organization: AI Goes Enterprise learning program, and discover the hands-on skills required to navigate the redefinition of work, HR teams, and organizations in the era of superworkers and superagents. Get Galileo: The Enterprise AI Agent for HR  Chapters (00:00:00) - All the HR Technology Announcements(00:07:29) - Oracle's strategy for growth(00:10:37) - Microsoft's Copilot, and More

Is your career site delivering the conversion you need? Dalia's plug-and-play tech turns any employer career site into a high-performance candidate conversion engine — no replatforming required, live in days.Visit dalia.co to learn more. AND by jobcase, "Are you struggling to find the right talent in a crowded job market? Jobcase connects you to a massive community of over 120 million registered workers, including the hourly, skilled, and gig professionals that other job boards often miss. Visit jobcase.com/hire today to post your roles and start building the team you need with tools designed to make hiring fast and easy." Alright rec techies…..here's what's happening this week. First up, SAN FRANCISCO — Juicebox, the outbound recruiting platform, announced $80 million in Series B funding at an $850 million valuation led by DST Global. The company has tripled ARR since its Series A in July 2025, and now serves 5,000 customers spanning fast-growing technology companies and Fortune 100 brands, with customers reporting up to 90% less time spent identifying top candidates. https://hrtechfeed.com/outbound-recruiting-platform-juicebox-raises-whopping-80m/ CAMBRIDGE, Mass.—-Talvy, a platform that offers video-first professional profiles, today announced its $2M seed round led by Link Ventures.  https://hrtechfeed.com/talvy-raises-2m-seed-for-video-resumes/ Persona, the identity platform for businesses worldwide, today launched a Candidate Verification solution to confirm job applicants' real-world identities at critical hiring stages. With integrations into Ashby, Greenhouse, and Workday, the offering enables talent acquisition and security teams to verify candidate identity as a natural part of their existing workflow. https://hrtechfeed.com/persona-launches-candidate-verification-to-stop-hiring-fraud-before-day-one/ I've just returned from Philadelphia where I attended this years I AM PHENOM user conference. This year it had about 2,500 customers and prospects gathered together at the convention center downtown. Unlike last year when they debuted 25 AI agents, this year saw no major product announcements rather they focused on product enhancements particularly around workflows and data orchestration. They called it WorkOps and They also announced a move into Public Sector HR  https://hrtechfeed.com/phenom-set-to-go-after-public-sector-hr-software-market/ Learn more about your ad choices. Visit megaphone.fm/adchoices

Genre free show, you never know what your going to get from week to week.Catch the Midweek Workday Chill Mix Weds check @labr@ravenation.club for updated times.Everything #LABR can be found at https://labr.onlineOur Mastodon account: https://ravenation.club/@labrIf you're on the go?https://www.radio-browser.info/usersDo A Search for LABR, & There You Are. Streaming 24/7 all the LABR Collective Members shows that you might've missed. And a few extra's in between.Enjoying this love we're spreading? Want to support LABR - Love a Brother Radio in spreading that love? Now you can.https://labr.online/donate Any little thing helps us feed the Keebler Elves to keep the wheels turning in the background. We're a 2 1/2 person operation. And a lot goes into making this work properly. With that said, we all thank you in advance for any support you lend. But most importantly. For your ears. 

In this episode of Cloud Wars Live, Bob Evans speaks with Bonnie Tinder, founder and CEO of Raven Intelligence, about the surge of hype, confusion, and opportunity surrounding AI in enterprise technology. As headlines claim AI could replace traditional software and “vibe coding” threatens SaaS vendors, Tinder brings a grounded perspective from years of advising organizations on enterprise systems like Salesforce, Workday, and SAP. Their conversation explores what AI can realistically do today, why enterprise software remains critical, and how companies can move forward without falling for hype. Episode 58: AI Hype vs. Reality The Big Themes: Why “Vibe Coding” Won't Replace ERP: The idea that AI-powered “vibe coding” could replace enterprise applications is a popular narrative, but both Evans and Tinder challenge its practicality. Even companies developing cutting-edge AI models are still relying on traditional enterprise systems. For example, Tinder notes that AI companies themselves are hiring administrators for established software platforms rather than replacing them. Leadership Must Guide AI Adoption: The discussion also emphasizes that AI adoption cannot be left solely to technology teams. According to Evans, the entire executive leadership team, especially the CEO, needs to be actively involved in defining how AI will shape the organization. AI initiatives affect workflows, job roles, data governance, and competitive strategy. Without clear leadership alignment, different departments may pursue conflicting approaches, slowing progress or introducing risk. Fear and FUD Are Slowing Progress: Ironically, the greatest threat from AI hype may be paralysis. Tinder argues that fear, uncertainty, and doubt in the market are causing many companies to delay decisions altogether. Organizations worry about choosing the wrong tools, implementing technology too early, or missing the next wave of innovation. This hesitation can prevent companies from making meaningful progress. Instead of waiting for perfect clarity, organizations should take practical steps. The Big Quote: “You can vibe code your way around [a] notion or a content system, that's way different though, than having an in-house solution for an enterprise software." More from Bonnie Tinder: Connect with Bonnie on LinkedIn. Visit Cloud Wars for more.

Oracle's earnings announcement yesterday demonstrates the company's shift to a new type of enterprise “software” company, challenging companies like SAP, Workday, Salesforce, and many others. In this podcast I explain this shift, the history of Oracle, and where AI is taking the enterprise software market. Here is the source article to read, and I look forward to further discussions on this topic with our clients and vendor partners. Reference Information Oracle's Earnings Prove That AI Infrastructure Is Eating Enterprise Software Nvidia Explains The New Software Stack (5-Layer Cake) Oracle Earnings Announcement   Chapters (00:00:00) - Analysts on Oracle's Earnings(00:01:35) - Oracle's Rise to the Cloud(00:08:43) - Oracle's Future in the AI World

Join Kyle, Nader, Vibhu, and swyx live at NVIDIA GTC next week!Now that AIE Europe tix are ~sold out, our attention turns to Miami and World's Fair!The definitive AI Accelerator chip company has more than 10xed this AI Summer:And is now a $4.4 trillion megacorp… that is somehow still moving like a startup. We are blessed to have a unique relationship with our first ever NVIDIA guests: Kyle Kranen who gave a great inference keynote at the first World's Fair and is one of the leading architects of NVIDIA Dynamo (a Datacenter scale inference framework supporting SGLang, TRT-LLM, vLLM), and Nader Khalil, a friend of swyx from our days in Celo in The Arena, who has been drawing developers at GTC since before they were even a glimmer in the eye of NVIDIA:Nader discusses how NVIDIA Brev has drastically reduced the barriers to entry for developers to get a top of the line GPU up and running, and Kyle explains NVIDIA Dynamo as a data center scale inference engine that optimizes serving by scaling out, leveraging techniques like prefill/decode disaggregation, scheduling, and Kubernetes-based orchestration, framed around cost, latency, and quality tradeoffs. We also dive into Jensen's “SOL” (Speed of Light) first-principles urgency concept, long-context limits and model/hardware co-design, internal model APIs (https://build.nvidia.com), and upcoming Dynamo and agent sessions at GTC.Full Video pod on YouTubeTimestamps00:00 Agent Security Basics00:39 Podcast Welcome and Guests07:19 Acquisition and DevEx Shift13:48 SOL Culture and Dynamo Setup27:38 Why Scale Out Wins29:02 Scale Up Limits Explained30:24 From Laptop to Multi Node33:07 Cost Quality Latency Tradeoffs38:42 Disaggregation Prefill vs Decode41:05 Kubernetes Scaling with Grove43:20 Context Length and Co Design57:34 Security Meets Agents58:01 Agent Permissions Model59:10 Build Nvidia Inference Gateway01:01:52 Hackathons And Autonomy Dreams01:10:26 Local GPUs And Scaling Inference01:15:31 Long Running Agents And SF ReflectionsTranscriptAgent Security BasicsNader: Agents can do three things. They can access your files, they can access the internet, and then now they can write custom code and execute it. You literally only let an agent do two of those three things. If you can access your files and you can write custom code, you don't want internet access because that's one to see full vulnerability, right?If you have access to internet and your file system, you should know the full scope of what that agent's capable of doing. Otherwise, now we can get injected or something that can happen. And so that's a lot of what we've been thinking about is like, you know, how do we both enable this because it's clearly the future.But then also, you know, what, what are these enforcement points that we can start to like protect?swyx: All right.Podcast Welcome and Guestsswyx: Welcome to the Lean Space podcast in the Chromo studio. Welcome to all the guests here. Uh, we are back with our guest host Viu. Welcome. Good to have you back. And our friends, uh, Netter and Kyle from Nvidia. Welcome.Kyle: Yeah, thanks for having us.swyx: Yeah, thank you. Actually, I don't even know your titles.Uh, I know you're like architect something of Dynamo.Kyle: Yeah. I, I'm one of the engineering leaders [00:01:00] and a architects of Dynamo.swyx: And you're director of something and developers, developer tech.Nader: Yeah.swyx: You're the developers, developers, developers guy at nvidia,Nader: open source agent marketing, brev,swyx: and likeNader: Devrel tools and stuff.swyx: Yeah. BeenNader: the focus.swyx: And we're, we're kind of recording this ahead of Nvidia, GTC, which is coming to town, uh, again, uh, or taking over town, uh, which, uh, which we'll all be at. Um, and we'll talk a little bit about your sessions and stuff. Yeah.Nader: We're super excited for it.GTC Booth Stunt Storiesswyx: One of my favorite memories for Nader, like you always do like marketing stunts and like while you were at Rev, you like had this surfboard that you like, went down to GTC with and like, NA Nvidia apparently, like did so much that they bought you.Like what, what was that like? What was that?Nader: Yeah. Yeah, we, we, um. Our logo was a chaka. We, we, uh, we were always just kind of like trying to keep true to who we were. I think, you know, some stuff, startups, you're like trying to pretend that you're a bigger, more mature company than you are. And it was actually Evan Conrad from SF Compute who was just like, you guys are like previousswyx: guest.Yeah.Nader: Amazing. Oh, really? Amazing. Yeah. He was just like, guys, you're two dudes in the room. Why are you [00:02:00] pretending that you're not? Uh, and so then we were like, okay, let's make the logo a shaka. We brought surfboards to our booth to GTC and the energy was great. Yeah. Some palm trees too. They,Kyle: they actually poked out over like the, the walls so you could, you could see the bread booth.Oh, that's so funny. AndNader: no one else,Kyle: just from very far away.Nader: Oh, so you remember it backKyle: then? Yeah I remember it pre-acquisition. I was like, oh, those guys look cool,Nader: dude. That makes sense. ‘cause uh, we, so we signed up really last minute, and so we had the last booth. It was all the way in the corner. And so I was, I was worried that no one was gonna come.So that's why we had like the palm trees. We really came in with the surfboards. We even had one of our investors bring her dog and then she was just like walking the dog around to try to like, bring energy towards our booth. Yeah.swyx: Steph.Kyle: Yeah. Yeah, she's the best,swyx: you know, as a conference organizer, I love that.Right? Like, it's like everyone who sponsors a conference comes, does their booth. They're like, we are changing the future of ai or something, some generic b******t and like, no, like actually try to stand out, make it fun, right? And people still remember it after three years.Nader: Yeah. Yeah. You know what's so funny?I'll, I'll send, I'll give you this clip if you wanna, if you wanna add it [00:03:00] in, but, uh, my wife was at the time fiance, she was in medical school and she came to help us. ‘cause it was like a big moment for us. And so we, we bought this cricket, it's like a vinyl, like a vinyl, uh, printer. ‘cause like, how else are we gonna label the surfboard?So, we got a surfboard, luckily was able to purchase that on the company card. We got a cricket and it was just like fine tuning for enterprises or something like that, that we put on the. On the surfboard and it's 1:00 AM the day before we go to GTC. She's helping me put these like vinyl stickers on.And she goes, you son of, she's like, if you pull this off, you son of a b***h. And so, uh, right. Pretty much after the acquisition, I stitched that with the mag music acquisition. I sent it to our family group chat. Ohswyx: Yeah. No, well, she, she made a good choice there. Was that like basically the origin story for Launchable is that we, it was, and maybe we should explain what Brev is andNader: Yeah.Yeah. Uh, I mean, brev is just, it's a developer tool that makes it really easy to get a GPU. So we connect a bunch of different GPU sources. So the basics of it is like, how quickly can we SSH you into a G, into a GPU and whenever we would talk to users, they wanted A GPU. They wanted an A 100. And if you go to like any cloud [00:04:00] provisioning page, usually it's like three pages of forms or in the forms somewhere there's a dropdown.And in the dropdown there's some weird code that you know to translate to an A 100. And I remember just thinking like. Every time someone says they want an A 100, like the piece of text that they're telling me that they want is like, stuffed away in the corner. Yeah. And so we were like, what if the biggest piece of text was what the user's asking for?And so when you go to Brev, it's just big GPU chips with the type that you want withswyx: beautiful animations that you worked on pre, like pre you can, like, now you can just prompt it. But back in the day. Yeah. Yeah. Those were handcraft, handcrafted artisanal code.Nader: Yeah. I was actually really proud of that because, uh, it was an, i I made it in Figma.Yeah. And then I found, I was like really struggling to figure out how to turn it from like Figma to react. So what it actually is, is just an SVG and I, I have all the styles and so when you change the chip, whether it's like active or not it changes the SVG code and that somehow like renders like, looks like it's animating, but it, we just had the transition slow, but it's just like the, a JavaScript function to change the like underlying SVG.Yeah. And that was how I ended up like figuring out how to move it from from Figma. But yeah, that's Art Artisan. [00:05:00]Kyle: Speaking of marketing stunts though, he actually used those SVGs. Or kind of use those SVGs to make these cards.Nader: Oh yeah. LikeKyle: a GPU gift card Yes. That he handed out everywhere. That was actually my first impression of thatNader: one.Yeah,swyx: yeah, yeah.Nader: Yeah.swyx: I think I still have one of them.Nader: They look great.Kyle: Yeah.Nader: I have a ton of them still actually in our garage, which just, they don't have labels. We should honestly like bring, bring them back. But, um, I found this old printing press here, actually just around the corner on Ven ness. And it's a third generation San Francisco shop.And so I come in an excited startup founder trying to like, and they just have this crazy old machinery and I'm in awe. ‘cause the the whole building is so physical. Like you're seeing these machines, they have like pedals to like move these saws and whatever. I don't know what this machinery is, but I saw all three generations.Like there's like the grandpa, the father and the son, and the son was like, around my age. Well,swyx: it's like a holy, holy trinity.Nader: It's funny because we, so I just took the same SVG and we just like printed it and it's foil printing, so they make a a, a mold. That's like an inverse of like the A 100 and then they put the foil on it [00:06:00] and then they press it into the paper.And I remember once we got them, he was like, Hey, don't forget about us. You know, I guess like early Apple and Cisco's first business cards were all made there. And so he was like, yeah, we, we get like the startup businesses but then as they mature, they kind of go somewhere else. And so I actually, I think we were talking with marketing about like using them for some, we should go back and make some cards.swyx: Yeah, yeah, yeah. You know, I remember, you know, as a very, very small breadth investor, I was like, why are we spending time like, doing these like stunts for GPUs? Like, you know, I think like as a, you know, typical like cloud hard hardware person, you go into an AWS you pick like T five X xl, whatever, and it's just like from a list and you look at the specs like, why animate this GP?And, and I, I do think like it just shows the level of care that goes throughout birth and Yeah. And now, and also the, and,Nader: and Nvidia. I think that's what the, the thing that struck me most when we first came in was like the amount of passion that everyone has. Like, I think, um, you know, you talk to, you talk to Kyle, you talk to, like, every VP that I've met at Nvidia goes so close to the metal.Like, I remember it was almost a year ago, and like my VP asked me, he's like, Hey, [00:07:00] what's cursor? And like, are you using it? And if so, why? Surprised at this, and he downloaded Cursor and he was asking me to help him like, use it. And I thought that was, uh, or like, just show him what he, you know, why we were using it.And so, the amount of care that I think everyone has and the passion, appreciate, passion and appreciation for the moment. Right. This is a very unique time. So it's really cool to see everyone really like, uh, appreciate that.swyx: Yeah.Acquisition and DevEx Shiftswyx: One thing I wanted to do before we move over to sort of like research topics and, uh, the, the stuff that Kyle's working on is just tell the story of the acquisition, right?Like, not many people have been, been through an acquisition with Nvidia. What's it like? Uh, what, yeah, just anything you'd like to say.Nader: It's a crazy experience. I think, uh, you know, we were the thing that was the most exciting for us was. Our goal was just to make it easier for developers.We wanted to find access to GPUs, make it easier to do that. And then all, oh, actually your question about launchable. So launchable was just make one click exper, like one click deploys for any software on top of the GPU. Mm-hmm. And so what we really liked about Nvidia was that it felt like we just got a lot more resources to do all of that.I think, uh, you [00:08:00] know, NVIDIA's goal is to make things as easy for developers as possible. So there was a really nice like synergy there. I think that, you know, when it comes to like an acquisition, I think the amount that the soul of the products align, I think is gonna be. Is going speak to the success of the acquisition.Yeah. And so it in many ways feels like we're home. This is a really great outcome for us. Like we you know, I love brev.nvidia.com. Like you should, you should use it's, it's theKyle: front page for GPUs.Nader: Yeah. Yeah. If you want GP views,Kyle: you go there, getswyx: it there, and it's like internally is growing very quickly.I, I don't remember You said some stats there.Nader: Yeah, yeah, yeah. It's, uh, I, I wish I had the exact numbers, but like internally, externally, it's been growing really quickly. We've been working with a bunch of partners with a bunch of different customers and ISVs, if you have a solution that you want someone that runs on the GPU and you want people to use it quickly, we can bundle it up, uh, in a launchable and make it a one click run.If you're doing things and you want just like a sandbox or something to run on, right. Like open claw. Huge moment. Super exciting. Our, uh, and we'll talk into it more, but. You know, internally, people wanna run this, and you, we know we have to be really careful from the security implications. Do we let this run on the corporate network?Security's guidance was, Hey, [00:09:00] run this on breath, it's in, you know, it's, it's, it's a vm, it's sitting in the cloud, it's off the corporate network. It's isolated. And so that's been our stance internally and externally about how to even run something like open call while we figure out how to run these things securely.But yeah,swyx: I think there's also like, you almost like we're the right team at the right time when Nvidia is starting to invest a lot more in developer experience or whatever you call it. Yeah. Uh, UX or I don't know what you call it, like software. Like obviously NVIDIA is always invested in software, but like, there's like, this is like a different audience.Yeah. It's aNader: widerKyle: developer base.swyx: Yeah. Right.Nader: Yeah. Yeah. You know, it's funny, it's like, it's not, uh,swyx: so like, what, what is it called internally? What, what is this that people should be aware that is going on there?Nader: Uh, what, like developer experienceswyx: or, yeah, yeah. Is it's called just developer experience or is there like a broader strategy hereNader: in Nvidia?Um, Nvidia always wants to make a good developer experience. The thing is and a lot of the technology is just really complicated. Like, it's not, it's uh, you know, I think, um. The thing that's been really growing or the AI's growing is having a huge moment, not [00:10:00] because like, let's say data scientists in 2018, were quiet then and are much louder now.The pie is com, right? There's a whole bunch of new audiences. My mom's wondering what she's doing. My sister's learned, like taught herself how to code. Like the, um, you know, I, I actually think just generally AI's a big equalizer and you're seeing a more like technologically literate society, I guess.Like everyone's, everyone's learning how to code. Uh, there isn't really an excuse for that. And so building a good UX means that you really understand who your end user is. And when your end user becomes such a wide, uh, variety of people, then you have to almost like reinvent the practice, right? Yeah. You haveKyle: to, and actually build more developer ux, right?Because the, there are tiers of developer base that were added. You know, the, the hackers that are building on top of open claw, right? For example, have never used gpu. They don't know what kuda is. They, they, they just want to run something.Nader: Yeah.Kyle: You need new UX that is not just. Hey, you know, how do you program something in Cuda and run it?And then, and then we built, you know, like when Deep Learning was getting big, we built, we built Torch and, and, but so recently the amount of like [00:11:00] layers that are added to that developer stack has just exploded because AI has become ubiquitous. Everyone's using it in different ways. Yeah. It'sNader: moving fast in every direction.Vertical, horizontal.Vibhu: Yeah. You guys, you even take it down to hardware, like the DGX Spark, you know, it's, it's basically the same system as just throwing it up on big GPU cluster.Nader: Yeah, yeah, yeah. It's amazing. Blackwell.swyx: Yeah. Uh, we saw the preview at the last year's GTC and that was one of the better performing, uh, videos so far, and video coverage so far.Awesome. This will beat it. Um,Nader: that wasswyx: actually, we have fingersNader: crossed. Yeah.DGX Spark and Remote AccessNader: Even when Grace Blackwell or when, um, uh, DGX Spark was first coming out getting to be involved in that from the beginning of the developer experience. And it just comes back to what youswyx: were involved.Nader: Yeah. St. St.swyx: Mars.Nader: Yeah. Yeah. I mean from, it was just like, I, I got an email, we just got thrown into the loop and suddenly yeah, I, it was actually really funny ‘cause I'm still pretty fresh from the acquisition and I'm, I'm getting an email from a bunch of the engineering VPs about like, the new hardware, GPU chip, like we're, or not chip, but just GPU system that we're putting out.And I'm like, okay, cool. Matters. Now involved with this for the ux, I'm like. What am I gonna do [00:12:00] here? So, I remember the first meeting, I was just like kind of quiet as I was hearing engineering VPs talk about what this box could be, what it could do, how we should use it. And I remember, uh, one of the first ideas that people were idea was like, oh, the first thing that it was like, I think a quote was like, the first thing someone's gonna wanna do with this is get two of them and run a Kubernetes cluster on top of them.And I was like, oh, I think I know why I'm here. I was like, the first thing we're doing is easy. SSH into the machine. And then, and you know, just kind of like scoping it down of like, once you can do that every, you, like the person who wants to run a Kubernetes cluster onto Sparks has a higher propensity for pain, then, then you know someone who buys it and wants to run open Claw right now, right?If you can make sure that that's as effortless as possible, then the rest becomes easy. So there's a tool called Nvidia Sync. It just makes the SSH connection really simple. So, you know, if you think about it like. If you have a Mac, uh, or a PC or whatever, if you have a laptop and you buy this GPU and you want to use it, you should be able to use it like it's A-A-G-P-U in the cloud, right?Um, but there's all this friction of like, how do you actually get into that? That's part of [00:13:00] Revs value proposition is just, you know, there's a CLI that wraps SSH and makes it simple. And so our goal is just get you into that machine really easily. And one thing we just launched at CES, it's in, it's still in like early access.We're ironing out some kinks, but it should be ready by GTC. You can register your spark on Brev. And so now if youswyx: like remote managed yeah, local hardware. Single pane of glass. Yeah. Yeah. Because Brev can already manage other clouds anyway, right?Vibhu: Yeah, yeah. And you use the spark on Brev as well, right?Nader: Yeah. But yeah, exactly. So, so you, you, so you, you set it up at home you can run the command on it, and then it gets it's essentially it'll appear in your Brev account, and then you can take your laptop to a Starbucks or to a cafe, and you'll continue to use your, you can continue use your spark just like any other cloud node on Brev.Yeah. Yeah. And it's just like a pre-provisioned centerswyx: in yourNader: home. Yeah, exactly.swyx: Yeah. Yeah.Vibhu: Tiny little data center.Nader: Tiny little, the size ofVibhu: your phone.SOL Culture and Dynamo Setupswyx: One more thing before we move on to Kyle. Just have so many Jensen stories and I just love, love mining Jensen stories. Uh, my favorite so far is SOL. Uh, what is, yeah, what is S-O-L-S-O-LNader: is actually, i, I think [00:14:00] of all the lessons I've learned, that one's definitely my favorite.Kyle: It'll always stick with you.Nader: Yeah. Yeah. I, you know, in your startup, everything's existential, right? Like we've, we've run out of money. We were like, on the risk of, of losing payroll, we've had to contract our team because we l ran outta money. And so like, um, because of that you're really always forcing yourself to I to like understand the root cause of everything.If you get a date, if you get a timeline, you know exactly why that date or timeline is there. You're, you're pushing every boundary and like, you're not just say, you're not just accepting like a, a no. Just because. And so as you start to introduce more layers, as you start to become a much larger organization, SOL is is essentially like what is the physics, right?The speed of light moves at a certain speed. So if flight's moving some slower, then you know something's in the way. So before trying to like layer reality back in of like, why can't this be delivered at some date? Let's just understand the physics. What is the theoretical limit to like, uh, how fast this can go?And then start to tell me why. ‘cause otherwise people will start telling you why something can't be done. But actually I think any great leader's goal is just to create urgency. Yeah. [00:15:00] There's an infiniteKyle: create compelling events, right?Nader: Yeah.Kyle: Yeah. So l is a term video is used to instigate a compelling event.You say this is done. How do we get there? What is the minimum? As much as necessary, as little as possible thing that it takes for us to get exactly here and. It helps you just break through a bunch of noise.swyx: Yeah.Kyle: Instantly.swyx: One thing I'm unclear about is, can only Jensen use the SOL card? Like, oh, no, no, no.Not everyone get the b******t out because obviously it's Jensen, but like, can someone else be like, no, likeKyle: frontline engineers use it.Nader: Yeah. Every, I think it's not so much about like, get the b******t out. It's like, it's like, give me the root understanding, right? Like, if you tell me something takes three weeks, it like, well, what's the first principles?Yeah, the first principles. It's like, what's the, what? Like why is it three weeks? What is the actual yeah. What's the actual limit of why this is gonna take three weeks? If you're gonna, if you, if let's say you wanted to buy a new computer and someone told you it's gonna be here in five days, what's the SOL?Well, like the SOL is like, I could walk into a Best Buy and pick it up for you. Right? So then anything that's like beyond that is, and is that practical? Is that how we're gonna, you know, let's say give everyone in the [00:16:00] company a laptop, like obviously not. So then like that's the SOL and then it's like, okay, well if we have to get more than 10, suddenly there might be some, right?And so now we can kind of piece the reality back.swyx: So, so this is the. Paul Graham do things that don't scale. Yeah. And this is also the, what people would now call behi agency. Yeah.Kyle: It's actually really interesting because there's a, there's a second hardware angle to SOL that like doesn't come up for all the org sol is used like culturally at aswyx: media for everything.I'm also mining for like, I think that can be annoying sometimes. And like someone keeps going IOO you and you're like, guys, like we have to be stable. We have to, we to f*****g plan. Yeah.Kyle: It's an interesting balance.Nader: Yeah. I encounter that with like, actually just with, with Alec, right? ‘cause we, we have a new conference so we need to launch, we have, we have goals of what we wanna launch by, uh, by the conference and like, yeah.At the end of the day, where isswyx: this GTC?Nader: Um, well this is like, so we, I mean we did it for CES, we did for GT CDC before that we're doing it for GTC San Jose. So I mean, like every, you know, we have a new moment. Um, and we want to launch something. Yeah. And we want to do so at SOL and that does mean that some, there's some level of prioritization that needs [00:17:00] to happen.And so it, it is difficult, right? I think, um, you have to be careful with what you're pushing. You know, stability is important and that should be factored into S-O-L-S-O-L isn't just like, build everything and let it break, you know, that, that's part of the conversation. So as you're laying, layering in all the details, one of them might be, Hey, we could build this, but then it's not gonna be stable for X, y, z reasons.And so that was like, one of our conversations for CES was, you know, hey, like we, we can get this into early access registering your spark with brev. But there are a lot of things that we need to do in order to feel really comfortable from a security perspective, right? There's a lot of networking involved before we deliver that to users.So it's like, okay. Let's get this to a point where we can at least let people experiment with it. We had it in a booth, we had it in Jensen's keynote, and then let's go iron out all the networking kinks. And that's not easy. And so, uh, that can come later. And so that was the way that we layered that back in.Yeah. ButKyle: It's not really about saying like, you don't have to do the, the maintenance or operational work. It's more about saying, you know, it's kind of like [00:18:00] highlights how progress is incremental, right? Like, what is the minimum thing that we can get to. And then there's SOL for like every component after that.But there's the SOL to get you, get you to the, the starting line. And that, that's usually how it's asked. Yeah. On the other side, you know, like SOL came out of like hardware at Nvidia. Right. So SOL is like literally if we ran the accelerator or the GPU with like at basically full speed with like no other constraints, like how FAST would be able to make a program go.swyx: Yeah. Yeah. Right.Kyle: Soswyx: in, in training that like, you know, then you work back to like some percentage of like MFU for example.Kyle: Yeah, that's a, that's a great example. So like, there's an, there's an S-O-L-M-F-U, and then there's like, you know, what's practically achievable.swyx: Cool. Should we move on to sort of, uh, Kyle's side?Uh, Kyle, you're coming more from the data science world. And, uh, I, I mean I always, whenever, whenever I meet someone who's done working in tabular stuff, graph neural networks, time series, these are basically when I go to new reps, I go to ICML, I walk the back halls. There's always like a small group of graph people.Yes. Absolute small group of tabular people. [00:19:00] And like, there's no one there. And like, it's very like, you know what I mean? Like, yeah, no, like it's, it's important interesting work if you care about solving the problems that they solve.Kyle: Yeah.swyx: But everyone else is just LMS all the time.Kyle: Yeah. I mean it's like, it's like the black hole, right?Has the event horizon reached this yet in nerves? Um,swyx: but like, you know, those are, those are transformers too. Yeah. And, and those are also like interesting things. Anyway, uh, I just wanted to spend a little bit of time on, on those, that background before we go into Dynamo, uh, proper.Kyle: Yeah, sure. I took a different path to Nvidia than that, or I joined six years ago, seven, if you count, when I was an intern.So I joined Nvidia, like right outta college. And the first thing I jumped into was not what I'd done in, during internship, which was like, you know, like some stuff for autonomous vehicles, like heavyweight object detection. I jumped into like, you know, something, I'm like, recommenders, this is popular. Andswyx: yeah, he did RexiKyle: as well.Yeah, Rexi. Yeah. I mean that, that was the taboo data at the time, right? You have tables of like, audience qualities and item qualities, and you're trying to figure out like which member of [00:20:00] the audience matches which item or, or more practically which item matches which member of the audience. And at the time, really it was like we were trying to enable.Uh, recommender, which had historically been like a little bit of a CP based workflow into something that like, ran really well in GPUs. And it's since been done. Like there are a bunch of libraries for Axis that run on GPUs. Uh, the common models like Deeplearning recommendation model, which came outta meta and the wide and deep model, which was used or was released by Google were very accelerated by GPUs using, you know, the fast HBM on the chips, especially to do, you know, vector lookups.But it was very interesting at the time and super, super relevant because like we were starting to get like. This explosion of feeds and things that required rec recommenders to just actively be on all the time. And sort of transitioned that a little bit towards graph neural networks when I discovered them because I was like, okay, you can actually use graphical neural networks to represent like, relationships between people, items, concepts, and that, that interested me.So I jumped into that at [00:21:00] Nvidia and, and got really involved for like two-ish years.swyx: Yeah. Uh, and something I learned from Brian Zaro Yeah. Is that you can just kind of choose your own path in Nvidia.Kyle: Oh my God. Yeah.swyx: Which is not a normal big Corp thing. Yeah. Like you, you have a lane, you stay in your lane.Nader: I think probably the reason why I enjoy being in a, a big company, the mission is the boss probably from a startup guy. Yeah. The missionswyx: is the boss.Nader: Yeah. Uh, it feels like a big game of pickup basketball. Like, you know, if you play one, if you wanna play basketball, you just go up to the court and you're like, Hey look, we're gonna play this game and we need three.Yeah. And you just like find your three. That's honestly for every new initiative that's what it feels like. Yeah.Vibhu: It also like shows, right? Like Nvidia. Just releasing state-of-the-art stuff in every domain. Yeah. Like, okay, you expect foundation models with Nemo tron voice just randomly parakeet.Call parakeet just comes out another one, uh, voice. TheKyle: video voice team has always been producing.Vibhu: Yeah. There's always just every other domain of paper that comes out, dataset that comes out. It's like, I mean, it also stems back to what Nvidia has to do, right? You have to make chips years before they're actually produced.Right? So you need to know, you need to really [00:22:00] focus. TheKyle: design process starts likeVibhu: exactlyKyle: three to five years before the chip gets to the market.Vibhu: Yeah. I, I'm curious more about what that's like, right? So like, you have specialist teams. Is it just like, you know, people find an interest, you go in, you go deep on whatever, and that kind of feeds back into, you know, okay, we, we expect predictions.Like the internals at Nvidia must be crazy. Right? You know? Yeah. Yeah. You know, you, you must. Not even without selling to people, you have your own predictions of where things are going. Yeah. And they're very based, very grounded. Right?Kyle: Yeah. It, it, it's really interesting. So there's like two things that I think that Amed does, which are quite interesting.Uh, one is like, we really index into passion. There's a big. Sort of organizational top sound push to like ensure that people are working on the things that they're passionate about. So if someone proposes something that's interesting, many times they can just email someone like way up the chain that they would find this relevant and say like, Hey, can I go work on this?Nader: It's actually like I worked at a, a big company for a couple years before, uh, starting on my startup journey and like, it felt very weird if you were to like email out of chain, if that makes [00:23:00] sense. Yeah. The emails at Nvidia are like mosh pitsswyx: shoot,Nader: and it's just like 60 people, just whatever. And like they're, there's this,swyx: they got messy like, reply all you,Nader: oh, it's in, it's insane.It's insane. They justKyle: help. You know, Maxim,Nader: the context. But, but that's actually like, I've actually, so this is a weird thing where I used to be like, why would we send emails? We have Slack. I am the entire, I'm the exact opposite. I feel so bad for anyone who's like messaging me on Slack ‘cause I'm so unresponsive.swyx: Your emailNader: Maxi, email Maxim. I'm email maxing Now email is a different, email is perfect because man, we can't work together. I'm email is great, right? Because important threads get bumped back up, right? Yeah, yeah. Um, and so Slack doesn't do that. So I just have like this casino going off on the right or on the left and like, I don't know which thread was from where or what, but like the threads get And then also just like the subject, so you can have like working threads.I think what's difficult is like when you're small, if you're just not 40,000 people I think Slack will work fine, but there's, I don't know what the inflection point is. There is gonna be a point where that becomes really messy and you'll actually prefer having email. ‘cause you can have working threads.You can cc more than nine people in a thread.Kyle: You can fork stuff.Nader: You can [00:24:00] fork stuff, which is super nice and just like y Yeah. And so, but that is part of where you can propose a plan. You can also just. Start, honestly, momentum's the only authority, right? So like, if you can just start, start to make a little bit of progress and show someone something, and then they can try it.That's, I think what's been, you know, I think the most effective way to push anything for forward. And that's both at Nvidia and I think just generally.Kyle: Yeah, there's, there's the other concept that like is explored a lot at Nvidia, which is this idea of a zero billion dollar business. Like market creation is a big thing at Nvidia.Like,swyx: oh, you want to go and start a zero billion dollar business?Kyle: Jensen says, we are completely happy investing in zero billion dollar markets. We don't care if this creates revenue. It's important for us to know about this market. We think it will be important in the future. It can be zero billion dollars for a while.I'm probably minging as words here for, but like, you know, like, I'll give an example. NVIDIA's been working on autonomous driving for a a long time,swyx: like an Nvidia car.Kyle: No, they, they'veVibhu: used the Mercedes, right? They're around the HQ and I think it finally just got licensed out. Now they're starting to be used quite a [00:25:00] bit.For 10 years you've been seeing Mercedes with Nvidia logos driving.Kyle: If you're in like the South San Santa Clara, it's, it's actually from South. Yeah. So, um. Zero billion dollar markets are, are a thing like, you know, Jensen,swyx: I mean, okay, look, cars are not a zero billion dollar market. But yeah, that's a bad example.Nader: I think, I think he's, he's messaging, uh, zero today, but, or even like internally, right? Like, like it's like, uh, an org doesn't have to ruthlessly find revenue very quickly to justify their existence. Right. Like a lot of the important research, a lot of the important technology being developed that, that's kind ofKyle: where research, research is very ide ideologically free at Nvidia.Yeah. Like they can pursue things that they wereswyx: Were you research officially?Kyle: I was never in research. Officially. I was always in engineering. Yeah. We in, I'm in an org called Deep Warning Algorithms, which is basically just how do we make things that are relevant to deep warning go fast.swyx: That sounds freaking cool.Vibhu: And I think a lot of that is underappreciated, right? Like time series. This week Google put out time. FF paper. Yeah. A new time series, paper res. Uh, Symantec, ID [00:26:00] started applying Transformers LMS to Yes. Rec system. Yes. And when you think the scale of companies deploying these right. Amazon recommendations, Google web search, it's like, it's huge scale andKyle: Yeah.Vibhu: You want fast?Kyle: Yeah. Yeah. Yeah. Actually it's, it, I, there's a fun moment that brought me like full circle. Like, uh, Amazon Ads recently gave a talk where they talked about using Dynamo for generative recommendation, which was like super, like weirdly cathartic for me. I'm like, oh my God. I've, I've supplanted what I was working on.Like, I, you're using LMS now to do what I was doing five years ago.swyx: Yeah. Amazing. And let's go right into Dynamo. Uh, maybe introduce Yeah, sure. To the top down and Yeah.Kyle: I think at this point a lot of people are familiar with the term of inference. Like funnily enough, like I went from, you know, inference being like a really niche topic to being something that's like discussed on like normal people's Twitter feeds.It's,Nader: it's on billboardsKyle: here now. Yeah. Very, very strange. Driving, driving, seeing just an inference ad on 1 0 1 inference at scale is becoming a lot more important. Uh, we have these moments like, you know, open claw where you have these [00:27:00] agents that take lots and lots of tokens, but produce, incredible results.There are many different aspects of test time scaling so that, you know, you can use more inference to generate a better result than if you were to use like a short amount of inference. There's reasoning, there's quiring, there's, adding agency to the model, allowing it to call tools and use skills.Dyno sort came about at Nvidia. Because myself and a couple others were, were sort of talking about the, these concepts that like, you know, you have inference engines like VLMS, shelan, tenor, TLM and they have like one single copy. They, they, they sort of think about like things as like one single copy, like one replica, right?Why Scale Out WinsKyle: Like one version of the model. But when you're actually serving things at scale, you can't just scale up that replica because you end up with like performance problems. There's a scaling limit to scaling up replicas. So you actually have to scale out to use a, maybe some Kubernetes type terminology.We kind of realized that there was like. A lot of potential optimization that we could do in scaling out and building systems for data [00:28:00] center scale inference. So Dynamo is this data center scale inference engine that sits on top of the frameworks like VLM Shilling and 10 T lm and just makes things go faster because you can leverage the economy of scale.The fact that you have KV cash, which we can define a little bit later, uh, in all these machines that is like unique and you wanna figure out like the ways to maximize your cash hits or you want to employ new techniques in inference like disaggregation, which Dynamo had introduced to the world in, in, in March, not introduced, it was a academic talk, but beforehand.But we are, you know, one of the first frameworks to start, supporting it. And we wanna like, sort of combine all these techniques into sort of a modular framework that allows you to. Accelerate your inference at scale.Nader: By the way, Kyle and I became friends on my first date, Nvidia, and I always loved, ‘cause like he always teaches meswyx: new things.Yeah. By the way, this is why I wanted to put two of you together. I was like, yeah, this is, this is gonna beKyle: good. It's very, it's very different, you know, like we've, we, we've, we've talked to each other a bunch [00:29:00] actually, you asked like, why, why can't we scale up?Nader: Yeah.Scale Up Limits ExplainedNader: model, you said model replicas.Kyle: Yeah. So you, so scale up means assigning moreswyx: heavier?Kyle: Yeah, heavier. Like making things heavier. Yeah, adding more GPUs. Adding more CPUs. Scale out is just like having a barrier saying, I'm gonna duplicate my representation of the model or a representation of this microservice or something, and I'm gonna like, replicate it Many times.Handle, load. And the reason that you can't scale, scale up, uh, past some points is like, you know, there, there, there are sort of hardware bounds and algorithmic bounds on, on that type of scaling. So I'll give you a good example that's like very trivial. Let's say you're on an H 100. The Maxim ENV link domain for H 100, for most Ds H one hundreds is heus, right?So if you scaled up past that, you're gonna have to figure out ways to handle the fact that now for the GPUs to communicate, you have to do it over Infin band, which is still very fast, but is not as fast as ENV link.swyx: Is it like one order of magnitude, like hundreds or,Kyle: it's about an order of magnitude?Yeah. Okay. Um, soswyx: not terrible.Kyle: [00:30:00] Yeah. I, I need to, I need to remember the, the data sheet here, like, I think it's like about 500 gigabytes. Uh, a second unidirectional for ENV link, and about 50 gigabytes a second unidirectional for Infin Band. I, it, it depends on the, the generation.swyx: I just wanna set this up for people who are not familiar with these kinds of like layers and the trash speedVibhu: and all that.Of course.From Laptop to Multi NodeVibhu: Also, maybe even just going like a few steps back before that, like most people are very familiar with. You see a, you know, you can use on your laptop, whatever these steel viol, lm you can just run inference there. All, there's all, you can, youcan run it on thatVibhu: laptop. You can run on laptop.Then you get to, okay, uh, models got pretty big, right? JLM five, they doubled the size, so mm-hmm. Uh, what do you do when you have to go from, okay, I can get 128 gigs of memory. I can run it on a spark. Then you have to go multi GPU. Yeah. Okay. Multi GPU, there's some support there. Now, if I'm a company and I don't have like.I'm not hiring the best researchers for this. Right. But I need to go [00:31:00] multi-node, right? I have a lot of servers. Okay, now there's efficiency problems, right? You can have multiple eight H 100 nodes, but, you know, is that as a, like, how do you do that efficiently?Kyle: Yeah. How do you like represent them? How do you choose how to represent the model?Yeah, exactly right. That's a, that's like a hard question. Everyone asks, how do you size oh, I wanna run GLM five, which just came out new model. There have been like four of them in the past week, by the way, like a bunch of new models.swyx: You know why? Right? Deep seek.Kyle: No comment. Oh. Yeah, but Ggl, LM five, right?We, we have this, new model. It's, it's like a large size, and you have to figure out how to both scale up and scale out, right? Because you have to find the right representation that you care about. Everyone does this differently. Let's be very clear. Everyone figures this out in their own path.Nader: I feel like a lot of AI or ML even is like, is like this. I think people think, you know, I, I was, there was some tweet a few months ago that was like, why hasn't fine tuning as a service taken off? You know, that might be me. It might have been you. Yeah. But people want it to be such an easy recipe to follow.But even like if you look at an ML model and specificKyle: to you Yeah,Nader: yeah.Kyle: And the [00:32:00] model,Nader: the situation, and there's just so much tinkering, right? Like when you see a model that has however many experts in the ME model, it's like, why that many experts? I don't, they, you know, they tried a bunch of things and that one seemed to do better.I think when it comes to how you're serving inference, you know, you have a bunch of decisions to make and there you can always argue that you can take something and make it more optimal. But I think it's this internal calibration and appetite for continued calibration.Vibhu: Yeah. And that doesn't mean like, you know, people aren't taking a shot at this, like tinker from thinking machines, you know?Yeah. RL as a service. Yeah, totally. It's, it also gets even harder when you try to do big model training, right? We're not the best at training Moes, uh, when they're pre-trained. Like we saw this with LAMA three, right? They're trained in such a sparse way that meta knows there's gonna be a bunch of inference done on these, right?They'll open source it, but it's very trained for what meta infrastructure wants, right? They wanna, they wanna inference it a lot. Now the question to basically think about is, okay, say you wanna serve a chat application, a coding copilot, right? You're doing a layer of rl, you're serving a model for X amount of people.Is it a chat model, a coding model? Dynamo, you know, back to that,Kyle: it's [00:33:00] like, yeah, sorry. So you we, we sort of like jumped off of, you know, jumped, uh, on that topic. Everyone has like, their own, own journey.Cost Quality Latency TradeoffsKyle: And I, I like to think of it as defined by like, what is the model you need? What is the accuracy you need?Actually I talked to NA about this earlier. There's three axes you care about. What is the quality that you're able to produce? So like, are you accurate enough or can you complete the task with enough, performance, high enough performance. Yeah, yeah. Uh, there's cost. Can you serve the model or serve your workflow?Because it's not just the model anymore, it's the workflow. It's the multi turn with an agent cheaply enough. And then can you serve it fast enough? And we're seeing all three of these, like, play out, like we saw, we saw new models from OpenAI that you know, are faster. You have like these new fast versions of models.You can change the amount of thinking to change the amount of quality, right? Produce more tokens, but at a higher cost in a, in a higher latency. And really like when you start this journey of like trying to figure out how you wanna host a model, you, you, you think about three things. What is the model I need to serve?How many times do I need to call it? What is the input sequence link was [00:34:00] the, what does the workflow look like on top of it? What is the SLA, what is the latency SLA that I need to achieve? Because there's usually some, this is usually like a constant, you, you know, the SLA that you need to hit and then like you try and find the lowest cost version that hits all of these constraints.Usually, you know, you, you start with those things and you say you, you kind of do like a bit of experimentation across some common configurations. You change the tensor parallel size, which is a form of parallelismVibhu: I take, it goes even deeper first. Gotta think what model.Kyle: Yes, course,ofKyle: course. It's like, it's like a multi-step design process because as you said, you can, you can choose a smaller model and then do more test time scaling and it'll equate the quality of a larger model because you're doing the test time scaling or you're adding a harness or something.So yes, it, it goes way deeper than that. But from the performance perspective, like once you get to the model you need, you need to host, you look at that and you say, Hey. I have this model, I need to serve it at the speed. What is the right configuration for that?Nader: You guys see the recent, uh, there was a paper I just saw like a few days ago that, uh, if you run [00:35:00] the same prompt twice, you're getting like double Just try itagain.Nader: Yeah, exactly.Vibhu: And you get a lot. Yeah. But the, the key thing there is you give the context of the failed try, right? Yeah. So it takes a shot. And this has been like, you know, basic guidance for quite a while. Just try again. ‘cause you know, trying, just try again. Did you try again? All adviceNader: in life.Vibhu: Just, it's a paper from Google, if I'm not mistaken, right?Yeah,Vibhu: yeah. I think it, it's like a seven bas little short paper. Yeah. Yeah. The title's very cute. And it's just like, yeah, just try again. Give it ask context,Kyle: multi-shot. You just like, say like, hey, like, you know, like take, take a little bit more, take a little bit more information, try and fail. Fail.Vibhu: And that basic concept has gone pretty deep.There's like, um, self distillation, rl where you, you do self distillation, you do rl and you have past failure and you know, that gives some signal so people take, try it again. Not strong enough.swyx: Uh, for, for listeners, uh, who listen to here, uh, vivo actually, and I, and we run a second YouTube channel for our paper club where, oh, that's awesome.Vivo just covered this. Yeah. Awesome. Self desolation and all that's, that's why he, to speed [00:36:00] on it.Nader: I'll to check it out.swyx: Yeah. It, it's just a good practice, like everyone needs, like a paper club where like you just read papers together and the social pressure just kind of forces you to just,Nader: we, we,there'sNader: like a big inference.Kyle: ReadingNader: group at a video. I feel so bad every time. I I, he put it on like, on our, he shared it.swyx: One, one ofNader: your guys,swyx: uh, is, is big in that, I forget es han Yeah, yeah,Kyle: es Han's on my team. Actually. Funny. There's a, there's a, there's a employee transfer between us. Han worked for Nater at Brev, and now he, he's on my team.He wasNader: our head of ai. And then, yeah, once we got in, andswyx: because I'm always looking for like, okay, can, can I start at another podcast that only does that thing? Yeah. And, uh, Esan was like, I was trying to like nudge Esan into like, is there something here? I mean, I don't think there's, there's new infant techniques every day.So it's like, it's likeKyle: you would, you would actually be surprised, um, the amount of blog posts you see. And ifswyx: there's a period where it was like, Medusa hydra, what Eagle, like, youKyle: know, now we have new forms of decode, uh, we have new forms of specula, of decoding or new,swyx: what,Kyle: what are youVibhu: excited? And it's exciting when you guys put out something like Tron.‘cause I remember the paper on this Tron three, [00:37:00] uh, the amount of like post train, the on tokens that the GPU rich can just train on. And it, it was a hybrid state space model, right? Yeah.Kyle: It's co-designed for the hardware.Vibhu: Yeah, go design for the hardware. And one of the things was always, you know, the state space models don't scale as well when you do a conversion or whatever the performance.And you guys are like, no, just keep draining. And Nitron shows a lot of that. Yeah.Nader: Also, something cool about Nitron it was released in layers, if you will, very similar to Dynamo. It's, it's, it's essentially it was released as you can, the pre-training, post-training data sets are released. Yeah. The recipes on how to do it are released.The model itself is released. It's full model. You just benefit from us turning on the GPUs. But there are companies like, uh, ServiceNow took the dataset and they trained their own model and we were super excited and like, you know, celebrated that work.ZoomVibhu: different. Zoom is, zoom is CGI, I think, uh, you know, also just to add like a lot of models don't put out based models and if there's that, why is fine tuning not taken off?You know, you can do your own training. Yeah,Kyle: sure.Vibhu: You guys put out based model, I think you put out everything.Nader: I believe I know [00:38:00]swyx: about base. BasicallyVibhu: without baseswyx: basic can be cancelable.Vibhu: Yeah. Base can be cancelable.swyx: Yeah.Vibhu: Safety training.swyx: Did we get a full picture of dymo? I, I don't know if we, what,Nader: what I'd love is you, you mentioned the three axes like break it down of like, you know, what's prefilled decode and like what are the optimizations that we can get with Dynamo?Kyle: Yeah. That, that's, that's, that's a great point. So to summarize on that three axis problem, right, there are three things that determine whether or not something can be done with inference, cost, quality, latency, right? Dynamo is supposed to be there to provide you like the runtime that allows you to pull levers to, you know, mix it up and move around the parade of frontier or the preto surface that determines is this actually possible with inference And AI todayNader: gives you the knobs.Kyle: Yeah, exactly. It gives you the knobs.Disaggregation Prefill vs DecodeKyle: Uh, and one thing that like we, we use a lot in contemporary inference and is, you know, starting to like pick up from, you know, in, in general knowledge is this co concept of disaggregation. So historically. Models would be hosted with a single inference engine. And that inference engine [00:39:00] would ping pong between two phases.There's prefill where you're reading the sequence generating KV cache, which is basically just a set of vectors that represent the sequence. And then using that KV cache to generate new tokens, which is called Decode. And some brilliant researchers across multiple different papers essentially made the realization that if you separate these two phases, you actually gain some benefits.Those benefits are basically a you don't have to worry about step synchronous scheduling. So the way that an inference engine works is you do one step and then you finish it, and then you schedule, you start scheduling the next step there. It's not like fully asynchronous. And the problem with that is you would have, uh, essentially pre-fill and decode are, are actually very different in terms of both their resource requirements and their sometimes their runtime.So you would have like prefill that would like block decode steps because you, you'd still be pre-filing and you couldn't schedule because you know the step has to end. So you remove that scheduling issue and then you also allow you, or you yourself, to like [00:40:00] split the work into two different ki types of pools.So pre-fill typically, and, and this changes as, as model architecture changes. Pre-fill is, right now, compute bound most of the time with the sequence is sufficiently long. It's compute bound. On the decode side because you're doing a full Passover, all the weights and the entire sequence, every time you do a decode step and you're, you don't have the quadratic computation of KV cache, it's usually memory bound because you're retrieving a linear amount of memory and you're doing a linear amount of compute as opposed to prefill where you retrieve a linear amount of memory and then use a quadratic.You know,Nader: it's funny, someone exo Labs did a really cool demo where for the DGX Spark, which has a lot more compute, you can do the pre the compute hungry prefill on a DG X spark and then do the decode on a, on a Mac. Yeah. And soVibhu: that's faster.Nader: Yeah. Yeah.Kyle: So you could, you can do that. You can do machine strat stratification.Nader: Yeah.Kyle: And like with our future generation generations of hardware, we actually announced, like with Reuben, this [00:41:00] new accelerator that is prefilled specific. It's called Reuben, CPX. SoKubernetes Scaling with GroveNader: I have a question when you do the scale out. Yeah. Is scaling out easier with Dynamo? Because when you need a new node, you can dedicate it to either the Prefill or, uh, decode.Kyle: Yeah. So Dynamo actually has like a, a Kubernetes component in it called Grove that allows you to, to do this like crazy scaling specialization. It has like this hot, it's a representation that, I don't wanna go too deep into Kubernetes here, but there was a previous way that you would like launch multi-node work.Uh, it's called Leader Worker Set. It's in the Kubernetes standard, and Leader worker set is great. It served a lot of people super well for a long period of time. But one of the things that it's struggles with is representing a set of cases where you have a multi-node replica that has a pair, right?You know, prefill and decode, or it's not paired, but it has like a second stage that has a ratio that changes over time. And prefill and decode are like two different things as your workload changes, right? The amount of prefill you'll need to do may change. [00:42:00] The amount of decode that you, you'll need to do might change, right?Like, let's say you start getting like insanely long queries, right? That probably means that your prefill scales like harder because you're hitting these, this quadratic scaling growth.swyx: Yeah.And then for listeners, like prefill will be long input. Decode would be long output, for example, right?Kyle: Yeah. So like decode, decode scale. I mean, decode is funny because the amount of tokens that you produce scales with the output length, but the amount of work that you do per step scales with the amount of tokens in the context.swyx: Yes.Kyle: So both scales with the input and the output.swyx: That's true.Kyle: But on the pre-fold view code side, like if.Suddenly, like the amount of work you're doing on the decode side stays about the same or like scales a little bit, and then the prefilled side like jumps up a lot. You actually don't want that ratio to be the same. You want it to change over time. So Dynamo has a set of components that A, tell you how to scale.It tells you how many prefilled workers and decoded workers you, it thinks you should have, and also provides a scheduling API for Kubernetes that allows you to actually represent and affect this scheduling on, on, on your actual [00:43:00] hardware, on your compute infrastructure.Nader: Not gonna lie. I feel a little embarrassed for being proud of my SVG function earlier.swyx: No, itNader: wasreallyKyle: cute. I, Iswyx: likeNader: it's all,swyx: it's all engineering. It's all engineering. Um, that's where I'mKyle: technical.swyx: One thing I'm, I'm kind of just curious about with all with you see at a systems level, everything going on here. Mm-hmm. And we, you know, we're scaling it up in, in multi, in distributed systems.Context Length and Co Designswyx: Um, I think one thing that's like kind of, of the moment right now is people are asking, is there any SOL sort of upper bounds. In terms of like, let's call, just call it context length for one for of a better word, but you can break it down however you like.Nader: Yeah.swyx: I just think like, well, yeah, I mean, like clearly you can engage in hybrid architectures and throw in some state space models in there.All, all you want, but it looks, still looks very attention heavy.Kyle: Yes. Uh, yeah. Long context is attention heavy. I mean, we have these hybrid models, um,swyx: to take and most, most models like cap out at a million contexts and that's it. Yeah. Like for the last two years has been it.Kyle: Yeah. The model hardware context co-design thing that we're seeing these days is actually super [00:44:00] interesting.It's like my, my passion, like my secret side passion. We see models like Kimmy or G-P-T-O-S-S. I'm use these because I, I know specific things about these models. So Kimmy two comes out, right? And it's an interesting model. It's like, like a deep seek style architecture is MLA. It's basically deep seek, scaled like a little bit differently, um, and obviously trained differently as well.But they, they talked about, why they made the design choices for context. Kimmy has more experts, but fewer attention heads, and I believe a slightly smaller attention, uh, like dimension. But I need to remember, I need to check that. Uh, it doesn't matter. But they discussed this actually at length in a blog post on ji, which is like our pu which is like credit puswyx: Yeah.Kyle: Um, in, in China. Chinese red.swyx: Yeah.Kyle: It's, yeah. So it, it's, it's actually an incredible blog post. Uh, like all the mls people in, in, in that, I've seen that on GPU are like very brilliant, but they, they talk about like the creators of Kimi K two [00:45:00] actually like, talked about it on, on, on there in the blog post.And they say, we, we actually did an experiment, right? Attention scales with the number of heads, obviously. Like if you have 64 heads versus 32 heads, you do half the work of attention. You still scale quadratic, but you do half the work. And they made a, a very specific like. Sort of barter in their system, in their architecture, they basically said, Hey, what if we gave it more experts, so we're gonna use more memory capacity.But we keep the amount of activated experts the same. We increase the expert sparsity, so we have fewer experts act. The ratio to of experts activated to number of experts is smaller, and we decrease the number of attention heads.Vibhu: And kind of for context, what the, what we had been seeing was you make models sparser instead.So no one was really touching heads. You're just having, uh,Kyle: well, they, they did, they implicitly made it sparser.Vibhu: Yeah, yeah. For, for Kimmy. They did,Kyle: yes.Vibhu: They also made it sparser. But basically what we were seeing was people were at the level of, okay, there's a sparsity ratio. You want more total parameters, less active, and that's sparsity.[00:46:00]But what you see from papers, like, the labs like moonshot deep seek, they go to the level of, okay, outside of just number of experts, you can also change how many attention heads and less attention layers. More attention. Layers. Layers, yeah. Yes, yes. So, and that's all basically coming back to, just tied together is like hardware model, co-design, which isKyle: hardware model, co model, context, co-design.Vibhu: Yeah.Kyle: Right. Like if you were training a, a model that was like. Really, really short context, uh, or like really is good at super short context tasks. You may like design it in a way such that like you don't care about attention scaling because it hasn't hit that, like the turning point where like the quadratic curve takes over.Nader: How do you consider attention or context as a separate part of the co-design? Like I would imagine hardware or just how I would've thought of it is like hardware model. Co-design would be hardware model context co-designKyle: because the harness and the context that is produced by the harness is a part of the model.Once it's trained in,Vibhu: like even though towards the end you'll do long context, you're not changing architecture through I see. Training. Yeah.Kyle: I mean you can try.swyx: You're saying [00:47:00] everyone's training the harness into the model.Kyle: I would say to some degree, orswyx: there's co-design for harness. I know there's a small amount, but I feel like not everyone has like gone full send on this.Kyle: I think, I think I think it's important to internalize the harness that you think the model will be running. Running into the model.swyx: Yeah. Interesting. Okay. Bash is like the universal harness,Kyle: right? Like I'll, I'll give. An example here, right? I mean, or just like a, like a, it's easy proof, right? If you can train against a harness and you're using that harness for everything, wouldn't you just train with the harness to ensure that you get the best possible quality out of,swyx: Well, the, uh, I, I can provide a counter argument.Yeah, sure. Which is what you wanna provide a generally useful model for other people to plug into their harnesses, right? So if youKyle: Yeah. Harnesses can be open, open source, right?swyx: Yeah. So I mean, that's, that's effectively what's happening with Codex.Kyle: Yeah.swyx: And, but like you may want like a different search tool and then you may have to name it differently or,Nader: I don't know how much people have pushed on this, but can you.Train a model, would it be, have you have people compared training a model for the for the harness versus [00:48:00] like post training forswyx: I think it's the same thing. It's the same thing. It's okay. Just extra post training. INader: see.swyx: And so, I mean, cognition does this course, it does this where you, you just have to like, if your tool is slightly different, um, either force your tool to be like the tool that they train for.Hmm. Or undo their training for their tool and then Oh, that's re retrain. Yeah. It's, it's really annoying and like,Kyle: I would hope that eventually we hit like a certain level of generality with respect to training newswyx: tools. This is not a GI like, it's, this is a really stupid like. Learn my tool b***h.Like, I don't know if, I don't know if I can say that, but like, you know, um, I think what my point kind of is, is that there's, like, I look at slopes of the scaling laws and like, this slope is not working, man. We, we are at a million token con

In today's Cloud Wars Minute, I explain why customer pressure is forcing SAP, Oracle, and Workday to overhaul traditional enterprise software sales models. Highlights 00:01 — Hello my friends. Welcome back to Cloud Wars Minute. We've got some big news here because we've got SAP, Oracle, and Workday all agreeing on a very key issue here and instituting some changes at the same time. What led to this unprecedented alignment between three companies that you know, day after day in the marketplace, are scratching each other's eyes out? 00:49 — It's really this notion about what's going on with customers here in these days of the AI revolution, with things moving so much faster. Customers are under enormous pressure to do things differently, to get AI throughout the organization and achieve better outcomes, but not spend too much money and not take risks. 01:20 — The very last thing that customers want or need or are willing to tolerate is old-fashioned approaches to how they engage with software companies. Especially now as the software itself is changing. They're not just apps vendors anymore, but agent vendors and data cloud vendors helping customers organize data and revise processes. 02:21 — Across the board these companies have decided they need to combine different sales organizations or flatten the existing ones to achieve a simpler point of contact for customers. Not so many different people from the same vendor calling on them. Workday says customers are moving faster and the old decision model doesn't work anymore. 03:08 — Rob Enslin, President and Chief Commercial Officer at Workday, said the company wants to push more decisions out to the point of the customer and have them spend less time with the inner workings of what Workday is doing. At SAP, the sales organization called Customer Success is now paired with the services and delivery team run by Thomas Saueressig. 04:00 — Customers are saying they want to give these companies their money but don't have time to hear endless presentations or meet half of a sales force. Either make it simpler or you're never going to see another nickel. In the early days of the AI revolution leading into the AI economy, customers cannot operate the old-fashioned way with software companies. Visit Cloud Wars for more.

Join Paul Spain as he sits down with Dr Ojas Mahapatra, Group CEO of Mars Bioimaging Ltd, to explore cutting-edge advances in portable CT scanning and the future of medtech innovation from Christchurch.Plus a look at tech news from the week including:NZ's new Online Scams Code2degrees and Ericsson's private 5G rollout at Lyttelton PortNZ cyber security stratergy 2026 - 2030Apple's budget MacBook NeoGoogle's Play Store fee cutsSpecial thanks to our show partners 2degrees, Fortinet, One New Zealand, Spark New Zealand, Workday and Gorilla Technology.

In this episode, we break down what really makes a “good” workday (hint: it's not doing more). You'll hear about a study that redefined productivity, plus a few practical strategies that help you make progress, finish strong, and feel better when you log off. If you've ever ended the day frustrated and empty, this one's for you.Episode Covers: - The surprising science behind “good” workdays - How to stop letting the day happen to you - Why batching communications helps you think clearly - A brain-based trick for feeling accomplished dailyFREE Resources: Watch this Free Class!: 3 Secrets to Always Having Enough Time For Your Work, Your Family and Yourself ( https://www.alexishaselberger.com/register-now ) ⁠⁠Click here to grab your free Distraction Action Plan today and start saving hours  each week! ( https://www.alexishaselberger.com/reduce-distraction )This show is brought to you by: ⁠Time Well Spent : the time management course for real people, just like you, who want to do more and stress less -  https://www.alexishaselberger.com/time-well-spent-course Stay connected!:Visit our website at  ⁠https://www.alexishaselberger.com⁠ Check out the " ⁠Time Well Spent: Time Management for Real People⁠ “ Course ( https://www.alexishaselberger.com/time-well-spent-course )Join the  ⁠Do More, Stress Less Facebook Community⁠  ( https://www.facebook.com/groups/domorestressless )Connect on  ⁠Linkedin⁠  ( https://www.linkedin.com/in/alexis-haselberger/ )Follow us for updates and more content: Youtube  ( https://www.youtube.com/c/DoMoreStressLess ) ⁠Instagram⁠  ( https://www.instagram.com/do.more.stress.less/ ) ⁠TikTok⁠  ( https://www.tiktok.com/@do.more.stress.less)  ⁠Facebook⁠  ( https://www.facebook.com/domorestressless )We want your feedback!:If you have constructive feedback, please email us at alexis+podcastfeedback@alexishaselberger.comIf you enjoyed this episode, please leave us a rating and share with a friend!Transcript:Read it  ⁠here⁠ !

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Jordan Cracknell is a UK-based financier and author. In addition to writing opinion pieces for publications such as TODAY.com, Metro.co.uk, City AM, and others, she is the author of You Can Count on Penny, a children's book that inspires young people to embrace their love of mathematics.A native New Yorker, she is a graduate of the University of Cambridge, where she obtained an MBA. Additionally, she has a MSc in Finance from Baruch College. Her parents inspired her to forge a career in Finance after attending ‘Take Your Daughter to Work Day' with her father, who worked in the World Trade Center.From being hired straight out of university putting pitch books together to working on the trading floor for companies such as Deutsche Bank and Renaissance Capital, she has never looked back.Jordan lives in the UK with her husband, the double Olympic gold medalist James Cracknell, and their dogs and cats, balancing her career goals with being a stepmother of three, writing, advocating for more women to enter finance, and inspiring the next generation to manage money and excel in the industry.www.jordancracknell.comhttps://www.linkedin.com/in/jconnell26/

Women are still greatly underrepresented in STEM. The latest figures from the Women Tech Network show women only make up around 26-27% of the STEM workforce and the organization estimates that at the current rate of change, it will be nearly 123 years before the economic gender gap is closed.It's International Women's Day 2026 – and the rate of women hired in tech continues to lag far behind that of men. As hiring teams turn to AI tools to automatically field candidates, we're at something of a crossroads – do we fix the bias inherent in hiring? Or simply replicate it at scale with similarly biased AI tools?In this episode, Jane are Rory are joined by Clare Hickie, EMEA CTO at Workday, to discuss how businesses can engage in bias-free talent acquisition in the age of AI.

In today's Cloud Wars Minute, I look at how Workday plans to blend AI agents with its core HR and finance platforms. Highlights 00:03 — One of the big stories of early 2026 is this whole wackiness around how AI is going to destroy the enterprise apps business, particularly SaaS companies. Will it change it? Absolutely and sometimes in profound ways, but not to the elimination of it. This idea that customers can either use agents or they can use apps is ridiculous. There's a very powerful role for both agents and applications. 01:14 — Workday's Aneel Bhusri's top priority for the company as he takes over again as CEO is he wants to grow. He came back in as CEO last month. Carl Eschenbach had been CEO for three years and did a great job building out the international business and scaling up the sales organization, making Workday a bigger, more well-run machine. 02:21 — Bhusri emphasized very strongly its [Workday's] core business of enterprise applications for HR and finance is very strong. It'll be able to help those customers find an even better way of using enterprise technology and that's the combination of its existing apps plus agents with its Data Cloud and its single data model. 03:29 — This year it's going to complement that by rolling out its own agents, specifically built around certain roles that are bound up tightly within HR organizations and finance. Bushri believes that's where AI-accelerated growth for Workday is going to happen in the second half of the year. 04:33 — Bhusri said he's a big fan of large language models, that's great. But this idea that you could take large language models, bypass applications, and connect those models to big stores of data and get great outcomes is ridiculous. This whole SaaS apocalypse thing is going to be a tremendous waste of time and energy. Visit Cloud Wars for more.

Genre free show, you never know what your going to get from week to week.Catch the Midweek Workday Chill Mix Weds check @labr@ravenation.club for updated times.Everything #LABR can be found at https://labr.onlineOur Mastodon account: https://ravenation.club/@labrIf you're on the go?https://www.radio-browser.info/usersDo A Search for LABR, & There You Are. Streaming 24/7 all the LABR Collective Members shows that you might've missed. And a few extra's in between.Enjoying this love we're spreading? Want to support LABR - Love a Brother Radio in spreading that love? Now you can.https://labr.online/donate Any little thing helps us feed the Keebler Elves to keep the wheels turning in the background. We're a 2 1/2 person operation. And a lot goes into making this work properly. With that said, we all thank you in advance for any support you lend. But most importantly. For your ears. 

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It was a major week for software with Workday, Salesforce, and OneStream announcing major fourth quarter and fiscal year financial results. Unit4 also announced it is streamlining Unit4 Success4U to simplify engagement for customers optimizing existing solutions or adopting Unit4 ERPx.Connect with us!https://www.erpadvisorsgroup.com866-499-8550LinkedIn:https://www.linkedin.com/company/erp-advisors-groupTwitter:https://twitter.com/erpadvisorsgrpFacebook:https://www.facebook.com/erpadvisorsInstagram:https://www.instagram.com/erpadvisorsgroupPinterest:https://www.pinterest.com/erpadvisorsgroupMedium:https://medium.com/@erpadvisorsgroup

Genre free show, you never know what your going to get from week to week.Catch the Midweek Workday Chill Mix Weds check @labr@ravenation.club for updated times.Everything #LABR can be found at https://labr.onlineOur Mastodon account: https://ravenation.club/@labrIf you're on the go?https://www.radio-browser.info/usersDo A Search for LABR, & There You Are. Streaming 24/7 all the LABR Collective Members shows that you might've missed. And a few extra's in between.Enjoying this love we're spreading? Want to support LABR - Love a Brother Radio in spreading that love? Now you can.https://labr.online/donate Any little thing helps us feed the Keebler Elves to keep the wheels turning in the background. We're a 2 1/2 person operation. And a lot goes into making this work properly. With that said, we all thank you in advance for any support you lend. But most importantly. For your ears. 

Tod Sacerdoti is the CEO and co-founder of Pipedream, which recently sold to Workday. Tod is also a general partner at Flex Capital, where he's invested in over 400 companies including Chime, Vercel, Replit, CodeRabbit, Mercury, and many others. He previously founded BrightRoll, a programmatic video advertising platform that sold to Yahoo for $640 million in 2014. In this episode of Summation, Tod and Auren discuss:Why seed investing has the highest annualized returns of any venture asset classFirst-gen AI companies being the most vulnerable to AI disruptionQSBS and why it's the most important tax benefit nobody talks aboutThe founder code and what happens when people break itYou can find Auren Hoffman on X at @auren and Tod Sacerdoti on X at @tod

Understanding the Frontline Workforce. As our research point out, more than 70% of all US workers (80% Worldwide) work in a frontline (customer facing or operational facing) role. We all have teams in these positions so it's important for business and HR leaders to understand this space. This is the first podcast in a series with Josh Secrest, the head of marketing at Paradox, an innovative AI company that pioneered conversational recruiting from end to end. Not only does Josh S. know a lot about the frontline, he has leadership roles at the National Restaurant Association and National Retail Federation, and also has experience leading talent management at McDonald's and leading culture at Abercrombie. Josh and I will be sharing a series of conversations to help you understand best-practices in high-volume recruiting, frontline workforce management, and the economics and financial business case for automation in this space. This episode features a deep discussion on the critical role of frontline workers in the workforce, exploring how technology, management, and strategic support can transform frontline work environments. It highlights innovative practices and future trends in supporting frontline employees across retail, hospitality, and healthcare sectors. Keywords frontline workers, workforce strategy, HR technology, AI in HR, employee retention, frontline management, retail, hospitality, workforce support, digital transformation Key topics Importance of frontline workers Impact of technology and AI on frontline support Role of frontline managers in business success Additional Information Powering the Frontline Workforce: How Frontline-First Companies Thrive (research) Josh Bersin Company Highlights Cost of Neglecting Frontline Workers (research) An Exploration into the Frontline Workforce with Josh Bersin (video) Tailor your HR and Management Programs for Frontline Work with Galileo, the Expert AI Agent for HR Chapters (00:00:03) - Josh Seacrest(00:01:16) - Workers on the Frontline(00:02:26) - The Power of a Front-Line Manager(00:03:39) - The Impact of Frontline on Business(00:05:37) - The Role of Frontline Workers(00:11:59) - McDonald's On AI & The Future of Workforce(00:14:46) - Backline Manager: The Future of Data-driven Business(00:16:52) - Employee Care in the Future(00:19:38) - Give Your Employees More Money(00:21:23) - Fast Food On The Podcast(00:22:30) - The New Talent: 711 and More(00:25:48) - Josh on the Business of Segmentation

Host Paul Spain is joined by James Pinner, CEO of New Zealand Growth Capital Partners (NZGCP), to explore the impact of New Zealand's venture capital and startup scene. Together, they dig into the origins of NZGCP, the changing landscape for Kiwi startups, and the crucial role government funding plays in building a thriving tech ecosystem. They discuss how government-backed funds, bold startups, and a thriving venture capital landscape are driving economic growth and innovation. From unicorn success stories like Rocket Lab to the challenges of early investment and KiwiSaver's role, this episode is packed with insights for founders, investors, and tech enthusiasts alike.Thanks to our Partners One NZ, Workday, 2degrees, Spark, Fortinet and Gorilla Technology

Dan Nathan and Guy Adami cover PPI, upcoming earnings, and this week's jobs report. They focus on mounting stress in the AI infrastructure and financing complex: CoreWeave's post-earnings drop, heavy customer concentration, funding challenges, and Jim Chanos' critique that its GPU-leasing model loses money and shows distress-level liquidity, alongside declines in Apollo, KKR, Blackstone, and banks. They contrast Nvidia's strong quarter and 60% growth outlook with stock stagnation, discuss Broadcom as a key AI barometer, and note ongoing software multiple and margin compression highlighted by volatile moves in Workday and Salesforce. Despite rising VIX swings, falling 10-year yields, and consumer-credit concerns signaled by AmEx, Capital One, Klarna, and Walmart trade-down commentary, the S&P remains near highs; they also discuss crude's rebound amid Middle East tensions and Bitcoin weakness pressuring MicroStrategy. After the break, Jen & Kristen join Dan and Guy live from the iConnections Global Alts conference in Miami to unpack an “AI panic” market day, why higher productivity could mean higher rates, and what private credit hiccups really signal for hedge funds and alts. They also explain how The Wall Street Skinny is turning arcane finance jargon into plain English for everyone from college students to the C‑suite, plus why there are no dumb questions when it comes to bonds, credit, and careers on Wall Street. Timecodes 0:00 - Intro 2:00 - CoreWeave & The Software Slide 17:30 - VIX, SPX & The Consumer 25:00 - Yields & Crude 28:30 - Bitcoin & Broader Market 33:20 - He Said, She Said

In today's Cloud Wars Minute, I break down Aneel Bushri's powerful case for pairing AI with enterprise apps. Highlights 00:02 — There are some wild things going on in the enterprise software business, some of it rational, much of it irrational. But the big issue right now is for customers, partners, and the software vendors, the Cloud Wars Top 10, to figure out what is going to be the right way forward, the optimal mix of AI with enterprise applications. 01:47 — I think the most important thing here was his [Workday CEO Aneel Bushri] take on the interplay between apps and AI. And also, he just had an utterly classic line about vibe coding. He said there is no amount of vibe coding that will ever produce an HR or ERP system that will meet all the requirements that modern business needs. 02:25 — "Whatever your problem is, AI is the solution." That's just not true. It's a tool. It's a fabulous tool. Might be the most important tool ever, but it can't do everything. And in his opening remarks on the Workday Q4 earnings call, Aneel Bushri did a great job of breaking that down. 04:08 — He said the combination of AI and many of the things it can do with its probabilistic capabilities and insights and predictive capabilities, plus the deterministic certainty of enterprise apps, is a really nice pair. He talked about the way forward and how he sees those two dynamics playing together. 05:18 — I just think he did one of his best jobs ever yesterday to step forward and say: "Here's what's real. Here's what isn't real. Here is the way forward. Here's the best combination for things. Here's the right outcome for customers." Brilliant performance by him on this earnings call. Visit Cloud Wars for more.

Senator Mark Kelly wins a preliminary injunction that will stop the Defense Department's disciplinary process in its tracks. Judge Aileen Cannon decides to hide Jack Smith's report on the MAL investigation from the public permanently. The list of Department of Justice failures in court continues to grow. Kash Patel has the best work day at the Olympics ever. Virginia Burger joins Andy to break down the situation around Senator Mark Kelly. More from Virginia Burger: https://www.pogo.org/about/people/virginia-burger Do you have questions for the pod?  Get this new customer offer and your 3-month Unlimited wireless plan for just $15 a month at  MINTMOBILE.com/UNJUST Follow AG Substack|MuellershewroteBlueSky|@muellershewroteAndrew McCabe isn't on social media, but you can buy his book The ThreatThe Threat: How the FBI Protects America in the Age of Terror and Trump Questions for the pod?https://formfacade.com/sm/PTk_BSogJ We would like to know more about our listeners. Please participate in this brief surveyListener Survey and CommentsThis Show is Available Ad-Free And Early For Patreon and Supercast Supporters at the Justice Enforcers level and above:https://dailybeans.supercast.techOrhttps://patreon.com/thedailybeansOr when you subscribe on Apple Podcastshttps://apple.co/3YNpW3P Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AI vs SaaS: Seat + Consumption Can CoexistSalesforce, Workday, Nvidia, Zoom, Block | Around the DeskThis week on Around the Desk, Sean Emory breaks down a pivotal week for AI and enterprise software.Are seat-based models being replaced?Or is AI expanding the value of platforms?Using earnings and data from Salesforce, Workday, Nvidia, Zoom, and Block, Sean argues AI is enhancing durable platforms, not eliminating them. The winners are likely multi-product ecosystems with compliance depth, proprietary data, and embedded workflows. Not point solutions.00:00 Welcome and Disclaimer00:43 AI vs SaaS Big Week02:13 Platforms vs Point Solutions03:46 Salesforce Seats + Agents07:06 Jobs Data10:08 Buybacks + Workday12:07 Inflation + Breadth14:17 Nvidia + Valuations17:26 AI Adoption + Limits19:03 Capitulation Setup22:08 Portfolio Updates26:19 ClosingDisclaimerThis content is for informational and educational purposes only and does not constitute investment advice, an offer, or a solicitation to buy or sell any security. The views expressed are as of the recording date and may change. The host and affiliated entities may hold positions in the companies discussed. Investing involves risk, including potential loss of principal. Always conduct your own research and consult a licensed financial advisor before making investment decisions.© 2026 Avory & Co. All rights reserved.

Send a textEver had someone CC half the company to “helpfully” point out your missing comma? We've been there. Today we unpack the office characters you secretly love to hate—the grammar police, the drama magnets, the nonstop talkers, and the tech dinosaurs—and share practical ways to set boundaries without burning bridges. We start human: the joy of a quiet morning with coffee and hockey, Olympic highlights, and the weird entertainment value of curling controversies. Then we get to the heart of HR work—how small details can derail big moments, and how to keep credibility when your tools lock after launch.We break down what separates a helpful edit from a public gotcha, why precision matters in performance reviews and open enrollment, and how to proof smarter in systems like Workday and UKG. We also dive into AI's double edge. Yes, it catches typos and speeds up drafting. But it can flatten everyone's voice and hallucinate facts. Our take: treat AI like a smart intern—use it for structure, then rewrite in your tone and verify every claim. On the hiring front, we explore subtle ways to spot AI-generated resumes and emails, from odd phrasing to robotic follow-ups, and we share prompts and cues that nudge applicants to reveal real thinking.From there, we tackle culture moments big and small: how to redirect bias in the room without escalating, how to build a mother's room and shut down rumor mills with clarity, and how to handle the six-hour orientation derailed by a single talker. Expect scripts you can steal, boundaries you can set, and playbooks for keeping meetings on track. We even admit our side gig as family IT support and the simple boundary hacks that save your sanity at home and at work.If you've ever wished your workplace had fewer what-the-heck moments and more calm, you'll find real talk and ready-to-use moves here. Subscribe, share with a friend who needs a laugh and a plan, and leave a review to tell us which “employee you love to hate” we should tackle next.Support the showWe want to hear from you.Text us or leave a voicemail (252) 564-9899‬email: feedback@jadedhr.comWant to:* Share a dumb employee question* Share a crazy story* Ask us a question* Share a best practice * Give us feedback Our Link Tree below has links to our social media sites, Patreon, Apple podcasts, Spotify & more.Please leave a review on your favorite podcast player and interact with us online!Linktree - https://linktr.ee/jadedhrFollow Cee Cee on IG - BoozyHR @ https://www.instagram.com/boozy_hr/

In der heutigen Folge sprechen die Finanzjournalisten Anja Ettel und Holger Zschäpitz über einen Absturz bei Nvidia, einen Rebound bei Software und eine Wende im Warner Brothers Drama. Außerdem geht es um Atlassian, Zscaler, Datadog, Applovin, Crowdstrike, Workday, Salesforce, Opendoor, Intuitive Machines, Carvana, IonQ, Rigetti, Netflix, Paramount Skydance, Allianz, Deutsche Telekom, Münchener Rück (Munich Re), Scout24, Heidelberg Materials, Deutsche Börse, Kion, Hensoldt, Puma, Block (Square), WiseTech, Amazon, Nike, Verizon, Papa Johns, Pinterest, Autodesk, Ebay, UPS, Hypoport, Xtrackers MSCI World Industrials ETF (WKN: A113FN), Amundi S&P World Industrials Screened ETF (WKN: A3DSTE), iShares MSCI Europe Industrials Sector ETF (WKN: A2QBZ6), iShares S&P 500 Industrials Sector ETF (WKN: A142N0). Wir freuen uns an Feedback über aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts und AAA-Newsletter. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Der Börsen-Podcast Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

The Shred is a weekly roundup of what's making headlines in the world of employment. The Shred is brought to you today by Jobcase.

Story 1: Humand's $66 Million Bet on the Deskless Workforce https://hrtechfeed.com/humand-secures-66-million-series-a-to-revolutionize-the-deskless-workforce-with-ai/ Story 2: ZipRecruiter's Rough 2025 Financials Story 3: WorkWhile Launches the 'AI Talent Coach' Story 4: Elly Debuts with $8M to End 'Tool Fatigue' Story 5: Workday's $9.5 Billion Year of AI Learn more about your ad choices. Visit megaphone.fm/adchoices

Plus: Shares of Workday pare early losses. And Samsung releases a new line of flagship smartphones with AI features. Julie Chang hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

The Olympics turned into the hockey team having a Take Your FBI Director to Work Day. Then we talk about the MAHA backlash against Trump trying to make all the children's water fountains spray Roundup. Finally, A..I. CEOs keep saying insane things that reveal they want us all dead, then cry when we don't just lay down in our graves. Weird. Join us.This episode is sponsored by BetterHelp. BetterHelp makes it easy to get matched online with a qualified therapist. Sign up and get 10%  https://www.betterhelp.com/skews This episode is sponsored by ZBiotics. Go to https://www.zbiotics.com/SKEW  now. You'll get 15% off your first order when you use SKEW at checkout

Carl Quintanilla, Jim Cramer and David Faber covered all of the bases on the AI trade: A preview of Nvidia's much-anticipated earnings due out after Wednesday's close of trading,  President Trump's State of the Union message to big tech about data centers and power plants, what Anthropic CEO Dario Amodei said on a podcast about AI risks.  Also in focus: The ball in Netflix's court after Paramount's sweetened offer to acquire Warner Bros. Discovery, billionaire investor David Tepper sends a scathing letter to Whirlpool, Workday shares extend their decline, Oracle gets upgraded, Cava soars while drinks giant Diageo tumbles, Lowe's falls as "uncertainty" overshadows an earnings beat.   Squawk on the Street Disclaimer   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In episode #356, Ben shares the results from the FP&A category of his 7th Annual SaaS Tech Stack Survey, highlighting the top financial planning and analysis solutions used in software companies today. With 37 FP&A solutions named in the survey, this remains one of the most competitive and fast-moving segments in the back-office tech stack. While spreadsheets still dominate usage—by a wide margin—dedicated FP&A platforms are gaining traction, especially as companies scale past $10M+ ARR and investor reporting requirements increase. Ben also compares this year's results to prior years and explains how FP&A tool adoption shifts by ARR size. Resources Mentioned 7th Annual SaaS Tech Stack Survey: https://www.thesaascfo.com/surveys/finance-accounting-tech-stack-survey/ What You'll Learn The most widely used FP&A solutions in SaaS and AI companies Why spreadsheets still dominate financial modeling workflows Which platforms are gaining momentum (Drivetrain, Mosaic, Aleph, Pigment, Planful, and others) How FP&A adoption changes as companies scale beyond $10M ARR Why enterprise-grade tools like Workday appear in larger organizations How funding and competition are reshaping the FP&A software landscape Why It Matters FP&A systems power your forecasting, budgeting, and board reporting Spreadsheet-based processes eventually break as complexity increases As ARR grows, investors expect more sophisticated financial modeling and analytics Selecting the right FP&A tool impacts forecasting accuracy, KPI visibility, and strategic planning Understanding market adoption trends helps founders and CFOs benchmark their financial systems

Since the start of 2026, Workday (WDAY) shares have fallen 40%. Earnings and a slew of downgrades didn't help the outlook picture, as Marley Kayden runs through the metrics in the report analysts took issue with. Prosper Trading Academy's Scott Bauer rounds out the bearish perspective with a put spread example options trade for Workday. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Options are pricing around a 4% to 5% move in Nvidia (NVDA) following its earnings, says Kevin Green when looking at the implied volatility. However, it's not the earnings themselves that can ignite volatility, but the guidance and call afterward. He explains how Nvidia can shatter a "gridlock" constraining Wall Street. Elsewhere in the tech stack, KG talks about the continuing weakness in software in Workday's (WDAY) earnings and turns to another ETF outside IGV to watch. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

$190 has been a key level to watch for Nvidia (NVDA) says Kevin Green. He provides a deep-dive preview on the highly-anticipated earnings event from the leading AI semiconductor name. KG adds thoughts on Pres. Trump's State of the Union address, Workday's (WDAY) slide after earnings and HP Inc. (HPQ) falling after disappointing guidance. For Wednesday's S&P 500 (SPX) levels to watch, KG is looking at $6945 to the upside with $6840 to the downside. He adds a break above the 100-day moving average as a key indicator to take out. ======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – / schwabnetwork Follow us on Facebook – / schwabnetwork Follow us on LinkedIn - / schwab-network About Schwab Network - https://schwabnetwork.com/about

Send a textBig Rocks, Small Peeps, and What ATS Systems Really Do to Teacher ResumesIn this episode, Vanessa talks about the physics of “yes” and “no,” how scope creep quietly expands your workload, a Peeps-inspired morale hack, and a deep dive into how Applicant Tracking Systems (ATS) actually interpret teacher resumes outside the classroom.If you've ever felt like you're doing everything right but nothing is landing… roll your chair up and have a listen!The Power of Yes & NoWhen you say yes to something, you're automatically saying no to something else — even if you don't see it immediately.In this segment we explore:Why teachers are conditioned to say yesHow scope creep grows one tiny task at a timeThe Big Rocks analogy and protecting what matters mostRecognizing gaslighting in professional environmentsAsking: Does this enhance your Big Three — or take away from them?Sometimes the most powerful boundary is a quiet, thoughtful no. Peeps: The Hack A lighthearted reset designed to support emotional regulation and morale — for students and staff.Ideas from today's episode:Peeps coloring for low-pressure brain breaksTurning small moments of joy into connectionWhy “sharpening the saw” often gives you time back later ATS Terrain: Naming Names!Last week we talked about terrain — this week we name names.You'll learn a little about United Talent, Workday, Taleo, and USAJobsKey takeaway: Your skills didn't change. The terrain did.It's isn't that you have to reinvent yourself. You just have to learn how the different systems listen.

Software faces it latest test with results from Workday. A look ahead to earnings from Salesforce and Snowflake. Plus, the CEO of Cava with his first reaction to earnings. The stock up more than 20% after the company says they are bridging the K-shaped economy. And the Department of Defense pressing anthropic for full access to its AI tools. The company's response and why it may not be so straight forward. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In der heutigen Folge sprechen die Finanzjournalisten Nando Sommerfeldt und Holger Zschäpitz über Anthropics Charme-Offensive, AMDs zweischneidigen Meta-Deal und den ersten Paypal-Bieter. Außerdem geht es um Thomson Reuters, FactSet, Salesforce, DocuSign, Intuit, Workday, Nvidia, HP, Fresenius Medical Care, MTU Aero Engines, VW, BMW, Xtrackers CSI300 Swap ETF (WKN: DBX0M2), HSBC Hang Seng Tech UCITS ETF (WKN:  A2QHV0), Deka MSCI China (WKN: ETFL32), iShares China Large Cap UCITS ETF (WKN: A0DK6Z), Invesco MSCI China Technology All Shares Stock Connect UCITS ETF (WKN: A3CMY8), UBS Solactive China Technology UCITS ETF (WKN: A2QJ9G), Kweichow Moutai, Invesco S&P 500 ETF (WKN: A1CYW7), UBS Core MSCI World (WKN: A2PK5J), Xtrackers Dax (WKN: DBX1DA), Amundi Core Stoxx Europe 600 (WKN: LYX0Q0), SPDR MSCI All Country World (WKN: A1JJTC) Wir freuen uns an Feedback über aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts und AAA-Newsletter. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Der Börsen-Podcast Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

Host Paul Spain sits down with Shane Smith, the co-founder of Education Perfect, for a fascinating dive into the world of edtech innovation. Shane shares the story behind Education Perfect's rise, building a gamified language learning platform that now helps millions of students and teachers across Australia and New Zealand. The conversation covers early development and key technical challenges, the role of AI in shaping personalised feedback and learning experiences, and thoughtful insights on balancing technology with integrity and data privacy in education.Special thanks to our show partners 2degrees, Fortinet, One New Zealand, Spark New Zealand, Workday and Gorilla Technology.

It's Episode 04 of Season 24. Enter the dark waters of the Cape Fear River as we present tales that will consume your minds."My Wife Keeps Feeding the Thing that Comes at Night" by Marcus Whalbring (Story starts around 00:03:30)TRIGGER WARNING!Produced by Jeff ClementCast: Narrator - Peter Lewis, Joan - Sarah Ruth Thomas"Residue" by Simon Bleaken (Story starts around 00:25:50)TRIGGER WARNING!Produced by Phil MichalskiCast: Narrator - Nikolle Doolin, Boy - Danielle McRae, Nick - Matthew Bradford, Steve - Jesse Cornett, Tom - Kyle Akers"Bring Your Slaughter to Work Day" by Abby Vail (Story starts around 01:01:10)TRIGGER WARNING!Produced by Phil MichalskiCast: Barnaby - Graham Rowat, Teller - Wafiyyah White, Liza - Sarah Ruth Thomas, Claire - Nichole Goodnight, Susan - Danielle McRae, Peter - Jeff Clement, Boy - Kyle Akers, Johnathan - Dan Zappulla"The Next Stage" by Beth Carpenter (Story starts around 01:13:25)TRIGGER WARNING!Produced by Claudius MooreCast: Jane - Erin Lillis, Daniel - Dan Zappulla, Mr. Ashe - Atticus Jackson, Mr. Pomp - Atticus Jackson, Mark - Jeff Clement"Fish Hook" by Rye Clarke (Story starts around 01:36:15)TRIGGER WARNING!Produced by Jesse CornettCast: Mike - Mike DelGaudio, Phil - Jesse Cornett, Danny - Matthew BradfordThis episode is sponsored by:Quince - Get cozy in Quince's high-quality wardrobe essentials highlighted by quality, sustainability, and affordability. Go to Quince.com/nosleep to get free shipping and a 365-day return period.Home Chef - Home Chef's meal kits are rated #1 in quality, convenience, value, taste, and recipe ease. Head to homechef.com/nosleep to get 50% off and free shipping for your first box plus free dessert for life!Click here to learn more about The NoSleep Podcast teamClick here to learn more about the Crimewave at Sea 2.0 Cruise!Click here to get your Crimewave at Sea discount code and bonus event!Click here to learn more about the anthology novel, "Hospital of Haunts"Click here to learn more about Abby VailExecutive Producer & Host: David CummingsMusical score composed by: Brandon Boone"Fish Hook" illustration courtesy of JörnThe NoSleep Podcast is Human-made for Human Minds. No generative AI is used in any aspect of work.Audio program ©2026 - Creative Reason Media - The copyrights for each story are held by the respective authors. No duplication or reproduction of this audio program is permitted without the written consent of Creative Reason Media. No part of this audio program may be used or reproduced in any manner for the purpose of training artificial intelligence technologies or systems. All rights reserved.

In this week's episode of WSJ's Take On the Week, co-host Miriam Gottfried and guest host Dan Gallagher, a tech columnist for Heard on the Street, chat with Jefferies software analyst Brent Thill about the recent turbulence in the business software market. They talk about the growing fears that AI will replace the need for traditional software-as-a-service, or SaaS, platforms like Intuit, Salesforce, and Workday. They analyze how the narrative around AI "vibe coding"—where businesses generate their own apps using simple text prompts—has led to a sharp selloff in cloud software stocks. They also note other factors weighing on the sector, including tech layoffs and the shift away from seat-based software pricing models and toward consumption-based metrics. After the break, Thill explains why he thinks the market's fears over AI disrupting major enterprise software are overblown. They explore why large companies won't trust AI with critical systems for payroll, accounting or taxes. Then Thill makes the case for why AI infrastructure and security companies remain safe bets, and why the current tech selloff and depressed valuations are setting the stage for a massive tech M&A boom driven by private-equity firms. This is WSJ's Take On the Week where co-hosts Telis Demos, Heard on the Street's banking and money columnist, and Miriam Gottfried, WSJ's investing and wealth management reporter, cut through the noise and dive into markets, the economy and finance—the big trades, key players and business news ahead. Have an idea for a future guest or episode? How can we better help you take on the week? We'd love to hear from you. Email the show at takeontheweek@wsj.com. To watch the video version of this episode, visit our WSJ Podcasts YouTube channel or the video page of WSJ.com Further Reading Threat of New AI Tools Wipes $300 Billion Off Software and Data Stocks AI Won't Kill the Software Business, Just Its Growth Story What You Need to Know About the AI Models Rattling Markets Meta Overshadows Microsoft by Showing AI Payoff in Ad Business Thoma Bravo's $34 Billion Fundraising Haul Bucks Private-Equity Slowdown IBM Strikes $11 Billion Deal for Confluent For more coverage of the markets and your investments, head to WSJ.com, WSJ's Heard on The Street Column, and WSJ's Live Markets blog. Sign up for the WSJ's free Markets A.M. newsletter.  Follow Miriam Gottfried here and Telis Demos here.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Bench Press: Pete Hegeseth. Secretary of War: 315. Avalanche. 9 of 15 people that died in Tahoe avalanche hiking/skiing backcountryMikaela ShiffrinGold in SlalomFirst Alpine skier to surpass 100 World Cup winsAnthony Kim. Started 5 shots back. 9 birdies final round!Anthony Kim talking about family and GodTiger on Anthony KimLike Kim Kardashian! Secret Edge of the Rockstar skier per WSJ.I'd say Golfer too.  MarketsMarkets 1% or so off ATH. Tech 5% or so whatever. The interesting story is.SaaSpocalypseiShares Expanded Tech-Software Sector ETF (IGV)31% off high!! Options tradingWorkday: Duration 482 or 16 months. June 2027Trading at $140. Call is $36 or 26%. % is key. Indicates cost. Bid / Ask: So buy 100 at $36.00, costs you $3600.IN summary. Workday goes up 26% to $176 you make money.To make “real Money” you buy $100k you get about 2700-2800 options. Workday doubles to $280. You make ($280- $140 - $36)*2700 = $280 - $300k.   AISpaceXaIXaI video on restructuring and next stepsRoboticsViral Video from China showing many robots dancing and flipping. Thank goodness we have some American Companies doing similar. Its actually INSANELY impressive. Real? https://www.unitree.com/Dog is $1600Figure in the US showing Robots doing real-world tasks.Billionaire Wealth Tax$519 Per Second: The Real-Time DestructionEconomicsRent control in Mass from WSJA group of housing advocates and labor unions want to stop landlords from raising rents by more than the state's annual rate of inflation—but no higher than 5%—a year.RecommendationsAI for Home: Using Gemini Gems: Home electronicsMusic studioChris Hemsworth on Theo VonPlay at 17.40. Purity of life and questioning meaning and why?“Falling in love”. Not rising or winning, but falling, like a sacrifice and risk.

AGENDA: 04:14 Anthropic's $30B Raise at $380B 06:18 Why SaaS Stocks Keep Getting Crushed 18:15 Wall Street's New Religion: AI Replaces Headcount  22:42 The Bear Case for Shopify: What Could Go Wrong? 31:51 Replit and Lovable are Proof Figma Missed Out: Figma; Buy or Sell?  48:42 Stripe Raises at $140BN: Is Stripe Wildly Overvalued or Adyen Undervalued?  54:36 OpenAI Buys OpenClaw 01:06:28 Thrive's $10B Growth Fund 01:09:10 Arif Janmohamed Leaves Lightspeed for New Firm 01:17:12 Workday's Founder Returns as CEO: Will it Work?  01:20:34 Which Founder Returns Next: HubSpot, Twilio, Gitlab? 01:24:03 Is Monday.com a Screaming Buy? 01:28:25 Jason and Harry Bet $200,000  

Welcome back to Truth, Lies & Work, the podcast where behavioural science meets workplace culture. This week we're diving into how AI is actually landing in the workplace — and what that means for managers, employees and the future of work. Our guest is Andrew Palmer, host of Boss Class from The Economist and author of the Bartleby management column. In Season 3 of Boss Class, Andrew goes hands-on with AI — not just talking about it, but living with it, testing it and asking the questions leaders need to answer as the technology transforms jobs and organisations. This episode isn't about hype. It's about what AI is actually good at today, what it's still terrible at, and how leaders should think about deploying it in ways that help people — not replace them.

https://rabbiefremgoldberg.org/living-with-emunah-part-377-take-god-to-work-day-is-everyday Wed, 18 Feb 2026 15:08:25 +0000 7262 Rabbi Efrem Goldberg Living with Emunah - podcast no

Episode Summary Alexis Haselberger is a time management and stress reduction coach who has helped over 215,000 people do more and stress less through coaching, workshops and online courses. Her clients include Google, Lyft, Workday, Capital One, Upwork and more. Who's your ideal client and what's the biggest challenge they face? What are the common mistakes people make when trying to solve that problem? What is one valuable free action that our audience can implement that will help with that issue? What is one valuable free resource that you can direct people to that will help with that issue? What's the one question I should have asked you that would be of great value to our audience? When was the last time you experienced Goosebumps with your family and why? Free Class: 3 Secrets to Always Having Enough Time for Your Work, Your Family, and Yourself Do More, Stress Less Podcast Get in touch with Alexis: Website, YouTube, Instagram Timing Validation Focus Validate your strategic timing with precision using the KAIROS assessment system. Book your 30-minute KAIROS Strategic Assessment (€147) and transform intuition into data-driven confidence. When you know exactly WHEN to move, not just HOW, transformation becomes inevitable. https://www.uwedockhorn.com/research