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Mastering Ecosystem Growth and AI Transformation Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ In this episode, Vince Menzione sits down with Rebecca Jones, Chief Growth Officer of Bridge Partners, to deconstruct the “Power of Three” co-selling model and the shift from AI experimentation to scalable business outcomes. They explore the critical importance of customer-centricity, the role of agentic workflows in solving complex B2B problems, and why the most successful leaders prioritize progress over perfection to show momentum within weeks rather than years. From her background in the financial sector to her experience scaling with industry titans like Microsoft, Rebecca provides a masterclass on navigating the current “tectonic shifts” in technology through strategic alignment and executive commitment. Key Takeaways Bridge Partners focuses on connecting strategy to execution, boasting a 90% referral rate driven by deep expertise in product marketing and partner ecosystems. The market is shifting from mere AI “dabbling” to purposeful applications in MVP and scale, specifically through agentic AI that tackles real business problems. Success in today's landscape requires knowing your underlying value and maintaining an unwavering focus on customer-centricity. The “Power of Three” (Hyperscaler, GSI, and ISV) remains the ultimate design for go-to-market scaling, provided there is a clear joint value proposition. To show immediate momentum, new executives should focus on “quick wins” achievable within six to eight weeks rather than long-term three-year plans. Effective co-selling requires removing blockers like compensation misalignment and securing top-down executive sponsorship across all leadership silos. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. https://youtu.be/nClWjCm6S6A At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags Rebecca Jones, Bridge Partners, Chief Growth Officer, co-selling, Power of Three, Hyperscaler, GSI, ISV, SAP, Microsoft, agentic AI, AI experimentation, pipeline velocity, pre-sales workshops, account-based marketing, ABM on steroids, GTM strategy, executive sponsorship, partnership ecosystems, B2B growth, tech industry trends 2026, Ultimate Partner, Vince Menzione, orchestration, value proposition. Transcript Rebecca Jones Audio Episode [00:00:00] Rebecca Jones: Because most of the agents I’ve seen drop into um, a lot of the areas where you and I can download are features. [00:00:07] Vince Menzione: Yes, [00:00:08] Rebecca Jones: they’re really feature agents. I love where we are ’cause we’re starting to tackle real business problems. [00:00:17] Vince Menzione: We just finished Ultimate Partners Winter Retreat here in beautiful Boca to a sold out crowd. Today I’m joined by Rebecca Jones, the Chief Growth Officer of Bridge Partners for this compelling discussion. Rebecca, welcome to the podcast. [00:00:33] Rebecca Jones: Thank you, Vince. [00:00:34] Vince Menzione: I am so thrilled to have you in Boca in the studio. [00:00:37] Vince Menzione: We’ve been working together now for a couple of years. We [00:00:39] Rebecca Jones: have, [00:00:40] Vince Menzione: and yesterday we were at the Ultimate Partner live executive winter retreat here in Boca. Uh, we’re recording in late February, early March timeframe. And, uh, just it was so thrilling to have everyone in the room yesterday. [00:00:55] Rebecca Jones: Was it? I mean, the energy. [00:00:56] Rebecca Jones: It was amazing. [00:00:57] Vince Menzione: Yeah, [00:00:58] Rebecca Jones: it was amazing. And thank you so much for having me. I mean, Florida’s gorgeous this time of year. It’s nice to get outta Seattle. [00:01:04] Vince Menzione: Well, it’s, it’s always, I, I, we, we love Seattle. Yes, we love, we do love to be in Seattle and especially in the spring, which we’ll be there together. We’ll talk about that in a little bit, but, um. [00:01:14] Vince Menzione: This is our first time actually having an interview. I mean, we’ve had you on stage. Yes. We’ve had Bridge as a part. Bridge Partners has been a partner. It’s ultimate partner. How’s that? And, uh, you’ve led some workshops. You help organizations to be successful and I thought just like to start out like, tell us more about you. [00:01:32] Vince Menzione: Yeah, bridge Partner and your role at Bridge Partners. And, uh, just to frame, to frame the conversation today. [00:01:40] Rebecca Jones: Okay. Of course. So let me tell you a little bit about my background. Um, I’ve been in the technology industry for a few decades now, and I started within the product and go to market, side of the house. [00:01:54] Nice. [00:01:54] Rebecca Jones: And I’ve navigated across a number of functional areas. From product to partner and sales. [00:02:02] Vince Menzione: So product development, [00:02:04] Rebecca Jones: engineering, [00:02:04] Vince Menzione: product marketing. Product marketing. [00:02:05] Rebecca Jones: Product marketing. [00:02:06] Vince Menzione: Yeah. [00:02:07] Rebecca Jones: Yes. And so when you look back on the areas of where I focus my time, it’s really how do you help customers grow and how do you help companies grow? [00:02:17] Rebecca Jones: Um, and a lot of my background is in B2B. [00:02:20] Vince Menzione: Very cool. [00:02:21] Rebecca Jones: Yeah. [00:02:21] Vince Menzione: And where’d you get your start? [00:02:23] Rebecca Jones: I started actually in the financial sector. [00:02:26] Vince Menzione: Very cool. [00:02:27] Rebecca Jones: Yeah, [00:02:27] Vince Menzione: very cool. That’s, well, that’s a good grounding and [00:02:30] Rebecca Jones: it’s an excellent grounding. And when you look back, and when I look back at what that provided as a foundation, it’s really the economics of a business and how do you help a business and what are the trend lines behind that by industry and and whatnot. [00:02:45] Rebecca Jones: And so I moved from that over to. More agency view, and so the real market facing view and then back inside to really look at how companies develop their products and bring ’em to market. [00:02:56] Vince Menzione: That’s an exciting, well, I think it’s exciting. I hope our listeners and viewers think it’s exciting and I know Bridge Partners because when I was at Microsoft, we worked with Bridge Partners. [00:03:06] Vince Menzione: But for the listeners and viewers that are with us today, maybe a little bit of background about the company and its, and its structure and go to market. [00:03:13] Rebecca Jones: Yeah, of course. So Bridge Partners is almost 20 years old. [00:03:18] Vince Menzione: Wow. [00:03:19] Rebecca Jones: Wow. [00:03:19] Vince Menzione: Yeah. [00:03:19] Rebecca Jones: Can you believe it? [00:03:20] Vince Menzione: We were newbies when I was working with you. [00:03:22] Rebecca Jones: We, we were newbies and uh, the company was really founded on the principle of how do you connect strategy to execution. [00:03:32] Rebecca Jones: And within that, our first customer was Microsoft. [00:03:36] Vince Menzione: Interesting. [00:03:37] Rebecca Jones: Yeah, yeah, yeah. Uh, and that was an incredible spot to be and an incredible time to be in a company that started to evolve and grow with one of the titans in the industry. And obviously a incredible market leader in the tech industry. [00:03:56] Vince Menzione: Well, and that time 20 years ago, ’cause I was, I was along for that journey. [00:03:59] Rebecca Jones: Yeah. [00:04:00] Vince Menzione: Uh, it was a time of tumultuous change at Microsoft. [00:04:03] Rebecca Jones: Yes. [00:04:04] Vince Menzione: Uh, in fact, we were talking about the, uh, entrepreneur’s dilemma earlier, uh, today, and Microsoft was going through that period where, you know, we, everyone loves Steve Bomber, but there was a time within the organization that it was stuck. [00:04:18] Rebecca Jones: Mm-hmm. [00:04:19] Vince Menzione: And it had to transform as an organization. [00:04:22] Rebecca Jones: A hundred percent. And so when you think about companies like Microsoft, it’s not only what they do, but how they bring that to market. Yep. And uh, so when you think about where Bridge Partners started and having the privilege to be in Microsoft of all places to, um, cut your teeth on you look at where we started and where we’ve grown from there. [00:04:44] Rebecca Jones: Uh, within the tech industry, we’ve worked across, um, multiple hyperscalers. We’ve worked across, uh. Really the top tier tech and telco, those top 100. Yep. And all the household names. And then throughout that, across the partner ecosystem, because you and I both know these companies grow and scale their businesses through the partner ecosystem, and so we’ve been privileged to work across. [00:05:08] Rebecca Jones: Multiple depth and breadth partners in that play. [00:05:12] Vince Menzione: And as an agency, are you more known for project management go to market? Uh, what, what are the areas and focus where the outcomes that you achieve? [00:05:21] Rebecca Jones: Yeah, so we’re known for. Being on the growth side of the house. And how I define that is you find us in marketing, but that center of gravity is in product marketing. [00:05:32] Vince Menzione: Yes. [00:05:32] Rebecca Jones: And then how you scale that through partner ecosystems and then supporting that field or that sales organization. So when you think about those three pillars within the organization, that’s where you’ll find us. [00:05:43] Vince Menzione: And why would I choose Bridge Partners? [00:05:46] Rebecca Jones: Oh, well, um, based on experience. Um, and then when you think about Bridge Partners, it’s not, um, just what we do, but when you take a look at our engagements and background, we’re over 90% referral. [00:06:01] Vince Menzione: Wow. [00:06:02] Rebecca Jones: And so people take us with them and um, what I look at is have we actually moved the needle or driven the customer outcomes? And when you think about the customers that we’ve worked with and the companies in this industry. It’s quite a roster and I don’t take that lightly because if you’re going to help support these companies and help them grow, it’s a testament to how we were able to accomplish that. [00:06:27] Rebecca Jones: Because all these companies have complex enterprise organizations. Their go to market is nuanced and how they want to, and then, um, get and grow. And so these are just a couple of the different ways that we’ve been able to be successful. [00:06:42] Vince Menzione: Fantastic. You know, you’ve done workshops at our events and talked to our community about how to help them achieve their greatest results. [00:06:50] Vince Menzione: What would you say to them? Now we’re living in this time? I, I I, I said this earlier, I don’t want to use the term tectonic shifts, but I’m running out of words to describe how tumultuous this time feels right now to me. [00:07:03] Rebecca Jones: It’s interesting you say that. I was thinking about that. ’cause both you and I have been in the industry for a bit. [00:07:08] Rebecca Jones: Yeah. And, um, there’s some pattern recognition happening right now for me and how I look at the go to market and these, these points in time and the evolution and. This point in time, it is a tectonic shift. But a lot of companies have other, have had to go through these challenges before. If you think about, um, the migration to the cloud and [00:07:33] Vince Menzione: yes, [00:07:33] Rebecca Jones: all of the unlocks that it has, and at the end of the day it’s, it’s shifting and thinking about new business models and it’s shifting and thinking about go to market, but there is. [00:07:43] Rebecca Jones: There are things that ring true no matter where you are. And one of the things I’ve always taken a look at is, do you know your underlying value and relevance in market? And are you being customer centric? That never goes outta style, right? Do [00:07:58] Vince Menzione: you know your value and are you customer centric? That makes a lot of sense, right? [00:08:02] Vince Menzione: Yeah. And do they, what do you do? And, and do they, how do what, how do they answer to that question? [00:08:07] Rebecca Jones: Well, that’s a, that’s a thinking question. Yes. Right? Yes. It takes a minute to think about that. Um, where is your moment of relevance with a customer? [00:08:16] Vince Menzione: Yeah. [00:08:17] Rebecca Jones: Where is your moment of relevance with a customer? [00:08:19] Rebecca Jones: And when you think about your reason to exist as a business, you have a really defined ICP, an ideal customer profile, and where’s your moment of relevance and. Yes. There’s a lot happening right now, and I think also because of where we sit in the industry and being in the midst of all of these giants with incredible technology to bring to market. [00:08:44] Rebecca Jones: Yeah. We’re, we’re in the front end of this wave or the, the, the tectonic shift that you’re talking about. It’s just, you know, it’s unsettling to a certain degree, but it’s really energetic and it’s. Dynamic and, and there’s so much opportunity out there. So [00:08:59] Vince Menzione: much so, you know, you had me thinking about the $600 billion that’ll be invested this year and just in cloud infrastructure and chips, right? [00:09:08] Vince Menzione: Yeah. So data centers and chips, and talk about that being like kind of creating this wave, this huge tsunami that’s coming for the beaches and, and everything seems to be. Every week there’s a new announcement, and recently it’s been philanthropic and clawed. And yes, uh, the markets are reacting. They’re, um. [00:09:30] Vince Menzione: They’re almost, uh, imploding in some ca in some cases because they’re trying to react the financial analysts, they’re trying to react to what’s happening right now. [00:09:38] Rebecca Jones: It, the investment is massive and it’s, it’s incredible and it’s massive. And over the last year, you saw a lot of experimentation. Yeah. And you saw a lot of dabbling, a lot of, you know, quite. [00:09:52] Rebecca Jones: Frankly, a little bit of concern about is this gonna pay off? [00:09:56] Vince Menzione: Yes. [00:09:57] Rebecca Jones: And when you look at where we are in this chain cycle and this adoption cycle, we’re right at the front end, the early adopters. And so a lot of the work that we’re doing, and where I’m focused on is how do you move from experimentation? To truly having some movement over into MVP and scale. [00:10:18] Rebecca Jones: And so I’ll just harken back to Yeah, [00:10:19] Vince Menzione: please. [00:10:20] Rebecca Jones: That product mindset of when you’re looking at opportunity within the business, there was a lot of, um, there was a lot of pockets of experimentation just for fun. Just for fun. And so when you look across the business, um, and what, what we observed was, um, businesses of all different sizes, experimenting and, and some were just, they’re fun, they’re dabbling, right? [00:10:45] Rebecca Jones: But it, it changed in the second half of last year, people became much more thoughtful, much more purposeful, um, thinking forward about how would this be applied to my business? Yeah, because the question now isn’t. Could we do this? It’s really, should we do this [00:11:03] Vince Menzione: right? And and there was a period of time, I don’t mean to interrupt you, but there was a period of time when we were talking about earlier in in last year, we were talking about halluc hallucinations still. [00:11:13] Vince Menzione: Yes. So there was a lack of confidence on the platform side. Yes. Microsoft had brought out. Uh, it’s copilot solutions early to market. And there was some, uh, pushback from the community saying, we’re not seeing the results of that. Yeah. From the financial community specifically. And then I think what you said is then the second half of the year things started to change. [00:11:35] Vince Menzione: There was greater confidence. The [00:11:36] Rebecca Jones: Yeah, [00:11:37] Vince Menzione: I’d say the models got better. [00:11:38] Rebecca Jones: The models got better. But when you think about innovation, that’s inherent risk, [00:11:43] Vince Menzione: right? [00:11:43] Rebecca Jones: Right. Yes. When, when you’re on an innovation curve, yes, that’s risk. And so you have to look at as any great CFO will tell you diversification innovation. [00:11:56] Rebecca Jones: When you start to look at that market landscape, you’re creating risks. Yes. So they’re investing a lot and they wanna know when the payoff is coming back into the business. Right? Or back into the market. [00:12:08] Vince Menzione: So Rebecca, where is the AI market right now? [00:12:13] Rebecca Jones: Oh, that is a tough and great question, Vince. [00:12:18] Vince Menzione: I mean, we’ve gone through it and I’ll, I’ll kind of frame this for, yes, for, for everyone, at least from my perspective of what’s happened, right? [00:12:24] Vince Menzione: So, uh, September, 2022. Chat, GBT. Yeah. So we get into chat bots or chat bot, chat bot, chat bot, chat bot the first year or so, beginning of last year, 2025. A agentic AI really starts to take hold. It’s, it becomes a new term. In fact, I don’t think we were even using the term agentic AI before the end of 24, beginning of 25. [00:12:47] Vince Menzione: And then agents have really proliferated, um, all of the marketplaces now have agents and people are developing their own agents and so on. And all the tools, like all, all the cloud tools have agent capabilities. And now, um. We’re in 2026 and we’re still in the first quarter. It feels like the agents are starting to rule the world and maybe taking over the world [00:13:10] Rebecca Jones: they might be. [00:13:11] Vince Menzione: Yeah, [00:13:11] Rebecca Jones: right. There is definitely a proliferation of agents and I’m anticipating a lot of consolidation of that. ’cause most of the agents I’ve seen drop into, um. A lot of the areas where you and I can download are features. [00:13:26] Vince Menzione: Yes. [00:13:26] Rebecca Jones: They’re really feature agents and those will get consolidated ’cause the where we are and you ask where we are in the market. [00:13:33] Rebecca Jones: What I love. I love where we are ’cause we’re starting to tackle real business problems. And what I’m observing and what we’re working on is really helping connect back into the business to really start that transformational work. [00:13:48] Vince Menzione: So take us through that. I’d love that. I’d love, give us a scenario or [00:13:51] Rebecca Jones: give us a use case. [00:13:52] Rebecca Jones: Do this. Yeah. I think’s really great scenarios here that I can walk you through. And first and foremost it is, and I’m gonna go back and I talked about specialization in specialty areas. Yes. That’s really important. Um, we talked yesterday during the conference around, um, industry. What industry are you in? [00:14:11] Rebecca Jones: You know, I’m in tech and that’s, that’s, we know that industry, we know those business models really well. That’s extremely important. And then you move within that. And what functions do you know and functions in this, you know, order are the product marketing function, how does that work? [00:14:30] Vince Menzione: Yeah. [00:14:30] Rebecca Jones: How does that work in an enterprise organization or a sales function or a. [00:14:36] Rebecca Jones: Partner function. And within that, what are all the workflows? How do these teams operate together? And so that’s where that curiosity comes in of not just how you did the work. How is the work orchestrated? [00:14:49] Vince Menzione: Inter orchestration is a huge topic area. [00:14:51] Rebecca Jones: Orchestration is a huge topic. Let’s, let’s go [00:14:53] Vince Menzione: there. [00:14:54] Rebecca Jones: E Exactly. [00:14:55] Rebecca Jones: And that’s where that curiosity, you know, I was talking about pattern recognition comes in how is the work designed? And that becomes. The blueprint for how you start to think about agentic workflows. And if you don’t have a great workflow, you don’t wanna replicate that in an agent, but Exactly. You definitely need to understand that. [00:15:18] Rebecca Jones: And so why don’t I take something that, um, I think will resonate for anyone listening to this podcast, because everyone is probably looking for growth this year and wanting to accelerate [00:15:28] Vince Menzione: Yes. [00:15:29] Rebecca Jones: Sales. Their pre-sales funnel. So if we just take that pre-sales motion and specifically now with where partners might play in that or where, um, technology companies might want to enable their partners better. [00:15:47] Rebecca Jones: When I start to break down a pre-sales function, you have areas within that. Whole workflow that your marketing department might be driving. They might be driving top of the funnel or or demand programs. And then as you move down the funnel, let’s call it mid funnel, that really has opportunities for partner and field sellers to come in and. [00:16:07] Rebecca Jones: You might be seen or observing that your, um, pipeline velocity is not where you want that, right? Mm-hmm. You might be, you know, as they say, stuck. Stuck. [00:16:18] Vince Menzione: Yep. [00:16:19] Rebecca Jones: And so when you start to look at what agents could do within that, I’ll use a real use case, um, around pre-sales workshops. You and I are both familiar with that. [00:16:28] Vince Menzione: We, we are, we were just talking about this last night, in fact, at dinner, about pre pre-sales workshops and how this is still such a vital component, how organizations work together. [00:16:37] Rebecca Jones: Such a vital component, um, for multiple reasons, right? You get to engage directly with the customer. You get to spend time with that customer. [00:16:46] Rebecca Jones: You get to ensure you understand what are their most pressing use cases and really help them design and buy into a solution far before you get to a proposal. And quite frankly, if you do this right. You also have an adoption plan, and then think about it from other functional areas in the organization. [00:17:02] Rebecca Jones: You start to pattern match across those presale workshops. You can start to see the use cases that are most valuable in market and start to put that into your messaging. So you think about presale workshop, it’s just not the activity of having a workshop, but if you could build an agent. To really help design around partners, enabling partners to deliver better presale workshops. [00:17:27] Rebecca Jones: Interesting. And how are you ingesting information that goes into the workshop? How are you helping, um, develop materials and first drafts faster for proposals post? How are you. Data is informing this. What are you collecting and what are you providing, and then what are you delivering? If you take that one simple component in a pre-sales process, you can see where I’m going. [00:17:53] Rebecca Jones: Yeah. All of a sudden, an ecosystem starts to show up around how could you connect better back with product marketing? What are they doing? What could you inform them with, with the data that you’re bringing in? [00:18:03] Vince Menzione: Interesting. [00:18:03] Rebecca Jones: And then what are the. Deterministic pathways outside of that, that you could be informing downstream down to first, first stress faster on proposals. [00:18:13] Rebecca Jones: Are you helping those partners with an adoption plan? The service partners in there. And so that is the designer and the architect of understanding how that workflow comes to life. And then you can really start to think about the outcomes that you wanna drive. And that’s where I love to start the conversations. [00:18:31] Rebecca Jones: That shouldn’t be an afterthought. That should be where you start. [00:18:35] Vince Menzione: So how do you, how do you, how do you start with this? You gave me a great example, but how do you apply this in the business? Like what do you take when you meet with a client to talk about pre-sales workshops as an example? [00:18:47] Rebecca Jones: Yeah. [00:18:47] Vince Menzione: You take a proforma of what a pre-sales workshop would look like. [00:18:51] Vince Menzione: I’m, I’m, I. I might be wrong on this, but you have, like, you, you now have, uh, AI or AI that they go out and pull the data that you would normally ask maybe in some, some, uh, process, uh, information flow process that we grab and, and pull this into the, to the, to the form. The [00:19:10] Rebecca Jones: first question I always ask is, why. [00:19:12] Rebecca Jones: Why is this so important and valuable? I might have an assumption why, based on my experience, but I want the facts, right? I wanna know how they’re measuring it today, so we have a baseline and I wanna understand what their goals are. [00:19:28] Vince Menzione: Okay? [00:19:29] Rebecca Jones: Are they looking to increase revenue? X percentage. Uh, how many deals are they anticipating? [00:19:38] Rebecca Jones: How many presale workshops do they typically deliver through partner a year? Are they looking to scale that? Probably, yes. Are they looking to increase the value that they’re getting into contract post presale workshop? Probably yes. But I want that empirical data. And then I also wanna know where are they storing that? [00:19:57] Rebecca Jones: Where are they sourcing that? And so it, it really. The question and the question set really is understanding the business outcomes and the why. I, I ask a lot of why, and it really helps you frame in what would be the best outcome or the best solution, and then where do you start? Because there’s a lot of appetite for a. [00:20:21] Rebecca Jones: A transformational workflow from A to Z. And that’s a hard place to, [00:20:26] Vince Menzione: it’s hard show momentum. It’s hard. It’s hard, [00:20:27] Rebecca Jones: right? [00:20:27] Vince Menzione: It’s, it’s hard to document your current workflow flows. [00:20:30] Rebecca Jones: Yeah. [00:20:30] Vince Menzione: Let alone come back and do this ally. [00:20:33] Rebecca Jones: Yes. [00:20:34] Vince Menzione: And create the best outcomes. [00:20:36] Rebecca Jones: Yes. [00:20:36] Vince Menzione: So I go back to this and I go, well, what, what creates the best outcomes? [00:20:39] Vince Menzione: Where the customer signs at the dotted line, and then how do you work back from that to the pre-sales workshop? Is that how [00:20:46] Rebecca Jones: you do it? A hundred percent. It’s a hundred percent. And then where do you start? How do you show, um, progress, not perfection. And so in this world, there’s a lot of, um, pressure. To show progress, outcomes, momentum. [00:21:00] Rebecca Jones: Yeah. And these very significant investments that are being made. And so how do you get them to quick wins? And so you know this, for any new executive coming into role, what are your quick wins? Yes. Right? Yes. You need to transform an organization, you need to transform a function. How do you set them up for success? [00:21:19] Rebecca Jones: And that’s always in my mind, that’s always in the mind of. The bridge partners, leaders of how do you set this leader up for success? And it’s that point between strategy and execution. How do you help them show quick wins? And so I broke you down that process. Yep. Of how would you think about in that use case, how to bring that back and help them show quick wins? [00:21:42] Rebecca Jones: Not in six months or a year, but in six weeks to eight weeks. How do you, how do you get them on that journey and then help them build to that next slide. And [00:21:51] Vince Menzione: in fact, that’s how you, you, you’ve made your, your name or your fame in the industry is really coming in and helping some of these executives, especially when they’re newer in role. [00:22:00] Rebecca Jones: Yes. [00:22:00] Vince Menzione: And those of us who’ve been around the Microsoft ecosystem know this well. Like you get asked day one, what’s your plan? The, while the fire, while the fire hose is blowing in your face at a hundred, a hundred miles an hour? Uh, what’s your plan? [00:22:14] Rebecca Jones: What’s your plan? What’s your [00:22:14] Vince Menzione: plan? [00:22:15] Rebecca Jones: What is your plan? [00:22:16] Vince Menzione: Yeah, yeah. [00:22:16] Vince Menzione: And then you have to show some measurable results fairly quickly. [00:22:19] Rebecca Jones: You have to [00:22:20] Vince Menzione: because you’re asked to get up in front of everyone. Yeah. Very soon. [00:22:23] Rebecca Jones: And that’s a blueprint that we have. We have, it’s a quick win. And when you think about all of these organizations that we’ve worked with, um, speed to market is a value signal. [00:22:36] Vince Menzione: Yep. [00:22:36] Rebecca Jones: Right? And that speed and quality. Where are you willing to take the risk? Where are you willing to fail fast? And what outcomes are non-negotiable and what are, and so when you look at that, there’s, there’s conversations that need to be had on. And being able to filter out the noise to get down to what’s really gonna move the needle, um, for our clients and for the executives that we work with. [00:23:06] Rebecca Jones: So they can show momentum and progress quickly. And then we talked a lot about it. We don’t do three year plans, right? We’re gonna help you show progress in months, [00:23:16] Vince Menzione: nice. [00:23:17] Rebecca Jones: And in quarters, right? It’s not, um, 10 years. [00:23:19] Vince Menzione: Can anybody even have a three year plan anymore? [00:23:22] Rebecca Jones: Who’s got one? [00:23:23] Vince Menzione: I’d love to spend some time on co-selling with you. [00:23:25] Vince Menzione: Yeah. Just because I know this was a topic that came up one of our workshops in the Yeah. We hosted, yes. Last year we hosted a session. With another partner. Bridge Partners. [00:23:34] Rebecca Jones: Yes. [00:23:35] Vince Menzione: And you talked about the power of three and I know you’ve published some information about the power of three. I thought maybe we’d talk about that. [00:23:41] Vince Menzione: ’cause I think that is fascinating and it seems very relevant even in yesterday’s conversation. Uh, there was a conversation about another partner, uh, that is looking to build an ecosystem that hasn’t really thought about building out an ecosystem before, as an example. And this, this, I think is some of the work that you do really applies against this. [00:24:01] Rebecca Jones: Yeah. This, I mean, it, it’s a hot topic, right? Yeah. Power of three, which fits under the umbrella of co-sell Yes. And co-selling. And everyone has a slightly different definition, so I’ll define where we play. Good in there. Um, and then I’ll talk to you about the power of three, um, because that’s one of. Um, I’ll call it the scenarios under co-selling. [00:24:23] Rebecca Jones: Yes. And it’s a very popular one. It [00:24:24] Vince Menzione: is pop Well, it is for v various reasons too because, and I’ll just set the context for this. We were used to co-selling being a technology organization and a and a hyperscaler, like a Microsoft. [00:24:37] Rebecca Jones: Yes. [00:24:37] Vince Menzione: Going to do something together and driving direct output or sales. Now we have finally seen where marketplaces, which has become the co-sell engine, have now enabled the channel. [00:24:49] Vince Menzione: Um, the reseller enabled, uh, offers now to now, uh, operate on behalf of, and so at least in that case, that’s three right there. Now, there might be more than just three. We talk about the seven seats of the table, but the power of three is palpable right now. [00:25:04] Rebecca Jones: Yeah. Let me tell you about that concept of the power of three. [00:25:07] Rebecca Jones: ’cause when you think about the classic one [00:25:10] Vince Menzione: yeah, [00:25:10] Rebecca Jones: it’s a hyperscaler. [00:25:11] Vince Menzione: Yep. [00:25:12] Rebecca Jones: A GSI. And then an ISB. [00:25:15] Vince Menzione: Yes. [00:25:15] Rebecca Jones: Right? [00:25:16] Vince Menzione: Yes. [00:25:16] Rebecca Jones: I mean that’s the, that’s the power, the powerful power, the three three, [00:25:19] Vince Menzione: the three giants in the [00:25:20] Rebecca Jones: room. The three giants. Yeah. And that’s rarefied air. [00:25:24] Vince Menzione: It is [00:25:25] Rebecca Jones: very [00:25:26] Vince Menzione: verified air. It’s, [00:25:26] Rebecca Jones: yeah. Right. And, uh, we do, we have a published article on that, um, and running a power three with SAP, uh, and it is, um, it changes the dynamics. [00:25:41] Rebecca Jones: Of how companies are gonna scale and grow in this market, right? [00:25:46] Vince Menzione: Yes. [00:25:46] Rebecca Jones: Because we know, um, that what got you to this point? Is likely not gonna get you to that next stage of growth. And all the conversations around the platform play is the partner ecosystem, right? And I look at the opportunity, not just with the power through, I’m gonna talk to you a little bit more about that story and what we’re doing there and how we’re looking at that. [00:26:12] Rebecca Jones: Um, but it is the ultimate. Design for your go to market. Yeah. When you think about how partners and the various types of partners can help you scale, but you need to know what you need. You absolutely need to know, [00:26:29] Vince Menzione: yeah. [00:26:30] Rebecca Jones: What are you trying to achieve in your go to market and what’s missing? [00:26:34] Vince Menzione: What are the gaps? [00:26:34] Vince Menzione: Gaps? [00:26:35] Rebecca Jones: What are the gaps? Are the gaps before you apply? Yes. The power of three, or I’ll talk to you about a couple other use cases within that. So the power of three. Has long been on everybody’s, you know, can, can we get this done right? Can you pattern match the customer set? I’ll often refer to it as a BM on steroids, account-based marketing and on steroids. [00:26:59] Rebecca Jones: Can you pattern match, um, the, the hyperscaler, let’s just use Microsoft in this scenario, the, the. High potential customers of Microsoft Joint with SAP joint, with A GSI. And the more specialized and specific you get in there, it’s not just any, because think about the size of these, you know, companies. Yeah, right. [00:27:24] Rebecca Jones: Then you start to look at, well, let’s get a little bit more specific on these product sets, these industries, these use cases. And then you start to refine that where you can start to identify your greatest opportunity for growth. So that’s the first stage of that. And it is, you know, we, we think about where is that overlap and where is that opportunity, but how do you activate that? [00:27:51] Vince Menzione: And it’s complex because, uh, as you, as you mentioned those three. Organizations, each of them have different go to markets. [00:27:59] Rebecca Jones: They do, [00:27:59] Vince Menzione: they have different, a different mapping of their geographies and their ideal customer profiles. [00:28:05] Rebecca Jones: Mm-hmm. [00:28:06] Vince Menzione: Um, and they, yeah, and they apply different tactics and selling tactics and channel tactics and so on that you have to layer in or you have to take into account when you build this. [00:28:15] Vince Menzione: And SAP’s a very different go-to market motion than a Microsoft, than a, than a, an EY or any name the GSI percent. Yeah. [00:28:23] Rebecca Jones: And so that is why not only is it, um, complex from a. Sharing and figuring out what data you’re going to share. Yeah. But how do you activate it? How [00:28:35] Vince Menzione: do you activate it? [00:28:36] Rebecca Jones: And uh, and that is what all companies are striving to do. [00:28:41] Rebecca Jones: Who are you gonna go to market with? Yeah. What is your best play in the industry? And so I, you know, while this one. There’s very few companies that are gonna be able to activate directly with the hyperscaler, right? Yes. Uh, Microsoft AWS or Google. Um, but there are ways in which you can apply this strategy no matter the size of your organization. [00:29:05] Rebecca Jones: And so when you think about. The power of three. It could be any combination. You are the designer, you are the decider of who is in your power of three. And when you start to kind of unpack that a little bit, it could be Microsoft, SAPN one ISV, or it could be a combination of complementary I ISVs that unlock a play. [00:29:28] Vince Menzione: Mm-hmm. [00:29:29] Rebecca Jones: Like migration to the cloud. [00:29:31] Vince Menzione: Right. [00:29:31] Rebecca Jones: Like it, it could be [00:29:33] Vince Menzione: backup and recovery. I could rattle off the different types of solutions. Yeah. [00:29:37] Rebecca Jones: What is, where are you seeing the greatest opportunity to scale and what ISVs could come in to help you do that? So when you extract that from the power of three, the classic power of three of Costone, you brought that down to, you know, how do you think about that in the masses of marketplace? [00:29:56] Rebecca Jones: Yeah. Or partners of any size. I like to bring this back to. Where do you believe your greatest opportunity is? Do you have, um, opportunity or weakness in your portfolio, your product set? Could a partner come in and help augment that? Do you have a tech platform and you need a services arm to help extend that? [00:30:19] Rebecca Jones: I I mean the, it it, the world’s your oyster. Yeah. You get to kit this together any way you need and then. The power of bringing these companies together. And you and I both know, and that was much of the conversation yesterday, is, um, the greater goodness of companies coming together Yes. To compliment one another to solve a customer problem. [00:30:39] Vince Menzione: How do you take it from concept to execution? Because to me, that’s. Especially when you’re talking about not just one organization like a micro, you’re working with a Microsoft or an SAP, but you’re layering in three types of organizations and you’re going across different sales motions. How do you get them all? [00:30:58] Vince Menzione: How do you get them all aligned in working together the right way? [00:31:02] Rebecca Jones: Magic. Magic. [00:31:03] Vince Menzione: Okay. [00:31:04] Rebecca Jones: I’m kidding. [00:31:04] Vince Menzione: Call bridge, call Rebecca [00:31:07] Rebecca Jones: Magic. [00:31:07] Vince Menzione: Nine nine nine five five five five. [00:31:09] Rebecca Jones: Let, let, let me, uh, let me talk about that because [00:31:13] Vince Menzione: Yeah, [00:31:13] Rebecca Jones: it’s one, there’s the good work, there’s the good thought work and the strategy of how to ensure you’re, you’re pointing and you’ve got the team lined up, right? [00:31:22] Rebecca Jones: Right. And the players lined up. But activation of that. Oh, [00:31:28] Vince Menzione: massive work. [00:31:29] Rebecca Jones: It’s massive work. Yeah. And it’s not a set it and forget it. [00:31:33] Vince Menzione: Right, [00:31:34] Rebecca Jones: right, [00:31:34] Vince Menzione: right. [00:31:35] Rebecca Jones: And when you think about the alignment, and you talked about we, we’ve got different fiscal year ends and we’ve got different sales and center plans. I will talk about a few things. [00:31:45] Rebecca Jones: One, executive sponsorship, top down. [00:31:48] Vince Menzione: Yep. [00:31:48] Rebecca Jones: Right. Um, ensuring, you know, compensation. You gotta get rid of the blockers and the barriers. [00:31:55] Vince Menzione: Yep. [00:31:56] Rebecca Jones: And you have to make it easy and you have to create that space because it’s really, and I’ll talk to you about some of the platforms and technology behind it, but it’s humans working together. [00:32:07] Rebecca Jones: There’s a lot of power in what we’re able to do now with, um, part tech platforms and with agentic solutions. And how do you automate this and how do you bring more power and visibility? Better than ever and, and more than ever. But at the end of the day, we’re activating teams. Across companies. Yep. To work together to bring this together. [00:32:34] Rebecca Jones: And there are playbooks, um, and any, there’s great playbooks out there, but you need to activate that. [00:32:41] Vince Menzione: You need to activate it. And you, you said you gotta get the executive commitment at the top? [00:32:45] Rebecca Jones: Yeah. [00:32:46] Vince Menzione: Not just at the CEO level, but across the leadership team. That’s right. In every silo. Uh, you’ve gotta get, uh, the organization, you have to get compensation taken care of because those, those can be blockers, those could be real blockers from getting the results you want to get. [00:33:00] Vince Menzione: And then you gotta get activation. [00:33:03] Rebecca Jones: Yeah. [00:33:03] Vince Menzione: Right? [00:33:04] Rebecca Jones: You gotta get activation and you have to be really clear on how you’re gonna activate what’s gonna move the needle. And you have to be ready to test, learn, optimize, and you need to put those into sprints. So I’ll give some examples around that. [00:33:20] Vince Menzione: Please do take us through the sprints. [00:33:21] Vince Menzione: ’cause this is, this is getting beyond the theory now. This is what I really wanted to capture with you. Take us through it. [00:33:28] Rebecca Jones: Yeah. [00:33:28] Vince Menzione: Yeah. [00:33:29] Rebecca Jones: So let’s just say we’ve got, we’ve got a power of three. [00:33:32] Vince Menzione: Yeah. [00:33:32] Rebecca Jones: You know, um, ready to roll and, and we’ve picked our industry and we have our use case. Um, between the three of us, the three players, you’re gonna start by allowing someone, and in this case it’s been Bridge Partners to really ensure we have a joint value prop, um, proposition for that end customer. [00:33:54] Rebecca Jones: Mm-hmm. And, you know, you gotta take a little ego out of the room. Typically on the power of three, you’ve got the leading companies coming in. But at the end of the day, if you’ve done this right, it’s, it’s customer first. It’s what’s gonna help solve this customer pain point in that language. And then when you think about activation, it’s who’s, who’s in role first? [00:34:20] Rebecca Jones: Right. And who’s taking point in these customer conversations. Right. Okay. And that is really, really, that’s important. Important. That is important. Who has the relationship? Yeah. Who is going to take lead and who’s gonna follow? And it gets all the way down to whose paper. Is this on? And that’s, that’s sometimes hard. [00:34:41] Rebecca Jones: You’ve got three players in the room, but it’s incredibly important to have those conversations and ensure that this is really end state for the customer. Yeah. So really going through roles and responsibilities and how are we gonna architect this for the customer’s success. Yeah. So that is a critical component of the playbook and then understanding. [00:35:02] Rebecca Jones: Where and what programs are we gonna drive, and then who’s taking what actions. And so I, I mentioned a BM on steroids a little before. Yes. There’s amazing things that you can be doing in market, [00:35:14] Vince Menzione: account-based marketing, [00:35:15] Rebecca Jones: m account-based based marketing, you dunno. Um, account-based marketing and there are some amazing things. [00:35:20] Rebecca Jones: Really truly connected sales and marketing, in this case. Connected sales, marketing and partner. Yeah. And how do you activate these partners together? [00:35:27] Vince Menzione: You used the term part tech, which. Not everyone understands partner technologies. Yes. Organizations like Partner Tap, work Span. Yeah. Tackle. [00:35:37] Rebecca Jones: Structured. Yeah. [00:35:38] Vince Menzione: Structured. If you, these are companies that help with co-selling methodologies, marketplace methodologies. [00:35:44] Rebecca Jones: Yes. [00:35:45] Vince Menzione: Or combining all of those, [00:35:46] Rebecca Jones: if you know, uh, J McBain, uh. Beautiful visual flat map of, um, it looks a little, the 28 moments. Yes. I was just, well, the 28 moments and he’s got the part tech landscape. [00:35:59] Vince Menzione: Oh, [00:35:59] Rebecca Jones: the islands. The islands. [00:36:00] Vince Menzione: Yes. The islands. [00:36:00] Rebecca Jones: Yes, we got it. But there are part tech solutions that support [00:36:03] Vince Menzione: Yeah. [00:36:03] Rebecca Jones: Partner programs, co-sell programs, partner marketing, you know. Yes. And really help to automate a lot of those processes. [00:36:11] Vince Menzione: Yes. [00:36:12] Rebecca Jones: Um, and a lot of those programs. [00:36:13] Vince Menzione: So Rebecca is such a great conversation today. [00:36:16] Vince Menzione: I mean, we can go. Thank you so deep on this. [00:36:18] Rebecca Jones: I know. [00:36:18] Vince Menzione: Which means that we’re all gonna have to be back together in Redmond. You live in the Seattle area? I do. And you’ll be with us. Um, we’ll be hosting the Ultimate Partner, live in, uh, may, May 11th to the 13th. If you’re marking your calendar as listeners and friends, uh, and you’ll be there and. [00:36:36] Vince Menzione: Probably driving some more of this conversation in a workshop format, I hope. [00:36:41] Rebecca Jones: I hope so too. Yeah, it was really rewarding last year. I mean, there’s nothing more powerful to be in the room with partners because the partners are frontline to customers. [00:36:51] Vince Menzione: Yes. [00:36:51] Rebecca Jones: And understanding what they’re seeing and hearing. [00:36:53] Rebecca Jones: And I always think voice of the customer is your ultimate signal. Yeah. So I can’t wait to be there. [00:36:58] Vince Menzione: Very cool. And I have a favorite question I ask all of my guests now. Uh, it is a favorite of mine. You are hosting a dinner party and you can choose where in the world you wanna host this dinner party, and you can invite only three guests, though from the present or the past to this amazing dinner party. [00:37:18] Vince Menzione: Whom would you invite Rebecca and why? And why? [00:37:22] Rebecca Jones: Yeah. Yeah. I’d, um, this is such a great question. I think on every single day I’d have a different collection of folks that I’d want at my home. Uh, I’ve had dinner at some amazing places for me. I would love to host this at my home. [00:37:38] Vince Menzione: Very cool, very [00:37:39] Rebecca Jones: cool. Uh, and the people that I would want there for this particular dinner party, I’m gonna pick, um, three iconic women. [00:37:51] Rebecca Jones: Coco Chanel, [00:37:52] Vince Menzione: Coco Chanel very cool [00:37:54] Rebecca Jones: designer. [00:37:55] Vince Menzione: Yeah. [00:37:56] Rebecca Jones: Um, really changed how women thought about an identity and wardrobe. Um, I would invite Georgia O’Keefe. Wow. She’s my favorite artist. [00:38:07] Vince Menzione: Yeah. [00:38:08] Rebecca Jones: Um, she is one of my favorite artists. Uh, I’m, uh, art and history background. And, uh, [00:38:16] Vince Menzione: that explains, [00:38:17] Rebecca Jones: that, explains that, um, a really interesting perspective. [00:38:22] Rebecca Jones: I love her view on landscapes and. She, [00:38:26] Vince Menzione: that’s why I know her as, you know, landscapes [00:38:28] Rebecca Jones: a landscape artist, um, and much more behind that. And then I would bring one of my favorite authors in, who’s Tony Morrison? [00:38:36] Vince Menzione: Tony [00:38:37] Rebecca Jones: Morrison. [00:38:38] Vince Menzione: I don’t know Tony Morrison. [00:38:39] Rebecca Jones: Oh, um, I would, beloved is her book and Oh, yes. When you think about. [00:38:45] Rebecca Jones: Um, and this is really my passion, my background in art and literature and design, and to have three, three women there, that voice of Tony Morrison, you’ve put that book on your list. Okay. It, it, it changed my life. Uh, and, um, Coco Chanel and, um, Giorgio O’Keefe, I think it would be a really interesting conversation. [00:39:07] Rebecca Jones: I love very cool trailblazers, women who really helped. I don’t know how much they recognize how much they really changed the narrative for other women, um, in their fields and together. But I think it’d be a really fun evening. [00:39:23] Vince Menzione: Very different. Very different. Uh, I was, I know a little bit about Cocoa Chanel ’cause my mom was always in the beauty and fashion industry. [00:39:31] Vince Menzione: So as a kid growing up, I mean her shoe was iconic. [00:39:34] Rebecca Jones: Yeah. [00:39:34] Vince Menzione: Iconic. Chanels an iconic brand was iconic. And, and she was a, wasn’t she a survivor of the. Of, uh, Nazi Germany maybe or something. There’s some, there’s some background or there’s [00:39:44] Rebecca Jones: some background. Flee. Flee [00:39:45] Vince Menzione: Nazi Germany [00:39:46] Rebecca Jones: or something. And what she’s really known for is, um, well many things, but yes, as a designer, really changing the tone and temperature Yes. [00:39:56] Rebecca Jones: Of um. How, you know, fashion and female identity. I think she, um, created the, what everybody knows is the little black dress and really got all that more structured and more modern look and feel of how to, how to wear and just really created a powerful path. [00:40:14] Vince Menzione: Very cool. Yeah. Very cool. [00:40:15] Rebecca Jones: So that’s who I’d have it, this one. [00:40:16] Vince Menzione: That will be a funer. [00:40:17] Rebecca Jones: Next time I’m on your podcast, I’d have a whole new crew. [00:40:21] Vince Menzione: Okay. Well I might. Bring dessert. If you don’t mind, I might bring a little, maybe a little chocolates I think maybe might be very appropriate would for this group and just maybe pop in for a few minutes. [00:40:29] Rebecca Jones: That would be great. [00:40:30] Vince Menzione: Because I don’t wanna inter interrupt the flow my, because this is be a great conversation. Oh my, [00:40:33] no, [00:40:33] Rebecca Jones: you would, I think you’d have a ball. [00:40:34] Vince Menzione: Okay. I, [00:40:35] Rebecca Jones: I mean, I know how close you were to your mother. [00:40:37] Vince Menzione: I am. [00:40:37] Rebecca Jones: And so, yeah. [00:40:39] Vince Menzione: So, um, this isn’t, again, I use this tumultuous term, but we are living in interesting times right now. [00:40:47] Rebecca Jones: We are. [00:40:47] Vince Menzione: And for all of our viewers and listeners. What is your advice to them? What is the one thing you would say? We’re in the first quarter of 2026. Yeah. This ball is moving fast or this puck is moving fast. Yeah. If you were a hockey player, um, what would you say to us now? What, what, what is the one thing you would go do if you’re not doing it now that you should be doing? [00:41:11] Rebecca Jones: Take a moment. Take a moment. As leaders. Your company and your organizations are looking for clarity. They’re looking for a path forward, and there’s a lot of energy out there, which is very exciting, but it can be also very distracting. [00:41:30] Vince Menzione: Yes. [00:41:31] Rebecca Jones: So hold some confidence and clarity for your organization and figure out where you need to be and where you’re going. [00:41:39] Rebecca Jones: That’ll help set your strategy, and this will all come into view. And so what I look to is how do we help enable the organization to grow? And by doing that, you ha you have to put the oxygen mask on yourself. Yeah. Take a moment. [00:41:53] Vince Menzione: Pause. [00:41:55] Rebecca Jones: Pause. Reflect, reflect. I told you I walked down to the beach this morning. [00:41:59] Rebecca Jones: It’s a great moment. Take a moment for yourself. It’s not passing you by. We’re just getting started. [00:42:06] Vince Menzione: Did you hear that? My friends and listeners? Take a moment. And so great to have you here in the room. Yeah. [00:42:13] Rebecca Jones: Thank you so [00:42:14] Vince Menzione: much. Thank you. And I want to thank our listeners, our viewers, for following along, ultimate Guide to Partnering and our YouTube channel Ultimate Partner. [00:42:23] Vince Menzione: And please, please, please come join us. We have an incredible year ahead. This was our event, number one of five. And Ultimate partner Live will be in Bellevue on the 11th through the 13th of May. [00:42:36] Rebecca Jones: Yeah, I’ll [00:42:36] Vince Menzione: see. You’ll see you there. Rebecca will be there. It’s [00:42:38] Rebecca Jones: in my backyard. [00:42:39] Vince Menzione: It’s in your backyard. And we are gonna have incredible leaders in the room. [00:42:42] Vince Menzione: So thank you for watching. Thank you for listening to The Ultimate Guide to Partnering. [00:42:47] Rebecca Jones: Don’t forget, ultimate Partner Live is coming [00:42:50] Vince Menzione: soon, May 11th through the 13th in beautiful Bellevue, Washington. I hope to see you there.s I, as I wrap up here, I just wanna make sure that what, where

Many people think physics / reality is either guided by a probabilistic distribution or is “determined.” Actually, there's a third, far‐more unsettling option. Curt Jaimungal explains why Einstein's general relativity isn't actually deterministic. He discusses how Cauchy horizons and closed time-like curves break predictability, showing that math and physics don't always guarantee a set future for our universe. This is a solo deep‑dive. One that he's been meaning to make for a while. As a listener of TOE you can get a special 20% off discount to The Economist and all it has to offer! Visit https://www.economist.com/toe FOLLOW: - Substack: https://curtjaimungal.substack.com/subscribe - Twitter: https://twitter.com/TOEwithCurt - Discord Invite: https://discord.com/invite/kBcnfNVwqs - Crypto: https://commerce.coinbase.com/checkout/de803625-87d3-4300-ab6d-85d4258834a9 - PayPal: https://www.paypal.com/donate?hosted_button_id=XUBHNMFXUX5S4 LINKS MENTIONED: - This Cosmologist Discovered Something Strange: https://youtu.be/73IdQGgfxas - The Most Abused Theorem in Math (Gödel's Incompleteness): https://youtu.be/OH-ybecvuEo - Harvard Scientist: "There Is No Quantum Multiverse" | Jacob Barandes [Part 3]: https://youtu.be/wrUvtqr4wOs - The Quantum Mechanics of Time Travel: https://youtu.be/yCQ_3qE6SmQ - The Dangerous Lie About Understanding: https://youtu.be/eASBzSNB8ts - Discovery That Changed Physics! Gravity Is Not a Force!: https://youtu.be/3pZNzF6LBII - Einstein's Amazing Theory of Gravity: Black Holes and Novel Ideas in Cosmology, Roger Penrose | LMS: https://youtu.be/xAcvNnSrkcM - The Geodesic Equation: Introduction and Derivation: https://youtu.be/5_79m-kHxts - Interpretation of the Wavefunction: https://youtu.be/R-5hjmV-bdY - Is the Future Already Set in Stone?: https://youtu.be/JBkB2D-_ZH0 - What Is Astrophysics Actually Explained: https://youtu.be/TCrRs_OBN0E - What Triggered the Big Bang? | How the Universe Works: https://youtu.be/gup4Cc0Ube0 - Visualization of the Gödel Universe: https://youtu.be/078jOiaevAQ - Iceberg of String Theory: https://youtu.be/X4PdPnQuwjY - The 300-Year-Old Physics Mistake No One Noticed: https://youtu.be/Tghl6aS5A3M - JB Manchak: Spacetime Asymmetry: https://youtu.be/lFbfhISreFY - Carlo Rovelli [TOE]: https://youtu.be/hF4SAketEHY - General Relativity Is Not (Technically) Deterministic: https://curtjaimungal.substack.com/p/general-relativity-is-not-deterministic - The Strong Cosmic Censorship Conjecture by Maxime Van de Moortel [Paper]: https://arxiv.org/pdf/2501.13180 - Some Black Holes Erase Your Past: https://www.sciencedaily.com/releases/2018/02/180221091334.htm - Determinism and General Relativity [Paper]: https://arxiv.org/pdf/2009.07555 - A Family of Local Deterministic Models for Singlet Quantum State Correlations [Paper]: https://arxiv.org/html/2408.09579v1 - Examples of Cosmological Spacetimes Without CMC Cauchy Surfaces: https://link.springer.com/article/10.1007/s11005-024-01843-7 - Asymptotic Dynamics on the Worldlines for Spinning Particles [Paper]: https://arxiv.org/abs/2009.07863 - World Line: https://en.wikipedia.org/wiki/World_line - Counterexamples in Topology [Book]: https://link.springer.com/book/10.1007/978-1-4612-6290-9 - Quantum Charged Black Holes [Paper]: https://arxiv.org/pdf/2404.07192 - Charged Hayward Black Hole with a Cosmological Constant and Surrounded by Quintessence and a Cloud of Strings [Paper]: https://arxiv.org/pdf/2511.02191 - Strong Cosmic Censorship in Charged Black-Hole Spacetimes: Still Subtle [Paper]: https://arxiv.org/pdf/1808.03631 - Chaos and Deterministic Versus Stochastic Non-Linear Modelling: https://academic.oup.com/jrsssb/article/54/2/303/7035838 - Reopening the Hole Argument by Klaas Landsman [Paper]: https://arxiv.org/pdf/2206.04943 - Is Time Travel Too Strange to Be Possible? [Paper]: https://arxiv.org/pdf/1704.02295 - Counterexamples in Topology [Book]: https://link.springer.com/book/10.1007/978-1-4612-6290-9 Learn more about your ad choices. Visit megaphone.fm/adchoices

Roy is a three-time founder who has cracked the code on enterprise AI. After selling his first company and realizing his second idea was too slow, he pivoted to solving a massive problem: customer service automation.In this episode, Roy breaks down how GetVocal went from zero to $1M ARR in just five months. He reveals the "Context Graph" technology that allows them to beat LLM wrappers, why he believes purely generative AI is useless for business, and how he turned a single deployment into an enterprise-wide contagion.Why You Should ListenHow to hit $1M ARR in 5 months with a single salesperson.Why "Context Graphs" are the secret to building AI that doesn't hallucinate.How to expand from a single agent to 80 agents across the enterprise.The critical difference between Deterministic and Probabilistic AI Why starting with a personal passion project failed, but pivoting to enterprise worked.Keywordsstartup podcast, startup podcast for founders, product market fit, enterprise AI, customer service automation, finding pmf, context graphs, AI agents, B2B sales, Roy Moussa00:00:00 Intro00:02:29 From Engineer to 3-Time Founder00:08:11 The Failed Pivot00:12:49 Solving Sales Efficiency First00:16:06 The Pivot to Customer Service00:18:57 Why Chatbots Failed & The Hybrid AI Solution00:25:43 What is a Context Graph?00:34:46 The "Contagion" Effect: 80 Agents in 8 Weeks00:39:34 Competing with Decagon & The Human-Centric Approach00:41:58 Hitting $1M ARR in 5 MonthsSend me a message to let me know what you think!

Tonight's WeatherBrains is all about the NWS Storm Prediction Center (SPC)'s convective outlooks. Guest WeatherBrain and SPC forecaster Bill Bunting joins us tonight.  He grew up in Virginia Beach, VA where we experienced a series of hurricanes and Nor'easters at a very young age.  He attended Old Dominion University as well as OU where he earned his Bachelor's Degree.  He's been with the National Weather Service since 1985, where he's worked primarily at local offices in Norman (OK), Kansas City (MO) and Fort Worth (TX).  He was Chief of Forecasting Operations at SPC in 2012, and is now the Deputy Director at SPC since 2024.  Bill, welcome to WeatherBrains! Second and last Guest WeatherBrain (but certainly not the least!) is the brand new Warning Coordination Meteorologist (MIC) for the SPC.  He grew up in Ohio and attended Valparaiso University, and has since worked for the NWS for over 15 years.  He loves historical weather statistics and visualization.  Evan Bentley, welcome to WeatherBrains! Our email officer Jen is continuing to handle the incoming messages from our listeners. Reach us here: email@weatherbrains.com. Organization/structure of NWS Storm Prediction Center (14:00) SPCS's Fire weather forecasting (16:00) Science behind SPC's new conditional intensity forecasts (20:30) Deterministic factors for a PDS (Particularly Dangerous Situation) watch (26:00) Improving communication of impactful weather events to family and co workers (40:00) SPC's philosophy on dealing with QLCS events (51:00) National team approach for warnings?  (01:03:00) The Astronomy Outlook with Tony Rice (01:29:45) This Week in Tornado History With Jen (01:32:15) E-Mail Segment (01:24:00) and more! Web Sites from Episode 1051:   Alabama Weather Network Picks of the Week: Bill Bunting - SPC Hazard Climatology James Aydelott - Okie James on Facebook: Two supercell rotation paths Jen Narramore - Southeast Severe Storms Symposium XXIV Rick Smith - NOAA Damage Assessment Toolkit Troy Kimmel - Foghorn Kim Klockow-McClain - Edwardsburg Schools offer support as community mourns loss of student John Gordon - Congressman Eric Sorensen introduces new legislation to investigate major weather disasters Bill Murray - Out James Spann - Out The WeatherBrains crew includes your host, James Spann, plus other notable geeks like Troy Kimmel, Bill Murray, Rick Smith, James Aydelott, Jen Narramore, John Gordon, and Dr. Kim Klockow-McClain. They bring together a wealth of weather knowledge and experience for another fascinating podcast about weather.

How do you ensure software quality when the system you're testing doesn't give the same output twice? Go to https://links.testguild.com/inflectra and start your free 30-day trial, no credit card, no contract required. That's the core challenge facing every QA team building or testing AI-powered applications today and it's breaking all the rules we've relied on for decades. In this episode of the TestGuild Automation Podcast, I sit down with Adam Sandman, co-founder of Inflectra, to get into what non-deterministic AI testing actually means in practice, why traditional pass/fail testing no longer cuts it, and what quality professionals need to do differently right now. We cover: Why AI-generated code is raising the stakes for QA teams while budgets stay flat The fundamental difference between deterministic and non-deterministic systems — and why it changes everything about how you test How to set acceptable risk thresholds for AI systems (hint: it depends on whether you're building an e-commerce chatbot or an air traffic control system) Why testers who embrace AI as a tool — not a threat — will be the ones leading their organizations forward How a live demo failure at a conference inspired Inflectra's new non-deterministic testing tool, SureWire If you're a tester, QA manager, or automation engineer trying to figure out how to keep up with AI-driven development without losing your mind — or your job — this one's for you.

Baby Blue Viper explores deterministic enforcement infrastructure across Bitcoin and AI systems.As capital moves without intermediaries and autonomous models scale without friction, governance must move from policy to infrastructure. This show examines transaction integrity, capability containment, sovereign compute, and the primitives required to embed constraint before execution.Episodes alternate between open enforcement briefings and member-only deep-dive transmissions — moving from surface signal to structural design.Enforcement must precede execution.Featured Tools & ResourcesΩmega Pruner — a non-custodial, PSBT-only enforcement layer for structured Bitcoin UTXO management.Live demo → https://omega-pruner.onrender.comCode → https://github.com/babyblueviper1/Viper-Stack-Omega This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.babyblueviper.com/subscribe

BONUS: When AI Decisions Go Wrong at Scale—And How to Prevent It We've spent years asking what AI can do. But the next frontier isn't more capability—it's something far less glamorous and far more dangerous if we get it wrong. In this episode, Ran Aroussi shares why observability, transparency, and governance may be the difference between AI that empowers humans and AI that quietly drifts out of alignment. The Gap Between Demos and Deployable Systems "I've noticed that I watched well-designed agents make perfectly reasonable decisions based on their training, but in a context where the decision was catastrophically wrong. And there was really no way of knowing what had happened until the damage was already there."   Ran's journey from building algorithmic trading systems to creating MUXI, an open framework for production-ready AI agents, revealed a fundamental truth: the skills needed to build impressive AI demos are completely different from those needed to deploy reliable systems at scale. Coming from the EdTech space where he handled billions of ad impressions daily and over a million concurrent users, Ran brings a perspective shaped by real-world production demands.  The moment of realization came when he saw that the non-deterministic nature of AI meant that traditional software engineering approaches simply don't apply. While traditional bugs are reproducible, AI systems can produce different results from identical inputs—and that changes everything about how we need to approach deployment. Why Leaders Misunderstand Production AI "When you chat with ChatGPT, you go there and it pretty much works all the time for you. But when you deploy a system in production, you have users with unimaginable different use cases, different problems, and different ways of phrasing themselves."   The biggest misconception leaders have is assuming that because AI works well in their personal testing, it will work equally well at scale. When you test AI with your own biases and limited imagination for scenarios, you're essentially seeing a curated experience.  Real users bring infinite variation: non-native English speakers constructing sentences differently, unexpected use cases, and edge cases no one anticipated. The input space for AI systems is practically infinite because it's language-based, making comprehensive testing impossible. Multi-Layered Protection for Production AI "You have to put in deterministic filters between the AI and what you get back to the user."   Ran outlines a comprehensive approach to protecting AI systems in production:   Model version locking: Just as you wouldn't randomly upgrade Python versions without testing, lock your AI model versions to ensure consistent behavior Guardrails in prompts: Set clear boundaries about what the AI should never do or share Deterministic filters: Language firewalls that catch personal information, harmful content, or unexpected outputs before they reach users Comprehensive logging: Detailed traces of every decision, tool call, and data flow for debugging and pattern detection   The key insight is that these layers must work together—no single approach provides sufficient protection for production systems. Observability in Agentic Workflows "With agentic AI, you have decision-making, task decomposition, tools that it decided to call, and what data to pass to them. So there's a lot of things that you should at least be able to trace back."   Observability for agentic systems is fundamentally different from traditional LLM observability. When a user asks "What do I have to do today?", the system must determine who is asking, which tools are relevant to their role, what their preferences are, and how to format the response.  Each user triggers a completely different dynamic workflow. Ran emphasizes the need for multi-layered access to observability data: engineers need full debugging access with appropriate security clearances, while managers need topic-level views without personal information. The goal is building a knowledge graph of interactions that allows pattern detection and continuous improvement. Governance as Human-AI Partnership "Governance isn't about control—it's about keeping people in the loop so AI amplifies, not replaces, human judgment."   The most powerful reframing in this conversation is viewing governance not as red tape but as a partnership model. Some actions—like answering support tickets—can be fully automated with occasional human review. Others—like approving million-dollar financial transfers—require human confirmation before execution. The key is designing systems where AI can do the preparation work while humans retain decision authority at critical checkpoints. This mirrors how we build trust with human colleagues: through repeated successful interactions over time, gradually expanding autonomy as confidence grows. Building Trust Through Incremental Autonomy "Working with AI is like working with a new colleague that will back you up during your vacation. You probably don't know this person for a month. You probably know them for years. The first time you went on vacation, they had 10 calls with you, and then slowly it got to 'I'm only gonna call you if it's really urgent.'"   The path to trusting AI systems mirrors how we build trust with human colleagues. You don't immediately hand over complete control—you start with frequent check-ins, observe performance, and gradually expand autonomy as confidence builds. This means starting with heavy human-in-the-loop interaction and systematically reducing oversight as the system proves reliable. The goal is reaching a state where you can confidently say "you don't have to ask permission before you do X, but I still want to approve every Y."   In this episode, we refer to Thinking in Systems by Donella Meadows, Designing Machine Learning Systems by Chip Huyen, and Build a Large Language Model (From Scratch) by Sebastian Raschka.   About Ran Aroussi Ran Aroussi is the founder of MUXI, an open framework for production-ready AI agents. He is also the co-creator of yfinance (with 10 million downloads monthly) and founder of Tradologics and Automaze. Ran is the author of the forthcoming book Production-Grade Agentic AI: From Brittle Workflows to Deployable Autonomous Systems, also available at productionaibook.com.   You can connect with Ran Aroussi on LinkedIn.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, a knowledge architect, community builder, and host of the Knowledge Graph Insights podcast. They explore the relationship between knowledge graphs and ontologies, why these technologies matter in the age of AI, and how symbolic AI complements the current wave of large language models. The conversation traces the history of neuro-symbolic AI from its origins at Dartmouth in 1956 through the semantic web vision of Tim Berners-Lee, examining why knowledge architecture remains underappreciated despite being deployed at major enterprises like Netflix, Amazon, and LinkedIn. Swanson explains how RDF (Resource Description Framework) enables both machines and humans to work with structured knowledge in ways that relational databases can't, while Alsop shares his journey from knowledge management director to understanding the practical necessity of ontologies for business operations. They discuss the philosophical roots of the field, the separation between knowledge management practitioners and knowledge engineers, and why startups often overlook these approaches until scale demands them. You can find Larry's podcast at KGI.fm or search for Knowledge Graph Insights on Spotify and YouTube.Timestamps00:00 Introduction to Knowledge Graphs and Ontologies01:09 The Importance of Ontologies in AI04:14 Philosophy's Role in Knowledge Management10:20 Debating the Relevance of RDF15:41 The Distinction Between Knowledge Management and Knowledge Engineering21:07 The Human Element in AI and Knowledge Architecture25:07 Startups vs. Enterprises: The Knowledge Gap29:57 Deterministic vs. Probabilistic AI32:18 The Marketing of AI: A Historical Perspective33:57 The Role of Knowledge Architecture in AI39:00 Understanding RDF and Its Importance44:47 The Intersection of AI and Human Intelligence50:50 Future Visions: AI, Ontologies, and Human BehaviorKey Insights1. Knowledge Graphs Combine Structure and Instances Through Ontological Design. A knowledge graph is built using an ontology that describes a specific domain you want to understand or work with. It includes both an ontological description of the terrain—defining what things exist and how they relate to one another—and instances of those things mapped to real-world data. This combination of abstract structure and concrete examples is what makes knowledge graphs powerful for discovery, question-answering, and enabling agentic AI systems. Not everyone agrees on the precise definition, but this understanding represents the practical approach most knowledge architects use when building these systems.2. Ontology Engineering Has Deep Philosophical Roots That Inform Modern Practice. The field draws heavily from classical philosophy, particularly ontology (the nature of what you know), epistemology (how you know what you know), and logic. These thousands-year-old philosophical frameworks provide the rigorous foundation for modern knowledge representation. Living in Heidelberg surrounded by philosophers, Swanson has discovered how much of knowledge graph work connects upstream to these philosophical roots. This philosophical grounding becomes especially important during times when institutional structures are collapsing, as we need to create new epistemological frameworks for civilization—knowledge management and ontology become critical tools for restructuring how we understand and organize information.3. The Semantic Web Vision Aimed to Transform the Internet Into a Distributed Database. Twenty-five years ago, Tim Berners-Lee, Jim Hendler, and Ora Lassila published a landmark article in Scientific American proposing the semantic web. While Berners-Lee had already connected documents across the web through HTML and HTTP, the semantic web aimed to connect all the data—essentially turning the internet into a giant database. This vision led to the development of RDF (Resource Description Framework), which emerged from DARPA research and provides the technical foundation for building knowledge graphs and ontologies. The origin story involved solving simple but important problems, like disambiguating whether "Cook" referred to a verb, noun, or a person's name at an academic conference.4. Symbolic AI and Neural Networks Represent Complementary Approaches Like Fast and Slow Thinking. Drawing on Kahneman's "thinking fast and slow" framework, LLMs represent the "fast brain"—learning monsters that can process enormous amounts of information and recognize patterns through natural language interfaces. Symbolic AI and knowledge graphs represent the "slow brain"—capturing actual knowledge and facts that can counter hallucinations and provide deterministic, explainable reasoning. This complementarity is driving the re-emergence of neuro-symbolic AI, which combines both approaches. The fundamental distinction is that symbolic AI systems are deterministic and can be fully explained, while LLMs are probabilistic and stochastic, making them unsuitable for applications requiring absolute reliability, such as industrial robotics or pharmaceutical research.5. Knowledge Architecture Remains Underappreciated Despite Powering Major Enterprises. While machine learning engineers currently receive most of the attention and budget, knowledge graphs actually power systems at Netflix (the economic graph), Amazon (the product graph), LinkedIn, Meta, and most major enterprises. The technology has been described as "the most astoundingly successful failure in the history of technology"—the semantic web vision seemed to fail, yet more than half of web pages now contain RDF-formatted semantic markup through schema.org, and every major enterprise uses knowledge graph technology in the background. Knowledge architects remain underappreciated partly because the work is cognitively difficult, requires talking to people (which engineers often avoid), and most advanced practitioners have PhDs in computer science, logic, or philosophy.6. RDF's Simple Subject-Predicate-Object Structure Enables Meaning and Data Linking. Unlike relational databases that store data in tables with rows and columns, RDF uses the simplest linguistic structure: subject-predicate-object (like "Larry knows Stuart"). Each element has a unique URI identifier, which permits precise meaning and enables linked data across systems. This graph structure makes it much easier to connect data after the fact compared to navigating tabular structures in relational databases. On top of RDF sits an entire stack of technologies including schema languages, query languages, ontological languages, and constraints languages—everything needed to turn data into actionable knowledge. The goal is inferring or articulating knowledge from RDF-structured data.7. The Future Requires Decoupled Modular Architectures Combining Multiple AI Approaches. The vision for the future involves separation of concerns through microservices-like architectures where different systems handle what they do best. LLMs excel at discovering possibilities and generating lists, while knowledge graphs excel at articulating human-vetted, deterministic versions of that information that systems can reliably use. Every one of Swanson's 300 podcast interviews over ten years ultimately concludes that regardless of technology, success comes down to human beings, their behavior, and the cultural changes needed to implement systems. The assumption that we can simply eliminate people from processes misses that huma...

In this episode of Run the Numbers, CJ Gustafson sits down with Dan Miller, CFO at RightRev. They unpack why leasing is underused in software, how RevTech emerged, and why revenue recognition may be the next AI battleground. Dan also shares how he evaluates durable growth vs. hypergrowth.—SPONSORS:Rillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.com—LINKS: Dan on LinkedIn: https://www.linkedin.com/in/danmillercpa/RightRev: https://www.rightrev.com/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:00:00:00 Preview and Intro00:02:41 Why Operating Experience Matters for CFOs00:04:08 Defining Durable Growth00:06:06 Snowflake and Consumption Revenue Complexity00:10:17 Forecasting in Consumption Models00:11:29 AI's Role in Revenue Forecasting00:12:14 Sponsors — Rillet | Tabs | Abacus AI00:15:39 Comping Sales in Usage-Based Models00:18:15 Leasing as a Software Monetization Tool00:20:47 The CFO's Role in Sales and GTM00:22:29 How CFOs Help Close Deals00:24:14 Rev Tech vs RevOps00:26:20 Sponsors — Brex | Metronome | RightRev00:29:40 Where AI Actually Helps Rev Rec00:31:55 Deterministic vs Probabilistic AI00:33:05 Why Enterprises Hesitate on AI Agents00:34:18 Startups vs Incumbents in the AI Race00:35:13 FOMO, Overfunding, and Market Distortions00:38:13 CFO Playbooks Without Hypergrowth00:39:38 Finding PMF as a CFO00:41:15 Career Advice: Growth vs Shiny Objects00:42:00 Building the CEO–CFO Relationship00:42:49 Learning Beyond the Back Office00:43:22 Lightning Round00:44:28 Advice to My Younger Self00:45:09 Finance Tech Stack00:46:36 Credits

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop explores the complex world of context and knowledge graphs with guest Youssef Tharwat, the founder of NoodlBox who is building dot get for context. Their conversation spans from the philosophical nature of context and its crucial role in AI development, to the technical challenges of creating deterministic tools for software development. Tharwat explains how his product creates portable, versionable knowledge graphs from code repositories, leveraging the semantic relationships already present in programming languages to provide agents with better contextual understanding. They discuss the limitations of large context windows, the advantages of Rust for AI-assisted development, the recent Claude/Bun acquisition, and the broader geopolitical implications of the AI race between big tech companies and open-source alternatives. The conversation also touches on the sustainability of current AI business models and the potential for more efficient, locally-run solutions to challenge the dominance of compute-heavy approaches.For more information about NoodlBox and to join the beta, visit NoodlBox.io.Timestamps00:00 Stewart introduces Youssef Tharwat, founder of NoodlBox, building context management tools for programming05:00 Context as relevant information for reasoning; importance when hitting coding barriers10:00 Knowledge graphs enable semantic traversal through meaning vs keywords/files15:00 Deterministic vs probabilistic systems; why critical applications need 100% reliability20:00 CLI tool makes knowledge graphs portable, versionable artifacts with code repos25:00 Compiler front-ends, syntax trees, and Rust's superior feedback for AI-assisted coding30:00 Claude's Bun acquisition signals potential shift toward runtime compilation and graph-based context35:00 Open source vs proprietary models; user frustration with rate limits and subscription tactics40:00 Singularity path vs distributed sovereignty of developers building alternative architectures45:00 Global economics and why brute force compute isn't sustainable worldwide50:00 Corporate inefficiencies vs independent engineering; changing workplace dynamics55:00 February open beta for NoodlBox.io; vision for new development tool standardsKey Insights1. Context is semantic information that enables proper reasoning, and traditional LLM approaches miss the mark. Youssef defines context as the information you need to reason correctly about something. He argues that larger context windows don't scale because quality degrades with more input, similar to human cognitive limitations. This insight challenges the Silicon Valley approach of throwing more compute at the problem and suggests that semantic separation of information is more optimal than brute force methods.2. Code naturally contains semantic boundaries that can be modeled into knowledge graphs without LLM intervention. Unlike other domains where knowledge graphs require complex labeling, code already has inherent relationships like function calls, imports, and dependencies. Youssef leverages these existing semantic structures to automatically build knowledge graphs, making his approach deterministic rather than probabilistic. This provides the reliability that software development has historically required.3. Knowledge graphs can be made portable, versionable, and shareable as artifacts alongside code repositories. Youssef's vision treats context as a first-class citizen in version control, similar to how Git manages code. Each commit gets a knowledge graph snapshot, allowing developers to see conceptual changes over time and share semantic understanding with collaborators. This transforms context from an ephemeral concept into a concrete, manageable asset.4. The dependency problem in modern development can be solved through pre-indexed knowledge graphs of popular packages. Rather than agents struggling with outdated API documentation, Youssef pre-indexes popular npm packages into knowledge graphs that automatically integrate with developers' projects. This federated approach ensures agents understand exact APIs and current versions, eliminating common frustrations with deprecated methods and unclear documentation.5. Rust provides superior feedback loops for AI-assisted programming due to its explicit compiler constraints. Youssef rebuilt his tool multiple times in different languages, ultimately settling on Rust because its picky compiler provides constant feedback to LLMs about subtle issues. This creates a natural quality control mechanism that helps AI generate more reliable code, making Rust an ideal candidate for AI-assisted development workflows.6. The current AI landscape faces a fundamental tension between expensive centralized models and the need for global accessibility. The conversation reveals growing frustration with rate limiting and subscription costs from major providers like Claude and Google. Youssef believes something must fundamentally change because $200-300 monthly plans only serve a fraction of the world's developers, creating pressure for more efficient architectures and open alternatives.7. Deterministic tooling built on semantic understanding may provide a competitive advantage against probabilistic AI monopolies. While big tech companies pursue brute force scaling with massive data centers, Youssef's approach suggests that clever architecture using existing semantic structures could level the playing field. This represents a broader philosophical divide between the "singularity" path of infinite compute and the "disagreeably autistic engineer" path of elegant solutions that work locally and affordably.

In this episode, Jeff Mains sits down with KG Charles-Harris, a serial entrepreneur who has founded six companies across industries ranging from genomics to AI. KG is the founder and CEO of Quarrio, a deterministic AI platform that solves a critical problem: getting accurate, consistent answers from corporate data in seconds instead of weeks.KG shares his unconventional path to entrepreneurship, explaining how his companies emerge from late-night conversations with brilliant people who share a common problem. He breaks down the crucial difference between deterministic and probabilistic AI systems, making the case that when decisions involve real money, real lives, or real consequences, accuracy isn't optional—it's essential.Key Takeaways[0:00] Introduction to KG Charles-Harris and his multi-industry entrepreneurial journey[1:18] How companies are born from conversations: The pattern behind KG's six startups[2:30] The genomics company origin story: From 4:30 AM conversation to Norwegian startup[3:28] Why Quarrio exists: Even data company CEOs can't get the data they need[4:31] The Quarrio platform: 100% accuracy, plain language queries, auto-visualization[5:27] Real-world impact: The $60M margin leak that took two quarters to find (would take 5 seconds with Quarrio)[7:00] Deterministic vs. probabilistic AI explained: Why autopilots don't hallucinate[11:30] The cycle time framework: Information → Decision → Action → Results[13:00] Why ChatGPT's inconsistency is a dealbreaker for enterprise decisions[18:30] Organizations as "decision-making machines" and democratizing decisions to every level[20:30] The data explosion: Managing 300+ structured data sources in mid-sized enterprises[23:00] Why Quarrio focuses on structured enterprise data (SAP, Salesforce, Oracle) instead of PDFs[30:00] Go-to-market strategy: Why they started with Salesforce and sales teams[32:30] The Salesforce incubation story: Free office space and immediate investment[33:30] Team building philosophy: Surrounding yourself with people smarter than you[37:00] Stewardship as core ethos: Taking care of family, team, customers, and partners[38:30] The founder's dilemma: Resilience vs. delusion—knowing when to persist[43:00] Where to connect with KG and learn more about QuarrioTweetable Quotes"An organization is essentially a machine for making decisions and taking actions that have certain types of results." — KG Charles-Harris"Cycle time to information shortens cycle time to decision, which shortens cycle time to action, which shortens cycle time to results." — KG Charles-Harris"Agentic AI without context is useless. You need determinism to trust what is enacted within your system." — KG Charles-Harris"Effectiveness requires redundancy. Efficiency optimizes for the shortest time or best expense, but effectiveness accomplishes the goal." — KG Charles-Harris"I'm not very smart, and because I realize that, I ensure I work with people who are very smart. Then they make me look smart." — KG Charles-Harris"Most of us give up before we should have. The break would have come had we stuck it out one more month." — KG Charles-Harris"If you don't have their back, you cannot expect them to have yours. It's a

Tyson Singer (Head of Tech & Platforms @ Spotify) joins us to unpack how Spotify is transforming its product development lifecycle across creation, experimentation and maintenance to shift from "localized speed" to "systematic speed." We explore why the industry's current obsession with the "Build It" phase of development is shortsighted, and how Spotify is aggressively deploying AI in the "Think It" (prototyping/strategy) and "Maintain It" (fleet management) phases. Tyson also details the internal tools driving this shift, including AiKA and Honk, and shares why the future of engineering relies on moving from I-shaped specialists to T-shaped generalists. ABOUT TYSON SINGERTyson Singer is the SVP of Technology & Platforms at Spotify, where he leads technology infrastructure, developer experience, cybersecurity, and finance IT. Tyson is the executive behind Spotify's internal developer portal, Backstage, and Spotify's experimentation system, Confidence, which are now both commercially available. He has a background as an engineer, architect, and product lead, and he holds a Master's in Computer Science from Stanford University. Tyson is also an avid outdoor adventurer. This episode is brought to you by Retool!What happens when your team can't keep up with internal tool requests? Teams start building their own, Shadow IT spreads across the org, and six months later you're untangling the mess…Retool gives teams a better way: governed, secure, and no cleanup required.Retool is the leading enterprise AppGen platform, powering how the world's most innovative companies build the tools that run their business. Over 10,000 organizations including Amazon, Stripe, Adobe, Brex, and Orangetheory Fitness use the platform to safely harness AI and their enterprise data to create governed, production-ready apps.Learn more at Retool.com/elc SHOW NOTES:Tyson's 9-year journey @ Spotify: From the "crucible" of hyper-growth to leading Tech & Platforms (3:46)The pivot from "localized speed" to "systematic speed" (7:27)Core principles of Spotify's Platform org: Partnering with customers & "Taking the pain away" (10:37)The "Think it, Build it, Ship it, Tweak it" lifecycle framework & why the industry obsession with "Build It" (coding agents) is missing the bigger picture (14:57)How Spotify is investing in the "Think It" phase: AI prototyping with deep business context (16:49)AiKA (AI Knowledge Assistant): Context engineering for humans and bots (18:47)"Honk": Spotify's internal framework for large-scale automated code changes (22:17)Addressing the decline of code quality and the bottleneck of human PR reviews (25:50)Probabilistic vs. Deterministic code reviews: A new approach to quality checks (29:43)Identifying bottlenecks to company value outside of R&D (Legal, Licensing, etc.) (32:12)Why systems change is fundamentally about people and identity shifts (35:57)Rapid fire questions (38:49) This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Today's guest is Emma Vitalini, Head of Global Digital Health Technology Innovation at Amgen, where she leads initiatives at the intersection of digital health, data strategy, and clinical innovation. Emma joins Emerj Editorial Senior Editor Marilie Fouche to explore how data and AI are reshaping patient recruitment, consent, and execution in clinical trials, with a focus on decentralized models, scalable compliance, and explainable AI in regulated environments. Emma also shares practical guidance for enterprise leaders on where AI is delivering near-term ROI today, including accelerating patient screening by surfacing unstructured data, reducing enrollment delays through digital and remote monitoring tools, and designing modular, plug-and-play AI platforms that balance speed, flexibility, and regulatory trust. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the 'AI in Business' podcast! If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

In this episode of Run the Numbers, CJ sits down with Bruno Annicq, CFO of Wellhub (formerly Gympass), to unpack a practical finance playbook built around cash discipline, sustainable growth, and simplicity. Bruno explains how he rebuilt forecasting using an AI-driven, probabilistic ensemble model, moving teams beyond single-scenario planning. They also dig into his EMPOWER planning framework, usable OKRs, and why tighter alignment between finance, HR, and wellbeing is becoming a durable lever for long-term performance.—SPONSORS:RightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.com—LINKS:Bruno on LinkedIn: https://www.linkedin.com/in/bannicq/Wellhub: https://wellhub.com/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:“Run Toward a Tough Market” — Developing the Hard and Soft Skills To Be a Great Finance Leaderhttps://youtu.be/iNHbkcG7YEo—TIMESTAMPS:00:00:00 Preview and Intro00:02:19 Sponsors — RightRev, Rillet, Tabs00:06:43 Accidental CFO Origin Story00:07:34 Consulting to Operations Pivot00:08:12 Why Finance Clicked for Bruno00:09:28 McKinsey Prioritization in Real World00:10:02 Eisenhower Matrix and Prioritization00:11:08 Investing in Non-Urgent Work00:13:30 Lessons From AOL Reinvention00:16:10 Sponsors — Abacum, Brex, Metronome00:20:01 Career Growth Through Hard Problems00:20:52 Broadening Skills Through Change00:23:12 Five Core Finance Principles00:24:02 Cash Is King00:25:14 Driving Sustainable Growth00:26:01 No Surprises and Forecasting00:26:07 Finance as Business Enabler00:27:22 Less Is More Philosophy00:28:47 Hardest Principle: Less Is More00:29:46 Deterministic vs Probabilistic Forecasting00:31:11 Marketplace Volatility and Forecast Error00:32:10 Ensemble Models Explained00:33:37 Forecast Accuracy Gains00:34:53 Building Models In-House00:36:46 Why Explainability Matters00:37:48 Empower Framework Introduction00:47:47 Urgency, Compounding, Long-Term Thinking00:48:10 Advice to Younger Self00:50:06 Finance Stack and Expense Stories00:52:51 Credits#RunTheNumbersPodcast #CFO #FinanceLeadership #Forecasting #AIinFinance This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cjgustafson.substack.com

Pixel Flow! works because it combines familiarity with novelty. The core loop feels intuitive if you've played sorting or bench-based puzzle games, but the slinging and conveyor mechanics add speed, mastery, and tension. Difficulty is created through execution pressure, not confusion. Players feel smart when they win and responsible when they lose, which massively boosts engagement.Monetization is deceptively simple and very effective. Interstitials trigger on failed and completed levels, rewarded videos are optional and placed where they don't break flow, and there are no unnecessary ad placements on app open. Ads already contribute roughly 15–20% of revenue and perhaps even more, with room to grow, but the team clearly prioritizes uninterrupted gameplay to protect conversion and retention.The biggest growth driver is UA. Pixel Flow! skipped a traditional soft launch, went hard on AppLovin and Mintegral, then expanded rapidly to TikTok, Unity, Google, and Meta. Creative velocity is extreme, with thousands of new creatives pushed in weeks, including playables, AI visuals, and gray-zone recognizable characters. Get our MERCH NOW: 25gamers.com/shop--------------------------------------PVX Partners offers non-dilutive funding for game developers.Go to: https://pvxpartners.com/They can help you access the most effective form of growth capital once you have the metrics to back it.- Scale fast- Keep your shares- Drawdown only as needed- Have PvX take downside risk alongside you+ Work with a team entirely made up of ex-gaming operators and investors---------------------------------------For an ever-growing number of game developers, this means that now is the perfect time to invest in monetizing direct-to-consumer at scale.Our sponsor FastSpring:Has delivered D2C at scale for over 20 yearsThey power top mobile publishers around the worldLaunch a new webstore, replace an existing D2C vendor, or add a redundant D2C vendor at fastspring.gg.---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Podcast: Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-2um8eguhf-c~H9idcxM271mnPzdWbipg00:00 — Intro and why Pixel Flow matters02:10 — First impressions and core loop04:10 — Sorting shooter mechanics explained06:40 — Bench, tray, and slinging mastery09:30 — Difficulty design and fail states12:10 — Comparison vs Voodoo-style sorting games14:00 — Growth timeline and revenue explosion16:00 — DAU, geo mix, and scale reality18:30 — Copycats and category pressure21:00 — Deterministic vs casino-style puzzles23:20 — Ceiling discussion: 15–30M per month?25:00 — Ad monetization breakdown27:30 — Why not to overpush rewarded ads29:30 — CPI, retention, and UA efficiency32:00 — Creative velocity as the real moat35:00 — IP lookalikes and gray-zone creatives38:30 — What Pixel Flow must do next41:30 — Final take and closing thoughts---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me

Get My NEW Book: Focus Like a Nobel Prize Winner: https://www.amazon.com/dp/B0FN8DH6SX Andrew Jaffe Book: The Random Universe: https://www.amazon.com/Random-Universe-Models-Probability-Cosmos/dp/0300250509 Is the universe intrinsically random? In this conversation, we dive deep into why the universe may be fundamentally, intrinsically random. Whether inflation on life support, the truth behind the Hubble tension, and whether cosmology is approaching the event horizon, limits beyond which humans can never know. Today we're joined by one of the architects of modern cosmological inference, Professor Andrew Jaffee, author of a new book called The Random Universe that argues that every observation in science is shaped by the models we bring to it, biases and all. KEY TAKEAWAYS 00:00–01:13 — Science and life rely on building models. 01:13–03:35 — Models of people and reality are often wrong and revised. 04:04–06:01 — Observation depends on prior theories. 06:01–07:32 — Models can't be escaped, only improved. 07:32–08:57 — No single scientific method exists. 08:57–11:25 — Science uses induction, not pure proof. 11:25–13:22 — Induction isn't certain, only probabilistic. 13:22–15:36 — Induction works because nature is regular. 17:44–19:08 — Big Bang emerges from well-tested models. 19:08–21:15 — Current cosmology is stressed, not broken. 29:19–30:36 — Probability gives meaning to models. 39:45–41:11 — Randomness often reflects limited knowledge. 43:46–45:00 — Quantum physics is fundamentally probabilistic. 49:09–50:04 — Inflation awaits decisive observational tests. - Additional resources: Get My NEW Book: Focus Like a Nobel Prize Winner: https://www.amazon.com/dp/B0FN8DH6SX?ref_=pe_93986420_775043100 Please join my mailing list here

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:DADVI is a new approach to variational inference that aims to improve speed and accuracy.DADVI allows for faster Bayesian inference without sacrificing model flexibility.Linear response can help recover covariance estimates from mean estimates.DADVI performs well in mixed models and hierarchical structures.Normalizing flows present an interesting avenue for enhancing variational inference.DADVI can handle large datasets effectively, improving predictive performance.Future enhancements for DADVI may include GPU support and linear response integration.Chapters:13:17 Understanding DADVI: A New Approach21:54 Mean Field Variational Inference Explained26:38 Linear Response and Covariance Estimation31:21 Deterministic vs Stochastic Optimization in DADVI35:00 Understanding DADVI and Its Optimization Landscape37:59 Theoretical Insights and Practical Applications of DADVI42:12 Comparative Performance of DADVI in Real Applications45:03 Challenges and Effectiveness of DADVI in Various Models48:51 Exploring Future Directions for Variational Inference53:04 Final Thoughts and Advice for PractitionersThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël...

Algorithms and automations have been buds for a decade plus.

The etcd project — a distributed key-value store older than Kubernetes — recently faced significant challenges due to maintainer turnover and the resulting loss of unwritten institutional knowledge. Lead maintainer Marek Siarkowicz explained that as longtime contributors left, crucial expertise about testing procedures and correctness guarantees disappeared. This gap led to a problematic release that introduced critical reliability issues, including potential data inconsistencies after crashes.To rebuild confidence in etcd's correctness, the new maintainer team introduced “robustness testing,” creating a framework inspired by Jepsen to validate both basic and distributed-system behavior. Their goal was to ensure linearizability, the “Holy Grail” of distributed systems, which required developing custom failure-injection tools and teaching the community how to debug complex scenarios.The team later partnered with Antithesis to apply deterministic simulation testing, enabling fully reproducible execution paths and easier detection of subtle race conditions. This approach helped codify implicit knowledge into explicit properties and assertions. Siarkowicz emphasized that such rigorous testing is essential for safeguarding the sensitive “core” of large open source projects, ensuring correctness even as maintainers change.Learn more from The New Stack about the etcd projectTutorial: Install a Highly Available K3s Cluster at the Edge Join our community of newsletter subscribers to stay on top of the news and at the top of your game.   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Listen now: Spotify, Apple and YouTubeIn the first-ever live recording of Supra Insider, Marc and Ben sat down with Jacob Bank, founder of Relay.app, to unpack one of the most urgent questions facing product leaders today: How do AI agents actually change the way we work? Instead of abstract predictions, Jacob shares the very real workflows, failures, and breakthroughs behind running a 10-person company that delegates work to more than 300 AI agents.Across the conversation, the three dig into what PMs must learn next: writing job descriptions for agents, architecting responsibilities, managing automated execution, and understanding how agents influence velocity, product quality, and cross-functional collaboration. Jacob also discusses why PMs are lagging behind engineering and ops in adopting agentic workflows, and what will happen to teams who don't catch up.If you're a PM, founder, or operator trying to understand how AI is reshaping product development, or you've struggled to translate “agent hype” into concrete, repeatable workflows, this episode gives you a realistic, practitioner-level framework for building with agents today, and preparing for what's coming next.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

Jamie and Don, freshly back from PAX Unplugged 2025 in Philadelphia, have been getting into some board, card, and dice rumbles lately, and they wanted to dig into the many styles of combat systems in board games. Deterministic versus random, complex versus streamlined, dice versus cards - there's a lot of ground to cover. Also, our yearly fundraiser is currently running on Kickstarter! Support us now at https://www.thesecretcabal.com/kickstarter

Send us a textHit replay on one of the most thought-provoking Agentic AI conversations on Making Data Simple. GTM Account Director Megan Gallagher makes the case for Agentic AI from the Maven AGI front lines, where AI agents stop following rigid decision trees and start acting with real autonomy over enterprise workflows.“We're still living like everything is deterministic,” Megan argues, “but this new generation of agents is inherently generative and predictive.” In this replay, she unpacks what that shift means for smaller specialized models, using real enterprise data, rethinking “assistant vs person,” and how to get started without boiling the ocean.If you want to understand how Agentic AI moves from slideware to shipped value, this is the episode to queue up again.01:30 All Great Podcasts start with Drinks05:27 Maven AGI 09:13 Smaller Models! 10:50 Why Maven AGI12:04 The Secret Sauce or Use Case15:13 Typical Client Persona 20:31 Using Enterprise Data 26:19 But AGI, Really?30:12 Assistant or Person?39:06 What's Next?40:28 My Thoughts on Getting Started?46:30 The AI Example49:30 The Maven AGI Pitch53:23 LearningMaven AGI: https://www.mavenagi.com/ Megan's LinkedIn: https://www.linkedin.com/in/megfgallagher/Al's LinkedIn: https://www.linkedin.com/in/al-martin-ku/#AgenticAI #FutureOfAI #MakingDataSimple #MavenAGI #AIAgents #EnterpriseAI #CustomerExperience #AIInProduction #PodcastReplay​Want to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Send us a textHit replay on one of the most thought-provoking Agentic AI conversations on Making Data Simple. GTM Account Director Megan Gallagher makes the case for Agentic AI from the Maven AGI front lines, where AI agents stop following rigid decision trees and start acting with real autonomy over enterprise workflows.“We're still living like everything is deterministic,” Megan argues, “but this new generation of agents is inherently generative and predictive.” In this replay, she unpacks what that shift means for smaller specialized models, using real enterprise data, rethinking “assistant vs person,” and how to get started without boiling the ocean.If you want to understand how Agentic AI moves from slideware to shipped value, this is the episode to queue up again.01:30 All Great Podcasts start with Drinks05:27 Maven AGI 09:13 Smaller Models! 10:50 Why Maven AGI12:04 The Secret Sauce or Use Case15:13 Typical Client Persona 20:31 Using Enterprise Data 26:19 But AGI, Really?30:12 Assistant or Person?39:06 What's Next?40:28 My Thoughts on Getting Started?46:30 The AI Example49:30 The Maven AGI Pitch53:23 LearningMaven AGI: https://www.mavenagi.com/ Megan's LinkedIn: https://www.linkedin.com/in/megfgallagher/Al's LinkedIn: https://www.linkedin.com/in/al-martin-ku/#AgenticAI #FutureOfAI #MakingDataSimple #MavenAGI #AIAgents #EnterpriseAI #CustomerExperience #AIInProduction #PodcastReplay​Want to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Ari Paparo sits down with Sergio Serra, PM Lead for RTB Fabric at AWS, to explore how Amazon is transforming the foundations of programmatic advertising. They break down how RTB Fabric eliminates data egress costs, improves latency through deterministic routing, and introduces a per-billion transaction model built specifically for ad tech. Recorded at Marketecture, this conversation reveals how AWS is creating purpose-built infrastructure for SSPs and DSPs, the power of modular services like real-time throttling and OpenRTB filtering, and why Fabric might redefine the economics of ad exchanges. Takeaways RTB Fabric removes the dual tax of data egress and load balancing costs. Deterministic availability zone routing cuts latency and boosts reliability. Built-in modules add rate limiting, filtering, and error masking without extra cost. The pricing model aligns with ad tech's transaction-based economics. AWS is opening Fabric beyond its own backbone, allowing external connectivity. Chapters 00:00 Introduction and AWS's Focus on Ad Tech 01:00 What RTB Fabric Solves for SSPs and DSPs 03:00 Eliminating Data Egress and Load Balancing Costs 05:00 How Deterministic Routing Improves Latency 07:00 Built-In Modules: Rate Limiting, Filtering, Error Masking 10:00 Pricing Model Based on Transactions 12:00 Internal vs. External Fabric Connections 14:00 Launch Partners and Future Expansion 15:30 Competitive Edge and Vision for RTB Fabric Learn more about your ad choices. Visit megaphone.fm/adchoices

This episode of The Fusaka Files explores EIP-7917: Deterministic proposer lookahead, focusing on validator operations, ecosystem readiness, and enterprise impact. Guest Justin Drake from the Ethereum Foundation joins hosts to discuss EIP-7917 and its implications for scalability and security. The Fusaka Files is a limited-episode podcast series exploring Ethereum's upcoming Fusaka upgrade through the lens of real-world use, ecosystem readiness, and enterprise impact.

David Lapin is a C-Suite Advisor, Executive Coach and Founder of Lapin International. David helps leaders align who they are with how they lead. Along the way we discuss – A Family of Rabbis (1:00), Leonard Bernstein's Eyebrows (4:00), Value Drivers (7:45), Deterministic vs. Humanist (11:15), Ethical Governance, South Africa (13:30), Character as a Competitive Advantage (19:30), Trust as a Strategic Lever (22:30), Humility (25:30), Managing out of Fear (29:30), and Reflection on Small Quantities of Profound Content (33:35).   Strengthen your business from within @ Lapin International. Order a copy of David's book @ Lead by Greatness: How Character Can Power Your Success. This podcast is partnered with LukeLeaders1248, a nonprofit that provides scholarships for the children of military Veterans. Send a donation, large or small, through PayPal @LukeLeaders1248; Venmo @LukeLeaders1248; or our website @ www.lukeleaders1248.com. You can also donate your used vehicle @ this hyperlink – CARS donation to LL1248.  Music intro and outro from the creative brilliance of Kenny Kilgore. Lowriders and Beautiful Rainy Day.

Ari Paparo and Eric Franchi are joined by Mathieu Roche, CEO of ID5, to discuss the recent acquisition of TrueData and its implications for the identity market. They explore the integration of identity graphs and IDs, the challenges of maintaining privacy and data security, and the evolving landscape of identity solutions. The conversation also touches on the importance of match rates, the role of identity in advertising, and the future of identity technology. Takeaways ID5's acquisition of TrueData aims to enhance identity solutions by integrating identity graphs with IDs. The acquisition increases ID5's staff and revenue by 30-40%, marking a significant expansion. ID5 focuses on making devices addressable and recognizable over time, enhancing match rates. TrueData specializes in connecting data at the user and household level, acting as a 'Rosetta Stone' for identity. The integration of ID5 and TrueData offers a unique end-to-end identity solution for clients. Identity is crucial for targeting, optimization, frequency capping, and measurement in advertising. The debate between deterministic and probabilistic identity solutions continues, with trade-offs in scale and precision. ID5's global presence and strong match rates provide a competitive edge in the identity market. The acquisition process involved extensive due diligence, highlighting the complexity of transatlantic deals. The future of identity technology involves balancing privacy concerns with the need for effective data solutions. Chapters 00:11 Introduction and Guest Welcome 03:42 ID5's Acquisition of TrueData 09:56 Identity Graphs and Device IDs 13:33 AI's Role in Advertising 29:49 The US Market and Global Scale 50:19 Deterministic vs. Probabilistic Solutions 57:23 Future of Identity in Ad Tech Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of The Digital Executive, host Brian Thomas sits down with Martin Lucas, founder and CEO of Decision Boundaries, to explore how decision science and deterministic AI are reshaping the way humans and machines think together.Martin unpacks the psychology of brand trust—how emotion, timing, and tone influence decision-making—and explains why most marketing fails to connect at a subconscious level. He then takes listeners inside his breakthrough work combining decision science, decision physics, and symbolic mathematics to create AI systems that reason with human-like understanding.Looking ahead, Martin shares his vision for the next decade: AI that understands intent, context, and humanity itself, ushering in an era where technology enhances—rather than replaces—human creativity.Whether you're a leader in AI, marketing, or innovation, this conversation offers a rare glimpse into the science driving the next evolution of intelligent systems.If you liked what you heard today, please leave us a review - Apple or Spotify. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI Assisted Coding: From Deterministic to AI-Driven—The New Paradigm of Software Development, With Markus Hjort In this BONUS episode, we dive deep into the emerging world of AI-assisted coding with Markus Hjort, CTO of Bitmagic. Markus shares his hands-on experience with what's being called "vibe coding" - a paradigm shift where developers work more like technical product owners, guiding AI agents to produce code while focusing on architecture, design patterns, and overall system quality. This conversation explores not just the tools, but the fundamental changes in how we approach software engineering as a team sport. Defining Vibecoding: More Than Just Autocomplete "I'm specifying the features by prompting, using different kinds of agentic tools. And the agent is producing the code. I will check how it works and glance at the code, but I'm a really technical product owner." Vibecoding represents a spectrum of AI-assisted development approaches. Markus positions himself between pure "vibecoding" (where developers don't look at code at all) and traditional coding. He produces about 90% of his code using AI tools, but maintains technical oversight by reviewing architectural patterns and design decisions. The key difference from traditional autocomplete tools is the shift from deterministic programming languages to non-deterministic natural language prompting, which requires an entirely different way of thinking about software development. The Paradigm Shift: When AI Changed Everything "It's a different paradigm! Looking back, it started with autocomplete where Copilot could implement simple functions. But the real change came with agentic coding tools like Cursor and Claude Code." Markus traces his journey through three distinct phases. First came GitHub Copilot's autocomplete features for simple functions - helpful but limited. Next, ChatGPT enabled discussing architectural problems and getting code suggestions for unfamiliar technologies. The breakthrough arrived with agentic tools like Cursor and Claude Code that can autonomously implement entire features. This progression mirrors the historical shift from assembly to high-level languages, but with a crucial difference: the move from deterministic to non-deterministic communication with machines. Where Vibecoding Works Best: Knowing Your Risks "I move between different levels as I go through different tasks. In areas like CSS styling where I'm not very professional, I trust the AI more. But in core architecture where quality matters most, I look more thoroughly." Vibecoding effectiveness varies dramatically by context. Markus applies different levels of scrutiny based on his expertise and the criticality of the code. For frontend work and styling where he has less expertise, he relies more heavily on AI output and visual verification. For backend architecture and core system components, he maintains closer oversight. This risk-aware approach is essential for startup environments where developers must wear multiple hats. The beauty of this flexibility is that AI enables developers to contribute meaningfully across domains while maintaining appropriate caution in critical areas. Teaching Your Tools: Making AI-Assisted Coding Work "You first teach your tool to do the things you value. Setting system prompts with information about patterns you want, testing approaches you prefer, and integration methods you use." Success with AI-assisted coding requires intentional configuration and practice. Key strategies include: System prompts: Configure tools with your preferred patterns, testing approaches, and architectural decisions Context management: Watch context length carefully; when the AI starts making mistakes, reset the conversation Checkpoint discipline: Commit working code frequently to Git - at least every 30 minutes, ideally after every small working feature Dual AI strategy: Use ChatGPT or Claude for architectural discussions, then bring those ideas to coding tools for implementation Iteration limits: Stop and reassess after roughly 5 failed iterations rather than letting AI continue indefinitely Small steps: Split features into minimal increments and commit each piece separately In this segment we refer to the episode with Alan Cyment on AI Assisted Coding, and the Pachinko coding anti-pattern.  Team Dynamics: Bigger Chunks and Faster Coordination "The speed changes a lot of things. If everything goes well, you can produce so much more stuff. So you have to have bigger tasks. Coordination changes - we need bigger chunks because of how much faster coding is." AI-assisted coding fundamentally reshapes team workflows. The dramatic increase in coding speed means developers need larger, more substantial tasks to maintain flow and maximize productivity. Traditional approaches of splitting stories into tiny tasks become counterproductive when implementation speed increases 5-10x. This shift impacts planning, requiring teams to think in terms of complete features rather than granular technical tasks. The coordination challenge becomes managing handoffs and integration points when individuals can ship significant functionality in hours rather than days. The Non-Deterministic Challenge: A New Grammar "When you're moving from low-level language to higher-level language, they are still deterministic. But now with LLMs, it's not deterministic. This changes how we have to think about coding completely." The shift to natural language prompting introduces fundamental uncertainty absent from traditional programming. Unlike the progression from assembly to C to Python - all deterministic - working with LLMs means accepting probabilistic outputs. This requires developers to adopt new mental models: thinking in terms of guidance rather than precise instructions, maintaining checkpoints for rollback, and developing intuition for when AI is "hallucinating" versus producing valid solutions. Some developers struggle with this loss of control, while others find liberation in focusing on what to build rather than how to build it. Code Reviews and Testing: What Changes? "With AI, I spend more time on the actual product doing exploratory testing. The AI is doing the coding, so I can focus on whether it works as intended rather than syntax and patterns." Traditional code review loses relevance when AI generates syntactically correct, pattern-compliant code. The focus shifts to testing actual functionality and user experience. Markus emphasizes: Manual exploratory testing becomes more important as developers can't rely on having written and understood every line Test discipline is critical - AI can write tests that always pass (assert true), so verification is essential Test-first approach helps ensure tests actually verify behavior rather than just existing Periodic test validation: Randomly modify test outputs to verify they fail when they should Loosening review processes to avoid bottlenecks when code generation accelerates dramatically Anti-Patterns and Pitfalls to Avoid Several common mistakes emerge when developers start with AI-assisted coding: Continuing too long: When AI makes 5+ iterations without progress, stop and reset rather than letting it spiral Skipping commits: Without frequent Git checkpoints, recovery from AI mistakes becomes extremely difficult Over-reliance without verification: Trusting AI-generated tests without confirming they actually test something meaningful Ignoring context limits: Continuing to add context until the AI becomes confused and produces poor results Maintaining traditional task sizes: Splitting work too granularly when AI enables completing larger chunks Forgetting exploration: Reading about tools rather than experimenting hands-on with your own projects The Future: Autonomous Agents and Automatic Testing "I hope that these LLMs will become larger context windows and smarter. Tools like Replit are pushing boundaries - they can potentially do automatic testing and verification for you." Markus sees rapid evolution toward more autonomous development agents. Current trends include: Expanded context windows enabling AI to understand entire codebases without manual context curation Automatic testing generation where AI not only writes code but also creates and runs comprehensive test suites Self-verification loops where agents test their own work and iterate without human intervention Design-to-implementation pipelines where UI mockups directly generate working code Agentic tools that can break down complex features autonomously and implement them incrementally The key insight: we're moving from "AI helps me code" to "AI codes while I guide and verify" - a fundamental shift in the developer's role from implementer to architect and quality assurance. Getting Started: Experiment and Learn by Doing "I haven't found a single resource that covers everything. My recommendation is to try Claude Code or Cursor yourself with your own small projects. You don't know the experience until you try it." Rather than pointing to comprehensive guides (which don't yet exist for this rapidly evolving field), Markus advocates hands-on experimentation. Start with personal projects where stakes are low. Try multiple tools to understand their strengths. Build intuition through practice rather than theory. The field changes so rapidly that reading about tools quickly becomes outdated - but developing the mindset and practices for working with AI assistance provides durable value regardless of which specific tools dominate in the future. About Markus Hjort Markus is Co-founder and CTO of Bitmagic, and has over 20 years of software development expertise. Starting with Commodore 64 game programming, his career spans gaming, fintech, and more. As a programmer, consultant, agile coach, and leader, Markus has successfully guided numerous tech startups from concept to launch. You can connect with Markus Hjort on LinkedIn.

In this episode of Crazy Wisdom, host Stewart Alsop talks with Jared Zoneraich, CEO and co-founder of PromptLayer, about how AI is reshaping the craft of software building. The conversation covers PromptLayer's role as an AI engineering workbench, the evolving art of prompting and evals, the tension between implicit and explicit knowledge, and how probabilistic systems are changing what it means to “code.” Stewart and Jared also explore vibe coding, AI reasoning, the black-box nature of large models, and what accelerationism means in today's fast-moving AI culture. You can find Jared on X @imjaredz and learn more or sign up for PromptLayer at PromptLayer.com.Check out this GPT we trained on the conversationTimestamps00:00 – Stewart Alsop opens with Jared Zoneraich, who explains PromptLayer as an AI engineering workbench and discusses reasoning, prompting, and Codex.05:00 – They explore implicit vs. explicit knowledge, how subject matter experts shape prompts, and why evals matter for scaling AI workflows.10:00 – Jared explains eval methodologies, backtesting, hallucination checks, and the difference between rigorous testing and iterative sprint-based prompting.15:00 – Discussion turns to observability, debugging, and the shift from deterministic to probabilistic systems, highlighting skill issues in prompting.20:00 – Jared introduces “LM idioms,” vibe coding, and context versus content—how syntax, tone, and vibe shape AI reasoning.25:00 – They dive into vibe coding as a company practice, cloud code automation, and prompt versioning for building scalable AI infrastructure.30:00 – Stewart reflects on coding through meditation, architecture planning, and how tools like Cursor and Claude Code are shaping AGI development.35:00 – Conversation expands into AI's cultural effects, optimism versus doom, and critical thinking in the age of AI companions.40:00 – They discuss philosophy, history, social fragmentation, and the possible decline of social media and liberal democracy.45:00 – Jared predicts a fragmented but resilient future shaped by agents and decentralized media.50:00 – Closing thoughts on AI-driven markets, polytheistic model ecosystems, and where innovation will thrive next.Key InsightsPromptLayer as AI Infrastructure – Jared Zoneraich presents PromptLayer as an AI engineering workbench—a platform designed for builders, not researchers. It provides tools for prompt versioning, evaluation, and observability so that teams can treat AI workflows with the same rigor as traditional software engineering while keeping flexibility for creative, probabilistic systems.Implicit vs. Explicit Knowledge – The conversation highlights a critical divide between what AI can learn (explicit knowledge) and what remains uniquely human (implicit understanding or “taste”). Jared explains that subject matter experts act as the bridge, embedding human nuance into prompts and workflows that LLMs alone can't replicate.Evals and Backtesting – Rigorous evaluation is essential for maintaining AI product quality. Jared explains that evals serve as sanity checks and regression tests, ensuring that new prompts don't degrade performance. He describes two modes of testing: formal, repeatable evals and more experimental sprint-based iterations used to solve specific production issues.Deterministic vs. Probabilistic Thinking – Jared contrasts the old, deterministic world of coding—predictable input-output logic—with the new probabilistic world of LLMs, where results vary and control lies in testing inputs rather than debugging outputs. This shift demands a new mindset: builders must embrace uncertainty instead of trying to eliminate it.The Rise of Vibe Coding – Stewart and Jared explore vibe coding as a cultural and practical movement. It emphasizes creativity, intuition, and context-awareness over strict syntax. Tools like Claude Code, Codex, and Cursor let engineers and non-engineers alike “feel” their way through building, merging programming with design thinking.AI Culture and Human Adaptation – Jared predicts that AI will both empower and endanger human cognition. He warns of overreliance on LLMs for decision-making and the coming wave of “AI psychosis,” yet remains optimistic that humans will adapt, using AI to amplify rather than atrophy critical thinking.A Fragmented but Resilient Future – The episode closes with reflections on the social and political consequences of AI. Jared foresees the decline of centralized social media and the rise of fragmented digital cultures mediated by agents. Despite risks of isolation, he remains confident that optimism, adaptability, and pluralism will define the next AI era.

In this episode Shiv is in conversation with Jason Casey, CEO and co-founder of Beyond Identity. Jason talks about his early fascination with technology, his career trajectory, and how he entered cybersecurity. He describes Beyond Identity's mission to eliminate passwords and improve identity verification. Jason shares insights on staying ahead in cybersecurity, touching on topics like AI, supply chain attacks, and trusted computing. He discusses the transition from CTO to CEO, the importance of curiosity and experimentation in both early and mid-career stages, and his personal practices for managing stress and staying grounded.00:25 Jason Casey's Early Fascination with Technology01:22 Journey into Cybersecurity02:23 Understanding the Hacker Mindset03:58 Staying Ahead in Cybersecurity06:57 Fundamentals of AI and Security09:01 Challenges and Innovations in AI Security10:16 Building Secure Systems with AI13:50 The Cost of Real-Time Security15:24 Deterministic vs. Probabilistic Security Controls17:19 The Role of Honeypots in Cybersecurity18:44 Adversarial Tactics and Device Posture21:15 AI in Software Development and Security25:17 Trusted Computing in Aerospace and Defense27:01 Hardware-Based Trusted Computing28:04 Genesis of Beyond Identity29:31 Meeting Jim Clark32:17 Beyond Identity's Mission34:04 Transition from CTO to CEO37:17 Career Tips for Cybersecurity50:06 Personal Practices to Stay Groundedhttps://www.linkedin.com/in/jassoncasey/Jasson Casey currently serves as Chief Executive Officer and Co-Founder at Beyond Identity, where he's built an identity security platform for enterprises to make identity-based attacks impossible.Jasson has 20+ years of experience delivering security and networking products to all markets and customer types, including global enterprises and carriers. He served as CTO at Security Scorecard, Fellow in CyberSecurity with the Center for Strategic and International Studies (CSIS), and as Advisor to IronNet CyberSecurity, a security startup founded by Gen. (Ret) Keith Alexander.Prior to Beyond Identity, Jasson was VP of Engineering at IronNet CyberSecurity and oversaw development of the vendor's revolutionary collective intelligence platform and pioneered new approaches to total network observability, including limitless wire rate packet capture with truly elastic retention abilities.He also brings a long history of innovation advocacy for Software Defined Networks (SDN) through his work founding and leading Flowgrammable, and serving as a member of the Software Leadership Council at the Open Networking Foundation. Previously he held roles in product management, business development and engineering with CenturyTel(CenturyLink), Level3(CenturyLink), and Alcatel(Nokia).Jasson has a BSEE from the University of Texas at Austin and has a Ph.D. in electrical engineering at Texas A&M University.

In this episode, Will Wilson, CEO and co-founder of Antithesis, explores Deterministic Simulation Testing (DST) with host Sriram Panyam. Wilson was part of the pioneering team at FoundationDB that developed this revolutionary testing approach, which was later acquired by Apple in 2015. After seeing that even sophisticated organizations lacked robust testing for distributed systems, Wilson co-founded Antithesis in 2018 to make DST commercially available. Deterministic simulation testing runs software in a fully controlled, simulated environment in which all sources of non-determinism are eliminated or controlled. Unlike traditional testing or chaos engineering, DST operates in a separate environment from production, allowing for aggressive fault injection without risk to live systems. The key breakthrough is perfect reproducibility -- any bug found can be recreated exactly using the same random seed. Antithesis built "The Determinator," a custom deterministic hypervisor that simulates entire software stacks including virtual hardware, networking, and time. The system can compress years of stress testing into shorter timeframes by running simulations faster than wall-clock time. All external interfaces that could introduce non-determinism (network calls, disk I/O, system time) are mocked or controlled by the simulator. The approach has proven effective with major organizations including MongoDB, Palantir, and Ethereum. For Ethereum's critical "Merge" upgrade in 2022, Antithesis found and helped fix several serious bugs that could have been catastrophic for the live network. The platform typically finds bugs that traditional testing methods miss entirely -- such as those arising from rare race conditions, complex timing issues, and unexpected system interactions. This episode is sponsored by Monday Dev

Patrick McKenzie (patio11) is joined by Will Wilson, CEO of Antithesis, to discuss the evolution of software testing from traditional approaches to cutting-edge deterministic simulation. Will explains how his team built technology that creates "time machines" for distributed systems, enabling developers to find and debug complex failures that would be nearly impossible to reproduce in traditional testing environments. They explore how this approach scales from finding novel bugs in Super Mario Brothers to ensuring the reliability of critical financial and infrastructure systems, and discuss the implications for a future where AI writes increasingly more code.–Full transcript available here: www.complexsystemspodcast.com/software-testing-with-will-wilson/–Sponsor: This episode is brought to you by Mercury, the fintech trusted by 200K+ companies — from first milestones to running complex systems. Mercury offers banking that truly understands startups and scales with them. Start today at Mercury.com Mercury is a financial technology company, not a bank. Banking services provided by Choice Financial Group, Column N.A., and Evolve Bank & Trust; Members FDIC.–Recommended in this episode:Antithesis: https://antithesis.com/––Timestamps:(00:00) Intro(01:23) Database scaling and the CAP theorem(08:13) Abstraction layers and hardware reality(15:28) The problem with traditional testing(19:43) Sponsor: Mercury(23:16) The fuzzing revolution(30:35) Deterministic simulation testing(42:36) Real-world testing strategies(47:22) Introducing Antithesis(59:23) The CrowdStrike example(01:01:15) Finding bugs in Mario(01:07:37) Property-based vs conventional testing(01:09:51) The future of AI-assisted development(01:14:51) Wrap

In this episode of Crazy Wisdom, Stewart Alsop sits down with Derek Osgood, CEO of DoubleO.ai, to talk about the challenges and opportunities of building with AI agents. The conversation ranges from the shift from deterministic to probabilistic processes, to how humans and LLMs think differently, to why lateral thinking, humor, and creative downtime matter for true intelligence. They also explore the future of knowledge work, the role of context engineering and memory in making agents useful, and the culture of talent, credentials, and hidden gems in Silicon Valley. You can check out Derek's work at doubleo.ai or connect with him on LinkedIn.Check out this GPT we trained on the conversationTimestamps00:00 Derek Osgood explains what AI agents are, the challenge of reliability and repeatability, and the difference between chat-based and process-based agents.05:00 Conversation shifts to probabilistic vs deterministic systems, with examples of agents handling messy data like LinkedIn profiles.10:00 Stewart Alsop and Derek discuss how humans reason compared to LLMs, token vs word prediction, and how language shapes action.15:00 They question whether chat interfaces are the right UX for AI, weighing structure, consistency, and the persistence of buttons in knowledge work.20:00 Voice interaction comes up, its sci-fi allure, and why unstructured speech makes it hard without stronger memory and higher-level reasoning.25:00 Derek unpacks OpenAI's approach to memory as active context retrieval, context engineering, and why vector databases aren't the full answer.30:00 They examine talent wars in AI, credentialism, signaling, and the difference between PhD-level model work and product design for agents.35:00 Leisure and creativity surface, linking downtime, fantasy, and imagination to better lateral thinking in knowledge work.40:00 Discussion of asynchronous AI reasoning, longer time horizons, and why extending “thinking time” could change agent behavior.45:00 Derek shares how Double O orchestrates knowledge work with natural language workflows, making agents act like teammates.50:00 They close with reflections on re-skilling, learning to work with LLMs, BS detection, and the future of critical thinking with AI.Key InsightsOne of the biggest challenges in building AI agents is not just creating them but ensuring their reliability, accuracy, and repeatability. It's easy to build a demo, but the “last mile” of making an agent perform consistently in the messy, unstructured real world is where the hard problems live.The shift from deterministic software to probabilistic agents reflects the complexity of real-world data and processes. Deterministic systems work only when inputs and outputs are cleanly defined, whereas agents can handle ambiguity, search for missing context, and adapt to different forms of information.Humans and LLMs share similarities in reasoning—both operate like predictive engines—but the difference lies in agency and lateral thinking. Humans can proactively choose what to do without direction and make wild connections across unrelated experiences, something current LLMs still struggle to replicate.Chat interfaces may not be the long-term solution for interacting with AI. While chat offers flexibility, it is too unstructured for many use cases. Derek argues for a hybrid model where structured UI/UX supports repeatable workflows, while chat remains useful as one tool within a broader system.Voice interaction carries promise but faces obstacles. The unstructured nature of spoken input makes it difficult for agents to act reliably without stronger memory, better context retrieval, and a more abstract understanding of goals. True voice-first systems may require progress toward AGI.Much of the magic in AI comes not from the models themselves but from context engineering. Effective systems don't just rely on vector databases and embeddings—they combine full context, partial context, and memory retrieval to create a more holistic understanding of user goals and history.Beyond the technical, the episode highlights cultural themes: credentialism, hidden talent, and the role of leisure in creativity. Derek critiques Silicon Valley's obsession with credentials and signaling, noting that true innovation often comes from hidden gem hires and from giving the brain downtime to make unexpected lateral connections that drive creative breakthroughs.

Identity is the root cause of over 70% of all security incidents, yet many organizations still rely on fundamentally flawed authentication methods. In this episode, Jasson Casey, CEO and co-founder of Beyond Identity, explains why even common forms of MFA are insufficient and why any system that relies on a "secret moving" is vulnerable to attack.The conversation dives deep into the architectural shift needed to truly secure identity: moving from probabilistic tools to deterministic proof. Jasson breaks down how to leverage the hardware-backed secure enclaves (like TPMs and the Secure Enclave) that already exist in our devices to create un-phishable, device-bound credentials that can't be stolen or copied.We also explore how this approach provides a necessary defense against the next wave of AI-enabled threats, including deepfakes and hyper-realistic social engineering attacks that will make it nearly impossible for humans to spot the difference.Guest Socials -⁠⁠ ⁠⁠⁠⁠⁠Jasson's LinkedinPodcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security BootCamp⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you are interested in AI Cybersecurity, you can check out our sister podcast -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AI Cybersecurity PodcastQuestions asked:(00:00) Introduction(02:10) Who is Jasson Casey?(04:00) What is the 2025 Version of IAM?(07:15) Why Hasn't The Identity Problem Been Solved?(08:00) The Fundamental Flaw: Relying on Secrets That Move(10:00) The Solution: Un-phishable, Hardware-Backed Identity(12:15) Why Your Current MFA is Insufficient and Easily Exploited(14:42) The Apple Pay Analogy: How Secure Identity Already Works in Your Pocket(18:58) The "Aha!" Moment: Reducing Help Desk & SOC Workload(25:25) The AI Adversary: How Deepfakes Will Break Authentication(30:00) The Answer to AI Threats: Cryptographically Attested, Device-Bound Proof(32:15) Challenges of Adopting a New World of Identity(34:30) Beyond Human Identity: Securing Workloads, Drones & IoT(36:20) Deterministic vs. Probabilistic: A New Blueprint for Security(45:20) Final Questions: Drones, Cooking, and Tex-MexThank you to Beyond Identity for sponsoring this episode

Sergey Nazarov, co-founder and CEO of Chainlink, explains why crypto has hit a growth wall and how traditional finance holds the key to its explosive future. From powering 80% of DeFi to building bridges for Wall Street's trillions, Sergey reveals why banks aren't crypto's enemy… they're its savior.__________________________________PARTNERS

Miguel Clement looks back on Deterministic's win in the Fourstardave and shares his thoughts on this weekend's stakes,

Presented by TwinSpires   Hall of Fame trainer Shug McGaughey discusses his runners in Saturday's Arlington Million, Dave Zenner previews the Million day stakes and special wagers, Miguel Clement looks back on Deterministic's win in the Fourstardave and shares his thoughts on this weekend's stakes, Kentucky Downs VP of Racing Ted Nicholson looks ahead to the upcoming meet. Plus, Hill 'n' Dale GM Jared Burdine talks about their sale topping colt at Fasig-Tipton Saratoga, James Scully previews three races to watch in this week's 'TwinSpires Triple Play', Kurt Becker takes you on a Stroll Through Racing History presented by Keeneland, and Dale Romans & Tim Wilkin tackle the sports hottest topics on 'I Ask, They Answer' presented by the University of Louisville Equine Industry Program in the College of Business. 

When we heard Eric Seufert talk at the Meta Summit we knew we had to have him on the show.Eric is the founder of Mobile Dev Memo and partner at Heracles Capital, and he joins us today for a deep dive into how today's smartest marketers approach measurement. We unpack the difference between deterministic and probabilistic attribution, why incrementality testing beats last-click reporting, and how to make sense of CAC, LTV, and payback periods across different business models. Eric shares insights on Meta's evolving AI infrastructure, signal loss, and platform opacity, explaining why a single tool can't give you the full picture, and why the greatest marketers are the ones that think like data scientists. He also introduces the concept of signal engineering: how to guide automated ad platforms by sending higher-quality signals and intent data.If you're enjoying the podcast, please hit the subscribe button, comment, share and like - it helps us reach more people, get more great guests on the show and keep bringing these episodes to you every week.Want to submit your own DTC or ecommerce marketing question? ⁠⁠⁠Click here⁠⁠⁠.00:00 Introduction 06:42 The Role of Discord in Gaming Advertising09:21 Eric's Journey in the Gaming Industry19:04 Understanding Freemium Models in Mobile Gaming26:08 Incentivized Advertising in Gaming29:55 Understanding Measurement Tools in Advertising30:24 Deterministic vs. Probabilistic Measurement33:14 Attribution Models and Measurement Tools39:16 Geo Lift Studies and Their Application43:03 Common Sense in Marketing Measurement54:10 Operationalizing Incrementality Testing56:25 Understanding Incrementality and Testing Strategies01:00:33 Navigating the Meta Ecosystem and AI Changes01:06:40 Signal Engineering and Optimizing for Conversions01:09:44 Radical Experimentation in Creative Strategies01:21:55 Breaking Out of Targeting LoopsMeta's AI advertising playbook (with Matt Steiner):https://podcasts.apple.com/us/podcast/season-5-episode-23-metas-ai-advertising-playbook-with/id1423753783?i=1000711081020Powered by:Motion.⁠⁠⁠https://motionapp.com/pricing?utm_source=marketing-operators-podcast&utm_medium=paidsponsor&utm_campaign=march-2024-ad-reads⁠⁠⁠https://motionapp.com/creative-trendsPrescient AI.⁠⁠⁠https://www.prescientai.com/operatorsRichpanel.⁠⁠⁠https://www.richpanel.com/?utm_source=MO&utm_medium=podcast&utm_campaign=ytdescAftersell.https://www.aftersell.com/operatorsHaus.http://Haus.io/operatorsSubscribe to the 9 Operators Podcast here:https://www.youtube.com/@Operators9Subscribe to the Finance Operators Podcast here: https://www.youtube.com/@FinanceOperatorsFOPSSign up to the 9 Operators newsletter here: https://9operators.com/

In this live session, the hosts discuss various aspects of game design, gameplay mechanics, monetization strategies, and the future of game development. They provide insights into the gameplay of a specific game, analyze its mechanics, and compare it with other games in the market. The conversation also touches on audience engagement and upcoming events in the gaming industry.takeawaysThe live session format allows for real-time audience interaction.Gameplay mechanics can often be misleading in marketing.Deterministic level design enhances player experience and engagement.Monetization strategies are crucial for game sustainability.Comparative analysis helps understand market positioning.Future trends in gaming focus on user engagement and innovative mechanics.Audience interaction can provide valuable feedback for developers.Events like Gamescom and ChinaJoy are important for networking.Game performance metrics are essential for assessing success.The gaming industry is evolving with new monetization models.titlesExploring Game Design in a Live SessionGameplay Mechanics UnveiledSound Bites"We are live!""This is a game changer!""We need to have a soundboard!"Chapters00:00 Introduction to the Live Session03:07 Gameplay Overview and Mechanics07:15 Game Design and Difficulty Progression10:01 Monetization Strategies and User Engagement12:52 Comparative Analysis of Game Performance18:37 Future of Puzzle Games and Market Trends28:10 The Scale of Game Revenue30:16 The Evolution of User Acquisition Strategies32:56 Insights on Game Development and Monetization39:21 Analyzing Game Performance and Revenue Projections45:41 The Future of Game Development Strategies---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me---------------------------------------If you are interested in getting UA tips every week on Monday, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠lancaric.substack.com⁠⁠⁠⁠⁠⁠ & sign up for the Brutally Honest newsletter by Matej LancaricLatest article - https://open.substack.com/pub/lancaric/p/stay-ahead-in-ua-for-2024-my-top?r=7qqaf&utm_campaign=podcast&utm_medium=web&showWelcome=trueDo you have UA questions nobody can answer? Ask ⁠⁠⁠⁠⁠⁠⁠⁠Matej AI⁠⁠⁠⁠⁠⁠ - the First UA AI in the gaming industry! https://lancaric.me/matej-ai

Feño (https://twitter.com/fenoxsky) gives you a quick recap on last week's events, some news, and what to expect from this week in the world of MMA.https://www.youtube.com/@FenoTFS Follow us on Twitter: https://twitter.com/FightSitedotcomFeño on Bluesky: https://bsky.app/profile/feno.bsky.socialFeño on Threads: https://www.threads.net/@feno_tfsCheck out our written content on the website: https://www.thefight-site.com/Support us directly on Patreon for exclusive content and access to the discord: https://www.patreon.com/fightsite** Commission a video analysis to Feño through Ko-fi https://https://ko-fi.com/fenotfs ** 

EIP-7917: Deterministic proposer lookaheadEIP-7917 proposes to pre-calculate and store a deterministic proposer lookahead in the beacon state at the start of every epoch. This mechanism aims to support faster Layer 2 preconfirmations by offering predictable validator responsibilities.Authors: Lin Oshitani and Justin DrakeStatus: Considered for InclusionResources:------------------ EIP-7917 on Ethereum.org- Ethereum Magicians Thread- EIP-7917 Full Spec HackMDUseful Links:------------------ Consensus Spec PR #4190- Randao Explanation- HackMD Notes by Pooja Ranjan- Docswell ResourcePlaylists:------------------ Pectra PEEPanEIP Playlist- General PEEPanEIP PlaylistFollow on Twitter:--------------------------- Lin Oshitani- Justin Drake- Pooja Ranjan- Edited by Akash KshirsagarTopics Covered:-------------------------00:00 - Quick Recap00:34 - PEEPanEIP Intro00:48 - Introduction01:08 - About EIP 791702:10 - Lin Introduction02:57 - Justin Introduction03:34 - Overview on EIP 7917 by Justin06:34 - Presentation on EIP 791706:45 - EIP 7917 Outline07:20 - What are Rollups?08:15 - What are Centralized Sequencers?08:48 - What are Preconfirmations?10:13 - Based Rollups11:24 - Based Preconfirmations14:32 - Issues with current L114:38 - First Issue: Lookahead Availability in EVM issue19:18 - Second Issue: Lookahead Instability Issue27:15 - Conclusion28:58 - Presentation End29:20 - Q&A Section29:31 - What factors dictate latency in real conditions?31:19 - What must happen post-implementation for noticeable improvement?35:46 - Is a longer lookahead useful?38:51 - Why “deterministic” if using randao?41:11 - What are threads and mitigation strategies?42:46 - Can L2 block being full prevent preconf?44:42 - Can a proposer renounce after preconf? Slot-level lookahead helpful?47:19 - Rapid Fire Round52:25 - Final message from Justin Drake54:02 - Final message from Lin55:21 - Closing Words-------------------------#EIP7917 #Ethereum #PEEPanEIP

00:00 Is the Hubspot ChatGPT Integration Any Good?13:56 Are Finance Teams Too Deterministic?Hear more from us:Subscribe to us on Youtube: https://www.youtube.com/channel/UCN-x5u0G03LWmU0Ds_4zR8wSubscribe to our newsletter here: https://www.cs2marketing.com/revenue-growth-architects#subscribe-to-newsletterFollow Crissy on LinkedIn: https://www.linkedin.com/in/crveteresaunders/Follow Charlie on LinkedIn: https://www.linkedin.com/in/charliesaunders/Follow Xander on LinkedIn: https://www.linkedin.com/in/xanderbroeffle/

In this eye-opening episode, host Wes Read, CPA, CFP welcomes Vivek Kinra, founder and CEO of Verific, for an in-depth conversation about artificial intelligence in dentistry.The discussion starts off unscripted and fresh, as Vivek shares his unique journey from software engineering to creating multiple dental technology companies—PPO Profits and now Verific. He details how Verific uses genuine AI to tackle the notoriously complex problem of insurance verification.Together, Wes and Vivek unpack the difference between real AI applications—like machine learning and large language models—and the marketing buzzwords that many companies use to inflate their valuations.Key Points Vivek Kinra's background: Software engineer turned dental technology entrepreneur; built PPO Profits (acquired) and now leads Verific.Insurance verification problem: Decades-old challenge with no single source of truth—Verific uses AI to gather and standardize disparate data.Deterministic vs. Non-Deterministic Software:Deterministic = same output every time (ideal for appointment reminders).Non-Deterministic = varied output (necessary for natural language and understanding context).True AI examples in dentistry:Machine learning for x-rays.Large language models parsing complex insurance documents.Virtual receptionists handling natural conversations.Marketing Hype: Many companies label simple software workflows as “AI” for better valuations and branding.How to spot real AI:Does the system generate new outputs based on context and training data?Is it using models like LLMs or neural networks to understand and adapt?#DentalAI #DentalTechnology #PracticeManagement #InsuranceVerification #ArtificialIntelligence #DentalBusiness #DentalPodcast #MachineLearning #Verific #DentalInnovation #DentistryHypeVsReality #DentalSoftware #DentalBoardroomPodcast

Highlights from this week's conversation include:Dashboard vs. Chatbot Discussion (1:40)The Future of Chat Interfaces (3:03)Vibe Revenue Concept (6:36)AI's Early Days Compared to the Internet (10:14)OpenAI and Hardware Collaboration (13:09)Challenges of Hardware Development (16:20)Productivity in Programming Languages (18:18)Critique of 'Language is Dead' Posts (21:34)Legacy Systems in Use (22:34)Deterministic vs. Non-Deterministic Workflows (23:37)Final Thoughts and Takeaways (23:45)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it's needed to power smarter decisions and better customer experiences. Each week, we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

On today's Legally Speaking Podcast, I'm delighted to be joined by Philip Young.Philip is the Co-Founder of Garfield AI, the first SRA-regulated AI legal services firm. He was a City Lawyer for 25 years and previously a Partner at a specialist law firm. Philip has experience in a range of commercial cases. Upon leaving the City, Philip focused his attention on large language models - and passionate about access to justice, leading him to create Garfield AI.So why should you be listening in? You can hear Rob and Philip discussing:- Garfield AI Being the First SRA-Regulated AI Legal Services Firm- How Philip Leveraged ChatGPT-4 Technology to Create Garfield AI- Using a Hybrid Approach of Deterministic, Expert and Probabilistic AI Systems- What Garfield AI aims to Improve by Making Legal Processes More Accessible and Affordable- The Future of AI in Legal Services and the Removal of Repetitive, Administrative TasksConnect with Philip here - https://uk.linkedin.com/in/philip-young-091b665

Is evidence from Artificial Intelligence and Quantum Computing devices legally admissible in court? And how are courts actually handling this influx? Let's find out with your hosts Kip Boyle, CISO with Cyber Risk Opportunities, and Jake Bernstein, Partner with K&L Gates.

Strategic tools can help you navigate organisational and market complexities. But how do you even begin to make sense of it all?In this episode, Simon Wardley, the creator of Wardley Mapping and one of the most profound thinkers and influencers in strategy, explains the power of mapping complex ecosystems. He speaks on how building resilient organisations goes beyond clever tactics and requires a deep understanding of the landscapes you're navigating. He highlights how most organisations still struggle to truly understand their customers, and emphasises why it's crucial to realign strategies, foster a shared language, and enable cohesiveness to create true “value.”For leaders, this conversation serves as a call to rethink how you approach both organisational structures and the strategies needed to stay adaptable in a constantly changing landscape.Simon brings a wealth of experiential knowledge in strategy, having contributed to the growth of some of the biggest organisations worldwide. In this podcast, he delves into the evolution of technology, organisational structures, and strategy, with a particular focus on the impact of AI. He challenges our fear of rapid technological advancement by sharing how tech's true development follows a more gradual process, ultimately leading to sudden bursts of change - a more nonlinear growth.He also explores other critical themes like - the evolution of organisational structures, referencing his explorers-villagers-town planners model, the need to have strong guiding principles, and also shares why the engineer is the true architect of a technology.So, for anyone seeking a roadmap to navigate complexity correctly, this conversation is a must-listen. Tune in. Key Highlights

Liam's Map is the sire of Grade 1 winners Basin, Hopeful S. (G1) and Arkansas Derby (G1), Colonel Liam, back-to-back editions of thePegasus World Cup Turf (G1) and Turf Classic S. (G1), Wicked Whisper, Frizette S. (G1) and Juju's Map, Alcibiades S. (G1).Not only do these top-level successes rank Liam's Map among the best young sires, it puts him alongside some of America's top stallions in history. Both Basin and Wicked Whisper hail from the first-crop of Liam's Map and since 1983, just eight other first-crop stallions have sired two Grade 1 winners, with only Danzig and Uncle Mo having sired both Grade 1 fillies and colts. An elite group of just five other stallions have sired winners of both the G1 Frizette and the G1 Hopeful—A.P. Indy, Storm Cat, Mr. Prospector, Bold Ruler, and Nasrullah.His success continued in 2024 with MGSWs Roses for Debra, Deterministic and Starting Over. Roses for Debra went on to top the Keeneland November Sale selling for $2,400,000. He saw 40 $150,000+ yearling sales, with a top price of $700,000.An $800,000 Keeneland September yearling, Liam's Map is out of the stakes-winning Trippi mare Miss Macy Sue, and is a half-brother to graded winner Not This Time and black-type winner Taylor S. Liam's Map's pedigree carries only one cross of Northern Dancer in the sixth generation, so his pedigree will be an ideal outcross for mares inbred to Northern Dancer. Since Liam's Map's pedigree largely consists of horses bred by or based on pedigrees of horses bred by John Nerud, it should also be beneficial to emphasize female families associated with Nerud other than Aspidistra.(UPDATED FEB 1, 2025)