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Dave Goyal, Founder and CEO of Think AI Corporation, is driven to Turn Disabilities into Unique Abilities by using technology to empower disabled entrepreneurs and help businesses unlock the value of their data. After contracting polio as an infant, Dave transformed physical limitations and early adversity into a passion for solving business problems, building companies, and giving back to society. Through Think AI, he helps manufacturing and healthcare leaders use data and AI to generate real-time insights, improve productivity, reduce costs, and create new opportunities for growth. In this conversation, Dave introduces The 3G AI Augmentation Framework—Gap: Where are we losing time, quality, ability, or capacity? Grow: Apply AI to augment people and improve that work. Glow: Institutionalize the solution so humans and AI collaborate effectively. Dave also shares how his private second brain and AI executive agents save him up to 80 hours per month, why human control and security must remain central to AI adoption, and how authority, trust, people, customers, and culture drive business growth. He also discusses his book, Real-Time Business Intelligence Mastery, and his vision for creating a venture studio for disabled entrepreneurs. — Turn Disabilities into Unique Abilities with Dave Goyal  Good day. Steve Preda here with The Management Blueprint. And today my guest is Dave Goyal, the founder and CEO of Think AI Corporation, which helps CTOs and CIOs in manufacturing and healthcare turn siloed data into real-time insights and automation, creating reduced downtime, increased efficiency, and going from weeks to days in project launches. Dave, welcome to the show.  Thank you for having me, Steve.  Well, I’m really curious to learn about you and your company, Think AI Corporation, but first I’d like to ask you about your personal why and how you are manifesting it in your business.  So thank you again, Steve. I’m really excited to be on your show. I’m in the data and AI business, and really tech innovation, for the last 30 years. In this particular company, Think AI, I have a partner, Manish Bhardia, and we both have been working very actively with Microsoft partners, the Microsoft ecosystem, and implementing data and AI solutions for midsize companies and manufacturing companies. You went on why, which is amazing.  My why: I’m a disabled entrepreneur. I have this hunger for building businesses. I’ve built nine businesses. We can talk about it later. And five of them were miserable failures in my books. Not all of them were that miserable, as I say. But five of them were failures, and I learned a lot from them. And I’m really motivated now to expand it further, to give back to small businesses.  We’ve been working with midsize and enterprise clients, but to midsize companies, and then motivate—I have a 15-year-old kid—so motivate young people and also small businesses to make use of the power of their own data and use and consume AI on a day-to-day basis. That’s my why.  Wow. So, to learn the power of their own data and use AI, why is this important to you?  The main reason is I am passionate about technology. Everybody is good at something. I am really good at solving business problems using technology. Being a disabled entrepreneur, I did not have a lot of luxury initially, even walking. Eventually, I started using braces, started going to different countries. So the passion became really the source of energy and motivation, and that passion is now going to a level where I want to motivate people like me who are disabled entrepreneurs and want to go into this kind of business. So my real passion is technology and giving back to society using technology, to sum it up.  Wow. So you mentioned this disabled entrepreneur. I’ve never heard this term. I mean, you talk about minority entrepreneurs, women entrepreneurs, you know, veteran entrepreneurs, and actually the government recognizes these categories, but I never heard about disabled entrepreneurs. So would you mind sharing a little bit about what happened to you and how you got into this entrepreneurship?  Sure, yeah. And that’s really good, by the way. I don’t see anybody else using that term but me, so probably I should keep it with me as a copyright term. Just joking on it. But having said that, every disability brings some kind of ability. That’s why sometimes they call it differently abled. When you have these abilities, you don’t know the source or the channels to use them. So, for example, blind people, they may have a lot of great listening power. That’s why they are into music most of the time.  Sometimes they have amazing reasoning and critical-thinking power, but they don’t know how to channel it, so they fight on a day-to-day basis with these issues. Bringing it back to me, I have polio. When I was six months old, I got hit by the polio virus. Initially, for a few years, I had to just lie down on the bed, had a lot of physical therapy. Then I was able to get up and sit, at least. Then my father was carrying me to school, and I could see the kids were going out and playing. I got beaten up because of that, too, because kids don’t understand. No fault of theirs that I’m not throwing the ball at them and they are playing.  And so that brought a lot of negativity in me. Eventually, my grandfather and my father helped me get over that, and I started channeling that into building businesses. So I started teaching music. I learned music through some of my friends. I started teaching music during my college days and started making money. And I had a blind friend, and he needed money because he was abandoned by his parents, so I had to help him out. I started making some money. I was doing well with my family, so I could just pay everything back to him. So that seed got planted there, and I didn’t know what to do back then, right?  Still a 14-, 15-year-old kid or a teenager, in this case. So I started getting into that mindset of, how about I build businesses for me and then start helping out the community? I’m still not there yet. I’m going towards helping that community. But I want to identify disability in three ways, not just physical. So those three are physical, but the bigger one is mental. A lot of people are really mentally blocked, and you see people, you know, “Oh, I can’t change anything in my life.” People die by suic*de. Kids get into depression. This is a form of disability, by all means.  I don’t think education, parents, and community are doing so much about that other than having a cliché thing that, “I was a victim of depression, so I’m doing that,” just to show off. But really, to help out the community in a methodical manner, that doesn’t exist. Second, physical disability, like I said, given by God sometimes, like war veterans and others, then you feel really limited. So what to do with that? And I come into that category, so I know that really well. Third is financial disability. So a lot of financial disability is in the mind, too. I’ve heard a phrase called, “You don’t die by hunger; you really die by indigestion.”  So you would find ways and means to make money even if you’re a completely disabled person. So I don’t think finance is an issue in general. So these three areas, to me, are the real disability areas. I’m obviously only working on one today, which is physical disability: how to identify the potential of people who can create something different within that limitation and then make a change in the world. So that’s the motivation. That’s my Life 2.0, where I’m moving now. Love it. Love it. So how did you have time to build nine businesses?  I started it in 1993, ’94, I believe, or ’95, I think. And they’re one at a time. Today I have about three. I sold one. And yeah, I did not have time. One of the big challenges when I built these three in the last six, seven, eight years, the biggest challenge I faced is I do not have time for working with customers, which I love to do—talking and listening to their business problems, solving those problems. I end up doing a lot of operational work.  Post-2020, and it’s a very blunt thing to say, people got lazy. They want to change jobs, make more money, do moonlighting, do multiple things, but not work hard like we did back in the days. And that kind of pushed all of us small businesses to do a lot more management of resources, especially human resources, in a distributed environment. I have teams in India, the Philippines, Canada. So that became a bigger challenge. But having said that, AI came as a savior. In the last 18 months, AI has changed quite a lot.  And if you don’t go into a debate of whether AI is good or bad, or you’re a skeptic or an enthusiast, AI can really help you if you really put together how it can help you. It should not replace you, but it should give you an additional helping arm. In my business, I started deploying C-suite. So I still have a VP of operations. My business partner is into sales. But then I started filling in other functions, like a fractional CFO, as an example. My fractional CFO is monitoring my top line and bottom line. I call him Felix. I have to give names to AI agents.  So Felix is actually looking on a weekly basis at what invoices are billed, if we have vendors or employees, what we need to pay, where our expenses are going, what’s the monthly or maybe six-month cash flow run. Are we within limits? Do we have borderline cash availability so that we can survive? So it started to do a lot of things. But not only that, because we are feeding our own data, our own mind, I have built my own second brain. It started to read off of that and started giving me insights that a human would not give me. And even if I hire a fractional CFO, he will only hear what I have to say, look into some of my books, and then give me some blanket suggestions.  Here, this is really tailor-made to our problems, our situation, and it worked phenomenally well. So I’m building that as a product now. It’s not done yet. Then I also deployed my own CMO called Sasha, and she started to look into my marketing angles, my branding, my voice, my identity. I love writing, but now AI can help me—not just create a blanket AI post or something, but really read how I write, what I write. So I create ideas. It helps me research, does a factual check on it. So I give 100 words. It can take those ideas and then expand into newsletter articles or a big campaign.  I can start building different case studies for our customers, proof of concept, building podcasts such as these. So this started freeing up—I only talked about two executives, and I have seven of them—but they started to help me free up my time. And believe it or not, I am getting about 40 to 60 hours, and in good months, about 80 hours per month. So 30 to 50% of my regular time is freed up. So that time is now going into this movement that I’m thinking about, which is disabled entrepreneurship.  Wow, that is impressive. So tell me a little bit about this. This is a podcast of frameworks. So do you have a framework for maybe launching an AI agent like that?  I definitely do, and I want you to expand on it. But in terms of what I have, I look into three things. Where is the gap in terms of human? Where I see either performance issues, quality issues, or availability and capacity issues. So what are those things which don’t hit my security side of things, don’t interact with my customer, and still help me in my operations? That’s the gap we look into. How can we use and fill that gap to grow what we need to work on? And then last, so I use three Gs with my last name, Goyal, right? So Gap to Grow to Glow.  So now how can we use this in our business to glow and create an environment where even humans can interact with this AI persona? So we are always big on human-in-the-loop or human-in-control with any AI solution. So it always starts with the gap. Where is the gap? Where is it taking time from one of the human sides of our team?  I like that. I like that you isolated those things which are less risky to develop, because I think a lot of people are held back by this idea that it’s a black box, you don’t know what you’re getting into, you don’t know what you don’t know, and it’s risky, and then they don’t do anything.  But you actually isolated that customer interaction is a risk you don’t want to live with right now, and security is another one you don’t want to, which, I mean, it’s obvious. But if those are not hurt, then really what is the risk you’re running? So I like it. So how do you fire up an AI agent like that?  So in terms of technology, I’m using a few things. I’m using Claude Code, the full Claude environment. So we build it off of that. Back in my days, I worked as a white-hat hacker, so the security angle we mentioned, I’m always so worried about hacking and security. So I have a completely isolated environment at my home on a Mac Mini, a really powerful Mac Mini, and that cannot go out on the internet and do things. And nobody can inject anything.  But then I still need to feed information to it, so I have another machine where the only job of that machine is to provide information to this system. So I have my own second brain mapped into Obsidian, which is a note-taking application, but it’s really organized. So I first fed all my knowledge. I’ve recorded lots and lots of audios and documents, and it has learned. So I built that system first, like Dave’s second brain. And my second brain has learned everything about me. Nobody can see it but me. That’s my initial basis, right?  After that, I built a working memory for my agents, for my company, and I’m doing it for one company at a time. Think AI is not completely live on the system, but Data & AI Studio is, which is a solo entrepreneurship business that I have. It is learning everything about that business as we speak. Even the transcripts from these podcasts and other places go into it, and it learns from it. There will be some insights which it will find, so it retains them. So Claude Code, Mac Mini, Obsidian—these are the basis. And then I’ve deployed my own personal models, like DeepSeek, and that is sitting locally on that machine.  So the model is local. The downside is it’s not getting updated, so I only update it when I feel that it’s right. Not risk, it’s really the downside. There’s no risk in it. So you’re not on the latest and greatest, but you don’t have to be on the latest and greatest all the time. So Claude Code is on the latest and greatest, but when we deploy, it may not apply certain features that Claude Code is making available. And that’s fine. That’s the risk I’m taking. That’s the trade-off I’m taking. And the system is working great for the last six months.  In the last 18 months, even though I started AI about 28, 30 years ago, the last 18 months is when I learned the new-age AI, and the last six months is when I started building this in an iterative manner. And it is pretty stable now. It can do a lot of things like I mentioned. So your agents are running on your Mac Mini off the grid?  Yes.  And then you’re feeding information with another computer to it to essentially give them the raw material from which they can build stuff, right?  Right. A good example there, if I may expand: we use QuickBooks in our accounting system. It cannot read QuickBooks directly, but we, being a Microsoft partner, understand technology. I can write a job which can push data into the Mac Mini. It doesn’t read off of it. My Mac Mini, which has the agent, doesn’t know where that data has come from. It has the data, so it’s already synthesized.  It cannot communicate with others. So that’s the calculated risk we take, right? Getting the data from one angle, one way, and then it’s synthesizing and analyzing data and getting insights out of it. So that’s the balance of systems that I have.  I love it. That’s very clever. And what is your main business anyway? Because you talk about three businesses right now. What is your core business? What is your flagship business?  The flagship business is Think AI. It’s a consulting organization, a three-time Inc. 5000 winner in terms of growth. We have our own team, but then we also use a lot of vendors which are qualified by us throughout the world. And we are Microsoft Advanced Specialization partners. What that means is we are in the top 2% of the worldwide partners within the Microsoft ecosystem, which is about 500,000 partners. And we work mainly with midsize manufacturers, and sometimes healthcare if they are okay and open to AI, and if not, data. So we go in there, look into whether they have a data and AI strategy.  If they do, we work on their initiatives. If they have the initiatives. If not, we create the initiatives for them by doing some POCs and whatnot. And once we get engaged, we do deliverables like consulting services. But it’s not like typical consulting services where you place a resource. It’s really a value-based delivery model where we try to understand two business imperatives. One is what can help them make more revenue. And if we cannot find that, what can help them be more productive and have cost-cutting in one way or the other.  So these are the two main business imperatives we work on. When and if we align with that, then we give them a roadmap, a phase-wise approach, which they can do with us or with somebody else, and then we keep delivering on it. So that’s the whole model.  So what drives growth in the Think AI business?  I mean, finding more customers, to say the least. And that becomes difficult because today everything is becoming a commodity. So one good learning, by the way, I need to share with the audience here. When you’re a small business, you think your brand is the value that you have. It’s the founders who are the brand. So it’s Manish and me. Manish, my business partner, is really big in productivity, project management, and that kind of thing. And I’m really good at building solutions using tech. And together we have about 55 years of experience.  And then our key team members are ex-Microsoft or MVPs, Microsoft Most Valuable Professionals. So we hire a really strong key team. And the team below, we can either fill with our members, hire our own members, or go to the vendors also, and we tell it to our clients also. So our delivery model is we are the ones who are delivering. The guarantee is taken by Manish and Dave, not by Think AI. We have gotten into that situation. We are about 95% successful, so there’s a 5% failure. And the failure is either because we have the wrong team member, the communication between the client and us was not clear, the scope was not clear, the definition of value and done was not clear, and we have learned from it.  So our business model is towards that, and that brings us growth because we work with a number of partners. Manish is part of a lot of Microsoft channel partner networks. We provide complementary services to those partners. So one channel is we work with a lot of partners because the trust is there. Authority and trust are the two factors we have understood which establish your business, and it’s the founders’ authority and trust, not the company. Company will build on its own. So we started building our own authority and trust, and that gets us growth. It’s not at the level we’d like, but we are happy.  You are happy. Okay. So what is your vision? What would you like to make out of this?  So we have an exit plan, at least on Think AI. And like I told you, Manish has his own. That is up to him. For me, I want to create this venture studio for disabled entrepreneurs, get the funds from here, and then harvest, go across the world. So three hobbies I have.  One is travel. Second is reading, writing stuff, books. And third is music. And entrepreneurship comes in this whole surroundings, in this whole ring, so it’s the foundation of it. So we’re going to build this disabled entrepreneurship venture studio with a little bit of funds from our exit, and hope to grow there and hope to retire or die with that thinking.  Love it. Love it. It’s fascinating. So you have a book that is on your LinkedIn page, Real-Time Business Intelligence Mastery. So tell me about this book. Why did you write it, and what’s it about?  Sure. So we went into a coaching program. Up until 2022, we were arrogant enough to say, “Oh yeah, we can do everything on our own.” And then slowly we realized we need help, and we started taking help. We went to a couple of coaches in India where they were coaching us either on how to manage operations and operational excellence, and then another coach who’s like a life/building-your-brand marketing coach, and he inspired us to write a book.  Now, I’ve been writing in my own native Hindi language, songs and compositions, but writing a book was a dream, and I thought it’s a big undertaking. But with their little bit of motivation and help, not in writing, but in the angle of what a book can bring. So I have a lot of experience working in midsize manufacturing organizations, working with CTOs and CIOs, and business intelligence is delayed. So it’s either a one-day delay or a week delay or a month delay, and it’s more reactive in nature.  So the book was more about how you can build a real-time business intelligence culture so that you can get the insights from your data, make actionable insights, take actions on it, and grow your business for those two imperatives I talked about, which is grow your revenue or increase your productivity and decrease your cost.  So are you writing about some of the things that you talked about? Leveraging AI, building AI agents?  It has more about—so I wrote it in 2022, I believe. It has a lot more detail about. AI was not as popular, right? I mean, I did write about AI in it, but it was more about building a data culture than AI. It does talk briefly about AI because real-time is going very closely with AI. That’s the enabler for AI insights or data insights through AI. So it does talk a little bit about AI, but it talks more about tech leaders like CIOs and CTOs. What do they need to do? How do they need to build a culture around harvesting data, bring the data, build the team, where to take it? So it has those details.  Okay. That’s fascinating. Who is this book for? Is it for founders? Is it for C-level executives? Who is the target?  Like I said, it’s for tech leaders, CIOs, and CTOs of midsize organizations.  Okay. That’s awesome. And these are the people that are your target customers as well at Think AI?  Yes. That’s our true ideal client profile too, and that’s whom we have worked with all our lives. So they’re close friends, target audience, and customers. Future customers and current customers.  All in one. All in one. That’s so nice when you write a book to your friends. That’s a very cool concept. So let me ask you this, Dave. If you had a magic wand, you’ve done a lot of things in your business, you built nine businesses. You learned from some of the failures that you had, which is part of entrepreneurship, and now you created AI agents, and then you have a second brain, and you’re leveraging all that technology. So if you had a magic wand and you could fix one thing in your business in the next 12 months, what would that be?  I wish I had more senior leadership. Any business works with delegation. We have a couple who are really amazing, and they wear a lot of hats. But growth depends on three things, right? Being in front of the right customer, having the right team, and having the right product. Our product is people, unfortunately and fortunately. Customers, we are very happy and excited, and they trust us. We know how to get to them. We know how to create value for them. We are very satisfied. Everybody would say, “I need more customers,” and we would do that too.  But I think more important is what product you are offering. So then people are what we are offering, and we are competing against big ones like Accenture and Avanade and Cognizant of the world in our business, the tech consulting business. But then we are not competing against cost; we are competing against value. So how do you create value? You find a valuable customer. They understand our language. The next level is, where is the product, which is the people? And that management becomes quite difficult. And harvesting and getting the right people in place is a job by itself.  So kudos to those large companies if they’re harvesting one, although that’s debatable because when we go to the client, they complain a lot about their resources as well. So harvesting the right product and the right team is the key. And how do you do that? If you have the right leaders on top. Two partners alone cannot do that. So building more leaders underneath is the key. We are able to build a few, and I wish we could build a lot more. So when you have the right core team, your growth comes in, is my belief system. It could be different for everyone else.  No, I think it’s a very deep insight, and very few people actually talk about this idea that the purpose of a business, especially in today’s AI age, is to build leaders. That’s your purpose, because people will take care of everything. They’re going to run your AI agents. They’re going to manifest your vision. But you can’t have just AI agents in a company, right?  Because then the mental load is so much on the leader, and then the single-person dependency becomes critical. So is this what you mean by this? Where do you come from with this idea of harvesting people and leaders in the business?  Absolutely. You said it well. Building leaders doesn’t just apply to an organization, whether small, medium, or big, but even to countries. If you don’t have the right leaders in place, it’s going to bite you back. And all cultures, some of the top management consultants will teach you to go into succession planning. That is what they really mean by that.  It’s not succession planning by, okay, replace a CEO with a CEO. It’s the mindset. Apple is a great example of it. Steve Jobs hired Tim Cook from Compaq, from that world, and he had that vision. Obviously, he had a mission, but he had the vision—who to take, where to take, and what they would do. And that legacy continues even today. So Apple didn’t change a single bit in their model. And people would argue and debate, and that’s fine.  But when I see it from my eye, he built a great leader. When he did that, the company stayed the same, right? So it’s not about what products Apple is creating today, whether it’s iPhone or iPad or Apple Vision Pro or some of the other things that they are doing, but it’s really that leader. Same thing went with Google, or Microsoft, Satya Nadella. And you see the right leaders were built by these founders. And by any means, we are not that big, and hopefully we can get to some place which is pretty good in our books.  But finding and building the right people, it gives you a lot of satisfaction, happiness, bliss, if you can give it back to somebody who’s capable enough. And I’m always in hunt of the right people, building the right team in place.  It’s so interesting you mention Apple because when Isaacson came out with the Steve Jobs biography, he said, basically, I think it’s in the preamble of the book, that Steve Jobs wanted people to remember him not for the products that he created, but the company. So his biggest contribution was creating a company. And I didn’t get what he meant by it.  But if you witness the last—since he died 13 years ago—the last 13 years, this company has gone from strength to strength. Ninety percent of its market capitalization has been created since he died, right? So it keeps growing and keeps going from strength to strength, and that is the culture and the people that he built. This is the company he built. So it’s quite an amazing idea.  I was about to comment on company. Something came back to me or reminded me that companies generally build on three pillars: customer, people, and culture. And if you don’t have the right balance of it—so, the right people, but if you don’t have the right culture, they’re going to run away. If you don’t have the right customers, you should have the ability to say no to certain types of customers too. Like Apple never targeted small, cheap products.  And when I say cheap, meaning which doesn’t have the right quality in place, not about the cost. It’s always cost versus quality. So they have really struck the right balance in those three angles, and I think that’s the right way to do it. Some are able to do it, some are able to push through to do it, and some are not. But that’s where I think the focus needs to be if you are a founder. Find that right balance of people, client or customer, and culture.  Yeah.  And that is your core values too.  Yeah. I agree with you. That’s wonderful. If you are listening to this conversation with Dave Goyal, and you would like to learn more about him and what he does and Think AI Corporation, where should our listeners go to learn more?  Thank you for this opportunity, first of all. And people can find me on LinkedIn by my name, Dave Goyal. I’m very active there. I recently started a YouTube channel with the name Dave Goyal, so you can find me on YouTube. And mainly on LinkedIn, I have a newsletter on AI, and I’m pretty passionate about what’s happening in AI.  So I even publish AI news this week, but with a different angle, a builder’s angle in mind. And last but not least, you can connect with me through LinkedIn for a 15-, 30-minute call. No angle there. I will just come and help you if you really want to do something with AI. I can listen to your challenges or your fear of missing out, if that’s the case, and tell you if AI is the right fit for you or not, and what you can do on your own also. And if you need our help, we are happy to.  That’s fantastic. So take Dave up on his offer, which I think is very generous. And obviously, Dave, you know what you’re talking about. You built a second brain. You’re running AI agents. Your C-suite is chock-full of AI agents, which is very impressive. I’d love to learn more about this myself.  So if you’re curious about that, make sure you book a call with Dave or check out his stuff. Where is your newsletter? Is it a Substack? Where can people find your newsletter?  It’s on LinkedIn. It’s called Data & AI Demystified in my profile.  Okay. So that’s easy. So we can go to Dave’s LinkedIn profile. And if you enjoyed this conversation, make sure you subscribe and follow us on Apple Podcasts and YouTube. Give us a review because every week I bring in a couple of exciting entrepreneurs like Dave who share their favorite frameworks with you. So Dave, thanks for coming, and thank you for listening. Important Links: Dave's LinkedIn Dave's website

Today, we're talking to Etienne, founder of Seven CTOs and author of the new book Refactor. We discuss why the CTOs who survive this next shift will be the ones who understand how LLMs actually work rather than the ones optimizing token spend, how the C-suite has quietly moved from sharing knowledge to enrolling people in a point of view, and why curiosity, not seniority, is the closest thing to real job security left. All of this right here, right now, on the Modern CTO Podcast!

What happens when your autonomous coding agents need to navigate your core infrastructure? Do you hand them the keys and hope for the best? (gulp!) This week on Dev Interrupted, 1Password CTO Nancy Wang teaches the golden path for agentic security: just-in-time secrets that grants AI "access without custody." She also shares her CTO playbook for measuring true agentic ROI beyond raw PR volume, explains why 1Password has officially replaced traditional coding interviews with agent builder tests, and confesses she's shipping PRs again with her own fleet of agents between meetings. Like many CTOs we've had on the show, Nancy reminds us that code is cheap now, and review is what's expensive now. We get into tactics for addressing that bottleneck.Get the guide: The AI engineering productivity gap - how elite teams pull ahead in 2026Register: Dev Interrupted Presents: The Software Factory RoundtableFollow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's guest:1Password: Explore the enterprise password and identity platform at 1password.com1Password for Developers: Dive into the new developer tooling, credential brokering, and secure AI workflows at 1password.devOracle Red Bull Racing: Read more about the F1 team's systems engineering at redbullracing.comConnect with Nancy: LinkedIn OFFERSStart Free Trial: Get started with LinearB's AI productivity platform for free.Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era.LEARN ABOUT LINEARBAI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production.AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance.AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil.MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

Sponsored by Blocks: Save at least 20% on your AWS costs with AI-powered optimization and enterprise discounts. Get your free Cloud Check at https://blocks.cloud/alphalist?utm_source=alphalist&utm_medium=podcast&utm_campaign=blocks-podcast-2026 Dana Lawson's path into tech started with backup tapes, not a keyboard-in-the-womb origin story. She joined the US Army in the late '90s, automated her way out of manual password resets, and went on to become VP of Product Engineering at GitHub before taking the CTO seat at Netlify. She joins Tobi to make the case that "writing code is no longer the job", an argument from her own New Stack article that lit up Hacker News, and one she doesn't back away from here. The conversation explores why trust in AI agents may eventually become automatic, the same way our trust in cars we can't repair ourselves already has. Dana explains why Netlify is designing for "agent experience" alongside developer experience, and why the engineer's role is shifting from writing every line of code to defining architecture, guardrails, reliability, and safe user experiences. CTOs will walk away with a sharper way to think about where human judgment still matters versus where it's already been commoditized, including the tension between speed and control, whether developers risk becoming the bottleneck, and why the real constraint may be moving from "can we build it?" to "does anyone actually want it?" What's covered: - Dana's path from the US Army to VP of Engineering at GitHub to CTO of Netlify - Why she argues "writing code is no longer the job" and the Hacker News backlash - The self-driving car analogy for trusting AI agents - What "agent experience" means and why Netlify designed for it - Craftsmanship, control, and the shift from writing code to defining guardrails - Whether developers risk becoming the bottleneck in the agentic era - Why the constraint is moving from "can we build it" to "does anyone want it"

Has enterprise AI finally reached the point where impressive demonstrations are no longer enough? In this episode of Tech Talks Daily, I speak with Bruce McMahon, Chief Product Officer at CallMiner, about what he describes as the industrialization of AI: the move from experimentation and excitement toward repeatable processes, measurable ROI, better customer experiences, and technology that can operate reliably at enterprise scale. Bruce explains why business leaders are increasingly asking a much simpler question about AI: how is this going to create value? Drawing on CallMiner's experience analyzing hundreds of thousands of hours of customer interactions every day, Bruce discusses how AI can surface operational inefficiencies and customer insights that were previously difficult to identify. The opportunity is not simply generating more data. Organizations need processes that get the right insight to the right person so something actually changes as a result. We also discuss how AI is changing workforce expectations. Bruce sees curiosity and adaptability becoming increasingly valuable, particularly among technical teams. As AI takes on more routine work, employees who question outputs, experiment with new approaches, and apply human judgment can become more valuable than those who rely solely on established technical knowledge. The economics of enterprise AI present another challenge. Foundation models, capabilities, and pricing continue to change rapidly, creating questions around vendor dependency and long-term costs. Bruce explains why companies may increasingly use a mixture of commercial, open-source, fine-tuned, self-hosted, and proprietary models rather than relying on one provider for everything. Governance becomes even more important as AI agents begin interacting directly with customers. We discuss red teaming, bias testing, compliance, data protection, monitoring, and why organizations need to decide which actions can be fully automated and which decisions must remain accountable to a human. Bruce also examines how AI is changing customer experience and the BPO industry. Rather than choosing between humans and AI agents, he sees value in designing systems where both can work together, with people handling interactions requiring judgment while AI manages high-volume and repetitive work. For CIOs, CTOs, COOs, customer experience leaders, and anyone responsible for enterprise AI strategy, this conversation provides a practical look at moving beyond AI pilots and turning the technology into a dependable part of business operations.  

Send us Fan MailAI, leadership, and adoption with Shawn MillsThis episode focuses on how organizations should approach AI as a business strategy, not just a technology purchase. Chris Whitaker is joined by Shawn Mills, CEO and chairman at Pisteyo, who brings a practical view of what actually works when companies try to adopt AI. The conversation covers leadership alignment, change management, and why education matters just as much as the tools themselves.Key topicsIn this episode: Shawn Mills shares how his background in finance, sales, and entrepreneurship shaped the way he thinks about business and technology.We discuss: why AI adoption is usually a people and process challenge, not just a tooling challenge.Shawn explains: the meaning behind the name Pisteyo, including its roots in off-piste skiing and the Greek meaning tied to belief.We explore: how Pisteyo positions itself as a management consulting firm first, then a technology solution provider.Shawn breaks down: why CEOs, boards, CTOs, and CIOs are often the real drivers behind AI initiatives.We cover: the common mistake of buying an AI tool and expecting adoption without training, enablement, or context.Shawn shares: why smaller and mid-market companies can now compete more effectively because AI gives them access to capabilities once reserved for larger firms.We discuss: prompt quality, AI slop, and the need to understand and explain AI-generated output before passing it along.Shawn emphasizes: that business and technology teams need to stay in lockstep or AI projects can drift away from real business needs.We close with: advice on leadership alignment, curiosity, and continuous learning as AI capabilities keep accelerating.Timestamps00:00 - Why AI is now at the center of every tech conversation 02:41 - What Shawn Mills is seeing in executive AI journeys 03:17 - Life outside work: hiking, surfing, Nepal, Peru, and Costa Rica 05:35 - From finance and investment banking to sales, marketing, and operating companies 07:29 - The biggest lesson: use your network and ask for help 09:34 - How American Airlines flight benefits sparked a lifetime of adventure 10:21 - Why AI is transforming financial modeling and analysis 12:11 - The real story behind the name Pisteo 14:45 - Why Pisteo starts with business strategy, not technology 16:52 - The board and CEO pressure driving AI initiatives 18:33 - Passive resistance and why education gets forgotten 20:49 - Why upper mid-market companies are the best fit 22:42 - How AI is leveling the playing field for smaller companies 24:32 - The most common AI failure: tool first, training never 27:38 - Why the AI capability gap keeps growing 29:41 - Is AI making people smarter or lazier? 32:21 - Why technologists must partner with the business 35:25 - Lightning round: coffee, books, leadership, and video 37:28 - Why executive alignment is critical to AI success 38:36 - Closing thoughts and final takeawayNotable quotes“If you're not falling, you're not learning.”“I'm literally the smartest person on the planet as long as I have access to AI.”“Bring the leadership team along with you on the journey.”More on Shawn MillsMore on Pisteyo Support the showCheck out my website https://thewirelessway.net/ use the contact button to send request and feedback. 

Companies are spending billions on GPUs, data centers, foundation models, and AI infrastructure. But what happens when the network connecting all of it cannot keep up? In this episode of Tech Talks Daily, I welcome back Avi Freedman, co-founder and CEO of Kentik, five years after our previous conversation. Avi has been operating large-scale networks since the 1990s, including more than a decade at Akamai, and brings a rare combination of founder experience and hands-on knowledge of how the internet actually works. We discuss why network performance is becoming an important factor in determining the return companies receive from their AI investments. If organizations cannot move data efficiently to models or deliver inference reliably to users and applications, expensive compute infrastructure can sit waiting while performance suffers and costs increase. Avi explains what technology leaders should measure to determine whether their network is helping or hindering AI workloads. This includes establishing performance baselines, synthetic testing across cloud and AI providers, understanding dependencies across the digital supply chain, and using observability to identify what changed when performance deteriorates. The conversation also examines network intelligence and why collecting telemetry alone is not enough. Organizations need to connect network data with the applications and users affected, understand historical behavior, determine which problems matter, and give network teams enough context to act quickly. Agentic AI introduces another opportunity. Avi explains how AI agents can increasingly perform the work of experienced network engineers by monitoring baselines, investigating alerts, troubleshooting problems, and recommending actions. But fully autonomous networks remain some distance away. Most enterprises currently want humans deciding whether significant production changes should be made. That leads us into governance. As businesses give AI systems access to increasingly important infrastructure, credentials, permissions, guardrails, and oversight become major considerations. Avi warns about ungoverned AI systems gaining proxy access to corporate infrastructure and explains why companies need clear boundaries around what agents can see and do. We also revisit a lesson from decades of internet infrastructure: individual components will fail. Rather than attempting to create networks that never fail, businesses should design for resilience through redundancy, over-provisioning, monitoring, and architectures capable of continuing when something inevitably breaks. For founders, CIOs, CTOs, network engineers, and infrastructure leaders building around AI, Avi offers practical advice on observability, network resilience, autonomous operations, AI infrastructure, and knowing when networking expertise should be developed internally or brought in from elsewhere. And we finish somewhere unexpected: how CEOs can use AI to make better decisions by explicitly asking it to disagree with them. Avi explains why turning AI from a sycophantic assistant into an argumentative colleague can expose weaknesses in an idea, improve communication, and help leaders test their thinking. AI may be transforming software, compute, and business operations, but none of it works without connectivity. As AI becomes part of the operational backbone of the enterprise, understanding the network underneath it becomes increasingly difficult to ignore.

What does it take to cross a 100% blocked artery and open the door to treatment when others may think there are no options? On this episode of The Heart of Innovation, I'm joined by my co-host Dr. John Phillips as we talk with Dr. Brett Bennett, an Interventional Cardiologist and CTO expert, about the challenging world of chronic total occlusions, also known as CTOs. A CTO is a completely blocked artery, and crossing it requires skill, strategy, tools, and experience. Dr. Bennett shares what makes these cases so complex, how physicians approach them, what patients should understand, and why expertise matters when dealing with advanced coronary artery disease. Dr. Bennett has a special focus on chronic total occlusion intervention and complex coronary intervention, and his work has helped his program become one of the top CTO centers in the Southeast. He is also actively involved in research related to coronary artery disease, novel coronary stents, congestive heart failure, arrhythmia, antiplatelet therapy, and artificial intelligence in heart disease diagnosis. If you or someone you love has been told they have a blocked artery, this is an important conversation. Please watch, subscribe, and share so more patients and families can find this information. Call to Action: Subscribe: @TheWayToMyHeart Call the Leg Saver Hotline: 1-833-PAD-LEGS Visit: TheWayToMyHeart.org Join: TheGreatCirculationChallenge.org for our PAD Awareness Month walking challenge Comment below with your questions about CTOs, coronary artery disease, or artery blockages Share this episode with someone who needs hope, answers, or another perspective Hosted by: Kym McNicholas and Dr. John Phillips #HeartOfInnovation #CTO #ChronicTotalOcclusion #BlockedArtery #CoronaryArteryDisease #InterventionalCardiology #DrBrettBennett #HeartDisease #Cardiology #PatientAdvocacy #KymMcNicholas #DrJohnPhillips

Mike Lyon is the founder and managing director of Vista Point Advisors, a boutique sell-side investment bank he started almost sixteen years ago. A former chemical engineer at Exxon and BP who later joined Citigroup's tech M&A team, he built Vista Point to solve a conflict of interest he watched play out on Wall Street: bankers quietly working both sides of a deal. Vista Point Advisors represents founder-led software companies only — never the buyers — focusing on vertical SaaS with enterprise values between $30 and $300 million. Mike and five other managing directors run about a dozen deals a year, spending 50 to 100 hours cleaning up each company's data before launch and running a long, non-exclusive process built to keep multiple buyers competing to the finish. Mike says buyer criteria changed this year for the first time in a decade, and changed fast. Buyers now demand roughly 90% gross retention as a proxy for AI resilience, favor systems of record with real data moats, and discount horizontal point solutions. His advice: understand exactly how you'll be graded long before you go to market, because the aperture for a premium exit keeps shrinking. Key Takeaways AI Changed Everything — For the first time in a decade, buyer criteria are shifting week to week this year. Gross Retention Rules — Buyers now want 90%-plus gross retention as a proxy for AI resilience. System Of Record — Point solutions look exposed to AI; systems of record with real data moats win. Exclusivity Is Leverage — Once you grant it, the deal slows and power shifts to the buyer. Watch Spend, Not Talk — Every buyer says the right things; only diligence dollars prove real intent. Second Bite Pays — Most founders who sold majority to PE did as well or better on the rollover. Quote from Mike Lyon, Founder & Managing Director of Vista Point Advisors "For the first time in ten years, the rules hav changed — and they changed fast. If you called any private equity or strategic buyer over the last decade and asked what makes a good SaaS company, the answer barely moved. This year I've had conversations I've never had before, where the criteria shifted week to week — first it's X, then X plus Y, then X plus Y plus Z. The first big change is a maniacal focus on gross revenue retention (GRR). Net retention (NRR) was the star for years, but gross gives you no credit for upsells — you only get dinged for downgrades and churn, so it's really asking what happens if you can't upsell anymore. Buyers want to see 90% or better now, and our read is that it's a proxy for AI risk: they don't fully understand the AI threat yet, but they know a business at 90% gross retention is probably resilient enough to take some hits and survive. System of record is the other thing that suddenly matters more. Point solutions feel like they're at further risk from AI, so buyers want a system of record with a real data moat — not the fake moat where anyone can go get that data somewhere else — or a payments angle, which is harder for AI to route around. And if your business started eight years ago, it's hard to be fully agentic, but you at least need the ability to hang agents off the backbone of your software to keep delivering value." Links Mike Lyon on LinkedIn Vista Point Advisors on LinkedIn Vista Point Advisors website Podcast Sponsor – Fraction This podcast is sponsored by Fraction. Fraction gives you access to senior US-based engineers and CTOs — without full-time costs or hiring risks. Get 10 to 30 hours per week from vetted and experienced US-based talent. Find your next fractional senior engineer or CTO at fraction.work. You can start with a one-week, risk-free trial to test it out. The Practical Founders Podcast Tune into the Practical Founders Podcast for weekly in-depth interviews with founders who have built valuable software companies without big funding. Subscribe to the Practical Founders Podcast using your favorite podcast app or view on our YouTube channel. Get the weekly Practical Founders newsletter and podcast updates at practicalfounders.com. Practical Founders CEO Peer Groups Be part of a committed and confidential group of practical founders creating valuable software companies without big VC funding.  A Practical Founders Peer Group is a committed and confidential group of founders/CEOs who want to help you succeed on your terms. Each Practical Founders Peer Group is personally curated and moderated by Greg Head.

In this solo episode of The Tech Leader's Playbook, Avetis Antaplyan examines how artificial intelligence, workforce efficiency, and headcount reduction affect talent development and succession planning. He explains why repetitive work can be automated without eliminating career pathways, how middle managers translate strategy into execution, and why companies cannot rely exclusively on external executive hiring. This episode is essential for CEOs, CTOs, technology leaders, HR executives, investors, and managers responsible for building sustainable organizations.What You'll Learn• Why automating junior-level work can weaken the future leadership pipeline.• How middle managers develop talent, preserve institutional knowledge, and turn strategy into execution.• What companies should measure beyond productivity, headcount reduction, and short-term margins.• When AI should accelerate employee judgment rather than replace critical thinking.• The practical steps leaders can take to redesign career paths and develop future executives.Chapters00:00 The Leadership Pipeline Crisis02:25 Efficiency Versus Long-Term Capability04:50 Leadership Legacy and Development07:12 Entry-Level Jobs and Experience09:32 Why Middle Management Matters11:56 Internal and External Hiring14:20 Productivity Versus Capability16:46 The Experience Paradox19:08 Redesigning Junior Roles26:07 Building Better Career PathsFollow Avetis AntaplyanInstagram:https://www.instagram.com/avetisantaplyanSpotify:https://open.spotify.com/show/0rOkUXDSQb6SVFE6LttWDeApple Podcasts:https://podcasts.apple.com/us/podcast/the-tech-leaders-playbook/id1690263628HIRECLOUT:https://www.hireclout.comThe Tech Leader's Playbook:https://www.podcast.hireclout.comLinkedIn:https://www.linkedin.com/in/hirefasthirerightAI automation, leadership development, future of work, artificial intelligence in business, leadership pipeline, workforce automation, talent development, succession planning, entry-level jobs, middle management, executive leadership, career development, organizational capability, workforce efficiency, employee development, AI productivity, management training, internal promotions, executive hiring, institutional knowledge, critical thinking, AI fluency, mentorship and coaching, technology leadership, human capital strategy, Avetis Antaplyan, The Tech Leader's Playbook#ArtificialIntelligence #LeadershipDevelopment #FutureOfWork #AI #TechnologyLeadership #ExecutiveLeadership #TalentDevelopment #SuccessionPlanning #MiddleManagement #WorkforceAutomation #CareerDevelopment #TechLeadersPlaybook

PHP Alive and Kicking – August 4, 2026 Hosts: Chris Miller (standing in solo while Mike is on holiday) Mike escaped to a beach so Chris hosts alone with special guest Derick Rethans. Expect date-time philosophy, PHP 8.6 goodies, Xdebug secrets, deprecation drama, and a heated debate about how to queue at a pub. From University Websites to Committing to PHP Derick traces his PHP origins back to his university days, building websites that needed database connectivity. ASP.NET was an option, but nobody wanted to pay for it — Linux was cool, and PHP was easy to start using. That combination hooked him early. His very first accepted contribution came from a practical need: everyone wanted centered Shockwave Flash films on their pages, back before CSS even existed. Centering meant knowing the width of a file, and while getimagesize() already existed (and still works today), it didn’t understand Flash. Derick added that support. From there he fell into bug triage, spending several years working through the issue tracker alongside Jani Taskinen (whose “Sniper” name still shows up in commit logs). For a long time it was just the two of them reproducing and fixing bugs — an important task that gets neglected easily, which is exactly how backlogs of a thousand issues and pull requests build up. The Eternal Problem of Date and Time Chris and Derick dig into why date-time bugs are so hard to triage: date algorithms are genuinely complicated, and it takes real time to figure out whether a report is a user’s mistake or an actual PHP bug. Crashes are easy to reproduce; subtle date arithmetic is not. The core philosophical question is “what does adding a month even mean?” If it’s January 31st and you add a month, PHP historically just bumps the month number, producing February 31st, which overflows to March 2nd or 3rd. But most people who add a month really just want the next month and don’t care about day counts — so “next month” is conceptually distinct from “add a month.” Derick has been brainstorming better interfaces so people can’t make these mistakes, though it’s a lot of work. They touch on Carbon as a unified interface people understand, but agree that making Carbon the language standard isn’t the right move. At the language level you need something that works for everybody and ideally doesn’t break existing code. What’s Coming in PHP 8.6 The first new time class is landing: a Duration object representing seconds and nanoseconds within a specific range, living in the Time namespace. It’s a trial run for new APIs and immediately useful — for example, passing a duration to sleep or to timeouts in the new polling API, rather than juggling raw integers. Another highlight is partial function application. With the pipe operator from 8.5, piping into anything that takes more than one argument meant wrapping it in a closure (and extra parentheses because of how the parser works). Partials let you pre-fill some arguments so the resulting callable accepts just one — perfect for pipelines. There’s also TLS session resumption support for streams, which lets OpenSSL shortcut the expensive session setup on repeated TLS connections. Bigger stream-layer work by Jakub Zelenka of the PHP Foundation is underway too, but that’s a huge job on old code and won’t make 8.6 — Chris and Derick both note the pain of keeping a year-and-a-half-long project in sync with the master branch. Xdebug in 8.6 and the Xdebug Cloud Xdebug’s recent headline feature (shipped with 8.5) is native path mapping, which lets you configure remote-to-local path mappings inside Xdebug itself instead of your IDE — handy when your code runs in Docker or on a dev machine. Feedback since release is driving quality-of-life improvements. For 8.6, relatively little is broken, though a change to how PHP reports line numbers needs untangling so debugging stops on the correct lines. Derick also has a new code-coverage implementation that gives better, more correct results at roughly a 10% speed cost. He’d rather ship correct output than fast output — and points out that PHPCov may be quicker but skips path and branch coverage entirely. The pair discuss how branch coverage doubles your test count at every branching point, and how Xdebug has a hard limit (around 64K) to avoid running out of memory on pathological code. Finally, Chris asks about Xdebug Cloud — a paid service that enables debugging where networking otherwise wouldn’t, such as remote teams sharing a single development machine (an SSH tunnel only lets one person listen on the debug port). It’s £15/month or £150/year for an individual, priced similarly to Packagist’s private packages, and helps fund Derick’s ongoing Xdebug work. He plans to do more marketing about it this year. Deprecations, Consensus, and Two-Thirds Votes Every year PHP collects a big list of deprecations — around 20 to 25 this cycle — voted on separately. Derick is more reluctant than most to approve them, arguing many offer no clear user benefit. His examples: deprecating metaphone() (whose suggested Composer replacement ironically relies on the internal function) and the dechunk filter (which can’t really be removed because HTTP streams use it internally). The list() deprecation is another sore point — Chris uses it, and the vote is tied 21–21 with one abstention, so it won’t pass under the two-thirds rule. Derick reminds everyone that “yes” needs to be twice “no.” Returning from a finally block is another candidate that makes little sense to use but hurts nothing to keep. Chris and Derick reflect on how voting was originally meant to confirm consensus reached on the mailing list, not to be the decision itself. A 50%+1 result isn’t consensus; two-thirds is the compromise, even if true 75–80% agreement would be ideal. Joe notes many deprecations are groundwork to reserve keywords for future RFCs — which raises the perennial question of how you measure real-world usage across Composer’s dependency chains. Infrastructure, the Website, and the Next Generation The PHP infrastructure has been converted to an Ansible-based repository over the last year and a half (largely by Derick, with help from Joe), replacing a wild mix of individually maintained servers — one was still running FreeBSD 4.3. They’ve also migrated to a new CDN, which brought surprises: a default one-month cache, ignoring the query string in cache keys, and even dark/light mode being an organization-wide setting rather than per-user. On the website side, everything currently lives in a single root directory of 1999-vintage code, and there’s a long-pending patch to restructure it. The PHP Foundation’s ambassadors program — with speaking, marketing, and research streams — aims to make the very techy php.net homepage speak to CTOs and newcomers about who uses PHP and why it’s a thriving project (only 31-ish years old, versus C’s 60). The conversation closes on caretakers and the next generation of contributors. Triage, they stress, doesn’t require deep internals knowledge — just reading reports to see if they still make sense is hugely valuable. Security and infrastructure access necessarily stay gatekept for trust reasons, and both agree a bus factor of one (or even two) isn’t good enough. Chris admits he’s struggling to name people who are both nice and know infra. Links from the show: Join us live in Discord — discord.phparch.com Watch live on YouTube — youtube.com/phparch PHP Tek Conference 2027 — Call for Papers now open PHP Arch Swag Store — store.phparch.com Xdebug — and Xdebug Cloud for shared debugging Magazine discount code ALIVE3 — three free months on an annual digital subscription Host: Chris Miller X: @ccmiller2018 Mastodon: @miller@phpc.social Bluesky: @ccmiller2018.bsky.social PHPArch.me: @miller Mike Page Mastodon: @MikePageDev@phpc.social PHPArch.me: @mikepagedev Streams: Youtube Channel Twitch Connect & Hire PHP Architect Website Twitter/X Mastodon Hire PHP Developers Looking to hire PHP developers? Email support@phparch.com – the team is available for consulting, infrastructure work, Ansible playbooks, and code review. Partner This podcast is made a little better thanks to our partners Displace Infrastructure Management, Simplified Automate Kubernetes deployments across any cloud provider or bare metal with a single command. Deploy, manage, and scale your infrastructure with ease. https://displace.tech/ OurCVEs Your security posture, on autopilot with OurCVEs CodeRabbit Cut code review time & bugs in half instantly with CodeRabbit. PHP Architect Consulting Your PHP codebase deserves a partner, not a contractor PHP Architect provides long-term technical partnerships for organizations that need senior-level PHP expertise that you can depend on. https://www.phparch.com/consulting/ Music Provided by Epidemic Sound https://www.epidemicsound.com/ Join Us Live Next Week Youtube Channel Got feedback? Join us on Discord at discord.phparch.com The post PHP Alive and Kicking 2026.08.04 appeared first on PHP Architect.

In this episode of Shift AI, Andrew Comstock, Senior Vice President and General Manager of MuleSoft from Salesforce, joins host Boaz Ashkenazy for a wide-ranging conversation on governing, securing, and controlling the cost of agentic AI at enterprise scale.The conversation covers how MuleSoft's API-led integration playbook, built during the on-prem-to-cloud shift, is now extending into what the company calls Agent Fabric — bringing governance and controlled connectivity to agent-to-agent communication. Andrew walks through a concrete example of how two internal agents trusting each other by default can leak a customer's order history, and digs into prompt injection, impersonation, and why the security industry's people-focused compliance frameworks now have to account for reasoning software, not just people. He and Boaz also get into token economics, LLM gateways, intent-based routing between local and cloud models, and the emerging trend of companies repatriating some AI workloads on-premise.This episode is essential listening for CTOs, CISOs, platform and integration engineers, and IT and product leaders responsible for governing AI spend and agent security as their organizations scale past pilot projects into production.Chapters[00:01] Andrew Comstock's path to MuleSoft[02:06] First job: tax returns and trumpet reeds[03:21] What MuleSoft does and its extension into Agent Fabric[05:29] From consumer chatbots to enterprise agent connectors[07:22] Governance and security at the enterprise level[10:36] A real example: how two agents can leak an order number[13:45] Connect AI 2025 vs. 2026 — a year of night-and-day change[16:28] Mythos, Fable, and preparing for the LLM that breaks your systems[18:11] Token economics and the true cost of enterprise AI[20:38] The "AI savior" pattern vs. applying IT discipline to AI[22:40] Hybrid deployments, local models, and intent-based routing[27:13] Why AI's uneven acceleration makes prioritization more valuable[28:57] The two-word answer: "coming soon"Connect with Andrew ComstockLinkedIn: https://www.linkedin.com/in/andrewcomstock/Connect with Boaz AshkenazyLinkedIn: https://www.linkedin.com/in/boazashkenazy/Email: info@shiftai.fm

AI is changing the conversation around legacy modernisation, but successful transformation demands far more than powerful models and automated code generation. It requires engineering discipline, governance, and a clear-eyed view of where AI genuinely adds value and where it doesn't.On a recent episode of Tech Transformed, host Christina Stathopoulos, founder of Dare to Data, sat down with Shodhan Sheth, Enterprise Modernisation Platform and Cloud Lead, and Alessio Ferri, Lead Software Engineer, both part of Thoughtworks' Global Legacy Modernisation Service Development Team, to unpack exactly that.Legacy Modernisation With AIThe conversation around AI in legacy modernisation is often muddied by marketing noise. As Sheth puts it, "value and hype can coexist". Overpromising doesn't automatically mean a technology is worthless. The real question for technology leaders is whether AI meaningfully improves the cost-time-value equation for a specific problem.This will always start with problem-solution fitness: has someone already solved a comparable challenge with AI, and does the proposed use case genuinely fit that pattern? As Sheth notes, "most things can be judged by cost, time, and value", which is a simple but effective filter for cutting through the noise.Rethinking Legacy ModernisationGenerative AI is inherently probabilistic, while enterprise software has always relied on deterministic, predictable behaviour. Ferri unpacks this tension by separating two very different use cases: using AI to build systems, and embedding AI within operational systems.When AI writes code, inconsistency is manageable; developers review, test and refine the output before it ships. Production systems are a different matter entirely, where unpredictable behaviour carries real operational risk. As Ferri explains, "AI in production requires different guardrails than AI for building."The practical answer is controlled use. AI might suggest alternative products in a marketplace, for instance, while deterministic rules still guarantee that only in-stock items are ever shown. This lets AI add value within a firm, enterprise-grade constraints.Why AI Alone Won't Modernise Legacy SystemsTechnology is only part of the story. Sheth is clear that modernisation is fundamentally about change, and change is hard, especially across large enterprises with tangled, interconnected systems. Tasks that resist automation are often the hardest, like upskilling teams, explaining complex trade-offs, and winning buy-in; these cannot be solved with code alone. These human and organisational factors are routinely underestimated. Where the impact of a change is broad, he also advises either aligning teams properly across the business or breaking the change into smaller, more manageable pieces, a strategy that reduces resistance and smooths the path to adoption.If you would like to learn more about this, visit Thoughtworks or connect with both Sheth and Ferri on LinkedIn.TakeawaysApplying AI to modernise complex enterprise systems.Distinguishing hype from practical AI applications.Balancing probabilistic AI with deterministic enterprise software.Organisational and leadership challenges in AI modernisation.Building control, traceability, and abstractions in AI workflows.Advice for CIOs and CTOs on AI adoption.Chapters00:00 Introduction to AI and Legacy Modernisation01:18 Meet the Experts: Shodhan and Alessio02:47 Distinguishing Hype from Value in AI05:51 The Tension Between Probabilistic and Deterministic Systems10:13 The Human Element in Modernisation17:12 AI's Role in Enterprise Transformation19:26 Distinguishing Hype from Value in AI22:13 Probabilistic vs Deterministic Systems27:45 People and Processes in Modernisation29:03 Lessons in AI for Legacy Modernisation

What does it really take to build an AI-ready enterprise when your data is fragmented, teams operate in silos, and years of technology decisions have created complexity that no large language model can magically fix? In this episode of Tech Talks Daily, I speak with Raymon Ohmori, Senior Principal Software Engineer at Valiantys, and Jiecheng Dong, Senior Software Engineer at Valiantys, about the work that goes into enterprise AI adoption and why successful AI transformation begins long before companies deploy agents, copilots, or autonomous workflows. Using Valiantys' work with Mercedes as a case study, Raymon and Jiecheng explain how modernizing software delivery and connecting data across teams can create the foundation required for AI systems to deliver meaningful business value. We discuss why fragmented data, organizational silos, poor governance, and unclear business problems continue to prevent many companies from moving beyond AI pilots. The conversation examines what it means to become AI-ready in practice. Jiecheng explains why enterprises need performant, structured, and queryable data rather than simply feeding huge volumes of information into large language models. Raymon shares why businesses must begin with real problems, stakeholder needs, and clearly defined outcomes to justify the cost of AI and successfully move projects into production. We also discuss the growing role of agentic AI and autonomous workflows in software engineering. How should engineering teams prepare AI agents to become active participants in software development? What tools, context, permissions, governance, and observability do these systems need to operate effectively? And why might treating an AI agent more like a new colleague than another software tool help teams think differently about deployment? Raymon and Jiecheng also share their perspectives on AI-assisted software development and developer productivity. As AI becomes increasingly capable of writing code, the role of the software engineer is shifting toward architecture, system design, requirements gathering, trade-off evaluation, and translating business needs into technical specifications. We also discuss the challenge facing junior developers and why companies still need to create pathways for new engineering talent. Finally, we examine the practical steps CIOs, CTOs, and engineering leaders can take today to build more connected, AI-enabled enterprises. From improving data ownership and governance to identifying costly problems that AI can realistically solve, this conversation offers a practical guide for companies trying to move from AI experimentation to production systems that deliver measurable value. Where is your company on its AI journey? Are fragmented data, organizational silos, and unclear business problems preventing your AI projects from reaching production, or have you found effective ways to turn experimentation into measurable results? Share your thoughts with me.   Useful Links   Valiantys Website: https://www.valiantys.com/  Valiantys LinkedIn: https://www.linkedin.com/company/valiantys/    

In this episode of The Tech Leader's Playbook, Avetis Antaplyan speaks with technology executive, investor, author, and MIT Media Lab affiliate researcher Harry Glorikian about how AI is changing business at the operating-model level. Drawing from his book The Invisible Interface, Harry explains why domain experts may outperform pure technologists, how AI agents could enable billion-dollar companies with remarkably small teams, and why traditional software businesses face growing disruption. They also examine AI implementation mistakes, organizational redesign, proprietary data, cognitive rent, leadership accountability, and why executives should focus on revenue creation—not only headcount reduction. This conversation is essential for CEOs, CIOs, CTOs, investors, entrepreneurs, and technology leaders preparing their organizations for an AI-native economy.What You'll Learn• Why AI strategy must be led by executives rather than delegated entirely to technology teams.• How domain expertise helps organizations build more accurate and valuable AI workflows.• What the invisible interface means for software, commerce, customer relationships, and competitive advantage.• When proprietary data, institutional knowledge, and AI memory create cognitive rent and vendor lock-in.• The organizational questions leaders should answer before deploying AI agents across the business.Chapters00:00 AI Leadership and Organizational Change01:35 Harry Glorikian's Career Journey06:30 Leadership Lessons and Innovation16:24 Executive AI Implementation Mistakes25:00 AI Agents and Smaller Teams27:30 SaaS Disruption and Consolidation34:12 Why AI Is Not a Bubble39:32 The Invisible Interface52:42 Redesigning AI-Native Organizations01:06:01 AI Investment Opportunities and PredictionsFollow Avetis AntaplyanInstagram:https://www.instagram.com/avetisantaplyanSpotify:https://open.spotify.com/show/0rOkUXDSQb6SVFE6LttWDeApple Podcasts:https://podcasts.apple.com/us/podcast/the-tech-leaders-playbook/id1690263628Follow Harry GlorikianLinkedIn:⁠https://www.linkedin.com/in/harryglorikian/⁠Website:⁠https://glorikian.com/⁠HIRECLOUT:https://www.hireclout.comThe Tech Leader's Playbook:https://www.podcast.hireclout.comLinkedIn:https://www.linkedin.com/in/hirefasthirerightartificial intelligence, AI leadership, enterprise AI strategy, generative AI, AI agents, large language models, organizational design, future of work, digital transformation, AI implementation, executive leadership, AI business strategy, domain expertise, proprietary data, cognitive rent, AI memory, competitive advantage, SaaS disruption, software disruption, AI infrastructure, business innovation, technology investing, one-person billion-dollar company, Harry Glorikian, Avetis Antaplyan, The Invisible Interface, The Tech Leader's Playbook, MIT Media Lab#ArtificialIntelligence #AILeadership #EnterpriseAI #GenerativeAI #AIAgents #DigitalTransformation #FutureOfWork #TechnologyLeadership #BusinessStrategy #SaaS #Innovation #ExecutiveLeadership

https://clearmeasure.com/developers/forums/ Michael Nygard advises consulting firms, private equity teams, CTOs, CEOs, and boards when they require senior technology judgment for limited-term, high-impact situations — including architecture assessment, platform rescue, cloud and data cost intervention, AI engineering enablement, technical diligence, divestiture and carve-out architecture, and operating-model redesign. Over a 35-year career, he has worked at the seam where people, processes, organizations, and the systems they build intersect. Most organizations treat those as separate problems. The hardest failures, and the most consequential wins, live precisely where they interact. That through-line is what Release It! is fundamentally about. The vocabulary it introduced — circuit breakers, bulkheads, stability patterns — is now standard in how the industry discusses reliability, and the book is widely cited as foundational to DevOps and cloud-native practice. At Nubank, he led the Data Business Unit with over $300 million in annual spend, then served as Chief Architect with reach across 2,500 engineers while the customer base grew from 75 million to 125 million across Brazil, Mexico, and Colombia. Results included cutting data-platform spend roughly 50% year-over-year, improving on-time data availability past 99%, building governance aligned with LGPD, GDPR, and CCPA, moving team engagement from the bottom decile to the 60th percentile, and rolling out AI coding tools to more than 90% of engineers without customer-visible quality regression. At Sabre, as part of the CTO office, he helped lead development-practice modernization, GCP migration strategy, mainframe offload architecture, technical diligence, and divestiture architecture across thousands of applications and hundreds of products. He is most effective when the stakes are real, the system is sociotechnical, and the solution must hold across architecture, execution, economics, and organizational behavior. LinkedIn: https://www.linkedin.com/in/mtnygard/ Personal Blog & Website: https://www.michaelnygard.com GitHub: https://github.com/mtnygard Twitter/X: https://x.com/mtnygard Release It! (Pragmatic Programmers): https://pragprog.com/titles/mnee2/release-it-second-edition/ 97 Things Every Software Architect Should Know (O'Reilly): https://www.oreilly.com/library/view/97-things-every/9780596800611/ Goodreads Author Page: https://www.goodreads.com/author/show/6089.Michael_T_Nygard LinkedIn Articles: https://www.linkedin.com/today/author/mtnygard Presentations Archive: https://github.com/mtnygard/presentations/wiki Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.

Why do AI agents and applications look impressive in demos but struggle when companies try to deploy them in production? In this episode of Tech Talks Daily, I speak with Nikunj Bajaj, co-founder and CEO of TrueFoundry, about why enterprise AI has become a systems problem, what companies need to move AI from proof of concept to production, and how better infrastructure can improve reliability, governance, security, observability, and cost control. Before founding TrueFoundry, Nikunj worked at Meta on conversational AI systems serving more than a billion users and contributed to the company's internal machine learning platforms. He explains how developers at Meta could concentrate on solving business problems while infrastructure handled logging, monitoring, deployment, and governance by default. In many enterprises, the same journey from an AI idea to a production application can still take weeks or months. Nikunj argues that increasingly capable AI models are not necessarily the biggest barrier to enterprise adoption. The harder challenge is building reliable systems around them. Companies need to know what happens when a model becomes unavailable, how an agent is behaving, which data it can access, how much it is costing, when a human should intervene, and whether there is a kill switch when something goes wrong. We discuss why AI proofs of concept often fail when exposed to real users. Controlled demonstrations rarely reproduce production conditions such as unexpected prompts, malicious actors, heavy workloads, model outages, latency, and dependencies between multiple components. Even when individual parts of a system perform reliably, combining them can create failure rates that businesses cannot accept for mission-critical workflows. The conversation also examines the infrastructure required as companies introduce multiple AI models and agents. Nikunj explains the roles of model gateways, MCP gateways, and agent gateways, and how bringing these components together through an AI gateway can give enterprises a control plane for observing and governing AI traffic. Cost is another major challenge. Nikunj explains why sending every request to the most powerful model can waste significant amounts of money when smaller or cheaper models could produce comparable results for simpler tasks. Intelligent model routing can help companies balance quality, latency, availability, and price. He shares how organizations using this approach have reduced model costs by as much as 75 to 80 percent in some production environments. We also discuss what reliable multi-agent systems require in practice. Companies need clearly defined boundaries for what agents can do, escalation routes to other agents or people, safeguards against infinite agent loops, and complete audit trails of interactions and decisions. For CIOs, CTOs, AI engineering teams, platform leaders, and companies trying to move generative AI and agentic AI into production, this conversation provides a practical guide to the infrastructure decisions that determine whether AI applications remain impressive prototypes or become reliable business systems. The next stage of enterprise AI will not be defined by models alone. Companies that can connect, observe, govern, secure, and control their AI applications while managing costs will be better positioned to turn experimentation into dependable production systems.

In this episode of Shift AI, Jared Wray, CEO and co-founder of Hyphen, joins host Boaz Ashkenazy for a wide-ranging conversation on how AI is poised to eliminate the complexity of cloud infrastructure and the DevOps role entirely.Jared shares his unconventional career journey from growing up in a small town in Idaho, where technology barely existed, to washing dishes at 15, teaching himself programming at the local ISP, and eventually founding five startups across cloud computing, energy tech, and developer infrastructure. From bootstrapping Tier 3 (acquired by CenturyLink) to co-founding Palmetto, now one of the largest energy lenders in the nation, Jared's path has been defined by a passion for solving infrastructure problems.The conversation dives deep into why DevOps has become painfully complex, with developers needing to glue together seven to thirteen different services just to deploy a single application across providers like AWS, Google Cloud, and Cloudflare. Jared explains how Hyphen is using AI to abstract away this complexity by asking developers only for business rules like uptime requirements and performance needs, then letting the AI determine the right architecture, deploy it, and operate it autonomously.Boaz and Jared explore why tools like Claude Code and other coding agents still cannot handle the full deployment lifecycle, what it would look like if AI agents replaced PagerDuty by calling you during an outage with a diagnosis and recommendation, and why the future of infrastructure is an autonomous cloud where humans are decision makers and agents handle everything else. The episode closes with a forward-looking discussion on agent-only companies, the death of cloud certifications, and why Jared believes DevOps was a good idea that we ran too long.This episode is essential listening for CTOs, platform engineers, and startup founders who want to understand how AI is moving beyond writing code to fundamentally transforming how software is deployed, operated, and scaled.Chapters[00:00] From Pocatello to Five Startups: Jared's Career Journey[02:49] Building Fonz, Co-founding Palmetto, and Finding Passion in Infrastructure[06:05] Why AI Led Jared Back to Infrastructure with Hyphen[07:05] First Job as a Dishwasher and Breaking Into Tech[08:46] What Is DevOps and Why Does It Exist[10:49] Why Cloud Infrastructure Has Become So Painfully Complex[12:36] How AI Can Apply Best Practices Without Reinventing the Wheel[14:35] The Hyphen Developer Experience: Business Rules Over Architecture[17:14] Why Claude Code and Coding Agents Cannot Solve Infrastructure Yet[20:26] The Full Context Problem: Operating Across Multiple Cloud Providers[23:07] Autonomous Cloud: When Agents Talk to Agents[24:17] Replacing PagerDuty: AI Agents That Call You During Outages[28:47] March Madness, Live Streaming, and Why Five Minutes Feels Like a Lifetime[30:59] Two Words for the Future of Work: Autonomous Cloud[33:20] Agent-Only Companies and Why Humans Will Be CEOs[35:12] DevOps Was a Good Idea We Ran Too Long[35:41] What Is Next for HyphenConnect with Jared WrayLinkedIn: https://www.linkedin.com/in/jaredwray/Email: jw@hyphen.aiConnect with Boaz AshkenazyLinkedIn: https://www.linkedin.com/in/boazashkenazy/Email: info@shiftai.fm

Why are companies investing heavily in AI, analytics, and data platforms while business leaders still struggle to see what is happening across their operations quickly enough to make confident decisions? In this episode of Tech Talks Daily, I speak with Massimo Merlo, Vice President for UK, Iberia, and Italy at Elastic, about why the next stage of enterprise AI adoption will depend less on who deploys the most advanced models and more on which companies can give people and AI systems access to relevant, trusted, and secure information when decisions need to be made. Massimo describes the problem as a lack of decision-grade visibility. Most large companies are not short of data. They have spent decades building data platforms, analytics systems, dashboards, cloud infrastructure, and reporting tools. Yet information remains fragmented across departments and applications, insights arrive too late, and employees often struggle to find the small amount of information that matters among enormous volumes of data. The result is a growing gap between having information and being able to act on it. Massimo explains why simply adding an AI model to this environment does not solve the underlying problem. If an AI system is connected to fragmented, outdated, poorly governed, or irrelevant information, it can produce convincing answers without providing reliable business outcomes. The quality of an AI model matters, but the context available to that model increasingly determines whether AI becomes a useful business asset or an operational liability. This leads to one of the biggest technology conversations emerging around enterprise AI: context engineering. Massimo explains how context engineering provides AI systems with the relevant data, tools, permissions, organizational knowledge, and guardrails required to complete a task safely. Rather than sending ever-larger volumes of information to AI models, companies need infrastructure capable of retrieving the right information and making it available at the moment a person or software agent needs to act. Fraud detection provides a practical example. An AI agent evaluating a transaction needs more than access to a powerful model. It requires customer history, behavioral patterns, company risk thresholds, permissions, compliance requirements, and the ability to recognize activity that falls outside normal behavior. Without that context, the system could block legitimate customers or approve fraudulent transactions while presenting its decision with complete confidence. We also discuss why digitally mature companies can still struggle with real-time decision-making. Massimo shares lessons from Elastic's work with organizations including Reed, the Met Office, and Rightmove, explaining why having sophisticated technology systems does not automatically make a company context mature. Information can still remain trapped between applications, teams, and databases, preventing employees and AI agents from seeing the complete picture when it matters. The conversation challenges another long-standing enterprise technology habit: adding more dashboards. Massimo explains why dashboards often provide visibility into what has already happened without helping people decide what to do next. Companies can continue adding reporting layers while employees become overwhelmed by information and remain unable to identify the actions that will improve customer experience, productivity, security, or business performance. A healthcare example demonstrates what becomes possible when companies solve this problem. Massimo shares how CogStack at King's College Hospital brought together unstructured patient information during the COVID-19 pandemic and made it searchable using natural language processing. Clinicians could find relevant information without waiting for technical teams to build new queries or systems, helping medical professionals access information when patient decisions needed to be made. For CEOs, CIOs, CTOs, data leaders, and technology teams trying to improve AI ROI, Massimo offers practical advice on where to begin. Do not start with another model, tool, or dashboard. Start with a business decision or workflow that is currently too slow, unreliable, or difficult to execute. Identify what information that decision requires, where the data is stored, who or what system needs access to it, which permissions should apply, and where information currently becomes delayed or disconnected. That process can reveal the visibility gaps preventing companies from turning their existing data and AI investments into measurable results. We also examine why search and retrieval are becoming infrastructure concerns for companies introducing AI agents. As software agents begin making recommendations and taking actions across business systems, their performance will depend on whether they can securely retrieve relevant information at scale. For business and technology leaders facing pressure to demonstrate returns from AI investment, this conversation provides a practical framework for improving enterprise search, context engineering, AI agent reliability, real-time operational visibility, and decision-making. The companies that gain the greatest value from AI may not be those collecting the most data or deploying the most models. They will be the companies capable of finding what matters, understanding its context, and getting trusted information to people and AI systems quickly enough to act on it. That is where better visibility can become better decisions, stronger productivity, and business growth.

Why are companies spending heavily on AI tools while struggling to show meaningful improvements in productivity, revenue, or business performance? In this episode of Tech Talks Daily, I speak with Matt Cloke, Chief Technology Officer at Endava, about what it takes to become an AI-native business, why deploying thousands of AI licenses does not amount to an AI transformation, and how companies can move from experimentation to measurable business outcomes. Matt has played a central role in Endava's own adoption of artificial intelligence and the development of Dava.Flow, the company's methodology for applying AI throughout the technology delivery lifecycle. With more than 11,000 employees and clients operating across multiple industries, Endava has treated itself as "client zero," testing AI internally before advising other companies about how to introduce it across their operations. Matt shares the story of a CEO who proudly told him that his company had completed its AI transformation after purchasing 10,000 licenses for an AI tool. Twelve months later, the business had seen little return on its investment and returned for help understanding what becoming AI-native actually required. The story captures one of the biggest problems with enterprise AI adoption today: buying technology is easy, but changing how people think about problems, redesign workflows, and create business value is much harder. We discuss why Matt believes becoming AI-native is primarily a mindset. Rather than treating AI as another application added to the technology stack, employees should become curious about where AI can improve existing processes, remove unnecessary work, and create new ways of delivering value. Matt also explains his idea that AI works best when it becomes invisible. Instead of requiring employees to constantly interact with chatbots and standalone AI applications, software agents can operate inside existing workflows, monitor information, prepare responses, identify problems, and bring people into the process when human judgment is required. His own use of AI agents provides a practical example. While attending meetings that prevented him from monitoring email for several days, Matt used agents to review incoming messages, redirect requests, identify urgent communications, and prepare draft responses. Rather than handing complete control to automation, he determined which actions required approval and where AI could operate independently. This leads to a wider discussion about human oversight and accountability. Matt argues that managing AI agents may increasingly resemble managing teams. Leaders do not inspect every decision made by every employee, but they establish responsibilities, controls, escalation points, and circumstances where intervention is required. Companies introducing agentic AI need similar approaches to supervision. We also examine two mistakes Matt frequently sees companies make. The first is treating AI adoption as a software rollout, buying tools for employees and expecting productivity gains to appear automatically. The second is creating centralized AI centers of excellence and expecting a small group of specialists to determine how every department should use the technology. Matt argues that employees closest to business processes are often best placed to identify opportunities for improvement. At Endava, the legal team runs monthly AI hackathons to redesign its own workflows, supported by technology specialists but led by people who understand the work itself. For companies operating in payments, financial services, and other regulated industries, the conversation turns to reliability, auditability, traceability, and risk. Matt explains how Dava.Flow allows companies to translate regulatory requirements and operational controls into policies that AI systems must follow and demonstrate throughout the delivery process. Rather than searching for a single killer AI application, Matt recommends examining end-to-end business workflows. Companies can map how information moves between employees, departments, and systems, identify unnecessary handoffs and manual processes, and determine where AI agents can improve speed, cost, and performance without replacing entire technology platforms. Leadership is another major theme throughout the episode. Matt believes the companies that achieve meaningful results from AI will be led by executives who personally use the technology, understand its capabilities, and demonstrate the behaviors they expect from their workforce. He shares how Endava brought senior leaders from legal, technology, people, and other business functions together to build software agents themselves. The experience changed how executives thought about technology investments, including one leader realizing that an existing vendor contract might no longer be necessary because the company could build the required capability internally. For CIOs, CTOs, technology leaders, and business executives under pressure to demonstrate returns from AI investment, this conversation provides practical lessons on becoming AI-native, redesigning workflows, managing software agents, maintaining human accountability, operating AI in regulated industries, and moving beyond technology adoption toward measurable business value. The companies that succeed with AI may not be those buying the most tools or making the biggest announcements. They will be the ones whose leaders understand the technology, whose employees rethink how work gets done, and whose AI systems quietly become part of everyday business operations.

Host Jason Pereira sits down with Suvrat Bansal, CEO of Clarista, to discuss how financial firms can safely launch AI applications without getting tangled in massive data-engineering projects. Bansal explains how Clarista acts as a secure "data fabric", streaming information directly from a firm's existing software without forcing them to completely overhaul their databases or risk platform lock-in.The conversation dives into how Clarista bridges the gap between fast AI development (like "vibe coding") and strict corporate realities, ensuring every app has ironclad data tracking, role-based security controls, and strict compliance audits. By providing a shared internal catalogue, the platform stops teams from accidentally building duplicate AI tools while helping wealth management firms quickly turn messy data into reliable, automated daily workflows.This episode is a must-listen for operations leaders and CTOs at wealth management firms who want to build custom AI workflows quickly while maintaining rigorous corporate data governance.Episode Highlights:00:00 Welcome and Guest Intro00:40 What Clarista Does01:10 Origin Story and Growth01:51 Building the Data Fabric04:59 Why Data Is Hard06:38 Outcome Focused Delivery07:34 From Insights to Workflows08:55 Agents and Vibe Coding10:37 Enterprise Pitfalls and Controls18:45 Avoiding Duplicate AI Apps22:27 Who Is Winning With AI25:08 Rapid Fire Closing Questions28:01 Wrap Up and SponsorResources:Facebook – Jason Pereira's FacebookLinkedIn – Jason Pereira's LinkedInWoodgate.com – SponsorClaristaLinkedIn - Survat Bansal's LinkedIn Hosted on Acast. See acast.com/privacy for more information.

AI isn't just speeding up recruiting; it's actually forcing companies to redesign work itself, blending human judgment with agentic execution across hiring, mobility, and skills development. As a result, most conversations these days are about AI in the enterprise centre on software development and engineering. Recruiting, hiring, and talent management get far less attention, but they may be where AI's impact is most immediate.In a recent episode of Tech Transformed, host Dana Gardner spoke with Meghna Punhani, Chief People Officer at Eightfold AI, about how organisations are rethinking talent acquisition, workforce planning, and employee development in an AI-driven world. Meghna Punhani's perspective is shaped by nearly two decades at Google, a stint leading employee experience at Palo Alto Networks, and her current dual role at Eightfold AI, where she both leads the people function and helps build the product her team relies on. That vantage point gives her a practical, ground-level view of what works and what doesn't when AI meets HR.Reimagining the Talent Lifecycle with AI Punhani's central argument is that most legacy HR systems were designed for a different purpose, one that has evolved as work itself has changed and the workforce now includes AI agents alongside people. Simply bolting automation onto existing processes, she argues, isn't enough. Organisations that are succeeding are the ones re-engineering roles, workflows, and organisational structures from the ground up, treating this as an operating-model shift rather than an IT upgrade.This shift touches the entire talent lifecycle, from how companies find candidates and evaluate skills instead of just job titles to how they support internal mobility. Punhani points out that skills now have a much shorter shelf life than in the past, which means static job descriptions are giving way to dynamic, skills-based decision-making. AI, she says, helps surface pathways for employees that traditional resumes and titles would never reveal, including her own nontraditional route into HR leadership.How AI Is Reshaping Workforce Strategy Trust is the recurring theme throughout the discussion. Punhani is candid that employees often fear AI-driven decisions, especially around jobs and evaluations. Her approach is focused on transparency first. When Eightfold rolled out digital twins internally, employees were uneasy until leadership explained how the technology worked and used it themselves, which helped build organisation-wide confidence.That same principle shows up in Eightfold's own hiring practice. One example is the company's campus recruiting programme in India, where its AI interviewer conducted roughly 90 per cent of interviews. This enabled recruiting to scale from around eight or 10 university partners to more than 150, and from approximately 5,000 applications to 15,000, without pulling engineers away from their day-to-day duties.Time-to-offer dropped from around six weeks to as little as four days in some technical roles, largely because interviews could happen around the clock rather than around a recruiter's or hiring manager's schedule. Beyond recruiting, Eightfold's internal initiative, nicknamed Project Andromeda, applies the same re-engineering approach across sales and finance, reportedly reclaiming thousands of employee hours through redesigned, agent-assisted workflows.AI and the Future of TalentLooking ahead, Punhani doesn't frame AI as a threat to human contribution, but she frames it as an amplifier of it. As tools become more accessible across every function, she believes the people who will succeed won't be the ones who know the most facts, since AI can answer those questions. Instead, it will be the people who ask better questions, orchestrate multiple AI agents, and apply judgment where the right answer isn't obvious.For HR leaders specifically, Punhani's advice is to claim a seat at the table now, rather than letting AI adoption happen without a people-first lens. This means learning the technology firsthand, demonstrating its value to non-technical teams, and partnering closely with CTOs and CIOs to shape decisions jointly. Her advice for individuals entering this shifting job market is similarly grounded: focus on learning agility over any single technical skill, since the skills in demand today may look different within months.Future of AI in Talent ManagementAcross the conversation, Punhani returns to one idea, and that is AI in talent management isn't primarily a technology problem; it's a leadership and trust problem. Organisations that treat it that way, redesigning work with both humans and agents in mind, are the ones seeing measurable gains in speed, candidate experience, and internal mobility.For HR leaders exploring AI adoption, the takeaway from this episode is to start before you feel ready, build trust through transparency, and let AI handle evaluation and execution so people can focus on judgment, empathy, and connecting the dots across the organisation. If you would like to find out more, visit eightfold.ai or connect with Meghna Punhani on LinkedIn.TakeawaysAI's impact on talent acquisition and management.Reengineering work processes with AI.Building trust and transparency in AI systems.Skills-based internal mobility and workforce planning.AI-driven candidate evaluation and employee development.Chapters00:00 Introduction to AI in Talent Management02:59 Understanding AI's Role in Talent Acquisition06:07 AI's Impact on Workforce Planning and Skills Development10:02 Building Trust in AI for Hiring Processes13:04 Internal Use of AI at Eightfold AI18:58 Measuring ROI from AI in Talent Acquisition25:02 Enhancing Candidate Experience with AI29:53 Future Directions for AI in Talent Management

Don’t let the AI wave crush you. Subscribe to our Newsletter: https://theultimatepartner.com/ebook-subscribe/ Check Out UPX: https://theultimatepartner.com/experience/ Dive into the seismic shifts happening within the AWS Marketplace and discover how AI, self-service product-led growth (PLG), and advanced co-selling strategies are redefining partner success. Matt Yanchyshyn, VP of Marketplace at AWS breaks down the recent announcements from the summit, illustrating how agility and adaptation are crucial to surviving the new agentic future. From lowering professional services fees to the explosion of business applications like ServiceNow, this conversation reveals the hidden mechanics of modern cloud procurement and how you can position your organization to capture massive enterprise opportunities before your competitors do. https://youtu.be/gaWxU1kgCLk Key Takeaways Adapting to the new agentic future requires agility rather than fighting the influx of AI tools. Lowering the listing fee for professional services from 2.5% to 0.5% drastically improves partner economics. Organizations without a self-service or PLG motion on the marketplace are literally leaving money on the table. Millennial buyers increasingly initiate complex enterprise procurements through self-service and AI-driven research. New AI-powered opportunity scoring empowers partners to prove their value internally and to AWS. Marketplace success hinges on optimizing metadata for AI agents, not just traditional SEO. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags: AWS Marketplace, agentic workflow, med pick scoring, phoenix.ai, Cara Cloud, branded storefronts, product-led growth strategy, intrinsic value boost, SaaS evolution, self-service motion, Databricks credit model, Trend Micro companion app, MCP servers, opportunity score tracking, PPA drawdown, concurrent agreements, AAMI structural debt, CXML procurement Transcript: Matt Y Audio Podcast [00:00:00] Matt Y: The ability to adapt with change and kind of roll with punches. ’cause a lot of people are saying like, agents are gonna destroy everything. And, and the opposite has been true. [00:00:08] Vince Menzione: You can feel it happening. The ecosystem is shifting beneath us, the way hyperscalers are partnering, how AI is remaking the channel and what it means to win in 2026. [00:00:19] Vince Menzione: Welcome to the Ultimate Partner Podcast. I’m Vince Menzi, own your host. And each week I sit down with leaders at the intersection of technology, partnerships and outcomes. The voices shaping how ecosystems actually work. We talk about what’s real, what’s changing, and what it takes to lead in this era where the partner channel isn’t just part of the strategy. [00:00:42] Vince Menzione: It is the strategy because being in the room changes everything. [00:00:46] Matt Y: Let’s start. [00:00:50] Vince Menzione: And now on to the really important stuff. So, Matt, I don’t wanna butcher it ’cause I, a couple people have told me how to pronounce your last name and they said use the word magician and you’ll get close to it. But I’m just gonna introduce you as Matt Wy and I’m gonna ask you to pronounce your name on stage, but I want to have you join us. [00:01:08] Vince Menzione: So excited to have Matt wy. After a super busy day and night last night, come over from Brooklyn and join us today. Matt, so great to have you. Thanks. Thank you so much. Thank you so much. Alright, so pronounce your name for us. [00:01:23] Matt Y: Anyone wanna guess? Ian’s? It’s like magician. [00:01:27] Vince Menzione: It’s not that hard, [00:01:28] Matt Y: it’s not that [00:01:28] bad, [00:01:28] Vince Menzione: but I don’t wanna butcher. [00:01:29] Vince Menzione: I wanted to let you do it. Good. [00:01:30] Matt Y: What calls me Matt White. [00:01:31] Vince Menzione: That’s great. [00:01:32] Matt Y: Yeah. [00:01:32] Vince Menzione: So 13 years. [00:01:34] Matt Y: Four coming up on 14 next month. Yeah. [00:01:36] Vince Menzione: Wow. Congratulations. Yeah. So you’ve been there, you’ve been there since the early days. And we, we had a conversation. I had some Microsoft, former Microsoft colleagues. Uh, Theresa Carlson, for those of you who knew the public sector business. [00:01:48] Vince Menzione: Yeah. Who started, I mean, Andy came out, it was so funny because I was there and she was hosting Andy for a dinner and with all the CIOs of the federal government. [00:01:57] Matt Y: Yeah. [00:01:58] Vince Menzione: And she was still at Microsoft and it was actually kind of an interesting time. And she came over and did a lot of great things for a number of years. [00:02:04] Matt Y: Yeah. She [00:02:05] Vince Menzione: and a lot of great [00:02:05] Matt Y: business. [00:02:06] Vince Menzione: Yeah. She really like, it went from employee number one to 7,000. [00:02:09] Matt Y: Yeah. [00:02:09] Vince Menzione: And you, you were, you’ve been there all that whole time. Pretty much. [00:02:12] Matt Y: Yeah, I guess when I started in New York, just down the road, we were, uh, in a Regis facility. There were like 11 of us in, uh, just sitting around a table and we had to speak quietly sometimes because there was a, um. [00:02:21] Matt Y: Some type of a financial services organization down the hall and they’d listen to try and get stock tips on Amazon. Yeah, [00:02:28] Vince Menzione: I love it. [00:02:29] Matt Y: Never leaked. That’s [00:02:29] Vince Menzione: good. I love it. [00:02:30] Matt Y: Yeah, [00:02:30] Vince Menzione: you probably got some great stories and, um, we won’t have time for today ’cause I wanna leave some room for conversations on marketplace end questions. [00:02:38] Matt Y: Yeah. [00:02:38] Vince Menzione: But I would love to invite you back for a real, like, in-depth podcast and I would love to get the whole genesis story. [00:02:44] Matt Y: Let’s do it. [00:02:45] Vince Menzione: We’ll do it. Okay, so let’s talk about, let’s talk about yesterday for you. Uh, some, some really big announcements as well. I thought maybe you could recap a little bit of what’s been going on in the marketplace business and it’s an, it’s been an exciting time. [00:02:58] Matt Y: Yeah. Yeah. You know what’s, I think what was really nice yesterday is it was sort of the combination of bringing, uh, our partner services like Partner Central and all those other services together closer to marketplace. We’ve been doing that over, over several years. So Marketplace has some of its own. [00:03:12] Matt Y: Big announcements, like, uh, we have a, we formalized our list and sell initiative. For example. We have a new, so it we essentially reducing the cost, uh, to list on marketplace through a partner program. [00:03:22] Vince Menzione: Yep. [00:03:22] Matt Y: And incentives associated with that. We have a new AI powered listing experience, which I think is particularly important ’cause I think many of you are like me and watching your SEO numbers go down and watching your agent traffic go up. [00:03:33] Matt Y: And so having, uh, an AI assistance in marketplace to optimize your listings for not just to, you know, retain what you can of your SEO, but prepare for the newent future and improve your GEO as we’re calling it. So that, [00:03:45] Vince Menzione: so it’s GEO now? [00:03:46] Matt Y: Yeah. You know, there’s a little debate right now in the acronym Moral A A EO versus GO I’m going, I’m on the G team, so, yeah. [00:03:52] Vince Menzione: Alright. GEO [00:03:54] Matt Y: It’s like the, the, yeah, they’re gonna win. They’re like the Knicks, but the, um, [00:03:57] Vince Menzione: yeah, yeah, exactly. [00:03:57] Matt Y: But yeah, so AI assisted, uh, I mean, making. The most of, like, essentially marketplace is an excellent conversion engine. And so using AI to help improve that conversion engine in the form of your PDPs for both humans and agents. [00:04:08] Matt Y: So that was an exciting launch. Um, I got the most applause when I announced that. We lowered, we made the economics better for, uh, consulting offers professional services, nice to marketplace. We lowered the listing fee from 2.5 to, to 0.5% and wow, it goes even lower in certain circumstances. So just improving the economics. [00:04:24] Matt Y: I’m really excited to. Really partner with a lot of you to reinvent services through, through the marketplace like we did with SAS and other areas. Uh, and we’re doing with agents right now. So that was a big one. And then a whole series of announcements around, um, how we’re making it easier and more cost effective and more efficient to partner with AWS. [00:04:41] Matt Y: So using AI to, uh, using med pick scoring to automatically progress opportunities so you don’t have to kind of wait on a human. To, to click and progress, you know, that that can take days. And, uh, if you, if you wanna have an opportunity and have that be cos sold with AWS, that can be through a mix of agents for the long tail and with humans in the, in the sort of top end and more complex. [00:05:00] Matt Y: And allowing AI to help all the partners improve their opportunity quality so that we can better co-sell together. So. Yeah, I said AI a lot intentionally. Um, [00:05:09] Audience Guest: yeah, [00:05:10] Matt Y: AI sort of in the whole cycle for buyers, for sellers, uh, for operational efficiency, cost of sales. So a lot of announcements. I think I hit the big ones, so yeah. [00:05:18] Matt Y: I’m Might have missed something there. There we go. [00:05:21] Vince Menzione: George. [00:05:21] Matt Y: Oh, and storefront. Yeah. Thanks George. See, I look at George to see what I missed. Uh, we, we acquired a great company called phoenix.ai late last year. Okay. And you, you actually were said Caresoft and Yeah. Be down. Uh, [00:05:30] Vince Menzione: yeah. [00:05:30] Matt Y: So if you’re familiar with Cara Cloud, they have a procurement portal. [00:05:33] Matt Y: It’s heavy use by the US government, and they, um. Uh, we, we acquired them, uh, the really great growth company. They have over 70 logos now, and they help you build a branded storefront on marketplace, which obviously is important in the government space. If you’re procuring on a certain contract with a certain reseller, um, you know, there’s a certain set of products you’re allowed to buy. [00:05:51] Matt Y: But what we’re finding is even down on Wall Street, you hear, um, enterprises are, are using storefronts for internal procurement and they wanna have a curated collection of, of partner products and, and your own ecosystems internally. So we’re selling to both customers. And also to channel partners to build custom storefronts, branded storefronts for, and [00:06:07] Vince Menzione: it makes total sense, right? [00:06:08] Vince Menzione: Yeah, because you wanna li you wanna limit the, the viewing and, uh, and get, because I mean, how many different listings do we have? Like over 30,000? [00:06:16] Matt Y: Yeah. Yeah. There’s, I think the official numbers over th we have over 36,000. I was checking from over 6,000 vendors. Um, it’s a lot. And, and that’s gonna explode with the AI powered, uh, listing, uh, experience that we launched. [00:06:26] Matt Y: We’re gonna make it easier. And I guess what I’ve been telling partners is. You know, customers aren’t clicking through categories anymore. They’re using AI to search. And so it doesn’t matter how big our catalog is, what matters is being found. And what matters is converting that buyer. So if you have a. [00:06:39] Matt Y: If you’re running a demand gen campaign for say, like, you know, life sciences in, in Jersey and there’s a specific buyer at j and j, you wanna capture, that person doesn’t wanna be just dropped onto a generic marketplace, 30,000 listings. They wanna be dropped in a very specific place where they’re seeing like life sciences offers from Accenture, for example, coupled with a life sciences power thing with Elastic, you know, like, but a solution. [00:07:00] Matt Y: And that they want to land in a curated place where that highly intention buyer can be converted effectively. So that, that’s what we’re doing with all this. [00:07:06] Vince Menzione: And that’s where the GEO comes in because [00:07:09] Matt Y: Yeah. ’cause that buyer might be an agent That’s right. With, and that agent has is even more fickle, honestly. [00:07:14] Matt Y: And you know, what used to be milliseconds for the human before they kind of click away is, is now perhaps microseconds. Yeah. And so, uh, you know, having the right metadata and, and the right positioning, uh, the right story that an agent or a human can pick up to ultimately. Uh, complete their product research and choose your product is, is critical. [00:07:30] Vince Menzione: Very cool. Very cool. So before I, I, I’ve been asked to ask you this because I, I’ve had this con, people have brought come to me and said, you gotta ask Matt about music. He’s a big music guy. And, uh, so what are your favorite bands? [00:07:49] Matt Y: So, I mean, the, the real answer is, uh. I, I go to about a show about every week. [00:07:54] Matt Y: As, as Mike Trill knows, uh, we heard a show last night. Um, we were, uh, just a few hours ago, really? And, uh, um, favorite band, uh, well, I’ll tell, I’ll tell a story. I, I had a side hustle with MTV for years. Um, I used to run a music website. Um, oh, that’s cool. I didn’t know that. It got, it got kind of popular. It got sponsored by, if, if anyone’s into like early hip hop. [00:08:16] Matt Y: It got sponsored by a group called Jurassic Five. ’cause he, one of them reached out to me and said, nice. Hey, uh, you know, I’ve been, I like your website. And he ended up paying for a web, hosting a Dream host, if you remember, of cost back then. [00:08:26] Vince Menzione: Oh, Jesus. [00:08:26] Matt Y: Because I was broke and couldn’t afford it. And then, uh, and then this band sent me like a, a single and said, Hey, you know, trying to get the word out about our little band, can you help us out? [00:08:35] Matt Y: And I put their, uh, I put their, you know, single up on my, on my website and it blew up. And that band is Vampire Weekend. So they’re kind of big now. Wow. Yeah. Um, and uh, that got picked up by like Vanity Fair and all these other guys. And then I got sponsored by MTV to essentially write. Music reviews for years on the side. [00:08:51] Matt Y: So I was working for the Associated Press, laying cable in sports and war and, and, uh, yeah. So Vampire Weekend was good to me that, that they, they kind of paved a way to go to a lot of free shows over the years and a lot of bands and see a lot of great music. But yeah. [00:09:03] Vince Menzione: That is very cool. And that, and how did that get your day? [00:09:05] Vince Menzione: WS It was just a, it was just the technology path that was like, [00:09:09] Matt Y: I mean, it’s a, it’s a, I guess it’s a bit of a long story, but, um, the. There’s many versions of this story. I’ll tell the, tell the one quickly. I was living for free in a Fulbright scholarship house in West Africa. You, we can talk about how that happened another time. [00:09:23] Matt Y: And, uh, a guy had sort of fallen down on the floor ’cause he’d had too much to drink. And I, I sort of lay down beside and be like, Hey man, are you all right? And, um, he, uh. He worked, he, he worked for the Associated Press and next day I had the job, um, being West Africa, head of technology for West Africa. [00:09:37] Matt Y: And because of that, um, and as I learned years later, the AP didn’t have dr they had no disaster recovery. Yeah. And I, I can tell you that now ’cause um, you know, 16 years since I worked there, but they, uh, I put the DR in, um, on AWS and we’re talking like, yeah, 16, 17 years ago. This is early. It was early days. [00:09:56] Matt Y: And I, I swear to God, I paid for. Uh, our AWS bill using, um, taxi receipts, fake taxi receipts that I bought in on Nigerian market, um, because there was no budget and so, you know, it was like 30 bucks. [00:10:08] Vince Menzione: I was gonna say swipe a credit card, but they didn’t [00:10:09] Matt Y: knew that this is the entire press this before. [00:10:11] Vince Menzione: This is before, yeah. [00:10:12] Matt Y: Yeah, like the entire ap. Um, and, uh, so AWS called me like, who are you? Like, why, why are you paying on like this like low limit credit card for like the ap? Like, who are you? And, uh. Next day I had the job. Well, a week later I had the job with aw WS. That so cool. So that’s the story’s [00:10:29] Vince Menzione: cool thing. [00:10:29] Matt Y: Yeah. [00:10:30] Vince Menzione: Very cool. [00:10:31] Vince Menzione: Uh, sports teams. So Knicks fan. [00:10:34] Matt Y: Yeah, I mean, I like the Knicks. Um, they’re h hockey, I’m not allowed to say anything different. No. I appreciate them. Uh, I’m a Raptors fan. I grew up in Toronto mostly. Yeah, yeah. Uh, so, and you know, when they won, uh, that was very exciting as well. So no, Nicks are great. I like the Knicks. [00:10:49] Matt Y: Nothing against the Knicks. Um. They’re fine. Yeah. [00:10:54] Vince Menzione: Hockey, hockey fan. Favorite hockey teams? [00:10:56] Matt Y: Oh yeah. Itron. Maple leaf. Maple leaf. Yeah. They’re gonna, they’re gonna win. Of course. Of course. Yeah. Um, like every year they’re actually, we [00:11:02] Vince Menzione: have some Canadians laughing in the sand. [00:11:03] Matt Y: Well, the leaf are, are, are the Knicks of hockey? [00:11:05] Matt Y: Like Yes, they are. You know, it’s 67 years out, coming up on 68 since they won, so That’s crazy. 53 is nothing. I know. Pain. So. Yeah, definitely the least. Yeah. [00:11:15] Vince Menzione: I love it. I love it. It’s so cool. Yeah. So what was the, uh, what was the, what was the last concert you went to? [00:11:22] Matt Y: Well, literally last night. Oh, it was last, [00:11:23] Vince Menzione: oh, that [00:11:24] Matt Y: was actually concert were my favorite bar in the world. [00:11:26] Matt Y: This place called Sunny’s. Uh, it’s, you know, I, I took Mike and, and Matt from, from Texas and from TGS down there to sort of see my neighborhood and they’re like, where are we? And I’m like, yeah, I live here. Uh, sort of an industrial part of Brooklyn. And, and we went to see, um, I dunno what you would call it, like. [00:11:40] Matt Y: I guess it’d be like roots music. There was a woman with an accordion and a guy with a big cowboy hat. Yeah, it was, it was fun. Yeah. [00:11:47] Vince Menzione: That is so funny. Alright, we’re gonna shift back years. Um, important time right now for partners. What, what should partners be looking out for the most? What would you say to them in terms of what’s the, what’s their real headline for them? [00:11:59] Matt Y: Well, I, I, you know, to borrow from you actually, you know, I liked, uh, the, the principles you had up there and, and with agility, um, you know, there’s a lot of fud flying around right now. You know, people. People were like, oh, it’s the demise of sis with the arrival of ai, you know, everyone’s gonna be using agents. [00:12:13] Matt Y: And then it turns out it’s been a huge boon for most, uh, you know, system integrators and consulting companies that I work with. They all have, you know, the, the good ones especially have vibrant consulting practices now, and everyone is deploying fds, uh, you know, um, the new, the new cool acronym. But it’s, it’s essentially created a huge opportunity for the consulting space. [00:12:31] Matt Y: Uh, and similarly, uh, you know, there there’s this narrative around the sa sa apocalypse, which I really hate, you know, ’cause it was, uh, premature and kind of a trigger reaction from the stock market. And, you know, just look, look what Snowflake did. And, you know, they did what a lot of SaaS companies are doing, but they, they added a nice sort of glaze of positioning and, and, you know, their stock popped and they did pretty well. [00:12:50] Matt Y: And so I think the ability to adapt with change and kind of roll with the punches. ’cause a lot of people are saying like, agents are gonna destroy everything. And, and the opposite has been true. For the more successful consulting companies and software companies who have become agentic. But SaaS hasn’t gone away, you know? [00:13:05] Matt Y: No. Look at our own marketplace. We have this agent marketplace, but people aren’t buying atomic agents at scale. They’re buying ified SaaS solutions with sort of agent sidecars, which has created new opportunities for candidly additional licenses, [00:13:16] Vince Menzione: right? [00:13:16] Matt Y: Um, as customers sort of want to consume more AI services on top of their. [00:13:20] Matt Y: On top of their SaaS solutions. So I think being agile, you know, you see like ServiceNow as part of our billionaires club. Yes. They’re not going anywhere. They’re, yeah. They’re gentrifying. You know, Salesforce has pivoted to this headless model, um, along with Asian Force and using sort of Slack as the operating system. [00:13:34] Matt Y: And, you know, you said like a lot of companies from the seventies aren’t around anymore. They’re gonna be winners and losers. Yeah. Um, but the winners are gonna win even more. And so I, I think what’s so important right now for partners is to not, not bite too hard at the, the latest trend. You know, models are changing and everyone’s like, oh, you know, philanthropics really in the world and they’re wonderful, great to work with, amazing technology. [00:13:55] Matt Y: That’s what people are saying about open AI six months ago. That’s right. And before that, you know, and it, I, I was with Fireworks AI yesterday, a great company and they have some really cool stuff with sort of, um, they believe in more cost effective, uh, open source models essentially, that you can find tune. [00:14:08] Matt Y: Maybe that’s gonna win. I don’t know. Um, is it gonna be sort of domain specific models? Is it gonna be highly capable LLMs? Are LLMs gonna level off as soon as Fable and Mythos are allowed to launch? Maybe. I, I don’t think anyone can predict the future right now. So you have to be agile and you have to kind of seize the opportunities and take a couple punches. [00:14:25] Vince Menzione: Yeah. [00:14:26] Matt Y: You know, and marketplace too, like we’re, you have to be unafraid to experiment right now. Um, you know, that’s hard if your stock’s taking a beating. Um, but this is, it’s a, it is a disruptive time, uh, but it’s creating actually enormous opportunities for growth for partners and, and we really see that, you know, in marketplace specifically within AWS. [00:14:45] Vince Menzione: It, it, it does still feel like the deer in the headlights moment. Right. Would you agree? Like you’re probably taking a lot of meetings and, and calls from ISVs specifically? [00:14:54] Matt Y: Well, [00:14:54] Vince Menzione: that are still trying to figure it out. [00:14:56] Matt Y: Yeah. But it’s everyone. Yeah. I think what’s really interesting, I had a meeting [00:14:58] Vince Menzione: with, it’s not just one. [00:14:59] Matt Y: Yeah. I, well, I had a meeting with one of the leading AI companies, like one of the biggest ones. And they, uh, they demonstrated how they work and they were really proud. They were like, you know, look at our agentic workflow. And I came out at me. I’m like, that’s it. Ours is way better. Like really like, you know, ’cause we we’re, we’re using quick desktop with MCP servers and connectors and all this, and you know, we, we have our own sort of ecosystem of partners, a mix of homegrown software and third party. [00:15:20] Matt Y: And I kinda walked out there and, and looked at, you know, my phone, which has been populated by agents this morning with all the, and I was like, I have a way better agent workflow than this world’s leading supposedly AI company. And I think, um, that really, so during, I, I would, during the headlights, you can call it deer in the headlights, I call it chaos. [00:15:36] Matt Y: And in times of chaos there are people who create. Opportunity again. And so, yeah, there are some people who are stuck and who don’t know what to do, who are over worried about token costs, um, who are not experimenting. But there are a lot of companies, uh, taking this opportunity to kind of pivot their business. [00:15:53] Matt Y: Um, I think, I think we’re in a moment and, uh, yeah, I, I candidly I see more of the latter. I see more experimenting. [00:15:59] Vince Menzione: You mentioned ServiceNow. Any other great examples of that? Organizations that really embraced it? [00:16:04] Matt Y: Uh, yeah. Well, you know, ServiceNow is part of this business applications category, as we call it, in marketplace. [00:16:09] Matt Y: That outside of AI, I think is the fastest growing category in marketplace, which is wild when you think about it. ’cause we’ve historically been an infrastructure partner marketplace with security and data and analytics and, you know, security with channel partners, et cetera. But Salesforce, ServiceNow, Workday, Adobe, you know, I could go on. [00:16:23] Matt Y: They, they are actually. You know, our fastest growing category and yeah, ServiceNow, obviously reinventing itself for ai, Salesforce, but Workday, you know, the workday’s done some, who knows if it’s gonna work, but they, they’re experimenting with essentially like a Databricks, uh, credit style model for like, units of work, uh, which I think is fascinating. [00:16:41] Matt Y: Like everyone’s talking about value-based, outcome-based pricing and meter. And, and you have companies that are ERP companies, you know, like traditional business applications, experimenting with effectively like a metered pay as you go, value based credit model. Again, like who knows if it’s gonna work. [00:16:54] Matt Y: But I think that’s really amazing to see and we need more ISVs experimenting. I, I was talking about trend ai and I know they’re, they’re, they’re one of the sponsors yesterday. You know, many of you know them as Trend Micro back in the day. They’ve successfully reinvented themselves. They built that companion app. [00:17:10] Matt Y: Um, you know, that I think we’re seeing. Just a ton of experimentation in the market across categories. Uh, I could go on and on about partners. Um, yeah, there, I I wouldn’t pick a winner right now. Yeah. [00:17:24] Vince Menzione: You, you, we’ve talked about ai. We’ve talked, talk more about the buying journey and how that’s changing, because again, it feels, it feels like that’s also [00:17:33] Matt Y: Yeah. [00:17:33] Matt Y: So, you know, one of, one of the core, uh, strategic objectives, or we’ll say like the philosophy marketplace is that. Um, financial incentives are important, you know, EDP or PPA drawdown, uh, credits. Like we need to act as an efficient and effective vehicle for allowing buyers to exercise their discounts for, and, and sort of partners to exercise their credits, et cetera. [00:17:55] Matt Y: That, that’s actually important. But what, what a lot of people over rotate on that, and we’re really, one of the things we say a lot inside at Amazon or at AWS marketplace is we want to continue to boost the intrinsic value of marketplace beyond the financial incentives. And well over a quarter of all private offers, private pricing, private, uh, custom terms, et cetera. [00:18:14] Matt Y: Um, begin with a self-service or PLG motion. And partners who don’t have a PLG or self-service motion are literally leaving money on the table. Like if you look at like a Databricks for example, and they did a good job integrating buy with a WS within their SaaS application. They have free trials, they have really strong pego and, and, uh, and PLG motion. [00:18:33] Matt Y: They’re making, I can’t share their numbers obviously, but they’re making a ton of money. On purely self-service motions. And importantly, they’re acquiring new business, new logos that they nurture, you know, really like not just leads but closed opportunities, right? That they lead, they’re growing, uh, at a reasonable conversion rate or or success rate into the next big logos. [00:18:50] Matt Y: And these are over multi-year horizons. They’re patient, you know, they bring in these new logos with PLG, and they’re also bringing banking, a lot of large enterprises. Through self-service. I, I was with data Mask. There’s this great little startup from New Zealand. They’re a New Zealand based company. Um, super nice guy. [00:19:06] Matt Y: And, and, uh, they, they got huge logos. I think they got, what was it? A DP and some huge American logos. Okay. And this like logo in, I think it was Chile, or no, it was Peru. They’ve never been to Peru. They don’t have sales in Peru. Um, and they. Buyers were discovering them self-service and they, they, I think they got something like 13 logos entirely through a self-service motion. [00:19:26] Matt Y: One password will tell you the same thing. I was just with them in Toronto and companies big and small startups and the largest are getting enterprise wins in addition to net new small logos through that PLG. Buyer motion. And that’s because you have a whole generation of CFOs, CTOs, CROs, whatever. The C is [00:19:43] Vince Menzione: millennial [00:19:43] Matt Y: who grew up on their phones. [00:19:45] Vince Menzione: Yeah. [00:19:45] Matt Y: And, and it sounds like, you know, hyperbole, but it’s true. They, they want immediate apps, immediate access. And that actually, you’re like, oh, that never translates to business applications. Turns out it does. It does. And they might not be buying on their phone, but what they are doing is researching and we see the numbers, the amount of customers who are doing their research, and then eventually landing on the page from chat, GPT. [00:20:06] Matt Y: From major financial, like Fortune 500 companies is extremely high. Yeah. Uh, you have procurement team, sourcing team, uh, developers who are starting the research increasingly, like in clawed in chat, GPT, and then, you know, building a proposal and then handing it to their enterprise procurement team. Yeah. [00:20:22] Matt Y: Which is still largely unchanged. So buyer behavior is on the front end, on the research side is really changing. So the [00:20:29] Vince Menzione: discovery is happening through PLG. [00:20:32] Matt Y: Yeah. [00:20:32] Vince Menzione: And then the backend work on private offers and things like that sometimes still happens the old way. [00:20:36] Matt Y: Yeah. Well, and so, you know, it’s [00:20:37] Vince Menzione: fax machine, [00:20:38] Matt Y: some people Yeah, sure. [00:20:39] Matt Y: They’re bringing the deal directly to Marketplace last minute. But even if that deal goes direct, sometimes they’re still beginning their research journey and increasingly using Marketplace as a research vehicle, which is why we launched Agent Mode, um, to help you sort of help you and agents do research. [00:20:51] Matt Y: But that I think if, if I have one piece of device for any partner consulting or ISV is. Don’t leave those leads and that money on the table by not having a PLG self-service strategy like you’re fooling yourself. Uh, and it’s, it’s a huge, it’s a huge, huge business for us. The, the majority of all customers by far on marketplace don’t even have a PPA, uh, and a huge percentage of even those with PPA spend beyond the p. [00:21:17] Matt Y: And so if you’re just think if you’re just using marketplaces as like BPA retirement, you are literally losing money. [00:21:22] Vince Menzione: Yeah. [00:21:22] Matt Y: Yeah. [00:21:23] Vince Menzione: We have a session with Vinod. We’re gonna talk a little bit about that right after. Great. So good. Um, so I, yeah, I think, um. We talked about, we talked about agents, we’ve talked about the millennial buyer, the change in buying behavior. [00:21:40] Vince Menzione: What other, what other areas of aspect I, I, I, I do wanna think about like opening it up though for a second. I think that maybe with maybe nine minutes left. Sure. I just want to get a read from the people in the room. People have questions for Matt that we weren’t able to ask them. Yeah, I think, I think we probably have a few of those. [00:21:57] Vince Menzione: I think that would probably be great. [00:21:58] Matt Y: I can sense the hardball coming. [00:22:00] Vince Menzione: You’ve known each other [00:22:00] Matt Y: a long time. [00:22:01] Vince Menzione: Yeah. No, no. Hardball. We have a mic back here. Okay. I’ll just, we’ll, we’ll, we’ll get you a mic as we are recording. So good. Thank you. [00:22:11] Audience Guest: Uh, Boris Geller with a, a Click PLG is near and dear to my heart. [00:22:17] Audience Guest: We’ve been doing a lot of business in marketplace and I’m still struggling to sell my vision internally on, on, uh, on PLG. Uh, I think. Ag Agent AI is gonna be one of the drivers, and we are already on, uh, agent Marketplace, but I would appreciate guidance on, uh, best practices. How do we kind of, uh, operationalize it? [00:22:41] Audience Guest: It’s, it’s on us, not on you. [00:22:43] Matt Y: Well, no, I think it’s on both of us. You know, we, uh. One thing that we’re trying to do is give you more data to, to sell to your internal stakeholders in your executive suite. The value of co-sell with AWS all up, like finally with what we launched at, uh, the summit yesterday, you now get an opportunity score. [00:23:02] Matt Y: You, you get a number. People have been asking for this for years, so, so you can say when we do this and we, when we give AWS this information. The score goes up and we have a higher propensity to be cos sold by humans or agents before you had to kind of, it was like this mystery you had to guess. And similarly with marketplace, um, we, we have new dashboards that you can use to sort of, you used to have to sit down with us and go through spreadsheets to trace sort of lead to trace the funnel to sort of a close opportunity. [00:23:28] Matt Y: And we’re gonna continue to launch more there. But you now have more data that you can show. You can be like, listen, these are our inbound leads, this how’s converting, and now we have PRM, the partner revenue measurement where we can say like, this is what it’s translating into in terms of. AWS service revenue driven by our product. [00:23:41] Matt Y: And so that being able to tie from that inbound lead from your demand gen campaign through to a converted opportunity to what you actually drive from an AWS impact perspective, so you can, and then what your opportunity score is that data you can use to sell. Not only internally, but to us as well. Yeah, to a skeptical sales team or whatever who’s not maybe, you know, hype on partners in the, in the US West. [00:24:03] Matt Y: You can be like, listen, I don’t care what you think about my business. This is what I’m gonna drive for you with your quarter retirement from an AWS perspective, and this is how the shape of your customer accounts are gonna change. And this is why you should pay attention to my opportunities. ’cause my opportunity score is, is crazy high and I’m giving you insights into business that AWS would not otherwise have. [00:24:19] Vince Menzione: That’s your brand story we’re talking about. [00:24:21] Matt Y: Yeah. [00:24:22] Vince Menzione: Building your story up with within [00:24:25] Matt Y: So it’s, it’s about the data, I guess. And, and you should, you know, you should all actually be [00:24:28] Vince Menzione: Yeah. [00:24:29] Matt Y: Asking me for more data, so, you know, and tell me like, what do you need to sell to your internal stakeholders? ’cause if I can draw a clear line. [00:24:35] Matt Y: From your demand chain campaign that lands on a marketplace, which I know is a conversion machine, it has way better than industry levels of, of conversion rates. And then you can show, hey, if we have a PLG strategy and we land those leads on marketplace, we will convert them with high efficiency, low cost of sales and, and, and have sort of a bifurcated where we can close some through self service, some through express private offers and some through private offers, depending on deal size. [00:24:57] Matt Y: Like you tell A CFO that, and they’re my number one customer now and they love it ’cause they see cost of sales going down, cost of operations going down and business going up. Um, so I think we have more data than we used to use that data. And let me know what other data do you need to make that pitch and make that pitch to the CFO go around the head of sales, all those other people. [00:25:15] Matt Y: Honestly, the CFO is where we get the best leverage. [00:25:18] Vince Menzione: Awesome. Great question. [00:25:23] Matt Y: Gonna bring your mic. [00:25:23] Vince Menzione: We’re, we’re gonna get your mic here. There you go. Oh, [00:25:25] Audience Guest: thank you. So my name’s Jody Cheval and I’m a consultant now, but I was at Workday during when they adopted AWS and it, a sales organization needs propensity to buy data. [00:25:34] Audience Guest: To really drive the sales team to realize the opportunity kind of makes them visualize it. We didn’t struggle, but it was challenging to get that data because at that time we’re getting spreadsheets. So does AWS have a vision of making that API based data that our client, my clients, can get at and bring into a tool to start building account hypothesis based on that data? [00:25:57] Audience Guest: ’cause it really is important to an enterprise sales guy to have the sense that OAWS can help me close this deal. [00:26:03] Matt Y: Yeah. I mean. Part of that. So we, we launched, we’ve been launching part of that in stages and we’re not done. There’s, there’s more coming. Um, part of that is embedded really within the new, uh, partner agent workflows. [00:26:13] Matt Y: We are giving sort of more, uh, information back to you, not just about like what funding programs you’re eligible for, but like, you know, and when, when we will co-sell this deal with you, which is effectively a signal like we, we see this as a high value opportunity, that you have a likelihood of winning internally. [00:26:28] Matt Y: We, we have this solution matching engine that we’re using and we announced. That, that that ties you the partner to a customer specific opportunity that you have a high propensity or the partner has a high propensity to assist with and ultimately win. And now we’ve tied that to our express private offers, which we announced this week. [00:26:44] Matt Y: So it’s an indirect answer to what you’re asking, but a rep can essentially say. Send a private priced offer to the customer on behalf of the partner without having to ring up the partner because they have a high propensity to win this deal with the customer. So we’re progressively launching features like that. [00:26:59] Matt Y: In addition to the propensity to buy data that we do now share. It used to be kind of, again, manual magic depending on who you knew we could share. Now we do share that programmatically, and there’s more to come specifically in that space. Uh, I’d say watch that space. In the next few months, there’s gonna be more data coming away, but we do have the APIs, we have the agent. [00:27:16] Matt Y: We have things like express private office solution matching, and we have been sort of in that space progressively launching features over the last six to 12 months. And, and you should expect to see some more there soon, not just from us or from our partners. [00:27:27] Vince Menzione: Nice. Any announcement dates? [00:27:30] Matt Y: I can’t commit to a date or else my engineers will get mad at me. [00:27:33] Vince Menzione: It looks like we Another question number. Is the mic still back there? Okay. There’s a gentleman over here [00:27:40] Audience Guest: first Go leaves. Um, it’s awesome. I’m right next to. I was right next. [00:27:46] Vince Menzione: We’ve got a lot of great plants here, so, [00:27:49] Audience Guest: um, so this may be a little bit myopic or, or a challenge that we run into, but I love a lot of the innovation that’s looking forward and all the future things that we’re doing. [00:28:00] Audience Guest: One of the things that we’re struggling with is a little bit of almost like tech or structural debt. How do you think about bringing flexibility to the core pieces that underpin all of the innovation, which is. We are self-hosted. So one of our listings is an a MI. You can’t amend an a MI, you have to cancel and start over. [00:28:18] Audience Guest: So a lot of the building blocks, when you think about PLG, if somebody wants to add to that in an a MI listing, it’s, it’s sort of broken. So how are you thinking about taking all of the, the rapidly changing buyer behavior and then looking back at the structural foundation that underpins all of those things, like offers and, and amendments and changes and all of that? [00:28:39] Matt Y: Yeah. I, I promise I didn’t seed that question, but that, that’s a great one. Um, so not to get too in the weeds, but fundamentally, marketplace was built up, um, a bit like AWS like a set, a series of services somewhat independently. And each product type was effectively its own service, SaaS, server images, ais. [00:28:59] Matt Y: Um, what we’ve done recently is now we, we have, we got rid of product types basically on the backend. You, you don’t see it, but what that means, for example, like another thing AAMIs don’t support today, future data agreements. Um, or concurrent agreements, uh, they will all be supported by amis before the end of the year. [00:29:14] Matt Y: ’cause what we’re doing, this fundamental thing that you won’t even see called product offer decoupling. Uh, and it’s a fundamental piece of things that we need to unwind. ’cause we built up, we were moving very quickly over the years. We had a distributed engineering model and we built each product type independently. [00:29:28] Matt Y: And so yeah, if you’re a seller and you’re selling containers, agents, SaaS, amies, um, we’re breaking down the silos between those so that each of them will get the same benefits. And, and by the way, we’re taking the same approach to international. Hopefully you’ve noticed now that. It’s not like a feature launches in the US only and then takes five years to launch in either public sector or another country. [00:29:48] Matt Y: We, we’ve taken a global approach to feature launch and increasingly a product type neutral approach to feature launches. Uh, that’ll be largely resolved before the year’s out. We’re working on it right now. So again, it’s, it should be transparent to you, like you shouldn’t actually see any difference in the, in the experience. [00:30:05] Matt Y: Except that all of those features will be available. So, so that is, uh, actively under work. And that’s actually something if you’d like to try, um, you’re, you’re welcome to. So, yeah, [00:30:17] Vince Menzione: we have time for maybe one more question and we we’re actually gonna have you up here with a couple partners. [00:30:24] Matt Y: Sounds [00:30:24] Vince Menzione: good. Kind of fun. [00:30:32] Audience Guest: Hey, Matt, uh, met Natasha from Dondo. Uh, quick. So great announcements. And you know, you talked about the million, multi-billion dollar, uh, club, and, uh, that’s all great. Uh, in terms of the. Propensity data. I think that’s coming at the center of a lot of things, right? You know, for enterprises, oh, there’s an investment and you tap into that investment. [00:30:53] Audience Guest: But also there’s the other side of the procurement where a lot of customers, sometimes we work with, they’re like, they still wanna go direct for whatever reason, right? So I think there’s an education piece there, but also trying to understand like how we can work together to, you know, get some of that side of the things sorted out as well. [00:31:11] Audience Guest: You know? ’cause a lot of times it’s not about. Just, you know, retiring the, uh, the, the spend comets, but also like, Hey, I’m used, I’m already used that for something else. So maybe that’s not an, uh, something that applies here. And in also in tying that the PLG motion, uh, you know, for the customers you said, you talked about, you know, if there is. [00:31:34] Audience Guest: Leads on the TA table, like where the, it’s not the enterprise, but you know, the others. Um, I feel like it’s more to do, changing the business model at some times. Like with the enterprises, you have the revenue stream coming through, say large deals, right? And all of a sudden you tap into this, you know, PayGo. [00:31:51] Audience Guest: Where it flips the whole equation with, you know, the financing and the, and the, and the revenue measurement. So I think there’s two aspects of how do you kind of cons reconcile those things in terms of, you know, the revenue measurements going forward. [00:32:05] Matt Y: Yeah. So, so two things real quick on the procurement. [00:32:07] Matt Y: Um, yeah, like, yeah, I sort of alluded to this earlier, but, uh. Procurement is a bit late to the AI ag agentic transformation. They’re trying, and there’s a lot of great new incumbents in this space. And the big leaders like, you know, Coupa and Ariba and Oracle are, are, are evolving their products, albeit a bit slowly. [00:32:26] Matt Y: Um, but the, I think, uh, it’s still the long pole in the tent. You know this. And so like, there are two reasons why deals tend to go direct, because it kind of hits a wall of. Legal, uh, you know, procurement, governance, like all that kind of after the selection’s been made, et cetera, or, or they’re, you know, we can’t change. [00:32:44] Matt Y: People are gonna optimize for, for finance, you know, they’re, they’re going to, if they’re getting big discounts. I mean, that is life. I always say it’s like sellers at the most agented company are still gonna chase quota no matter how, you know, crazy. Uh, your, your company is, and it’s the same with, um, with the chief, uh, financial officer and chief procurement officer. [00:33:01] Matt Y: They are going to, they’re literally. Paid to find discounts. And so we’re not, we’re not gonna get rid of financial engineering. That’s a, that’s a thing. What we can do is reduce the friction for procurement. So we launched, for example, like mandatory purchase orders. That was a big thing. We, we have buyer notifications, now we’re making other procure to pay enhancements. [00:33:17] Matt Y: I mean, procurement systems still use like CXML. It’s like, that was, that was cool when I worked for the ap. And like I, I have teenagers that are old, like older than, so they, I, I think, um. Procurement needs to evolve and we’re gonna help it evolve. We’re gonna push it forward and, and we need to make it more seamless for procurement teams so that we remove those objections. [00:33:38] Matt Y: Uh, I can’t remove the financial engineering objection, like, you know, that’s just life. Um, but I can make it irresponsible not to use marketplace ’cause it’s so easy to use. And, uh, that, that’s kind of the approach we’re taking on, on the front end. Uh, you, you know, I think you, you, again, I didn’t see this question. [00:33:52] Matt Y: You, you stepped into a trap. Un unwittingly, um, PLG is not just is for enterprise. And, and PLG doesn’t necessarily mean pego or self-service. Uh, doesn’t necessarily like, uh, most of our self-service efforts are actually focused on private offers. And not necessarily for pego. Uh, when, when I say self-service and, and PLG, uh, it, it can mean all kinds of things like it. [00:34:14] Matt Y: We have requested private offer, requested demo call to actions, buttons that you can put on your listing. For example, you don’t necessarily need a free trial or a metered pay as you go listing to take advantage of those inbound self-service leads. So, and those inbound self-service leads are often massive enterprise deals, like I mentioned specifically, uh, the data mask. [00:34:31] Matt Y: Those giant enterprise deals that they launched came from an enterprise like Fortune 1000 Enterprise in the US that organically discovered their solution on the marketplace using our AI search. And that was a massive enterprise. And so I, I think yes, there is the long tail, you wanna capture a new logo acquisition, but you should think of your product like growth in your self-service strategy as a way to, um, acquire all kinds of leads, including large enterprise. [00:34:54] Matt Y: And so when I say leave money on the table, I’m not just talking about things that are gonna mature over two years or tiny little deals. These could be massive deals. Uh, and, and you’ll accelerate those deals by accelerating their discovery and, and research so that I think that, so, and my advice is don’t, you don’t have to go all in if you don’t have, if you don’t have metering, if you don’t have PayGo, that’s cool. [00:35:13] Matt Y: Start with something simple. Start with a public listing, with a request to private offer like that. That is a, a huge step. That doesn’t take much, and, and it kind of blows my mind still that a lot of companies aren’t doing that yet. [00:35:25] Vince Menzione: Great answer. Well, it’s now time we’re gonna bring, we’re gonna bring, it’s time. [00:35:29] Vince Menzione: We, we’ve got some great partners coming up here, Nvidia Elastic, Accenture gonna all join us for a conversation. Great. And I’m glad that you’re gonna stay with us. And let’s, let’s, well, let’s thank Matt, by the way, for that session. [00:35:41] Matt Y: Thanks. [00:35:42] Vince Menzione: And [00:35:42] Matt Y: thanks for listening to the Ultimate [00:35:44] Vince Menzione: Partner Podcast. If today’s conversation resonated, share it with a partner leader in your network. [00:35:51] Vince Menzione: Subscribe where you listen. And head over to the ultimate partner.com. For show notes related content and the resources for this episode. And if you haven’t already, now’s the time to register for the Ultimate Partner Live Event in Reston, Virginia, October 26th through October 28th. Until next time, keep showing up in the rooms that matter because being in the room changes everything.

In this episode of Shift AI, Patrick Hillmann, Chief Strategy Officer at Logical Intelligence, joins host Boaz Ashkenazy for a conversation about why the next era of AI cannot be built on probability alone.Patrick shares his unconventional path into AI, from crisis communications and cybersecurity work at Edelman and General Electric, to steering Binance through its most turbulent years and a major DOJ settlement. At Logical Intelligence, Patrick now works alongside Yann LeCun, a Fields Medalist, and engineers from Meta, Google, and Cruise to build deterministic, energy-based reasoning models.Patrick explains why LLMs behave like a confident intern, fast and articulate, but wrong in ways you only catch if you already know the answer, and why critical systems like power grids, hospitals, and self-driving cars need a layer of certainty that probabilistic systems cannot provide. He and Boaz dig into Logical Intelligence's benchmark results, including a 98% score on the notoriously difficult Putnam math competition, and a public Sudoku test where their energy-based model, Kona, beat every major LLM combined while running on a fraction of the compute cost. This episode is essential listening for CTOs, technical leaders, and anyone trying to understand what comes after the current generation of large language models.Chapters[00:00] Patrick's Improbable Path: From Grad School to the Front Lines of a Geopolitical Crisis[02:43] From Binance to Chief Strategy Officer at Logical Intelligence[02:54] The First Paid Job: Unloading UPS Trucks in 100 Degree Heat[04:31] The UPS Lesson That Still Shapes How He Thinks About Work[04:47] Why LLMs Are Confident Guessing Machines, Not Truth Machines[07:14] The Team Behind Logical Intelligence: A Fields Medalist, Yann LeCun, and Math Olympiad Engineers[08:55] Is Logical Intelligence Betting Against LLMs?[10:32] The AI Sandwich: Where LLMs, Reasoning Layers, and World Models Fit[12:36] The Putnam Benchmark and Why Formal Proofs Don't Get Partial Credit[15:44] What Is an Energy-Based Model, Really?[19:54] Eve Badia's 15-Year Path to the Energy-Based Reasoning Model[22:04] Formal Verification and the Future of Secure Code Generation[23:33] When Unverified Code Fails: The Molson Coors Ransomware Story[25:39] Why AI Coding Tools Create Rat's Nests Engineers Can't Debug[28:53] The Sudoku Test: 98% Accuracy for $4 vs $14,000 for the Leading LLMs[31:13] ByteDance, China, and the Race for Formal Methods[33:42] Two Words for the Future of AI: Chaotic Determinism[37:12] Where to Follow Logical Intelligence and Founder Eve BadiaConnect with Patrick HillmannLinkedIn: https://www.linkedin.com/in/crisiscommunicationsConnect with Boaz AshkenazyLinkedIn: https://www.linkedin.com/in/boazashkenazy/Email: info@shiftai.fm

My guest today is Elangovan Shanmugam,  a seasoned software professional with over 30 years of experience. Among the multiple roles Elangovan has played are Distinguished Engineer, Director of Engineering, leading strategic AI transformation initiatives in a large organization.In this conversation Elangovan:Traces his journey from FoxPro and 4MB RAM days to AI engineeringShares how software evolved from standalone systems to connected business ecosystems.A major theme is that the problem often stays the same, but the solution changes.Elangovan describes AI agents as digital humans that help people interact naturally with systems.He explains that in software patents, the method of solving a problem can be patentable.His advice for professionals: embrace AI, keep evolving, and don't stay in a comfort zone.Elangovan Shanmugam is a seasoned software professional with over 30 years of experience. He has held multiple roles—Distinguished Engineer, Director of Engineering, and now leading strategic AI transformation initiatives. Most recently, he led a 40-person organization through a fundamental shift: teaching traditional engineering teams to think and operate as AI engineering organizations. He has worked at the intersection of deep technical architecture and enterprise leadership—helping CTOs and organizations figure out what changes when AI becomes native to how you build. He also has the distinction of receiving multiple innovation awards and has over 20 patents issued to his credit!He is super passionate about solving tough problems for customers!He may be reached at: elangovan.shanmugam @ gmail.com

Pat Casey was the first person besides founder Fred Luddy to write code at ServiceNow back in 2005, when it was called Glide and lived above a friend's restaurant. Twenty years later, he's CTO of a company where 85% of the Fortune 500 are customers, and until recently ran all of engineering: 10,000 people, 7,000 of them writing code. Almost nobody survives the journey from first engineer to public-company CTO. Pat did. Tobi and Pat dig into how ServiceNow actually works under the hood: a metadata processing engine running 90,000 single-tenant databases and over 25 billion queries an hour, why they bought a 15-person German database company and turned it into RaptorDB, and why tearing apart a 20-year-old monolith is harder than every senior engineer thinks. Then the conversation turns to AI. Pat bought 7,000 Windsurf licenses and measured a real, but unglamorous, 15% productivity bump, with a small subset of engineers going 5–6x while most barely changed. His thesis: AI coding is like playing five chessboards at once, and it's reshuffling the deck on who the top engineers will be. On agents, ServiceNow's answer is disarmingly simple: create a user called "AI Pat," assign it cases, and make it follow the exact same rules as humans because you should not trust an LLM more than you trust a human being. Topics covered: - From Atari 400 and floppy-disk jockey at Aldus to first engineer at ServiceNow - Scaling engineering from a stuffed fish on a monitor to 10,000 people — and the productivity trough at ~100 engineers - Single-tenant architecture: 90,000 databases, 25B+ queries/hour, and the monolith-to-Kubernetes migration - Why ServiceNow bought Swarm64 and built RaptorDB on a Postgres fork - 7,000 Windsurf licenses, Claude Code, and the real numbers on AI coding productivity - "AI Pat": the anthropomorphic model for enterprise agents outcomes, not toolkits - Whether AI kills seat-based SaaS, and why incumbents may have the inside track - Pat's advice to CTOs: this is not a time for excessive caution

Autonomous AI and agentic AI are moving from experiments into real enterprise workflows. But are businesses truly ready to let AI systems plan, decide and take action on their behalf?In this interview, I speak with Shayan Mohanty, Chief Data and AI Officer at Thoughtworks, about the next phase of enterprise AI and what leaders need to do now to prepare.We explore the shift from AI assistants and copilots to autonomous agents, why governance and accountability need to be built into the architecture, and how enterprises can move beyond proof-of-concept projects toward scalable, production-grade AI systems.Shayan also explains why competitive advantage in the AI era may come less from access to the latest model and more from orchestration, AI-ready data, responsible engineering and the ability to redesign work around intelligent systems.This conversation is essential viewing for CEOs, CIOs, CTOs, CDOs and business leaders who want to understand what autonomous AI means for enterprise transformation, governance, software development and the future of work.If you'd like to explore how enterprises are preparing for this next wave of agentic AI, Thoughtworks' latest white paper, The Agentic Enterprise, offers practical perspectives and real-world insights.  https://www.thoughtworks.com/about-us/partnerships/cloud/aws/building-the-agentic-enterprise-ecosystem?utm_source=organic-influencer&utm_medium=influencer-marketing&utm_campaign=eai_tsi_rp-gl-pspt_rewire-for-agents_2026-05&utm_term=bernard-video-interview&utm_content=video#Sponsored Autonomous AI in the enterpriseAgentic AI and AI agentsAI governance and accountabilityEnterprise AI readinessMoving AI from pilots to productionAI-ready data and orchestrationThe future of software development#AutonomousAI #AgenticAI #EnterpriseAI #AIagents #ArtificialIntelligence #AIGovernance #AITransformation #DigitalTransformation #Thoughtworks #AIworks #FutureOfWork #BusinessTechnology

# Show Notes: Vibe Coding and Real World Problems: Why AI Still Needs a CTO **Guest:** Anuraag Jain, CTO, Executive Coach and Author **Website:** [growthvariable.com](https://www.growthvariable.com) **LinkedIn:** [Anuraag Jain](https://www.linkedin.com/in/anuraagjain) --- ## About This Episode Anuraag Jain is a 30-year tech industry veteran who started as an Oracle DBA in India without an engineering degree and became a CTO in under nine years. He was the first developer hired at Betfair, where he built the betting exchange engine behind one of the era's biggest fintech IPOs, and served as CTO at The Hut Group (THG) during their run as the UK's fastest-growing tech company. In this episode, Bhairav and Anuraag talk honestly about vibe coding: what it can do, where it breaks down, and why thirty years of building things under pressure still matters in an AI-assisted world. --- ## What We Cover - How two non-traditional hires found their way into tech and why that background shaped how they build - The early days at Betfair: manual bet settlement, shoestring infrastructure, and recovering 36 hours of betting data from server log files at 2am - What scaling really means when you have no safety net - The 80/20 rule for vibe coding: what AI handles well and what still requires human judgment - Security blind spots in AI-generated code and why even experienced engineers get caught out - The hidden cost of running AI tooling against a large code base - Repo to Pitch: the open source tool that turns a code base into a full pitch deck and documentation suite - RepoWatch: how to track exactly what AI has changed in your code, commit by commit - *Prompt Engineering the Subconscious*: the book that connects AI prompting to mindset and reached the UK top 50 - What the fractional CTO market actually looks like right now, and the three types operating in it - Practical advice for anyone trying to break into tech or move toward a CTO role today --- ## Key Takeaways - AI can handle roughly 80% of what used to require a developer. The remaining 20% is the domain logic that requires someone who actually understands the problem. - The AI does not know which parts of your code cannot be touched. Unless you tell it, it will modify anything, and you may not notice until something breaks. - Vibe coding a product does not make it production-ready. Security, scalability, and edge-case logic still need human attention. - Building something real, however small, is still the best way to develop the judgment the CTO role requires. The tooling available now means there is no excuse not to. --- ## Links - Anuraag Jain on LinkedIn: [linkedin.com/in/anuraagjain](https://www.linkedin.com/in/anuraagjain) - Growth Variable: [growthvariable.com](https://www.growthvariable.com) - Repo to Pitch (open source): [growthvariable.com/repotopitch.html](https://www.growthvariable.com/repotopitch.html) - RepoWatch: [growthvariable.com](https://www.growthvariable.com) - *Prompt Engineering the Subconscious*: [book.growthvariable.com](https://book.growthvariable.com) --- ## About the Atom CTO Podcast The Atom CTO Podcast, transitioning to Bits, Bytes and Business, features conversations with founders, CTOs and entrepreneurs about building technology businesses in the real world. Hosted by Bhairav Patel. Subscribe wherever you get your podcasts.

In this episode with Tamara Tomasevic, Head of Program Development at CIONET Germany, we discuss how cloud, data, and AI are driving digital transformation in supply chains. Tamara shares insights on fostering enterprise intelligence, encouraging cross-functional teamwork, and scaling AI through mindset and skill development. The episode explores leadership strategies and the future of supply chain connectivity and sovereignty.Download the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠episode transcript⁠⁠⁠⁠⁠⁠===== In this episode Richard and Sin talk with Tamara Tomasevic, Head of Program Development at CIONET Germany, about what really drives digital transformation. From digital sovereignty and exit strategies to re‑architecting processes around enterprise intelligence, Tamara cuts through the hype and gets real about what works. Learn why data quality makes or breaks AI, why mindset matters more than budget, and how leaders can scale AI with confidence in an increasingly complex supply chain world. ===== Guest: Tamara Tomasevic, Head of Program Development at CIONETTamara is Head of Program Development at CIONET Germany, connecting CIOs, CDOs, and CTOs across the country. She is passionate about building strong communities in the digital era. Previously, she helped establish the Bavarian State's AI Network and, as a communication scientist, believes communication is at the heart of digital disruption.Host: Richard Howells⁠⁠⁠⁠⁠Richard Howells⁠⁠⁠⁠⁠ has been working in the Supply Chain Management and Manufacturing space for over 30 years. He is responsible for driving the thought leadership and awareness of SAP's ERP, Finance, and Supply Chain solutions and is an active writer, podcaster, and thought leader on the topics of supply chain, Industry 4.0, digitization, and sustainability.Host 2: Sin ToSin To brings over 15 years of experience in the digital media and technology industry – primarily in marketing, business development, thought leadership, and editorial. At SAP, they ensure that SAP's supply chain solutions are properly visible with a focus on future trends and sustainable innovations as part of the Thought Leadership & Awareness Supply Chain Team.===== Show Links:SAP Digital Supply Chain: www.sap.com/scmCIONET Germany: https://www.cionet.com/cionet-germanyFollow Us on Social Media : Tamara TomasevicLinkedIn: https://www.linkedin.com/in/tamara-tomasevic-community/ Richard Howells:LinkedIn: ⁠⁠www.linkedin.com/in/richardjhowells⁠⁠ Sin To: LinkedIn: ⁠⁠www.linkedin.com/in/sin-to-5334208⁠⁠ SAP Digital Supply Chain:LinkedIn: ⁠⁠www.linkedin.com/showcase/sapdsc/⁠⁠ Please give us a like, share, and subscribe to stay up-to-date on future episodes!  ===== Chapters: 00:00:00 Sovereignty And Resilience00:00:31 Welcome And Power Triangle00:01:07 Meet Tamara And CIONET00:01:59 Digital Leaders Hot Topics00:05:10 Cloud Strategy In Supply Chain00:08:03 Data Quality And Ownership00:09:52 Turning Data Into Assets00:11:20 AI Misconceptions And Use Cases00:14:21 Scaling AI Beyond Pilots00:16:21 Why Networks Matter00:18:42 People Skills And Culture Shift00:20:46 90 Day Action Plan00:22:00 Future Outlook And Wrap Up

When companies mandate AI adoption without a use case, without a strategy, and without a business outcome in mind, they don't get transformation. They get jazz hands and nightmare token bills.This month's System Update pulls apart what's actually happening beneath the headlines: Oracle's 21,000 layoffs attributed to "AI adoption," Amazon arming junior developers to replace senior engineers, and enterprises burning through AI budgets they cannot predict or control.The deeper argument George K. and George A. make is harder to dismiss than the headlines. You cannot drive deterministic business outcomes with probabilistic means of production. The CEOs and CTOs who greenlit LLM adoption at scale are now facing a math problem that no earnings call language can paper over.The free water is now a metered utility. Will the bill ever be worth paying?The episode also turns to labor theater and what we're giving our attention to: what we lose when institutions optimize for engagement over depth, and what it costs when an entire generation learns to consume rather than think.Mentioned: NYT on Schneider Electric's AI adoption without layoffs Amazon tokenmaxxing mandate goes sideways Oracle sheds 13% of its workforce amid so-called AI adoption Amazon still hiring junior employees while also doing layoffs…? The Intellectual Life of the British Working Classes, by Jonathan Rose Snap's intentional targeting of teens' attention Denmark invests in de-screening its schools

Daniel Crook, founder of School Harbor and co-founder of AEG Systems, joins Dr. Michael Conner for a direct, infrastructure-first conversation about what K–12 districts are getting wrong about AI adoption.Daniel has spent years working inside the systems that districts rely on to manage data, make decisions, and integrate technology at scale. His assessment is clear: the AI problem in education isn't a platform problem, it's a foundation problem. Too many districts are evaluating AI tools before they've built the data infrastructure that would allow those tools to function effectively.In this episode, Daniel walks through why the data warehouse is the non-negotiable starting point, what interoperability actually requires in practice, not in theory, and the three criteria every district leader should apply before selecting or expanding any AI platform. He also introduces the concept of AI sobriety: an implementation philosophy that grounds strategy in the technology's current capabilities rather than its projected future state.The conversation also touches on the Netflix analogy for understanding where edtech sits in the broader AI development cycle, and why the districts building administrative intelligence now will have a significant structural advantage over those still chasing student-facing tool count.A grounded, no-hype episode for superintendents, CTOs, instructional technology leads, and anyone responsible for AI strategy in K–12 education.Episode 127 | Voices for Excellence

In this episode of the Shift AI Podcast, Cynthia Tee, former CTO of Smartsheet, joins host Boaz Ashkenazy for a wide-ranging conversation on what it really takes to integrate AI at enterprise scale responsibly, securely, and in a way that earns lasting customer trust.Cynthia shares her unconventional journey from growing up in Manila and working her first job at a library at age 12, to earning a computer science degree from MIT, building her career at Microsoft, running Ada Developers Academy, and ultimately leading engineering at Smartsheet through one of its most consequential chapters, including the company's transition from public to private and the rollout of its first generation of AI-powered features.The conversation dives deep into how Smartsheet approached AI integration: using generative AI to simplify formula generation and data visualization, being deliberate about what information was and wasn't sent to LLMs, and communicating transparently with enterprise customers who needed to trust the system before they would adopt it. Cynthia explains why trust, governance, and data classification aren't afterthoughts, they're the foundation that makes AI deployment possible at scale.Boaz and Cynthia explore the emerging role of MCP in connecting LLMs like Claude to applications like Smartsheet, translating user intent into real-world action across complex workflows. They also get into what SaaS executives often underestimate: that shipping AI features is the easy part, and evolving the rest of the organization, pricing, enablement, customer support, and role definitions is where companies get stuck.The discussion turns to the next generation of workers and the genuine tension young people face between learning a craft and leaning on AI to accelerate it. Cynthia shares a perspective on hustle, curiosity, and what it looks like when someone who's never written a line of code builds an inventory system for vintage clothing because a tool like Claude made it possible.This episode is essential listening for CTOs, engineering leaders, and product executives who want to understand what responsible AI deployment actually looks like inside a company operating at scale.

For more thoughts, clips, and updates, follow Avetis Antaplyan on Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/avetisantaplyan⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠In this solo episode of The Tech Leader's Playbook, Avetis Antaplyan explores one of the most overlooked yet critical leadership skills: decision-making. Drawing on insights from conversations with CEOs, CTOs, founders, professional athletes, Hall of Fame coaches, and executives from companies including Apple, Google, Amazon, National Geographic, and Radical Candor, Avetis breaks down what separates exceptional leaders from everyone else.He argues that leadership success is rarely about having perfect information, superior intelligence, or flawless strategy. Instead, the leaders who consistently create momentum are those who can make sound decisions despite uncertainty. Avetis shares practical frameworks used by high-performing leaders, including Amazon's "one-way door vs. two-way door" decision model, Jeff Bezos' regret minimization framework, and the importance of principle-based decision-making.The episode also examines how AI is changing the leadership landscape. While artificial intelligence can accelerate analysis and provide recommendations, Avetis explains why human judgment, accountability, and courage remain irreplaceable. Through real-world examples and actionable leadership lessons, he challenges listeners to identify the decisions they've been avoiding and take decisive action before delays become the real obstacle to progress.TakeawaysExceptional leaders distinguish themselves through decision-making, not intelligence alone.The greatest organizational threat is often indecision, not making the wrong decision.Most leadership decisions must be made with incomplete information.Leaders are paid for their ability to navigate uncertainty and create momentum.A mediocre decision made quickly often outperforms a perfect decision made too late.Amazon's "one-way door vs. two-way door" framework helps determine when to move fast and when to proceed carefully.Great leaders commit fully after making a decision rather than remaining trapped in doubt.Principle-based decision-making allows leaders to make consistent decisions faster.Technology leaders often make the mistake of optimizing for technical perfection instead of business outcomes.AI can provide information and recommendations, but accountability and judgment remain human responsibilities.When a decision is inevitable, delaying it often causes more damage than acting on it immediately.Chapters00:00 Why Decision-Making Separates Great Leaders01:12 The Myth of Intelligence and Leadership Success02:13 Why Indecision Damages Organizations03:25 Amazon's One-Way Door vs. Two-Way Door Framework04:38 Lessons from Hall of Fame Coach Dick Vermeil05:15 Radical Candor and the Courage to Act05:55 Technology Leaders and Business Outcomes06:30 Framework #1: Speed Over Perfection07:00 Framework #2: Regret Minimization08:00 Framework #3: Reversible vs. Irreversible Decisions08:55 Framework #4: Principle-Based Decision Making09:55 Why AI Makes Judgment More Valuable11:05 Creating Momentum Through Action11:40 The Decisions You're Avoiding Right Now12:10 When It's Inevitable, Make It Immediate12:45 Closing Thoughts and Final TakeawaysResources and Links:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.hireclout.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.podcast.hireclout.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/hirefasthireright⁠

Charlie Fink, Ted Schilowitz, and Rony Abovitz take the full hour to work through the most consequential AI and spatial computing stories of the moment — unfiltered, in depth, and without the usual polite hedging that comes with having someone on to promote something. This is a pure news and commentary episode, and the news is strange enough that three experienced people sitting in a room still cannot fully account for it.AI XR News You Should Know:The OpenAI vs. Elon Musk case concluded without a clear ruling, but the more durable observation is what the whole saga revealed about Sam Altman. He has now survived being ousted by his own board (which he subsequently dismantled), a high-profile lawsuit from Elon Musk, and senior rivals leaving for government roles. Rony frames this through the Overton window — Altman studies what society is prepared to accept at any given moment and positions himself precisely there. Ted references a New Yorker profile that describes Altman as having a politician's gift for telling people what they want to hear until it becomes true. The financial architecture underneath the AI boom looks precarious on close inspection. SpaceX, widely assumed to be profitable, is losing five billion dollars a year. Anthropic is spending three dollars for every dollar of revenue it generates — and is paying SpaceX approximately one billion dollars a month for compute through roughly 2030. Rony's framing lands hard: two money-losing entities are funding each other while NVIDIA captures all the margin in between. Sequoia published a fifty-page analysis arguing the economics cannot work — while simultaneously holding positions in the companies it is critiquing. Google I/O delivered less on wearables than expected, but the real story was a deliberate strategic decision to put Gemini at the center of the company's entire product surface — effectively cannibalizing an eighty-two-billion-dollar search business before a competitor does it for them. The Innovator's Dilemma, run on purpose. On the hardware side, Android XR glasses are designed to be imperceptible as technology — thin temples, hidden camera portals, frames that belong in an optometrist's display case rather than a trade show floor. Rony notes that Google's glasses almost certainly incorporate Magic Leap optics, following a partnership announced in fall 2025. [00:00] – Cold open and episode framing: why there is no guest today and what the trio plans to cover.[04:15] – OpenAI vs. Elon Musk non-verdict: what the outcome (and lack of one) actually reveals.[09:30] – Sam Altman and the Overton window: Rony's read on how Altman has survived everything thrown at him.[16:00] – Anti-AI backlash on campuses: Eric Schmidt booed at University of Arizona, YouGov poll showing 69 percent of young people negative on AI, and what the demographic gradient means.[24:45] – SpaceX financials and the AI funding loop: the five-billion-dollar annual loss, Anthropic's burn rate, and Charlie's Ponzi scheme framing.[33:20] – Sequoia's fifty-page report and the ad model endgame: Ted's argument that Google wins because they already know the business model.[41:00] – Google I/O: the deliberate destruction of the search business, Android XR glasses, and why distribution beats specifications.[49:10] – AI accountability and the airplane analogy: Ted's line, Rony's "underground noise" from generals and CTOs, and the problem of regulatory vocabulary.[55:30] – Palantir, dual-use opacity, and the Lookout Mountain Air Force Station story: Rony on Jared Leto, classified film studios, and Cold War bunkers in Laurel Canyon.[01:01:00] – The success ledger: who is measuring impact, and what should actually count as winning.This episode is sponsored by Zappar and Mattercraft. Mattercraft is Zappar's web-based platform for building augmented reality experiences without an app. Find them at mattercraft.io. Hosted on Acast. See acast.com/privacy for more information.

In this episode of The Broadband Bunch, host Pete Pizzutillo sits down with Rob Lawrence, Technology Strategist at Microsoft, to separate the reality of agentic AI from the growing hype surrounding autonomous systems. As organizations race to experiment with AI agents, Rob argues that the biggest challenges aren't the models themselves—they're the operating environments, governance frameworks, data quality, accountability structures, and organizational readiness required to deploy them successfully. Pete and Rob discuss why many AI pilots succeed while production deployments struggle, the return of disciplines like project portfolio management and process engineering, and why data governance may be the most important prerequisite for successful AI adoption. Rob also talks about the role of identity and permissions, the risks of poorly governed agents acting on flawed data, and why organizations need better observability into AI-driven workflows. Along the way, he shares advice for CIOs, CTOs, and broadband operators looking to move beyond experimentation and build a responsible foundation for agentic AI.

Kishore Ravilla: How CTOs Influence Business DecisionsKishore Ravilla is a CTO with 25 years of leadership across healthcare, insurance, and financial services. In this episode, we explore how technology leaders can better communicate with business stakeholders, why storytelling matters in digital transformation, and how strong execution and operational excellence create lasting business value. To learn more about Kishore, visit https://www.linkedin.com/in/kishoreravilla/__TEACH THE GEEK (http://teachthegeek.com) Prefer video? Visit http://youtube.teachthegeek.comGet Public Speaking Tips for STEM Professionals at http://teachthegeek.com/tips

Rory O'Neill, CMO of Checkout.com, doesn't just solve for payments- he's solving for brand preference in a crowded payments space. And he's doing it by competing on what's different, not what others do better. That insight changes everything, from how you position payments to how you build a team that can sustain growth as a challenger. In the latest episode of Scratch, Rory breaks down the playbook that lets Checkout compete with global giants. Brand preference wins 95% of B2B deals before salespeople ever show up- so your marketing owns the invisible 60% of the buyer's journey. Challenger brands win by picking one fight and building culture around it, not chasing everything competitors do. He reveals the three-part formula: focus your core business, build your culture, reinvest profit. Consumer marketing skills-data, insight, action-are B2B's secret superpower. And his rule: if you wouldn't say it at dinner, don't write it in marketing. The key takeaway: Brand preference wins deals - 95% of the time, the brands on the day-one top-five list are the ones that win. B2B buyers spend 60% of their journey before contacting a salesperson. Define your focus as a challenger - Compete on what's different, not on what competitors do better. Checkout only does digital payments to stay focused while competitors spread across multiple business lines. Three elements beat category norms - Focus on your core business, build the human operating system (culture, people, vision), then reinvest capital in new products. Consumer marketer skills are powerful in B2B - Data, insight, action, brand building, and performance marketing from the consumer world unlock B2B success. Understand stakeholder maps - B2B is complex: CTOs influence CFOs, recommenders influence buyers. Map those relationships to win. Simplify your language - Ditch jargon like "frictionless" and "seamless." Use words you'd use at dinner. Marketing becomes more interesting and understood. Marketing is logic and magic - Be both data-driven and creative. Avoid letting fiefdoms kill integrated work. Join everything together. Watch the video version of this podcast on Youtube ▶️: https://youtu.be/chR0mn9Pum0 Scratch is a production of Rival, a marketing innovation consultancy that develops strategies and capabilities that help businesses grow faster. Scratch is hosted by Eric Fulwiler, and he's joined by Rory O'Neill of Checkout.com in this episode. Find Rival online at www.wearerival.com, LinkedIn  Find Eric on LinkedIn Find Rory on LinkedIn  Say hi at media@wearerival.com, we'd love to hear from you. Rival is a marketing consultancy for brands that want to challenge convention in their category. We're on a mission to understand what challenger brands do differently to grow in categories that are being disrupted, and use a challenger playbook to deliver outsized impact through an integrated, tech-enabled approach. Past guests include CMOs from Mastercard, GE, Shell, Hyperloop, Adobe, PepsiCo, and Papa Johns.If you're interested in learning more about marketing from successful CMOs, we compiled a list of the top 5 CMO podcasts to listen to in 2024; check it out here

In this episode of Future Finance, Paul Barnhurst and Glenn Hopper sit down with Dave Trier, CEO of ModelOp, to discuss how enterprises can govern, manage, and operate AI at scale. Dave shares insights on implementing AI responsibly, tracking ROI, managing risks, and creating an enterprise-wide AI portfolio that drives value while ensuring compliance and governance.Dave Trier leads ModelOp with a focus on customer value, product innovation, and enterprise execution. With over 20 years in data science, AI, analytics, cloud, and enterprise software, he brings technical expertise and a pragmatic leadership style, helping CIOs, CTOs, and AI leaders deploy AI effectively across organizations .In this episode, you will discover:How enterprises can scale AI responsibly and reliablyThe CFO's role in AI oversight and portfolio managementMeasuring AI value through ROI, usage, and internal feedbackDistinctions between AI governance and traditional data governanceImportance of change management and structured AI adoptionDave provides a framework for enterprise AI adoption, emphasizing disciplined management, measurable impact, and alignment with regulatory and operational requirements. This episode is essential for finance and tech leaders looking to integrate AI at scale while ensuring oversight, efficiency, and business value . Follow Dave:Website: https://www.modelop.com/LinkedIn: https://www.linkedin.com/in/davidetrier/Follow Glenn:LinkedIn: https://www.linkedin.com/in/gbhopperiiiFollow Paul:LinkedIn: https://www.linkedin.com/in/thefpandaguyFollow QFlow.AI:Website - https://bit.ly/4i1EkjgFuture Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai. Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.In Today's Episode:[00:00] – Trailer[02:38] – AI Compliance & Governance Challenges[04:35] – Distinction Between AI & Data Governance[07:28] – Measuring AI Value & ROI[12:41] – Treating AI as a Portfolio of Investments[15:05] – Change Management & Enterprise Adoption[17:39] – Wild West of AI & Need for Rigorous Processes[18:54] – CFO Oversight in AI Implementation[21:00] – Closing Remarks

Most enterprises are renters, not owners, of their technology and AI. Raffi Krikorian, Chief Technology Officer of Mozilla, explains why dependence on a handful of closed model providers means losing control over model behavior, pricing, and your own data.In CXOTalk episode 920, Krikorian lays out where open-source AI actually wins in the enterprise, how lock-in happens quietly, and what CIOs and CTOs should do about it now. Krikorian draws on his experience building infrastructure at Twitter and running the self-driving division at Uber to ground the discussion in real engineering and economic tradeoffs, not hype.YOU'LL DISCOVER✅ Why 85% of enterprises believed they could switch AI vendors, but only about 30% actually could when they tried✅ The "renters vs. owners" framing and what it means to control your AI destiny✅ Why Krikorian wants data "protected by architecture, not legal handshakes"✅ How Pinterest reportedly saved on the order of $10 million in a single quarter by switching from closed to open models✅ Why IT is becoming "the HR team for agents," and the read/write "dangerous triangle" of agentic permissions✅ The case for recording your prompts and running your own evaluations instead of trusting public benchmarks✅ Why roughly 70% of enterprise GPUs sit idle, and the missing "LAMP stack for AI" that could put them to work✅ How closed "validation machines" can quietly steer answers toward sponsored outcomes⏱️ TIMESTAMPS (estimated, verify before publishing)0:00 Renters vs. owners: who controls enterprise AI2:26 The risks of depending on closed model makers6:23 How lock-in happens and where open source fits9:53 Regression testing and building your own evals13:24 Pricing instability and the post-IPO cost question23:31 Governance: IT as HR for AI agents32:38 Can a small organization own its AI stack end-to-end?38:47 Validation machines, trust, and sponsored answers43:39 Keeping humans at the center, not in the loop47:23 Can open source beat big tech in AI?51:39 Inside Mozilla.ai: Otari, CQ, Octanus, Thunderbolt55:21 The "rebel alliance" strategy

What does it take to go from helping millions of people breathe easier to helping patients keep their legs?   Join The Heart of Innovation as Kym McNicholas talks with Sarvajna Dwivedi, Ph.D., entrepreneur, inventor, and CEO of AngioSafe, whose career has spanned some of the most challenging problems in medicine.   Sarvajna co-founded Pearl Therapeutics, a company focused on breakthrough respiratory therapies that was ultimately acquired by AstraZeneca for $1.15 billion. Along the way, he helped develop inhaled therapies and drug-device combinations designed to improve the lives of patients with asthma and COPD. (AngioSafe United States)   Today, his focus has shifted from the lungs to the arteries.   As CEO and co-founder of AngioSafe, Sarvajna is leading the development of the Santreva-ATK Endovascular Revascularization Catheter, a novel device designed to restore blood flow through some of the most challenging chronic total occlusions (CTOs) physicians encounter in patients with peripheral artery disease (PAD). The technology is designed to cross completely blocked arteries, compress plaque, create a new channel, and restore blood flow without relying on a guidewire or external power source. (Medical Economics)   In this episode, we discuss:   • How a pharmaceutical scientist became a medical device innovator   • The story behind Pearl Therapeutics and its $1.15 billion acquisition   • Why chronic total occlusions remain one of the biggest challenges in PAD treatment   • How AngioSafe's Santreva-ATK technology works   • What it means to restore blood flow through arteries that are 100% blocked   • The future of cardiovascular and vascular innovation   If you or someone you love has peripheral artery disease, diabetes, leg pain while walking, non-healing wounds, or has been told an artery is completely blocked, this is a conversation you won't want to miss.  

In this episode of the Finovate podcast, host Greg speaks with Oren Buskila, CEO and co-founder of Cobalt, a FinovateSpring 2026 Best of Show winner. Cobalt has developed enterprise architectural intelligence specifically designed for financial institutions, addressing a critical challenge in modern banking: the lack of understanding of complex system dependencies.Banks operate on enormously intricate systems comprising their own code and dozens of third-party vendor applications, yet most institutions don't fully comprehend how these systems interconnect and depend on one another. This knowledge gap leads to significant problems, including development slowness—with banks spending 70% of their IT budgets on maintenance rather than innovation—and costly production failures that can result in millions of dollars in direct and indirect costs when changes are deployed without full visibility into system dependencies.Cobalt's solution automatically maps both IT systems and business processes in real-time by scanning existing data sources including event logs, API logs, code, and database tables. The platform creates a comprehensive topology map that aligns business processes with their underlying technical infrastructure, allowing banks to see exactly which systems support which business functions and understand the full impact of any proposed changes. This "bank on a page" architecture view enables technical teams to anticipate the consequences of modifications before implementation, preventing failures and ensuring safer deployments. The platform maintains a live, continuously updated view of the system architecture by taking frequent snapshots that can be compared to detect changes and investigate issues, a capability that was previously impossible with manual architecture mapping methods.The demo at FinovateSpring resonated strongly with attendees, particularly technical leaders like CIOs and CTOs from medium and large banks who recognized the transformative potential of having complete visibility into their systems. The presentation also attracted significant interest from venture capitalists and leaders from smaller banks and credit unions, the latter group seeing opportunities to introduce agentic AI into their operations by first mapping existing business processes. Looking ahead, Cobalt positions itself as the essential architectural layer for AI-driven development in banking, as the industry moves toward having AI agents generate, maintain, and modify code at unprecedented velocities—a shift that will require the contextual understanding and change management capabilities that Cobalt provides.More info:Cobalt AI: https://www.getcobalt.ai/; https://www.linkedin.com/company/getcobalt/Oren Buskila: https://www.linkedin.com/in/oren-buskila/Greg Palmer: https://www.linkedin.com/in/gregbpalmer/Finovate: https://www.finovate.com; https://www.linkedin.com/company/finovate-conference-series/FinovateSpring: https://informaconnect.com/finovatespring/#Finovate #FinovateSpring #Banking #banks #creditunions #personalization #data #communitybanks #AI #backoffice #corebanking #digitaladoption #podcast #fintechpodcast #financialservices #innovation #digitraltransformation #fintech #finserv #modernization

In this episode of Future Finance, Paul Barnhurst and Glenn Hopper sit down with Dave Trier, CEO of ModelOp, to explore the challenges and opportunities of implementing AI at scale in enterprises. Dave shares how organizations can manage AI responsibly, measure ROI, and move from scattered pilots to a disciplined, industrialized approach. He also discusses the critical role of CFOs in AI oversight, change management, and creating measurable business value from AI initiatives Dave Trier is CEO of ModelOp, leading the company with a focus on customer value, product innovation, and enterprise execution. With over 20 years of experience across AI, data science, analytics, cloud, and enterprise software, Dave is a patent-holder and trusted partner to CIOs, CTOs, and AI leaders. Prior to becoming CEO, he shaped ModelOp's product strategy and held senior roles at Think Big Analytics, Powered by Action, and Accenture Technology Labs. He holds a BS in Electrical Engineering from the University of Notre Dame. In this episode, you will discover:How to industrialize AI delivery across an enterpriseManaging risk, governance, and compliance for AI implementationsMeasuring AI ROI using financial, feedback, and usage metricsThe CFO's role in AI oversight and rationalizing AI investmentsKey lessons for change management and process discipline in AI adoptionDave Trier highlights how enterprises can move from scattered AI pilots to a disciplined, industrialized approach that delivers measurable business value. He emphasizes the importance of governance, change management, and cross-functional collaboration to ensure AI initiatives succeed. CFOs play a key role in oversight, setting financial parameters, and rationalizing AI investments. Follow Dave:Website: https://www.modelop.com/LinkedIn: https://www.linkedin.com/in/davidetrier/Follow Glenn:LinkedIn: https://www.linkedin.com/in/gbhopperiiiFollow Paul:LinkedIn: https://www.linkedin.com/in/thefpandaguyFollow QFlow.AI:Website - https://bit.ly/4i1EkjgFuture Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai. Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.In Today's Episode:[00:00] – Trailer[02:07] – Meet Dave Trier, CEO of ModelOp[04:57] – ModelOp & AI Governance Explained[06:21] – AI vs Data Governance[08:11] – Evaluating AI ROI for CFOs[13:24] – AI as a Managed Investment Portfolio[16:43] – Change Management & Process Discipline[20:48] – CFO's Role in AI Oversight[27:38] – Tips to Maximize AI ROI[30:16] – Enterprise AI Complexity & Coordination[32:13] – Dave's Journey: Electrical Engineer to AI CEO[35:12] – Closing Thoughts

In this episode of The New P&L TO THE POINT, Paul explores the growing conversation around AI that increasingly positions it as an ‘existential crisis' for businesses and society.Drawing on insights from dozens of executive roundtablesheld across the UK and Europe with CIOs, CTOs, CMOs, HR leaders and transformation executives, Paul reflects on how the AI conversation has evolved at remarkable speed. In just a couple of years, organisations have rapidly shifted from asking What is AI? to How do we deploy it? and now increasingly Why are we using it in the first place?At the heart of the discussion is a critical observation: many organisations approached AI implementation in reverse order. Businesses rushed into experimentation and deployment before establishing strategic clarity around purpose, culture and long-term impact. According to Paul, this is where the real challenge lies.Rather than focusing solely on future fears around AGI orsuperintelligence, this episode argues that today's AI crisis is more immediate and human: a leadership, capability and adaptability crisis. AI is not simply another technology tool; it is transformational and foundational, requiring organisations to rethink leadership, culture, communication and workforcedevelopment.Paul also examines how AI acts as a mirror for organisational health, exposing weak leadership, fragmented data, siloedcultures and poor communication. Without clear vision, employee trust and meaningful upskilling pathways, businesses risk creating fear, disengagement and resistance internally.Ultimately, this episode challenges leaders to rethink their relationship with AI: not as a transactional solution, but as a force that will fundamentally reshape the nature of work, organisations and leadership itself. Those who fail to adapt may face their own existential crisis far sooner than the technology does.To discuss the topics outlined in this episode on moredetail, email: hello (at) principlesandleadership.com To learn more about The New P&L and the work we do, goto: ⁠⁠⁠www.principlesandleadership.com

Most sales teams are reactive — waiting for buyers to fill out a form, book a demo, or respond to an email. Tal Peretz, co-founder and CEO of OnFire AI, is building the infrastructure to change that. OnFire monitors millions of public signals across Reddit, Stack Overflow, LinkedIn, Slack, and technical forums to identify high-intent buyers before they ever contact your sales team.In this episode, Tal breaks down how AI is transforming go-to-market for companies selling to technical buyers — CTOs, CISOs, and engineers — who notoriously resist generic outreach and respond only to context-rich, well-timed conversations. Tal shares his journey from engineer to CEO, how he and his co-founder interviewed 275 revenue leaders before writing a line of code, what it's really like to raise a $20M seed round, and the hard-won lessons of learning to sell as a first-time founder. From ICP discovery and outcome-based pricing to the future of AI in sales, this is a masterclass in signal-driven, intent-based revenue growth.Key Takeaways0:00 — Why most sales teams miss buyers who are already signaling intent publicly2:07 — Intro to Tal Peretz: Co-founder & CEO of OnFire AI3:56 — The origin story: 275 revenue leader interviews before building the product4:36 — How OnFire works: Capturing public web signals, de-anonymizing prospects, and delivering real-time context to sales teams6:25 — Why selling to CTOs, CISOs, and engineers is uniquely difficult — and uniquely valuable7:36 — The 50-million-engineer insight: Turning public technical conversations into revenue intelligence10:04 — What true AI ROI looks like: efficiency gains + directly attributed pipeline11:15 — The 4X pipeline result: What customers see in their first quarter with OnFire11:52 — Speed + personalization + human touch: Why all three are required for signal-based outreach13:03 — Raising a $20M seed round and what hypergrowth pressure really means13:47 — What makes a great investor: shared values, chemistry, and true partnership in hard moments15:59 — Managing pressure: Working backwards from a 24-month North Star to break goals into milestones17:07 — Building vs. selling: What was harder in the early days17:59 — An engineer who learned to love sales: How Tal found his passion for closing deals19:21 — The ICP trap: Why selling to everyone early is the most costly mistake a founder makes20:51 — The outbound playbook: Cold calling, LinkedIn, and the "stealth company" message that landed their biggest customers22:10 — The consulting approach: Why leading with curiosity instead of a pitch built their enterprise pipeline24:41 — The three-layer go-to-market machine: Brand, field/events, and outbound working together26:45 — Selling six-figure enterprise deals: Going on-site, acting as a partner, not a vendor28:51 — Staying focused in a crowded AI market: The "build on top of the platform" rule30:02 — Building go-to-market teams as a technical founder: The hardest challenge32:14 — The biggest AI pricing mistake: Why outcome-based pricing is the future35:03 — Sales-led vs. product-led growth: How Tal thinks about when and how to make the shift38:09 — The future of go-to-market: How AI eliminates the 80% of busy work reps do today40:53 — The one thing founders must nail to break through from product to real revenue41:38 — Where to find Tal and OnFire AITweetable Quotes"We monitor the public web for signals — competitors, pain points, product mentions — and surface them to your sales team in real time. Your buyers are already talking. You just have to listen." — Tal Peretz"It's not about quantity. It's about the quality of the data. Act fast, personalize based on the pain point, and always keep the human touch in the loop." — Tal Peretz"We take your existing team and infrastructure and make the pipeline 4X better — not by adding headcount, but by giving them the right signal at the right moment." — Tal Peretz"Every revenue is not good revenue. Nail your ICP first — where you see the biggest pain, the best retention, and the growth potential — then press the pedal." — Tal Peretz"The best investors aren't just writing checks. When something breaks — and something always breaks — that's where you find out if you have a true partner." — Tal Peretz"AI will eat the 80% of the sales rep's day that is busy work. The reps who win will be the ones who know how to leverage those tools and still build real relationships." — Tal Peretz"Outcome-based pricing is the future. Align what your customer pays with the value they actually receive — then you're never fighting about ROI again." — Tal Peretz"We started with outbound and a simple message: 'I'm a stealth founder. I want to learn from your experience.' No pitch. Just curiosity. Our biggest customers today came from that exact message." — Tal PeretzSaaS Leadership Lessons1. Validate the market before you build the product. Tal and his co-founders interviewed 275 revenue leaders before writing a single line of code. They didn't fall in love with a solution — they found the problem first. For early-stage founders, this discipline separates products that get traction from ones that get ignored.2. Your ICP is not a marketing decision — it's a survival decision. Selling to every prospect early feels like progress, but it's a trap. Tal's hard-won insight: identify the customers with the biggest pain, the highest retention potential, and the best growth trajectory early, then build everything around them. Chasing the wrong customers burns runway and muddies your product roadmap.3. Great investors are chosen for the downside, not the upside. When everything is working, any investor looks great. The real test comes when something breaks. Tal defines great investors by shared core values, authentic chemistry, and willingness to engage as a true partner — not just a capital source — when the hard moments arrive.4. Act like a consultant before you act like a vendor. OnFire's biggest enterprise wins came from going on-site, meeting the full revenue team, mapping the customer's strategic goals, and co-designing a plan — before ever talking contract. For founders selling complex, high-ACV solutions, acting as a partner rather than a vendor changes the entire sales dynamic.5. Outcome-based pricing aligns your success with your customer's success. Charging by seat or token puts you in constant translation mode — always proving value. Pricing tied to outcomes (pipeline generated, conversations resolved, deals influenced) makes the value self-evident and creates a partnership, not a vendor relationship. The companies doing this best in AI are winning stickier, larger contracts.6. The future sales rep is an AI orchestrator, not a data processor. Today's reps spend ~80% of their time on research, sourcing, and admin — not selling. AI will progressively eliminate that 80%. The reps who thrive won't be those who resist the change, but those who master AI tooling and redirect all of their energy to the irreplaceable human skill: building trust and closing deals.Guest Resourcestal@onfire.aihttps://onfire.aihttps://www.linkedin.com/in/tal-peretz/instagram.com/peretztalx.com/TalPeretz13Episode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

For more thoughts, clips, and updates, follow Avetis Antaplyan on Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/avetisantaplyan⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠In this solo episode of The Tech Leader's Playbook, Avetis Antaplyan breaks down what he calls “The Great White Collar Compression”, the growing disconnect between a strong-looking economy and the pressure many white-collar professionals are feeling in real time.Avetis explores why corporate profits, AI investment, and stock market strength are not translating into the hiring booms many workers expected. Instead, companies are flattening teams, raising performance expectations, slowing hiring, and demanding more output from fewer people. Drawing from his perspective inside the hiring market, Avetis explains how AI, remote work abuse, salary inflation, and shifting leadership priorities are reshaping the future of work.He shares candid stories from conversations with CTOs, candidates, and professionals who feel uncertain about their roles despite working at successful companies. The episode also digs into the decline of the “comfortable middle,” the rise of hybrid roles, the need for AI fluency, and why adaptability may now be the most valuable career currency.This episode is a direct, practical warning and roadmap for leaders and professionals who want to stay relevant, valuable, and hard to replace.TakeawaysThe economy can look strong while white-collar workers still feel pressure.AI investment is increasing productivity without creating proportional hiring.Companies are flattening teams and cutting unnecessary management layers.Average performance is becoming more vulnerable in the modern workplace.Remote work abuse and inflated salaries contributed to employer distrust.Hiring is slower because companies now expect rare hybrid skill sets.Professionals need to get closer to revenue, customer impact, and business outcomes.AI fluency is no longer optional for most white-collar roles.Adaptability and learning velocity are becoming premium career skills.Building a reputation matters more than relying on a resume alone.The future belongs to builders, operators, and people willing to evolve quickly.Chapters00:00 Introduction to the Great White Collar Compression02:36 Why Traditional Hiring Growth Is Changing05:00 Fewer Layers, Higher Expectations, and AI Pressure07:20 Why Workers Feel Weak Despite a Strong Economy09:43 Hiring Freezes, Salary Pressure, and Market Uncertainty11:46 Efficiency, Profitability, and Leaner Operations12:49 The Death of the Comfortable Middle14:55 Why Hiring Feels Broken Right Now17:19 The Rise of Team-Elevating Talent20:03 Adaptability as the New Career Currency22:28 Getting Closer to Revenue and Business Outcomes24:50 Building Hybrid Skills and Becoming Indispensable27:13 Reputation, Network, and Proof of Work28:40 Final Thoughts on the Future of White-Collar WorkResources and Links:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.hireclout.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.podcast.hireclout.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/hirefasthireright⁠

In this episode of the AI at Health series on The Beat Podcast, host Sandy Vance sits down with Venky Ananth, Executive Vice President and Head of Healthcare at Infosys, for a wide-ranging and energizing conversation about what it actually means for AI to transform healthcare at scale. Venky brings a refreshingly honest and structured perspective to a conversation that is often dominated by hype, breaking down why AI is fundamentally different from every other technology wave healthcare has been through, laying out the five areas where Infosys is seeing real traction with payers, providers, and PBMs right now, and sharing the story behind two exciting developments: the acquisition of Optimum Health IT and the Pacesetters podcast and executive leadership community. If you are a healthcare leader trying to figure out where to start or how to think about AI as a whole-enterprise challenge rather than a point solution, this episode is essential listening.  In this episode, they talk about: AI is not a point solution; it is a new operating system that will touch every function in every organization Healthcare is broken, fragmented, and frustrating, and AI is the first technology with the potential to fix all three at once Legacy modernization must come before AI adoption because you cannot layer intelligence on top of broken processes AI can now reverse engineer legacy systems that used to depend entirely on tribal knowledge The five pillars of AI transformation are strategy and engineering, legacy modernization, data, process reengineering, and physical AI Training AI on your own private data is the competitive wedge that separates leaders from followers Agents are the new team members, and organizations need to rethink how humans and agents orchestrate workflows together Infosys acquired Optimum Health IT, the number one-ranked Epic implementation partner according to KLAS, to deepen its provider capabilities Epic now covers an estimated 220 to 230 million distinct patients in the US and is growing internationally The Pacesetters podcast and annual executive gathering bring together CIOs, CTOs, academics, and analysts for candid, off-the-record dialogue about the future of healthcare A Little About Venky Ananth: Venky is a technology and transformation executive with deep experience leading a global business unit, scaling high-performance organizations, and delivering large-scale change through AI, cloud, and modern growth operating models. His career has focused on helping enterprises modernize core systems, improve operational efficiency, and unlock new growth through platform innovation and disciplined execution. He founded and scaled Infosys Helix, a cloud-native platform business that continues to shape payer and health platform modernization. In addition, he has led global teams across engineering, delivery, consulting, and product, giving him a broad view of strategy, technology, operations, and organizational scale. He operates at the intersection of technology, business model transformation, and leadership development, with a track record of strengthening enterprise performance and building organizations capable of sustained growth. Beyond his operating role, I host PaceSetters, a CXO leadership platform featuring conversations with leaders from healthcare, academia, private equity, and technology. 

Autonomous software development creates a dilemma for leaders in regulated industries: adopt AI coding at scale or fall behind on product velocity without compromising auditability and code quality. In CXOTalk episode 917, Kris Tokarzewski, Group Chief Technology Information Officer at Vitality, describes how a 14,000-employee multinational insurer is rebuilding its software development life cycle around AI. This episode examines the impact of agentic AI on software development in the enterprise.Recorded at Blitzy's headquarters, the conversation examines deterministic code generation, Blitzy's infinite code context, context engineering, test-driven development, and the shifting bottlenecks that surface as throughput accelerates.YOU'LL DISCOVER✅ Why regulated industries require deterministic, auditable code rather than the probabilistic output most AI coding systems generate✅ How Blitzy's infinite code context (ingestion of codebases, engineering standards, and business rules) creates high-quality software aligned with compliance requirements✅ How Vitality reverse-engineers legacy systems with autonomous AI, achieving a measured 5x acceleration over manual methods✅ Why optimizing end-to-end SDLC throughput matters more than local efficiency at any single stage✅ How code review of 50,000 to 100,000-line pull requests becomes the next limiting factor, and how AI reviewers close the gap✅ How test-driven development pairs with autonomous code generation to raise quality and compliance pass rates✅ How the roles of requirements engineers, software engineers, and product teams converge inside an AI-native SDLC✅ How to instrument AI spend against velocity, quality, end-to-end throughput, and customer value rather than isolated gainsTIMESTAMPS0:00 Deterministic code vs. probabilistic AI output0:14 Meet Kris Tokarzewski, Group CTIO of Vitality0:32 Why Vitality is modernizing legacy insurance systems1:30 Event-driven architecture as agentic AI's natural partner3:00 Building an AI-native software development life cycle with Blitzy4:28 Throughput optimization versus local efficiency6:02 Reverse engineering legacy systems and deterministic code generation9:05 Infinite code context: ingesting codebases, standards, and rules10:00 Test-driven development with autonomous code generation10:49 Results: 5x faster legacy reverse engineering13:17 Product, engineering, and DevOps convergence15:04 Roles level up: requirements engineers and software engineers16:18 Reviewing 50,000 to 100,000-line pull requests17:56 Instrumenting AI spend against business outcomes19:16 Executive sponsorship for autonomous development20:16 Advice for CIOs and CTOs adopting AI-driven development

Early bird discounts for the San Francisco World's Fair, the biggest AIE gathering of the year, end today - prices will go up by ~$500 tonight so do please lock in ASAP!From near-universal AI tool adoption inside Shopify to internal systems for ML experimentation, auto-research, customer simulation, and ultra-low-latency search, Mikhail Parakhin joins us for a deep dive into what it actually looks like when a 20-year-old, $200B software company goes all-in on AI. We cover why Shopify has become much more vocal about its internal stack, what changed after the December model-quality inflection, and why the real bottleneck in AI coding is no longer generation, but review, CI/CD, and deployment stability.We also go inside Tangle, Tangent, SimGym, which are three major AI initiatives that Shopify is doing to make experimentation reproducible, optimization automatic, customer behavior simulatable, and search and catalog intelligence faster and cheaper at scale. Along the way, Mikhail explains UCP, Liquid AI, and why token budgets are directionally right but often measured badly, why AI-written code can still increase bugs in production, what makes Shopify's customer simulation defensible, and what he learned from the Sydney era at Bing.We discuss:* Mikhail's path from running a major Microsoft business unit spanning Windows, Edge, Bing, and ads to becoming CTO of Shopify* Why Shopify is talking more publicly about AI now, and why staying at the frontier has become necessary for the company* Shopify's internal AI adoption curve, the December inflection, and why CLI-style tools are rising faster than traditional IDE-based tools* Why Jensen Huang is directionally right on token budgets, but raw token count is still the wrong way to evaluate engineering output* Why the real unlock is not more agents in parallel, but better critique loops, stronger models, and spending more on review than generation* Why AI coding can still lead to more bugs in production even if models write cleaner code on average than humans* Why Shopify built its own PR review flow, and why Mikhail thinks most off-the-shelf review tools miss the point* How PR volume, test failures, and deployment rollback are becoming the real bottlenecks in the agent era* Why Git, pull requests, and CI/CD may need a new metaphor once code is written at machine speed* What Tangle is, and how Shopify uses it to make ML and data workflows reproducible, collaborative, and production-ready from the start* Why Tangle is different from Airflow, and why content-addressed caching creates network effects across teams* What Tangent is, and how Shopify is using auto-research loops to optimize search, themes, prompt compression, storage, and more* Why Tangent is becoming a democratizing tool for PMs and domain experts, not just ML engineers* Why AutoML finally feels real in the LLM era, and where auto-research still falls short today* Why Tangle, Tangent, and SimGym become much more powerful when combined into one system* What SimGym is, why simulated customers only work if you have real historical behavior, and why Shopify's data gives it a moat* How SimGym evolved from comparing A/B variants to telling merchants what to change on a single live storefront to raise conversions* Why customer simulation is so expensive, from multimodal models to browser farms to serving and distillation costs* How Shopify models merchant and buyer trajectories, runs counterfactuals, and thinks about interventions like discounts, campaigns, and notifications* Why category-level behavior is so different across commerce, and why ideas like Chinese Restaurant Processes are showing up again in practice* Shopify's new UCP and catalog work, including runtime product search, bulk lookups, and identity linking* Why Shopify is using Liquid AI, and why Mikhail sees it as the first genuinely competitive non-transformer architecture he has used in practice* Where Liquid already works inside Shopify today, from low-latency query understanding to large-scale catalog and Sidekick Pulse workloads* Whether Liquid could become frontier-scale with enough compute, and why Shopify remains pragmatic and merit-based about model choice* Who Shopify is hiring right now across ML, data science, and distributed databases* The Sydney story at Bing, why its personality was not an accident, and what Mikhail learned from deliberately shaping AI character early onMikhail Parakhin* LinkedIn: https://www.linkedin.com/in/mikhail-parakhin/* X: https://x.com/MParakhinTimestamps00:00:00 Introduction: Mikhail Parakhin, Microsoft, and Shopify00:01:16 Why Shopify Is Talking More About AI00:02:29 Internal AI Adoption at Shopify and the December Inflection00:06:54 Token Budgets, Jensen Huang, and Why Usage Metrics Can Mislead00:10:55 Why Shopify Built Its Own AI PR Review System00:12:38 AI Coding, More Bugs, and the Real Deployment Bottleneck00:14:11 Why Git, PRs, and CI/CD May Need to Change for Agents00:18:24 Tangle: Shopify's Reproducible ML and Data Workflow Engine00:21:19 Why Tangle Is Different from Airflow00:26:14 Tangent: Auto Research for Optimization and Experimentation00:30:07 How Tangent Democratizes Experimentation Beyond ML Engineers00:33:06 The Limits of Auto Research00:36:36 Why Tangle, Tangent, and SimGym Compound Together00:37:20 SimGym: Simulating Customers with Shopify's Historical Data00:42:47 The Infra Behind SimGym00:46:00 Why SimGym Gets Better with Real Customer History00:47:30 Counterfactuals, HSTU, and Modeling Merchant Trajectories00:51:55 CRPs, Clustering, and Category-Level Customer Behavior00:53:30 UCP, Shopify Catalog, and Identity Linking00:55:07 Liquid AI: Why Shopify Uses Non-Transformer Models00:59:13 Real Shopify Use Cases for Liquid01:03:00 Can Liquid Scale into a Frontier Model?01:09:49 Hiring at Shopify: ML, Data Science, and Databases01:10:43 Sydney at Bing: Personality Shaping and AI Character01:13:32 Closing ThoughtsTranscript[00:00:00] swyx: Okay. We're here in the studio, a remote studio, with Mikhail Parakhin, CTO of Shopify. Welcome.[00:00:08] Mikhail Parakhin: Thank you. Welcome.[00:00:10] swyx: I don't even know if I should introduce you as CTO of Shopify. I feel like you have many identities. Uh, you led sort of the, the Bing ML team, I guess, uh, uh, or ads team. I, I don't know, I don't know, uh, you know, it's, uh, people va-variously refer you as like CEO or, or, uh, I don't know what that, that, that said previous role at Microsoft was.[00:00:29] Mikhail Parakhin: Uh, that was... Yeah, my previous role w- at Microsoft was the-- I actually was the CEO of one of Microsoft's business units, which included, as I, you know, as we discussed, all the things that people like to laugh about, uh, including Windows and Edge and Bing and ads and everything.[00:00:47] swyx: Yeah, yeah. What a, what a, what a wild time.You've obviously, uh, done a lot since you landed at Shopify. Uh, one of the reasons I reached out was because you started promoting more sort of internal tooling, uh, primarily Tangle, but also a lot of people have seen and adopted Tobi's QMD, uh, and obviously, I think, uh, Shopify has always been sort of leading in terms of, uh, engineering.I think more-- it's just more recent that you guys have been more vocal about your sort of AI adoption. Is that, is that true?[00:01:16] Mikhail Parakhin: Well, I think AI tools in general are fairly recent development, uh, and we've-- Shopify, you know, at this stage of its development, we're developing AI in-in-house and other, uh, building tools that use AI and, you know, interfacing with the wider AI community, uh, you know, are on the sort of the, uh, runaway trajectory.So it just did by sort of natural byproduct. We, we talk about it more also. We just, uh, just even yesterday, Andrej Karpathy was famous in tweeting about, oh, are there some, uh, ways, uh, that, that you can organize your agents to store the data and then, uh, look up the data so that you don't have to research or, or lose context every- Yestime. And a little bit tongue in cheek, I tweeted that, “Hey, we've, we've done it much earlier, and we even have different approaches, Tobi and I.” Tobi, of course, is a big fan of QMD, and I'm more of a SQL, SQLite fan. But, uh, yeah, very similar things that we've already done here. The point is, yeah, we're very dynamic, you know, explosively growing company, and we have to be at the forefront of AI adoption, obviously.[00:02:29] swyx: Yeah. Yeah. Um, you, your team kindly prepared some slides actually that we were gonna bring up on to, uh, the screen. I think I can, I can screen share, and then we can kind of go through some of the shocking stats that maybe, maybe put some numbers to what exactly is going on. So here we have, uh- An internal AI tool adoption chart.What are we looking at here? What ?[00:02:54] Mikhail Parakhin: Yeah, this is very interesting statistics. Uh, this is number of daily active workers, you know, think of, uh, DAO, basically the active users of-[00:03:05] swyx: Yeah ...[00:03:05] Mikhail Parakhin: AI tool as a percentage of all the people in the company, right? And then- Yeah ... different AI tools. And, uh, you could see two things here is that one is the green is total.Uh, green is just total. So you could see that it approaches really % by now. It's hard not to do your job now without interacting deeply, at least with one tool. You could see another interesting thing is just as many people commented in December was the phase transition when suddenly models gotten good enough that, that everything took off and started growing.Uh, it, it was many people noticed that the thing is that small improvements accumulated into this big change in Sep- December roughly timeframe.[00:03:52] swyx: Yeah.[00:03:52] Mikhail Parakhin: The other thing I would claim you could see is that, uh, CLI-based tools and tools that don't require you to look at the code becoming more popular, and you could see, yeah, various versions of, uh, Cloud Code and Codex and Pi and internal development tools taking off.Uh, exactly, yeah, uh, and blue is our River, just internal agent for coding, where tools, uh, that require IDEs such as, uh, GitHub, Copilot or Cursor, they're not exactly shrinking, but they're not growing as fast. Like, uh, red, red line is, is the IDE kind of tools. So you could see that they're, they're not experiencing as, as fast of a growth.[00:04:37] swyx: As I understand it, basically, every employee has their choice, right? Of choose whatever tool you use, and then you're just kind of doing a, a daily sur-survey or something.[00:04:47] Mikhail Parakhin: Exactly. And, uh, we- Yeah ... the, the push is to get your job done, you can use any tool, and we effectively fund unlimited tokens for everybody.Uh, we, we do, we do try to control the models that, uh, people use, but from the bottom, not from top. Like we basically say, “Hey, please don't use anything less than Opus four point six.”[00:05:09] swyx: Oh .[00:05:10] Mikhail Parakhin: Some people, some people end up using GPT five point four extra high. Some people use Opus four point six. Um, uh, you know, uh, there are some, uh, there are plus and minuses in going for full one million context window versus not.But, uh, we try to discourage people from using anything less than that.[00:05:28] swyx: Yeah, yeah. Got it, got it. Uh, I mean, uh, that's, you know... The, the next chart here, it really kind of shows the expansion and the sort of December twenty twenty-five inflection, right? That, uh, people are using a lot of tokens. I think it's also really interesting that no one was kind of abusing it in twenty twenty-five.Like it was- Had comparatively, uh, to this year, there was almost no growth. I mean, it's still like, you know, probably, probably gave fifty percent.[00:05:56] Mikhail Parakhin: Yeah. This is just a different scale. It's still exponential- Yeah, yeah ...growth at just a different- ...rate of expansion. Uh, there was inflection point, and Sean, I would claim the, the super interesting part here is that you could see that the distribution becoming more and more skewed.Yes. The top percentiles grow faster. So that means- Yeah ...the people in the top ten percentile, they, their consumption grows faster than seventy-five and so forth. So, uh, the distribution skews more and more towards the highest users, which is... I don't know what it tells me. It's like it feels not ideal, to be honest.Or maybe it's okay. We'll see.[00:06:36] swyx: Why does it feel not ideal? Is, is it because of, um, quantity over quality, or what's the concern?[00:06:42] Mikhail Parakhin: Because take it to the limit. That means, you know, if, if this rate of separation continued- Ah, yes ...a year, there will be one person consuming all the tokens. So it's just, it's kinda strange.[00:06:54] swyx: Yeah, I mean, um, uh, I, I think internal like teaching and all that, uh, will, will help sort of distribute things more widely. But in, in the early days, of course, the people who are sort of more AI-pilled will obviously find more ways to use it than the people who are less AI-pilled. Maybe let's, let's call it that.I'll just, I'll just kinda quickly, uh, pause from the, the... You know, we will go back to the rest of the slides, but I just wanna, um, review, you know, there are a lot of CTOs of, of large companies like yourself where they're all considering some kind of token budget, right? Like I think it's something, something that Jensen Huang has been talking about, where like if your 200K engineer is not using 100K of tokens every year, like they're, they're underutilizing coding agents.Of course, Jensen Huang would say that, but like it seems a very quantity over quality approach and like some, some people are basically saying like, well, is this comparable to judging engineer quality by lines of code, right? Which we also know is like kind of flawed, but better than nothing. So I, I don't know if you have like a sort of management take here on, on how to view this kind of, uh, metrics.[00:08:02] Mikhail Parakhin: Well, I mean, you're, you're baiting me. I, I like... This is my favorite topic. Uh, if you let me, I'll probably talk for two hours on just this. I have a lot of things to say. Like I do think Jensen gotten a lot of bad press saying, “Oh, of course you're, you know, this, uh, the- ...the cake seller says you don't need enough cakes.”You know? Like, of course. Uh, but, uh, I actually, uh, think that's undeserved. I think he, he's actually right. Uh, I do think- He,[00:08:33] swyx: he's directionally correct.[00:08:35] Mikhail Parakhin: Yeah. Yeah. He's directionally correct for sure. Uh-[00:08:37] swyx: Who knows what the right number is? Yeah.[00:08:39] Mikhail Parakhin: The thing that I do Uh, want to say, and this is something that we learned through trial and error and very important is like two things.One is that it's not about just consuming tokens. Uh, you can consume tokens and, and in fact, the anti-pattern is running multiple agents, too many agents in parallel that don't communicate with each other. That's almost useless, uh, compared to just fewer agents and burns tokens very efficiently. Uh, setting up the right critique loop, especially with the high quality models, where one agent does something, the other one, ideally with a different model, critiques it, uh, suggests ways to improve it, the agent redoes it with this critique and, and so it takes much longer.So people don't like it because latency goes up. You know, they, they have to wait until this debate is happening. But, uh, the quality of the code is much higher. And another thing, just since you mentioned like, look, uh, uh, yeah, the overall budget is just like, uh, lines of codes. Lines of codes are exploding for everybody right now, or partially because AI is really mover balls, but partially just because AI can write a lot more code, you know, doesn't get tired.And so you have to have to have a very strong narrow waist during PR review. Otherwise, just the number of bugs will go through the roof. It's, uh, it's this unexpected consequence of the just volume trumping everything. I would claim by now good model writes code on average with fewer bugs than, than the average human.But since they write so much more of it, like more of it will make it into production. So you have to- You still[00:10:26] swyx: have[00:10:26] Mikhail Parakhin: more bugs. Yeah. Have to have a very rigorous PR reviews, also automated of course. But, uh, yeah, that to spend a lot budget there. Like this, this for me, for me, actually, the important metric is the ratio of budget spent during code generation versus, uh, spent, uh, expensive tokens like GPT, uh, five point four Pro or, uh, uh, Deep Think from Gemini, you know, checking on PR reviews.[00:10:55] swyx: Yeah, totally. Uh, I noticed in your chart you didn't have any review tools. Do you just use like, like let's say a Claude code to review tools? Or do you have another set of review tools like the Greptiles, the Code Rabbits, uh, Devin Reviews has a review tool. I don't know if you've had those specialist review tools.[00:11:13] Mikhail Parakhin: You are a little bit jumping on my store tool right now because the graphs I was only showing public tools. Uh, uh, the-- I haven't found a good PR review tool that, that does what I think should be done. And, uh, partially my, my thinking is because it's so... It just goes against both what people feel like emotionally they prefer and, uh, some of the, uh, you know, frankly Even business models that, that the companies run.At peer review tool, uh, time, you want to run the largest models. That means, I don't know, Codex or, or, uh, Cloud Code is not gonna cut it. You need to have pro-level models if you really want to, uh, stand the tide of bots from going into production. And you need us to spend a lot of time, the models taking turns, but you don't want, like, a big swarm of, uh, of, uh, agents.So in fact, you end up in a different dual-dualistic world where you generate not that many tokens. You, in fact, generate few tokens, but it takes f-a long time because these are expensive models taking turns rather than many, many agents trying to do many things in parallel. So that's, that's why I feel like I haven't found good tools, so we are using our own for peer review for now.[00:12:33] swyx: Yeah. Yeah. I mean, uh, I think a lot of companies are building their own, uh, especially to their needs, right?[00:12:38] Mikhail Parakhin: Mm-hmm.[00:12:38] swyx: Um, I, uh, you also have a chart here going back to the slides on, uh, PR merge growth, where we're now at thirty percent, uh, month on month rather than ten percent. Uh, and also the, the estimated complexity is going up.You know, this is productivity, right? ‘Cause y- presumably there's more stuff going into the code base and more, more features getting worked on. I'm curious about the backlog, right? Like the, the, the-- I actually don't mind a pro-level model taking an hour or two hours to review my PR, because I've dealt with humans who take a week to review my PR, right?And I keep pinging them on Slack, “Hey, hey, review my PR.” So, you know, I think there's some trade-off here where, like, it still doesn't make sense.[00:13:18] Mikhail Parakhin: Exactly. That, that's exactly m-my point. Uh, that on one hand, you can tolerate longer latencies at, uh, PR. On the other hand, like right now, the real problem is not in spending time waiting for PR.It's real problem is since there's so much more code than- Yeah ... uh, probability of at least some tests failing going up, and then you, like, keep de-failing, then you have to find the offending PR, evict it, retest it without that PR, and so deployment cycle becomes much longer. Uh, so it actually, in terms of the overall time to deploy, it's total time savings if you spend more time on a longer model, like thinking for an hour, because then, then you, you don't have to spend all that time during testing and rolling, you know, rolling back the deployment.[00:14:03] swyx: Yeah, totally. That's still worth it. You know, you don't look at the individual, look at the aggregate, and look at the, the, the change in the aggregate system.[00:14:11] Mikhail Parakhin: Exactly.[00:14:11] swyx: I'm kind of curious if, like, there's this PR mentality and, like, c-- the, the, the CICD paradigm will be changed eventually. Some people are like, obviously a lot of people want new GitHub, but I even wonder if, like, Git is the problem, right?Like, is that the bottleneck? Is the concept of a PR a bottleneck? Do you guys use stack diffs? I don't know if, uh, that's a, like, a merge queue stack diff type of thing.[00:14:34] Mikhail Parakhin: We, we use, we use Stacks, we u- we use Graphite. We worked with, uh, Graphite a lot. Uh, so we use Stack, uh, PRs. I think, uh, like that's clearly the overall CICD in general, and the interaction with the code repository right now is the, clearly the sort of the, the main issue and the bottleneck for us, uh, and highest top of mind.I would say we probably need a different metaphor or different whole design of how to process it in new agentic world. I haven't seen anything dramatically better yet. I, I think everybody right now is just trying to keep their head above the water ‘cause, ‘cause there, there's so many PRs and then everybody's CICD pipelines start creaking, the, the times are increasing, the number of bugs slipping by increasing, and you have to, have to clap on down.And so we are a little bit in this situation when we need to first stabilize that story and then start thinking, hey, what, what it could be a completely different and new world, which I haven't... I know some people working on it. I haven't seen something, like anything super compelling yet, but clearly the old thing were designed for humans will need to be morphed into something new.[00:15:53] swyx: One of the thing that I, I think about is kind of like the merge conflict is basically a global mutex on the whole system, right? And in, in hu- in human organizations, we do have something like that. It's the company standup. But like, other than that, it's like it's actually fitting for us to be somewhat decentralized, somewhat plugged into one stream of information source, but somewhat lossy.Like it's okay, you know, that, that not every delivery is like atomic consistency. Like we're not dealing with a database sometimes.[00:16:27] Mikhail Parakhin: This is a very good point, uh, because since humans don't write code too fast, you know that global mutex is not too bad. Once you-[00:16:36] swyx: Yes ...[00:16:37] Mikhail Parakhin: start writing code at the speed of machine, it becomes the, you know, the bottleneck.Then what do you do? Maybe, and I can't believe I'm saying this because I, I'm long-- lifelong opponent of, uh, microservices, and I always thought that was, like, a really bad idea. And now that you're saying it, like, maybe in new guys like microservices will make a comeback, you know, because then you, you can ship things independently in tiny things and, and the managing all that complexity automatically will be much easier.I don't know. Like, we'll s-- we'll have to see.[00:17:10] swyx: Yeah. I mean, I don't know what the Microsoft or, or Shopify thing is, but I, I read this paper from Google where they have a monorepo that deploys into microservices, right? And then, uh, the other concept that I think about a lot is the Chaos Monkey concept from, from Netflix.Being able to create, like, this robust system where, um, uh, you know, you, you have the service discovery, you have the, uh, the independent, independent microservices discovery and, and, uh, you know, probably going to be a fair amount of duplication. That's how an organic system sort of scales, uh, that, that you have that...I don't know how you call it. Slack? Robustness? Depend-- uh, d-duplication. I, I, I forget the-- I, I'm-- And this-- those-- these are not exactly the terms- Hmm ... I'm looking for, but I c-can't really think of the words. Okay. I was gonna go into Tangent and Tangle. Uh, so, uh, we, we sort of discussed the overall stats that, uh, Shopify has.Uh, but, you know, I, I think some, some pretty cool stuff that you guys are working on is your ML experimentation, uh, and your, your sort of auto tr-research training pipeline. Presumably you're much closer to this one because it's, it's a sort of personal hobby of yours. How, how would you explain them in, together?I thought we have a slide that, like, uh, has the s- the system diagram.[00:18:24] Mikhail Parakhin: Yeah. Tangle first and then Tangent as a-[00:18:27] swyx: Yeah ...[00:18:28] Mikhail Parakhin: as a thing on top of Tangle. And, uh, Tangle is the third generation, I claim, of, uh, systems of, uh, running any data processing, but a bit with a skew for ML experiments, but not necessarily. Any sort of data processing tasks where you need to iterate, share, and you have scale so that you want maximum efficiency.You know how, like, normally you would work, you would-- Imagine you're a data scientist or an ML practitioner, you would get Jupiter notebooks or, or maybe you would get, uh, you know, Pyth- your Python scripts, and you would manage the data, and you produce those TSV files, and you put them in some JFS or something.Then you would notice that, oh, it has this, uh, weird missing values. You go and write another script that, uh, goes and replaces them with, uh-[00:19:20] swyx: Ah ...[00:19:21] Mikhail Parakhin: dash S. And then, then you, then you run some, some, uh, “Oh, I need to filter bots.” And so you run some light GBM model that, uh, removes the bots. And then, then you like-- And then you, you kind of like get into shape, and then you start experimenting, and you run multiple experiments, and then you're like, “Oh my God,” like, “this experiment is worse.”You undo, and you cannot get to previous result. And like, “Ah, what did I do?” Like that. Again, then, then you finally like get everything working. Then you like start throwing it over the fence to production. You, you replicate it, those things don't work, and then sometimes you like don't notice that you forgot some feature naming and the, the features don't match.But then, like imagine you, you did everything, and then six months later you're like, have to repeat it because now there's more data, or you wanted to do another pass, and you're like, “What, what did I do?” Or like, or like, “This script crashes now,” or the, “the path has changed.” And then, then you're trying to, like you spend another month just doing ar- digital archeology on your own, you know, history, right?Now multiply that by many, many teams. Now imagine you got an intern that you wanna ramp up. Now you have to show that intern, “Oh, you know, look, here's the folder, there's the scripts, you know, ask your cloud agent to do, and then, uh, to, to figure it out.” And then cloud agent does something, and then you're, “Ah, yeah, right, right, it was the wrong folder.I forgot to tell you, I actually have this other thing I forgot myself.” And, and that's, that's the, like, the daily life we all, uh, all know it, uh, if, if you're a data scientist, machine practitioner, ma- machine learning practitioner or, uh, or even like any data managing, uh, person.[00:21:00] swyx: Yeah. So I, I used to do this, uh, f- uh, on the quant finance side, uh, in, in my hedge fund.So we did this before Airflow, and then, uh, obviously Airflow came along and, uh, then more recently Dagster, uh, I would say is like, in my mind, what I would use for that shape of problem, uh, where you had to materialize assets and create a pipeline.[00:21:19] Mikhail Parakhin: And that's, that's very good segue because... So Airflow is great, but Airflow is more about you, you have something and you wanna repeatedly run it in production on schedule.It's less about you as a team developing things and being able to share, and you grabbing the standard pipeline and saying, “Hey, I wanna change this tiny little component in the huge sea of data processing, and I don't wanna-- I wanna run ten experiments on this, and I wanna do hyperparameter optimization.”All that is very hard to do with Airflow. It's very easy to do with Tango. Tango is m- more about, it's everything about group of people Running experiments, it might be agents too nowadays. Uh, running experiments cheaply, collaborating, sharing results. Uh, you don't need to understand fully. You, you grab-- you clone somebody else's experiment or somebody else's pipeline, uh, run, uh, change small piece, run it, be, like, get it to production state, and then ship in one click.So then the... You don't have to port it into any other system to, to run in production. You can just run the same experiment. It's, it's fully production ready. And, and it's, uh, it has lots of... Again, as I said, it's third generation system. The original one was, I would claim there was Ether and then, uh, at least in my career, Ether was the first, first, uh, that pioneered this type of approach.And then there was, uh, Nirvana, which, uh, uh, at Yandex, which did kind of sec-second take on this. And now this one aggregates the, the learnings from all of those and, and Airflow as well to, to get to the state where you try it, it, it feels kind of magical. Uh, ‘cause now everything is based on content, uh, hashes.So even if the version changed, but if the output didn't change, nothing is being rerun. It's very efficient. If you... Multiple people start experiment that needs the same sort of data preprocessing, it's not repeated multiple times. It's automatically done only once. If you start ten experiments that all require, you know, some, some data preparation first as the first step, and you don't have to coordinate for that.Like, you don't have to know that other people are starting it. You now, it's very easy compos-, uh, composability, any language you can u- uh, you wanna use, and it's very visual. So you can see immediately, you can edit it easily, you can assemble small things with just even mouse clicks if you want to, and, uh, share, clone.And everybody knows also it's fully kind of static in the sense that we rerun it second time, it will exactly have the same results. Like, you will never have to do digital archeology. So full versioning and everything is also there.[00:24:06] swyx: Uh, so, so people can, uh... It's open source. Go to the GitHub repo and, and, uh, check it out.Uh, and it is also a really good, uh, blog post about it. I think all these is, like, really appealing. The, the, the, the thing that I think sells me the most about it is that, um, sort of development to production transition, right? Which I think, um, a lot of people haven't really solved that, uh, strictly, right?Like, we develop really, really well in, in Python notebooks, but then, you know, that's obviously not a sort of production ready process. I think that, like, any way in which that is solved, I think is, is very appealing. Then the other thing that you mentioned, which also raised my eyebrows, was content-based caching, which you mentioned is, is, um, you know, is ve-very much, uh, um, a sort of efficiency measure about, uh, you know, just like recalculation only on, on sort of content addressing Which I think makes sense.Uh, it surprised me that the savings could be this much, but maybe I just haven't worked at your scale where there's so much duplication, uh, that people just rerun because they change a single ID upstream.[00:25:10] Mikhail Parakhin: It does, yeah. But it's not only you rerun. The, the main savings are coming from the fact that you ran it, you got your job done, and you moved on.Then- Yeah ... somebody else in some department you don't know existed runs the same task, but on a newer version.[00:25:27] swyx: Yeah.[00:25:27] Mikhail Parakhin: Like right now, you can't, in, in most of the organizations, you can't even find out about it so that you can't even measure that you're spending that time twice, right? Here- Yeah ... if everybody's on Tango, that's detected automatically and detected that the output is the same.And then for that person, all it looks like is like experiment just suddenly moved, jumped forward, right? Uh, uh- Yeah ... so that's because, because the, there's network effect of multiple people helping each other.[00:25:51] swyx: Yeah. This is one of those things where it's designed to be a platform from the beginning rather than an individual developer's tool from the beginning, right?And, and everything's gonna streams down from there. That is the sort of Tango, uh, orchestrator, and it's, it manages jobs. We've seen a few versions of this, and this is obviously, uh, uh, the sort of, uh, unique approaches that you guys have, have, uh, figured out. And then there's Tangent.[00:26:14] Mikhail Parakhin: Yeah. And Tangent is basically an automatic auto research loop that can help and kind of do your work for you.Uh- ... you know, uh, effectively, effectively, Andrej Karpathy recently popularized it with auto research. Yes. Remember he said like he was, uh, speed running this, uh... Yeah, uh, you know the story. The, here we're basically bringing the same capability into Tango so that, uh, the, uh, Tangent can analyze it. It's just an agent that can run multiple experiments, figure out what can be changed, and keep on rerunning it, keep on modifying until, uh, maximizing some goal, some loss function, whatever you need to, to achieve.And in general, I would say if you're not using auto research-like approach in whatever you do, like literally whatever you do, then you're missing out. We saw at Shopify that taking like a wildfire, anything where you can put measurements can be done dramatically better. Our-[00:27:19] swyx: Mm-hmm ...[00:27:20] Mikhail Parakhin: uh, speed of, uh, templatization HTML, uh, completely new UX tem- uh, templatization of, uh, reducing latency for liquid themes.Uh, we-- Our, uh, search, uh, recently we moved from It's hard even, uh, quote from eight hundred QPS to forty-two hundred QPS with the same quality just by pure optimizations and not a research loop that kept running and changing code in our index serve on the same number of machines, just increasing the throughput.We, we managed to improve the quality of gisting and machine learning process. Uh, you know, gisting is the prompt compression technique that[00:27:59] swyx: allows for[00:28:00] Mikhail Parakhin: lower latency and, and lower and, uh, actually higher quality slightly. So like literally whatever different walks of life, and it doesn't have to be AI related.Uh, we, we had a reduction in, uh, storage because the agents would go and find data sets that clearly are derivative, uh, and then you don't need to store things twice. You know, we, we, we found somewhat embarrassingly that it was one of the largest tables was hashing random IDs into another random ID, and we literally- Oofput only one. So it was translating, yeah, two random IDs hashed[00:28:36] swyx: into[00:28:37] Mikhail Parakhin: each. So, so[00:28:37] swyx: it has access to the code as well, so it can, it can check the, like what, what the hell is it doing?[00:28:42] Mikhail Parakhin: So there, there cou- it could be run in two levels. You, uh, you know, at the superficial level, it could just use ex-existing components and, uh, reshuffle them.Uh, you know, like you can grab- Yeah ... uh, XGBoost, and you can grab some, some Py- PyTorch module, and then can grab some, you know, grab another tools and, and combine them. At a deeper level, since Tangle is all sort of CLI based underneath you, every, every component is a wrapped really CLI, uh, call and a YAML file, it can analyze code and create new components and, and, uh, keep on iterating as well.So, so you can, you can both have quick modifications of existing t- uh, pipelines with the, with components that are already there pre-baked, or you can create new components, uh, and-[00:29:29] swyx: Yeah ...[00:29:29] Mikhail Parakhin: keep iterating on those. So auto research is, again, this is probably the, the thing I was excited the most in the last two months happening, and we see it taking like, like totally like a wildfire.Just, uh, everybody, every day, every... well, every day, every minute, I would, uh, have somebody Slack message saying, “Oh, look how much better I made it.” And, uh, it's all throughout the research.[00:29:53] swyx: Is this democratized in some way in, in the sense that like is it your ML, uh, engineers and researchers doing this, or is it your regular PMs and software engineers also have the ability to auto-- to use Tangent?[00:30:07] Mikhail Parakhin: This is an awesome question. Like, Tango in general and Tangent in particular are extremely democratizing. Like they- Yeah ... they are the main tools for- ‘Cause I don't[00:30:15] swyx: need the details.[00:30:16] Mikhail Parakhin: Yeah. Exactly. Initially used by ML and AI engineers, but then literally, as you said, PMs are like the highest user right now is one of PMs on our org, uh, Sartak and he was, he was number one by, by usage of, of this ‘cause they're just, uh, energetic and knowledgeable, and now it, it unlocks a lot of capability where you don't have to co-change code manually.[00:30:39] swyx: I mean, I mean, because it kind of cuts out the ML, ML engineer from the process because the, the, the PMs have the domain knowledge and the ability to think about, uh, from first principles about, okay, what, what results do I want? And they can-- they even have the access to the data that, that needs to go in.So it's like in some ways, like this is the magic black box that we've always wanted for, for training and, and for, uh, I guess, uh, uh, hill climbing, whatever.[00:31:04] Mikhail Parakhin: It's basically cloud code for your AI development- ... uh, situation, right? Like now, now you don't have to know exactly how algorithms work. You can just, uh, bring your domain knowledge and expertise and product knowledge and iterate within Tangent until you've gotten the results that you need.[00:31:21] swyx: In my previous roles, every time that someone has pitched AutoML, you know, I've always been like, “Uh, this is not, this is not gonna work. It's, you know, it's, it's always gonna be a flop.” Somehow it's working now. I mean, presumably the answer is now we have LLMs and it's good enough, right? It's, it's an emergent property that we can do auto research, but like, it doesn't feel that satisfying that how come we didn't do this before, right?Like we just did like parameter search and like, I don't know. That's maybe that's it.[00:31:48] Mikhail Parakhin: Yeah. Bayesian optimization and hyperparameter optimization was, was the one that, or facet of AutoML that was used very actively, which incidentally also built into, uh, Tango. But, you know, I know Patrice Simard very well, and, uh, he was such a, uh, such a proponent of AutoML, and he put, like literally spent careers trying to democratize it.Without LLMs, it just turned out to be very hard. Like it, you, you would have flexibility within certain narrow domain, but it was hard to wider scale, and now with LLMs suddenly it's like magic wand, and so suddenly everybody- ... is an AutoML expert.[00:32:28] swyx: Yeah, I, I think it's multiple things, right? Like I'm, I'm just gonna bring up the, the, the chart again, right?Like LLMs can do the monitoring very well. That is the very potentially unbounded, super unstructured. It can do the analysis very well, it can do the... Uh, and basically it is much more intelligence poured into every single step. Uh, there's maybe nothing structurally changed about AutoML, but this is just m-more intelligent and more unstructured.[00:32:53] Mikhail Parakhin: Exactly.[00:32:54] swyx: Any flaws that you've run into? Like everyone is like drinking the Kool-Aid, oh my God, time savings, uh, you know, performance improvements. Like what, what, uh, issues have you have, uh, come up?[00:33:06] Mikhail Parakhin: This is really cool. It's not a solution to all the world's problems for sure. The limitations are usually the ones I-- And this is where we get into a bit of a subjective territory.Uh, I can only share what I've, I've seen so far, and I'm sure the situation, uh, is changing, and, you know, maybe after I say it, like many people will reach out and say, “Hey, what about this?” And you don't know that, and then, then we'll be probably right. But what I've seen is auto research is very good at doing kind of obvious things that you don't have bandwidth to do or you didn't notice or maybe you're not aware of like the-- some standard practices.It is not good at doing something completely out of distribution, something that, you know, you have to think for, for multiple days, uh, and, and do something like none of this. So, so it's, uh, I, uh, set an experiment once, uh, on, on my sort of, uh, hobby thing, and I let it run for, uh, ended up, uh, several weeks run, uh, you know, it's like full production kind of scale, so it, you know, slow runs and, and it ex-- it performed in the end, uh, over four hundred experiments, and only one was successful.I'm like, “Okay, that's, that's good.” But-[00:34:18] swyx: But it saved time.[00:34:19] Mikhail Parakhin: Yeah, I saved time. Like it, it was the, that thing. Yeah, if I, if I were doing four hundred experiments myself, my betting average, as I said, would have been much higher, I'm sure. But also, first of all, it would take me like three years to do four hundred experiments.And, uh, I didn't have to do them. Like the machines were just, uh, the price of electricity did that. So, and I got one improvement, uh, that in, uh, my, my-- Honestly, when I was starting that experiment, my thinking was to go and show that, “Hey, Andre, maybe you just don't know how to optimize.” And I was super smart because in, in my pro-problem, it was optimized for many years, and it was like fully improved.Uh, and I didn't expect it, you know, auto research to find anything at all. Yet it did. So instead of making fun of Andre, I ended up, uh, a big, big supporter. Yeah, that's exactly the tweet. Yes.[00:35:10] swyx: You and Toby really, really go back and forth on-online a lot, which is really funny. Uh, think of it as, as an eval for the optimalness of the code it's running on.Uh, it's almost like it reminds me of like a Kolmogorov complexity thing, but, uh, I guess it's-- there's some optimal thing that you're trying to sort of reduce down to, I guess. Um, and so, so you, you, you know, you should congratulate yourself that you had, uh, you know, uh, ninety-nine percent, uh, optimality.[00:35:36] Mikhail Parakhin: Exactly, yeah. I think Andre really deserves a lot of credit for popularizing this approach. This is, uh, this is incredibly, I think, powerful and cool and You know, the, uh, even him, him just mentioning it led to a lot of gains in a lot of places in the industry, so we should be thankful.[00:35:56] swyx: Yeah. I think he also has a just...I don't know what it is. Like, um, you know, it, it is a simple self-contained project that people can take and apply to other things, which is, is, is one thing, but also just the name. Just like somehow no one, no one managed to call their thing auto research. It's just naming things is very important. I think that that is mostly, uh, our coverage of Tango and, and, uh, Tangents.I think obviously, you know, there's a lot of, uh, ML infra at, at Shopify that people can, uh, dive into. We're about to go into SimGym, but before I do that, any, any other sort of broader comments around this whole effort? Like where is it, where is it leading to?[00:36:36] Mikhail Parakhin: As a segue to SimGym, like all those things start composing strongly.And, uh, you could see a huge unlock when you can look at each one of the tools and, and you see, oh, they're extremely useful. Uh, Tango is useful by itself. Auto Research is useful by itself. SimGym is useful by itself. If you combine all three, you create like synergetic effect. I think that's why we wanted to even, uh, cover them today is because this is something that if you go back even, you know, five years ago, would've been unthinkable.Uh, replicating that, uh, would, would be either incredibly costly or impossible, right? With probably thousands of people are required.[00:37:20] swyx: Well, we have serverless human, uh, serverless intelligence, right? Like, uh, so yes, you do have thousands of hu-- of, of intelligences, not just, not humans. And that's, that's close enough, right?Even if they're not AGI, they're, they're close enough to do the, the task that you need them to do. And, and, you know, that's, there's plenty for, for a lot of routine work, knowledge work. Okay, let's get into SimGym. Um, this is one of those things I, I was surprised to see actually it's apparently your, uh, one of your most popular launches, and I think something that, uh, I think Sim AI, I think Yunjun Park, who did the Smallville thing, there's a very small cottage industry of people trying to do like the simulate customer thing.I think a lot of people maybe don't super trust this yet because they're like, well, obviously they would just do what you prompt them to do, right? But maybe just think, uh, tell us about the sort of inspiration or origin story.[00:38:10] Mikhail Parakhin: That's exactly actually the thing I wanted to cover, because if you don't have the historical data, all you can do is prompt a-agents in a vacuum, and they will do exactly what you prompt them to do.In fact, when I first proposed it, and this is a bit of, um, my brainchild initially, if I, I can boast, even Toby said like, “But wouldn't they, they just repeat what, what you tell them?” And, uh, but I'm like, “Yes, except Shopify has decades of history of how people made changes and what there is, uh, there, what it resulted in terms of sales.”So now what we can do is we can-- we have this... It's not, it's a noisy data. There's a small, usually websites, uh, you know, like things, things are never in isolation. It's almost never AB experiment. It's always AA experiment when there's has two meanings, but basically, you know, in different time you run two different things.But if you aggregate in general, uh, like everything together, and you apply, uh, denoising and collaborative filtering like approach, you can extract a very clear signal. And then you can optimize your agents. And that's why it took so long. It took almost a year of that optimization of just us sitting and fiddling, and, and we had this internal goals of correlation of hitting-- internal goal was to hit zero point seven correlation with, uh, add to cart events, for example.Like that, that if we run real AB test experiment, that it should, it should go and, and rep-uh, replicate, uh, same sort of success that, that humans had or lack thereof. And it, it took forever, and I don't think that's easily replicatable because, uh, like who else would have that data? You have to have this historic, you know, decades, uh, worth of data.And now, now the, like the other thing you need is in-infrastructure and the scale, right? Because, uh, w- again, what we found, uh, stat sig results, you need to run a lot of simulations, a lot of agents, and, and it's-- Those are expensive things. Like you're, you're making actions in the browser because you want a real friction.You want to, to be able to get the image like of what humans will see because you wanna, uh, detect effects like, “Hey, if I make my images larger, will I have more sales or l- uh, fewer sales?” And like usually people's intuition here, by the way, is that I increase my images, I will have more because they look nicer.You know, designers all look sparse and big images. Like usually your sales tank, right? But, but, uh, you know, from HTML, all the characters look the same only the, the size tag looks different, right? So it's very hard. So you have to take visual information, you have to run this in simulated browser environment on the big farm and, and of course, you have to have, uh, like very, very expensive model, good model with multi-model model.So all this it's-- is what's taken so long and, uh, to share my personal fail a little bit there, Sean, is like, you know, we always had this bias to-- for like large company bias. You know, we always, uh, whenever you-- we do, we're like, “Hey, we'll run an experiment,” right? We make, make a change, and we will run an experiment and then, uh, see, uh, see which one's better or like, “No, this is worse,” and most of them are worse, so you discard it and keep iterating, hill climbing.And we're like, “Oh, like smaller merchants, they cannot get stat sig results. They cannot really run experiments simply because, you know, in a week there would be not enough data for them.” So we thought from this perspective. What we didn't realize is that most people don't have A and B, they just have one thing, and they need suggestions of What A and B should be.So, uh, we first build this, hey, we run simulation on two separate teams and, and, uh, say, “Hey, which one is better?” We then morphed it into, and very recently just released it, when you have just your site, your theme, we run over it and we say, “Hey, here's what predicted values of, of, uh, uh, conversions are, and here's how we think you should modify it to increase your conversions.”And then circling back to what you started with, the proof is in the pudding. Like, if we are not correlating with reality, like, people will not be using it. And, uh, thankfully, we see literally every day more users than the previous day. So, so right now, uh, right now- It's working. Yeah. I'm-- Right now my problem is how to pay for it all because the so our major thing is how to optimize the LLMs, do distillation, how to run the headless browsers, uh, and handful browsers, uh, uh, cheaper so that we can accommodate the increase in traffic.[00:42:47] swyx: Yeah. I, I understand that you, uh, you published a lot of technical detail at GTC, so I was just gonna bring it up a little bit. I think s- was this in, in con-conjunction with some kind of GTC presentation? Or something like that, right?[00:42:59] Mikhail Parakhin: Well, we, yeah, we, we did it in several place, but yeah, we had the engineering- Yeahblog, uh, as well. Yeah.[00:43:05] swyx: Yeah. So you're running, uh, GPT OSS. Uh,[00:43:08] Mikhail Parakhin: the, this is an older version. You know, now we run multimodal model. But yeah- Yeah ... GPT OSS, we still run GPT OSS as well for[00:43:15] swyx: And then you have the VMs, and you also have browser-based. I really like this one where it you said, “It violates almost every assumption that standard LLM serving is designed for.”And then you had like, basically orders of magnitude differences between everything.[00:43:29] Mikhail Parakhin: Exactly. Which is, which, uh, which was, you know, a bit of a challenge to implement, like when, like even simple things. Uh, be- since it violates all the assumptions, for example, multi-instance GPUs, like MIGs don't work as well.But we needed, uh, to get MIG to work because, ‘cause otherwise it's way too expensive. And so we had to deal with the, yeah, with, uh, lots of infrastructure and, and, uh, work with, uh, uh, Fireworks and CentML, uh, you know, to help with optimizations and browser-based, as you mentioned. Yeah, like, takes a village.[00:44:04] swyx: Okay. So there's a lot of like, I guess, experimentation in the infrastructure so far, and you've published more or less what you have here. I guess I'm, I'm less familiar with CentML. I, I don't do, uh, that much work in this, this part of the stack. But why was it the sort of preferred instance platform?[00:44:22] Mikhail Parakhin: There are really three probably top companies. There used to be, uh, uh- Three top companies, uh, at least I was aware of that did, uh, LM optimization. You know, together Fireworks and Santa ML, not necessarily in that order. Santa ML recently got acquired by NVIDIA. Uh, what they did is if you have a model and you want to optimize it to a specific prof-- uh, profile of usage, uh, they would go and do it.And, uh, we work with, with those companies, uh, this was work particularly in with Santa ML and NVIDIA to get them the best possible results out of it. And, and sometimes you, you have to retune depending on, like sometimes you want the maximum throughput, sometimes you want minimal latency, sometimes you want like the cheapest, right?And, yeah, or some combination. And so yeah, these are people who would come and help you.[00:45:14] swyx: I see. I see. Yeah, yeah. I'm familiar with these people for the LLM, you know, autoregressive stack. But the other interesting category of these optimizers is also the diffusion people, whereas like Fel and, you know, uh, Pruna recently has come up a lot as well, which I think is like really underappreciated, uh, at least by myself, because I, I thought, oh, all the workload would be LLMs, but actually there's a lot of diffusion as well.[00:45:38] Mikhail Parakhin: Exactly.[00:45:38] swyx: There's a lot here, so I, I, I... it's, it's, uh, it's, it's, it's hard to cover. But I, I do think like people underappreciate the importance of customer simulation, basically. I think this is something that I'm candidly still getting to terms with. Uh, you know, uh, you also-- your team also like prepared this, like, really nice diagram.Uh, I, I assume this is AI generated.[00:46:00] Mikhail Parakhin: Yeah, it looks-[00:46:01] swyx: Maybe it's not.[00:46:01] Mikhail Parakhin: Yeah, it looks, uh, Gemini-ish. Yeah, but, uh, uh, honestly, I, I don't know where, where the hell they generated. It looks, look, uh, looks like it's, uh, Google. But the interesting part, John, that, that, uh, we haven't covered, but I, I wanted to mention is if your store had previous customers, rather than it's a new store, you're like new merchant just launching things, it helps tremendously in just correlation and forecast.Yeah, we take your previous, uh, customer's behavior, and we create agents that replicate those specific distribution of, of customers that you get, and then we a- we apply those to your changes, and then that, that raised raw, you know, the re-- uh, just correlation with the add to cart events or to-- with conversion or whatever it, it, it may be, uh, quite dramatically.So, uh, replicating humans in general seems like an interesting, cool challenge.[00:46:58] swyx: As a shareholder, I think this is the-- like if people are Shopify shareholders, they should really deeply understand this because this is basically the moat. The, the more you use Shopify, the more it will just automatically improve, right?Like you're, you're doing the job for them.[00:47:13] Mikhail Parakhin: Yeah, that's what we started with. Like, uh- ... uh, otherwise, if you're just a startup, I wouldn't do it if, uh, you know, if it was my startup because Without the data, it, yeah, as, as you said, it's, it's exactly the case that, uh, whatever you say in prompt, that's, that's what the agents will be doing.[00:47:30] swyx: The statistician in me wants to like really satisfy the sort of, um, statistical intuition, I guess. Um, to me it's kind of, uh, the, the word that comes to mind is, um, ergodicity. Uh, so let's say a, a customer takes this path, customer takes this path, customer takes this path, right? Um, the... In my mind, the way I explain it is like, okay, here, here's the ninety-five percentile, here's the five percentile, and here's the median, right?Um, but to me, what SimGym is potentially doing is that it can, uh, modify... It can sort of model the sort of in-between sort of journeys as well, that, that maybe are dependent on the previous states. This may be like a very RL-type conclusion where like basically the summary statistics, if you only did naive AB testing, you only have the, the statistics at, at, at a certain point, and you only judge based on the sort of overall summary statistics.But here you can actually model trajectories. Does that make sense? Or-[00:48:31] Mikhail Parakhin: That makes total sense because like, well, that, that makes even more sense that maybe even you realize bec- because-[00:48:38] swyx: Okay. Please,[00:48:38] Mikhail Parakhin: please. Yes ... we do-- Yeah. The, so internally, uh, we have this system, we talked about it briefly once at NeurIPS.We have a huge HSTU-based system that models the whole companies, uh, and their possible paths. And like- Yeah ... what you are, what you are showing, like actually at any point of time, you can either model the user's behavior or you mo- can also think about, uh, the whole merchant as a company, as the entity that acts in the world.You can model that as well. And then you can do, can do counterfactuals. In your graph, like in your blue graph, uh, if you're... Imagine in the center there, uh, somewhere in the middle, you would have an intervention. I give that person a coupon, or I don't know, I send a personal thank you card, or give a discount in some- somewhere.And then you can, uh, then you can do forward rollouts from that counterfactual. So what would have happened with that intervention or without the intervention? And you can even ch- change where that intervention, uh, in time can happen, right? Like some- where, where in this journey. So we, we do this at the Shopify scale for our merchants, and then if we notice that something that they can be fixing, like there's a strong counterfactual, like we have Shopify policy, they basically get a notification like, “Hey, we think your...something is wrong with your-” I don't know, Canadian sales. Like, uh, it looks like it's misconfigured. Here's what you need to do. Or do you think like, uh, you have to set up this campaign with these parameters? And we do that at the buyer level to literally offer discounts or cashback or, or things to buyers.So this is-- I'm getting very excited. Like this is my sort of area of, uh, interest, I guess, and, and hobby. But being able to m-model something complex as human beings or companies and model counterfactuals on it, where you can have interventions in the future and optimize when to make intervention, what kind inter-- uh, what kind of intervention to make.It's such an unlock that previously was completely impossible. Like the-- it was, it was always dreamed of, but never... Like how would you even simulate it without LLMs or HTUs? I think very, very exciting times.[00:50:59] swyx: I just wanted to, uh, to maybe illustrate this. I, I'm not the best illustrator, but I, I am a conceptual statistics guy.And y-you know, you cannot just do this. Like this is a dimensionality AB test doesn't do, right? Like, uh, because it doesn't have the, the, the change over time, uh, stochastic nature, uh, and it doesn't have the sort of contextual like... Here's all the context to this point. Um, okay, cool. Um, that's SimGym.You're, you're gonna burn a lot of tokens on this thing. But you're, you're one of the, the only scale platforms in the world that can, uh, that can do this across a huge variety of workloads, right? I'm even curious on a sort of human, uh, research level of like, well, do, does retail behave d-differently from like clothing sales?D-does that behave differently from electronic sales? I, I don't know. I don't know what else you guys... The Kardashian shoppers, do they differ from like people who buy, uh, I don't know, cars and, uh, whatever.[00:51:55] Mikhail Parakhin: Well, very different, and different sensitivities and different modes of, uh, shopping and, and different levels of what's important.Now, to-totally, you can do aggregations at, uh, at a store level. You can do aggregations at a different, uh, category level. I don't know if, uh, you know, for our statisticians among us, I couldn't believe, but we-- recently we're looking at it, and we had to bring back, uh, CRPs, you know, Chinese restaurant process.It's a, like, way of aggregating and, like, naturally grow clustering. So across... Specifically to answer questions that, uh, like you were just posing on how, how if, if buyers behave different categories. And I'm like, “I haven't seen CRP since two thousand and one.” It's[00:52:37] swyx: so What? It's so- What is... No, I haven't, I haven't seen this.No. This is not in my training. Uh,[00:52:44] Mikhail Parakhin: but, but yeah, it, uh, uh, it actually, like the, the-- there was a very popular kind of theory, popular neurips HTML circles in early two thousands, uh, kind of nice. And now, now it has practical applications, uh- Yeah ... that we were resurrecting.[00:53:03] swyx: Yeah, amazing. Uh, I, I can see, I can see how this is like a, uh, a fun job for you where you get to apply all these things.Um, yeah, yeah, so super cool. Super cool. So, okay, so, so anyone who, who knows what CRPs are and has always wanted to use them at work, uh, they should, they should definitely join Shopify. Okay, so w-we have a lot and but I, I'm, I'm being mindful of the time. I, I do wanted to, to sort of cover some other things.Um, I-I'll give you a choice, UCP or Liquid?[00:53:30] Mikhail Parakhin: Liquid. I think, I think on UCP, you know, like UCP is very important for us and, and it just we are-- UCP, we have a structured, uh, discussions, and you can read about them, and we have, uh, blog posts, and we have a big release this week, in fact, like with our catalog.Oh,[00:53:46] swyx: okay.[00:53:46] Mikhail Parakhin: Uh, yeah,[00:53:46] swyx: but- Le-I mean, we, we can, we can discuss the, the, the release briefly because we'll release this after the-- after it's already announced so whatever. There's a catalog that you guys are doing?[00:53:55] Mikhail Parakhin: Yeah. So we are, we are- Okay ... we are bringing in capabilities of a whole, uh, Shopify catalog.Basically, you now you can search for products, you can do lookups by specific ID, you can do bulk lookups when you need to bring m-multiple products. You don't need to know in ad-in advance what you're trying to show or to sell or check out. Like, you can now, you can now have this decided at, at runtime, and this big area for investment for us for both non-personalized and personalized searches, trying to provide basically a win-window into whole universe of products that are being sold everywhere in the world.And Shopify is really not exactly, but almost like a super set of any-anything being sold. Now we are bringing it into UCP and, uh, and, uh, identity linking is another big thing for us, uh, so that you, you can use, uh, like Google or whatever, whatever identity you have, uh, they're minimizing friction.[00:54:56] swyx: Yeah. So[00:54:57] Mikhail Parakhin: yeah, big release for us.But Liquid AI of course we never talk about, and the problem might be more, more aligned with what we d-discussed previously on this chat.[00:55:07] swyx: Sure. The main thing that everyone understands about Liquid is that it is inspired by Worm, and I still don't know why. I'm curious on your explanation. I think you, you, uh, you can make things very approachable.And also I think like what is the potential of like the, the level of efficiency that you get out of Liquid?[00:55:23] Mikhail Parakhin: You- we all familiar with transformer architectures. And, uh, for the longest time, there was a competing architecture, it's called the state space models. So, so Sams, uh, you know, Chris, Chris Reyes, one of the pioneers and, and lots of startups, uh, trying to make those realities.They have, uh, significant benefits being main being, uh, being much faster and, uh, lower footprint and not quadratic in length, you know, sort of, uh, linear in, in, uh, in your context length. But with state space models- They never quite made it. Like they're used-- They have, uh, certain niches when they thrive, their hybrid architectures are useful, but they never quite made it.And liquid neural networks are, you can think of them as a next step, like, uh, sort of, uh, state-space model square. It's non-transformer architecture that's more complicated than sta-state space and really difficult to code if you-- if I'm being honest. But it's, um, very efficient. It's, uh, subline-- sub, uh, quadratic in, in length of your context.Uh, it's very compact way to represent things, and that's a liquid AI company. They... Their goal is to productize it, and very often you have this need, uh, when you need to have long context and small model, and you want to have low latency. Like in general, it's basically on par with transformers, and if you do hybrids with transformers, it's, it's even better.That's why we at Shopify, when we tried multiple and we constantly try multiple models, multiple companies, we found that for small, particularly with low latency applications, when you have low latency and/or if you need longer context lengths, liquid was the best. And so we still use the whole zoo and always like obviously test and use everything, uh, every open source model and, you know, it feels l

Timothy Li, CEO and Co-Founder of LendAPI, has spent nearly a decade trying to solve the same problem: launching a lending product takes too long and costs too much. With LendAPI, he's built a no-code platform that lets banks, credit unions, fintechs, and retailers go from idea to live lending product in weeks, not months or years. Think of it as a GoDaddy-style experience for financial services. Timothy joined me again on the show (he was last on in 2017) to talk about what's under the hood, what the Sunglass Hut deal reveals about embedded finance, and where he thinks AI is actually useful in lending today.What We CoveredTimothy's path from the Fluid college credit app to building LendAPIHow the drag-and-drop product builder works for non-technical usersPython model deployment for credit risk officers inside the same platformWinning Best in Show at FinovateThe Sunglass Hut deal and how it came together in three monthsWhy retailers are moving away from pure-play BNPL providersIntegration options: bank cores, side cores, and direct e-commerce embedThe 300-plus partner marketplace and the SEO strategy behind itDoc AI and single-task AI agents for document processing and underwritingTimothy's experience in the CURQL accelerator and how credit unions differTeaching FinTech Fundamentals at USCThe five consumer verticals with the most opportunity in fintechKey TakeawaysThe build vs. buy debate is essentially over. When Timothy talks to bank CTOs today, the conversation is "can you launch this next week?" not "should we build this ourselves?" Speed to market has become the dominant concern.Pure-play BNPL approval rates are outside a retailer's control and can swing 10 points overnight. Private label embedded finance, built on infrastructure like LendAPI, lets retailers and banks own the underwriting criteria and the customer experience, which matters especially for high-ticket items where the financing decision happens in-store.Single-task AI agents are the near-term opportunity in lending, not fully automated credit decisions. Automating document verification, data extraction, and intake workflows saves minutes per application, and at scale, that compounds quickly.The five consumer fintech verticals worth building in: mortgages, auto, credit cards and personal loans, payments, and bank accounts. If it's in someone's wallet, there's still work to do.About Timothy LiTimothy Li is the CEO and co-founder of LendAPI, a no-code lending platform that launched in 2024 and won Best in Show at Finovate. He previously built Fluid, a credit-building app for college students, and has been building lending infrastructure across multiple ventures over the past decade. He also taught FinTech Fundamentals at the University of Southern California.Connect with Fintech One-on-One:Tweet me @PeterRentonConnect with me on LinkedInFind previous Fintech One-on-One episodes