Talking AI

HatchWorks

Welcome to the Talking AI podcast, where we dive deep into the world of artificial intelligence with host Matt Paige. Formerly known as the Built Right podcast, Talking AI brings you insightful conversations with AI experts, founders of AI products, and industry leaders who are leveraging AI in their businesses. Whether you're an AI expert or a beginner, our episodes will help you understand how AI technology works and how early adopters are deriving value from it.

  1. 2일 전

    From Coding Agents to AI Coworkers: Where Verifiability Ends and Taste Begins

    AI got very good at coding first, and the reason is less flattering than it sounds. Coding is work where the machine can check its own answer. The test passes or it doesn’t. The build compiles or it doesn’t. Almost nothing else people do all day comes with a test suite — strategy, brand voice, a hiring call, a pricing decision. That is the boundary the entire agent economy is now walking up to, and whoever crosses it first gets to rewrite what a company looks like. In this episode of Talking AI, Matt Paige sits down with Jay Hack, Head of AI at ClickUp and the founder of Codegen, one of the early autonomous coding-agent companies, which ClickUp acquired in late 2025. Jay spent years on the frontier of engineering automation and came away with a claim that sounds small and isn’t: a coding agent is just a general-purpose agent. The code was never the point. The loop was. The conversation covers why the best coding model tends to be the best model at everything, why the era of token maxing is ending and what a hard compute cap actually does to a team, how ClickUp turns a company’s docs, chats, and meetings into a context engine, why verification rather than generation is now the bottleneck on shipping, and what happens to an org chart when the scarcest resource on the team is high agency. In this episode, you’ll hear about: Why verifiability made software engineering the first domain AI genuinely transformed. Positive transfer, and why getting better at code makes a model better at everything else. What happened when Jay asked one model a question and it spawned 200 sub-agents to answer it. The coming compute-budget reckoning, and why a cap wouldn’t dent day-to-day productivity. A marketplace for ideas: allocating compute to people based on the quality of their pitch. Ultra coding, and the class of project that went from impossible to routine. The data silos problem, and why Jay sold Codegen to a company that already owned the context. Roll-ups, and using cheap models to distill signal so the expensive model never reads the noise. What ambient context does to onboarding, alignment, and the five-meetings-a-day habit. Hiring for high agency in an agent-first org. Why building ten features doesn’t mean shipping ten features. The zero-person company, and Jay’s timeline for it. --- Key Moments 00:01:30 — Why coding went first: verifiability, low stakes, and Stack Overflow00:04:37 — “Build me Netflix”: level five self-driving for software engineering00:07:02 — Positive transfer: why the best coding model is the best model at everything00:10:08 — Fable spins up 200 sub-agents nobody asked for00:11:00 — A marketplace for ideas: how compute gets allocated inside a company00:13:43 — The a16z claim that humans are now cheaper than software00:14:04 — The $10K-a-month token cap, and why productivity wouldn’t drop00:15:00 — Ultra coding, and the projects that went from impossible to routine00:17:17 — Context is everything: the data silos problem and why he sold Codegen00:19:00 — Roll-ups: cheap models distilling signal so the expensive one skips the noise00:22:00 — Why ClickUp, and the realization that a coding agent is just a general-purpose agent00:24:01 — When context goes ambient: the org as a brain that finally sobers up00:31:51 — Agent pilled: hiring for an era of valid chess moves00:33:00 — High agency is the scarcest resource on your team00:34:20 — Why any roadmap past three months is performative00:36:06 — Ten features is not ten shipped features: verification is the bottleneck00:37:56 — Does human in the loop still matter? The zero-person company --- Key Links ClickUpConnect with Jay on LinkedIn Mentioned in this episode: AI Opportunity Finder Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

  2. 9월 1일

    Why the Future of AI May Be Smaller: The Rise of Domain-Specific Models

    Legal is the department that can stop a business transaction cold. A contract goes into review and two weeks disappear. Procurement waits. Sales waits. And the tools that were supposed to fix that — an assistant bolted into Word, a chat window with a contract pasted into it — ask an in-house lawyer to trust a system that can give one answer today and a slightly different answer next week. In a field where the human carries the liability and the model does not, that is not a rounding error. That is the whole problem. In this episode of Talking AI, Matt Paige sits down with Emad Khazraee, co-founder and CTO of RiskVantage AI, previously VP of AI at Xometry, a data science and AI leader at Turing, an information science professor, and a fellow at Harvard’s Berkman Klein Center. For years Emad told his co-founder, Mark Afshar — a practicing lawyer turned in-house counsel for big pharma — that legal AI was a bad idea: a wrapper has no moat, and Anthropic or OpenAI will do it better than you overnight. What changed his mind was an architecture, not a market: a deterministic ontology that owns the legal reasoning, and small domain-specific language models that handle the language. The conversation covers why a nine-billion-parameter model running sub-second on a commodity GPU can match a frontier model inside a single domain, how subsidized token prices are distorting the entire legal AI market, why RiskVantage AI sells to procurement and sales ops rather than to lawyers who bill by the hour, what a failed PhD project on symbolic AI taught him about where determinism belongs, and whether the billable hour survives the decade. In this episode, you’ll hear about: What ChatGPT can’t know about your company: its risk appetite, its baselines, and the practices it expects every single timeWhy the legal services market — north of $900 billion, by Emad’s count — has every frontier lab gunning for itThe objections that made him refuse to build a legal AI company, and the one that still holdsWhy a Word plugin stopped being defensible the moment Anthropic shipped its ownHow subsidized token pricing echoes Uber and Lyft, and who gets hurt when the subsidy endsThe consistency problem: one answer today, a different answer next week, and a lawyer’s confidence goneNeuro-symbolic AI in plain English — a deterministic ontology for legal risk, LLMs for document understandingThe three years Mark Afshar spent codifying legal risk before there was a productWhy a 9B domain-adapted model is “dumb enough” that it can’t wander outside its sandboxKnowledge distillation, silver datasets, and self-distillation policy optimization in practiceThe sovereign-cloud niche: ITAR data, commodity GPUs, and customers whose data will never leaveOutcome-based pricing, AI-enabled law firms, and what happens to the billable hourThe access-to-justice case: pro se filings, public defenders, and what a $20 subscription changes Key Moments 00:01:30 — What ChatGPT can’t know: your company’s risk appetite and baselines00:05:12 — $700 an hour, a tenth at a time — and Coinbase’s AI mandate to outside counsel00:08:12 — Why he told his co-founder no: a wrapper has no moat00:10:22 — Subsidized tokens, Uber and Lyft, and Legora’s move to consumption pricing00:14:31 — The sovereign-cloud niche: ITAR data, commodity GPUs, and data that can’t leave00:16:56 — “I am on the hook for the liability, not which model I used”00:18:15 — Same question a week later, a different answer, and confidence gone00:22:13 — If a rule can govern it, you should never use an LLM00:23:00 — The PhD failure: narrative machines, Frege, and symbolic AI’s rigidity00:26:53 — Mark Afshar’s three years codifying legal risk into an ontology00:29:00 — Neuro-symbolic AI, explained00:31:03 — Don’t use a missile to hit a fly: why smaller models are safer00:35:47 — A 9B model, sub-second on a commodity GPU, matching Fable 5 in-domain00:38:00 — Does the billable hour survive? Outcome pricing and AI-enabled firms00:42:40 — Why affordable legal access is a democratic-society problem00:44:00 — The pro se surge: people filing their own cases with ChatGPT and Claude00:48:30 — “I’m talking with Copilot.” “That’s not research.” Key Links RiskVantage AIConnect with Emad on LinkedIn Mentioned in this episode: AI Opportunity Finder Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

  3. 8월 18일

    More Agents Than Employees: How Zapier Disrupted Itself Before AI Could

    The best AI model in the world just scored 18.1%. On Zapier's own benchmark for real business work — the cross-app tasks any white-collar worker does every day — even the top frontier model completes them barely one time in five. That's the number Wade Foster keeps pointing at, and he runs an automation company that stands to gain from the hype. Instead, he makes the case for what actually works right now: not turning a model loose, but blending deterministic workflows with agents where each is strong. In this episode of Talking AI, Matt Paige sits down with Wade Foster, co-founder and CEO of Zapier, who built a scrappy Y Combinator startup into the $5 billion plumbing of the SaaS era on barely a million dollars raised. Foster called a company-wide “code red” the week GPT-4 launched, and he's spent the years since rewiring how Zapier — and its customers — actually use AI. The conversation covers why he shut the company down for a week in 2023, how AI habits actually stick, what Zapier's AutomationBench reveals about the gap between benchmark scores and real-world reliability, why coding models improve faster than knowledge-work models, how to tell a workflow from an agent, and the difference between individual AI and the institutional AI almost no company has cracked. In this episode, you'll hear about: The three things about GPT-4 that triggered Zapier's first-ever code redHow daily AI use jumped from 11% to over 50% in a single hackathon weekThe moves that make AI habits stick: show-and-tell, repeat hackathons, and “not yet”Why the best model on AutomationBench still scores only 18.1%Why coding is easy to verify — and subjective knowledge work isn'tThe power of hybrid setups that blend deterministic workflows with agentsWade's prediction: most tokens on open-source models, most spend on the frontierWhat actually makes a good eval — hard for models, easy for humans, private dataA plain-English definition of an “agent” versus a deterministic workflowThe daily recap workflow Wade thinks everyone is sleeping onFloor raisers vs. ceiling raisers — and why individual AI isn't enoughWhy the six-month product roadmap is dead Key Moments 00:04:40 — Making AI habits stick: show-and-tell and repeat hackathons00:06:38 — Differentiation when AI is best at the thing you sell00:09:34 — AutomationBench: the best model scores just 18.1%00:11:31 — Why the top model stalls: verifiable code vs. subjective work00:14:19 — Getting squeezed on both sides: AI in the company and the product00:15:20 — Model efficiency, Coinbase, and the token-maxing debate00:17:18 — What makes a good eval00:19:30 — What actually counts as an “agent”00:23:12 — Iterating on workflows with your own mini-evals00:26:15 — The kind of worker thriving right now00:27:36 — Wade's favorite workflow: the daily recap00:30:44 — Floor raisers vs. ceiling raisers for AI adoption00:34:55 — From individual AI to institutional AI00:37:58 — Why the six-month roadmap is dead Key Links: ZapierConnect with Wade on LinkedIn Mentioned in this episode: AI Opportunity Finder Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

  4. 8월 11일

    The State of AI 2026 Mid-Year Reality Check

    The value is real. The spend is real. And the gap between the companies getting one in exchange for the other and the companies getting neither has never been wider. Six months into 2026, the top one percent of firms spend $7,450 per employee per month on AI while the median firm spends $11 — a 680x gap. The question in every boardroom has sharpened from “does AI work?” to “show me the ROI.” In this special episode of Talking AI, host Matt Paige hands the mic to an AI. Hatchworks AI just released its State of AI 2026: Mid-Year Reality Check — a comprehensive look at what has fundamentally changed since January and where AI is headed in the second half of the year — and instead of publishing it only as a written report, the team used ElevenLabs to turn the full report into an audio experience. The voice is AI-generated. The research, analysis, and point of view come directly from co-authors Brandon Powell, Matt Paige, and Omar Shanti. The report covers the step change in model capability that ended the plateau debate, the shift from token maxing to “show me the ROI,” the lab landscape’s new equilibrium, the 18-day Fable 5 ban and the arrival of trust-tiered AI, sovereign AI moving into procurement reality, open models as the enterprise hedge, Coinbase’s five tactics for blended intelligence, the new enterprise AI stack, the double agent problem, the jobs data that runs against the doom narrative, and nine calls for the second half of 2026. In this episode, you’ll hear about: The ten numbers that define AI at mid-year — from a 3x jump in long-horizon capability to a 680x spend gap between the top 1% of firms and the medianWhy January’s “models are plateauing” consensus got overtaken — and why “the technology isn’t ready” has expiredThe three places ROI variance actually lives: data connection, workflow embedding, and adoptionThe lab landscape’s new equilibrium — Anthropic as the enterprise incumbent, OpenAI’s agentic comeback, and two confidential IPO filings near $1 trillion valuationsSpaceX’s $60 billion all-stock acquisition of Cursor’s parent company, Anysphere, and why distribution is now the gameThe 18-day Fable 5 ban, identity verification, and what trust-tiered AI means for enterprise buyersSovereign AI getting real — Palantir, NVIDIA Nemotron, and owned weights in air-gapped environmentsOpen source as the enterprise hedge, and the advisor model pattern for blending frontier and open modelsCoinbase’s five tactics for cutting AI spend roughly in half while token usage kept growingThe new enterprise AI stack: the intelligence layer, skills, loops and the agent harness, and bring your own agentThe double agent problem, agentic zero trust, and why agents need first-class identityThe jobs data — heavy AI adopters growing headcount 10%, entry-level roles 12% — plus the rise of the forward deployed engineer and nine predictions for H2 2026 Key Moments: 00:01:30 — Chapter 1: Mid-year by the numbers — ten numbers, ten storylines00:03:35 — Chapter 2: The plateau that wasn’t — the step change in model capability00:06:55 — Chapter 3: From token maxing to “show me the ROI”00:11:10 — Chapter 4: The lab landscape’s new equilibrium — Anthropic, OpenAI, and the IPO filings00:14:20 — Chapter 5: The distribution and price frontier — Google, Nemotron, SpaceX–Cursor, and the Chinese open weight labs00:18:20 — Chapter 6: Fable, the 18-day ban, and the arrival of trust-tiered AI00:23:30 — Chapter 7: Sovereign AI gets real00:26:00 — Chapter 8: Open source is the enterprise hedge00:29:30 — Chapter 9: Case study — Coinbase and five tactics for blended intelligence00:33:05 — Chapter 10: The new enterprise AI stack00:39:00 — Chapter 11: Agent identity and the double agent problem00:42:35 — Chapter 12: The jobs question — watch the net, not the headlines00:46:30 — Chapter 13: The bottleneck is still human — the forward deployed engineer00:49:20 — Chapter 14: Nine calls for the second half of 202600:51:10 — Chapter 15: CEO commentary — the view from the field with Brandon Powell Key Links: Download the State of AI 2026 Mid Year Reality Check Mentioned in this episode: AI Opportunity Finder Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

  5. 8월 4일

    Context, Control, Collaboration: Why Capability Was Never the Bottleneck

    The models have never been better — so why do so many companies still struggle to turn AI into real, repeatable value? The answer, Tom Scott argues, isn’t the technology. It’s everything around it: messy workflows, scattered data, no clear governance. Drop even the best tool on top of that and it struggles, and piling on more tools can make things worse, not better. Capability was never the bottleneck. In this episode of Talking AI, Matt Paige sits down with Tom Scott, CEO of Wrike — the intelligent work management platform used by 20,000+ organizations, from NVIDIA to Jaguar Land Rover. Scott came up through finance and operations, including a stint as CFO at Zebra Technologies, so his lens is the operator’s, not the evangelist’s. He’s now steering a 20-year-old SaaS company through its own AI reinvention while watching thousands of customers attempt the same thing. The conversation covers Wrike’s three-part framework — context, control, and collaboration — why context, not capability, is the real bottleneck, and why the collaboration piece is the most underrated of the three. From there it moves into the strategy-to-execution gap, the case for hands-on leadership, the “bring your own agent” question reshaping SaaS, the full-stack professional replacing the specialist, and the honest, messy reality of leading transformation from the top. In this episode, you’ll hear about: Why capability was never the AI bottleneck — and what actually isWhy everyone is experiencing this technology wave at the same time, unlike prior onesContext, control, and collaboration — the three Cs behind Wrike’s valueWhy collaboration is the least understood and most important of the threeConnecting your own models to a system of record via MCP to kill duplicated researchThe “bring your own agent” shift and what it means for SaaS platformsWhy hands-on leaders — not top-down mandates — close the strategy-to-execution gapThe risk of automating mediocrity instead of rethinking the processWhy transformation is messy and has to be owned by the CEOHiring for curiosity and resilience over deep single-domain expertiseThe full-stack professional and the collapse of the middle of the org chartA humanist take on AI’s job impact — and why we lack full-stack peopleHow Tom personally uses AI to align his executive team and sweep up follow-upsThe advice he’d give his pre-AI self: move faster, and the one-way/two-way door test Key Moments 00:01:19 — Why value stays trapped in silos: it’s people, process, and tech, all at once00:03:19 — Defining the three Cs — context, control, and collaboration00:06:21 — From individual wins to consistent, repeatable value across a team00:07:26 — A research use case: connecting your model to Wrike via MCP00:11:09 — Do you really want 30 agents across 30 tools, or bring your own?00:12:50 — The open, “headless” architecture customers actually want00:17:32 — The hard part isn’t strategy — it’s execution00:18:17 — Hands-on leadership: “I built this over the weekend…”00:21:00 — Don’t just automate mediocrity — rethink the process first00:23:20 — Transformation is messy and has to be owned by the CEO00:29:06 — The ideal hire: curiosity first, then resilience00:31:31 — The org of the future and the rise of the full-stack professional00:36:38 — A humanist read on AI’s job impact00:39:31 — How Tom personally uses AI to drive alignment and execution00:44:21 — Advice to his pre-AI self: move faster00:45:38 — The one-way vs. two-way door decision test Key Links WrikeConnect with Thomas on LinkedIn Mentioned in this episode: AI Opportunity Finder Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

  6. 7월 22일

    Past the Productivity Ceiling: Rebuilding the Enterprise from First Principles

    Most enterprises rolling out AI are quietly optimizing for the wrong thing: speed, volume, lines of code shipped. Manu Narayan, CIO of GitLab, argues that efficiency gains alone are about to drive companies straight into a productivity ceiling they can't engineer their way out of. The reason is simple and uncomfortable—a faster version of a pre-AI workflow is still a pre-AI workflow. The real unlock isn't speeding up what you already do; it's rebuilding it from first principles. In this episode of Talking AI, Matt Paige sits down with Manu Narayan, GitLab's first-ever CIO, who owns the company's internal AI strategy, enterprise technology, and data infrastructure—in effect, putting GitLab to work inside GitLab. Manu makes the case for moving beyond incremental AI adoption toward a genuine operating model for enterprise AI. The conversation covers GitLab's hub-and-spoke operating model and its embedded "AI transformation owners," why the team measures adoption against business KPIs instead of token counts, how "human in the loop" is evolving into an orchestration role, and why context and traceability—not raw speed—are the new differentiators in software development. In this episode, you'll hear about: Why efficiency gains alone lead straight into a productivity ceilingThe gap between AI "haves and have-nots" and how to close itGitLab's hub-and-spoke (really hub-spoke-hub) operating modelWhat an "AI transformation owner" does inside each division"Full stack" people: stretching roles end-to-end across a life cycleThe difference between a skill and an agent—and why it mattersBuilding an internal skill library with governance built inWhy token maxing is the wrong scoreboard, and what to measure insteadHow human-in-the-loop shifts to a higher level of abstractionWhat "loops" mean and the move to being a manager of agentsWhy context and traceability beat commoditized speedLocal vs. repo-side development and where guardrails belongHandling shadow AI with a genuine "happy path to production"The first move for a CIO stuck optimizing the old workflow Key Moments 00:03:11 — The AI "haves and have-nots" inside every enterprise00:04:30 — The hub-and-spoke operating model and "AI transformation owners"00:07:00 — "Full stack" people: stretching roles across the whole life cycle00:09:06 — Skills vs. agents — human-invoked versus autonomous00:12:00 — The daily to-do skill that briefs Manu every morning00:12:58 — Building an internal skill library with a review-and-promote pipeline00:16:13 — Why GitLab doesn't ascribe to "token maxing"00:18:02 — Measuring adoption by role — beyond lines of code and MRs00:24:30 — Local vs. repo side: where governance and guardrails actually live00:27:39 — How "human in the loop" is evolving as agents outpace review00:30:49 — What "loops" really are, and the manager-of-agents shift00:33:52 — Why context and traceability are the new differentiators00:37:29 — The maintainability fear and the bottleneck that moved to review00:39:55 — SaaSpocalypse, agent sprawl, and the limits of MCP00:42:51 — Shadow AI and the "happy path to production"00:45:29 — The first move Monday morning: executive alignment on scope00:47:33 — Advice to his pre-AI self: stay nimble, it's okay to pivot Key Links GitLabConnect with Manu on LinkedIn Mentioned in this episode: AI Opportunity Finder Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

  7. 7월 7일

    The VC's Lens: How AI Is Rewriting the Rules of Defensibility

    Every company building AI right now is asking the same question: if the models keep getting better and anyone can access them, what actually makes us defensible? Avi Bharadwaj writes the checks that answer that question. As an Investment Director at Intel Capital, he focuses on the software infrastructure layer of AI, backing companies like Scale AI, Bria, TrueFoundry, and Twelve Labs. In this episode of Talking AI, Avi sits down with Matt Paige to break down exactly where moats are showing up as frontier models commoditize intelligence. He walks through five specific layers of defensibility for application companies (unique data, workflow and system of action, product reimagination, integration, and trust and compliance) and explains why the infrastructure between the model and the application is where most enterprise AI projects actually stall. The conversation covers why building for the gap between what frontier models can and can't do is a losing strategy (because the gap is ever-shrinking), why the chatbot era was brief and agents are now first-class citizens, how Avi uses an agent on Claude Cowork to scan Hacker News and Reddit overnight and enter emerging companies into his CRM by morning, and why he's most excited about world models and the emergent abilities that might come from scaling them. The episode closes with Avi's advice for founders: don't build things that fit the current gap in model capability. Build things that improve as the model improves. And his honest take on being a VC: at best you're a sidekick for founders, at worst you're a detractor. In this episode, you'll hear about: Five layers of defensibility that frontier models can't commoditize. Why unique data, not just more data, is the moat that still matters. The shift from chatbots to deeply embedded agentic workflows in enterprise. How Avi uses Claude Cowork agents to automate deal sourcing and financial analysis. Why specialized foundation models still win in domains like licensed imagery, industrial robotics, and edge inference. The Figma/Claude Design moment and what it means for how VCs underwrite platform risk. Why context engineering is becoming its own discipline and the mistake of treating models like if-else loops. World models, emergent abilities, and what comes after language as an abstraction. How Avi went from Goldman Sachs engineer to IBM data scientist to Intel Capital investor. The coolest and most overrated parts of being a VC. -- Key Moments00:01:41 — "It's a mistake to think better models kill moats"00:02:30 — Unique data as the new defensibility: proprietary CRM triggers, healthcare, industrial00:03:25 — Workflow and system of action moats00:04:00 — UX and product reimagination as a moat00:04:30 — Integration moats: 50 to 100 systems upstream and downstream00:05:10 — Trust and compliance as the fifth layer00:05:30 — Infrastructure layer defensibility: evaluation, benchmarking, security, identity00:06:27 — Jack Dorsey's "From Hierarchy to Intelligence" and the YC thesis00:09:55 — From data scientist to frontier model commoditization: what changed00:13:12 — How a VC uses AI: seeing, picking, winning, and supporting00:15:00 — Claude Cowork agent scanning Hacker News, Reddit, and PitchBook overnight00:18:58 — Specialized models vs. the ever-shrinking gap: where do they survive?00:20:30 — Bria's licensed data moat and Field AI's industrial deployment data00:22:45 — "Build things that improve as the model improves"00:24:14 — Why frontier models win bottom-up but can't crack top-down enterprise adoption00:25:43 — The chatbot era was brief: agents are first-class citizens00:27:50 — Memory: session, long-term, and standardized enterprise memory00:31:41 — "Don't use models like a very long if-else statement loop"00:35:08 — World models, emergent abilities, and what comes after language00:38:34 — Robotics: narrow industrial use cases first, Jetsons life in ten years00:41:26 — From Goldman Sachs engineer to IBM data scientist to Intel Capital VC00:43:10 — The coolest and most overrated things about being a VC -- Key LinksIntel CapitalConnect with Avi on LinkedIn Mentioned in this episode: Free report from HatchWorks AI — State of AI 2026 What’s real in AI this year, what’s hype, and what leaders should prioritize — including production lessons, designing for agents, and governance. https://hatchworks.com/state-of-ai-2026/ AI Opportunity Finder Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

  8. 6월 9일

    99% Correct Is Still Failure: The Last Mile for Mission-Critical AI

    AI can now write code faster than any human alive, and most of the time it's more than good enough. That's the magic powering the entire vibe coding wave. But there's a category of software where "most of the time" just doesn't cut it: the code running a fighter jet, a power grid, an autonomous vehicle, a piece of medical hardware. When that code is wrong, the consequences aren't a bug. They're a recall, an accident, a national security incident. In this episode of Talking AI, Matt Paige sits down with Ryan Aytay, the former CEO of Tableau and now President and COO of CodeMetal, which just raised $125 million to close that gap. Ryan explains what he calls "the last mile" for mission-critical industries: the verification, validation, and provability layer that sits between AI-generated code and the systems where failure is catastrophic. The conversation covers why 99% correct is still failure in defense and autonomous systems, how CodeMetal translated a million lines of legacy C++ to Rust in weeks (like rewiring a city without the power going out), and why the real problem isn't code generation, it's behavioral assurance at scale. Ryan also shares how he's using AI to run a sub-100-person startup, why the biggest risk for any company right now is doing nothing, and what an operator who lived through 19 years of per-seat SaaS at Salesforce thinks about outcomes-based pricing in the age of AI. In this episode, you'll hear about: Why every AI coding tool says "almost, but not quite" when asked about production-ready guarantees. The difference between code generation and behavioral assurance at scale. How CodeMetal translates legacy C++ to Rust with provable correctness in weeks, not years. The concept of V&V (verification and validation) and why it's the missing layer in AI code gen. Real use cases in defense, autonomous vehicles, and simulation environments. Why hardware in the loop matters as much as human in the loop. How a sub-100-person company uses AI across M&A, recruiting, marketing, and operations. Ryan's take on token economics, outcomes-based pricing, and the SaaS evolution. Why the biggest risk is inaction, not AI errors. What attracted Ryan to CodeMetal after 19 years at Salesforce and leading Tableau. Key Moments 02:47 — From Tableau fanboy to the trust gap in AI03:52 — Why Ryan left Salesforce/Tableau for CodeMetal05:55 — "Is it safe for the things I depend on every day?"06:45 — 99% correct is still failure for mission-critical systems08:20 — The sycophantic nature of AI: "Heck yeah, I can do that"09:22 — It's not a coding problem, it's a behavioral problem at scale11:22 — Human in the loop isn't enough: hardware in the loop14:30 — What is fuzzing? Formal methods explained in plain English16:02 — How a sub-100-person company leverages AI across every function18:19 — The Shopify mandate: using AI reflexively21:33 — Rewiring the city without the power going out: the million-line translation24:38 — Defense use cases: drones, autonomous vehicles, and simulation26:28 — "Prove is even a stronger word than guarantee"28:32 — Accountability and the coming wave of AI insurance32:54 — Token usage, the Uber CTO's blown budget, and outcomes-based pricing36:26 — SaaS isn't dead, it's evolving: Ryan's Salesforce/Tableau perspective40:08 — The biggest risk is doing nothing42:07 — Where to find CodeMetal (and they're hiring) Key Links CodeMetalConnect with Ryan on LinkedIn Mentioned in this episode: AI Opportunity Finder Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/ Free report from HatchWorks AI — State of AI 2026 What’s real in AI this year, what’s hype, and what leaders should prioritize — including production lessons, designing for agents, and governance. https://hatchworks.com/state-of-ai-2026/

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소개

Welcome to the Talking AI podcast, where we dive deep into the world of artificial intelligence with host Matt Paige. Formerly known as the Built Right podcast, Talking AI brings you insightful conversations with AI experts, founders of AI products, and industry leaders who are leveraging AI in their businesses. Whether you're an AI expert or a beginner, our episodes will help you understand how AI technology works and how early adopters are deriving value from it.

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  • 제작진
    HatchWorks
  • 방송 연도
    2023년 - 2026년
  • 에피소드
    84
  • 등급
    전체 연령 사용가
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    © A podcast by HatchWorks®
  • 웹사이트 보기
    Talking AI

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