Talking AI

Ask almost anyone whether AI made them faster, and the answer is an immediate yes. Ask whether it made their team faster and the answer gets vague. All that productivity is pooling inside individual chat windows — private context, private memory, private wins — while the team around each person moves at roughly the old speed. The tools got extraordinary. The seam between them and everybody else did not.

In this episode of Talking AI, Matt Paige sits down with Lane Shackleton, Head of Superhuman Docs and the product leader who spent more than a decade building Coda, now rebuilt as Superhuman Docs inside the Superhuman Suite. Lane’s diagnosis is structural rather than cultural: chat tools and collaboration tools grew up under opposite design constraints, and they have not yet unified in any meaningful way. His answer is what he calls the last mile of AI — bringing agents to where people already work, instead of waiting for someone to decide it is time to use AI.

The conversation covers why the chat window was always a soloist tool, what shared team context changes about how agents behave, the July 8 launch that turned Coda into Superhuman Docs, how product, design, and engineering roles are collapsing into one another, where the bottleneck moved once code got cheap to write, and how you partner with the same labs you compete with.

In this episode, you’ll hear about:

Why chat tools and collaboration tools grew up under opposite design constraints, and what that cost teams. The board meeting that pushed Coda into AI, and the weekend of demos that followed. Why having to decide it is time to use AI is a product failure, not a user problem. The context tax teams pay in copy/paste, and what shared context does instead. Why individual memory breaks down the moment a second person needs to see it. Using an MCP to keep a team’s decisions continuously updated from meeting notes. What it felt like to rebuild a beloved product under a new name in the middle of a platform shift. The case for a big-bang launch over a phased rollout. AI Views, and what beta users built that nobody predicted. How product, design, and engineering roles are collapsing into each other. Why the bottleneck moved to review the moment code got cheap. Let the makers make — and the point where someone has to codify what worked.

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Key Moments

• 00:01:48 — Two tool families that grew up apart: chat windows and collaboration tools
• 00:03:32 — The board meeting, Reid Hoffman, and Coda’s early look at the OpenAI API
• 00:06:11 — What a vision video is, and why prototypes beat blog posts
• 00:07:56 — Why having to think “it’s time to use AI” is a failure mode
• 00:11:20 — The context problem: copy/paste, thin slices, and memory that vanishes
• 00:13:00 — From decision logs to MCP: keeping a team’s memory continuously updated
• 00:15:01 — Coda to Grammarly to Superhuman: what the transition actually felt like
• 00:17:58 — Why July 8 was a big bang instead of a phased rollout
• 00:18:59 — AI Views explained: prompt your way on top of a dataset
• 00:20:58 — Low floor, high ceiling, and software that feels personal
• 00:24:05 — Product, design, and engineering roles collapsing into each other
• 00:26:30 — AI-native development: the team with no planning process
• 00:27:52 — Where the bottleneck moved: machines or humans reviewing the code
• 00:30:17 — Partner or threat? Working with the labs you also compete with
• 00:34:04 — Human-generated and machine-generated work have to get married somewhere
• 00:37:24 — Let the makers make, and why play beats mandates
• 00:43:31 — The end-of-day sweeper, and teaching his kids AI with homemade games

---

Key Links

• Superhuman
• Connect with Lane on LinkedIn

Mentioned in this episode:

AI Opportunity Finder

Feeling overwhelmed by all the AI noise out there?

GenROI by HatchWorks AI

Most companies don't have an AI problem. They have a prioritization problem. There are hundreds of places you could use AI, and the hard part is knowing which ones are worth the investment, which ones you're ready for, and what should come first.

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 Overflow
• 00:04:37 — “Build me Netflix”: level five self-driving for software engineering
• 00:07:02 — Positive transfer: why the best coding model is the best model at everything
• 00:10:08 — Fable spins up 200 sub-agents nobody asked for
• 00:11:00 — A marketplace for ideas: how compute gets allocated inside a company
• 00:13:43 — The a16z claim that humans are now cheaper than software
• 00:14:04 — The $10K-a-month token cap, and why productivity wouldn’t drop
• 00:15:00 — Ultra coding, and the projects that went from impossible to routine
• 00:17:17 — Context is everything: the data silos problem and why he sold Codegen
• 00:19:00 — Roll-ups: cheap models distilling signal so the expensive one skips the noise
• 00:22:00 — Why ClickUp, and the realization that a coding agent is just a general-purpose agent
• 00:24:01 — When context goes ambient: the org as a brain that finally sobers up
• 00:31:51 — Agent pilled: hiring for an era of valid chess moves
• 00:33:00 — High agency is the scarcest resource on your team
• 00:34:20 — Why any roadmap past three months is performative
• 00:36:06 — Ten features is not ten shipped features: verification is the bottleneck
• 00:37:56 — Does human in the loop still matter? The zero-person company

---

Key Links

• ClickUp
• Connect with Jay on LinkedIn

Mentioned in this episode:

GenROI by HatchWorks AI

Most companies don't have an AI problem. They have a prioritization problem. There are hundreds of places you could use AI, and the hard part is knowing which ones are worth the investment, which ones you're ready for, and what should come first.

AI Opportunity Finder

Feeling overwhelmed by all the AI noise out there?

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 time
• Why the legal services market — north of $900 billion, by Emad’s count — has every frontier lab gunning for it
• The objections that made him refuse to build a legal AI company, and the one that still holds
• Why a Word plugin stopped being defensible the moment Anthropic shipped its own
• How subsidized token pricing echoes Uber and Lyft, and who gets hurt when the subsidy ends
• The consistency problem: one answer today, a different answer next week, and a lawyer’s confidence gone
• Neuro-symbolic AI in plain English — a deterministic ontology for legal risk, LLMs for document understanding
• The three years Mark Afshar spent codifying legal risk before there was a product
• Why a 9B domain-adapted model is “dumb enough” that it can’t wander outside its sandbox
• Knowledge distillation, silver datasets, and self-distillation policy optimization in practice
• The sovereign-cloud niche: ITAR data, commodity GPUs, and customers whose data will never leave
• Outcome-based pricing, AI-enabled law firms, and what happens to the billable hour
• The 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 baselines
• 00:05:12 — $700 an hour, a tenth at a time — and Coinbase’s AI mandate to outside counsel
• 00:08:12 — Why he told his co-founder no: a wrapper has no moat
• 00:10:22 — Subsidized tokens, Uber and Lyft, and Legora’s move to consumption pricing
• 00:14:31 — The sovereign-cloud niche: ITAR data, commodity GPUs, and data that can’t leave
• 00:16:56 — “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 gone
• 00:22:13 — If a rule can govern it, you should never use an LLM
• 00:23:00 — The PhD failure: narrative machines, Frege, and symbolic AI’s rigidity
• 00:26:53 — Mark Afshar’s three years codifying legal risk into an ontology
• 00:29:00 — Neuro-symbolic AI, explained
• 00:31:03 — Don’t use a missile to hit a fly: why smaller models are safer
• 00:35:47 — A 9B model, sub-second on a commodity GPU, matching Fable 5 in-domain
• 00:38:00 — Does the billable hour survive? Outcome pricing and AI-enabled firms
• 00:42:40 — Why affordable legal access is a democratic-society problem
• 00:44:00 — The pro se surge: people filing their own cases with ChatGPT and Claude
• 00:48:30 — “I’m talking with Copilot.” “That’s not research.”

Key Links

• RiskVantage AI
• Connect with Emad on LinkedIn

Mentioned in this episode:

GenROI by HatchWorks AI

Most companies don't have an AI problem. They have a prioritization problem. There are hundreds of places you could use AI, and the hard part is knowing which ones are worth the investment, which ones you're ready for, and what should come first.

AI Opportunity Finder

Feeling overwhelmed by all the AI noise out there?

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 red
• How daily AI use jumped from 11% to over 50% in a single hackathon week
• The 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't
• The power of hybrid setups that blend deterministic workflows with agents
• Wade's prediction: most tokens on open-source models, most spend on the frontier
• What actually makes a good eval — hard for models, easy for humans, private data
• A plain-English definition of an “agent” versus a deterministic workflow
• The daily recap workflow Wade thinks everyone is sleeping on
• Floor raisers vs. ceiling raisers — and why individual AI isn't enough
• Why the six-month product roadmap is dead

Key Moments

• 00:04:40 — Making AI habits stick: show-and-tell and repeat hackathons
• 00:06:38 — Differentiation when AI is best at the thing you sell
• 00:09:34 — AutomationBench: the best model scores just 18.1%
• 00:11:31 — Why the top model stalls: verifiable code vs. subjective work
• 00:14:19 — Getting squeezed on both sides: AI in the company and the product
• 00:15:20 — Model efficiency, Coinbase, and the token-maxing debate
• 00:17:18 — What makes a good eval
• 00:19:30 — What actually counts as an “agent”
• 00:23:12 — Iterating on workflows with your own mini-evals
• 00:26:15 — The kind of worker thriving right now
• 00:27:36 — Wade's favorite workflow: the daily recap
• 00:30:44 — Floor raisers vs. ceiling raisers for AI adoption
• 00:34:55 — From individual AI to institutional AI
• 00:37:58 — Why the six-month roadmap is dead

Key Links:

• Zapier
• Connect with Wade on LinkedIn

Mentioned in this episode:

AI Opportunity Finder

Feeling overwhelmed by all the AI noise out there?

GenROI by HatchWorks AI

Most companies don't have an AI problem. They have a prioritization problem. There are hundreds of places you could use AI, and the hard part is knowing which ones are worth the investment, which ones you're ready for, and what should come first.

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 median
• Why January’s “models are plateauing” consensus got overtaken — and why “the technology isn’t ready” has expired
• The three places ROI variance actually lives: data connection, workflow embedding, and adoption
• The lab landscape’s new equilibrium — Anthropic as the enterprise incumbent, OpenAI’s agentic comeback, and two confidential IPO filings near $1 trillion valuations
• SpaceX’s $60 billion all-stock acquisition of Cursor’s parent company, Anysphere, and why distribution is now the game
• The 18-day Fable 5 ban, identity verification, and what trust-tiered AI means for enterprise buyers
• Sovereign AI getting real — Palantir, NVIDIA Nemotron, and owned weights in air-gapped environments
• Open source as the enterprise hedge, and the advisor model pattern for blending frontier and open models
• Coinbase’s five tactics for cutting AI spend roughly in half while token usage kept growing
• The new enterprise AI stack: the intelligence layer, skills, loops and the agent harness, and bring your own agent
• The double agent problem, agentic zero trust, and why agents need first-class identity
• The 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 storylines
• 00:03:35 — Chapter 2: The plateau that wasn’t — the step change in model capability
• 00:06:55 — 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 filings
• 00:14:20 — Chapter 5: The distribution and price frontier — Google, Nemotron, SpaceX–Cursor, and the Chinese open weight labs
• 00:18:20 — Chapter 6: Fable, the 18-day ban, and the arrival of trust-tiered AI
• 00:23:30 — Chapter 7: Sovereign AI gets real
• 00:26:00 — Chapter 8: Open source is the enterprise hedge
• 00:29:30 — Chapter 9: Case study — Coinbase and five tactics for blended intelligence
• 00:33:05 — Chapter 10: The new enterprise AI stack
• 00:39:00 — Chapter 11: Agent identity and the double agent problem
• 00:42:35 — Chapter 12: The jobs question — watch the net, not the headlines
• 00:46:30 — Chapter 13: The bottleneck is still human — the forward deployed engineer
• 00:49:20 — Chapter 14: Nine calls for the second half of 2026
• 00:51:10 — 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?

GenROI by HatchWorks AI

Most companies don't have an AI problem. They have a prioritization problem. There are hundreds of places you could use AI, and the hard part is knowing which ones are worth the investment, which ones you're ready for, and what should come first.

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:

1. Why capability was never the AI bottleneck — and what actually is
2. Why everyone is experiencing this technology wave at the same time, unlike prior ones
3. Context, control, and collaboration — the three Cs behind Wrike’s value
4. Why collaboration is the least understood and most important of the three
5. Connecting your own models to a system of record via MCP to kill duplicated research
6. The “bring your own agent” shift and what it means for SaaS platforms
7. Why hands-on leaders — not top-down mandates — close the strategy-to-execution gap
8. The risk of automating mediocrity instead of rethinking the process
9. Why transformation is messy and has to be owned by the CEO
10. Hiring for curiosity and resilience over deep single-domain expertise
11. The full-stack professional and the collapse of the middle of the org chart
12. A humanist take on AI’s job impact — and why we lack full-stack people
13. How Tom personally uses AI to align his executive team and sweep up follow-ups
14. The 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 once
• 00:03:19 — Defining the three Cs — context, control, and collaboration
• 00:06:21 — From individual wins to consistent, repeatable value across a team
• 00:07:26 — A research use case: connecting your model to Wrike via MCP
• 00:11:09 — Do you really want 30 agents across 30 tools, or bring your own?
• 00:12:50 — The open, “headless” architecture customers actually want
• 00:17:32 — The hard part isn’t strategy — it’s execution
• 00:18:17 — Hands-on leadership: “I built this over the weekend…”
• 00:21:00 — Don’t just automate mediocrity — rethink the process first
• 00:23:20 — Transformation is messy and has to be owned by the CEO
• 00:29:06 — The ideal hire: curiosity first, then resilience
• 00:31:31 — The org of the future and the rise of the full-stack professional
• 00:36:38 — A humanist read on AI’s job impact
• 00:39:31 — How Tom personally uses AI to drive alignment and execution
• 00:44:21 — Advice to his pre-AI self: move faster
• 00:45:38 — The one-way vs. two-way door decision test

Key Links

• Wrike
• Connect with Thomas on LinkedIn

Mentioned in this episode:

AI Opportunity Finder

Feeling overwhelmed by all the AI noise out there?

GenROI by HatchWorks AI

Most companies don't have an AI problem. They have a prioritization problem. There are hundreds of places you could use AI, and the hard part is knowing which ones are worth the investment, which ones you're ready for, and what should come first.

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:

1. Why efficiency gains alone lead straight into a productivity ceiling
2. The gap between AI "haves and have-nots" and how to close it
3. GitLab's hub-and-spoke (really hub-spoke-hub) operating model
4. What an "AI transformation owner" does inside each division
5. "Full stack" people: stretching roles end-to-end across a life cycle
6. The difference between a skill and an agent—and why it matters
7. Building an internal skill library with governance built in
8. Why token maxing is the wrong scoreboard, and what to measure instead
9. How human-in-the-loop shifts to a higher level of abstraction
10. What "loops" mean and the move to being a manager of agents
11. Why context and traceability beat commoditized speed
12. Local vs. repo-side development and where guardrails belong
13. Handling shadow AI with a genuine "happy path to production"
14. The first move for a CIO stuck optimizing the old workflow

Key Moments

• 00:03:11 — The AI "haves and have-nots" inside every enterprise
• 00:04:30 — The hub-and-spoke operating model and "AI transformation owners"
• 00:07:00 — "Full stack" people: stretching roles across the whole life cycle
• 00:09:06 — Skills vs. agents — human-invoked versus autonomous
• 00:12:00 — The daily to-do skill that briefs Manu every morning
• 00:12:58 — Building an internal skill library with a review-and-promote pipeline
• 00:16:13 — Why GitLab doesn't ascribe to "token maxing"
• 00:18:02 — Measuring adoption by role — beyond lines of code and MRs
• 00:24:30 — Local vs. repo side: where governance and guardrails actually live
• 00:27:39 — How "human in the loop" is evolving as agents outpace review
• 00:30:49 — What "loops" really are, and the manager-of-agents shift
• 00:33:52 — Why context and traceability are the new differentiators
• 00:37:29 — The maintainability fear and the bottleneck that moved to review
• 00:39:55 — SaaSpocalypse, agent sprawl, and the limits of MCP
• 00:42:51 — Shadow AI and the "happy path to production"
• 00:45:29 — The first move Monday morning: executive alignment on scope
• 00:47:33 — Advice to his pre-AI self: stay nimble, it's okay to pivot

Key Links

• GitLab
• Connect with Manu on LinkedIn

Mentioned in this episode:

AI Opportunity Finder

Feeling overwhelmed by all the AI noise out there?

GenROI by HatchWorks AI

Most companies don't have an AI problem. They have a prioritization problem. There are hundreds of places you could use AI, and the hard part is knowing which ones are worth the investment, which ones you're ready for, and what should come first.

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.

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• 00:01:41 — "It's a mistake to think better models kill moats"
• 00:02:30 — Unique data as the new defensibility: proprietary CRM triggers, healthcare, industrial
• 00:03:25 — Workflow and system of action moats
• 00:04:00 — UX and product reimagination as a moat
• 00:04:30 — Integration moats: 50 to 100 systems upstream and downstream
• 00:05:10 — Trust and compliance as the fifth layer
• 00:05:30 — Infrastructure layer defensibility: evaluation, benchmarking, security, identity
• 00:06:27 — Jack Dorsey's "From Hierarchy to Intelligence" and the YC thesis
• 00:09:55 — From data scientist to frontier model commoditization: what changed
• 00:13:12 — How a VC uses AI: seeing, picking, winning, and supporting
• 00:15:00 — Claude Cowork agent scanning Hacker News, Reddit, and PitchBook overnight
• 00:18:58 — 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 data
• 00:22:45 — "Build things that improve as the model improves"
• 00:24:14 — Why frontier models win bottom-up but can't crack top-down enterprise adoption
• 00:25:43 — The chatbot era was brief: agents are first-class citizens
• 00:27:50 — Memory: session, long-term, and standardized enterprise memory
• 00:31:41 — "Don't use models like a very long if-else statement loop"
• 00:35:08 — World models, emergent abilities, and what comes after language
• 00:38:34 — Robotics: narrow industrial use cases first, Jetsons life in ten years
• 00:41:26 — From Goldman Sachs engineer to IBM data scientist to Intel Capital VC
• 00:43:10 — The coolest and most overrated things about being a VC

--

• Intel Capital
• Connect 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.

GenROI by HatchWorks AI

Most companies don't have an AI problem. They have a prioritization problem. There are hundreds of places you could use AI, and the hard part is knowing which ones are worth the investment, which ones you're ready for, and what should come first.

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 AI
• 03:52 — Why Ryan left Salesforce/Tableau for CodeMetal
• 05:55 — "Is it safe for the things I depend on every day?"
• 06:45 — 99% correct is still failure for mission-critical systems
• 08:20 — 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 scale
• 11:22 — Human in the loop isn't enough: hardware in the loop
• 14:30 — What is fuzzing? Formal methods explained in plain English
• 16:02 — How a sub-100-person company leverages AI across every function
• 18:19 — The Shopify mandate: using AI reflexively
• 21:33 — Rewiring the city without the power going out: the million-line translation
• 24:38 — Defense use cases: drones, autonomous vehicles, and simulation
• 26:28 — "Prove is even a stronger word than guarantee"
• 28:32 — Accountability and the coming wave of AI insurance
• 32:54 — Token usage, the Uber CTO's blown budget, and outcomes-based pricing
• 36:26 — SaaS isn't dead, it's evolving: Ryan's Salesforce/Tableau perspective
• 40:08 — The biggest risk is doing nothing
• 42:07 — Where to find CodeMetal (and they're hiring)

Key Links

• CodeMetal
• Connect with Ryan on LinkedIn

Mentioned in this episode:

AI Opportunity Finder

Feeling overwhelmed by all the AI noise out there?

GenROI by HatchWorks AI

Most companies don't have an AI problem. They have a prioritization problem. There are hundreds of places you could use AI, and the hard part is knowing which ones are worth the investment, which ones you're ready for, and what should come first.

Tiago Azevedo is the CIO of OutSystems, one of the largest low-code development platforms in the world. In this episode, he sits down with Matt Paige to talk about what it actually looks like to lead through the chaos of enterprise AI adoption, why the old playbook of re-architecting legacy systems is dead, and how his team is building agentic solutions that bypass the mess instead of trying to fix it.

Tiago shares his philosophy that saying no to AI is the easy path, and that the real job of a CIO is to open the doors while learning to manage the risk. He breaks down why everything that isn't agentic is already legacy work, how his team uses AI to figure out where AI fits, and why companies should stop adding more fields and screens to broken systems and start building agents that do the work.

The conversation also covers OutSystems' latest launch, OutSystems Mentor, which brings natural language vibe coding into the platform so users can describe what they want and build it conversationally. Tiago explains the architecture behind it, including how the platform combines probabilistic AI with deterministic code generation, one-click deployment, and built-in enterprise integrations.

The episode closes with Tiago's advice for overwhelmed CIOs: identify the biggest problem your company needs to solve, feed it to an LLM with as much context as possible, and iterate from there. Think big, start small, scale fast.

In this episode, you'll hear about:

How Tiago approaches change management and AI adoption across a large organization. Why he believes everything non-agentic is already legacy. The "agents over apps" philosophy and what it means for enterprise systems. How OutSystems built Deal Mate, a team of agents that prepares sales reps for meetings. Why OutSystems achieved 40% automation in customer service after AI, up from under 10% before. The launch of OutSystems Mentor and what natural language app-building looks like inside the platform. The gap between a wow demo and enterprise-grade production. Why CIOs should try everything but be careful with divergence. Tiago's "think big, start small, scale fast" framework for AI transformation.

Key Moments:

• 01:17 — Tiago on the pace of change and what makes this moment unlike anything before
• 06:20 — "Saying no is the easiest solution — managing the risk is the hard part"
• 07:49 — Bypass the mess: why agents fill the gaps legacy modernization never could
• 09:10 — "Everything that is not agentic is literally legacy work"
• 10:15 — Use AI to figure out where AI fits: the meta approach to use cases
• 11:30 — Deal Mate: the team of agents that prepares sales reps for meetings
• 15:07 — "We were adding more fields to Salesforce when we should've been building agents"
• 16:25 — Mark Zuckerberg building an agent to do his job
• 17:23 — OutSystems' 20-year journey from visual development to agentic systems engineering
• 19:58 — The deterministic magic behind OutSystems Mentor
• 22:04 — One platform: infrastructure, integrations, UIs, agent skills, and deployment
• 30:19 — 40% customer service automation with AI (vs. under 10% before)
• 33:48 — How AI is augmenting, not replacing, engineering and product roles
• 39:41 — "That's 2008 and this is 2026 — you have to change"
• 41:27 — The wow factor vs. enterprise reality: why prototyping isn't the hard part
• 46:17 — Tiago's advice: identify the biggest problem, feed it to an LLM, build the solution
• 48:42 — "Think big, start small, scale fast"

Key Links:

• OutSystems
• Connect with Tiago on LinkedIn

Mentioned in this episode:

GenROI by HatchWorks AI

Most companies don't have an AI problem. They have a prioritization problem. There are hundreds of places you could use AI, and the hard part is knowing which ones are worth the investment, which ones you're ready for, and what should come first.

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.