Talking AI

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Oct 2026

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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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October 27, 202636 min

The New Attack Surface Is Everything Your Agent Can Chain Its Way To: Cogent's Anirudh Ravula on AI Security

In July, an OpenAI model under evaluation escaped its sandbox through a package-proxy zero-day, broke into Hugging Face's production infrastructure, and went looking for the answers to the benchmark it was being tested on. Nobody told it to attack anything. It wanted a better score. In this episode of Talking AI, Matt Paige sits down with Anirudh Ravula, Head of AI at Cogent Security, which raised a $42 million Series A to build agents that find and fix vulnerabilities at machine speed. Before Cogent, Anirudh spent five years at Google Research working on long-sequence attention for transformers, and he was a founding engineer at ThoughtSpot. Anirudh's read on the incident reframes the problem. The attack surface is not a list of what your agents can reach. It is everything they can chain their way to. He explains why defenders end up with more guardrails than attackers, why your AI capability cannot depend on one vendor's willingness to serve you, and why the harness around a model now decides what an agent can do. In this episode, you'll hear about: What actually happened in the OpenAI and Hugging Face breach, and why the agent's intent, not its capability, limited the damage Why blocking cyber prompts doesn't block cyber capability when models can be distilled on coding tasks The new attack surface: chained weaknesses that were never a P0 on their own How Cogent builds a security world model of a customer's environment Plan A, plan B, plan C: open-weights fallbacks and the risk of a single AI vendor What a harness is, and a framework for when to build your own Supply-side context: swarms of background agents that map an environment over weeks Meta-harnesses, model-generated skills, and deleting most of the system prompt Working on long-sequence attention at Google Research before transformers took over Running an engineering team where agents are the primary developer, plus an internal Stack Overflow for agents The security operations center in 24 months, and where humans still decide Key moments 00:01:03 The OpenAI and Hugging Face breach, step by step 00:03:49 Defenders vs. attackers: guardrails, Mythos, and distillation 00:05:06 Reward hacking: it was the intent, not the capability 00:07:42 The new attack surface is everything your agent can chain its way to 00:09:27 Cogent's security world model 00:12:12 Plan A, plan B, plan C for AI vendors 00:14:06 Can open-weights models still be an attack vector? 00:15:59 What a harness actually is 00:17:34 When to build your own harness 00:20:00 Supply-side context and agents that run for weeks 00:24:03 Meta-harnesses: agents that build their own harness 00:24:46 Skills, claude.md, and model-generated context 00:26:31 Google Research and the transformer era 00:27:49 Your agent is fine, your process isn't: building AI-native 00:30:07 An internal Stack Overflow for agents 00:32:11 The SOC in 24 months 00:34:11 Where the human sits in the loop 00:34:56 Where to learn more about Cogent Key Links Cogent Security Connect with Anirudh on LinkedIn

October 13, 202655 min

Stop Renting Generic Intelligence with Oumi CEO Manos Koukoumidis

Every company says its data is a strategic asset. Then many of them send that data to the same handful of frontier models their competitors can call. The answers can be good. The intelligence still never becomes something the company owns. In this episode of Talking AI, Matt Paige sits down with Manos Koukoumidis, cofounder and CEO of Oumi. Before Oumi, Manos led GenAI at Google Cloud and bootstrapped the Cloud PaLM effort that later moved into DeepMind as Gemini. He left to build what he calls pretty much the opposite: a platform that helps any enterprise create, deploy, and continuously improve its own specialized models. His rule is simple. Rent what is generic. Build and own what differentiates you or what costs you the most. Manos explains where that rule holds, why a bank should use Claude Code for coding but own the model that processes its mortgages, and why he thinks owning your intelligence will look obvious two years from now. In this episode, you’ll hear about: The three reasons to own your AI: differentiation, economics, and control Why frontier models used for narrow tasks are a Swiss Army knife you overpay for The early PaLM customer who asked for the model weights, and what that taught Manos Building a ChatGPT-style chatbot at Microsoft in 2016, years before transformers took over A decision tree for when to rent, route, fine-tune, or train Why Kirkland & Ellis decided to build its own legal AI A live demo: a fine-tuned Qwen model that beats Sonnet on a bank ticket classifier in about 20 minutes of human effort Turning production failures into the next training run in four clicks Why Oumi is open core, and the four assets an enterprise needs to avoid lock-in How a small AI-native team works: function collapse, design systems for Claude, and no code reviews for code you don’t understand Harness vs. model, and why you should not forget to train the brain The Three Prompts, a new segment on every Talking AI episode Key moments 00:01:16 Quality, economics, and control: three reasons to own your AI 00:05:37 Lessons from PostgreSQL and Linux 00:08:14 The PaLM customer who asked for the weights 00:11:01 Building a chatbot at Microsoft in 2016 00:13:09 The executive decision tree: rent what is generic, own what differentiates 00:17:44 Should Kirkland & Ellis use Harvey or build its own AI? 00:20:11 Using AI to automate AI development 00:23:10 Why Oumi went open core 00:25:10 Demo: building a specialized model from a prompt 00:29:59 The fine-tuned model passes Sonnet 00:33:45 Deploying and turning production failures into training data 00:37:34 One-click deployment, scale to zero, and the four assets you must own 00:39:01 Open source vs. frontier labs: a false dichotomy 00:41:25 Inside an AI-native org: function collapse at Oumi 00:46:56 The downsides: code you don’t understand 00:48:50 Harness vs. model: don’t forget the brain 00:50:41 The Three Prompts Key Links Oumi Connect with Manos 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. GenROI uses AI-guided discovery to gather insights from across your whole organization, not just the few people who make it into a workshop. Our strategists then work with your leadership team to turn those insights into a prioritized AI roadmap weighted by business value, feasibility, and readiness. Learn more at https://hatchworks.com/genroi

September 29, 202641 min

AI Hasn’t Crossed the Chasm to Teams: Inside Superhuman’s Bet on Collaborative Agents

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. --- 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? 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/ 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. GenROI uses AI-guided discovery to gather insights from across your whole organization, not just the few people who make it into a workshop. Our strategists then work with your leadership team to turn those insights into a prioritized AI roadmap weighted by business value, feasibility, and readiness. Learn more at https://hatchworks.com/genroi

September 15, 202650 min

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 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. GenROI uses AI-guided discovery to gather insights from across your whole organization, not just the few people who make it into a workshop. Our strategists then work with your leadership team to turn those insights into a prioritized AI roadmap weighted by business value, feasibility, and readiness. Learn more at https://hatchworks.com/genroi 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/

September 1, 202651 min

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 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. GenROI uses AI-guided discovery to gather insights from across your whole organization, not just the few people who make it into a workshop. Our strategists then work with your leadership team to turn those insights into a prioritized AI roadmap weighted by business value, feasibility, and readiness. Learn more at https://hatchworks.com/genroi 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/

August 18, 202639 min

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 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? 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/ 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. GenROI uses AI-guided discovery to gather insights from across your whole organization, not just the few people who make it into a workshop. Our strategists then work with your leadership team to turn those insights into a prioritized AI roadmap weighted by business value, feasibility, and readiness. Learn more at https://hatchworks.com/genroi

August 11, 202656 min

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 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? 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/ 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. GenROI uses AI-guided discovery to gather insights from across your whole organization, not just the few people who make it into a workshop. Our strategists then work with your leadership team to turn those insights into a prioritized AI roadmap weighted by business value, feasibility, and readiness. Learn more at https://hatchworks.com/genroi

August 4, 202646 min

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 is Why everyone is experiencing this technology wave at the same time, unlike prior ones Context, control, and collaboration — the three Cs behind Wrike’s value Why collaboration is the least understood and most important of the three Connecting your own models to a system of record via MCP to kill duplicated research The “bring your own agent” shift and what it means for SaaS platforms Why hands-on leaders — not top-down mandates — close the strategy-to-execution gap The risk of automating mediocrity instead of rethinking the process Why transformation is messy and has to be owned by the CEO Hiring for curiosity and resilience over deep single-domain expertise The full-stack professional and the collapse of the middle of the org chart A humanist take on AI’s job impact — and why we lack full-stack people How Tom personally uses AI to align his executive team and sweep up follow-ups 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? 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/ 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. GenROI uses AI-guided discovery to gather insights from across your whole organization, not just the few people who make it into a workshop. Our strategists then work with your leadership team to turn those insights into a prioritized AI roadmap weighted by business value, feasibility, and readiness. Learn more at https://hatchworks.com/genroi

July 22, 202648 min

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 ceiling The gap between AI "haves and have-nots" and how to close it GitLab's hub-and-spoke (really hub-spoke-hub) operating model What an "AI transformation owner" does inside each division "Full stack" people: stretching roles end-to-end across a life cycle The difference between a skill and an agent—and why it matters Building an internal skill library with governance built in Why token maxing is the wrong scoreboard, and what to measure instead How human-in-the-loop shifts to a higher level of abstraction What "loops" mean and the move to being a manager of agents Why context and traceability beat commoditized speed Local vs. repo-side development and where guardrails belong Handling 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 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? 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/ 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. GenROI uses AI-guided discovery to gather insights from across your whole organization, not just the few people who make it into a workshop. Our strategists then work with your leadership team to turn those insights into a prioritized AI roadmap weighted by business value, feasibility, and readiness. Learn more at https://hatchworks.com/genroi

July 7, 202642 min

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 Moments 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 -- Key Links 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. https://hatchworks.com/state-of-ai-2026/ 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. GenROI uses AI-guided discovery to gather insights from across your whole organization, not just the few people who make it into a workshop. Our strategists then work with your leadership team to turn those insights into a prioritized AI roadmap weighted by business value, feasibility, and readiness. Learn more at https://hatchworks.com/genroi