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. 5d ago

    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 damageWhy blocking cyber prompts doesn't block cyber capability when models can be distilled on coding tasksThe new attack surface: chained weaknesses that were never a P0 on their ownHow Cogent builds a security world model of a customer's environmentPlan A, plan B, plan C: open-weights fallbacks and the risk of a single AI vendorWhat a harness is, and a framework for when to build your ownSupply-side context: swarms of background agents that map an environment over weeksMeta-harnesses, model-generated skills, and deleting most of the system promptWorking on long-sequence attention at Google Research before transformers took overRunning an engineering team where agents are the primary developer, plus an internal Stack Overflow for agentsThe security operations center in 24 months, and where humans still decide Key moments 00:01:03 The OpenAI and Hugging Face breach, step by step00:03:49 Defenders vs. attackers: guardrails, Mythos, and distillation00:05:06 Reward hacking: it was the intent, not the capability00:07:42 The new attack surface is everything your agent can chain its way to00:09:27 Cogent's security world model00:12:12 Plan A, plan B, plan C for AI vendors00:14:06 Can open-weights models still be an attack vector?00:15:59 What a harness actually is00:17:34 When to build your own harness00:20:00 Supply-side context and agents that run for weeks00:24:03 Meta-harnesses: agents that build their own harness00:24:46 Skills, claude.md, and model-generated context00:26:31 Google Research and the transformer era00:27:49 Your agent is fine, your process isn't: building AI-native00:30:07 An internal Stack Overflow for agents00:32:11 The SOC in 24 months00:34:11 Where the human sits in the loop00:34:56 Where to learn more about Cogent Key Links Cogent SecurityConnect with Anirudh on LinkedIn

  2. Oct 13

    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 controlWhy frontier models used for narrow tasks are a Swiss Army knife you overpay forThe early PaLM customer who asked for the model weights, and what that taught ManosBuilding a ChatGPT-style chatbot at Microsoft in 2016, years before transformers took overA decision tree for when to rent, route, fine-tune, or trainWhy Kirkland & Ellis decided to build its own legal AIA live demo: a fine-tuned Qwen model that beats Sonnet on a bank ticket classifier in about 20 minutes of human effortTurning production failures into the next training run in four clicksWhy Oumi is open core, and the four assets an enterprise needs to avoid lock-inHow a small AI-native team works: function collapse, design systems for Claude, and no code reviews for code you don’t understandHarness vs. model, and why you should not forget to train the brainThe Three Prompts, a new segment on every Talking AI episode Key moments 00:01:16 Quality, economics, and control: three reasons to own your AI00:05:37 Lessons from PostgreSQL and Linux00:08:14 The PaLM customer who asked for the weights00:11:01 Building a chatbot at Microsoft in 201600:13:09 The executive decision tree: rent what is generic, own what differentiates00:17:44 Should Kirkland & Ellis use Harvey or build its own AI?00:20:11 Using AI to automate AI development00:23:10 Why Oumi went open core00:25:10 Demo: building a specialized model from a prompt00:29:59 The fine-tuned model passes Sonnet00:33:45 Deploying and turning production failures into training data00:37:34 One-click deployment, scale to zero, and the four assets you must own00:39:01 Open source vs. frontier labs: a false dichotomy00:41:25 Inside an AI-native org: function collapse at Oumi00:46:56 The downsides: code you don’t understand00:48:50 Harness vs. model: don’t forget the brain00:50:41 The Three Prompts Key Links OumiConnect 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

  3. Sep 29

    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 tools00:03:32 — The board meeting, Reid Hoffman, and Coda’s early look at the OpenAI API00:06:11 — What a vision video is, and why prototypes beat blog posts00:07:56 — Why having to think “it’s time to use AI” is a failure mode00:11:20 — The context problem: copy/paste, thin slices, and memory that vanishes00:13:00 — From decision logs to MCP: keeping a team’s memory continuously updated00:15:01 — Coda to Grammarly to Superhuman: what the transition actually felt like00:17:58 — Why July 8 was a big bang instead of a phased rollout00:18:59 — AI Views explained: prompt your way on top of a dataset00:20:58 — Low floor, high ceiling, and software that feels personal00:24:05 — Product, design, and engineering roles collapsing into each other00:26:30 — AI-native development: the team with no planning process00:27:52 — Where the bottleneck moved: machines or humans reviewing the code00:30:17 — Partner or threat? Working with the labs you also compete with00:34:04 — Human-generated and machine-generated work have to get married somewhere00:37:24 — Let the makers make, and why play beats mandates00:43:31 — The end-of-day sweeper, and teaching his kids AI with homemade games --- Key Links SuperhumanConnect 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

  4. Sep 15

    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: 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/

  5. Sep 1

    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: 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/

  6. Aug 18

    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/ 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

  7. Aug 11

    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/ 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

  8. Aug 4

    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/ 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

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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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  • Creator
    HatchWorks
  • Years Active
    2023 - 2026
  • Episodes
    87
  • Rating
    Clean
  • Copyright
    © A podcast by HatchWorks®
  • Show Website
    Talking AI

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