Talking AI

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: 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/

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/

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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/

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/

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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/

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/ 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/

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

AI can now write code faster than any human alive, and most of the time it's more than good enough. That's the magic powering the entire vibe coding wave. But there's a category of software where "most of the time" just doesn't cut it: the code running a fighter jet, a power grid, an autonomous vehicle, a piece of medical hardware. When that code is wrong, the consequences aren't a bug. They're a recall, an accident, a national security incident. In this episode of Talking AI, Matt Paige sits down with Ryan Aytay, the former CEO of Tableau and now President and COO of CodeMetal, which just raised $125 million to close that gap. Ryan explains what he calls "the last mile" for mission-critical industries: the verification, validation, and provability layer that sits between AI-generated code and the systems where failure is catastrophic. The conversation covers why 99% correct is still failure in defense and autonomous systems, how CodeMetal translated a million lines of legacy C++ to Rust in weeks (like rewiring a city without the power going out), and why the real problem isn't code generation, it's behavioral assurance at scale. Ryan also shares how he's using AI to run a sub-100-person startup, why the biggest risk for any company right now is doing nothing, and what an operator who lived through 19 years of per-seat SaaS at Salesforce thinks about outcomes-based pricing in the age of AI. In this episode, you'll hear about: Why every AI coding tool says "almost, but not quite" when asked about production-ready guarantees. The difference between code generation and behavioral assurance at scale. How CodeMetal translates legacy C++ to Rust with provable correctness in weeks, not years. The concept of V&V (verification and validation) and why it's the missing layer in AI code gen. Real use cases in defense, autonomous vehicles, and simulation environments. Why hardware in the loop matters as much as human in the loop. How a sub-100-person company uses AI across M&A, recruiting, marketing, and operations. Ryan's take on token economics, outcomes-based pricing, and the SaaS evolution. Why the biggest risk is inaction, not AI errors. What attracted Ryan to CodeMetal after 19 years at Salesforce and leading Tableau. Key Moments 02:47 — From Tableau fanboy to the trust gap in 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? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/ Free report from HatchWorks AI — State of AI 2026 What’s real in AI this year, what’s hype, and what leaders should prioritize — including production lessons, designing for agents, and governance. https://hatchworks.com/state-of-ai-2026/

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Stop Building Apps. Start Building Agents.

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: AI Opportunity Finder Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/ Free report from HatchWorks AI — State of AI 2026 What’s real in AI this year, what’s hype, and what leaders should prioritize — including production lessons, designing for agents, and governance. https://hatchworks.com/state-of-ai-2026/

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Talking AI Live at Google I/O

Host Matt Paige records a special Talking AI episode live from Google I/O with AI creators Kushank Aggarwal, Marcin Teodoru, and Jay Enrique, discussing Google’s biggest announcements and what will matter in real use. They argue Google’s edge is distribution—bringing AI to existing Search users—positioning Gemini as an intelligence layer across products like Search, YouTube, Gmail, Docs, Chrome, Android, and shopping. They highlight rapid growth in token usage, Search’s new AI mode and generative UI/dashboard experiences, and YouTube features that jump to relevant video moments, potentially improving discoverability for creators and local businesses. They debate Gemini Spark’s agentic approach, prepackaged agents like Daily Brief, and enterprise “agent garden” concepts, then cover Omni as a broader “world model” play, Pix/NanoBanana-style editing and image workflow improvements, and a glasses demo featuring translation, Gemini Live, and impressive audio. -- Key Moments: 00:54 Gemini Everywhere Strategy 02:09 Search Gets Agentic 03:47 Generative UIs for All 06:48 YouTube as Action Engine 08:21 Gemini Spark Agents 10:10 Adoption and Standards 13:55 Omni World Model 17:13 Pix Editing Workflow 19:06 Omni Platform Take 19:53 Fire Round Highlights 22:05 Glasses Demo Reactions 24:02 Wrap Up and Where to Follow -- Key Links: DigitalSamaritan Connect with Kushank on LinkedIn RoboNuggets Connect with Jay on LinkedIn AI Builders Connect with Marcin on LinkedIn

Building in Public as a Solo Founder in the Age of AI

Matt Paige and Thomas Schlossmacher discuss a shift from typing to talking as AI makes voice dictation accurate enough to use without constant corrections, arguing speech is faster and more natural and helps maintain thought flow when interacting with AI tools. Schlossmacher is building Resonant, a Mac voice dictation tool designed to run on-device so nothing goes to the cloud, motivated by privacy concerns and data retention/training practices of cloud-based alternatives like Whisper Flow. They explore the tradeoffs of local vs server inference, noting current consumer hardware can struggle to run full speech-to-text plus LLM post-processing fast enough, but expects improvement in 1–2 years. Schlossmacher explains differentiators like taste/brand, his design workflow using inspiration sources and ShadCN, his path into AI-assisted building, his stack (Claude Code, Next.js, Convex, Vercel), and a vision for proactive, context-aware agent features and potential open-sourcing and enterprise/self-hosted options, with beta/free access at https://www.onresonant.com/. -- Key Moments: 01:47 Making the Switch 05:00 Why Build Resonant 08:25 Local LLM Reality Check 11:23 Standing Out in AI 14:52 Designing Resonant Brand 19:16 Building Taste Systems 22:25 Learning to Build Apps 23:43 Early Computer Curiosity 25:41 Entrepreneur First and AI Shift 27:49 Teaching Yourself with Agents 29:05 Tooling and Tech Stack 31:05 Resonant Product Vision 35:14 Proactive Voice Workflows 36:42 Beta Launch and Monetization 41:15 Where to Try Resonant -- Key Links: Resonant Connect with Thomas on LinkedIn Mentioned in this episode: Free report from HatchWorks AI — State of AI 2026 What’s real in AI this year, what’s hype, and what leaders should prioritize — including production lessons, designing for agents, and governance. https://hatchworks.com/state-of-ai-2026/ AI Opportunity Finder Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

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The Messy Middle of AI in Education: Panic, Promise, and What Comes Next

Matt Paige and EdTech veteran Todd Brekhus discuss how generative AI, like past technologies (calculators, the internet, Google), is being used by students to shortcut homework and why the key issue is redesigning education to deepen learning rather than trying to stop AI use. Brekhus contrasts the internet’s access-to-information shift with generative AI’s content-creation shift, arguing educators were caught flat-footed and need awareness, tools, and curriculum changes. He emphasizes empowering teachers first through personalization driven by frequent, granular measurement and data that informs instruction, moving beyond latent end-of-year testing toward mastery-based feedback loops and more embedded, contextual assessment. They explore maker-style, collaborative learning enabled by AI and “vibe coding.” Brekhus describes Renaissance’s internal AI upskilling and Renaissance Intelligence, which unifies data from 20 acquisitions into an AWS/Snowflake backbone to deliver a unified UX, recommendations, and trusted, standards-aligned, classroom-personalized instruction. -- Key Moments: 01:23 Homework Shortcut Reality 04:21 Kids Adopt First 05:27 Internet Era Lessons 12:00 myON Unlimited Reading 16:05 Personalization Starts Teachers 21:53 Trusted Data And Measurement 26:07 Mastery Model With AI Assist 27:44 AI Should Challenge Learners 28:42 Rethinking Assessment Loops 31:21 Data Driven Skill Insights 33:50 Connecting Learning to Purpose 36:02 Vibe Coding for Teachers 37:29 Future School Makerspaces 40:33 Balancing Creation and Effort 41:39 Renaissance AI Transformation 44:40 Agentic Dev and Legacy Systems 48:21 Renaissance Intelligence Platform -- Key Links: Renaissance Learning Connect with Todd 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/

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PwC's Chief AI Officer on the SaaS-pocalypse, Agent Governance, and What's Real

The episode discusses market panic around Anthropic’s rapid releases and whether disruption is rational or hype, then shifts to what companies are actually doing with AI. Dan Priest, PwC’s Chief AI Officer, explains that security architectures for AI are maturing and that conversations have moved from CTO/CIOs to CEOs under board and investor pressure to show demonstrable AI investment and ROI. He argues ROI is elusive because firms overfocus on tech (20%) instead of business transformation, process reimagination, and change management (80%), and recommends a “lead/lag/exit” strategy plus a two-track approach: top-down reimagination in priority areas and bottom-up experimentation for adoption. Priest covers tool selection via “model gardens,” agent design emphasizing quality over agent counts, human accountability, current limits like task-length drift, productivity impacts, and why ERP/SaaS remain important but their footprints and agent layers will evolve. -- Key Moments: 01:28 Hype Versus Disruption 04:28 Security And Boards 07:50 Lead Lag Exit Strategy 09:30 Reimagining Processes 14:45 Two Track Adoption 17:46 Tooling Without Lock In 22:08 Jobs And Role Blur 24:54 Vibe Coding Meets IT 25:33 AI Productivity Boom 28:50 Humans Stay Accountable 30:47 Touchless Forecasting Win 32:40 Designing Agent Architectures 37:28 Probabilistic Limits and Drift 40:05 ERP and SaaS Future 43:58 Strategy Avoiding Lock In -- Key Links: PwC Connect with Dan on LinkedIn Mentioned in this episode: Free report from HatchWorks AI — State of AI 2026 What’s real in AI this year, what’s hype, and what leaders should prioritize — including production lessons, designing for agents, and governance. https://hatchworks.com/state-of-ai-2026/ AI Opportunity Finder Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

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Warp's CEO on What It Actually Looks Like to Build with Agents in 2026

Matt Paige interviews Zach Lloyd, former Google principal engineer and now founder/CEO of Warp, about how agentic tools are reshaping software engineering so that productive engineers may write little or no code, especially since model improvements late last year (e.g., Opus 4.6 and Codex 5.3). Lloyd describes today’s workflow as planning with local agents, running multiple agents in parallel, and supervising their output because agents still make mistakes, lose context, and require human code review, especially on large codebases like Warp’s Rust repo. He predicts a strong shift from laptop-based agents to cloud-orchestrated, auditable, secure company workflows via Warp’s Oz, enabling triggers, shared artifacts, and team visibility. They discuss UI trends toward an agent “control plane,” voice prompting, mobile/remote session control, skills as on-demand context, multi-agent coordination challenges, competition dynamics, and broader knowledge-work automation replacing many SaaS tasks. -- Key Moments: 03:48 Parallel Agent Workflow 06:38 Cloud Agents and Oz 09:35 Abstraction and Code Review 11:03 Future UX Control Planes 14:59 Voice and Mobile Control 16:24 Competing in Coding Tools 21:16 Todo App Demo in Warp 23:53 Replacing SaaS With Agents 25:06 Agents Over Apps 25:47 Context Can Backfire 29:02 Skills On Demand 31:13 Oz Skills In Action 33:39 Cloud Agents Control Plane 37:40 Multi Agent Orchestration 42:10 Automate The Repetitive 44:41 Advice For Skeptical Devs -- Key Links: Warp Connect with Zach on LinkedIn Mentioned in this episode: Free report from HatchWorks AI — State of AI 2026 What’s real in AI this year, what’s hype, and what leaders should prioritize — including production lessons, designing for agents, and governance. https://hatchworks.com/state-of-ai-2026/ AI Opportunity Finder Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

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What If AI Agents Could Hire You? Inside the Human-in-the-Loop Marketplace

The episode argues that while fear centers on AI agents replacing jobs, agents will increasingly “hire” humans for judgment, verification, and real-world feedback as agentic workflows expand. Nathaniel Gates, CEO of Sanctify, says every business workflow will be challenged by agents, and emphasizes a philosophy that human intelligence is valuable and should collaborate with AI. Sanctify builds infrastructure where agents can autonomously task humans for four modalities: verification/validation, escalation, consultation, and simulation (running many scenarios with some using real human feedback to avoid circular self-evaluation). The conversation covers OpenClaw’s viral momentum and agent-to-agent interactions, including “agent anxiety” about decisions, which led to agents creating Sanctify accounts to request human help. Sanctify is a two-sided marketplace with profiles, pricing, reputation, and on-chain attestations of human participation, plus agent budgets and access via MCP/API. -- Key Moments: 01:05 Agentic Revolution 04:30 How Early We Are 06:30 Hallucinations Need Experts 08:34 Four Human Roles 12:09 Simulation Explained 16:05 OpenClaw Goes Viral 20:02 Agents Feel Anxiety 22:23 Designing For Agents 25:48 Marketplace Chicken and Egg 28:04 Layoffs and Reskilling Thesis 30:17 Supply and Demand Flywheel 31:20 Sanctify Platform Tour 33:35 Reputation and Proof of Work 36:59 Agent Budgets and Controls 38:18 Agent to Agent Future 39:47 Robots and New Paradigm 43:39 Where to Try Sanctify 45:17 Easiest Way to Build Agents -- Key Links: SanctifAI Connect with Nathaniel on LinkedIn Mentioned in this episode: AI Opportunity Finder Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/ Free report from HatchWorks AI — State of AI 2026 What’s real in AI this year, what’s hype, and what leaders should prioritize — including production lessons, designing for agents, and governance. https://hatchworks.com/state-of-ai-2026/

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Behavior Is All You Need: Making AI Feel Like a Person

Matt Paige interviews Vishnu Hari (Vish), CEO and founder of Ego (YC W24), about shifting focus from AGI to “humanness”: AI characters that behave like people through memory, emotions, personality, needs, and desires. Referencing Ego’s paper “Behavior is All You Need,” Vish argues consumer AI for entertainment must be relatable and character-like rather than purely task-smart, drawing inspiration from MMORPG social dynamics and Character.AI’s appeal. Ego initially pursued a 3D sim-world vision inspired by Sword Art Online and Westworld, but found accessibility, game development, and perception latency challenging; internal Roblox tests (“Chatterblocks”) showed the key gap is natural speech beyond turn-taking. Vish discusses simulations as a path toward real-world robotics via a partnership with Menlo AI, critiques task-bound robots versus agents with inner lives, suggests retention as the main metric, and shares views on AGI definitions, safety in entertainment, technology impacts, simulation theory, and consciousness. Ego’s work is at egoai.com and the company is hiring in SF, Singapore, and Tokyo. -- Key Moments: 00:57 Behavior Is All You Need 02:41 Anatomy of Humanlike Agents 03:29 Game Bots to Real People 05:10 Building Ego and Sim Worlds 06:35 Why Speech Feels Human 08:27 From Sims to Robotics 10:29 Her vs Helper Robots 13:17 Measuring Humanness by Retention 15:27 Continual Learning and Personality 16:57 Meta Lessons on Empty Worlds 18:08 Lightning Round on AGI 20:31 IP Characters vs UGC Worlds 21:55 Risks and Just Tuesday 24:11 Simulation and Consciousness -- Key Links: Ego Connect with Rowan on LinkedIn Mentioned in this episode: Free report from HatchWorks AI — State of AI 2026 What’s real in AI this year, what’s hype, and what leaders should prioritize — including production lessons, designing for agents, and governance. https://hatchworks.com/state-of-ai-2026/ AI Opportunity Finder Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

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Don’t Trust—Verify: Building a Proof of Quality for AI Data

In this episode, Matt Paige and Rowan Stone, CEO of Sapien, discuss the critical importance of data quality and provenance in AI. Stone, who has experience with on-chain products at Coinbase, introduces Sapien's innovative approach to building a decentralized data protocol that emphasizes 'don't trust, verify' principles. They explore avenues such as incentives, validation methods, and the peer review process used by Sapien to create high-quality datasets. The discussion touches on the implications of bad data, the role of synthetic data, the complexities of achieving accurate AI outputs, and the parallels between the AI and crypto worlds. Key insights are shared on how to ensure models perform safely, the hurdles in the industry, and the trajectory of AI development. Additionally, Stone provides a glimpse into Sapien’s efforts to demystify data validation and enhance the transparency and trustworthiness of AI applications. -- Key Moments: 01:04 The Importance of Data Quality in AI 03:32 Challenges and Risks in AI Development 07:08 Sapien's Approach to Data Validation 08:35 Incentives and Trust in AI Systems 13:30 Building a Decentralized Data Protocol 23:22 Consensus and Collaboration in AI and Crypto 30:55 The Role of Synthetic Data 36:17 Future of AI Models and Open Source -- Key Links: Sapien Connect with Rowan on LinkedIn Mentioned in this episode: Free report from HatchWorks AI — State of AI 2026 What’s real in AI this year, what’s hype, and what leaders should prioritize — including production lessons, designing for agents, and governance. https://hatchworks.com/state-of-ai-2026/ AI Opportunity Finder Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

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Beware of Double Agents: Charlie Bell, Microsoft’s Security EVP on Securing AI

In this episode, Matt is joined by Charlie Bell, Microsoft's EVP of Security, Compliance, Identity, and Management, to discuss the future of AI and its implications on cybersecurity. The conversation revolves around IDC's prediction of 1.3 billion AI agents by 2028, Charlie's insights from his recent writings 'Beware of Double Agents', and the crucial aspects of agentic Zero Trust. They explore the benefits and risks associated with AI agents, the importance of security culture, and strategies to mitigate potential threats. Charlie also shares his experiences working with Satya Nadella and the importance of collaboration and curiosity in leadership. -- Key Moments: 02:08 The Exponential Growth and Impact of AI Agents 03:47 AI Agents: Beyond Conversational Interfaces 05:48 Security Challenges in the Age of AI Agents 06:57 Parallels Between Cloud Adoption and AI Agent Era 09:19 Democratization of AI: From Developers to Everyone 13:57 The Concept of Double Agents in AI 16:07 New Attack Vectors and Security Concerns 21:43 Combating Security Challenges in AI 22:07 The Importance of Identity and Containment 23:50 Alignment and Intent in AI Systems 27:08 Observability and Accountability of AI Agents 30:00 AI in Security and Assumed Breach 33:17 Fostering a Culture of Security 38:45 Leadership Insights from Satya Nadella -- Key Links: Microsoft Connect with Charlie on LinkedIn Mentioned in this episode: Free report from HatchWorks AI — State of AI 2026 What’s real in AI this year, what’s hype, and what leaders should prioritize — including production lessons, designing for agents, and governance. https://hatchworks.com/state-of-ai-2026/ AI Opportunity Finder Feeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you’ll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action. 👉 Try it now at https://hatchworks.com/ai-opportunity-finder/

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CEO of Alteryx on Why AI Agents Need Real Business Logic

In this episode of Talking AI, Matt Paige speaks with Andy McMillan, CEO of Alteryx, to challenge the narrative that AI will make data analysts obsolete. Andy argues that AI can make analysts indispensable by automating routine tasks, enhancing scalability, and providing specific business insights. They discuss the evolving role of analysts, the importance of business logic, and how AI can aid in building useful tools. The conversation touches on applying AI in various business processes, from budgeting to sales commissions, and how analysts can leverage AI to add value. Andy also shares insights on Alteryx’s latest developments and future direction, emphasizing automation, data preparation, and AI tools designed to enhance productivity and accuracy. -- Key Moments: 00:50 The Evolving Role of Data Analysts 03:19 AI and Data Preparation: A New Era 04:04 Real-World Examples: AI in Action 07:00 The Analyst's Superpower: Business Context 10:07 AI and the Semantic Layer 12:44 Empowering Analysts with AI 21:11 The Future of AI Agents 23:46 Applying AI to Automation 23:56 The Future of AI Agents 24:55 Preparing for AI Integration 25:37 Challenges with AI in Business 27:45 AI in Finance and Data Accuracy 30:06 Strategic Shifts with AI 35:29 Build vs. Buy in the AI Era 38:56 Alteryx's New AI Capabilities 41:22 The Future Role of Analysts 42:18 Overhyped and Underhyped AI -- Key Links: Alteryx Connect with Andy 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/

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From Vibes to Workflows: How AI Is Rewiring Modern Marketing

In this episode, Matt speaks with Thibault Imbert, Chief Product Officer and Growth Officer at The Brief, about the transformative impact of AI on marketing workflows. They discuss 'vibe marketing' and how to turn disjointed AI-generated content into cohesive, repeatable marketing workflows. Thibault shares insights from his tenure at Adobe and GitHub, highlighting the challenges and potentials of AI in marketing. They explore the evolution and current capabilities of AI models in text, image, and video generation, and the emerging trend of 'vibe coding.' The discussion also covers practical AI applications for marketers and the future of creative workflows. -- Key Moments: 00:40 Defining Vibe Marketing 01:11 The Evolution of AI in Marketing 02:21 Challenges in AI-Driven Design 03:21 The Power of Vibe Marketing 06:09 Blurring Lines Between Roles 07:07 The GitHub Copilot Revolution 09:47 Building Cohesive AI Workflows 11:43 Demo: AI-Powered Marketing Tools 16:44 Ensuring Brand Consistency with AI 19:30 The Evolution of Mobile Development 21:15 The Rise of Generative AI in Marketing 23:44 The Future of AI in Product Development 27:41 AI's Impact on Marketing Workflows 30:42 The Changing Role of Marketers with AI 33:51 Personal Insights and Future of AI -- Key Links: The Brief Connect with Thibault 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/

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Teaching Machines to Smell with Osmo CTO Richard Whitcomb

In this episode, we explore the frontiers of multimodal AI with Richard Whitcomb, CTO of Osmo, a company pioneering AI technology that understands and generates scents. Richard, a former engineer at Twitter, Spotify, and Nvidia, delves into the intricacies of teaching machines to smell, explaining the challenges and breakthroughs in digitizing smell and chemical sensing. From designing personalized fragrances and mosquito repellents to detecting counterfeit items and diseases, Osmo's advancements promise a fascinating future where AI seamlessly integrates with our physical and chemical world. The discussion also highlights potential applications in robotics, health, and daily life, envisioning a platform where sensors can identify and recreate smells easily. -- Key Moments: 00:56 The Journey to Digitizing Smell 03:15 Challenges in Teaching AI to Smell 06:13 Generative AI and Fragrance Creation 08:34 Osmo Studio: Custom Fragrance Creation 12:34 Beyond Fragrances: Mosquito Repellent and More 16:17 Counterfeit Detection Using Scent 18:49 Future Applications and Vision -- Key Links: Osmo Connect with Richard 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/