Podcast
Talking AI
Hosted by HatchWorks · EN
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.
22episodes
Episodes
Newest firstAll episodes
Context, Control, Collaboration: Why Capability Was Never the Bottleneck
1w ago00:45:58Tap to summarizeThe 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 testKey Moments00:01:19 — 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 testKey LinksWrikeConnect with Thomas on LinkedInMentioned in this episode:AI Opportunity FinderFeeling 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/
Transcribe →Past the Productivity Ceiling: Rebuilding the Enterprise from First Principles
3w ago00:48:24Tap to summarizeMost enterprises rolling out AI are quietly optimizing for the wrong thing: speed, volume, lines of code shipped. Manu Narayan, CIO of GitLab, argues that efficiency gains alone are about to drive companies straight into a productivity ceiling they can't engineer their way out of. The reason is simple and uncomfortable—a faster version of a pre-AI workflow is still a pre-AI workflow. The real unlock isn't speeding up what you already do; it's rebuilding it from first principles.In this episode of Talking AI, Matt Paige sits down with Manu Narayan, GitLab's first-ever CIO, who owns the company's internal AI strategy, enterprise technology, and data infrastructure—in effect, putting GitLab to work inside GitLab. Manu makes the case for moving beyond incremental AI adoption toward a genuine operating model for enterprise AI.The conversation covers GitLab's hub-and-spoke operating model and its embedded "AI transformation owners," why the team measures adoption against business KPIs instead of token counts, how "human in the loop" is evolving into an orchestration role, and why context and traceability—not raw speed—are the new differentiators in software development.In this episode, you'll hear about:Why efficiency gains alone lead straight into a productivity ceilingThe gap between AI "haves and have-nots" and how to close itGitLab's hub-and-spoke (really hub-spoke-hub) operating modelWhat an "AI transformation owner" does inside each division"Full stack" people: stretching roles end-to-end across a life cycleThe difference between a skill and an agent—and why it mattersBuilding an internal skill library with governance built inWhy token maxing is the wrong scoreboard, and what to measure insteadHow human-in-the-loop shifts to a higher level of abstractionWhat "loops" mean and the move to being a manager of agentsWhy context and traceability beat commoditized speedLocal vs. repo-side development and where guardrails belongHandling shadow AI with a genuine "happy path to production"The first move for a CIO stuck optimizing the old workflowKey Moments00:03:11 — The AI "haves and have-nots" inside every enterprise00:04:30 — The hub-and-spoke operating model and "AI transformation owners"00:07:00 — "Full stack" people: stretching roles across the whole life cycle00:09:06 — Skills vs. agents — human-invoked versus autonomous00:12:00 — The daily to-do skill that briefs Manu every morning00:12:58 — Building an internal skill library with a review-and-promote pipeline00:16:13 — Why GitLab doesn't ascribe to "token maxing"00:18:02 — Measuring adoption by role — beyond lines of code and MRs00:24:30 — Local vs. repo side: where governance and guardrails actually live00:27:39 — How "human in the loop" is evolving as agents outpace review00:30:49 — What "loops" really are, and the manager-of-agents shift00:33:52 — Why context and traceability are the new differentiators00:37:29 — The maintainability fear and the bottleneck that moved to review00:39:55 — SaaSpocalypse, agent sprawl, and the limits of MCP00:42:51 — Shadow AI and the "happy path to production"00:45:29 — The first move Monday morning: executive alignment on scope00:47:33 — Advice to his pre-AI self: stay nimble, it's okay to pivotKey LinksGitLabConnect with Manu on LinkedInMentioned in this episode:AI Opportunity FinderFeeling 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/
Transcribe →The VC's Lens: How AI Is Rewriting the Rules of Defensibility
Jul 700:42:25Tap to summarizeEvery company building AI right now is asking the same question: if the models keep getting better and anyone can access them, what actually makes us defensible? Avi Bharadwaj writes the checks that answer that question. As an Investment Director at Intel Capital, he focuses on the software infrastructure layer of AI, backing companies like Scale AI, Bria, TrueFoundry, and Twelve Labs.In this episode of Talking AI, Avi sits down with Matt Paige to break down exactly where moats are showing up as frontier models commoditize intelligence. He walks through five specific layers of defensibility for application companies (unique data, workflow and system of action, product reimagination, integration, and trust and compliance) and explains why the infrastructure between the model and the application is where most enterprise AI projects actually stall.The conversation covers why building for the gap between what frontier models can and can't do is a losing strategy (because the gap is ever-shrinking), why the chatbot era was brief and agents are now first-class citizens, how Avi uses an agent on Claude Cowork to scan Hacker News and Reddit overnight and enter emerging companies into his CRM by morning, and why he's most excited about world models and the emergent abilities that might come from scaling them.The episode closes with Avi's advice for founders: don't build things that fit the current gap in model capability. Build things that improve as the model improves. And his honest take on being a VC: at best you're a sidekick for founders, at worst you're a detractor.In this episode, you'll hear about:Five layers of defensibility that frontier models can't commoditize. Why unique data, not just more data, is the moat that still matters. The shift from chatbots to deeply embedded agentic workflows in enterprise. How Avi uses Claude Cowork agents to automate deal sourcing and financial analysis. Why specialized foundation models still win in domains like licensed imagery, industrial robotics, and edge inference. The Figma/Claude Design moment and what it means for how VCs underwrite platform risk. Why context engineering is becoming its own discipline and the mistake of treating models like if-else loops. World models, emergent abilities, and what comes after language as an abstraction. How Avi went from Goldman Sachs engineer to IBM data scientist to Intel Capital investor. The coolest and most overrated parts of being a VC.--Key Moments00:01:41 — "It's a mistake to think better models kill moats"00:02:30 — Unique data as the new defensibility: proprietary CRM triggers, healthcare, industrial00:03:25 — Workflow and system of action moats00:04:00 — UX and product reimagination as a moat00:04:30 — Integration moats: 50 to 100 systems upstream and downstream00:05:10 — Trust and compliance as the fifth layer00:05:30 — Infrastructure layer defensibility: evaluation, benchmarking, security, identity00:06:27 — Jack Dorsey's "From Hierarchy to Intelligence" and the YC thesis00:09:55 — From data scientist to frontier model commoditization: what changed00:13:12 — How a VC uses AI: seeing, picking, winning, and supporting00:15:00 — Claude Cowork agent scanning Hacker News, Reddit, and PitchBook overnight00:18:58 — Specialized models vs. the ever-shrinking gap: where do they survive?00:20:30 — Bria's licensed data moat and Field AI's industrial deployment data00:22:45 — "Build things that improve as the model improves"00:24:14 — Why frontier models win bottom-up but can't crack top-down enterprise adoption00:25:43 — The chatbot era was brief: agents are first-class citizens00:27:50 — Memory: session, long-term, and standardized enterprise memory00:31:41 — "Don't use models like a very long if-else statement loop"00:35:08 — World models, emergent abilities, and what comes after language00:38:34 — Robotics: narrow industrial use cases first, Jetsons life in ten years00:41:26 — From Goldman Sachs engineer to IBM data scientist to Intel Capital VC00:43:10 — The coolest and most overrated things about being a VC--Key LinksIntel CapitalConnect with Avi on LinkedInMentioned in this episode:Free report from HatchWorks AI — State of AI 2026What’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 FinderFeeling 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/
Transcribe →99% Correct Is Still Failure: The Last Mile for Mission-Critical AI
Jun 900:42:27Tap to summarizeAI 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 Moments02:47 — From Tableau fanboy to the trust gap in AI03:52 — Why Ryan left Salesforce/Tableau for CodeMetal05:55 — "Is it safe for the things I depend on every day?"06:45 — 99% correct is still failure for mission-critical systems08:20 — The sycophantic nature of AI: "Heck yeah, I can do that"09:22 — It's not a coding problem, it's a behavioral problem at scale11:22 — Human in the loop isn't enough: hardware in the loop14:30 — What is fuzzing? Formal methods explained in plain English16:02 — How a sub-100-person company leverages AI across every function18:19 — The Shopify mandate: using AI reflexively21:33 — Rewiring the city without the power going out: the million-line translation24:38 — Defense use cases: drones, autonomous vehicles, and simulation26:28 — "Prove is even a stronger word than guarantee"28:32 — Accountability and the coming wave of AI insurance32:54 — Token usage, the Uber CTO's blown budget, and outcomes-based pricing36:26 — SaaS isn't dead, it's evolving: Ryan's Salesforce/Tableau perspective40:08 — The biggest risk is doing nothing42:07 — Where to find CodeMetal (and they're hiring)Key LinksCodeMetalConnect with Ryan on LinkedInMentioned in this episode:AI Opportunity FinderFeeling 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 2026What’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/
Transcribe →Stop Building Apps. Start Building Agents.
May 2700:44:24Tap to summarizeTiago 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 before06:20 — "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 could09:10 — "Everything that is not agentic is literally legacy work"10:15 — Use AI to figure out where AI fits: the meta approach to use cases11:30 — Deal Mate: the team of agents that prepares sales reps for meetings15:07 — "We were adding more fields to Salesforce when we should've been building agents"16:25 — Mark Zuckerberg building an agent to do his job17:23 — OutSystems' 20-year journey from visual development to agentic systems engineering19:58 — The deterministic magic behind OutSystems Mentor22:04 — One platform: infrastructure, integrations, UIs, agent skills, and deployment30:19 — 40% customer service automation with AI (vs. under 10% before)33:48 — How AI is augmenting, not replacing, engineering and product roles39:41 — "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 part46:17 — Tiago's advice: identify the biggest problem, feed it to an LLM, build the solution48:42 — "Think big, start small, scale fast"Key Links:OutSystemsConnect with Tiago on LinkedInMentioned in this episode:AI Opportunity FinderFeeling 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 2026What’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/
Transcribe →Talking AI Live at Google I/O
May 2000:25:02Tap to summarizeHost 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 Strategy02:09 Search Gets Agentic03:47 Generative UIs for All06:48 YouTube as Action Engine08:21 Gemini Spark Agents10:10 Adoption and Standards13:55 Omni World Model17:13 Pix Editing Workflow19:06 Omni Platform Take19:53 Fire Round Highlights22:05 Glasses Demo Reactions24:02 Wrap Up and Where to Follow--Key Links:DigitalSamaritanConnect with Kushank on LinkedInRoboNuggetsConnect with Jay on LinkedInAI BuildersConnect with Marcin on LinkedIn
Transcribe →Building in Public as a Solo Founder in the Age of AI
May 1200:42:30Tap to summarizeMatt 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 Switch05:00 Why Build Resonant08:25 Local LLM Reality Check11:23 Standing Out in AI14:52 Designing Resonant Brand19:16 Building Taste Systems22:25 Learning to Build Apps23:43 Early Computer Curiosity25:41 Entrepreneur First and AI Shift27:49 Teaching Yourself with Agents29:05 Tooling and Tech Stack31:05 Resonant Product Vision35:14 Proactive Voice Workflows36:42 Beta Launch and Monetization41:15 Where to Try Resonant--Key Links:ResonantConnect with Thomas on LinkedInMentioned in this episode:Free report from HatchWorks AI — State of AI 2026What’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 FinderFeeling 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/
Transcribe →The Messy Middle of AI in Education: Panic, Promise, and What Comes Next
Apr 2800:53:28Tap to summarizeMatt 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 Reality04:21 Kids Adopt First05:27 Internet Era Lessons12:00 myON Unlimited Reading16:05 Personalization Starts Teachers21:53 Trusted Data And Measurement26:07 Mastery Model With AI Assist27:44 AI Should Challenge Learners28:42 Rethinking Assessment Loops31:21 Data Driven Skill Insights33:50 Connecting Learning to Purpose36:02 Vibe Coding for Teachers37:29 Future School Makerspaces40:33 Balancing Creation and Effort41:39 Renaissance AI Transformation44:40 Agentic Dev and Legacy Systems48:21 Renaissance Intelligence Platform--Key Links:Renaissance LearningConnect with Todd on LinkedInMentioned in this episode:Free report from HatchWorks AI — State of AI 2026What’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/
Transcribe →PwC's Chief AI Officer on the SaaS-pocalypse, Agent Governance, and What's Real
Apr 1400:50:43Tap to summarizeThe 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 Disruption04:28 Security And Boards07:50 Lead Lag Exit Strategy09:30 Reimagining Processes14:45 Two Track Adoption17:46 Tooling Without Lock In22:08 Jobs And Role Blur24:54 Vibe Coding Meets IT25:33 AI Productivity Boom28:50 Humans Stay Accountable30:47 Touchless Forecasting Win32:40 Designing Agent Architectures37:28 Probabilistic Limits and Drift40:05 ERP and SaaS Future43:58 Strategy Avoiding Lock In--Key Links:PwCConnect with Dan on LinkedInMentioned in this episode:Free report from HatchWorks AI — State of AI 2026What’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 FinderFeeling 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/
Transcribe →Warp's CEO on What It Actually Looks Like to Build with Agents in 2026
Mar 3100:49:15Tap to summarizeMatt 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 Workflow06:38 Cloud Agents and Oz09:35 Abstraction and Code Review11:03 Future UX Control Planes14:59 Voice and Mobile Control16:24 Competing in Coding Tools21:16 Todo App Demo in Warp23:53 Replacing SaaS With Agents25:06 Agents Over Apps25:47 Context Can Backfire29:02 Skills On Demand31:13 Oz Skills In Action33:39 Cloud Agents Control Plane37:40 Multi Agent Orchestration42:10 Automate The Repetitive44:41 Advice For Skeptical Devs--Key Links:WarpConnect with Zach on LinkedInMentioned in this episode:Free report from HatchWorks AI — State of AI 2026What’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 FinderFeeling 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/
Transcribe →