Talking AI

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/

Every company building AI right now is asking the same question: if the models keep getting better and anyone can access them, what actually makes us defensible? Avi Bharadwaj writes the checks that answer that question. As an Investment Director at Intel Capital, he focuses on the software infrastructure layer of AI, backing companies like Scale AI, Bria, TrueFoundry, and Twelve Labs.
In this episode of Talking AI, Avi sits down with Matt Paige to break down exactly where moats are showing up as frontier models commoditize intelligence. He walks through five specific layers of defensibility for application companies (unique data, workflow and system of action, product reimagination, integration, and trust and compliance) and explains why the infrastructure between the model and the application is where most enterprise AI projects actually stall.
The conversation covers why building for the gap between what frontier models can and can't do is a losing strategy (because the gap is ever-shrinking), why the chatbot era was brief and agents are now first-class citizens, how Avi uses an agent on Claude Cowork to scan Hacker News and Reddit overnight and enter emerging companies into his CRM by morning, and why he's most excited about world models and the emergent abilities that might come from scaling them.
The episode closes with Avi's advice for founders: don't build things that fit the current gap in model capability. Build things that improve as the model improves. And his honest take on being a VC: at best you're a sidekick for founders, at worst you're a detractor.
In this episode, you'll hear about:
Five layers of defensibility that frontier models can't commoditize. Why unique data, not just more data, is the moat that still matters. The shift from chatbots to deeply embedded agentic workflows in enterprise. How Avi uses Claude Cowork agents to automate deal sourcing and financial analysis. Why specialized foundation models still win in domains like licensed imagery, industrial robotics, and edge inference. The Figma/Claude Design moment and what it means for how VCs underwrite platform risk. Why context engineering is becoming its own discipline and the mistake of treating models like if-else loops. World models, emergent abilities, and what comes after language as an abstraction. How Avi went from Goldman Sachs engineer to IBM data scientist to Intel Capital investor. The coolest and most overrated parts of being a VC.
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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

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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/

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/

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/

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.
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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

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Key Links:
DigitalSamaritan
Connect with Kushank on LinkedIn
RoboNuggets
Connect with Jay on LinkedIn
AI Builders
Connect with Marcin on LinkedIn