Talking AI

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/

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/

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.
--
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/

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/