Talking AI

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.

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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/

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/