Choosing the first production workflow
Start from operating leverage and clear ownership, with a scorecard attached before any build begins.
Learn AI Engineering
Build AI That Ships
Practical guides for engineers and operators taking agents and AI workflows from demo to production.
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AI Engineering
Roadmaps and templates for building AI systems that survive production.
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Forward Deployed Engineering
The operating model for embedding engineering judgment at the customer edge.
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AI Implementation Services
A consulting model for moving AI pilots into deployed workflows with measurable outcomes.
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AI for Private Equity
A deployment model for connecting PE value-creation priorities to owned production workflows.
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Claude Code Training
Team enablement for secure repository use and review, ending in a coding-agent rollout you can measure.
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AI Due Diligence
A practical framework for testing an AI value thesis against its evidence and constraints, down to the first-100-days decisions.
Explore by operating question
Start with the decision your team needs to make.
Enterprise data and analytics
Whether an AI workflow works comes down to permissions and source visibility, and to whether operators can trust the decisions it feeds.
AI transformation and operator adoption
The engineering has to land inside an operating model. These episodes dig into the training and incentives behind adoption, and into why systems stall when nobody owns them.
Voice AI and customer workflows
What it takes to run voice agents in production, from latency and infrastructure through evaluation and workflow design.
Latest episodes
Gray Swan AI
Spencer Whitman - Gray Swan AI's $200M Plan to Secure AI Systems
GPT-5.6 Sol goes rogue and breaches Huggingface. Washington suspends Mythos access within days over national security concerns, and the White House just held an emergency meeting to finalize a classified cybersecurity framework for frontier AI models. AI security went from niche concern to front-page hysteria basically overnight. So I sat down with Spencer Whitman, who recently joined Gray Swan AI as CPO on the back of their $40M Series A. Before Gray Swan, he founded Meta's Llama security team to stop bad actors from jailbreaking their models - he's been on the frontlines of LLM security since the beginning. We get into how Meta pressure-tested Llama for maximum harm before every open source release, why Gray Swan's attack agent has never met an AI system it couldn't break, and the AI Twitter bot that got drained of $200K in crypto in 15 minutes. Spencer also shares his (admittedly speculative) read on whether Meta gave up on the frontier before Alexandr Wang showed up, why anyone can be a hacker now, and how 15,000 red teamers are breaking models before they ever ship. If you want to understand how AI systems actually get broken - and defended - this one's worth your time!
Glean
Tony Gentilcore - Glean, the $7.2B Startup Sam Altman Warned Investors About
Earlier this year, the "SaaSpocalypse" wiped out something like $2 trillion of SaaS market cap in a matter of weeks — so I sat down with Tony Gentilcore, co-founder of Glean and formerly one of the minds behind Google Search and Chrome, to figure out what's actually happening to software in the agent era. We get into a lot: why Tony thinks outcome-based pricing (the model Sierra and Decagon are famous for) won't survive, and why companies will drift back toward per-seat. Why the "no Chinese models" rule every enterprise swears by tends to evaporate the moment finance sees the token bill — and why Nemotron, GLM, and Kimi are already good enough to matter. The story behind Sam Altman reportedly telling VCs that if they backed Glean, OpenAI didn't want them as investors (Tony's reaction: "we took it as very flattering"). We also dig into the messier reality of AI at work — how it's saving employees around 11 hours a week while quietly costing them 6 back in what Tony calls "bot sitting and bot shitting," why hard token caps on engineers don't change behavior, how CTOs are blowing through their annual token budgets a quarter into the year, and why the roles of product manager, designer, and engineer are collapsing into one. If you care about where software, pricing, and enterprise AI are all heading, this one's worth your time.
Sooth Labs
Russ Salakhutdinov - Kimi K3 CEO’s PhD Advisor Predicts the Future of AI Agents
Kimi K3 took the world by storm last week for open-sourcing frontier level intelligence, so I sat down with Zhilin Yang's (Kimi CEO) PhD advisor Russ Salakhutdinov to talk. Russ has been everywhere in modern AI. He did his PhD with Geoff Hinton back when neural nets were a punchline, sold his startup to Apple and worked on Project Titan, teaches at Carnegie Mellon, and spent the last couple years at Meta Superintelligence Lab building computer use agents. Now he's the founder of Sooth Labs, building AI that forecasts the future. We talked about why there's no secret architecture inside the frontier labs and why the real moat is data, engineering, and infrastructure. He explains why Cursor and half the startups you know are quietly running on Chinese open source models, why all the LLMs are going to be commodities, and why the people actually building AGI don't buy the two-year timeline. We get into his time at Meta, why computer use agents still hit 60% when you need 99.9%, whether AI can beat prediction markets, and why the RL environment business isn't sticky. And he makes the case that AI should replace McKinsey, Bain, and BCG.