Ep #34 When Your Vibe Coder Doesn't Know What They Don't Know

Anthropic and OpenAI didn’t deploy billions into embedded engineering organizations because they suddenly got interested in consulting. They did it because a trillion dollars a year in AI infrastructure only gets paid for one way: token consumption at scale. And that only happens when organizations actually know how to use the tools. The McKinseys of the world are handling the Fortune 500 side of that equation. Which means mid-market and SMB companies are being left entirely outside the conversation designed to solve the problem they’re all struggling with.

In this episode, Kevin Williams and Matt Grahams break down the forward deployed engineer land grab what it actually is, why it’s happening, and what it means for the organizations that McKinsey was never going to call anyway. They also get into what’s happening inside mid-market companies right now: smart, well-meaning builders moving fast without governance, technical debt that compounds quietly, and the real cost of cleaning it up after the fact.

The conversation also covers the nuts and bolts of building agentic systems with precision how to make probabilistic AI outputs more deterministic when the stakes are high, why model quality can shift on a random Wednesday and what to do about it, and what the actual switching costs look like when your organization commits to a platform.

Key topics covered

Why OpenAI and Anthropic’s forward deployed engineer strategy is a token economics play, not a consulting play
The structural gap leaving mid-market companies without access to embedded AI expertise
What happens when vibe coders build fast without governance and who gets called to clean it up
How to make AI outputs more deterministic for sensitive financial and operational workflows
The real switching costs of committing to a team-level AI platform (GPT Teams vs. Anthropic Teams)
Why 2026 is the year organizations stop piloting and start activating