Engineering Agency out of the Happy Path

Official Schedule Context

I spent ‘24 and ‘25 structuring the entire written history of biopharma - through drugs, trials,

deals, etc. This was a ~500B token effort that translated into a production system now used by 19 of

the 20 largest pharmas. We achieved PhD-level performance at scale with 99.95% accuracy over

critical concepts. The hard parts were solving questions of domain and organizational “shape”. This

involved identifying which critical concepts and which bundle of tasks were worth the organizational

investment to automate. And the biggest spillover win wasn't actually about time savings, it was

about refocusing scarce expert judgment on error exhaust - out of which falls potential high value

roadmap. I'll walk through real examples and non-obvious, transferable wins. While the case example

is in biopharma, the pattern applies to any business that relies on expert domain judgement to

deliver differentiated value.

No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

Pending transcript synthesis when an official recording or confirmed matching video is available.