What happens when the technology adoption curve compresses from decades into a couple of years?
In this episode of CTO Confidential, we sit down with Philip Kiely, software developer at Baseten and author of Inference Engineering, to unpack the economics, careers, and engineering reality behind running AI in production.
We get into why inference is not the money losing operation everyone assumes it is, why most applications do not need a frontier model at all, and how owned intelligence has gone from a multi year project to something a team can build in days or weeks. Philip also makes the case that inference engineering is the next great career in software, and that the field is young enough for newcomers to become experts fast.
We close on something that applies to every builder and founder. The best career advice is not about networking. It is about becoming someone people want to connect with.
What we cover:
- Why the AI adoption curve moved from decades to years
- Debunking the myth that inference runs on negative margins
- Task specific models and the future of specialized intelligence
- Why you almost never need the whole model
- How owned intelligence became realistic for normal teams
- Inference engineering as the next career frontier in software
- Why a background outside of computing can be an advantage
- Career advice, book recommendations, and a few thoughts on House of Leaves
Connect with Philip Kiely:
Website: philipkiely.com
Inference Engineering book: https://www.baseten.co/inference-engineering
Find Philip on LinkedIn and X as @philipkiely
If you build software, lead a team, or run a company, this conversation is worth your time.
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