Laurie Voss co-founded npm and watched it grow exponentially for ten years. Now head of developer relations at Arize, he says the typing-the-code part of engineering is going to someone else, and the part that matters is deciding what to build.

In this conversation he lays out where agents still fall short (code review at an 80% hit rate, operations, knowledge nobody wrote down), why he would no longer tell someone to spend $15,000 on a bootcamp, and why AI right now looks like the web in 1997: real change underway, valuations ahead of reality, and the big winners not yet decided.

He also predicts how the bubble pops, and what comes back in its place: the systems analyst, renamed the product engineer.

We cover:

Why the "aha moment" of solving a problem survives even when agents type the code
Where agents still fail: 80% code-review hit rates, operations, and tacit knowledge
Whether a $15,000 coding bootcamp or a theory-heavy CS degree is still worth it
Why AI in 2026 looks like the web in 1997, and what that says about the bubble
How open source felt AI pull-request overload six months before everyone else, and what Cloudflare's WordPress clone means for closed-source moats
Why the systems analyst returns as the product engineer, and why that means niche software for bakeries and auto parts
How 80% API margins, open models, and scaling laws decide when frontier-lab valuations collapse
Who trains junior engineers now that the entry-level ladder is gone

Chapters:

(0:00) Cold open
(0:44) Why Laurie is optimistic about the code generation explosion
(3:06) The aha moment moves from typing code to thinking
(5:49) What agents still can't do: 80% code review, operations, tacit knowledge
(8:51) Is college still worth it?
(9:54) Bootcamps versus theory-heavy CS courses
(13:04) We are all product engineers now
(15:55) AI is the web in 1997
(18:19) Exponential growth, the labs' pause, and npm's ten-year curve
(20:07) Barring AGI, AI is a normal technology
(21:48) Block's layoffs and companies staying smaller
(23:14) What the labor data shows: fewer people, more capital
(26:29) Open source as the canary: drowning in AI pull requests
(28:11) Cloudflare cloned WordPress in a week
(30:26) Personal software and the kill-my-SaaS hackathon
(32:32) The bakery and the return of the systems analyst
(34:09) Niche software for specific industries
(35:36) Bootstrapping and the DevTools opportunity
(37:13) What this means for the model companies
(38:20) 80% margins and the open models right behind
(39:53) How the bubble pops: scaling laws and diminishing returns
(43:24) Staying private and the trough of disappointment
(45:51) Get good at a domain, not the technology
(50:44) The missing junior ladder is the question of our time
(53:32) Closing thoughts: it's 1997, you can retrain

Connect with Laurie Voss:

Blog: https://seldo.com/
LinkedIn: https://www.linkedin.com/in/seldo/
Twitter/X: https://x.com/seldo
Bluesky: https://bsky.app/profile/seldo.com
Arize: https://arize.com/

Connect with Chain of Thought host Conor Bronsdon:

Newsletter: https://newsletter.chainofthought.show/
Twitter/X: https://x.com/ConorBronsdon
LinkedIn: https://www.linkedin.com/in/conorbronsdon/
YouTube: https://www.youtube.com/@ConorBronsdon

More episodes: https://chainofthought.show

Thanks to Svix, presenting sponsor of season four of Chain of Thought. Svix delivers billions of reliable webhooks for startups and the Fortune 500. Get started at https://link.svix.com/cot. Qualified startups get $12,000 in credits, and YC companies get $50,000.

Thanks to Walrus, presenting sponsor of season four of Chain of Thought. Walrus Memory gives AI agents portable, verifiable memory that carries context across apps, sessions, and other agents. Get started at https://walrus.xyz/cot

Thanks to G2i for sponsoring this episode - for over a decade, they vetted and placed engineers at other companies, from startups to FAANG. Two years ago, they turned that same judgment inward, building their own bench to review RL environments, evals, and training data that models are trained on. Get access: https://fandf.co/3SFxVm6

Thanks to Inngest, presenting sponsor of season four of Chain of Thought. Agents in production run long - they call models and wait on APIs and people. But the longer agents run, the more they break. Inngest handles that with durable execution. You build your agent as steps in TypeScript, Python, or Go. When a step fails, Inngest retries it with exponential backoff, and completed steps are saved and skipped. Try it out: https://inngest.link/cot-pod

AI Codes: Product Engineers Decide What to Build

Chain of Thought | AI Agents, Infrastructure & Engineering

AI Codes: Product Engineers Decide What to Build