Breaking into GTM Eng (applied AI)

Hey there,

Two new posts this week plus a bunch of stuff I've been thinking about.

​Two Waves of AI — Most people treat AI as one wave. It's actually two. Wave 1 is LLMs as cheap, scalable intelligence (AI added to products). Wave 2 is 10x productivity in verifiable domains like coding (AI added to the creation process).

​How to Break Into GTM Engineering — We're hiring at Vercel and I wrote up the trends I’m seeing. Four paths in: solutions engineers, former founders, product-obsessed engineers, and RevOps folks who want to level up. Come ship with us.

A few other things worth your time this week:

​Turbopuffer case study — Our knowledge base work got featured. Building a KB that powers all our agents (Gong, Slack, Salesforce indexed on turbopuffer) has made them considerably smarter. Getting the right tokens in your context window pays dividends. We’ll be talking a lot more about how we use this soon.

​Notion custom agents — The new Notion agents beta is impressive. I rebuilt our Eng intake process in a few hours using Claude Cowork + Notion MCP. This will be how a lot of the world builds their first agent.

Podcast I keep thinking about: Tobi Lütke on Shopify — "Companies are a social technology that let people commit intensely to a counterfactual world."

Also: Benedict Evans on AI and software had a wild thesis that AI might make central planning actually work. And Martin Casado on why this isn't the dot-com bubble — infrastructure dominates AI spend right now, but that's not a bad thing. The new HBR study shows workers are expanding the tasks they’re doing with more multitasking. That feels like we’re on the path of continued returns.

One tweet that resonated: Alex Lieberman nailed it — "Models will improve. AI products will get better. But we'll see a lag in noticeable impact." The tech is no longer the constraint. Integration is. This is basically the AGI: Some Assembly Required thesis.

That's all for this week. Keep shipping.

— Drew