I wasn’t able to put up a post last Wednesday because I was at the Engineering Leadership Conference here in San Francisco. The big theme was, of course, AI. Easily 90% of the presentations and 100% of the conversations touched on AI in some way. Here are some impressions I came away with:
Everyone is scrambling. No one is confident. The people who act confident are bullshitting.
10-20% of teams are heavily using AI and living in the present. 80-90% are falling behind.
The big struggle is to even start using AI coding assistant tools. Lots of teams just don’t use them at all, or use them in very limited ways. People leading these teams know they are going to lose if they don’t change but are struggling to get their orgs to let them.
Even fewer teams have figured out how to use LLMs to build compelling product. Everyone is starting to figure out that LLMs are not a magic bullet, and real product and engineering work is needed to create something that customers will love.
The teams that seem furthest ahead in terms of tooling are using Claude Code, Cursor, and building internal chatbots for surfacing hidden team knowledge.
The teams that seem furthest ahead in terms of product are building targeted LLM features that automate labor-intensive services work. Think features like automating research, data entry, data normalization, report generation, and other work that is hard to deterministically automate but requires limited human judgement to get 80-90% of the way there.
The best teams also keep humans in the loop because LLMs aren’t deterministic and aren’t perfect. They know there’s value in that last 10-20% from a human, and they find ways to leverage AI and people together to make services cheap, fast, and good.
No one is sure what the future holds. Almost everyone I talked to acts vaguely like we’re either near the top of an S-curve or are going to see linear improvements in AI capabilities, even if they say they expect exponential growth.
Final assessment: engineering leadership across the industry is struggling to respond to AI quickly. If you use AI tools at all you’re ahead of the game. If you’re building usable AI features you’re further ahead. And if we see continued exponential growth, it’s going to hit teams like a ton of bricks.