With the festive break almost here, some of you might be thinking about making a change in the new year! I’ve had a bunch of email threads asking for my take on pivoting a career into AI, so here’s a public version:

I used to give people longer-formed advice about how to get into machine learning—there was so much to learn before you could “do” anything. In the last 2 years I no longer say that: you can now work “in” AI as much as you can work “on” AI.

While working “on” AI usually takes the spotlight (e.g. designing & evaluating prompts in agentic workflows), the opportunity to work “in” AI has never been greater & is full of fascinating, mind bending challenges:

💡 Making the execution engines that underpin AI agents safe, scalable, observable, and configurable—a “200 OK” no longer means everything went right.

🔎 Enabling an engineering process that now, more than ever, relies on evaluation: “it works in staging” is almost meaningless.

✨ Designing & building products that exist to empower people to understand, test, and control AI, not to sprinkle a bit of AI on the side.

And there’s so much more! AI is the future, but companies are still groups of people who build & learn together. So if you’re thinking of making a change, know that what you already do as an Engineer, Data Scientist, PM, or designer is almost certainly in high demand within the AI world.