Speakers

Today that's a small part of what I do: AI can take care of the implementation. My job now is to formulate the problem for the AI to solve - choose the metrics to optimize and the constraints to respect - then make it fast and easy for the AI to iterate. This sometimes feels like herding the model: cutting off all the bad routes, so its randomness can only push in the direction of the improvement I want. In this talk, I'll review three real-world projects through this lens, and propose practical ways to set up feedback for the model. This is not a software-factory pitch.

I still write code, and my review is often the most important constraint, but I don't think it makes sense to pretend that me writing and reading all the code is the best way to build software anymore. Luckily, in a world where AIs write the code, the human who formulates the problem is more valuable than the human who wrote the code was a year ago.

Can agents just do all the planning, coding, reviewing, and testing for us? Do humans have something that LLMs don't?

I think there are some hard limits on what can be delegated, and how. While machines can do a lot of very intelligent work, there are things they won't be doing unless Skynet takes over the world.