Mechanize: How to Fully Automate Software Engineering

Current AI presents the Moravec's paradox, it finds easy tasks that are really hard for humans (math, coding) and very hard tasks that are easy for humans (a baby chosing the right shape), some people think we can't get over this paradigm.

The bitter lesson (Richard Sutton) tells us that scale (brute force) can solve this problem. RL is also a solution to this problem but stuck on narrow problems.

This blog argues that full automation needs more than just bigger models, it's about training on tons of real human coding data and then letting AI learn in rich, realistic environments using RL. Right now, those RL environments are way too basic, and grading open-ended engineering work is tough.

So, AI shifts engineers to higher-level planning, testing, and coordination. And here's the twist: software engineering might be both the first and last white-collar job to be totally automated, since building a "drop-in remote worker" AI is a much bigger challenge than just writing code.

https://x.com/MechanizeWork/status/1945528661131849790