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"Even if AI progress totally stalls, it's sufficiently easy to collect data on all these different white collar job tasks that we should expect to see them automated within the next 5 years."

The key bottleneck to automating software engineering right now isn't compute or pre-training data, but rather the absence of rich, realistic training environments.

Just as scaling pretraining unlocked powerful meta-learning in LLMs, scaling the volume and diversity of RL environments may be key to giving AIs the meta-skills to continually learn, adapt to new tools, and operate productively in open-ended settings. x.com/MechanizeWork/…

Most AI labs talk about merely “augmenting” humans at work. They say this because AI currently falls short, not out of some deep conviction. I'm not here to bullshit anyone. Mechanize’s explicit goal is to automate all work as quickly as possible. nytimes.com/2025/06/11/tec…

Most AI companies say they're trying to augment workers, not replacing them. But this week, I wrote about Mechanize, a buzzy new AI start-up whose explicit goal is automating all jobs. I don't love that, but I'm glad they're not hiding the ball. nytimes.com/2025/06/11/tec…

This morning's NYT piece on Mechanize , presents them as being rather un-empathetic to those whose jobs they're trying to take. But I think there's a lot more nuance than the article had space to include. It's not (just) techno libertarians gone wild.

Before GPT-3, achieving good performance required specialized fine-tuning for each task. Today's RL is similar: models need to be carefully trained to handle tasks like deep research, web search, or coding. But we think RL will soon have its GPT-3 moment.

How much do we need to scale RL to enable its GPT-3 moment? We expect we'll soon need roughly 10,000 years of human-equivalent task time, comparable to the cumulative effort behind major projects like GTA V or Windows 2008.

Economists really don't like to entertain the possibility that AI might actually generate new knowledge, and instead insist on dubious claims like it can only "interpolate between known points of knowledge."

You know you've had an impact when leadership decides the only path forward is a clean-room team reboot.

If your interviewing and vetting process for early hires doesn’t catch frauds, then you’re probably doing something wrong. Get references, fly them out, reconstruct their past work on the Wayback Machine to see how productive they are, etc.

“life, liberty & the preservation of happiness” → “life, liberty & the pursuit of happiness” I like this edit because it shifts emphasis from safeguarding ("preservation," cautious, European) to striving ("pursuit," distinctly American, growth-oriented).