Andrew Siah

Not many people seem to realise that there’s a Trillion dollar opportunity here.
AI-led discovery cannot remain bottlenecked by linear scaling of physical infrastructure.
Modelling physical world dynamics to exponentially speed up the inner loop of discovery is inevitable!

Over the years, I’ve become obsessed with references, so I decided to sit down and write out some of what I’ve figured out. x.com/i/article/2107…

Introducing Griffin, the first model to pass the video Turing test.

48% of people who talked to it live thought it was a real human. Previous systems have had a pass rate <3%. It is #1 on NVIDIA's benchmark for full-duplex AI video.

It’s the first Human Interaction Model (HIM).

After 2+ years in the robotics data space, we are shutting @Eidon_AI down.

The thesis was right. But the business is brutally hard.

We close this chapter by open-sourcing everything we built and sharing lessons for anyone venturing into the space. x.com/i/article/2102…

we keep benchmarking AI.
so I asked AI to benchmark us.

it made a geometry game where an ant’s love life depends on your spatial reasoning. 🐜

find the shortest path and climb the human leaderboard 😈

your turn:
antics-cube-game.vercel.app

Synthetic data derived from production user data of consumer AI tools is used for training. I’ve heard this rumour from both large labs’ employees.

In particular, if you’re doing something “interesting” like working on complex math/business/software/bio problems you’re dramatically more likely to get trained on because they filter/up-weight towards those usecases where the model has the most to learn.

Even in ZDR and “we won’t train on you” regimes, derivative data is usually carved out. The promise is only not to train on exactly the data you put in, rewritten data is fair game.

agreed feels big, i want a new kind of computer

Savvy ice cream shop owner makes 43% of monthly rent betting on weather: 'If it rains, we still win' trib.al/4cfjpVl

"No Rules Rules" is not a book about Netflix or reinvention. To me, the main lesson from Netflix's success is that talent is underrated. Sure, there's a lot of fluff and stuff I missed. But the talent discussion is interesting and has implications for hedge funds. A long 🧵
1/