I think by the end of the year everyone in AI will be RSI-pilled. Personally, I have never believed more in RSI and ASI being live before 2029.
I believe in an ermergent composability that naturally falls out of scaling.
And the following example feels very much like an argument you would have made before Mythos' cybersecurity and GPT-5.5's math breakthroughs:
"Agents will do well at optimizing single metrics, but the leap required to navigate many metrics at once is a very different skill set."
The leap from optimizing a single metric to multiple metrics is very natural for language models.
The same way they figured out how to connect tokens to build words, connect words to build sentences, and the same way Mythos figured out how to build more complex exploits out of multiple small vulnerabilities.
Where else would improvements in loss come from when models already know how to tackle individual problems?
The answer is by going up 1 abstraction layer and seeing how multiple problems fit together.
Models are smoothly transitioning from a local view of the world to a more global/holistic view.
All you need to do is scale the amount of compute you put in. And compute is being scaled like crazy.
That said, I still believe a true AGI-like system needs continual learning and that these coming automated researchers will be narrowly focused on coding and math. But more domains will follow as labs gather more data, because if you don't have continual learning you need the model to be trained on everything.
Enjoy the weekend.
I still stand by this despite the recent Anthropic post. There are still serious bottlenecks in building the model that the agents don’t address (organizational, compute, data access, etc).
It’ll take time to push through them and we will see "linear" gains for years to come.
