This is a *way* bigger deal than it seems... Frontier AI companies will *never* own the frontier again I kid you not... I've been waiting for someone to show this result for like 4 years... this is a huge deal. The short reason: combinations of models will *always* outperform individual models The long reason: this is the gateway to a million times more data... and huge leaps in compute efficiency. The AI scaling laws always win. More in article below 👇

BREAKING: American in-fighting vs Chinese focus

FYI: all closed-source AI products degrade performance based on the prompt w/o telling the user. There are layers of "filters" on the output. Most obvious is a "recitation filter"... checks every LLM output to prevent an AI product from accidentally outputting exact copies of information from its training data. The problem: exact-quoting high quality sources is often the most accurate response to give... but it can be a liability for closed-source products. So the filter catches it... and a less-exact output is produced instead. Open source models don't do this. This is also why attribution-based control is such a crucial feature to long-term trustworthy AI. Attribution (and corresponding credit/payment layers) would allow frontier AI systems to more reliably exact-quote sources. Important research area.

- Anthropic on Filters: support.claude.com/en/articl… - Microsoft on Filters: learn.microsoft.com/en-us/az…

Fwiw - I understand that this is the concensus view, but I think history will look back with surprise that it didn't bear out in the end. In the 1960s, an employee at IBM or Bell Labs would have said the same thing about the Mainframe computer... and they were incredible (and many are still in use today). But it wasn't just "bigger mainframes forever" anymore than the library was just "bigger library of alexandria forever". I say that as someone who joined DeepMind nearly 10 years ago doing language modeling research. I have had access to large scale and small scale compute during that time. I personally think there's an enormous amount of low-hanging fruit which doesn't require. The future is networks of neural networks: - better routers - better benchmarks - better access to non-public / niche information - better pricing mechanisms - better source attribution - better unlearning - ... There's so much great research to be done. And much of it remains low-hanging because there are some subtle reasons why highly resourced orgs don't tend to pursue them.

One final thing - I think the biggest barrier to breakthrough research is allowing yourself to subscribe to industry groupthink (or to the polar opposite of that groupthink). Go in a 3rd direction. Follow the scaling laws. Look for bridges across fields (especially deep learning, cryptography, and distributed systems). It's never been a better time to do research.