AI companies have high incentive to make score go up. They may employ human to write similar-to-benchmark training data to hack benchmark (while not directly train on test).
Throwing your hard problem at work to LLM is a better metric than benchmarks.
This remains an open problem for LLMs - we don’t have true AGI benchmarks and the LLMs are frequently learning the benchmark problems without actually necessarily getting that much better in real world. Gemini 3 has been hailed precisely because it’s delivered huge gains across the board that aren’t overfitting to benchmarks.
This has been tried multiple times by multiple people and it ends up not doing so great over time in terms of retaining immunity to “cheating”.
Not really. I have a set of disclosures on my blog here: https://simonwillison.net/about/#disclosures
I'm beginning to pick up a few more consulting opportunities based on my writing and my revenue from GitHub sponsors is healthy, but I'm not particularly financially invested in the success of AI as a product category.
The counter-incentive here is that my reputation and credibility is more valuable to me than early access to models.
This very post is an example of me taking a risk of annoying a company that I cover. I'm exposing the existence of the ChatGPT skills mechanism here (which I found out about from a tip on Twitter - it's not something I got given early access to via an NDA).
It's very possible OpenAI didn't want that story out there yet and aren't happy that it's sat at the top of Hacker News right now.