I’m more and more convinced that all of AI engineering is Neijuan (内卷, meaning curl inwards). In China it describes a system that demands ever more effort and competition without improving output.
they started from a place of corporate governance where they were looking out for fraud. And so so risk mitigation rather than shareholder interest. And so when you come at it from that perspective, you're like, there should be rules and people should adhere to the rules and when people get outside of the rules, that's bad.
What probably the single most important proxy metric that you can have it in a company today is token burn. Because what token burn says is your engineers are trying to do stuff or your non-engineers.
And so I would say, yes, we are just giving proxy names to things we don’t understand, but to dismiss that as some kind of, “Oh, they just don’t know…” It is actually quite the opposite.
A more intelligent agent is more capable of finding "holes" in the design of reward function and exploiting the task specification-in other words, achieving higher proxy rewards but lower true rewards.
You might be skeptical of using synthetic data. After all, it’s not real data, so how can it be a good proxy? In my experience, it works surprisingly well. Some of my favorite AI products, like Hex use synthetic data to power their evals
Speed and reliability are often intuited hand-in-hand. Speed can be a good proxy for general engineering quality. If an application slows down on simple tasks, then it can mean the engineers aren’t obsessive detail sticklers.
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