I texted Don about a side gig: €5,000 for every 1% you shave off a zip file.

It's a real contest. For 20 years, Marcus Hutter has offered €500,000 to anyone who can losslessly compress a gigabyte of Wikipedia below 110 megabytes. Gzip gets you to 322. After that you're on your own.

So we tried. Run-length encoding. Pointing back instead of repeating yourself. Huffman coding worked out by hand. Arithmetic coding, which spends less than one bit per character. Then Claude Shannon's 1951 guessing game, and a paper where an LLM beats every record and still wins nothing, because the model counts against you.

Don wanted the euros. I had other reasons for bringing him over.

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I've been trying to understand how machine learning actually works. Not use it, understand it, down to the ifs and loops. How does a program built out of plain conditionals get better on its own?

So late one night I sent Don a paper. Three words in the title: reward is enough. The claim is that all of intelligence, the whole thing, comes down to a system maximizing a reward. Don thought that was far too reductive. I wanted to pull it apart and see if it held up.

We backed up through the history to find out how far "reward is enough" really goes: B.F. Skinner training pigeons, a backgammon program that taught itself, the Go move no human would have played.

It's a story about machine learning, and what that leaves for the rest of us who still do it by hand.

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I've been confusing Don with frontier-lab links late at night for a bit.

Ilya Sutskever told a NeurIPS audience that pre-training as we know it would unquestionably end. There's only one internet, and the data isn't growing. The frontier labs call this the pre-training wall.

A leaked Google memo from 2023 argued they had no moat. R1 is on GitHub. Llama is on Hugging Face. OpenAI's secondary-market valuation has climbed past $850 billion.

Don was confused. So he came over and we made an episode about it.

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