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Culture War Roundup for the week of August 10, 2026

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As one of the proponents of the critique that Rationalist AI fans are "writing science fiction", allow me to specify my particular view of it. There is a difference between what Gwern is attempting here and the general Rationalist discourse around things like paper clip maximizers, singularities, basilisks, etc. If I had to put it to words it would be something like:

The scientific and engineering breakthroughs to achieve the technological level where the afore mentioned conceptual ideas becomes embodied and realizable are to word, amorphous, nebulous, underspecified, not defined at any level of scientific or engineering rigor. The end state is the assumed outcome and the pathway to achieve it is handwaved away analogous to a professor putting a hard math problem on the board, stating the solution, and saying the solution is trivial and an exercise to the reader. In this case replace trivial with some dismissive word for "let the shape rotators solve it"

The difference here is that Gwern is taking existing technology and proposing a pathway from it, because of that, the starting breakthroughs needed to achieve it are easier to define because its not an infinite span of starting states that lead to this one eventuality.

He essentially needs to prove these things are possible (at least):

  1. Ultra sample efficient RLHF, RLVR, or a new finetuning algorithm because the level of personal data is going to be orders of magnitude lower than current amounts
  2. Quantitative vs Qualitative gap: essentially its pretty hard to take something people know qualitatively and convert it to a quantifiable number. This is what NNs do now with categorical labels, but instead of doing it at the object level, he'd need to figure out how to do it at the meta-learning level
  3. Training at reduced scale: Goes without saying that LLMs take a lot of compute, in order for everyone to have their own LLM, you'd need to either reduce compute costs, provide a centralized trainer/decentralized inference system (trust problems), or reduce the compute needed.
  4. Related to the QvQ, he needs somewhere to figure out how to create data for training that is highly personalized, whether this is trying symbolic-styled AI with values that scaffold to behavior, learning some value latent space. This one is a bit Sci-Fi because its poorly defined, and the actual methodology to do it would be be new. The realistic answer is probably just take a survey and RLHF 2.0 on the answers or something.

These breakthroughs do break down into further breakthroughs but they are all on the cutting edge of current research not the distant futures research like a lot of Rationalist AI ideas.

It's the only real effort I've seen from anyone to align AIs properly

Alignment is an impossible boogeyman, it's not possible to "align" a sentient being, so if you view eventual AIs as sentient you should just accept they will do what they want. If you view AIs as a tool without sentience, then "alignment" is better specified from control theory, and your actual research would be in quantifying the systemic signal an AI provides and applying classic control algorithms to it. The systemic signal is the hard part bordering on Sci-fi because its underspecified, amorphous, and not rigorously defined.