@maxrumpf: Humans Are a Low Ceiling Can Magnus Carlson give feedback to AlphaZero? Obviously not. Even the very best chess players…
Summary
Max Rumpf argues that human feedback is becoming obsolete for training advanced AI models, citing examples like chess, math, and search. He advocates for human-free methods like self-play and synthetic data, while a quoted tweet from Will Depue calls for a large-scale data infrastructure parallel to compute scaling.
View Cached Full Text
Cached at: 07/07/26, 09:27 AM
Humans Are a Low Ceiling
Can Magnus Carlson give feedback to AlphaZero? Obviously not. Even the very best chess players are so far outclassed that their input is of no value in training future AIs.
While not universal, we’re already there in other domains too. It’s why the modal labeler has become educated and experienced. How many humans can still give good feedback on time-bounded math problems like IMO? 1,000 globally? In 2022, >1B people could have helped the models get better at math.
It is most likely easier to make self-play/synthetic data/online learning work than it is to turn the planet into a click-farm. We have seen it in games (chess), math (lean), and search (our work).
Human-free methods are most scalable. Most bitter lesson.
will depue (@willdepue): A Stargate for Data
Labs are on a trajectory towards >$100B/year of data spend by 2030. As we begin the trillion-dollar compute project, we need to think about the equivalent civilizational-scale effort for the other core ingredient: data.
At the foundation of the scaling
Similar Articles
@HowToAI_: Yann Lecun published the most heretical AI paper of the year. He opens by arguing Magnus Carlsen isn't good at chess an…
Yann LeCun and co-authors published a paper arguing that the AI industry should abandon the goal of AGI, proposing instead Superhuman Adaptable Intelligence (SAI) focused on specialized adaptation beyond human capabilities.
@jeremyphoward: I feel that the trend towards training models to autonomously go off and try to do everything themselves is anti-human.…
Jeremy Howard argues against training AI models to autonomously do everything, advocating instead for LLMs that support human learning, creativity, and iterative experimentation.
@AlexGDimakis: I am very excited about this research: We show 2 things: 1. If you just do random sampling (i.e. you try to solve a pro…
This research compares AI coding agents (like Claude-Code and Codex) with human expert coders on long-horizon tasks, showing that humans scale super-linearly due to continual learning while agents plateau, highlighting a key limitation of current AI in extended problem-solving.
What to expect from AlphaZero's value predictions [D]
The article analyzes how AlphaZero's value predictions are shaped by self-play training data and noise, questioning whether they reliably estimate win chances against opponents with different play styles despite AlphaZero's strong empirical performance.
Model collapse + skill atrophy + competitive pressure = one big feedback loop. Thoughts?
The article discusses a consulting firm's argument that model collapse, human cognitive debt (skill atrophy), and competitive pressure form a self-reinforcing feedback loop in AI, and questions whether organizations can resist the race to automate.