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The exchange is a case study with transferable rules. A conclusion resting on failures needs a failure taxonomy. Every instance must be verified solvable before anyone is scored against it. Output format is a confound whenever answers get long. And when two explanations fit the same data, the productive move is to find the prediction on which they differ, then test it.

The response disputed none of the data and argued the experiment measured something other than reasoning. Models had to print move lists exceeding their output limits, and said so in the transcripts. Some instances had no solution and were scored as failures anyway. And asking for a program instead of a move list produced high accuracy on instances reported as total collapse.

Apple researchers built an evaluation designed to fix a real problem with benchmarks: puzzles where difficulty turns up smoothly while the logic stays identical, and every step can be checked. They found accuracy collapsing to zero past a threshold, and not improving when the solution algorithm was handed to the model. This lesson covers the design and why it was a good one.

A model went from 27 percent to around 57 percent, so it is more than halfway to AGI and the rest arrives shortly. That inference is wrong in at least four ways, and working through why is more useful than the score itself. This lesson covers the linearity assumption, construct validity, contamination, and what the framework is good for once you stop reading it as a progress bar.

The framework scores ten cognitive domains at ten percent each. Running it produces something more useful than the headline totals of 27 percent for GPT-4 and around 57 for GPT-5: a jagged profile, where a model is at or near full marks on some domains and at zero on others. This lesson walks the domains, reads both profiles column by column, and shows what the jaggedness explains.

For a term that anchors company charters, safety policy and hundreds of billions in investment, AGI has been remarkably undefined. A 2025 paper with over thirty authors proposes fixing that by borrowing psychology's most validated framework for human intelligence. This lesson covers why the definition was missing, what the paper anchors to, and what the borrowing assumes.

The paper's answer to safety is that reward signals adapt to human feedback: a network inside the reward function learns which grounded signals to weight from how people respond. This lesson works through that proposal and the published critique arguing it fails twice over, on specification gaming and on goal misgeneralization, and what an honest reading of the disagreement leaves you with.

The paper is concrete about what an experiential agent would differ on, and names four: it lives in a continuous stream rather than episodes, acts in the world rather than emitting text, takes rewards from grounded signals rather than human judgement, and plans in terms it worked out rather than imitating human chain of thought. This lesson works through each.

David Silver and Richard Sutton argue that the current approach has a ceiling built into it, because a system trained to predict what humans wrote is aiming at human performance by construction. This lesson works through their three eras, the claim about data exhaustion, why they think superhuman performance needs a different learning signal, and the honest counter-arguments.