FrontierAugust 26, 2026via MIT Technology Review
AI models flub these intelligence tests. Can you fare any better?
Why it matters
AI capability benchmarking through puzzle and game performance reveals gaps in model reasoning and generalization — a key metric for practitioners evaluating which models to adopt and for enthusiasts tracking the frontier labs' progress toward AGI-adjacent capabilities.
Key signals
- Article frames puzzles/games as standard capability tests for AI models
- References historical context: 'machine learning' term popularized in 1959 by Arthur Samuel at IBM
- Suggests current frontier models have measurable failures on specific benchmark categories
- MIT Technology Review publication — credible source on AI benchmarking
- AI models underperform on intelligence test puzzles
- Puzzles used as capability benchmarking mechanism since early ML era
- Story pegs to historical precedent (1959 Arthur Samuel, machine learning term)
- Interactive/comparative structure (human vs. model performance)
- Published by MIT Technology Review (credible source on frontier capability)
The hook
Current frontier models still fail these benchmarks. Here's why it matters for capability eval.
Puzzles and games have been central to AI development since the very beginning. Just as we humans like to test our smarts with crosswords or logic puzzles, developers can test how far models have advanced with a gaming gauntlet. The term “machine learning” was popularized in a 1959 article by the IB…