One of the key insights that led to the recent explosive advancement in AI capability is this: to build better AI, just give it more data and more computational power (i.e., larger neural networks, longer training time, etc.). In other words, simply scaling up produces qualitatively new capabilities: you use the same data, but make the neural network larger, and all of a sudden the AI can do all sorts of things it couldn’t do before; you give the model more data, and all of a sudden it can do advanced mathematics.
In the past, AI scientists thought we needed new scientific insights in order to build smarter machines. But during the “scaling era,” AI scientists realized they just needed to scale up existing ideas.
This is PURE GOLD 10/10 must read, every sentence is accidental poetry.
Link to the full dosage ai100.stanford.edu/sites/…/ai100_report_2026.pdf
This kind of thing just puts hardworking comedy and satire writers out of business, it is cruel and it needs to stop!
A growing body of work demonstrates that improvements in data quality—through deduplication, pruning, and prioritization—can reduce the need for larger datasets while maintaining or improving performance.237 These findings suggest a shift from treating data as a quantity to be maximized toward treating it as a resource to be optimized, with carefully curated datasets often yielding greater value than continued expansion of raw data volume.23