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• Classical AI scaling laws describe how training loss decreases with compute in a power-law form, making progress predictable at a declining rate.technical 95%
• Scaling laws are largely empirical and observational but appear repeatedly across model families and training-adjacent regimes.result 90%
• Progress in practice has continued through rapidly improving efficiency despite diminishing returns predicted by scaling laws.market 85%
• The compute variable in scaling laws is best understood as logical compute, an implementation-agnostic notion of model-side work.technical 88%
• The practical burden of scaling depends on how efficiently real resources are converted into logical compute.technical 87%
• AI scaling depends not only on larger raw budgets but on sufficiently rapid gains in efficiency to keep the law economically productive.market 90%
• Diminishing returns create rising pressure for cost reduction, system-level innovation, and breakthroughs needed to sustain Moore-like efficiency doublings.limitation 82%
• The industry has been using something close to logical compute efficiency in disguised and peak-normalized form through metrics like MFU.technical 80%

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