Research · The Scaling Timeline
One picture for the whole build-out: the forces of AI scaling, each on a single comparable log scale, indexed to their 2020 baseline. Three race upward — compute, context, and power. The cost to run a model collapses downward, pulled by efficiencies that compound just as fast. The gap between them is the cycle.
Indexed to each series' baseline (≈2020 = 1×), log scale. The efficiencies overlays the compounding denominators; Absolute · units shows each force in its own real units. Solid = observed; dashed = scenario at the rates set below. Hover any point for the raw figure. Sourced anchors at the foot.
What-if · drag to bend the curves to 2030
What it says
Read the engine first. Frontier training compute has grown about 5× a year since 2020 — doubling roughly every five months, an order of magnitude faster than the two-year cadence that defined classic Moore's Law. Context windows and cluster power ride the same exponential: a model's working memory went from 2,048 tokens to a million in four years, and a frontier training site went from a 100-megawatt "titan" to gigawatt campuses that draw as much power as a city.
Now read the counter-curve. The cost to run a model of fixed capability has fallen about 10× every year — a GPT-3-class model went from $60 to $0.06 per million tokens in three years. That is the scissors: the price of building the frontier explodes while the price of using it collapses. Switch to "The scissors" to watch the two curves cross, "The efficiencies" for the compounding denominators that make it possible, or "Absolute · units" for each force in its own real numbers.
The efficiency frontier
The cost collapse isn't magic — it rests on two compounding efficiencies underneath the price. Algorithmic efficiency: per Epoch AI, the compute needed to reach a fixed capability has halved roughly every eight months — about 3× a year. Hardware efficiency: the leading AI accelerators deliver about 34% more FLOP per watt each year. Stack those beneath the falling token price and you get the engine of "LLMflation" — the same intelligence, cheaper, every year. The "The efficiencies" view overlays all three rising denominators: smarter algorithms, more efficient silicon, and more tokens per dollar.
The fourth vector
The physical vectors are measurable. The fourth — adoption and labour — is where the scaling curves land in the real economy, and it resists a clean log line. So we mark it as the event it is: the capex pivot.
Method & sources
Each series is indexed to its ~2020 baseline and drawn on a log scale so growth rates are directly comparable; the "Absolute · units" view shows the same data in native units on per-panel log axes. Hover a point for the raw figure. Anchors are rounded to convey orders of magnitude, not false precision; the dashed segments past 2026 are scenario extrapolations at the rates you set in the what-if panel, not forecasts.
Compute & efficiency trends (AlexNet→GPT-3→PaLM→GPT-4→Llama 3.1→frontier): Epoch AI. Context windows: model documentation (OpenAI, Google). Cluster power (Colossus 150MW→1.2GW→~2GW): SemiAnalysis / datacenters.com. Inference-cost decline ("LLMflation"): a16z. Algorithmic efficiency (~3×/yr, halving every ~8 months): Epoch AI. Hardware energy efficiency (~+34%/yr FLOP/watt): Epoch AI. Capex & layoffs: our own Capex Watch and Layoffs.