# Cost & Economics

URL: https://justuju.org/concern/cost/
Cross-cutting concern, on every layer

Energy, chips, training runs and per-token prices: cost shapes decisions at every layer.

## D1 · Story

Making an AI costs a lot of money. Builders pay for special chips, big buildings full of computers, and lots of power.
Then each answer costs a little too. The computers must work every time someone asks a question.
So builders look for ways to do the same job with less. One trick is to save work they did before, so they need not do it twice.
Like: Building a bakery costs a lot once; every loaf after that costs a little.

## D2 · Picture

Costs come in two shapes. Build cost is paid once per model: chips, data, staff and the training run. Run cost is paid on every request: the computing behind each answer, usually priced per million tokens.
Training the most compute-hungry models has grown about 2.4 times more expensive each year since 2016. Chips and staff are the biggest items; energy is only 2 to 6 percent.
Run cost grows with use and with how a question is answered. Anthropic measured its own agents using about 4 times as many tokens as a chat, and its multi-agent research system about 15 times.
Much optimisation work is cost work: batching many requests together, caching repeated text (on one major platform, cached input is billed at a tenth of the normal price or less), and smaller or compressed models.
Like: Building a bakery costs a lot once; every loaf after that costs a little. Where it breaks: A loaf costs about the same every time. An AI answer can cost a hundred times more or less depending on its length, the model, and whether an agent loops many times to produce it.

## D5 · Frontier

Per-token prices make AI look like a metered utility, but the bill depends on how each answer is produced and who pays for the build.
Reading the headline. The capital-spending figure mixes AI with everything else the four companies build, and filings don't split it by layer.
Training costs. If costs keep rising 2.4 times a year, the largest runs would pass a billion dollars by 2027, leaving frontier development to the best-funded organisations (Epoch AI, 2024).
Cost moves to every answer. Recent capability gains come largely from post-training and extra computing at the time of use, which is paid per request (International AI Safety Report, 2026).
Agents multiply tokens. Anthropic measured its own agents using about 4 times the tokens of a chat and its multi-agent research system about 15 times; token use alone explained 80% of performance differences on one browsing test (Anthropic, 2025).
Energy. A small share of training cost but a growing share of electricity: data-centre use is projected to more than double to around 945 TWh by 2030 (IEA, 2025).

## Examples as of 2026-10-30

- Big Tech capital spending: Microsoft, Alphabet, Amazon and Meta
- Per-token API price lists from OpenAI, Anthropic and Google
- Hourly GPU rental from hyperscalers and neoclouds
- The Stargate data-centre programme (OpenAI, Oracle, SoftBank)

## Sources

- The rising costs of training frontier AI models (Epoch AI): https://arxiv.org/abs/2405.21015
- How we built our multi-agent research system (Anthropic): https://www.anthropic.com/engineering/multi-agent-research-system
- Prompt caching (Claude API documentation): https://platform.claude.com/docs/en/build-with-claude/prompt-caching
- International AI Safety Report 2026: https://internationalaisafetyreport.org/publication/international-ai-safety-report-2026
- IEA: Energy and AI, executive summary: https://www.iea.org/reports/energy-and-ai/executive-summary
- Navigating the High Cost of AI Compute (Andreessen Horowitz): https://a16z.com/navigating-the-high-cost-of-ai-compute/


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Justuju · https://justuju.org/ · Content CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Quote with a link to the page.