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Technology and Economics

Why Not Just Use ChatGPT, Claude or Gemini?

General models are excellent, and for many tasks they are the right answer. The comparison is not a subscription against a platform: it is a general model plus the integration, evaluation and operations we build around it, against a licensed platform that already carries financial and blockchain capability.

Main finding

Commercial AI APIs can power sophisticated systems, and in some workloads they are the better answer. The ATLAS claim is different: it is an integrated platform with financial and blockchain capability accumulated in advance, which must be demonstrated against those same APIs rather than assumed superior.

Benchmarks required before any claim

  • Held-out directional accuracy on a fixed evaluation set, against a naive baseline and against the desk's current process.
  • Execution and slippage error: modelled fill cost compared with realised fills on the same orders.
  • Liquidity response under stress, compared with observed order-book behaviour in the recorded period.
  • Cost per finished analysis, including integration, review time and human correction, not the API price alone.
  • Time to add a new data source, measured in engineering days on both routes.
  • Operating reliability: availability, reproducibility of the same question, and a stated failure rate.
  • Security review against an agreed threat model, including key custody and data retention.
  • Calibration of scenario ranges: does the stated interval contain the realised outcome at the rate claimed.

What supports it

  • A general model plus retrieval reaches strong domain performance without retraining, which lowers the barrier to building specialized systems (Lewis et al. 2020).
  • Tool use and multi-step acting extend what an API-based system can do operationally (Yao et al. 2022).
  • Both routes carry integration, evaluation and operating cost. The comparison has to include all three, not the subscription price alone.
  • Open-weight models are a third route, with hosting, tuning and governance cost of their own and no vendor support.

What this does not claim

  • No head-to-head benchmark has been run on our tasks. The advantage is a hypothesis until it is measured.
  • General models improve continuously and are available on commercial terms; a fixed licence can lag behind them.

Planning assumptions used

  • Both alternatives are evaluated on the same tasks, data and acceptance criteria.
  • Integration effort is costed for the licence route as well, not only for the API route.

Open before signature

  • What is the evaluation set, and who signs off on it?
  • What is the cost per finished task on each route?
  • Which capabilities genuinely exist only inside the licensed platform?

Sources

Figures marked illustrative are planning assumptions. This note separates published evidence from project assumptions and does not present a supplier quotation, a valuation or a forecast of returns.