AI Boss Tools | Diagnostic 02 • Back to the Hub
The price of AI capability keeps falling: inference costs for GPT-3.5-class performance dropped more than 280-fold in two years. Owning your own AI stack makes sense for some companies and wastes capital for others. Twelve questions assess which is true for yours.
For a company, sovereign AI means controlling your own models, data, and inference rather than renting everything through vendor APIs. The case for it rests on four things: how sensitive your data is, how much you spend at what volume, whether your team can actually run the stack, and whether owning it creates an advantage competitors cannot copy.
Both directions of error are expensive. A company that over-builds spends capital on hardware it cannot keep busy, while a company that under-builds pushes confidential data through tools it does not control. IBM found that breaches involving unsanctioned AI cost US$670,000 more than average.
The twelve questions cover four dimensions and end with a recommendation, from Use the APIs through to Sovereign Stack, plus a one-pager you can put in front of your board.
Documents the collapse in inference pricing (over 280-fold for GPT-3.5-class output in two years) and rising frontier training costs.
Independent analysis of what training and serving large models actually costs, from single fine-tunes to gigawatt data centres.
Breaches involving unsanctioned AI tools cost US$670,000 more than average. 97 percent of AI-related breaches lacked proper access controls.
A score for every dimension, a median benchmark to compare against, a specific next step for each dimension, and a one-pager sized for a board pack. Your name and email open it, and there is no charge.