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 stack still makes sense for some companies, and is an expensive vanity project for others. Answer 12 questions and find out which one you are.
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.
Get it wrong in either direction and it costs you. Companies that over-build burn capital on GPUs they cannot keep busy. Companies that under-build push confidential data through tools they do not control: IBM found that breaches involving unsanctioned AI cost US$670,000 more than average.
Twelve questions, four dimensions, and a recommendation from Use the APIs through to Sovereign Stack, with 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.