The best AI model in the world scores 4% on genuine reasoning tasks.
Not 4% on some obscure academic test. 4% on ARC-AGI-2 — the benchmark designed to measure whether AI can actually think through a novel problem. Humans score 95% on the same test.
Meanwhile, the average mid-market company is spending $200K-$500K/year on tools built on these models. And nobody in the building can answer: what did we get for it?
Why Is AI Spend an Accountability Problem?
The models are useful. They generate text, summarize documents, write code, draft emails. Nobody is arguing otherwise.
But "useful" and "worth $200K with no measurement" are two different conversations.
Most companies have no system for tracking which AI tools are being used, by whom, how often, and whether the output justifies the cost. They have subscriptions. They have invoices. They don't have proof.
What Does AI Spend Accountability Look Like?
Proof means every number is tagged:
- [verified] — came from an admin export, invoice, or billing system
- [calculated] — computed from verified inputs using defined rules
- [estimated] — based on an assumption, and we show the assumption
Proof means 7 systematic waste detection rules, not opinions. Proof means a board-ready brief that auto-generates from your actual data. Proof means measuring at 30, 60, and 90 days — not projecting and walking away.
How Do You Know If Your AI Spend Is Worth It?
The question isn't whether to use AI. The question is whether you can prove what it's doing for you.
If you can't, that's not an AI failure. That's a measurement failure. And it's fixable.
Source: ARC-AGI-2 benchmark results, arcprize.org (2025). Model performance: best frontier model ~4%, humans ~95%.