By Roman Bodnarchuk
10XAI.news | Special Series 6 of 14 | Research checked October 18, 2026
The AI answer that matters is the one you can check.
That principle connects this special series: measure useful work, give the system relevant context, choose a worthwhile problem, and inspect the evidence.
The story
On October 16, Anthropic reported that an internal research model traced the seeds for five NIST prime curves to a C-library pseudorandom generator initialized with Unix timestamps. Anthropic says the discovery took under half an hour and publishes reproduction code. [4]
This was an investigation into how public cryptographic parameters were generated. It was not a cryptographic break or a method for recovering private signing keys. Anthropic says no operational change is required. It also lists unresolved questions, including one binary-curve seed, several base points, and why some candidate curves were skipped. [4]
My take: make important findings inspectable
Most business owners will never need to investigate elliptic curves. We do need to know whether an important AI-generated conclusion rests on evidence we can examine.
Consider a weekly sales report. An assistant might report that your sales cycle is getting shorter. Before changing your plan, ask it to identify the records included, the start and end dates used, and how incomplete deals were handled.
Then have the calculation checked against the underlying records. If the conclusion changes when a few unusually large deals are removed, you want to know that before presenting it to your team.
My recommendation is to require a short evidence note for any AI finding that could affect a meaningful business decision:
- Claim: what the analysis concludes, in one sentence.
- Inputs: the records, documents, or measurements used.
- Method: the steps needed to repeat the work.
- Check: a calculation, test, or qualified review performed outside the original answer.
- Limits: what remains unknown and what could change the conclusion.
This takes effort. Use it where the decision warrants it. A rough brainstorming list and a recommendation to change your pricing deserve different levels of review.
Your 10X action
Take one AI-generated business claim from this week. Ask for its evidence note. Check one consequential detail against the original material before acting.
Copy this prompt
Review this claim: [claim]. Using only the materials I provide, list its supporting inputs, explain the method, and identify assumptions and missing evidence. Propose a reproducible check and show what result would weaken the claim. State clearly which checks have actually been performed. Do not present a proposed test as a verified result.
Reply with the business claim you would most like to verify.
You have the same 24 hours in a day as the people building these systems. Use a little of that time to choose a better problem, complete useful work, and check the result.
Roman Bodnarchuk
10XAI.news
Adem B10: Claude Solved a 27-Year NSA Mystery in Half an Hour — scope and remaining questions at 7:32. Independent commentary, not an Anthropic presentation.