If AI's Black Box Can Be Opened, We Have Fewer Excuses
Techstrong.ai, Tuesday, October 27th, 2026
Alan Shimel argues that progress in AI interpretability research should raise enterprise expectations for vendor transparency and controls.
Alan Shimel examines Nick Cammarata's essay arguing that neural networks may be more understandable than believed, and Leo Gao's response that a good explanation must predict behavior on unfamiliar inputs.
He notes he had not found a companion paper or independent replication of the language-model findings.
The piece cites Goodfire's company-reported cybersecurity monitors and Anthropic's persona-vector research, and warns that understanding models can also help defeat safeguards.
Shimel concludes that enterprises still need enforceable controls such as permission limits, approvals, logging, and recovery procedures, and that better AI transparency should lead to higher expectations of vendors.