AI news Oct 24, 2026

Looking back · SecondSource, August 25 issue

Dalton ties agent autonomy to failure cost; it's unmeasured

Michael Dalton, an infrastructure and security engineer at OpenAI, said at the Black Hat security conference in August that fully automated attack already has real examples, while fully automated defense has none. In Dalton's argument, an attacker who misses only keeps the status quo, and one who hits wins outright. A defender who patches correctly only keeps the status quo, and one who patches wrongly breaks the service, so keeping a human in the loop on defense is rational. SecondSource's August 25 issue drew on this to write that whether a process can be handed to an AI agent to run autonomously may depend on whether the cost of one failure and the payoff of one success are asymmetric, not on whether the model is strong enough (August 25 issue). That issue inferred from this yardstick that processes with low failure costs suit agents first, such as internal tools, unmaintained old reports and work nobody is doing now. That inference hasn't been measured. The counterexample stays: Databricks said in August that it had put AI into incident investigation across more than 150 teams, more than 2,000 times a day, doing automated diagnosis only, with no automated remediation. Dalton is an OpenAI employee, and his conclusion points toward the kind of product OpenAI sells. Extending the same yardstick to "incumbents are more reluctant to let go than startups" has no measurement behind it so far. Today's main-line item 2 is a new reading on the attack side: once an attack starts, Mythos 5 rarely stops on its own. How we checked →