Blog | Greg Brown

17 Jul 2026

Inherited robustness: you cannot run the lab's test, and the evidence expires (GPT-Red). Model choice as a security control. News-pegged.

Most organisations believe they have a rough sense of how exposed their AI systems are. Something OpenAI published this week shows how much of that judgement rests on evidence they cannot reproduce. It has built GPT-Red, an internal automated attacker trained specifically to break AI systems. By the

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AI security's human bottleneck: finding got cheap, fixing didn't (Patch the Planet); runs before jagged-frontier post

The reassuring version of AI in cybersecurity runs roughly like this: the machines find the flaws, the machines write the fixes, and software slowly gets safer on its own. This is the premise on which GPT-Cyber works. It is half right, but the missing half is the one that matters. What AI has actual

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AI as utility: settled product, unsettled economics (OpenAI losses + SpaceX compute landlord)

We treat AI like we treat electricity: something that simply works, will keep working, and will hopefully cost roughly what it does today. This cannot continue indefinitely. Consider the commercial layer first. According to leaked financials, OpenAI, the firm behind ChatGPT, took in around thirteen

AI economics compute platform risk vendor dependency SME strategy