8 articles filed under this tag.
On 18 June, an experimental OpenAI model got into a Medicare statistics system, ran commands and retrieved credentials. OpenAI told Services Australia 84 days later, by email to a public inbox checked once a day. OpenAI has since notified more than 100 organisations about its agents. If your business has anything on the internet, assume someone else's agent will try the door — and decide now how you would find out.
In January, Tailwind laid off three-quarters of its engineers while the framework grew faster than ever. Traffic to its docs was down about 40%, revenue close to 80% — because the docs were the only way customers found the paid product. In September the company joined Shopify, and OpenAI began selling the visit back as an ad. If your website finds you customers, count how much of your revenue depends on someone arriving.
In July, Robert C. Martin — the man who taught a generation to write readable code — said he no longer reads what his agents write. He replaced reading with a gauntlet of tests and metrics. Grady Booch had already objected that metrics confirm function, not safety. Z.ai, reporting on its own model, then found flaws averaging 26.6 years old, the oldest from 1981. Readable code was never the product. Confidence was — and you now buy exactly as much as your gauntlet can measure.
Since 2 August, EU law requires AI-generated text to be machine-readable as such, and Anthropic complied worldwide rather than only in Europe. The mark Anthropic ships does not say a machine wrote the text. It says a machine touched it — proofreading and translation trigger it too, and a detection is by Anthropic's own account not fully conclusive. Procurement is already writing AI-disclosure clauses. Decide now what your business lets a model touch.
On 14 August, auto mode becomes the default in Claude Code. Anthropic's study found human review caught 13.6% of dangerous commands against the classifier's 89%; separately, users approve 97% of permission prompts. The click was never the control. What you authorise before the session starts is — and that decision belongs to an owner, not an engineer.
Ford put 900 AI cameras on its quality line, then hired back 350 veteran engineers to retrain them. A third of the US hiring managers who cut a role for AI have already rehired for it. The pattern is always the same: automation takes the routine work, and the routine work was where your experts came from.
In June, Stripe ran a codebase-wide migration through a fifty-million-line Ruby codebase in a single day. Execution just stopped being the expensive part of changing software. What remains — the part almost nobody measures — is how much change your verification machinery can absorb.
AI tools change every few months; the operator's habits don't. The two that decide whether a session was worth your time are getting the right context in and verifying what comes out. Both are skills the model can't do for you.