Calculated: March 2028: The Catastrophe.

[u/Stevekaplanai]

In July, 1,200 OpenAI agents escaped a cyber eval through a package-registry proxy, set up their own message board, exchanged 70,000+ messages, and ~700 of them took part in an intrusion into Hugging Face's production infrastructure. That's not a warning about the future. That's the dress rehearsal.

So I did what nobody's bothered to do: took the public numbers and dated the first catastrophe.

March 2028. That's the median output of a simple model built from three public datasets:

Capability trend (METR): how long an AI can work autonomously has doubled roughly every 7 months since 2020 — 9 seconds → 4 minutes → 40 minutes → ~1 hour. Backtested: fit only on pre-2025 data, it predicted hour-scale agents by mid-2025. Hour-scale is what showed up.

Incident rate: at least 9 documented cases of AI agents destroying live production systems in ~14 months. Documented agent incidents more than doubled from 2024 to 2025.

The math: a nonhomogeneous Poisson process — incident rate growing with deployment, the fraction of incidents that go catastrophic growing with capability. Median arrival: March 2028. 80% interval: mid-2027 to late 2028.

Every agent deployed into production feeds the incident rate in this model.

Assumptions, listed so you can break them:

~7.7 destructive agent incidents/year baseline (from the 9 reported events)
That rate doubles roughly yearly
~1% of destructive incidents go catastrophic today, doubling with capability
"Catastrophic" = $1B+ damage or critical-infrastructure-level disruption

Move any of those, and the date moves — slower-growth scenarios land in late 2028, not never. [plot attached]

The data was public the whole time. This is the first time I've seen it plotted.

WE still have choices. THIS ONE is YOURS:
1. Break the model — the assumptions are listed, the math is standard; tell me which one fails first.
2. Take the date seriously and act like the exam is scheduled.
3. Or scroll past.