Another (outer) alignment failure story by Paul Christiano

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This is: Another (outer) alignment failure story, published by Paul Christiano on the AI Alignment Forum.
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This is a story where the alignment problem is somewhat harder than I expect, society handles AI more competently than I expect, and the outcome is worse than I expect. It also involves inner alignment turning out to be a surprisingly small problem. Maybe the story is 10-20th percentile on each of those axes. At the end I’m going to go through some salient ways you could vary the story.
This isn’t intended to be a particularly great story (and it’s pretty informal). I’m still trying to think through what I expect to happen if alignment turns out to be hard, and this more like the most recent entry in a long journey of gradually-improving stories.
I wrote this up a few months ago and was reminded to post it by Critch’s recent post (which is similar in many ways). This story has definitely been shaped by a broader community of people gradually refining failure stories rather than being written in a vacuum.
I’d like to continue spending time poking at aspects of this story that don’t make sense, digging into parts that seem worth digging into, and eventually developing clearer and more plausible stories. I still think it’s very plausible that my views about alignment will change in the course of thinking concretely about stories, and even if my basic views about alignment stay the same it’s pretty likely that the story will change.
Story
ML starts running factories, warehouses, shipping, and construction. ML assistants help write code and integrate ML into new domains. ML designers help build factories and the robots that go in them. ML finance systems invest in companies on the basis of complicated forecasts and (ML-generated) audits. Tons of new factories, warehouses, power plants, trucks and roads are being built. Things are happening quickly, investors have super strong FOMO, no one really knows whether it’s a bubble but they can tell that e.g. huge solar farms are getting built and something is happening that they want a piece of. Defense contractors are using ML systems to design new drones, and ML is helping the DoD decide what to buy and how to deploy it. The expectation is that automated systems will manage drones during high-speed ML-on-ML conflicts because humans won’t be able to understand what’s going on. ML systems are designing new ML systems, testing variations, commissioning giant clusters. The financing is coming from automated systems, the clusters are built by robots. A new generation of fabs is being built with unprecedented speed using new automation.
At this point everything kind of makes sense to humans. It feels like we are living at the most exciting time in history. People are making tons of money. The US defense establishment is scared because it has no idea what a war is going to look like right now, but in terms of policy their top priority is making sure the boom proceeds as quickly in the US as it does in China because it now seems plausible that being even a few years behind would result in national irrelevance.
Things are moving very quickly and getting increasingly hard for humans to evaluate. We can no longer train systems to make factory designs that look good to humans, because we don’t actually understand exactly what robots are doing in those factories or why; we can’t evaluate the tradeoffs between quality and robustness and cost that are being made; we can't really understand the constraints on a proposed robot design or why one design is better than another. We can’t evaluate arguments about investments very well because they come down to claims about where the overall economy is going over the next 6 months that seem kind of alien (even the more recognizable claims are just kind of incomprehensible predictions about e.g. how t...