Our Collective Bike Shed Moment
In 1957, Parkinson postulated his "Law of Triviality" using a fictitious committee reviewing plans for a nuclear power plant. The reactor design gets 10 minutes because nobody understands it, so nobody argues. The bike shed gets 45 minutes because everyone has opinions about the paint color.
I feel like we are living this committee meeting at scale every day.
LLMs are already better engineers than most of us. They are better at formal methods, and better at reasoning under pressure than most people. They run at incredible speed and don't get tired. They improve continuously. But, some people keep moving the goalposts on LLMs. First they said LLMs couldn't code. Then they said they hallucinated too much. All of these barriers fell, but some people are still scoffing at these systems. What chutzpah!
If aliens landed in Central Park tomorrow, I don't think the reaction would be that different. With AI, an alien form of intelligence has already arrived in our laps, and we gave it a collective shrug and kept scrolling.
Psychologists call this the normalcy bias. We tend to assume things will continue roughly as they have, even when confronted with something that requires revising our picture of the future.
Douglas Adams introduces an adjacent phenomena, called the Somebody Else's Problem (SEP) field. The SEP field does not try to make something invisible directly, rather it makes your brain classify it as somebody else's problem, so it gets actively skipped. An alien spacecraft hovers over a crowded park unnoticed, because everyone has silently agreed it's someone else's problem.
The individual adaptation strategy makes sense of course. Learn the tools, and stay ahead of the curve to save your own ship. Why not? Stay afloat the next couple years. But what about the coming decades?
I am not saying everyone should engage with everything. I know about the circles of control, influence, and concern. But, unfortunately, even the people who should be debating this (technologists, researchers, economists, policymakers, ethicists, military planners, educators) are not engaging with this problem at the depth it deserves.
We are having the bike shed debate about the reactor, and unfortunately the reactor design doesn't get enough attention.
Comments
The part that feels most important is that the debate is still stuck on whether LLMs are “good enough” at isolated tasks, while the real systems question has already arrived: what happens when they become persistent actors inside real workflows?
That is the reactor-design question. Not “can it code?” or “does it hallucinate?”, but what invariants, control planes, audit trails, rollback mechanisms, and human authority boundaries are needed when AI systems operate inside software, organizations, education, and infrastructure.
I originally followed your work for distributed systems and TLA+/formal methods, and I increasingly think that lens is exactly what this moment needs: less debate around demos and benchmarks, more work on failure modes, safety properties, authority, observability, and recovery.
Thanks for writing this.