In AI discourse, there seems to be this eternal dance between people who put a lot of emphasis on process and people who focus on outcomes. It seems pretty visible in the 'is it reasoning?' debate. I think both are right in different ways: of course a model is reasoning in some sense, and functionally I don't really care if the mechanism that leads to the CoT and output is not analogous to how biological brains do it. But also, there are important considerations about the kinds of reasoning used, the rationale for certain chosen logical chains, and the degree to which these generalise robustly in out of distribution situations. The 'process people' are not always blind naysayers, and the 'functional equivalence' people aren't fundamentally incorrect either. The process failures (e.g. R in strawberries) used to be easier to catch though, and a lot of 'ideological skeptics' rely on them to make all sorts of unsupported claims, which makes it tempting for 'narrative activists' to dismiss process concerns entirely. Clearly the models are improving at an incredible rate, and this is great. But there remain failures or lacunae in the process by which an outcome is generated; this is less of an issue in coding, math, formal logic or areas where verification is easy, but more so in blurrier domains where we value diversity of processes precisely because we don't know the 'correct way', to the degree there even is a single one. With humans you had this cultural and scientific evolution that over time refines heuristics and mechanisms; I think it's important that we maintain a degree of model multiplicity and cognitive diversity with models too. If you optimize hard enough on outcomes alone, you could easily converge on reasoning monocultures that perform well in-distribution but fail in precisely the situations where diverse reasoning approaches would have generated useful signal. Hence why I'm so keen for the 'letting a thousand flowers bloom' approach to normative alignment, and generally insistent that a much wider set of people and groups should be able to customize and align models, beyond whoever happens to be in position to do so at the labs. Of course a lot of human cognitive diversity can also be noise like motivated reasoning, systematic biases, and cultural path dependencies that don't track truth, so you don't just want diversity for the sake of it. You need verification mechanisms that actually stress-test reasoning: e.g. adversarial collaborations are underused. You need institutions designed to promote truth-seeking, which are genuinely hard to build, as well as strong cultural and legal protections for the marketplace of ideas, which are increasingly under pressure. And you need better epistemic infrastructure broadly: there's an enormous amount we could do to improve how science is done.