These are all additions to the existing treaty that could have been added without the drama, anger and stupid memes, without Trump blustering about "deals" and without terrifying the inhabitants of Greenland.
when AIs are extremely extremely capable my view is that those AIs will be harder to align than current systems. So for current systems, we have this feedback loop where we basically like we create an AI. We do some evaluations on it. We see that it has some kind of messed up behavior that we can kind of quickly understand. Then we like can like go look in training and be like, "Oh, the these training environments led to this problematic behavior. Let's like tweak that training data. Let's introduce some additional training data to like correct this other issue and then move forward from there." But in a regime where the AIs are extremely situationally aware, very very very very capable and um you know uh we don't necessarily understand what they're doing, this feedback loop breaks down.
reliability and performance lives on this exponential ladder of nines. So, getting to that first 90% or 99%, that's the easy part. But then every next nine that you want to add, that takes about 10 times more effort.
I think that, you know, the model that we've put together collectively about the relationships between archaic and modern humans is sort of accreted over time. There was this, you know, idea that modern humans are distinct and that Neanderthals and Denisovans are like sisters of each other. And then over time we developed and detected additional mixture events like this modern human into Neanderthal and then this other ones I didn't even talk about like super divergent lineage going into Denisovans and like all this other stuff.
I mean, I I I I do believe that that hybrid um human plus AIs will will dominate mathematics for a lot longer. It it's It will depend It will require some additional breakthroughs uh beyond what we already have.
My sense is that this hybrid is fairly common in practice; solvers aren't magical and if you can deduce additional structure using domain-specific analysis, it will often give the solver an important boost.
I think basically what takes the long amount of time and the way to think about it is that it's a march of nines and every single nine is a constant amount of work.
I I like the term context engineering because I think the fundamental skill of using AI well is to be able to state a problem with enough context in such a way that without any additional piece of information, the task is plausibly solvable.
The principle in Perplexity is you’re not supposed to say anything that you don’t retrieve, which is even more powerful than RAG because RAG just says, “Okay, use this additional context and write an answer.” But we say, “Don’t use anything more than that too.” That way we ensure a factual grounding.
If my class is so boring that I have to devise an additional carrot-and-stick system to get students to pay attention, the problem is my class is boring.
Code has mass. Every additional line of code you don't need is ballast. It weighs your codebase down, making it harder to steer and change direction if you need to.
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