the value of standard social science approaches, which attempt to discover long term regularities, is likely to depreciate in relative terms until the system settles down again.
If there is a 3x productivity boost now, the correct assumption is that in the future the productivity boost, in places it carries over, will a lot more than 3x. Indeed, given how this scales, a better model might be in practice 10x or 100x or even 1,000x or more.
No one was happy. Because no one knows what they want until they receive it. You don't know what a program should do until you play with it. So in the agentic age, you should resist the temptation to be overly specific upfront. Be as vague as you can to manifest something, then interact with the something. The way you arrive at good software is you write a little bit of software, and then you try to use it. It is in the process of using software that you discover what you really want.
The revelation I've had working on Omarchy the last three months is that to get that magical 10X, 100X, in a few rare cases, 1000X productivity boost, you have to interact with the agents directly, and you cannot intermediate that bandwidth with another human because it's simply too slow.
Then there's another possibility which is that it actually already has worked but just the productivity boost isn't as big as would be obvious if people got 10% more productive. That would still be pretty impressive because it's hard to get a 10% across the board uplift.
If you start from a low baseline of performance, you might get some positive boost. It's quite small, but you might get it. But if you are actually quite a high performer, then you risk getting worse. with this is called the inverse U-shaped curve.
People pleasing is based on insecure attachment. We become people pleasers when we discover that others only value us when we're cooperative to the point of being submissive and deferential.
So, the lesson here is to bet on a system that's maximally learned and minimally constrained and leverage structure intentionally to boost performance and scaling laws both in training and in evaluation.
So, we're going to see people get into those where they're like, "Well, I vibe code the tip of the iceberg. I throw away the rest of the iceberg and now I'm in trouble because now I don't know what to do. Now I get to these downstream problems that I didn't even know existed." So, we're going to see on the on the side of the the the vibe coding replacers, we're going to see that kind of a naivity play out.
And the reason they came, I think, is absolutely because of the the better tooling. And I think we were totally right there that like adding a an erasable type system and then using that to enable great tooling is really where the pro where the programmer productivity boost is realized.
Um, there's just too many interesting ways of combining things. There's too many sort of deep ideas waiting to be uh, discovered and when it you know, not only we, but but nobody ever is going to to discover most of them. So, choices about how to make how to do the exploration actually matter quite a bit.
I expect AI to increase productivity of top mathematicians much more than that of top writers, because formalization is a kind of translation task, it doesn't require much judgement, and as long as it compiles noone needs to read the result.
Um I think with within a decade, a lot of things that mathematicians currently do um we we spend a lot of the bulk of our time doing and a lot of stuff we put in our papers today can be done by AI. Um but we will find that that actually wasn't the most important part of what we do.
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.
It's our choice whether we should say oh they are our offspring and we should be proud of them and we should celebrate their achievements or we should we could say oh no they're not us and we should be horrified.
The dream is you just feed it all this data, and this is here is a new patent that we didn't see before, but it actually, even the current state of the art even struggles to discover old laws of physics from the data.
Yet common sense insists the best way to discover useful ideas is to search for useful ideas — not to search for whatever fascinates you and pray it turns out to be useful.
But what you have is this scaffolding that we all contributed to built on top of. No one person is going to go build the iPhone. No one person is going to go discover all of science, and yet you get to use it. And that gives you incredible ability.
The cost to discover and develop a drug today is orders of magnitude higher than in the 1950s. Despite this, the probability that a drug entering clinical trials will eventually reach the market has hardly improved in the intervening years.
Large language models are more or less generalizations of stuff that we have. There’s still breakthroughs in AI waiting to happen, and maybe they are happening and maybe they’ll be good, maybe not, but that’s not quite the same.
The former is a personal touchstone which shaped me deeply; the later is merely very good. Looking up something else, I happened to discover that a bunch of his books are now available through our library electronically, so I plunged in.
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