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Being a computer scientist who refuses to find anything about LLMs interesting right now is a bit like being a geneticist who refuses to find anything interesting about the recently opened Jurassic Park.
One way to look at this is that there's a part of the labor market that competes purely on price-in this case, basically by being the poorest place rich enough to afford fast Internet-and this category is the most vulnerable to technological disruption.
To me, the possibility of partnership is the feeling that despite the fundamental chasm between two human beings, love allows us to bridge the gap; alienation is the feeling that the gap cannot be bridged, and moreover, that I do not want to.
and they're creatively pursuing goals much like very ambitious aggressive power-seeking humans creatively pursued their goals And so there are just structural analogies here that make it silly to not talk about agents as having motives and goals.
The world would be so fucking boring if we constantly sought 100% compatibility in our opinions and in our politics. And when it comes to my code generator, which is what Claude is, I don't need it to share all my politics.
I find there's a lot of like received programming wisdom that's just nonsense. Like clearly no one's ever tested it and if they did they would have found out that it's that there's no actual basis for it.
I predict that in the next 10 years software development will survive, but it will become like any other white-collar professional work. No more $200,000 salaries, unlimited vacation, or incredible employee bargaining power.
Some people think CI means you reject grammar books. I disagree. I often reference grammar materials and find these explanations useful. However, the focus of my learning is not to “master the rules”. Grammar resources simply help me notice and make sense of patterns in the language.
elevators are the rare part of the economy where Europeans have embraced market dynamism while Americans choose overregulation and labor market rigidity.
Like right now we're endowed with labor that can turn into uh that can turn into income. When that is no longer the case and we are now at the mercy of the of the elected official for like basic needs, right? So that to me feels like a power sharing arrangement that's really dangerous.
They just recently released a report, and I think like you really have to squint to see anything happening. Like basically, if you want to take kind of like uh an an approach across the entire economy and looking at even looking at like software engineering, like the most exposed sort of sectors, there's just like not really anything going on. There might be a little bit of a signal about like junior developers getting jobs less than before, and that but that's like a less than before rather than a level shift.
rather than thinking about individual forecasts like what me and Phil are going to do, rather looking at kind of like basically generating prediction markets, where you get aggregate forecasts, where you get like kind of wisdom of the crowd effects. And kind of the reason that I think this is because we have been famously terrible at forecasting.
In a sense, we're all turning into project managers, right? And and we can have an army of junior programmers called agents that will just spit out reams of code, but someone's got to have the big picture and review all of that. And so, increasingly our craft is going from one of writing the code to one of of reviewing the code and and building the architecture of the code and overseeing the work, if if you will.
Yeah, AI is an amplifier. And if uh if you're young and learning quickly, AI is going to amplify that or can amplify that. So, I I personally think this is this is the golden age of the junior programmer.
As we move from, you know, hey, [clears throat] these companies are selling tokens where they provide the entire uh reasoning chain and all that to uh selling automated, you know, white collar work, right? Automated software engineer, send them the request, they give you the result back and there's a bunch of thinking on the back end that they don't show you. The ability to distill out of American models into Chinese models will be harder.
Everybody thinks our schools are in crisis all the time because they’re being forced to do something they were never meant to do, which is to make everyone college-ready, and they’re being forced to do that because we have seen jobs that provide a living wage without a college diploma evaporate.
I- I can relate that if you very deeply identify that you are a programmer, that it's scary and that it's threatening because what you like and what you're really good at is now being done by a soulless or not entity. But I don't think you're just a programmer. That's a very limiting view of your craft. You are, you are still a builder.
Long lead times and soft costs, fueled by the world-leading US wages for "white collar" work, are the root cause of poor performance in low-volume production because there are few units to spread the soft costs over.
This is a topic that I warned about very publicly in 2025 , where I predicted that AI could displace half of all entry-level white collar jobs in the next 1–5 years, even as it accelerates economic growth and scientific progress.
So premium is one source of our revenue. We also have ads, but they're context-based, not targeted. Of course, we leave probably 80% of value on the table because we're not ready to engage in all this practices, exploiting personal data.
There will be very hard parts like whole classes of jobs going away, but on the other hand the world will be getting so much richer so quickly that we’ll be able to seriously entertain new policy ideas we never could before.
Assuming you are still hiring junior engineers (you really should be even in this AI era), the good ones will learn quickly and want to see career progress in their first few years of working.
The scale word gets a lot of attention in this. The interpretation that I use is effectively to avoid adding the human priors to your learning process. And if you read the original essay, this is what it talks about is how researchers will try to come up with clever solutions to their specific problem that might get them small gains in the short term while simply enabling these deep learning systems to work efficiently, and for these bigger problems in the long term might be more likely to scale and continue to drive success.
Indeed, it no longer feels amusing to see the Telegram organization urge people away from default-encrypted messengers, while refusing to implement essential features that would widely encrypt their own users’ messages.
a new relationship is like fresh powder. It is new and shiny and exciting but it will not be a new relationship for long. And then you're still stuck with an old relationship and the question is will your old relationship a year from now be better than your old relationship that you currently have is? And if the answer is yes then yes sunk costs completely apply. But if the answer is no then what you're really doing is shopping for novelty not ignoring sunk costs.
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