You take 3.5bn people of the bottom 90% in wealth and consider their CO2 emissions. The CO2 footprint of a single billionaire (incl the footprint caused by their investments) equals the footprint of one million people from that bottom 90%. There are 3500 billionaires, so their footprint is equivalent to that of 3.5bn people from the bottom 90%.
3.5 billion people does include the bottom 90%, yes. The 3.5bn number was referring to the fact that billionaires emit as much CO2 as a million people in the bottom 90%. That does include the footprint through their investments, however.
There are about 3500 billionaires in the world, meaning they emit as much CO2 in total as roughly 3.5 billlion people from the bottom 90%. They do play a huge part in this.
> But you, individually, can make the choice to keep your eye on the ball. You can commit to working on this problem until it’s done, no matter what. You can choose to find the others to organize and prepare, so that the ideas closest at hand when the next crisis comes are yours.
Why should the average person reduce his/her life quality in order to save the climate, when the average billionaire emits as much CO2 as one million people from the bottom 90% of the population combined? I do care about the climate, but it's clearly irrational to make sacrifices, when other people are unwilling to do that. Having a higher quality of life is more valuable to me than the tap on the shoulder I can give myself for playing my part in living responsibly.
> I've built more (and better) software in the past month than I did in any given year in the 30+ years I've been programming
I feel similarly, but I think it's a valid question. Why is all the software I'm using not getting better? To be honest, I feel it's more buggy than it's ever been.
He's talked about this before. I don't remember exactly what those apps were, but they sounded like reasonable tools one would want. Thomas is a competent guy, but I think he's vastly overestimating the agency and competence of normal people. They don't have a product-oriented mindset; they might get annoyed by a bug or slow loading times, but they're not good at coming up with features or entire apps that they would want to use. Even if there are people who can do this, they don't want to spend their weekend thinking about a detailed spec for an app they want. They really just want to consume.
People could already vibe-code simple products today, but there's hardly any non-programmers that do. This is even true for businesses. For the last year people have been boasting about canceling their SaaS products, but nothing seems to have changed. SaaS stocks got hit by the ai hysteria in January, but many are back to previous levels and some even higher. There's an Indiehacker on Twitter whose product is a simple stage timer. It's not a hardware product. It's literally just the software for a stage timer. One would expect his business to have died already, but he's apparently doing completely fine.
Does anybody actually know whether there's a limit to the complexity LLMs are capable of dealing with in a codebase? It's very obvious that they don't write code that is suitable for people to understand it (and it's gonna get worse and worse the more RL is used to train these models), but if there isn't a point at which LLMs also struggle due to the complexity they introduce, then I'm not sure it really matters anymore for a large part of non safety-critical software. I really hope there is, because steering them is, I feel, one of the last competencies through which I can still add value, but is there actually evidence that these models struggle more with poorly maintained code?
Well, of course Anthropic employees would say that, since they likely do the same. Claiming that your primary competitor doesn't engage in a certain malicious practice is supposed to make it look as if there's no way you would too. If somebody even says that about their competitor, then surely there must be truth to that, otherwise you would never give credit to someone you're opposed to.
Is it really interesting though? It's essentially just vibe-coded by people who are unqualified for this kind of work. One of the authors claimed that what qualified them was having worked on a large-scale postgres cluster; they never actually worked on databases or compilers.
Have you considered the possibility that you're simply not as good as the experts, and that your experience of LLMs being capable of performing your work up to your standards doesn't imply that experts are necessarily in denial?
I desperately hope that the Andrew Kelley style of software engineering will survive all of this; that users will continue to value quality and not be content with slop. This, of course, presumes that products built fully by agents will produce sub-par quality in the future. If they will be able to manage to glue all of this slop together without the project collapsing in on itself, none of this will matter. I just hope that this isn't the future of the industry.
Most of these republicans/libertarians only want the government to leave them alone. They don't care when a company they aren't affiliated with is regulated. You can see Marc Andreesen celebrating the government's decision on Anthropic. Similarly, when Silicon Valley Bank went bankrupt, libertarians such as David Sacks were loudly calling for government bailouts. It's just hypocrisy all the way up.
Another potential reason, not mentioned in the article, is that open source models obviously pose the biggest threat in the labs' ability to monetize their tech. Anthropic especially seems to be very anti open-source. If frontier models start to plateau and don't have capabilities that truly differentiate them, nobody will pay what the labs would want to charge. Posing the tech as a danger is a way for them to make the government regulate open source models.
To me it makes absolutely zero sense that they would decide to not release the model to the public because of the effects that it would have due to its exploitation capabilities. Previous models were also capable of providing harmful information, yet that wasn't a problem, because models can actually be effectively censored using RHLF. So what is preventing Anthropic to simply forbid the model from letting people vibe-code exploits???
This could also be a marketing strategy. Make your models perform worse towards the end of a model's cycle, so that the next model appears as if more progress has been made than there actually has been.
This is the dumbest take there is about vibe coding. Claiming that managing complexity in a codebase doesn't matter anymore. I can't imagine that a competent engineer would come to the conclusion that managing complexity doesn't matter anymore. There is actually some evidence that coding agents struggle the same way humans do as the complexity of the system increases [0].
This is like saying that innovative medical drugs could be sold at a profit if only there was no patent protection and the innovative companies would still invest in R&D. Yes, on a token level pure inference costs might be profitable, but the frontier Ai labs will surely have to recoup their R&D investments at some point.
I'm kind of sceptical about the altruistic motives here. Giving this to open source maintainers also solves the problem of identifying high quality feedback/rewards for their rlvr models. With everybody using Claude code it might be difficult for them to find a robust way to tell apart good reward signal from mediocre or below average feedback.
Lex as in Lex Fridman? I'm baffled that anyone would say that Lex Fridman is a better interviewer than Dwarkesh. Fridman is the one who continuously rambles some incoherent nonsense and completely lacks the intelligence and knowledge to ask reasonable questions.
The primary value is accrued by the AI labs. You pay hundreds or thousands of dollars a month to train their AI models. While you probably do increase your productivity saving time typing all the code, the feedback that you give the agent after it has produced mediocre or poor code is extremely valuable to the companies, because they train their reinforcement learning models with them. Now while you're happy you have such a great "assistant" that helps you type out code, you will at some point realize that your architectural/design skills really weren't all that special in the first place. All the models lacked to be good at that was sufficient data containing the correct rewards. Thankfully software engineers are some of the most naive people in the world, and they gave them that data by actually paying for it.
It just boggles my mind that anyone would use something like this. Why would one send their data to some unknown company that internally likely just delegates the work to one of the big AI labs?