Curious why it is so hard to find an owner to the issue. Gates' experience started with the web UI, then I'd expect that the team who owns the microsoft.com or owns the experience. To them there are only two types of bottlenecks: their web pages (usability, JavaScript performance, ways to get backend data, and etc), and their immediate dependencies. So, they drive improvement on both types of the bottlenecks, and the owners of the immediate dependencies recursively handle their own. For instance, the web team will identify that calling the catalog API has a P99 latency of 5 seconds and the network is fine, then they ask the catalog API to improve the API latency. If the catalog team does not do the obvious, the team's manager gets punished.
Of course, I'm being naive here, as I've seen too many companies fail to achieve such basic ownership. So, curious what I have missed. Of if the ownership is not a clean DAG, well, it goes back to Gates, as he was responsible for both the org charts and the company culture.
It wouldn't surprise me that what seems like whole things from the outside are split up even further on the inside of Microsoft. There's probably no single person responsible for microsoft.com (nor was there at the time). Instead the front page is one team, Windows Update (at the time) was yet another team, and sales another, and then there are 5 other teams doing other things, and no single individual or group is responsible for the whole thing. They've all got permission to push things to the website and change whatever just from time immemorial, and not through any defined authority, since that authority doesn't actually exist.
You can still see symptoms of this today. MS will (or at least would) spin up new domains rather than just update microsoft.com, or create newthing.microsoft.com, since both of those require talking to whomever is responsible for the main domain. Even if that person exists, knowing who they are in an org like MS is already a big ask when it's organised the way it is. Meanwhile, buying a new domain likely takes less than an hour, even if you include billing and such.
All luxuries indeed. Are they enough, though? Particularly, don't many books talk about how important it is to have productive hobbies or output-oriented hobbies? The underlying thesis is that one would quickly get bored and start seeking the meaning of life if he does not output something consistently. I was wondering if being able to find such hobby and being able to afford it is also a luxury.
There's an interesting dynamic, too. Even if an engineer reads the output of the AI and understands the root cause of the problems and how to diagnose the incident, somehow it's hard for them to internalize the learning and apply it next time to a new incident. As a result, the engineer loses touch with the system anyway.
It looks like our brains somehow have to experience the failures during a diagnosis and in gemerak perform this kind of pathfinding by themselves to truly understand the system. I don't know if this has to do with how our brains actually learn.
I think using open-source AI is no longer about API cost but about company survival.
Take Anthropic for an example. Anthropic has successfully destroyed customer trust, at least for me. DHH in a recent interview mentioned that Claude refused to translate an article about immigration. Not summarize. Not editorialize. Translate! I think this reveals an unacceptable level of paternalism: Anthropic fundamentally believes that it possesses a moral authority superior to the people actually paying for the API. If such basic and mechanical translation is already too sensitive to touch, the goalposts have moved from safety into outright censorship. What prevents them from quietly deciding tomorrow that your proprietary business logic, financial data, or legal documents cross their invisible moral line?
Let alone how Anthropic treats Cursor and Figma - not that they are wrong as companies are free to compete legally, but nonetheless it shows that companies can't outsource their intelligence to a potential competitor.
I get what you're saying and it's concerning how much power these big labs have amassed and how little transparency there is in what they do with it...
But I doubt this a major factor in the trend. I just don't think it's something most corporate users run into. My understanding is these guardrails are negotiable for enterprise customers anyway.
And, not for nothing, but if I owned a human-powered translation company I would've refused to translate it too.
I like the Claude constitution overall - I hope it becomes something representatives vote on and amend, to avoid the centralized corporate censorship you describe. In the meantime, I am fine with it abstaining from doing DHH’s bidding, especially because there are so many AI alternatives.
Ah, yes, I'm sure the article that moral paragon DHH wished to translate was not at all harmful, and that this was a good-faith effort on his part /s
While I agree that Claude can be overly paternalistic at times, how should it respond to a request to translate, say, bomb-making instructions? It's reasonable to me that it might refuse this.
It was an incredibly racist post claiming “gypsies” are like invading wolves and that something more drastic must be done to get rid of them before they kill all the “sheep” in Copenhagen.
I'm quite curious why few people are interested in what Uber has done. 70% of the code gets auto merged is a pretty impressive number. Companies have achieved way more than that? Uber's AI infra turns out to be not so useful? Or something else?
In some interviews, OAI mentioned that they didn't think that GPT-3.5 would be a success. They thought it would be a cool toy and they decided to launch it to see how users react. That means that they didn't think GPT-3.5 was intelligent enough. But somehow once GPT-3.5 became a huge hit, people conveniently ignored the anecdote, and started to believe that AGI had been eminent.
> they can afford the penalties and continue doing it.
I thought they could've bought just a single copy of each book and use the content to train their models. In that case, it falls into the fair use doctrine and they wouldn't need to pay the fine. And that will be way less expensive than the $1.5B price tag.
I'm not sure if this was ever really confirmed, but it's been said that in the beginning, before they were big enough for record labels to care, they used unlicensed MP3s to build up the library.
> The coding part of my career is over. LLMs are capable of doing everything I've ever been paid to write.
Remember we used to spend enormous amount of time in school and in our spare time studying computer science? Algorithms, operating systems, compilers, and etc. All kinds of insights. All kinds of fun. All kinds of hard engineering. Yet, how much time do we really need to spend in our day-to-day work implementing or using the algorithms and etc that we have learned?
Engineers have done amazing work of abstracting away the hard algorithms and data structures. In the meantime, there has been little progress or few new fields in the past 10 years or so in business that ask for implementation of new algorithms. In contrast, getting LLM to work is a new field, so it requires tons of new implementations: KV caches, speculative decoding, all kinds of variants of attention like FlashAttention, all kinds of parallel processing techniques, RL pipelines, post-training pipelines, and etc. It's just that the field is so concentrated that only luck few get to work on them.
So, maybe it's not LLM per se that removes the need of writing code. It is the maturity of the software engineering that has done so. It's just that LLM fills the last gap: making knowledge transfer so much faster and cheaper - if all that's left for most of us is slicing and dicing of what has been already been implemented, then LLM can reliably take over.
> Remember we used to spend enormous amount of time in school and in our spare time studying computer science? Algorithms, operating systems, compilers, and etc. All kinds of insights. All kinds of fun. All kinds of hard engineering. Yet, how much time do we really need to spend in our day-to-day work implementing or using the algorithms and etc that we have learned?
Because great insight and expertise stems from foundational knowledge.
I am a huge hockey fan. In the NHL, the players do not stop practicing the fundamentals once they make it to the NHL. They practice the fundamentals even more. Many practice the same drills as youth leagues -- stick handling, passing back and forth, shooting, edgework, shooting, rebound control, etc..
The best of the NHL might not hit the hardest, have the most accurate shot, or skate the fastest. What separates them from the rest of the lot is that they are fundamentally better than everyone else.
Yeah, but actually implementing those algorithms was extremely key to really grokking them. Testing those edge cases, seeing them fail, fixing them, learning "oh, that didn't fix it", repeat. Yeah, you can do it on paper (which I did), but the whole idea of going through the motions of writing yet another binary search algorithm was to lock in those concepts.
But that isn't the real problem here. Computer Science was always a theoretical concept, really. The meat of the issue is the rug being pulled out from the juniors and mids.
Reading code and writing it are two different skills. I believe that both are needed to maintain a codebase. It is much, much easier to understand what a service is doing when you're actually writing code that supports it, just like it's easier to remember the contents of a report that you wrote versus one that you read.
I think it follows the path of the spreadsheet. For a long while, only geeks and finance or accountants or data monkeys used spreadsheets. Then, it was such that anyone could create a spreadsheet. Vlookup was something you learned early in school or on your home computer tinkering around. There’s still some modeling gurus out there but largely everyone is developing solutions using spreadsheets everyday. And they’re doing it rather autonomously even prior to AI helping. We can talk about how they’re ugly and crappy spreadsheets but they generally solve the problem the user had.
With AI, people can build and collaborate on applications much more complex with much less technical knowledge. It might be ugly and crappy but I bet they’ll be mostly autonomous and not need to work through their IT team, or go through the hell of PM and requirements. If I know my requirements, I don’t need you. Hell, I can just start building and add requirements as I come across them. It’s not a major risk to the project like it used to be.
It will, and is, going much faster than the spreadsheet did.
> there has been little progress or few new fields in the past 10 years or so in business that ask for implementation of new algorithms
Transit routing is still moving fast in this area. We're still figuring out the best ways to return thousands-by-thousands transit time matrices with query-time truck dimensions and traffic updates. It can't be the only field!
Boris: "I don't prompt Claude anymore. I have loops prompting Claude and figuring what to do".
Boris: "I haven’t written a line of code by hand in, I think, eight months now… Claude Code, 100% written by Claude Code".
Boris: "There’s no manually written code anywhere at the company… All of the SQL is written by models. Everything is just built by the models... Claude instances communicate with each other (e.g., over Slack) in autonomous loops"
This does not sound like they review the code either. So, either the frontier labs like Anthropic have figured out something that very few companies could replicate, or they are being incredibly deceptive. I don't know which is true.
> No, they haven't figured out anything. Just vibe coding it with their bleeding dege models.
I'm sure you were saying "bleeding edge" but my first impression was to translate dege to mean degen/degenerate which made this so much more fun to read.
So with #2 they are betting that the future models will be able to reign in the mess that the current generation leaves in its wake. It's a bold bet but not an outrageous one either.
Where I'm at we use Claude for many coding tasks but we don't have infinite token pockets like Anthropic does. Everything is very focused on how to get the most out of the tokens and prove you're the one who deserves to have a ton extra granted next month. We can't afford things like have one guy tossing the equivalent in $165k at 64 simultaneous instances for days on end to just see if a Rust rewrite really works well or not like they did with Bun - but Anthropic still did that regardless. This doesn't prove the opposite either, it just appears asking whether or not others are doing the same is a bad oracle for Anthropic's honesty here.
I think that will be one of the most interesting things with models in the future. Even if we somehow 100% stopped dead where they are from a a model perspective, being able to run it twice and fast for half the price in the future will enable a hell of a lot more practical usage.
They work on research problems you can define a clear solution criteria for.
The kinds of business software I work on don't have those characteristics. If I needed something like a utils library, I think I could easily have Claude write the whole thing and not read the code.
What puzzles me is this: research means that we are exploring something that has not done before, yet using Claude to generate code means slicing and dicing what has been done many times before. So, I'm not sure how to make sense of both at the same time: Anthropic is pushing the boundary of AI, yet all the knowledge and engineering in form of code can be generated from the previous work?
How I interpreted their thoughts on "loops" and "graphs" etc, is that they 1. have unlimited token allowances, and 2. are working on problems where the solution itself can be described by code.
It reminds me of property based testing. I really liked the idea when I heard about it, but most of what I work on does not tend to behave in a way that can be easily described mathematically like that.
> I don't know why anyone should care about understanding the results if the AI is better at math than us
This is a big if, right? AI can still generate subtle or even silly mistakes that any normal human, let alone a mathematician, wouldn't make. Besides, math is more than just getting a conclusion but to understand and to generalize new ways of solving problems. After all, mathematicians are a curious bunch. To quote Hilbert's epitaph: We must know. We shall know.
I'm reminded of the joke about the two friends who come across a bear in the woods. When one puts on running shoes, his friend chides him that he can't outrun the bear. He responds, "I don't need to outrun the bear, I just need to outrun you."
AI doesn't have to implement Hilbert's vision and be able to prove everything. I just has to out-prove human mathematicians.
Of course, I'm being naive here, as I've seen too many companies fail to achieve such basic ownership. So, curious what I have missed. Of if the ownership is not a clean DAG, well, it goes back to Gates, as he was responsible for both the org charts and the company culture.
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