I'm an AI advocate but that question makes me feel that AI is simply "very useful" rather than being a historical game changer for humanity.
The earliest used models in this category would easily 10x (what did I just say) the creation of one-off short scripts, but you're not writing a 30-year app out of just a bunch of short scripts.
The METR time horizons graph suggests we can now get 10x (ahem) speedup on solving most coding problems that take us a few hours and about half of problems that take 2 days. Amdahl's law bites: even infinity speedup on half your problems is only 2x overall.
If you let an LLM loose, with a huge budget, what's the biggest artefact it can make before it drowns under the weight of bad decisions? The C compiler and web browser headlines a while back? The maths papers we see now that solve problems which stumped the maths world for decades?
But this is the other side of the same coin: teamwork. One person getting a thing made in twelve months vs a team of a hundred, you can scale up fast with money when you have a proof of concept, and an LLM can make a lot of proofs of a lot of concepts even in free accounts.
I do agree that the port would've taken a lot longer without LLMs though.
The actual hard part is getting it into idiomatic, safe rust, and I don't believe the LLM port makes the full transition any easier than doing things the old fashioned way: a dual lang code base like Linux.
Doesn't matter.
The vibe coded rust port did not get them any closer to full compiler verified memory safety. They still need to go file by file, bit by bit, and make it memory safe, at which point, why not just do that from zig.
because for the same effort you get a better result if you can and do use a better tool?
But I don't think they did it in a way that actually provided a meaningful gain. My point is that the state of the codebase right after the code is just a worse version of the original zig code with few of the rust advantages. Going from that Frankenstein rust code to actual memory safe rust is a similar leap to going from zig.
...
how so?
let's say Linus opens a branch, rust-temp, and in 2 weeks pushes ~15 million lines of code deleting most of the old C code. and then it gets merged in a few weeks. and then there's still a few months of the "merge window" and RC process. and each day folks report bugs, and automatic fuzzers make sure that both versions "behave the same".
I was pretty skeptical (still am), but what the Bun team did is pretty great so far.
https://bun.com/blog/bun-in-rust details the process, we see the results (Node.js compat [0], more than 3 thousand of issues fixed since since 1.3 [1], more than 900 issues fixed in ~24 hours [2], and it's live/in-prod [3])
> 50 dynamic workflows in Claude Code run continuously over the course of 11 days.
so let's say 500 workflows could do something similar for the kernel. the reported cost is 165K USD, so let's say this would cost 1.65M USD, plus CI costs [4] (which is ~200K/year for bun, so let's say it's 2M USD for the kernel)
How much the world is spending on kernel bug bounties and various security programs each year? (rough estimate says that just the visible kernel testing programs cost at least 15-30M / year)
The bar is not perfect. The bar is something better.
[0] https://xcancel.com/bunjavascript/status/2087436767054213517...
[1] https://xcancel.com/bunjavascript/status/2082298681223680002...
[2] https://xcancel.com/bunjavascript/status/2080882189663907859...
Long term stability is the thing that matters, such a move would be immensely threatening to long term stability.
the point is that it's a quite obvious opportunity that was just some pipedream years ago.
by the time v8.0 comes around we'll have more data on the bun rewrite. also we'll see how LLMs will affect the productivity of kernel developers, and the overall stability/maintainability.
You're not wrong. We're already seeing something similar with all the outages and buggy software updates these days.
We little choice in the matter, you will have ze (software) bugs!
I'm actually basing my opinion off the blog post, specifically the code. While they do get a few changes for free with the port, most of the code looks identical. And the number of unsafe statements certainly agrees.
And now they're going little by little, playing whack a mole with seg faults... My point is that the step they're at right here is the important part, and that mechanically converting the entire codebase to rust was an optional step when incrementally rewriting would've worked just as well if not better.
Being "only" 165k USD doesn't mean anything, because you didn't do a complete port, you did a port in a trench coat. If they went for the more targeted module by module approach, and did a clean and proper rewrite, LLM assisted or not, they would reach their end goal much less turbulently and without the effort of that initial frankenport.
for me it was already "don't run in prod" quality before. (at least after this I'm considering adding it to the CI to see how it fares.)
segfaults. I don't know. I looked at their CI and GH issues over the past few weeks. (though now GH is down so I can't do a search, but I didn't see thousands of segfaults.)
by all accounts and measures it seems it made their house of cards more manageable. despite the frankenport, no?
they already had a zig compiler fork, wanted to upstream it, but the zig maintainer(s) said it's low-effort. now they don't have to maintain their compiler fork. (or wait for the zig team to deliver the features they wish for.) no need to maintain a hybrid codebase. (though it has C++ because of the embedded JSC.)
also I have no idea what's the zig-rust FFI status, but getting over with a rewrite faster is usually better, even if you are left with non-idiomatic code.
After an aquisition earlier this year I got the task of doing an SAP-Integration for the new company, last time I did this 5 years ago it was a 6 month task, but with the experience and skills ive gained since I estimated it would be a 3 month project (with or without AI, most work is just logistics, AI cant help much there).
In those 3 months I was able to not only integrate SAP but also deliver a completely modernised user-facing software for that integration. While I could have written that software myself in a vacuum it would have never been worth it financially, since it would have delayed the launch of the integration by 6+ months. Building the software post-launch of the integration would have easily taken 2.5 years at minimum.
But this is also basically a "spherical cow in a vacuum" scenario, where I was essentially acting as a solo dev, in full operational control of the project, with deep domain knowledge of the topic and an allready fully set up codebase that I knew perfectly while working down ideas I've had in my backlog for 5+ years.
What you have said is correct, it lets you build software much faster. The question however is: is that software making money for the company? (Not talking about what you built but in general)
I think, with AI, companies are saying yes to a lot of things they would have said No to ik say 2020. And as a result realizing “just building it” is not the answer.
Previously your GTM team or Product team would say “If we ship some big project X, we unlock $Y in revenue” but now people are realizing that those projections were really more of a hope. So companies are spending so much more tokens and shipping so many more PRs based on hope but a lot of it just doesn’t turn into meaningful revenue, especially not in short term
This sort of system only works with internal software and an unusual amount of data. If we were in the business of selling that software we could not have charged a higher price for the new version over the old, the tweak could only be unlocked because we were able to control staff hiring and staff onboarding fully to make use of the new changes.
As part of the aquisition I got access to their previous codebase which was some sort of incomprehensible PHP monolith, with the persons who wrote that code long gone. Thanks to LLMs I was actually able to extract the core useful concepts (again, sufficiently deep domain knowledge that I knew exactly what to look for). Without LLMs i would have probably extracted the absolute minimum and let the rest rot.
There is no reason a dev of comparable skill and domain knowledge would not be able to do that for what I built here.
By that point it might no longer matter though, but I suspect that such code would have a lot more exposed edge cases than one where someone actually thought things through before coding.
Second, the Internet didn't show up much in GDP and similar measures either!
But your point stands. Where are the amazing digital products/stuff? I get that it might take time to arrive as we scale up compute and learn new paradigms. But so much infra already exists (deployment pipeliens, everyone reachable on a smartphone) that we should be seeing something.
But I am just as amazed with how little real life consequence it seems to have! Even software houses were hit more by interest rates than by this magical revolution.
If I couldn't directly observe Fable in action, I wouldn't believe in AI.
>> What's moving the goalposts? I am very much amazed at what Opus 4.8 can do. I push its code straight to prod.
>>> What's moving the goalposts? I am very much amazed at what Opus 4.6 can do. I push its code straight to prod.
>>>> What's moving the goalposts? I am very much amazed at what GPT5 can do. I push its code straight to prod.
>>>>>> What's moving the goalposts? I am very much amazed at what Opus 3.5 can do. I push its code straight to prod.
What would produce huge "gains" (depends on who you ask) again is the automation of the myriad non-digital roles/companies, but good luck with that...
I'm definitely seeing indie-sized games that appear to have had significant input from AI, though I'm not sure the balance between AI for coding and AI for assets. My experience attempting this directly suggests that the current level they work at can make very simple games as one-shots, but anything more than trivial will produce outputs only as good as the developer's combined willingness to put in effort tweaking things and taking it all one step at a time, and their taste about what "good" even is.
I'm using spare credits to build and improve an isochrone map renderer, which I otherwise wouldn't have had time for (apart from anything else, I'd have had to become skilled in JS+wasm, somewhat of a pivot from iOS). This also requires taking it all one step at a time, having UX and UI taste.
Having lived through GeoCities since before it was bought by Yahoo!, taste is… well. Most people make things that nobody else actually wants.
I've done amounts of refactoring and fixes and written tooling that just wouldn't have happened before.
I'm not sure what amazing new stuff y'all expect but the amount of technical debt in my projects is actually going down, cause I can finally get good enough test coverage, including E2E/load tests that actually prove whether the software works and scales or doesn't - just last week I diagnosed issues with SeaweedFS failing under concurrent writes when backing Sentry and could swap it out for Garage in a day, caught by a monitoring tool I slopped together that integrates with the Sentry API, no issues since.
The environment around me has gone from drowning in tech/ops debt to sort of swimming and at least holding above water for now (cause nobody will pay for 5x more tokens).
It's also insanely good for prototyping and being able to actually explore various ideas and shoot the bad ones down quickly instead of handwaving and looking at a loaded calendar, alongside being able to address well bounded tasks in parallel, better than human developers can - like I can give 5 GitHub issues to the slop machine and have it fix all of the annoying bugs. Issue with how some data shows up? Just feed it the DB dump and let it find out what's up.
Some projects have gone from around 500 code tests to around 4000, and before anyone says they're meaningless, at least 5% of those have caught real issues and helped a bunch, alongside linters and other tooling (including some tools I wrote myself). I've also written both native utilities and some web platforms for myself, side projects that I never would have gotten around to.
I'm measurably more productive than I've ever been (since I did measure that, looking at my commits over the last 2 years) but also burnt out. Still, it's the kind of burnout that's the consequence of context switching and lots of work, rather than the kind that I had years ago, where I had to manually untangle deeply nested Spring Boot service logic all over the place at like 2 AM cause the made up deadlines were kicking my butt.
In contrast to others, I don't need to move the goalposts - the productivity for me is here and now. Any future models will just make it better, unless we experience model collapse.
Disclaimer: you do need a LOT of code tests and validations, otherwise it all goes to shit. Maybe I'm just extending how much time it will be until it goes to shit for me as well, but go figure. You also have to babysit the models more than anyone would like or should, most of my work usually has 20-60 minutes of planning before dispatching the agent.
Implement features at a viable pace and drown any unrealistic expectations in reports of CVE fixes - usually a version bump of dependencies, but quite often also some migration work within the app; write tools to make that work visible like I did with scanning and reports, and enjoy living without having to complain about how you can’t keep up with releases while letting your software rot until it’s a multi-year migration.
Alongside that, do the same for test coverage as well because it’s the humans that have been sloppy for years and didn’t work enough on the test harnesses or good enough coverage, introduce Testcontainers and load testing tools and anything else that makes sense.
Use their money for tokens to build software that is actually good and make your life easier and ensure that their shortsightedness won’t sink the product on a few years.
Of course you could also get fired if they’re stupid, but then I don’t imagine that working for those that are delusional is exactly great either, unless the goal is explicitly to make slop to get investor money in which case the whole thing will probably need to be rewritten from scratch anyways so who cares.
So yes, many aspects of my job are now 10x as productive, but turns out that improves my overall throughput only very little.
The way it changes the game is by lowering the cost of making radical bets so we end up trying more moonshots.
Or an example of MS - their main cost like most software companies are people, especially software devs, which are to be replaced by AI so on the surface they would greatly benefit from it. But their products are centered around helping out people do stuff on the computer. Why would you need that when the AI will do it better and faster directly operating on the data or using e.g. Python?
Maximalists: 10x
Minimalists: were always close to 0x individuals, so opinion rejected 99% of the time
Realists: 30-50% productivity gains
80% of the engineers out there are the 0.5-1x baselines that we have to optimize the 10-20% around to carry...
The last 30 years of tech has been about big, the dot com boom, the next unicorn, the magnificent 7 or 11 or whatever.
The thing is that the missing solutions people want have never been the one sized fits all multi Tennant we need 6 rounds of seed funding kinds of problems.
The "app boom" should have been a clue that smaller, niche services are viable.
IF tech were like construction, then most of HN, most of SF Bay Area, most of "tech" seems to be concerned with building the tallest building, or a massive housing development. Your average office worker is more akin to a home owner, who has a busted outlet, a sticky door, a dripping faucet, they just work around it because the resources to fix it (programers) are going to build the next big thing.
Those workers now have tools to address those problems, themselves.
I have lawyers and writers (far removed from tech) talking to me about cron jobs and scripts they are running that solve real problems for them. AI has allowed them to accomplish this.
These things arent showing up on GitHub. They dont make quarterly reports at a publicly traded company. The mountain(s) of change are happening by stacking grains of sand, not one solid rock.
Hey, I just found 3 niche businesses that I can run because of AI and clear a million a year after costs.
Fight club rules apply, you dont talk about your advantages.
You get lawyers talking about their AI use (sometimes) because they are protected by the requirements of the bar. You people in niche businesses, or where it's about personal productivity, talking about it cause you cant replicate what they have done. You get folks in the sciences talking about how they are using LLM's to generate the python & scripts that make their world function with easy (rather than be a bottomless pit of time). These are people who might talk about their AI gains, but often have better things to do and don't run in programing / tech circles.
Unless you seek these people out you're not going to find their gains. It would be pretty hard for someone like anthropic to even begin to quantify time savings from generated code by "non programers" - because they don't have the data.
A product still requires a lot of handholding and human thinking, at least if one does not want everyone even throwing a glance at it to immediately be repulsed by the usual AI slop tells.
> I'm an AI advocate but that question makes me feel that AI is simply "very useful" rather than being a historical game changer for humanity.
It absolutely already is a historical game changer on par with the Industrial Revolution when it comes to the amount of jobs destroyed and economies screwed up - and the impact will be even worse in 10+ years as existing seniors retire but no new seniors rise as AI has destroyed entry level career paths.
Which economies are already screwed up?
IMO it can’t ever be on par with the Industrial Revolution because AI can only really affect the information economy. Things people do with their hands/bodies have either already been automated or can’t be with current tech. If you’d asked people decades ago they might say no one will ever work in factories by 2026 because they’ll all be automated. It didn’t work out that way. I think AI will go the same way: absolutely game changing to some industries (of which software engineering will be one) but a great many will still survive with less dramatic changes.
If anything it might result in more focus on the human aspects. How many people out there earn their stripes putting together slide decks? In a world where an AI can put together the snazziest presentation you’ve ever seen in a heartbeat it’s going to matter more how you stand at the front of the room and present those slides than it does today.