Feel like this is the equivalent of a traffic engineer standing at the grand opening of the 5th lane for the highway saying there’s no reason for traffic to be slow anymore.
That is, there’s a misunderstanding of why software (traffic) was slow in the first place, and it has nothing to do with our ability to generate code (number of lanes), even if that code is “high quality”.
you dont mesaure the quality of a plane by how much it weighs. but yea i think everything is rot to the core. app to os if just wackness that nobody cared about.
One article said secure software was here because of AI, this one says it can now be performant.
Yet when I ask for code it writes, by default, both slow and insecure code that mostly works. Kinda.
As I try to get AI to rewrite it into more secure, less bloated and optimized code is when it starts to randomly crash.
Then I read articles about how AI is "moving too fast" and cry.
It's all about the test suite. The test suite becomes an executable specification, and the better the spec, the better the results you can get from AI.
How do you write a spec for correctness? Only the small and unimpressive programs can be checked exhaustively.
The very first thing many people did with AI is start calling the automated tests it writes good enough to capture the desired behavior. Writing tests isn't a whole lot of fun for most, so now the thing that it is supposed to help ensure we're not evolving our software into piles of trash is, in fact, one of the most neglected parts of vibed out codebases.
It's part of it but you need good systems thinking in order to write good tests. The kind of thinking which requires years of software engineering experience to acquire.
Also the architecture really matters now because a poor architecture will lead to more weird edge cases and require more exotic tests to plug all the holes.
If you have such a test suite then you really don't need AI to write the code for you.
Writing a benchmark test is about 100x to 1000x easier than writing optimizations. A benchmark test can be as simple as:
func Benchmark(b *testing.B) {
for range b.N {
runCodeUnderBenchmark()
}
}
Actually optimizing the runCodeUnderBenchmark() function is far more difficult.
Here's this boiled down:
> A stochastic search process with an executable optimization objective over space of programs S can only improve the objective
This is superoptimization. We've known this since the 80s. The only novelty is that the proposer is now way better with LMs.
Further, there's a large number of reasons for software to be slow:
- LMs still don't do data or hardware-oriented design well out of the box, and therefore if you're engaging in any sort of serious novel work, beyond porting an extremely well-understood program with extremely well-understood workloads, you're going to be spending hours tracking down bad allocation decisions (c.f. why TigerBeetle doesn't use agents), which are often the root of evil (before you'd reach for anything further)
- The knobs you'd need to get serious performance are nearly unreachable in languages which LMs are good at (even Rust requires a discipline that the default language doesn't enforce). When you drop into the lower realms, you're trading consumption context for access to these levers. The levers are also "soft": you find yourself writing a bunch of skills, and tools to try and enforce the discipline.
The reality is to get performant code (quickly) out of an agent, you need to know how to write performant code (and you need to know how to surface the information that you'd use to create a verifier for such a thing to the agent), which 99% of developers do not know in 2026.
Sure, agents can teach you how to do this -- but it's one of these things where iykyk.
Experience: I've poured 10s of billions of tokens into Zig with the best agents and I have the time and space to try these things.
If you want to start learning the discipline, I'd recommend matklad's + TigerBeetle blog -- as well as hardware-oriented design.
100% sure software in the coming years will just feel slower and slower.
It will be the equivalent of that Excel =COPILOT(“sum the numbers above”) meme.
I’m a lot more optimistic now that RAM is scarce. A lot of focus will be on maximizing software performance.
See: iOS 27. It’s faster than the previous version, even on very old phones.
I also think AI will contribute to removing a lot of the tedium surrounding optimization.
People these days are saying “don’t look at the code”.
People are shipping all sorts of weird architectures, non-performance code, etc.
I wish that the ram scarcity would drive more performant software however I just think that the way people are shipping software currently will not lead to this
> People are shipping all sorts of weird architectures, non-performance code, etc.
And they weren’t before AI?
Is iOS 27 particularly impressive, or was iOS 26 a shitheap? Maybe a bit of both.
It fucking better be faster, iOS 26 basically bricked my iPhone SE, which had never been the slightest bit laggy or glitchy before that cursed update.
One of the biggest causes of slowness is just waiting for web requests. The fact that so much software is either online or built using the same stack even if it isn't, puts all that software in this blocked/waiting state constantly while using it.
Anyone not in the US feels this even more since so much online is US hosted, 300ms for every little interaction adds up quick.
If your software has the affordance of a waiting dialogue or loading wheel for many of its UI controls, you are building with this default blocked assumption. Even if you are building something web based, ask yourself if that's actually necessary for your software or if you could build it differently to avoid constant UI blocking.
It's not very hard to engineer software with these two constraints at the same time:
* Must feel very responsive
* Network requests can take up to 500ms end to end
Wow, my browser's Reader Mode saved my bacon on this one.
Otherwise, I would have left immediately.
I'm all for speedy, simple, plaintext websites, but it is a negligible amount of work in 2026 to throw some barebones CSS in and make it approachable.
Ironically a perfect thing to iterate on with your favorite coding agent. "make this page look better, present me 2 versions, I'll pick my favorite, and we'll iterate until I say stop"
You must not look at many academic websites.
I don’t know why you got downvoted but you have a point. It looks as if OP doesn’t write for readership and his posts contents have similar characteristics of reckless abandon for conventional norms for a tech post. Too many cross references and link-backs to his own other articles that makes it really hard to follow the main thread of the essay.
The downvotes may be related to community enforcing what's in the HN guidelines:
> Please don't complain about tangential annoyances—e.g. article or website formats, name collisions, or back-button breakage. They're too common to be interesting.
https://news.ycombinator.com/newsguidelines.html
Dan Luu is a bit famous around HN.
I think he might subscribe to the philosophy of everyone should style websites however they like and not be prescribed by the author.
Nah, he's just trying to be different. It's vanity.
He's had a usable website in the past. This one is explicitly made to be shitty. "Look at me, I'm such a geek" energy.
His site has always looked like this, i'm sure of it. This is what sites look like if you just write them as text. Not everybody knows how to use sql or css or java or whatever thing it is that lets you pick the font.
I've been using computers for 4 decades. They have gotten no faster. The nuclear plant computer system we built in 1989 had to present selected screens in 1 second. I don't think any apps I use today can do that.
What if we need pretty, rounded, softly opaque corners on all our transparent windows hovering above a gently shadowed background?
Giving an LLM a program that does a well defined thing correctly and asking it to make a faster version and a crapload of tests to show that it performs exactly the same task is a great way to burn through a bunch of tokens and getting a faster program in return.
I currently have one running working on a multi path problem trying to make an A* heuristic that produces the same or better score for search time, paths crossed, corners taken (plus a few other metrics, like proximity of opposite direction corners of unconnected paths.)
I have another where I'm testing how well it can iteratively rewrite the same program without regressions and successively stricter resource conditions. Instead of progressively adding lines of code to make an over engineered behemoth, starting over with the previous version as a template of functionality lets it consider the problem increasingly holistically
I would be genuinely interested to see how it evolves the same program over a large number of generations. Given a whole lot of software is more a collection of programs rather than a singular "program", I have to imagine that the outcomes would be vastly different in shape when dealing with smaller targeted, articulable-in-a-few-paragraphs programs vs many-years-of-business-domain layered programs.
But software seems to be getting slower and less user friendly by the hour.
Both can be true.
AI SDLCs simply compress the same incentives that were always there: you could invest in optimizing your architecture and improving your UX, neither of which make for big bullet points that close sales, or you could invest in new features that do.
The investment that was previously measured in months of developer time may now be measured in token spend across days, but the underlying dynamics shaping where it’s directed haven’t changed.
Because it's not built with client side optimization in mind any more. It's built with circular investment in mind.
Big tech funds startups (through obfuscated legal and financial means) and that funding goes straight back into big tech cloud hosting and SaaS services.
Isn't this just enshittification? Where money is the driver, enshittification is just bound to happen.
The private equity and venture capitalist playbook has always been enshittification: you either take an existing high quality product or innovate one subsidized with investment capital, then once it’s time to flip the business you 10x the price and cut costs (which usually cuts quality).
AI is the perfect tool for enshittification, because it can get you 80% of a finished product with a fraction of the costs it used to take, but the last 20% takes 2-5x times longer. So you just ignore the last 20%, make it up in volume by producing 100 mediocre products instead of 1 great one.
And that’s basically the state of the tech industry right now. I don’t think it’ll be like this forever, I think there will be better ways to collaborate with AI rather than wholesale delegation and prompting. But for now we’re stuck with mass enshittifation. People who have no sense of quality, taste, or craftsmanship don’t even understand, they skim over some generic blog post from Claude that takes five paragraphs to say one thing or watch the Coca Cola AI ad and don’t event notice it’s a different train in every shot and they think it’s so amazing. I hope this isn’t just the way things are now.
If that euphemism makes sense of it for you, sure.
In my experience AI coding is often about making lots of additive changes without a lot of (or any) thinking in advance.
Not sure how either of those things are conducive to performance
Incentive has shifted. It’s now more important to worry about “developer productivity” and churning out as much code as quickly as possible than it is to spend time optimizing that code.
As long as there’s a trade off between experimentation and performance, software will always be slightly too slow.
I recently built a piece of code which downloads a bulk set of data, indexes it for search, and then serves a pretty web UI on top of this with the help of some AIs. Normally I would have reached for sstables, sqlite, etc. This time, because the lookup patterns actually would not have been too efficient on sstables and SQLite would have been overkill, I had an agent take the data structures, pack the text effectively, and build a prefix tree for fast auto completion from the search bar. It was great. I could have done this all before but I wouldn't have. I would have felt sqlite was fast enough. The resulting web server is significantly faster feeling (because the optimized lookup speeds) than an sqlite implementation would feel like.
I think engineers building very complex systems now have a lot of performance knobs to twiddle that would have just been too costly for human effort. Since we constrain the responsibilities of the agent slop is less of a problem. We relegate it to defined tasks with clear API boundaries and test harnesses.
This makes sense for a verifiable task like a regex engine - but those are clearly defined and I doubt an LLM will have a meaningful speed increase over current human regex engines.
Anything else kind of just feels like "make GTA 6, make no mistakes". The ability for you to write fast and correct software is really going to depend on well you can specify your problem, and having the ability to specify correct programs in such detail an LLM can reasonably performance tune it is a big enough reason for me to believe there will still be slow software
Jevon's paradox implies we're getting more fast software, and more slow software. Sturgeon’s law implies the ratio will remain the same. 90% of what ships will be slow, buggy, slop. Just as it was before LLMs.
>LLMs are causing slow, bloated, code are going to eat crow once they re-write everything in super-optimized assembly
LLMs have caused such an increase in ram prices that pine64 is no longer making linux machines. When you can't afford RAM, as a direct result of LLMs, it's going to make your assembly app slower too. Sorry, this is the future you chose.
> performance work that used to require a person or team that had a rare set of skills can be done by anyone who can type a few sentences
This idea is the reason why software will keep getting slower and less reliable. Because it's wrong and yet people believe it.
Not only do people believe this, even if they didn't, they would still have the problem that most people (including many engineers) are bad at telling who is a good software engineer.
So this means that code will get worse and worse; less performant, less reliable, less secure.
It will be impossible to fix because nobody would know what the problems are let alone identify who is able to solve those problems.
I don't know how bad it has to get before people understand we have to rebuild everything from scratch on much more rigid guardrails.
Well, at least with the RAM crunch that the AI boom has caused, I hope there will be some sort of pressure to write efficient software, otherwise device manufacturers are quite fucked.
Yah, maybe $1k/dimm on 32 dimm system is a slight price bump.
"There's no reason" being very different than "we can now do the thing easier". There are still tons of reasons for software to be slow, the biggest of which is priorities.
If you want your software to be fast, stop writing it in Python and TypeScript and instead write it in Go, Rust, C++ or C. But nobody's going to do that, because humans are emotional creatures who get attached to things (like programming languages). Or if you want your software to be fast, stop spending your time and money (tokens) on features, and start spending it on profiling log replays to find a bottleneck causing a 50ms delay for the 95th percentile. But nobody's going to do that either, because how slow is too slow? Answer: when it's slow enough to scare the shareholders or annoy the developer on their high-powered laptop.
If you want faster, more efficient software, force it to run on a 100MHz CPU with 512KB RAM and a 56k modem. Then you will definitely prioritize speed.
There is no reason for it to be non-extensible either and depending on the use cases people might want to trade speed/optimization for more runtime customizations which will inherently be slower than pre-baked SIMD regex and whatnot.
The vast majority of users - weighting by the profits they generate for software companies - obviously don't care about "slow".
And Munger's Law never cares if you hate its consequences.
we should just have the LLM rewrite everything in Rust :D
problem solved
Eh, not sure. Very few humans do software optimization productively, so I'm not optimistic about machines built by humans, either. Every encounter I've had with agentic optimization involved trying to apply a bunch of myths to an existing code base, in a way that seemed cool (unrolling loops, eliminating apparent branches, SIMD) but which was all pointless because the only credible optimizations were going to come from doing fewer loads and taking up fewer itlb slots and stuff like that.
Data point of one, but after a few months of uselessness, I have managed to get some pretty serious, measurable performance improvements with AI optimizations -- order-of-magnitude speedups of business critical processes which took days as well as significant latency reductions.
But you need a really solid workflow, solid benchmarks which run quickly, and a lot of tokens -- plus a rigorous profiling workflow.