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Comment by stabbles | Hacker News Reader

Wouldn't AI make the field of mathematics more ambitious? In software development it feels that way: there are often tasks I can take on that would have been too risky in 2025, because it was unclear if they were worth it. Now you generate a prototype and can make much better judgement calls what is possible and what is worth pursuing.

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It might feel that way, but I'll ask again: where's the payoff? Where's all the amazing software that everyone is now supposedly shipping 10x faster than before?

If I look at the software I'm actually using day-to-day, or that my friends are using, all this stuff looks exactly the same as it did in 2021. Not a single product release from Google, Microsoft, or more scrappy companies in the past 6 months made me go "wow, they couldn't have pulled that off before". All the vibecoded "Show HN" projects seem to be half-broken and then abandoned before being finished.

It feels like we've gotten less ambitious, not more. Because yes, you can prototype more easily, but this means less commitment to what we create.

Mathematics is probably the same way. There's a short-term rush when you pull the lever, but there's less desire to get invested in what comes out.

I think software from major companies is in worse shape than in 2021. But that's on purpose, the enshittification continues. Otherwise I agree with you.

From personal experience in a small (total <10 people) company, we have definitely 10x our product in the last two years. What 4 devs did in 4 years have been dwarfed by what 2 devs were able to do in 1 year with AI. I'm not so familiar with the giant companies, but from afar it seems like they already have practically all the code-writing capacity they wanted anyway. Google could say "let's build a browser" or "let's build a mobile phone OS" or "lets build an experimental Fuschia" and throw all the people they needed on it already.

You might have delivered 10x more code, but did you deliver 10x more value?

Or put another way, are you making 10x more money?

It's easy to spend excess productivity effectively wasting time. Most companies did it before AI, and will continue doing it after.

It's easy to believe you are not wasting time because you have more bugs fixed or more features delivered, but if it doesn't move the bottom line, what is the point?

I mean it doesn't have to bring 10x more value, but it can be a 10x better experience for existing users. There have been so many niche bugs that were never ever fixed because there's more important things to do, but because fixing a niche bug is now 1 click away that really changes the economics quite a bit especially for small teams.

I'm personally working on a product solo that would not be possible without a team of 3-4 people that are all knowledgeable in that field and would take at least 1 year to get it started. It only took me 3 months to get a fully working solution with $1200 worth of AI subscriptions, the value multiplication is nearly x100 here.

With all due respect when you strip away the noise - unless it shows up in the financials it hasn’t done anything for the firm.

This is something many firms are struggling to grasp.

The world does not revolve around money, specifically non profits come to mind where this can be a huge boost in quality for everyone involved.

Again, I've heard that countless times on HN. Every time you ask, everyone is 10xing it and building amazing things that will go to market very soon now. But it's been a while; where is it? Somehow, everyone is just sitting on all these revolutionary advances while selling the exact same stuff as they were selling before, with the same warts and the same annoyances. Gmail is the same, Chrome is the same, Photoshop is the same, Slack is the same, Excel is the same... the only thing that seems to have changed is how SWEs feel about their jobs.

To be fair, what revolution were you expecting to see, exactly? You can 10x a "dashboard but with AI" app all you want but it's only ever going to be a "dashboard but with AI" app. And I note that nobody seems to be claiming that the AI helped them create a brand new kind of app.

Anyway, Sturgeon's law and all that.

Large companies move slowly and have lots of red tape. Give it a decade before making this call as far as large companies are concerned. That's how long it takes to change processes.

Generating AI images is something we couldn’t do before. AI assistants are better at web search than anything that came before.

This has a clear answer in systems theory; the ability to bang out code was never the limiting factor for Google et al.; large organizations are limited by coordination costs.

It's the small teams you need to pay attention to, the organizations that really were limited by engineering capacity. These are by definition also less visible - for now.

That’s not true at all.

The constraint is taste and vision.

You can’t buy either.

That "for now" has been going on for ~10 months. That was enough for some startups to make a splash in the pre-AI era; now that they can move 10x faster, shouldn't we be seeing evidence left and right? All the contenders stealing lunch from Big Tech by offering better products or exploring new frontiers?

>Mathematics is probably the same way. There's a short-term rush when you pull the lever, but there's less desire to get invested in what comes out.

Perhaps it will take until the next generation to come along to really embrace the new AI-assisted way of doing mathematics. The current generation has too many reservations.

Beautiful post.

Especially the implicit trade off. - if it becomes ‘easy’ to produce little thought and care is put into it.

Personally I saw this coming ages ago.

After all we live off the contributions of the few in relative terms.

No surprise - most humans don’t have much imagination or creativity.

I'm not a huge fan of AI, but there _are_ examples of mostly-AI generated software that are being used by real people:

- Nourish is a nutrition coach/food diary that is recommended by dieticians. It's mostly AI generated, but logging food is easy even if it's not correct (it's better to log and have it be slightly off than to not log at all --- I've been doing it for almost 20 years now)

- Cronometer isn't AI generated but its photo logging feature, which I use heavily, definitely uses a vision model to guess at what you're eating. This is SUPER STUPIDLY HELPFUL when I'm out with friends at, let's say a Korean BBQ joint, and don't have the time to log everything that I'm eating as I eat it. (The right thing to do is pre-plan your meal, but this isn't always possible.)

- Feeling Good! is a CBT therapy app that was written mostly by one of the creators of CBT with Claude. My therapist recommended it to me recently. I haven't used it yet but I'll find a way to.

- The creator of SparkPeople is re-releasing it as an almost completely AI-generated platform written by Claude. He's super upfront about this (on the landing page, in the privacy policy, in the onboarding docs), which I highly respect, but I actually hit him up on LinkedIn because the landing page was on Vercel and looked so suspect.

- Boris has said multiple times that Claude Code is increasing writing/maintaining Claude Code, which I deeply respect as a person who's a fan of compilers compiling themselves (like Go, which has since 1.4).

- Have a look at /r/apple on Reddit on a Sunday or even here on "Show HN" threads. Most of the stuff coming out is vibeslop, but some of what's being published looks like solutions to real problems.

All vibe-coded software is complete unusable trash. There are no exceptions.

So no, we're not advancing, we're in fact regressing. The crash and disillusionment of the post-vibe-code world will be immense.

Maybe, but what remains of the human professional mathematics will be unrecognizable (at least for those without tenure I guess). All of our credit assignment systems are breaking and access to computational/financial resources is becoming way more important. Math has been one of the most open academic fields but everyone is becoming afraid of sharing their ideas. Personally, my job has slowly been moving from open-ended brainstorming to prompting/digesting LLM output (or LLM output transmitted by grad students). And the students are so demoralized! Also, the academic funding structures which have supported math departments are looking less and less stable - I imagine many of them will shrink.

I think it's possible that it will play out that way, and that it's just too soon to see it. Tao was very optimistic about AI up until a few months ago, and I think what's changed is that an AI generated proof isn't that informative unless it is understandable by humans. So far experts are finding the solution to Navier-Stokes incomprehensible, so we only learn one thing (it's false), instead of the hundreds of things we learn from reading a proof we can understand.

Maybe this is a one-off, or maybe in a few weeks we'll figure out how to get AI to explain the proof in terms we can understand. Then math research will accelerate. But maybe it's not a one-off, and by this time next year we will have an oracle that just answers all of our questions, but in such a way that we don't even know what questions to ask anymore. Then AI will just mop up the existing and the subject will end.

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