Andrew Ng says bottleneck in AI startups isn't coding – it's product management

Many people are seeing huge gains in coding productivity with AI. If you're not one of those people it might be useful to evaluate why you aren't experiencing any benefits, instead of claiming that there are none.

> If you're not one of those people it might be useful to evaluate why you aren't experiencing any benefits, instead of claiming that there are none.

It might be that the gp is smart enough to code without a crutch.

> Many people are seeing huge gains in coding productivity with AI.

I don't want to speak for the person you replied to, but I think that their main point is... are they?

I see lots of articles about huge increases in productivity, but I think it's fair to argue that we've yet to see the huge increases in useful products that would surely (we hope) result from that if it were true.

Maybe because it's become second nature to you, due to a long enough career spent practicing the actual skill rather than using a crutch or tangential skill?

I'm speaking just to coding productivity. I think AI does very little for business development or creativity type issues.

People should realize that denying that AI can boost productivity in coding makes it look like they don't know how to use it, or believe in some conspiracy that no one is actually benefitting and it's all market hype from tech bros.

I'll bet he's strong enough to push nails into wood so he never uses a hammer.

This is Gavin Belson level stuff.

I admit that I don't know how to use it and it does seem like marketing hype and tech bros with some mild value and I'm waiting to be proven wrong and crushed, John Henry the Steel Driving Man style, and it's just not happening.

I believe in some conspiracy that no one is actually benefitting. I don't want to blame marketing, it's genuinely very cool that I can give Cursor a description of what I'm trying to do and get a result that's like 80% correct. But I've repeatedly found that going from 80% to 100% takes just as much time and effort as it would have to do it myself from the start.

I've shadowed people who believe AI is helping them, and it seems to me that some of them don't notice how much effort they're spending while others don't bother to correct the 80% version once tests are passing.

I don't think this line of reasoning holds. The only thing people should look at are peer reviewed studies, lots of them ideally, and with no conflict of interest. Who's getting productivity gains? What kinds of work are they doing? What doesn't work so well? All of these questions should be investigated by studies. People feeling productivity gains doesn't imply the gains exist.

Otherwise it sounds like "many people have had their lives changed by {insert philosophical/religious movement}, so if you're not finding it true you should look into what's wrong with you."

There are a lot of different niches to software development. It could be that your specific niche isn't seeing much benefit yet. However, continuing to hold onto a view that AI is hype garbage could very possibly hurt your career in the near future.

The people I know who are experiencing these gains were never more than juniors in skills, despite the number of years they've been on the job.

Makes me evaluate indeed.

If they were, where are their products and open source projects? Let's circle back.

One thing I've noticed about the super-AI enthusiasts on HN is that not a single one ever have a single comment linking to a repo of work they've made with it.

I check. I actually always do because I'm really keen to learn how to use these magical super-AI workflows. I've watched streams, replicated clause MD files, tried all the context tricks.

I'm not even saying AI doesn't help, it's great for getting me over the blank page writer's block. It's just not great at much else.

So I've just checked your comments and not only do you not have any examples of your super-duper AI skills, but it looks like you've been in the industry less than a year, graduating from a PhD last year?

You also admit it took you a week trying to debug a problem before an AI fixed it for you. Because you'd missed some parentheses in an algo.

I'm not trying to shame you, but that does signal your inexperience. If you'd have made the code well and easy to test, you should have spotted your bad algo quickly.

So is it that we're all bad at using AI? Or is it that AI benefits inexperienced programmers more?

> The only thing people should look at are peer reviewed studies, lots of them ideally, and with no conflict of interest.

"Ignore your own direct experience, only research papers matter" is certainly a take.

The beautiful thing about the current generation of tools is that they are so incredibly cheap relative to historical tools intended to improve engineering productivity. You can't just run out and pick up CASE tools for less than ~$CAR to ~$HOUSE. A pro subscription to whichever AI tool you want to try is $20.

Ignore research, try them, if you have success, use them. There's no dogma here. Just empiricism.

Damn bro. Deep dive into the comments.

The bad algo was a scaling problem for one equation. That particular equation wasn't some y = mx + b thing, it was the result of a discontinuous galerkin finite element scheme that I wrote from scratch. The actual equation was one that I found after about 2 pages of hand written derivations with high level math. Not really a coding issue, just an algebra issue after really intense manipulations of partial differential equations.

The fact that AI found that problem, a problem that could only be found by someone able to do complex manipulations of PDEs is incredible to me. Perhaps I didn't tell the story well in the past comment, but it isn't like I didn't know python syntax and AI held my hand.

I don't post repos because I keep my hacker news life separate from my personal life, and my repos are tied to my name.

Most major software companies are demanding that their employees use AI, so you should be able to look at any open repo from Google, Microsoft, Facebook, etc for examples of AI use in code.

While you're looking at peer reviewed studies, I'm over here being way more productive with AI and getting shit done.

Doesn't need a conspiracy, just excessive enthusiasm about the new shiny that hasn't been tempered by experience and reflection yet.

I, on the other hand, tried to vibe-code BDF-like ODE solver. Not some rough prototype, because I can do the rough prototype easily myself, but something robust and fast, with event handling etc. AI couldn't do it. Actually it couldn't do correctly anything more complex than out-of-the-textbook explicit RK4, but this is something undergrad students do while learning numerical methods for the first time.

And solvers are actually a simpler aspect of the project I am working on. It also includes (or rather aims to include) optimizing compiler with DAE to ODE reduction, advanced numerical debugging etc.

This is why these discussions are pointless - AI works well for some people in some contexts, for others not so much, yet both sides extrapolate their experience as universal.

I see what your saying but I would argue that vibe coding an ODE solver is an incorrect use of the tool. For something like and ODE solver you need to have a really solid understanding of what data structures you will use,and solid general knowledge of the numerical methods you want to implement. Then, you can use AI as an assistant when you get stuck, or to deepen your understanding, look over your implementation, etc.

It seems like developers used to always joke about how much they used stack exchange (even senior devs). Now it seems like there are suddenly so many people who claim to never need any help and can just smoothly bust out beautiful code all day long.

> I see what your saying but I would argue that vibe coding an ODE solver is an incorrect use of the tool.

Agreed. No true Scotsman would use the tool this way.

> When developers are allowed to use AI tools, they take 19% longer to complete issues—a significant slowdown that goes against developer beliefs and expert forecasts. This gap between perception and reality is striking: developers expected AI to speed them up by 24%, and even after experiencing the slowdown, they still believed AI had sped them up by 20%.

Maybe you just think you're being more productive ;)

https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...

But that is the case - I know how to implement production-ready ODE solver. My issue with AI was that it was able to help with basics, but not with those really important bits, so it couldn't really deepen much.

> For something like and ODE solver you need to have a really solid understanding of what data structures you will use,and solid general knowledge of the numerical methods you want to implement.

For basically every thing you program, you need to have a really solid understanding of what data structures you will use, and solid general knowledge of the methods you want to implement.

I claim that as a conservative estimate at least 90 % (likely more than 95 %) of what I code at work (and even more for what I code privately) is of this kind.