> This article is probably not accurate. AI allows me to create hugely complex apps.

To prove the article wrong even within the context of AI coding (which wasnt the focus of the article) you would have to present proof that creating these new AI apps resulted in you making lots of new money.

On personal projects AI has multiplied my velocity by about 10x.

In a corporate setting I've seen it reduce velocity by 80% in some cases.

https://news.ycombinator.com/item?id=48777257#48778335

I have been an AI sceptic for a long time. But we now throw in a native Android and native iOS app with apps we work on for clients now, because the amount of time saved makes it possible.

I could not move this fast on a corporate env though. I mean, I’m using half a dozen Chinese models, a Google AI pro sub, an OpenAI pro sub, and probably will add Claude to the list soon. We have also completely changed our workflows to align with how agentic programming works, including all the way to product management on the first place.

This article isn't focused on LLM for code generation.

Who is "making apps" at work? I keep seeing AI enthusiasts talk about how many apps they are making. I've never seen a real life dev whose job is churning out apps.

The article could be accurate but misleading.

It focuses on the average (mean), but does not talk about the distribution at all. It groups ALL uses of AI together, which isn't very helpful in determining if it is worth it or not to use AI for a specific purpose.

For example, these stats could mean every single use gets 3% time savings, or some uses get 80% time savings and other use cases actually take more time.

This has always caught up to me in a bad way.

All it takes is some brittleness to need to take apart the app code and understand it. AI fixes one thing, breaks something completely unrelated.

And usually it’s full of lots of spaghetti code slop. Then I need to find ways to modularize it, make it testable, and at least at some level clean it up.

This is why I say AI sw eng is basically just working with legacy code. It has always turned into unraveling and refactoring the ball of mud to be sane for me and agents to continue working with.

More and more though I just go slower. Write much of the code myself. And setup good validation for the specific parts I trust an agent to work autonomously. I try to expand the surface of things that can be done well autonomously without losing my own grip on the code.

> Apps that I mostly don't understand the underlying code

So you’re just flinging tokens at the wall until something sticks. That’s not engineering.

> This article is probably not accurate. AI allows me to create hugely complex apps.

> That said, in the corporate world, it's not that easy.

The article is accurate, and describes this very paradox.

> Apps that I mostly don't understand the underlying code but can evaluate that it works fairly quick and effectively.

This is a contradiction. If you don't understand the code, you cannot in fact evaluate that it works correctly. If that's the approach you are taking, you are doing sloppy work that is going to blow up in your face sooner or later.

Doesn't read like it's AI-written to me.

I think we have to accept that most/all prose in the future will either be AI written or AI edited at least.

Fine so long as the content checks out (!important). I'm not reading it for its artistic or human value.

> A year ago AI wrote roughly 0% of my code and now it writes roughly 100%.

Did the amount of money you or your employer make rise by anywhere near 100% as well?

Because that is really the primary question behind articles like this focusing on the economic benefits.

But that itself doesn't tell us yet how much time it saved you at the end of the day.

The study is two months old (March 13, 2026).

Edit: Also, it's about writing not coding and it's point isn't that time isn't saved but even with time saved at the task, you don't get a broad decrease in total worked time for many/most workers.

AI hasn't gotten better, you've just given in to the hype.

In what sense is AI like paying for ads? In one case you legitimately lose to the competition if you stop (prisoner's dilemma). In other case, whether and how much you lose depends almost entirely on how much it actually impacts your productivity in practice (or not).

If you're referring to the public perception benefits (supposing those even exist, which isn't clear to me), then it seems easy to make a lot of noise via PR while doing the minimal amount internally to explore the use case and not actually push it as hard as you say.

The article gives valid advice in using AI on tasks AI is good at, but the article could be clearer on is what AI is good at and what AI is not so good at.

> I HOPE people aren’t spending money on AI writing emails.

They very definitely are. I'm seeing more and more front-line responses to customer support emails that are very clearly written by AI.

Here's an example from this week:

  Thank you for reaching out and providing all of those tracking and
  return details right away. I am currently checking in with our
  Returns Team to track down your package and see why there has been a
  delay in processing it since its delivery.

  As soon as I receive an update from our warehouse specialists, I
  will reach back out to you directly via this email thread with the
  status of your credit. Thank you so much for your patience in the
  meantime!

> I HOPE people aren’t spending money on AI writing emails. That’s definitely not worth it.

The engineers use the coding AI for everything they don't like to do that distracts them from doing what they like to do. So that's certainly a lot of writing emails, status updates, meeting notes, journaling, Slack messages, and prettifying slide presentations and so so so many docs.

So if you're aghast that hundreds of dollars of AI tokens per engineer per month is being spent on writing emails, you should be even more offended that the more expensive human engineer time used to be wasted on those tasks.

I use AI to shorten my emails, I write as I talk, and I ramble a lot in my head.

This comment is odd given that it's high on opinion without ever refuting the actual data given in the article.

Like it or not, many business people spend hours a day writing emails. The article pointed out specific data showing AI could reduce these hours spent while raising quality.

I'll take the article's actual data and research over your data-free spouts of "That's definitely not worth it" and "Useless".

Except that many companies are pushing "Use AI for everything" -- seriously. Because the CEO was told by KPMG or whoever that "AI is the future". And so yeah, they're pushing it regardless of whether it makes actual sense to use it or not.

But is it a 3% gain if we include the costs? Or would it actually be cheaper to just hire humans with the money going to tokens?

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Like what

Can you say what exactly you've accomplished with this productivity? Can you provide revenue numbers and how profit increased or does your definition of productivity not include this?

I notice the study is "in the past" when models weren't as good and also it appears to focus on office workers writing text.

Ya, using AI to write your emails isn't that huge of a time savings. But coding is different matter.

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In research that studies a large enough population there will always be outliers. Maybe you are one of them. Your counterexample does not prove them wrong.

plot twist: the vast majority of "studies" are like this, complete garbage, but called "science" because they put a table with p-values in