That said, in the corporate world, it's not that easy. There are tons of hoops to jump through and context switching all day long. So while my code is 100% AI generated these days, and I can make extremely complicated apps quickly at home, at work however, I'm burned out and completely checked out for the most part, entirely due to AI.
We don't have the same capabilities to burn tokens in the corporate world like we do at home. We don't have the creative freedoms we have at home. AI productivity is just not easily measured.
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.
But, I'm running a small, two person business and we are being paid by a large company to develop a robotics project. We're working at a very fast velocity and have fielded a prototype within a few months. I've been doing this kind of work for years, and we're more productive than larger teams (8-10 developers) I've employed before. Five years ago, I would have needed at least 4 senior engineers to do the work we're doing now, and we're moving faster than we could even then.
And I'm being compensated at a flat rate by a major company so we're making really good money. The customer is happy with the results. Claude easily writes 90% of the code we use.
It has vastly increased my hobby programming "velocity", but has improved my day job performance by perhaps a quarter at most; so much in programming is not the task of programming, but mapping the problem and dealing with the outside world.
I feel like I am an outlier, but I do exist.
In a corporate setting I've seen it reduce velocity by 80% in some cases.
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.
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.
I used to work with a guy who at some point had heard the term "macro" in regards to Word or Excel and at that point anything to do with computers was a "macro."
We'd get calls from him at the help desk saying "my mouse macros are not working" meaning i.e. that his mouse wasn't working, in that era because the roller ball was dirty.
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.
So you’re just flinging tokens at the wall until something sticks. That’s not engineering.
Many of these things seem like they have the possibility of being their own products, but honestly, I'm just tired and burned out. I'm mostly building this stuff to remain relevant and prepared to pivot when the inevitable layoff comes. This layoff will come, for the same reason I'm able to create all this shit. CTO's and CEO's think this stuff will really work.
What the C-Suite doesn't know is... this is a fucking disaster waiting to happen. At some point the realization will come, that you have no moat. You have no unique ideas. If you do, they are easily copied. You're also giving over your IP to a company that is losing a fuck ton of money and will keep raising their prices. Worst of all, nobody including yourself will understand what your code is doing or forgetting to do. I foresee a bleak future.
I've had a decent amount of success in this industry. My plan is to ride this shit storm and see where it takes me, or do my best to help bring the entire system crashing down. By that, I mean, I'll release / contribute free and open source software, which mimics every business model that doesn't have a legal or financial moat. For instance... Slack, Miro, Shopify, the million AI wrapper products that are springing up every month etc.
> That said, in the corporate world, it's not that easy.
The article is accurate, and describes this very paradox.
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.
I believe this is where the huge divide in perceived AI productivity in SW comes from. It’s folks working on low-understanding-required domains talking to folks working on high-understanding-required domains.
That'll be a no from me dawg.
you COULD create a writer/reviewer loop hook to catch and rewrite sentences like this, but almost no one does
the result is usually very long essays that say very little (kind of like in grade school where we're graded on "length" so we just add words as filler)
Fine so long as the content checks out (!important). I'm not reading it for its artistic or human value.
If we can gain 3% across the board gains on AI based tasks without subsidized expenses, that would be a great win!?
Which is to say any AI study from a year ago is fairly out of date with the speed of advancement.
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 AI can write code much faster than a human. In 4 hours it might be able to write what would have taken me a week. Or more. Assuming it had proper specifications for that volume of code.
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.
Given that, I actually think the conclusions of the article are backwards. If the gpt-4/gpt-4o era had a measurable 3% improvement in productivity, how much more improvement are we getting from models today that are way way better?
https://bfi.uchicago.edu/wp-content/uploads/2025/04/BFI_WP_2...
Oh so this article isn’t really about where AI spending is actually happening.
I HOPE people aren’t spending money on AI writing emails. That’s definitely not worth it.
Companies are spending the most on coding tools, not email writing. That’s the main value proposition right now.
It also doesn’t get into media generation and industries that use video or music.
It’s just a really narrow look at probably the worst use case for AI. Useless.
I wouldn’t expect productivity to increase paychecks all that quickly except in special cases like that memory factory in South Korea. Higher productivity sometimes eventually results in more revenue and some companies competing for better workers by offering higher salaries (as has happened for software engineers), but this takes time and depends on companies believing that hiring better workers benefits them.
We aren’t really seeing that yet. We are seeing layoffs and companies being cautious about hiring. If it happens at all, it will take time.
Engineers, (and many other types of specialists/professionals) can use it to speed up their work and increase output (even increase quality in some cases if done right).
But it's not gonna make inherently inefficient, political, and corrupted internal/company processes any more efficient, it might actually multiply those existing inefficiencies.
In fact, I would argue the that with AI, companies should expect to spend more on average, without necessarily seeing any meaningful cost savings nor increase in profits.
That does not mean they can escape this though. It is just like paying for ads, backlinks etc.
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 people using the AI tools to improve their output will set higher consumer expectations for their product or service. If you do not also use AI, you will not be able to keep up, and so you will lose business over time.
So you have to pay for AI just to keep up with the other people who pay for AI.
But then if everyone is paying for and using AI to improve their product or service, no one can steal market share from each other. So you are all now paying a lot and AI is doing a lot and the buyers are getting better results, but that does not necessarily lead to better financial results in terms of market share, revenue, costs or profits for the business that is now using AI
The size of projects I've done in the past year compared to before is mind boggling. There is NO way I could have reached this level of productivity without a properly trained and properly prompted LLM.
Ya, using AI to write your emails isn't that huge of a time savings. But coding is different matter.
not my experience at all
Fascinating prose, implying that it’s possible that AI can inauthentically speed up the right tasks, which makes no sense at all. Good job Claude!
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!You don’t need a pro account for emails.
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.
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".
I’m saying it shouldn’t hardly cost any money, emails are not token intensive and most people could likely use the free version of chatbots just fine.
Typically it takes me as long to review code as it takes me to write it myself. The exception is repetitive coding tasks that cause my attention to drift, which LLMs can do very quickly.
If you don’t understand the output you don’t get to judge the quality.
When tractors came along, farmers became dramatically more productive. Were the farmers who did things the old way "forced" to buy tractors in order to stay afloat? Over time, sure. But this was in no way zero-sum. More products made it to market (literally), more of the workforce was able to shift away from manual labor, and society became much better off overall. The people who moved to cities made dramatically more money, and the farmers who remained made more money too.
Edit: this is literally on the front page right now: https://news.ycombinator.com/item?id=48775979
This is not a prisoner's dilemma in any meaningful sense, unless you just like being inefficient and wasting a bunch of human effort.
(I'm not convinced yet whether AI actually works this way or not. I'm just saying, if it works this way, the economic theory is well-developed and we can predict fairly accurately how it's going to play out.)
My experience has been that small-ish projects like these are the most likely to contain code with bond-villain level of complexity (and success).
More than once have I been stuck going on Da Vinci Code-esque adventures to uncover bugs in prototypes years after the fact because the business pivoted away from what it was trying to do and later decided to pivot back, only to discover that despite the prototype and systems/libraries it worked with not changing, somehow it mysteriously doesn't work at all or it fails in convoluted ways despite being perfectly functional when it was originally shelved.
Those things shouldn't be hugely complex. They sound actually very simple.
The first client has paid, and signed a 3 year contract. Not huge money, but 5 figures per year. That client didn't even see what the F50 demo user saw.