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.
Fwiw, I currently consider code generated by models below opus level borderline unusable. It really is horrendous there.
With opus, it's still not good - but it's a lot better then some of my colleagues write.
Well written code, carefully planned out and thought through is still only possible by either hand writing or holding the hand of the model so much that you may as well just write by have, as you'll be quicker.
However, the code opus produces is good enough... So I rarely take action anymore in private projects
But as the owner of the company, I was still liable for the product my employees made. That is the same thing here. It's not negligent to have Claude code write code for you at 10x the speed you can do it yourself. If I hadn't supervised my employees and put practices in place to control quality and safety, I would be liable. I'm actually far less worried about liability now, because I have my hands in every part of the system.
Calling it vibe coding is pejorative at this point, meant to imply that someone who has no skill or craft in software development just types a few prompts and gets something they didn't have a hand in the design or development. That's not what the professionals who are using AI coding are doing.
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.
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.
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.
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.
When producing code is basically a solved problem, generating worthless amounts of code is literally one click away. Execution remains key, and still means iterating upon an idea for a long time, rather than generating a prototype with Claude and calling it a day.
Honestly for some it's just a compulsion that they can later brag about on forums, but it never results in anything worth showing the world.
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.
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.
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.
If you don’t understand the output you don’t get to judge the quality.
> 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.
Those things shouldn't be hugely complex. They sound actually very simple.
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.
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.