It's why software has become far more unstable. There's nobody around to actually maintain it.
Don’t get me wrong, I use these tools daily. That being said I’m having a very hard time finding where the productivity gains are.
I imagine I’m far from alone in that search and when you pair that with the constant marketing and glowing “analysis” from some of the enthusiasts about how this technology is “solving coding” or “changing the face of security” or even leading to AGI it starts to tickle that part of my brain where I keep blockchain, NFTs and copper bracelets.
So TLDR the tech is good but the hype-slaves and their masters are killing it with overpromising and under delivering.
I'm really struggling to understand why you would use them that much if you aren't sure they are more productive. Is it just a more enjoyable workflow for you?
I ask because I find AI assisted workflows extremely painful. Constantly pulling me out of flow, like driving in gridlock traffic.
That and using it like a search engine feels a little like having good Google back.
> for the love of all things holy why are using them if they don’t provide any benefit?
tbf, there is some benefit there but its much more nuanced than the hype suggests (as usual)
So why are you using the tools? Personal curiosity? Workplace mandate?
I've made measurably more and faster progress on both professional and personal projects since adopting these tools. Sometimes assisted is less productive than unassisted, but the net gain is pretty obvious to me.
An AI is like delegating it to the junior programmer you don't have. You spend 5 minutes writing the spec rather than an hour coding.
It's usually something you could do yourself, and just can't get motivated to type out the code in the moment.
I use the tools, but I'm under no delusions that I'm not just being lazy. I could just do it myself, and in some cases it would take roughly the same amount of time, but I can scroll TikTok while it dutifully churns out code.
This is simultaneously one of the easier management KPIs for employees to hit and one of the least meaningful.
https://www.wsj.com/tech/ai/ai-work-use-performance-reviews-...
There really are many programming jobs that are rote and I have no problem believing that an LLM based tool can learn the pattern and regurgitate with the tweak de jour. In those jobs LLMs probably do increase productivity.
But there are other programming jobs that are not rote and there is no pattern to learn because you haven't done the thing yet. LLMs aren't any more useful than a normal base library would be, and if you're already good at using a library of code, they're not a productivity booster and often, in my experience, a hinderance.
I think another point is the prompt actually forces the engineer to spend a moment to actually think about what they're doing and make some kind of plan. Pre-AI tools way too many programmers just jumped straight into problems without thinking what they were doing figuring they could code their way out of anything and ending up stuck in some cul de sac and having to back track. And if they just stopped and made a basic plan they wouldn't have that issue. Forcing engineers to make a plan, who wouldn't otherwise do so, before they start, could definitely be a productivity booster for them.
I’m not surprised about productivity though. Efficiency gains are limited by the actual bottlenecks. And truthfully, I think people are deluding themselves a bit about how effective vibe coding is and how much faster they are actually moving when you consider developers still need to form an understanding of the codebase and its systems.
Outside of coding, is there really a use case for LLMs that has the potential to make big efficiency gains? Idk.
So I'm not actually being more productive, but I've cut my costs significantly to do the same things I could do before.
Of course ymmv, and if you find yourself paying subscriptions for stuff you can replace with vibe coded apps, all power to you.
The most likely outcome is an AI bubble correction that will be somewhat painful and wipe out many/most AI startups, followed by AI settling into day to day in a way that’s useful and found in many places, but not world-as-we-know-it-ending like the AI bros predict.
So, basically the easiest robotaxi market on the planet? Call me when it works in Bucharest, Mumbai, Istanbul, Cairo, etc.
For software the last 80% of effort needed to finish the 20% remaining items is the hardest and hardware is even harder.
I am sure you can find truly out-of-distribution cases where the car will make a mistake, but the data shows that this is more rare than a human driver making a mistake.
https://www.thecity.nyc/2026/04/06/waymo-driverless-cars-tes...
Phoenix is probably about as good a location as you could get for a self driving car. It’s not yet clear how wide their success will be outside of that niche.
[1] https://www.electronicdesign.com/technologies/embedded/digit...
P.S. I'm not saying fuzzy logic doesn't have applications, I know rice cookers are a thing, but I think it's safe to say we have other options for controlling non-linear systems these days.
Our stock price has also gone down 70% in the last few months
Naturally, we're pivoting our platform to put AI front and center
The evolution of harnesses like claude code or open cause, and metaharnesses like Ralph loops, gas town, claws, etc. Will progressively allow for gradually better results and abilities even if models stopped evolving, and if the Mythos eval numbers are to be believed, there is still no hard ceiling to be felt yet.
At the same time, small models that can run on PCs VRAM/UNIFIED RAM have like Qwen are becoming more useful.
I predict that having more and more loops within loops within loops and layers of cloud/local models of different capabilities will solve a great many limitations of LLMS today...at the cost of speed and token count.
We've never had a tool that is at the same time so unreliable and complicated as GenAI before. It will take us a minute to figure out how to use it properly.
For example, consider a commodity business for software product X. All vendors of this product had their costs reduced by a factor of 100 over night for developing new product. They could increase their profits, lower their price, or re-invest the dividend. In software, the buyer usually buys on quality - so they all re-invest.
Now they are spending the same amount on product development, for the same price tag, and earning the same profit - but they might be shipping much faster.
On one side, there is the usual process of figuring out how to properly use this new tech. It is to be expected that some experimentation is necessary to figure out what applications AI boosts productivity for and what applications it doesn't. There is unusually strong evangelism pushing AI into everything, so the negatives are going to be salient and may make it hard to spot some of the successes.
On the other side is something a little bit new: Deliberate enshittification. OpenAI and others no doubt saw the power crunch coming years in advance, yet it's still happening and is, ostensibly, the reason why prices are starting to go up. This was not unexpected. It's the business model. Build to the capacity that is cheaply available while offering your customers a sweetheart deal to get them addicted, and then jack up the prices when the competition has no cheap power to build upon. The result is locked in customers and locked out competition.
On one side, you have people learning when AI is appropriate and how to use it efficiently. On the other side, you have a small number of AI companies trying to extract every last bit of value so that any productivity gains wind up in their owners' pockets. Will the gains of more appropriately applying AI be entirely wiped out by enshittification?
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