Your CEO is suffering from AI psychosis
handyai.substack.com
handyai.substack.com
And yeah, what is it with C level executives and AI? I'm pretty much getting it forced on me by the higher ups. Management has insisted all employees come up with ideas on how to "leverage AI most effectively for the business". And now we're hiring an AI expert... For the small internal IT team of a publishing company...
At one point, I spent so much time fixing the disastrous results of people using ChatGPT to generate oodles of CSS that they would put in the WordPress Customizer that I had to resort to disabling the custom CSS functionality entirely. All because they wanted a link to be a different colour.
My assumption is that they are extremely excited about AI, because they are also extremely excited about being able to reduce their workforce while expecting more output from smaller teams.
LLM is bullshitter too, they assume everybody else does the same, so LLM does everything they think everyone does.
There is so much hype around AI and using it to move faster that the extreme focus on ‘product’ and prioritizing ‘impact’ has greatly relaxed. For the first time in my entire career, I’m able to prioritize addressing technical debt and friction in our SDLC processes.
Normally, these types of changes get bundled in with regular product work and cause timelines to grow. Nobody paid down technical debt unless it came due and was no longer optional.
I am using AI to address tech debt, but these are all things I identified as problems - and big improvements - long before AI was a regular tool in the toolbox. I just could never demonstrate how addressing them early would pay off in the long run through faster project turnaround. Fixing known issues simply doesn’t rank against new features when it comes to prioritization.
Anyway, I’m thankful to finally get to fix these things, and our ticket-to-deployment time is going way down - but none of this matters if ‘product’ doesn’t streamline their goals and priorities as well.
My boss is injecting hopium. He barely sleeps compiling massive massive documents which don't answer anything. We literally have a "(LILR Design Language)" spec which when you keep slicing through it, doesn't actually say anything.
It takes 70 seconds to full scroll through it with the fast scroll mechanism, but the whole thing boils down to "basically if it looks good then its good".
And even worse, he is asking for access to the codebase of the product since "I can do 90% of what we need and all the devs need to do is review the PR".
What is this called?
Had all the classic LLM signs. Underuse of commas. No longer sentences. No other punctuation except full stops and em dashes. Just sudden negation at the end of three barely related concepts.
I'm so unbelievably sick of reading this slop. For the love of God, it's more work to turn bullet points into an unreadable multipage document, so just send me the bullet points. I don't want to communicate with a gigantic vector representing the average person's literacy anymore.
I noticed shit like that pre-AI, but at least then it was written by a person and conceivably useful info, even if redundant and not always necessary. I can't imagine how bad it's going to get with all the AI slop now.
One step up on the ladder is that the fish aren’t being caught anymore due to regulations pertaining the hooks (disallowed) and the length of the line. The fishermen complaining are being told that the regulators are also complaining and that management has decided that complaints everywhere are the best balance.
A manic episode.
This. So much this.
It's so hard to sell an agentic platform that's designed to push real work compared to one that is designed to look flashy. (Spoiler alert: the one that outputs real work is way more useful, way more difficult to build, and less flashy because they can't just sell pure fantasies like the idea of automated CEO/managers.)
It's not just the CEOs that have the psychosis problem, it's investors as well. And for even technical users it's hard to tell the difference, but comparatively it's much easier to sell AIs on actual merit to very technical people than to non-technical people.
The big catastrope for valuations is if supply outruns demand. Videogen has not reached the expected traction and profitability, so now we're talking about 1000x code output as a success metric. But that's not quite the same thing as solving 1000x the number of user problems.
If anything these huge codebases are just creating new problems and atrophying from context rot due to the sheer amount of noise.
It's not. A lot of companies who wouldn't otherwise be terribly exposed are getting in on it. The steelman argument for it is - if you think that the optimal software development strategy is going to be much different in 2027 than it was in 2025, and you have a lot of money to burn, it may well be worth having your devs go wild trying everything to see what works.
(Of course, that steelman is very compatible with a story where the best strategies don't end up using very many tokens and a bubble pop happens anyway.)
I mean - none of this is great, but if these are the very worst examples they can find then it feels a bit like scraping the barrel.
While not the end of the world - we routinely deal with similar crap produced by humans - it's a clear marker of the kind of quality you get from AI without real supervision.
Rapid iteration of a bad code base, what could possibly go wrong?
Ah yes, the same guy that said implementing lidar with cameras is hard (like Kalman filters aren't a thing). Same guy who spoke positively about Musks engineering talents AFTER he went crazy. That genius...
Basically, I feel like if you are suffering from psychosis, your talent is measured by how much stuff you have memorized, and how much of it you can type on keyboard in a given timeframe. And now that LLMs are doing it for you, you feel worthless.
I remember when I first started learning python, having been in Java/C++ land. It felt like a hack. You could just pip install stuff, import it, dynamically hack things around if you needed to, and make stuff work in much shorter time. I wrote tools that let me write other tools quicker. For example, back before you could ask LLMs to write code, you basically had to google stuff and search for examples. So one of the first things I wrote was essentially web page to api converter. Now I had a tool that programmatically let me pull content from web, which included things like code samples.
I then wrote a tool to search documentation and github, and pull things that were styled as code, using my previous tool, and put them into opensearch, so when i had a question about something, I could search a function in opensearch and see examples.
E.t.c and so on.
Agents these days have replaced a lot of the manual work. But complex tasks, with decision making, repeat loops, and unknown unknowns is still something that agents cant reliably do. Anyone can put together a UI with agents very quickly. But then, if you leave a lot of stuff to the agents and not specify how you want the code written, you are going to get bounded into code that is going to quickly degrade performance, introduce edge case bugs, and so on. Sure, you can have llms fix all that, but to do that automatically is something nobody has done yet.
The real skill in the future is going to be writing agentic programs to work on features for you instead of working on features. You invest time up front to do this, and spend minimal time maintaining. Much in the same way that you invested time into writing OOP code with clean separation in packages and classes, build systems with verification, all so that anyone can come in and write code and have a safe way of testing and committing changes.