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.

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?

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.

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.

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.