Just don’t play Microsoft games. They crippled or outright destroyed all their former good game studios anyway. So it’s not like they will be making great games you can’t live without anyway.

We need to outlaw ads.

I guess the AI isn’t iterating over its “ideas” and code yet. I come up with overcomplicated and overcomplex code too. But my natural neural network keeps reevaluating the code while I think about it, write it down, test it, and often even come back to it later after I moved on to another part of the codebase.
It looks to me like AI is using the infamous waterfall method to write code. It does the “thinking” phase and does bugfix iterations when compilation or tests fail. But never does it question the design it came up with after it wrote it down. It doesn’t reevaluate whether the last round of fixes made tests work that test irrelevant implementation details. It doesn’t question whether there really need to be four methods sharing 90% of their body for doing the basically the same thing only subtly different.
I hold the suspicion that AI will be as slow as humans when it becomes as reliable because what makes us reliable is the redundancy in our thinking processes. The constant reevaluation in subtly different states of mind helps to catch design flaws and silly mistakes.

Reevaluation of finalized code is probably not something you can just put into memory. You would need to modify the harness to automatically inject a new prompt triggering reevaluation of the design and implementation after each step. This would likely increase token use and execution time by an order of magnitude. And if you do it by spawning fresh agents, there would be a high chance of having the AI go in circles, switching between a few different versions of the same shitty design or implementation. So you need to keep the context, which grows each iteration. So context rot may become a real problem. The AI might deviate from the initial goal just like some human devs tend to do…

And natural neural networks learn on the job. They adapt to the codebase and industry they are working with/in. claude.md and memory text files help a lot. But they are still sometimes ignored. And I don’t know why. Also, human rules are almost always somewhat fuzzy. Long lines are to be avoided - but unit tests and that one massive dict of dicts really are a lot more readable when ignoring that rule. There might be a lack of good training material on what makes code readable for humans.

The “handmade” label has been used in marketing before. I saw it far more often a few decades ago than now.

AI code assistants currently are like that senior dev who knows everything, has experience in everything, and always likes to help everyone. But sadly he does a lot of shrooms in his spare time and sees things that aren’t there. He also has quite the opinionated ideas about what good code should look like.

That implies that you just can’t let him do the quality assurance. He can write code. You can ask him stuff. And he excels at debugging.
But you can’t let him plan the architecture. You can ask him about his ideas and select the ones that are actually good, though.
And you definitely can’t let him review his own code. Someone else has to do that.

So if they literally just let the AI do everything, there is absolutely no way anyone has a chance to ever review the result because AI code is harder to review (for now), and AI loves to repeat itself (for now).
And sure, they can tell it to write tests. But AI is dogshit at writing tests. It tends to forget the important cases, test the wrong thing, and/or obsess about completely irrelevant implementation details.
I also tried the multi-round approach with a committee of agents trying to iteratively find and fix flaws. It only works well for finding bugs, not fixing them (for now).

But while AI can’t replace me, it is really nice to have as a tool - just like I have syntax highlighting in the IDE, I have a code assistant that can autocomplete a few lines of code, find out why my code misbehaves, or tell me what some library method is supposed to do. Sure, I have to still verify the results. But often, finding the bug or information was the hard part.
Coding assistants are great. Just don’t let them write too much code at once. You need someone who still actually understands the codebase. That sadly can’t be an AI (yet).

Yeah, good employees state their grievance, but if management insists, they just go into bot mode and do what has been commanded until they switch jobs. And bad employees are happy to blame AI.
So if management demands use of AI rather than just offering AI as yet another tool that may or may not be used at the discretion of the employee, output quality will either stay the same or degrade because AI can’t do the actually important job - quality assurance.

The moral of the story: if you aren’t actually experienced doing the job yourself, do not tell your workers how to do the job. Trust your senior workers to do that.

People feel betrayed because they can’t feel the benefits of economic growth. Jobs aren’t safe anymore. But funding a family still requires a stable income. Humans love having social and economic security. If a government refuses to make that happen, they will vote for whoever promises to punish the old government - even if no fix for any of the actual problems is proposed.

If you are a politician, the solution is disgustingly trivial: stop taking bribes and do your job. Governments are infrastructure. They are meant to support the people governed by them.
So far the solution implemented by the big old parties was to double down on making everyone but the Epstein class poorer until voter morale improves.

AI is indeed to blame - but not like you think.
Trump’s ChatGPT prompt after translation into natural language: I want to kill some sand people for fun and giggles. Which country did fight back the least in the past against aerial attacks?

Bethesda really missed the memo with Starfield. All those wasted loading screens…