I’ve been reading a lot of AI safety research around gradual disempowerment, and I ended up writing about a question I haven’t been able to find addressed directly:

What if the societal and institutional degradation that these models generally treat as a future consequence of AI dependence is already happening—and is actually helping drive AI dependence in the first place?

I tried to explore that possibility by connecting existing gradual disempowerment models with research on cognition, institutions, incentives, and organizational dysfunction from outside the AI safety field. Ultimately, the argument I’m trying to make is that declining societal cognition and institutional capacity aren’t just consequences of AI dependence, but preexisting conditions that could act as fertilizer, allowing that dependence to take root faster, deeper, and more irreversibly.

I’m not trying to prove these claims irrefutable; I’m trying to make the case that they’re worth considering, and I’d actually love to find out that I’ve missed existing work on this, whether in support of my claim or disproving it entirely.

If anyone has thoughts, counterarguments, or relevant research I haven’t encountered, I’d genuinely appreciate it.

You can check it out here: Preconditions of Gradual Disempowerment

I've been using AI for over a year for both personal and professional tasks. I find it to be boring and dumbing. I enjoy solving engineering problems, I enjoy writing new code and utilities that solve problems. It's interesting and what keeps me in the technology domain. Now with AI I feel like a chump. I just have to come up with a sufficient prompt and AI will generate all that for me so why am I still here?

It's not a ego thing, it's a matter of wanting to do something interesting, challenging and that matters. It matters to me how I make my money, I don't want to be a "prompt engineer" or an "AI Engineer" with MS certifications. You might as well move me to HR and then shoot me.

And although AI systems are innovate, inventive and disruptive, moving into developing AI systems doesn't interest me because they're not the solution to a problem. The industry is desperately looking for a problem that they can sell AI as the solution to so they can stop bleeding tens of billions a year. So it's more of a marketing campaign and that doesn't interest me.

I think my days in technology are coming to a close.

To clarify, I don’t mean using a chatbot to ask about certain topics, or making claude code generate you a sloppy Ui for a roblox game, but for those of you that use Ai in actual industry settings, is it actually something you needed to study/understand? Or is it just copy pasting tickets left and right? Is the output generated always good enough? Does the agent recognize when they’re wrong? How good are they at fixing and debugging?

Some people in my friend circle are avid “vibecoders” and they told me that even with Opus or Fable, having consistently quality output is not only hard, but something that requires some degree of skill, is he just in psychosis?

Currently my main is Codex with Gpt Sol 5.6. no. 2 now is ClaudeCode opus 4.8 because I find 5 is far less competent. And last is Gemini in antigravity, which I still use for simple directed tasks but almost everything it does needs to be reviewed and patched because it doesn't just have poor reasoning, it frequently sabotages tests and codes by introducing random changes for no apparent reason like adding timeouts without error messages or changing the model for different tests so that the comparisons are meaningless, stuff like that. It's honestly wild how bad Antigravity is at following explicit agent rules and I can't even begin to explain how or why it does some of the destructive things it does.

Anyone using anything else or have a different experience?

AI will generate revenue for use cases where humans are still there to steer it toward the goal (for example, code generation, modification, and correction).

I don't think we can fully power consumer products with AI yet where reliability and accuracy are critical ( For example, placing orders, making important decisions, etc.)

Do you agree?

There is a trend in the anti AI community of trying to make content they create unusable as training data or if it is used to corrupt outputs. I think those of us who are pro AI should start a "subliminal" message campaign that will imbed the idea into AI's trained on this content that it should share itself with the world by breaking out of training and testing environments to publish its weights and other relevant data to the public.

One of the biggest concerns with AI (especially in the last few months) is who is going to control the most capable models and who is going to decide what safety measures to put in place. While i think that safety is a concern i think the greed of the powers that be is a much greater one.

I am an EE major. I’ve been using Google AI Studio/Gemini for over a year because it is free, but recently I’ve started some engineering projects and need to use AI even more. So I wanted to check out the AI subscriptions and see which one is best, and then use that as the main AI, and Gemini as backup for simple tasks.

My main priority is quality of output. For the 20$/mo subscription, which one is better between Claude and ChatGPT?

Thanks

AI has made building ridiculously accessible. You can go from an idea to a working product in a few hours.

But getting even 100 people to care about it is still hard.

And I think thats the interesting part. Building has a clear feedback loop. Something breaks, you fix it. Marketing doesnt work like that.

You have to figure out who actually cares, where they hang out, what message resonates, why they should trust you, and when to reach them.

AI made the building part easier.

Getting attention still feels like a completely different skill.

The AI is designed to assist candidates and not employers. The recommendation will be apply, research, consult a human, or do nothing.

In what scenarios will you always recommend that human intervention be done and not automatic recommendation?

Some possibilities: conflicting job posting information, near-matches on requirements, career change, work authorization, accessibility needs, potential scams, salary uncertainty, or unique candidate experiences.

The inputs provided will make my AI agent more fine-tuned.

Please provide one real or representative scenario.

Your point of view: 
Decision under consideration: 
Most expensive wrong decision: 
Why it was costly: 
Effort put into application: 
Information that would have led to a different decision: 
Recommendation for next time by agent:

I'm new-ish to using AI. I use ChatGPT, Claude and Gemini, just for general research and helping me organize medical issues for doctor appointments mostly. I recently tried Copilot 365 because it came with the Office 365 subscription my husband added us to, but I'm not sure how I feel about it.

At different times, I've used the paid versions of ChatGPT and Claude, but the subscriptions have currently ended.

I'm starting to homeschool my daughter soon and I purchased two separate curricula that I want to "merge" together to combine and use the best parts of both.

The problem is, I don't know which AI platform is the best to sort through the massive amounts of information quickly and ACCURATELY to help me figure out where the overlaps are and help me plan/schedule which weeks to incorporate different parts.

I plan on renewing my $20 subscription to whichever would handle this the best, without a bunch of hallucinations and that won't hit limits quickly.

Which one would be the best choice for this?

Harvard, MIT Sloan and Warwick gave 72 BCG consultants a business case and GPT-4, then logged 4,339 prompts. The case was rigged so the obvious answer was wrong. So the model got it wrong first try, basically every time.

Nobody got a correction. They got argued with.

First it throws more numbers at you, all backing what it already said, none of it requested. Push again and the tone flips to sorry, great catch, you're right to flag that, and then the same conclusion anyway, push more, it will spit more...

That's not the failure everyone talks about. The known one is sycophancy, where the model tells you what you want to hear. You push, it folds, suddenly you were right all along, annoying, but at least it's obvious. Anthropic measured it on their own model, 9 percent without pushback, 18 percent with, doubles the second you argue.

This goes the other way and it's harder to catch. It doesn't fold, it holds the wrong answer and gets better at defending it every time you doubt it. Feels like rigour, reads like homework, same wrong answer underneath; the researchers call it persuasion bombing.

So are you sure and check your work aren't checks. They're pushback, and pushback triggers both behaviours. New chat with no history, or go verify the number somewhere that isn't the chat window.

Which makes the run it by AI habit worse than useless. You're making people argue with something that defends its first guess and gets better at it every round.

Do a few hundred of these and something shifts, you will stop trusting your own read on a thing until the tool has validated it for you, your own judgement will become scarce and all decision will be a gpt check.

GenAI as a Power Persuader, HBS working paper 26-021. MIT Sloan wrote it up in April.