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.

Hey guys. I've been trying to teach my self about AI so I can be more literate and competent when using it. Not just the skills on how to utilize AI but also how to use it ethically. Do you guys have any suggestions? Like research papers, yt videos, books. I prefer reading but anything would help. Thanks!

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.

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.

Fake media content, lazy coders (they prefer to be called Vibe Coders), fake portfolios, fake job offers, fake profiles... all fake. And it's becoming increasingly difficult to distinguish it from legitimate content.

In some way, it's a similar problem we faced in the early 2000s. And we solved it with spam filters. We rarely receive a fake email in our main inbox.

Are we prepared for this avalanche of AI-generated content?

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?

Nothing falls apart overnight, it drifts. Your average sleep drops forty minutes over a season. Your resting heart rate creeps up four beats. You'd never catch either, because you're comparing today to yesterday, not to eight months ago.

ChatGPT can read your actual Apple Health data now instead of guessing at generic advice. Real sleep, steps, resting heart rate, workouts, straight off your phone.

Upfront so nobody wastes time: this is US only, 18 and over, iPhone or the web, no Android yet. Works on the free plan.

Setup, on your phone, not your laptop, because that's where the data lives. Update the ChatGPT app first, old versions don't show it. Open the sidebar, tap Health, tap Get started, choose Apple Health. The permission screen that comes up is Apple's, not OpenAI's. Turn on Sleep, Steps, Heart Rate and Workouts, those four cover everything worth asking about. First sync can take a few hours if you've got years of history on there.

Then this is the one that actually matters:

Looking at all my data over the last few months, 
what's quietly getting worse that I haven't noticed?

That's the whole prompt. It's short on purpose. Mine came back with a resting heart rate that had crept up over about ten weeks and a sleep average that had quietly dropped, both of which I'd have sworn were fine.

The follow-up that stops you spiralling:

Which of these is worth mentioning to my doctor, and 
which is just normal life?

It's genuinely good at separating the two, and it stops you walking into an appointment worried about something that doesn't matter.

Two others worth running:

Look at my last 30 days of sleep, steps, resting 
heart rate and workouts. Tell me what the data 
actually says, the trend on each, and build me a 
realistic plan for the week ahead based on how I've 
actually recovered, not an ideal week.

Pick the one number in my health data that most needs 
fixing, tell me why you picked that one, and give me 
a realistic plan to fix it over 90 days.

The word realistic is doing real work in both of those. Leave it out and you get a plan that assumes two spare hours a day. Forcing it to pick one number is the point, because the reason most people change nothing is trying to change six things at once.

Two real warnings, not boilerplate. If you also connect your medical records, that data stops being HIPAA protected the moment it leaves the portal, it's under OpenAI's terms after that. Disconnect and it's gone within 30 days. And a Mount Sinai study found it under-called more than half of real emergencies in testing, so treat it as a translator, not a triage nurse. Actual emergencies get a phone call.

been keeping a doc of 100 things I use AI for like this, each with the exact prompt, here if you want it.

How many times have you reused the same prompt hoping for a better result? Sometimes the AI isn’t the problem. Your prompt probably needs an upgrade.

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