Problems the AI industry is not addressing adequately

Thanks for the read. I think it's a highly relevant article, especially around the moral issues of making addictive products. As a normal person in the Swedish society I feel social media, shorts and reels in particular, has an addictive grip on many in my vicinity.

And as a developer I can see similar patterns with AI prompts: prompt, wait, win/lose, re-prompt. It is alluring and it certainly feels.. rewarding when you get it right.

1) I have been curious as to why so few people in Silicon Valley seems to be concerned with, even talking about, the good of the products. The good of the company they join. Could someone in the industry enlighten me, what are the conversations in SV around this issue? Do people care if they make an addictive product which seems to impact people's lives negatively? Do the VCs?

2) I appreciate the author's efforts in creating conversation around this. What are ways one could try to help the efforts? While I have no online following, I feel rather doomy and gloomy about AI pushing more addictive usage patterns out in to the world, and would like to help if there is something suitable I could do.

> A sufficiently good simulation of understanding is functionally equivalent to understanding.

This is just a thing to say that has no substantial meaning.

  - What is "sufficiently" mean? 
  - What is functionally equivalent? 
  - and what is even understanding?

All just vague hand waving

We're not philosophizing here, we're talking about practical results and clearly, in the current context, it does not deliver in that area.

> At that point, the question of whether the model really does understand is pointless.

You're right it is pointless, because you are suggesting something that doesnt exist. And the current models cannot understand

I think it's important to not let valid criticisms of implausibly short AGI timelines cloud our judgments of AGI's potential impact. Compared to babies born today, AGI that's actually AGI may have many advantages:

- Faster reading and writing speed

- Ability to make copies of the most productive workers

- No old age

- No need to sleep

- No need to worry about severance and welfare and human rights and breaks and worker safety

- Can be scaled up and scaled down and redeployed much more quickly

- Potentially lower cost, especially with adaptive compute

- Potentially high processing speed

Even if AGI has downsides compared to human labor, it might also have advantages that lead to widespread deployment.

Like, if I had an employee with low IQ, but this employee could work 24 hours around the clock learning and practicing, and they could work for 200 years straight without aging, and they could make parallel copies of themselves, surely there would have to be some tasks at which they're going to outperform humans, right?

I feel I could argue the counterpoint. Hijacking the pathways of the human brain that leads to addictive behaviour has the potential to utterly ruins peoples lives. And so talking about it, if you have good intentions, seems like a thing anyone with the heart in the right place would.

Take VEO3 and YouTube integration as an example:

Google made VEO3 and YouTube has shorts and are aware of the data that shows addictive behaviour (i.e. a person sitting down at 11pm, sitting up doing shorts for 3 hours, and then having 5 hours of sleep, before doing shorts on the bus on the way to work) - I am sure there are other negative patterns, but this is one I can confirm from a friend.

If you have data that shows your other distribution platform are being used to an excessive amount, and you create a powerful new AI content generator, is that good for the users?

> Hallucination does seem to be much less of an issue now. I hardly even hear about it - like it just faded away.

Nonsense, there is a TON of discussion around how the standard workflow is "have Cursor-or-whatever check the linter and try to run the tests and keep iterating until it gets it right" that is nothing but "work around hallucinations." Functions that don't exist. Lines that don't do what the code would've required them to do. Etc. And yet I still hit cases weekly-at-least, when trying to use these "agents" to do more complex things, where it talks itself into a circle and can't figure it out.

What are you trying to get these things to do, and how are you validating that there are no hallucinations? You hardly ever "hear about it" but ... do you see it? How deeply are you checking for it?

(It's also just old news - a new hallucination is less newsworthy now, we are all so used to it.)

Of course, the internet is full of people claiming that they are using the same tools I am but with multiple factors higher output. Yet I wonder... if this is the case, where is the acceleration in improvement in quality in any of the open source software I use daily? Or where are the new 10x-AI-agent-produced replacements? (Or the closed-source products, for that matter - but there it's harder to track the actual code.) Or is everyone who's doing less-technical, less-intricate work just getting themselves hyped into a tizzy about getting faster generation of basic boilerplate for languages they hadn't personally mastered before?