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AGI Was Never Going To Kill Us Because Suicide Happens At The End of Doomscrolling
I'll go ahead and call this the peak of AI version one-dot-oh
The headline reads "OpenAI Is Preparing to Launch a Social App for AI-Generated Videos." People will, I guess, be able to share AI generated videos with their friends (and who doesn't have THE ALGO as a friend). Awesome. This is also on the heels of the introduction of live ads within OpenAI's ChatGPT.
Some of us were waiting for The Matrix. I know I've always wanted to learn Kung Fu. Others were sharpening our pointing sticks so that when the paperclip machine came, we'd be ready. Most of us just want to look forward to spending a quiet evening with AI Waifu before we initiate her kink.exe module.
But we'll never get there. Because Silicon Valley just can't help itself. Hockey sticks and rocketships. Series E-F-G. If I can just get 5 million more Americans addicted to my app, I can buy a new yacht made completely out of bitcoin.
I am a daily "AI" user and I still have very high hopes. My current operating theory is that a combination of whatever the MCP protocol eventually settles into plus agents trading some sort of crypto or stable coin will create a kind of autonomous, goal-seek driven economy. It will be sandboxed but with (semi) real money. I don't think we, humans, will use it to actually drive the global economy, but as a kind of just-over-the-horizon global prediction market. Think of it as a way for us to have seen 2008 coming in 2006. I also was looking forward to a team of maybe 10 people making a legit billion dollar company and this paving the way for groups of 3 - 5 friends running thousands of $10 + $50 million dollar companies. No more corporate grind if you're willing to take a little risk and team up with some people you work well with. No bullshit VC games - just ship the damn thing.
And I think these things are still possible, but I also, now, think the pure consumer backlash to this silicon valley lobotomy of AI could be very much Dot-Com-2-point-O. The normies at my watering hole are making jokes about AI slop. Instead of "lol I doomscrolled into 3 am again" people are swapping stories about popping in old DVDs so that they can escape the ads and the subscription fatigue.
Culturally, this could be great. Maybe the damn kids will go outside and touch some grass. In terms of advancing the frontier of human-digital knowledge, it seems like we're going to trade it in early not even for unlimited weird porn, but for pink haired anime cat videos that my aunt likes.
To what extent is it or will it become possible or practical to run a homebrew jailbroken LLM on local hardware? That's the big question in my mind.
I'm late to the party, and I'm aware of it, in that I'm only just now using LLMs beyond a toy for research and education purposes. But essentially every day I'm aware there's an expiration date, that the product is just a few bad days for the SP500 from being enshittified. Whether that comes in the form of censorship and legal caution that makes it useless for my purposes, or in the form of pricing that makes it prohibitive, or commercialization and monetization in ways that make it unreliable (pay extra for your product to be recommended!), or optimization for it as people start to operate their products specifically to be seen and understood by LLMs. There's going to come a time when I can't just log into ChatGPT and get a good result, I'm sure the old timers are already complaining; and there's going to come a time when there isn't enough VC money sloshing around to fund a competitor like Grok that throws off shackles.
So at that point, can I or will I be able to operate a homebrew LLM for my personal and business purposes? I'm not handy enough to know how possible that currently is, or how user friendly, I'm at the level of "I can run a Linux machine but I'll need to look stuff up once a week or so."
I think that one aspect is the question which performance you actually require from the model.
A fundamental difference between free / open source software and open weight models is that for software, the bottleneck is mostly developer hours, while for models, it is computing power on highly specialized machines.
For software, there have been large fields of application where the best available options are open source, and that has been the case for decades -- for example, try even finding a browser whose engine is proprietary, these days. (Of course, there are also large fields where the best options are all proprietary, because no company considered it strategically important to have open source software, nor was it a fun project for nerds to play with, e.g. ERP software or video game engines.)
For LLMs, tens of billions of dollars worth of computing power have to be sacrificed to summon more powerful shoggoths forth from the void. For the most part, the business model of the AI companies which produce the most advanced models seems to be to sell access to it. If Llama or DeepSeek had happened to be more advanced than OpenAI's models, their owners would not have published their weights but charged for access. (The one company I can imagine funding large open-weight models would be Nvidia, as part of a commodize your complement strategy. But as long as no AI company manages to dominate the market, it is likely more lucrative to sell hardware to the various competitors than to try to run it yourself in the hope of enticing people to spend more on hardware than on model access instead.)
That being said, for a lot of applications there is little gain from running a cutting edge model. I may be nerdier than most, but even I would not care too much what fraction of IMO problems an AI girlfriend could solve.
The trick's that the same chips used to produce a model are also usable to run the model for someone else, and a lot of the technologies used to improve training has downstream benefits on inference or implementation improvements. Every AI vendor has its own complement to turn into a commodity.
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