I miss the old Steve Yegge that actually knew how to write coherently. AI psychosis is apparently a hell of a drug... this is pages and pages of complete dreck.
I find it very interesting to read blogs like this which describe and predict the societal impact of AI. However, this impact is all framed in terms of developers. In the end this is a niche (albeit an important one) use of AI. I don’t believe for a moment that software development will be the primary use of LLMs that changes society, at least not in the long term.
Nuclear weapons… somewhat complicated to build and complicated to maintain, mass-produce, transport, smuggle, hide, and use.
Models on the other hand are just software. You can download Fable / Mythos / Praxia / Concordicon and hide a copy in your shoe. You can make two copies even! One per shoe!
We have lived through decades now of the glorious open source revolution so it’s easy to forget about a different time, in the 1990s, when a black market in CDs jammed full of cracked, slightly off branded copies of Windows / Photoshop / Corel / Office, etc. was all the rage.
Execution isn’t trivial. If anything should be controlled like nuclear weapons, again like the 90s, it ought to be compute hardware. I’m not sure it should.
I found a lot of interesting, if speculative, thoughts in the article, but...
> Superhuman means unverifiable
is not true for at least large classes of problems. The recent solution of the "unit distance" problem comes to mind, or any future AI-solved math problem that was beyond the capabilities of humans. You can tell it's superhuman (it's doing things humans can't) and you can easily verify its results are correct.
For other classes of problems (eg, policy suggestions for large scale systems like the economy), the point is fair.
I disagree with Steve Yegge's assessment that the curve is close to leveling off. It's not the models, it's the harnesses and the result automation possibilities that are the true unlock. LLMs stabilizing around a current local maximum is actually not much of a big deal. If we just use the models we have today there is so much more unlock available.
We have only just begun our ascent up the hockey stick and the most intense change is yet to come.
The real danger is how big of a gap will exist once the curve does level off. If we are just at the start of the sigmoid curve and starting our ascent, then many jobs will be thrown off by the time we hit the peak and begin to level off.
No politician or corporation is preparing for this sufficiently.
The focus is placed on "AI Literacy", but it seems to use this to just mean 'volume of AI use'. The discussion of the Netflix case study is extra perplexing, since the summary here admits they didn't find any actual productivity improvement, just that only a few hours of "training" could induce on the order of $50/person/day on tokens.
This long-winded screed appears to be an AI proselytizer trying to convince people that, no, actually, you're just holding the model wrong if you don't believe they're exponentially growing in intelligence every generation. The proof? His react client.
Far too dismissive of oss models. This sounds like MS employees talking about linux circa 2002. This attitude would have written off linux in the early 00s as doomed for not "keeping up" with windows. The oss option will always appear behind the curve but they inevitably catch up... if they even need too. AI is no different. The free/oss option will be niche and disregarded by the bigs but it will survive and thrive, just as linux has.
I think that the open source systems being behind is sort of the point, they are the baseline. A paid proprietary system must be better than this. An example.
If you want me to pay for your compiler it must be that much better than the compiler I can get for free that allows me much more freedom of agency in how I use it, if it is not I am going to use the free compiler. repeat for operating systems, word processors, databases and now LLM's
I miss the old Steve Yegge that actually knew how to write coherently. AI psychosis is apparently a hell of a drug... this is pages and pages of complete dreck.
Why is it dreck? Other than being predictions so probably wrong in that sense?
I do disagree with parts of it, but dreck?
Same. Used to publish some good stuff 10+ years ago.
I find it very interesting to read blogs like this which describe and predict the societal impact of AI. However, this impact is all framed in terms of developers. In the end this is a niche (albeit an important one) use of AI. I don’t believe for a moment that software development will be the primary use of LLMs that changes society, at least not in the long term.
Nuclear weapons… somewhat complicated to build and complicated to maintain, mass-produce, transport, smuggle, hide, and use.
Models on the other hand are just software. You can download Fable / Mythos / Praxia / Concordicon and hide a copy in your shoe. You can make two copies even! One per shoe!
We have lived through decades now of the glorious open source revolution so it’s easy to forget about a different time, in the 1990s, when a black market in CDs jammed full of cracked, slightly off branded copies of Windows / Photoshop / Corel / Office, etc. was all the rage.
Execution isn’t trivial. If anything should be controlled like nuclear weapons, again like the 90s, it ought to be compute hardware. I’m not sure it should.
I found a lot of interesting, if speculative, thoughts in the article, but...
> Superhuman means unverifiable
is not true for at least large classes of problems. The recent solution of the "unit distance" problem comes to mind, or any future AI-solved math problem that was beyond the capabilities of humans. You can tell it's superhuman (it's doing things humans can't) and you can easily verify its results are correct.
For other classes of problems (eg, policy suggestions for large scale systems like the economy), the point is fair.
NP problems can be verified in P time.
I disagree with Steve Yegge's assessment that the curve is close to leveling off. It's not the models, it's the harnesses and the result automation possibilities that are the true unlock. LLMs stabilizing around a current local maximum is actually not much of a big deal. If we just use the models we have today there is so much more unlock available.
We have only just begun our ascent up the hockey stick and the most intense change is yet to come.
The real danger is how big of a gap will exist once the curve does level off. If we are just at the start of the sigmoid curve and starting our ascent, then many jobs will be thrown off by the time we hit the peak and begin to level off.
No politician or corporation is preparing for this sufficiently.
I'm pretty confused by most of the article.
The focus is placed on "AI Literacy", but it seems to use this to just mean 'volume of AI use'. The discussion of the Netflix case study is extra perplexing, since the summary here admits they didn't find any actual productivity improvement, just that only a few hours of "training" could induce on the order of $50/person/day on tokens.
That seems... the opposite of literacy?
Measuring education in dollars spent is rather consistent with measuring the productivity of LLMs in lines of code excreted.
If the most expensive models yet to come will end up behind bars by default, what is the economic incentive to make them in the first place?
GPT-2 was already extremely dangerous.
So was the computational capabilities of Playstation 2. It could be used to simulate nuclear weapons, I heard.
Is this a troll
This long-winded screed appears to be an AI proselytizer trying to convince people that, no, actually, you're just holding the model wrong if you don't believe they're exponentially growing in intelligence every generation. The proof? His react client.
Far too dismissive of oss models. This sounds like MS employees talking about linux circa 2002. This attitude would have written off linux in the early 00s as doomed for not "keeping up" with windows. The oss option will always appear behind the curve but they inevitably catch up... if they even need too. AI is no different. The free/oss option will be niche and disregarded by the bigs but it will survive and thrive, just as linux has.
I think that the open source systems being behind is sort of the point, they are the baseline. A paid proprietary system must be better than this. An example.
If you want me to pay for your compiler it must be that much better than the compiler I can get for free that allows me much more freedom of agency in how I use it, if it is not I am going to use the free compiler. repeat for operating systems, word processors, databases and now LLM's