161–170 of 777 posts
Not sure why they put so much investment into videoSlop and imageSlop. Anthropic seems to be more focused at least.
Because as with the internet 99% of the usage won’t be for education, work, personal development, what have you. It will be for effing kitten videos and memes.
AI is going to be a highly-competitive, extremely capital-intensive commodity market that ends up in a race to the bottom competing on cost and efficiency of delivering models that have all reached the same asymptotic performance in the sense of intelligence, reasoning, etc. The simple evidence for this is that everyone who has invested the same resources in AI has produced roughly the same result. OpenAI, Anthropic,…
It already is. In terms of competition, I don't think we've seen any groundbreaking new research or architecture since the introduction of inference time compute ("thinking") in late 2024/early 2025 circa GPT-o4.
The majority of the cost/innovation now is training this 1-2 year old technology on increasingly large amounts of content, and developing more hardware capable of running these larger models at more scale. I think it's fair to say the majority of capital is now being dumped into hardware, whether that's HBM and research related to that, or increasingly powerful GPUs and TPUs.
But these components are applicable to a lot of other places other than AI, and I think we'll probably stumble across some manufacturing techniques or physics discoveries that will have a positive impact on other industries.
> that ends up in a race to the bottom competing on cost and efficiency of delivering
One could say that the introduction of the personal computer became a "race to the bottom." But it was only the start of the dot-com bubble era, a bubble that brought about a lot of beneficial market expansion.
> models that have all reached the same asymptotic performance in the sense of intelligence, reasoning, etc.
I definitely agree with the asymptotic performance. But I think the more exciting fact is that we can probably expect LLMs to get a LOT cheaper in the next few years as the current investments in hardware begin to pay off, and I think it's safe to assume that in 5-10 years, most entry-level laptops will be able to manage a local 30B sized model while still being capable of multitasking. As it gets cheaper, more applications for it become more practical.
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Regarding OpenAI, I think it definitely stands in a somewhat precarious spot, since basically the majority of its valuation is justified by nothing less than expectations of future profit. Unlike Google, which was profitable before the introduction of Gemini, AI startups need to establish profitability still. I think although initial expectations were for B2C models for these AI companies, most of the ones that survive will do so by pivoting to a B2B structure. I think it's fair to say that most businesses are more inclined to spend money chasing AI than individuals, and that'll lead to an increase in AI consulting type firms.
AI is going to be a highly-competitive, extremely capital-intensive commodity market that ends up in a race to the bottom competing on cost and efficiency of delivering models that have all reached the same asymptotic performance in the sense of intelligence, reasoning, etc. The simple evidence for this is that everyone who has invested the same resources in AI has produced roughly the same result. OpenAI, Anthropic,…
There is a pretty big moat for Google: extreme amounts of video data on their existing services and absolutely no dependence on Nvidia and it's 90% margin.
On the radio they mentioned that the total global chocolate market is ~100B, I googled it when I was home and it seems to be about ~135B. Apparently that is ... all chocolate, everywhere.. OpenAI's valuation is about 500B. Maybe going up to like 835B. I'd love to see the rationale that OpenAI (not "AI" everywhere) is more valuable than chocolate globally. ... so crash early 2026?
Ignoring that those numbers aren't directly comparable, it did make me wonder, if I had to give up either "AI" or chocolate tomorrow, which would I pick? Even as an enormous chocolate lover (in all three senses) who eats chocolate several times a week, I'd probably choose AI instead. OpenAI has alternatives, but also I do spend more money on OpenAI than I do on chocolate currently.
Not sure why they put so much investment into videoSlop and imageSlop. Anthropic seems to be more focused at least.
Because almost everyone involved in AI race grew up in "winner takes it all" environments, typical for software, and they try really hard to make it reality. This means your model should do everything to just take 90% of market share, or at least 90% of specific niche. The problem is, they can't find the moat, despite searching very hard, whatever you bake into your AI, your competitors will be able to replicate in f…
Been saying this since the 2016 Alice case. Apple jumped into content production in 2017. They saw the long term value of copyright interests.
https://arstechnica.com/information-technology/2017/08/apple...
Alice changed things such that code monkeys algorithms were not patentable (except in some narrow cases where true runtime novelty can be established.) Since the transformers paper, the potential of self authoring content was obvious to those who can afford to think about things rather than hustle all day.
Apple wants to sell AI in an aluminum box while VCs need to prop up data center agrarianism; they need people to believe their server farms are essential.
Not an Apple fanboy but in this case, am rooting for their "your hardware, your model" aspirations.
Altman, Thiel, the VC model of make the serfs tend their server fields, their control of foundation models, is a gross feeling. It comes with the most religious like sense of fealty to political hierarchy and social structure that only exists as hallucination in the dying generations. The 50+ year old crowd cannot generationally churn fast enough.
Earlier quoted context omitted.
Because almost everyone involved in AI race grew up in "winner takes it all" environments, typical for software, and they try really hard to make it reality. This means your model should do everything to just take 90% of market share, or at least 90% of specific niche. The problem is, they can't find the moat, despite searching very hard, whatever you bake into your AI, your competitors will be able to replicate in f…
> your competitors will be able to replicate in few months. Will they really be able to replicate the quality while spending significantly less in compute investment? If not then the moat is still how much capital you can acquire for burning on training?
Earlier quoted context omitted.
Because for all the incessant whining about "slop," multimodal AI i/o is incredibly useful. Being able to take a photo of a home repair issue, have it diagnosed, and return a diagram showing you what to do with it is great, and it's the same algos that power the slop. "Sorry, you'll have to go to Gemini for that use case, people got mad about memes on the internet" is not really a good way for them to be a mass consu…
Can Claude not do that? I've sent it pictures for simpler things and got answers, usually Id of bugs and plants.
AI is going to be a highly-competitive, extremely capital-intensive commodity market that ends up in a race to the bottom competing on cost and efficiency of delivering models that have all reached the same asymptotic performance in the sense of intelligence, reasoning, etc. The simple evidence for this is that everyone who has invested the same resources in AI has produced roughly the same result. OpenAI, Anthropic,…
There is a pretty big moat for Google: extreme amounts of video data on their existing services and absolutely no dependence on Nvidia and it's 90% margin.
Earlier quoted context omitted.
Because almost everyone involved in AI race grew up in "winner takes it all" environments, typical for software, and they try really hard to make it reality. This means your model should do everything to just take 90% of market share, or at least 90% of specific niche. The problem is, they can't find the moat, despite searching very hard, whatever you bake into your AI, your competitors will be able to replicate in f…
> your competitors will be able to replicate in few months. Will they really be able to replicate the quality while spending significantly less in compute investment? If not then the moat is still how much capital you can acquire for burning on training?
Earlier quoted context omitted.
There is a pretty big moat for Google: extreme amounts of video data on their existing services and absolutely no dependence on Nvidia and it's 90% margin.
I have yet to be convinced the broader population has an appetite for AI produced cinematography or videos. Independence from Nvidia is no more of a liability than dependence on electricity rates; it's not as if it's in Nvidia's interest to see one of its large customers fail. And pretty much any of the other Mag7 companies are capable of developing in-house TPUs + are already independently profitable, so Google isn'…