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I have an overwhelming feeling that what we're trying to do here is "Netflix over DialUp." We're clearly seeing what AI will eventually be able to do, just lik

• nutjob2 1y ago
  > We're clearly seeing what AI will eventually be able to do I think this is one of the major mistakes of this cycle. People assume that AI will scale and improve like many computing things before it, but there is already evidence scaling isn't working and people are putting a lot of faith in models (LLMs) structurally unsuited to the task. Of course that doesn't mean that people won't keep exploiting the hype with hand-wavy claims.
• skeezyboy 1y ago
  >I have an overwhelming feeling that what we're trying to do here is "Netflix over DialUp." I totally agree with you... though the other day, I did think the same thing about the 8bit era of video games.
  • echelon 1y ago
    Speaking of Netflix - I think the image, video, audio, world model, diffusion domains should be treated 100% separately from LLMs. They are not the same thing. Image and video AI is nothing short of revolutionary. It's already having huge impact and it's disrupting every single business it touches. I've spoken with hundreds of medium and large businesses about it. They're changing how they bill clients and budget projects. It's already here and real. For example, a studio that does over ten million in revenue annually used to bill ~$300k for commercial spots. Pharmaceutical, P&G, etc. Or HBO title sequences. They're now bidding ~$50k and winning almost everything they bid on. They're taking ten times the workload.
    • AnotherGoodName 1y ago
      Fwiw LLMs are also revolutionary. There's currently more anti-AI hype than AI hype imho. As in there's literally people claiming it's completely useless and not going to change a thing. Which is crazy.
      • lokar 1y ago
        That’s an anecdote about intensity, not volume. The extremes on both sides are indeed very extreme (no value, replacing most white collar jobs next year). IME the volume is overwhelming on the pro-LLM side.
        • whatevertrevor 1y ago
          Yeah the conversation on both extremes feels almost religious at times. The pro LLM hype feels more disconcerting sometimes because there are literally billions if not trillions of dollars riding on this thing, so people like Sam Altman have a strong incentive to hype the shit out of it.
      • Jensson 1y ago
        One sides extremes says LLM wont change a thing, the other sides extremes says LLM will end the world. I don't think the ones saying it wont change a thing are the most extreme here.
        • wyre 1y ago
          Luckily for humanity reality is somewhere in between extremes, right?
      • skeezyboy 1y ago
        >Fwiw LLMs are also revolutionary iterative. we had a shittier version of this 20 years ago
    • mh- 1y ago
      It's quite incredible how fast the generative media stuff is moving. The self-hostable models are improving rapidly. How capable and accessible WAN 2.2 (text+image to video; fully local if you have the VRAM) is feels unimaginable from last year when OpenAI released Sora (closed/hosted).
  • dml2135 1y ago
    It's a logical fallacy that just because some technology experienced some period of exponential growth, all technology will always experience constant exponential growth. There are plenty of counter-examples to the scaling of computers that occurred from the 1970s-2010s. We thought that humans would be traveling the stars, or at least the solar system, after the space race of the 1960s, but we ended up stuck orbiting the earth. Going back further, little has changed daily life more than technologies like indoor plumbing and electric lighting did in the late 19th century. The ancient Romans came up with technologies like concrete that were then lost for hundreds of years. "Progress" moves in fits and starts. It is the furthest thing from inevitable.
    • jopsen 1y ago
      True, but adoption of AI has certainly seen exponential growth. Improvement of models may not continue to be exponential. But models might be good enough, at this point it seems more like they need integration and context. I could be wrong :)
      • tracker1 1y ago
        At what cost though? Most AI operations are losing money, using a lot of power, including massive infrastructure costs, not to mention the hardware costs to get going, and that isn't even covering the level of usage many/most want, and certainly aren't going to pay even $100s/month per person that it currently costs to operate.
        • martinald 1y ago
          This is a really basic way to look at unit economics of inference. I did some napkin math on this. 32x H100s cost 'retail' rental prices about $2/hr. I would hope that the big AI companies get it cheaper than this at their scale. These 32 H100s can probably do something on the order of >40,000 tok/s on a frontier scale model (~700B params) with proper batching. Potentially a lot more (I'd love to know if someone has some thoughts on this). So that's $64/hr or just under $50k/month. 40k tok/s is a lot of usage, at least for non-agentic use cases. There is no way you are losing money on paid chatgpt users at $20/month on these. You'd still break even supporting ~200 Claude Code-esque agentic users who were using it at full tilt 40% of the day at $200/month. Now - this doesn't include training costs or staff costs, but on a pure 'opex' basis I don't think inference is anywhere near as unprofitable as people make out.
  • dormento 1y ago
    > I did think the same thing about the 8bit era of video games. Can you elaborate? That sounds interesting.
    • skeezyboy 1y ago
      too soon to get it to market, though it obviously all sold perfectly well, people were sufficiently wowed by it
• Q6T46nT668w6i3m 1y ago
  There’s no evidence that it’ll scale like that. Progress in AI has always been a step function.
  • ninetyninenine 1y ago
    Uh it’s been multiple repeated step ups in the last 15 years. The trend line is up up up.
  • ghurtado 1y ago
    There's also no evidence that it won't, so your opinion carries exactly the same weight as theirs. > Progress in AI has always been a step function. There's decisively no evidence of that, since whatever measure you use to rate "progress in AI" is bound to be entirely subjective, especially with such a broad statement.
    • dml2135 1y ago
      What is your definition of "evidence" here? The evidence, in my view, are physical (as in, available computing power) and algorithmic limitations. We don't expect steel to suddenly have new properties, and we don't expect bubble sort to suddenly run in O(n) time. You could ask -- well what is the evidence they won't, but it's a silly question -- the evidence is our knowledge of how things work. Saying that improvement in AI is inevitable depends on the assumption of new discoveries and new algorithms beyond the current corpus of machine learning. They may happen, or they may not, but I think the burden of proof is higher on those spending money in a way that assumes it will happen.
    • abc_lisper 1y ago
      What do you call GPT 3.5?
    • ezst 1y ago
      > There's also no evidence that it won't There are signs, though. Every "AI" cycle, ever, has revolved around some algorithmic discovery, followed by a winter in search for the next one. This one is no different and propped up by LLMs, whose limitations we know quite well by now: "intelligence" is elusive, throwing more compute at them produces vastly diminishing returns, throwing more training data at them is no longer feasible (we came short of it even before the well got poisoned). Now the competitors are stuck at the same level, within percent points of one another, with the difference explained by fine-tuning techniques and not by technical prowess. Unless a cool new technique come yesterday to dislodge LLMs, we are in for a new winter.
• bryanlarsen 1y ago
  Some people even figured it out in the 80's. Sears founded and ran Prodigy, a large BBS and eventually ISP. They were trying to set themselves up to become Amazon. Not only that, Prodigy's thing (for a while) was using advertising revenue to lower subscription prices. Your "Netflix over dialup" analogy is more accessible to this readership, but Sears+Prodigy is my favorite example of trying to make the future happen too early. There are countless others.
  • tombert 1y ago
    Today I learned that Sears founded Prodigy! Amazing how far that company has fallen; they were sort of a force to be reckoned with in the 70's and 80's with Craftsman and Allstate and Discover and Kenmore and a bunch of other things, and now they're basically dead as far as I can tell.
    • htrp 1y ago
      Blame short sighted investors asking Sears to "focus"
      • dehrmann 1y ago
        They weren't wrong. Its core business in what is still a viable-enough sector collapsed. Or if it were truly well-managed, running an ISP and a retailer should have been enough insight to be Amazon.
        • deleted 1y ago
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        • svnt 1y ago
          Timing is a difficult variable.
        • KerrAvon 1y ago
          It wasn't possible for them to be well managed at the time it mattered. Sears was loaded with debt by private equity ghouls; same story for almost all defunct brick and mortar businesses; Amazon was a factor, but private equity is what actually destroyed them.
• thefourthchime 1y ago
  I'm starting to agree with this viewpoint. As the technology seems to solidify to roughly what we can do now, the aspirations are going to have to get cut back until there's a couple more breakthroughs.
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• me551ah 1y ago
  Or maybe not. Scaling AI will require an exponential increase in compute and processing power, and even the current LLM models take up a lot of resources. We are already at the limit of how small we can scale chips and Moore’s law is already dead. So newer chips will not be exponentially better but will be more of incremental improvements, so unless the price of electricity comes down exponentially we might never see AGI at a price point that’s cheaper than hiring a human. Most companies are already running AI models at a loss, scaling the models to be bigger(like GPT 4.5) only makes them more expensive to run. The reason why internet, smartphones and computers have seen exponential growth from the 90s is due to underlying increase in computing power. I personally used a 50Mhz 486 in the 90s and now use a 8c/16t 5Ghz CPU. I highly doubt if we will see the same form of increase in the next 40 years
  • mindcrime 1y ago
    Scaling AI will require an exponential increase in compute and processing power, A small quibble... I'd say that's true only if you accept as an axiom that current approaches to AI are "the" approach and reject the possibility of radical algorithmic advances that completely change the game. For my part, I have a strongly held belief that there is such an algorithmic advancement "out there" waiting to be discovered, that will enable AI at current "intelligence" levels, if not outright Strong AI / AGI, without the absurd demands on computational resources and energy. I can't prove that of course, but I take the existence of the human brain as an existence proof that some kind of machine can provide human level intelligence without needing gigawatts of power and massive datacenters filled with racks of GPU's.
    • lawlessone 1y ago
      Deepmind where experimenting with this https://github.com/google-deepmind/lab https://github.com/google-deepmind/lab a few years ago. Having AI agents learn to see, navigate and complete tasks in a 3d environment. I feel like it had more potential than LLMs to become an AGI (if that is possible). They haven't touched it in a long time though. But Genie 3 makes me think they haven't completely dropped it.
    • fluoridation 1y ago
• bob1029 1y ago
  > Netflix over DialUp https://en.wikipedia.org/wiki/RealNetworks https://en.wikipedia.org/wiki/RealNetworks
• i_love_retros 1y ago
  Is some potential AGI breakthrough in the future going to be from LLMs or will they plateau in terms of capabilities? Its hard for me to imagine Skynet growing from chatgpt
  • whatevaa 1y ago
    The old story of paperclip AI shows that AGI is not needed for sufficiently smart computer to be dangerous.
• kokanee 1y ago
  I'm not convinced that the immaturity of the tech is what's holding back the profits. The impact and adoption of the tech are through the roof. It has shaken the job market across sectors like I've never seen before. My thinking is that if the bubble bursts, it won't be because the technology failed to deliver functionally; it will be because the technology simply does not become as profitable to operate as everyone is betting right now. What will it mean if the cutting edge models are open source, and being OpenAI effectively boils down to running those models in your data center? Your business model is suddenly not that different from any cloud service provider; you might as well be Digital Ocean.
• deegles 1y ago
  > We're clearly seeing what AI will eventually be able to do Are we though? Aside from a narrow set of tasks like translation, grammar, and tone-shifting, LLMs are a dead end. Code generation sucks. Agents suck. They still hallucinate. If you wouldn't trust its medical advice without review from an actual doctor, why would you trust its advice on anything else? Also, the companies trying to "fix" issues with LLMs with more training data will just rediscover the "long-tail" problem... there is an infinite number of new things that need to be put into the dataset, and that's just going to reduce the quality of responses. For example: the "there are three 'b's in blueberry" problem was caused by so much training data in response to "there are two r's in strawberry". it's a systemic issue. no amount of data will solve it because LLMs will -never- be sentient. Finally, I'm convinced that any AI company promising they are on the path to General AI should be sued for fraud. LLMs are not it.
  • hnfong 1y ago
    I have a feeling that you believe "translation, grammar, and tone-shifting" works but "code generation sucks" for LLMs because you're good at coding and hence you see its flaws, and you're not in the business of doing translation etc. Pretty sure if you're going to use LLMs for translating anything non-trivial, you'd have to carefully review the outputs, just like if you're using LLMs to write code.
    • mdemare 1y ago
      Exactly. Books are still being translated by human translators. I have a text on my computer, the first couple of paragraphs from the Dutch novel "De aanslag", and every few years I feed it to the leading machine translation sites, and invariably, the results are atrocious. Don't get me wrong, the translation is quite understandable, but the text is wooden, and the translation contains 3 or 4 translation blunders. GPT-5 output for example: Far, far away in the Second World War, a certain Anton Steenwijk lived with his parents and his brother on the edge of Haarlem. Along a quay, which ran for a hundred meters beside the water and then, with a gentle curve, turned back into an ordinary street, stood four houses not far apart. Each surrounded by a garden, with their small balconies, bay windows, and steep roofs, they had the appearance of villas, although they were more small than large; in the upstairs rooms, all the walls slanted. They stood there with peeling paint and somewhat dilapidated, for even in the thirties little had been done to them. Each bore a respectable, bourgeois name from more carefree days: Welgelegen Buitenrust Nooitgedacht Rustenburg Anton lived in the second house from the left: the one with the thatched roof. It already had that name when his parents rented it shortly before the war; his father had first called it Eleutheria or something like that, but then written in Greek letters. Even before the catastrophe occurred, Anton had not understood the name Buitenrust as the calm of being outside, but rather as something that was outside rest—just as extraordinary does not refer to the ordinary nature of the outside (and still less to living outside in general), but to something that is precisely not ordinary.
• armada651 1y ago
  > The groundwork has been laid, and it's not too hard to see the shape of things to come. The groundwork for VR has also been laid and it's not too hard to see the shape of things to come. Yet VR hasn't moved far beyond the previous hype cycle 10 years ago, because some problems are just really, really hard to solve.
  • jimbokun 1y ago
    Is it still giving people headaches and making them nauseous?
    • armada651 1y ago
      Yes, it still gives people headaches because the convergence-accommodation conflict remains unsolved. We have a few different technologies to address that, but they're expensive, don't fully address the issue, and none of them have moved beyond the prototype stage. Motion sickness can be mostly addressed with game design, but some people will still get sick regardless of what you try. Mind you, some people also get motion sick by watching a first-person shooter on a flat screen, so I'm not sure we'll ever get to a point where no one ever gets motion sick in VR.
      • duderific 1y ago
        > Mind you, some people also get motion sick by watching a first-person shooter on a flat screen Yep I'm that guy. I blame it on being old.
• matthewdgreen 1y ago
  As someone who was a customer of Netflix from the dialup to broadband world, I can tell you that this stuff happens much faster than you expect. With AI we're clearly in the "it really works, but there are kinks and scaling problems" of, say, streaming video in 2001 -- whereas I think you mean to indicate we're trying to do Netflix back in the 1980s where the tech for widespread broadband was just fundamentally not available.
  • tracker1 1y ago
    Oh, like RealPlayer in the late 90's (buffering... buffering...)
    • matthewdgreen 1y ago
      RealPlayer in the late 90s turned into (working) Napster, Gnutella and then the iPod in 2001, Podcasts (without the name) immediately after, with the name in 2004, Pandora in 2005, Spotify in 2008. So a decade from crummy idea to the companies we’re familiar with today, but slowed down by tremendous need for new (distributed) broadband infrastructure and complicated by IP arrangements. I guess 10 years seems like a long time from the front end, but looking back it’s nothing. Don’t go buying yourself a Tower Records.
      • tracker1 1y ago
        While I get the point... to be pedantic though, Napster (first gen), Gnutella and iPod were mostly download and listen offline experiences and not necessarily live streaming. Another major difference, is we're near the limits to the approaches being taking for computing capability... most dialup connections, even on "56k" modems were still lucky to get 33.6kbps down and very common in the late 90's, where by the mid-2000's a lot of users had at least 512kbps-10mbps connections (where available) and even then a lot of people didn't see broadband until the 2010's. that's at least a 15x improvement, where we are far less likely to see even a 3-5x improvement on computing power over the next decade and a half. That's also a lot of electricity to generate on an ageing infrastructure that barely meets current needs in most of the world... even harder on "green" options.
• deleted 1y ago
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