5 ms·

> at least for now. I know I could be eating my words, but there is basically no evidence to suggest it ever becomes as exceptional as the kingmakers are hopin

by ehnto 1y ago

> at least for now. I know I could be eating my words, but there is basically no evidence to suggest it ever becomes as exceptional as the kingmakers are hoping. Yes it advanced extremely quickly, but that is not a confirmation of anything. It could just be the technology quickly meeting us at either our limit of compute, or it's limit of capability. My thinking here is that we already had the technologies of the LLMs and the compute, but we hadn't yet had the reason and capital to deploy it at this scale. So the surprising innovation of transformers did not give us the boost in capability itself, it still needed scale. The marketing that enabled the capital, that enables that scale was what caused the insane growth, and capital can't grow forever, it needs returns. Scale has been exponential, and we are hitting an insane amount of capital deployment for this one technology that, has yet to prove commercially viable at the scale of a paradigm shift. Are businesses that are not AI based, actually seeing ROI on AI spend? That is really the only question that matters, because if that is false, the money and drive for the technology vanishes and the scale that enables it disappears too.

delusional 1y ago
> Yes it advanced extremely quickly, but that is not a confirmation of anything. It could just be the technology quickly meeting us at either our limit of compute, or it's limit of capability. To comment om this, because its the most common counter argument. Most technology has worked in steps. We take a step forward, then iterate on essentially the same thing. It's very rare we see order of magnitude improvement on the same fundamental "step". Cars were quite a step forward from donkeys, but modern cars are not that far off from the first ones. Planes were an amazing invention, but the next model of plane is basically the same thing as the first one.
- ehnto 1y ago
  I agree, I think we are in the latter phase already. LLMs were a huge leap in machine learning, but everything after has been steps on top + scale. I think we would need another leap to actually meet the markets expectations on AI. The market is expecting AGI, but I think we are probably just going to do incremental improvements for language and multi modal models from here, and not meet those expectations. I think the market is relying on something that doesn't currently exist to become true, and that is a bit irrational.
  - zamalek 1y ago
    Transformers aren't it, though. We need a new fundamental architecture and, just like every step forward in AI that came before, when that happens is a completely random event. Some researcher needs to wake up with a brilliant idea. The explosion of compute and investment could mean that we have more researchers available for that event to happen, but at the same time transformers are sucking up all the air in the room.
  - alganet 1y ago
    Several people hinted at the limits this technology was about to face, including training data and compute. It was obvious it had serious limits. Despite the warnings, companies insisted on marketing superintelligence nonsense and magic automatic developers. They convinced the market with disingenous demonstrations, which, again, were called out as bullshit by many people. They are still doing it. It's the same thing.
NitpickLawyer 1y ago
> I know I could be eating my words, but there is basically no evidence to suggest it ever becomes as exceptional as the kingmakers are hoping. ??? It has already become exceptional. In 2.5 years (since chatgpt launched) we went from "oh, look how cute this is, it writes poems and the code almost looks like python" to "hey, this thing basically wrote a full programming language[1] with genz keywords, and it mostly works, still has some bugs". I think the goalpost moving is at play here, and we quickly forget how 1 year makes a huge difference (last year you needed tons of glue and handwritten harnesses to do anything - see aider) and today you can give them a spec and get a mostly working project (albeit with some bugs), 50$ later. [1] - https://github.com/ghuntley/cursed https://github.com/ghuntley/cursed
- ehnto 1y ago
  I don't disagree with you on the technology, but mostly my comment is about what the market is expecting. With such a huge capex expenditure it is expecting a huge returns. Given AI has not proven consistent ROI generally for other enterprises (as far as I know), they are hoping for something better than what is right now and they are hoping for it to happen before the money runs out. I am not saying it's impossible, but there is no evidence that the leap in technology to reach wild profitability (replacing general labour) such investment desires is just around the corner either.
  - baxtr 1y ago
    After 3 years, I would like to see pathways. Let say we found a company that already realized 5-10% of savings in the first step. Now, based on this we might be able to map out the path to 25-30% savings in 5% steps for example. I personally haven’t seen this, but I might have missed it as well.
    - mikkupikku 1y ago
      Three years? One year ago I tried using LLMs for coding and found it to be more trouble than it was worth, no benifit in time spent or effort made. It's only within the past several months that this gas changed, IMHO.
wkat4242 1y ago
> Yes it advanced extremely quickly, It did but it's kinda stagnated now especially on the LLM front. The time when ever week a groundbreaking model came out is over for now. Later revisions of existing models, like GPT5 and llama4 have been underwhelming.
- CuriouslyC 1y ago
  GPT5 may have been underwhelming to _you_. Understand that they're heavily RLing to raise the floor on these models, so they might not be magically smarter across the board, there are a LOT of areas where they're a lot better that you've probably missed because they're not your use case.
  - bangaroo 1y ago
    every time i say "the tech seems to be stagnating" or "this model seems worse" based on my observations i get this response. "well, it's better for other use cases." i have even heard people say "this is worse for the things i use it for, but i know it's better for things i don't use it for." i have yet to hear anyone seriously explain to me a single real-world thing that GPT5 is better at with any sort of evidence (or even anecdote!) i've seen benchmarks! but i cannot point to a single person who seems to think that they are accomplishing real-world tasks with GPT5 better than they were with GPT4. the few cases i have heard that venture near that ask may be moderately intriguing, but don't seem to justify the overall cost of building and running the model, even if there have been marginal or perhaps even impressive leaps in very narrow use cases. one of the core features of LLMs is they are allegedly general-purpose. i don't know that i really believe a company is worth billions if they take their flagship product that can write sentences, generate a plan, follow instructions and do math and they are constantly making it moderately better at writing sentences, or following instructions, or coming up with a plan and it consequently forgets how to do math, or becomes belligerent, or sycophantic, or what have you. to me, as a user with a broad range of use cases (internet search, text manipulation, deep research, writing code) i haven't seen many meaningful increases in quality of task execution in a very, very long time. this tracks with my understanding of transformer models, as they don't work in a way that suggests to me that they COULD be good at executing tasks. this is why i'm always so skeptical of people saying "the big breakthrough is coming." transformer models seem self-limiting by merit of how they are designed. there are features of thought they simply lack, and while i accept there's probably nobody who fully understands how they work, i also think at this point we can safely say there is no superintelligence in there to eke out and we're at the margins of their performance. the entire pitch behind GPT and OpenAI in general is that these are broadly applicable, dare-i-say near-AGI models that can be used by every human as an assistant to solve all their problems and can be prompted with simple, natural language english. if they can only be good at a few things at a time and require extensive prompt engineering to bully into consistent behavior, we've just created a non-deterministic programming language, a thing precisely nobody wants.
walleeee 1y ago
The question in your last paragraph is not the only one that matters. Funding the technology at a material loss will not be off the table. Think about why.
- FromTheFirstIn 1y ago
  Just tell us why you think funding at a loss at this scale is viable, don’t smugly assign homework
  - walleeee 1y ago
    Apologies, not meant to be smug
    - 48terry 1y ago
      ...But you did fully intend to assign homework? Why are you even commenting, what are you adding?
bccdee 1y ago
> Yes it advanced extremely quickly The things that impress me about gpt-5 are basically the same ones that impressed me about gpt-3. For all the talk about exponential growth, I feel like we experienced one big technical leap forward and have spent the past 5 years fine-tuning the result—as if fiddling with it long enough will turn it into something it is not.
- timmytokyo 1y ago
  When building their LLMs, the model makers consumed the entire internet. This allowed the models to improve exponentially fast. But there's no more internet to consume. Yes, new data is being generated, but not at anywhere near the rate the models were growing in capability just a year ago. That's why we're seeing diminishing returns when comparing, say, GPT-5 to GPT-4. The AI marketers, accelerationists and doomers may seem to be different from one another, but the one thing they have in common is their adherence to an extrapolationist fallacy. They've been treating the explosion of LLM capabilities as a promise of future growth and capability, when in fact it's all an illusion. Nothing achieves indefinite exponential growth. Everything hits a wall.
ludicrousdispla 1y ago
>> The marketing that enabled the capital, that enables that scale was what caused the insane growth, and capital can't grow forever, Striking parallels between AI and food delivery (uber eats, deliveroo, lieferando, etc.) ... burn capital for market share/penetration but only deliver someone else's product with no investment to understand the core market for the purpose of developing a better product.

by ehnto 1y ago

delusional 1y ago

> Yes it advanced extremely quickly, but that is not a confirmation of anything. It could just be the technology quickly meeting us at either our limit of compute, or it's limit of capability. To comment om this, because its the most common counter argument. Most technology has worked in steps. We take a step forward, then iterate on essentially the same thing. It's very rare we see order of magnitude improvement on the same fundamental "step". Cars were quite a step forward from donkeys, but modern cars are not that far off from the first ones. Planes were an amazing invention, but the next model of plane is basically the same thing as the first one.

ehnto 1y ago
I agree, I think we are in the latter phase already. LLMs were a huge leap in machine learning, but everything after has been steps on top + scale. I think we would need another leap to actually meet the markets expectations on AI. The market is expecting AGI, but I think we are probably just going to do incremental improvements for language and multi modal models from here, and not meet those expectations. I think the market is relying on something that doesn't currently exist to become true, and that is a bit irrational.
- zamalek 1y ago
  Transformers aren't it, though. We need a new fundamental architecture and, just like every step forward in AI that came before, when that happens is a completely random event. Some researcher needs to wake up with a brilliant idea. The explosion of compute and investment could mean that we have more researchers available for that event to happen, but at the same time transformers are sucking up all the air in the room.
- alganet 1y ago
  Several people hinted at the limits this technology was about to face, including training data and compute. It was obvious it had serious limits. Despite the warnings, companies insisted on marketing superintelligence nonsense and magic automatic developers. They convinced the market with disingenous demonstrations, which, again, were called out as bullshit by many people. They are still doing it. It's the same thing.

NitpickLawyer 1y ago

> I know I could be eating my words, but there is basically no evidence to suggest it ever becomes as exceptional as the kingmakers are hoping. ??? It has already become exceptional. In 2.5 years (since chatgpt launched) we went from "oh, look how cute this is, it writes poems and the code almost looks like python" to "hey, this thing basically wrote a full programming language[1] with genz keywords, and it mostly works, still has some bugs". I think the goalpost moving is at play here, and we quickly forget how 1 year makes a huge difference (last year you needed tons of glue and handwritten harnesses to do anything - see aider) and today you can give them a spec and get a mostly working project (albeit with some bugs), 50$ later. [1] - https://github.com/ghuntley/cursed https://github.com/ghuntley/cursed

ehnto 1y ago
I don't disagree with you on the technology, but mostly my comment is about what the market is expecting. With such a huge capex expenditure it is expecting a huge returns. Given AI has not proven consistent ROI generally for other enterprises (as far as I know), they are hoping for something better than what is right now and they are hoping for it to happen before the money runs out. I am not saying it's impossible, but there is no evidence that the leap in technology to reach wild profitability (replacing general labour) such investment desires is just around the corner either.
- baxtr 1y ago
  After 3 years, I would like to see pathways. Let say we found a company that already realized 5-10% of savings in the first step. Now, based on this we might be able to map out the path to 25-30% savings in 5% steps for example. I personally haven’t seen this, but I might have missed it as well.
  - mikkupikku 1y ago
    Three years? One year ago I tried using LLMs for coding and found it to be more trouble than it was worth, no benifit in time spent or effort made. It's only within the past several months that this gas changed, IMHO.

wkat4242 1y ago

> Yes it advanced extremely quickly, It did but it's kinda stagnated now especially on the LLM front. The time when ever week a groundbreaking model came out is over for now. Later revisions of existing models, like GPT5 and llama4 have been underwhelming.

CuriouslyC 1y ago
GPT5 may have been underwhelming to _you_. Understand that they're heavily RLing to raise the floor on these models, so they might not be magically smarter across the board, there are a LOT of areas where they're a lot better that you've probably missed because they're not your use case.
- bangaroo 1y ago
  every time i say "the tech seems to be stagnating" or "this model seems worse" based on my observations i get this response. "well, it's better for other use cases." i have even heard people say "this is worse for the things i use it for, but i know it's better for things i don't use it for." i have yet to hear anyone seriously explain to me a single real-world thing that GPT5 is better at with any sort of evidence (or even anecdote!) i've seen benchmarks! but i cannot point to a single person who seems to think that they are accomplishing real-world tasks with GPT5 better than they were with GPT4. the few cases i have heard that venture near that ask may be moderately intriguing, but don't seem to justify the overall cost of building and running the model, even if there have been marginal or perhaps even impressive leaps in very narrow use cases. one of the core features of LLMs is they are allegedly general-purpose. i don't know that i really believe a company is worth billions if they take their flagship product that can write sentences, generate a plan, follow instructions and do math and they are constantly making it moderately better at writing sentences, or following instructions, or coming up with a plan and it consequently forgets how to do math, or becomes belligerent, or sycophantic, or what have you. to me, as a user with a broad range of use cases (internet search, text manipulation, deep research, writing code) i haven't seen many meaningful increases in quality of task execution in a very, very long time. this tracks with my understanding of transformer models, as they don't work in a way that suggests to me that they COULD be good at executing tasks. this is why i'm always so skeptical of people saying "the big breakthrough is coming." transformer models seem self-limiting by merit of how they are designed. there are features of thought they simply lack, and while i accept there's probably nobody who fully understands how they work, i also think at this point we can safely say there is no superintelligence in there to eke out and we're at the margins of their performance. the entire pitch behind GPT and OpenAI in general is that these are broadly applicable, dare-i-say near-AGI models that can be used by every human as an assistant to solve all their problems and can be prompted with simple, natural language english. if they can only be good at a few things at a time and require extensive prompt engineering to bully into consistent behavior, we've just created a non-deterministic programming language, a thing precisely nobody wants.

walleeee 1y ago

The question in your last paragraph is not the only one that matters. Funding the technology at a material loss will not be off the table. Think about why.

FromTheFirstIn 1y ago
Just tell us why you think funding at a loss at this scale is viable, don’t smugly assign homework
- walleeee 1y ago
  Apologies, not meant to be smug
  - 48terry 1y ago
    ...But you did fully intend to assign homework? Why are you even commenting, what are you adding?

bccdee 1y ago

> Yes it advanced extremely quickly The things that impress me about gpt-5 are basically the same ones that impressed me about gpt-3. For all the talk about exponential growth, I feel like we experienced one big technical leap forward and have spent the past 5 years fine-tuning the result—as if fiddling with it long enough will turn it into something it is not.

timmytokyo 1y ago
When building their LLMs, the model makers consumed the entire internet. This allowed the models to improve exponentially fast. But there's no more internet to consume. Yes, new data is being generated, but not at anywhere near the rate the models were growing in capability just a year ago. That's why we're seeing diminishing returns when comparing, say, GPT-5 to GPT-4. The AI marketers, accelerationists and doomers may seem to be different from one another, but the one thing they have in common is their adherence to an extrapolationist fallacy. They've been treating the explosion of LLM capabilities as a promise of future growth and capability, when in fact it's all an illusion. Nothing achieves indefinite exponential growth. Everything hits a wall.

ludicrousdispla 1y ago

>> The marketing that enabled the capital, that enables that scale was what caused the insane growth, and capital can't grow forever, Striking parallels between AI and food delivery (uber eats, deliveroo, lieferando, etc.) ... burn capital for market share/penetration but only deliver someone else's product with no investment to understand the core market for the purpose of developing a better product.