7 ms·

All these improvement in a single year, 2025. While this may seem obvious to those who follows along the AI / LLM news. It may be worth pointing out again ChatG

by ksec 10mo ago

All these improvement in a single year, 2025. While this may seem obvious to those who follows along the AI / LLM news. It may be worth pointing out again ChatGPT was introduced to us in November 2022. I still dont believe AGI, ASI or Whatever AI will take over human in short period of time say 10 - 20 years. But it is hard to argue against the value of current AI, which many of the vocal critics on HN seems to have the opinion of. People are willing to pay $200 per month, and it is getting $1B dollar runway already. Being more of a Hardware person, the most interesting part to me is the funding of all the developments of latest hardware. I know this is another topic HN hate because of the DRAM and NAND pricing issue. But it is exciting to see this from a long term view where the pricing are short term pain. Right now the industry is asking, we have together over a trillion dollar to spend on Capex over the next few years and will even borrow more if it needs to be, when can you ship us 16A / 14A / 10A and 8A or 5A, LPDDR6, Higher Capacity DRAM at lower power usage, better packaging, higher speed PCIe or a jump to optical interconnect? Every single part of the hardware stack are being fused with money and demand. The last time we have this was Post-PC / Smartphone era which drove the hardware industry forward for 10 - 15 years. The current AI can at least push hardware for another 5 - 6 years while pulling forward tech that was initially 8 - 10 years away. I so wished I brought some Nvidia stock. Again, I guess no one knew AI would be as big as it is today, and it is only just started.

coffeebeqn 10mo ago
Seems like Nvidia will be focusing on the super beefy GPUs and leaving the consumer market to a smaller player
- _s 10mo ago
  AMD owns a lot of the consumer market already; handhelds, consoles, desktop rigs and mobile ... they are not a small player.
  - utopiah 10mo ago
    They said "smaller" not small.
  - ac29 10mo ago
    Intel's client computing revenue was greater than AMD's entire revenue last quarter
- Flow 10mo ago
  I don't get why Nvidia can't do both? Is it because of the limited production capabilities of the factories?
  - ACCount37 10mo ago
    Yes. If you're bottlenecked on silicon and secondaries like memory, why would you want to put more of those resources into lower margin consumer products if you could use those very resources to make and sell more high margin AI accelerators instead? From a business standpoint, it makes some sense to throttle the gaming supply some. Not to the point of surrendering the market to someone else probably, but to a measurable degree.
    - ksec 10mo ago
      We will have to wait and see but my bet is that Nvidia will move to Leading Edge node N2 earlier now they have the Margin to work with. Both Hopper and Blackwell were too late in the design cycle. The AI hype and continue to buy the latest and great leaving Gaming at a mainstream node. Nvidia using Mainstream node has always been the norm considering most Fab capacity always goes to Mobile SoC first. But I expect the internet / gamers will be angry anyway because Nvidia does not provide them with the latest and greatest. In reality the extra R&D cost for designing with leading edge will be amortised by all the AI order which give Nvidia competitive advantage at the consumer level when they compete. That is assuming there are competition because most recent data have shown Nvidia owning 90%+ of discreet market share, 9% for AMD and 1% for Intel.
utopiah 10mo ago
> All these improvement in a single year > hard to argue against the value of current AI > People are willing to pay $200 per month, and it is getting $1B dollar runway already. Those are 3 different things. There can be a LOT of fast and significant improvements but still remain extremely far from the actual goal, so far it looks like actually little progress. People pay for a lot of things, including snake oil, so convincing a lot of people to pay a bit is not in itself a proof of value, especially when some people are basically coerced into this, see how many companies changed their "strategy" to mandating AI usage internally, or integration for a captive audience e.g. Copilot. Finally yes, $1B is a LOT of money for you and I... but for the largest corporations it's actually not a lot. For reference Google earned that in revenue... per day in 2023. Anyway that's still a big number BUT it still has to be compared with, well how much does OpenAI burn. I don't have any public number on that but I believe the consensus is that it's a lot. So until we know that number we can't talk about an actual runway.
- aspenmartin 10mo ago
  > People pay for a lot of things, including snake oil, so convincing a lot of people to pay a bit is not in itself a proof of value But do you really believe e.g. Claude code is snake oil? I pay $200 / month for Claude, which is something I would have thought monumentally insane maybe 1-2 years ago (e.g. when ChatGPT came out with their premium subscription price I thought that seemed so out of touch). I don't think we would be seeing the subscription rates and the retention numbers if it really was snake oil. > Finally yes, $1B is a LOT of money for you and I... but for the largest corporations it's actually not a lot. For reference Google earned that in revenue... per day in 2023. Anyway that's still a big number BUT it still has to be compared with, well how much does OpenAI burn. I don't have any public number on that but I believe the consensus is that it's a lot. So until we know that number we can't talk about an actual runway. this gets brought up a lot but I'm not sure I understand why folks on a forum called YCombinator, a startup accelerator, would make this sound like an obvious sign of charlatanism; operating at a loss is nothing new and anthropic / openAI strategy seems perfectly rational: they are scaling and capturing market share, and TAM is insane.
- jimmaswell 10mo ago
chias 10mo ago
These are not all improvements. Listed: * The year of YOLO and the Normalization of Deviance * The year that Llama lost its way * The year of alarmingly AI-enabled browsers * The year of the lethal trifecta * The year of slop * The year that data centers got extremely unpopular
- steveBK123 10mo ago
  > * The year that data centers got extremely unpopular I was discussing the political angle with a friend recently. I think Big Tech Bro / VC complex has done themselves a big disservice by aligning so tightly with MAGA to the point AI will be a political issue in 2026 & 2028. Think about the message they’ve inadvertently created themselves - AI is going to replace jobs, it’s pushing electric prices up, we need the government to bail us out AND give us a regulatory light touch. Super easy campaign for Dems - big tech trumpers are taking your money, your jobs, causing inflation, and now they want bailouts !!
- mbesto 10mo ago
  Said differently - the year we start to see all of the externalities of a globally scaled hyped tech trend.
- Y_Y 10mo ago
  Not that YOLO, PJ Reddie released that in 2015
jillesvangurp 10mo ago
2025 was the year of development tool using AI agents. I think we'll shift attention to non development tool using AI agents. Most business users are still stuck using chat gpt as some kind of grand oracle that will write their email or powerpoint slides. There are bits and pieces of mostly technology demo level solutions but nothing that is widely used like AI coding tools are so far. I don't think this is bottle necked on model quality. I don't need an AGI. I do need a secretary type agent that deals with all the simple but yet laborious non technical tasks that keep infringing on my quality engineering time. I'm CTO for a small startup and the amount of non technical bullshit that I need to deal with is enormous. Some examples of random crap I deal with: figuring out contracts, their meaning/implication to situations, and deciding on a course of action; Customer offers, price calculations, scraping invoices from emails and online SAAS accounts, formulating detailed replies to customer requests, HR legal work, corporate bureaucracy, financial planning, etc. A lot of this stuff can be AI assisted (and we get a lot of value out of ai tools for this) but context engineering is taking up a non trivial amount of my time. Also most tools are completely useless at modifying structured documents. Refactoring a big code base, no problem. Adding structured text to an existing structured document, hardest thing ever. The state of the art here is an ff-ing sidebar that will suggest you a markdown formatted text that you might copy/paste. Tool quality is very primitive. And then you find yourself just stripping all formatting and reformatting it manually. Because the tools really suck at this.
- arcatech 10mo ago
  > Some examples of random crap I deal with: figuring out contracts, their meaning/implication to situations, and deciding on a course of action This doesn’t sound like bullshit you should hand off to an AI. It sounds like stuff you would care about.
  - nrclark 10mo ago
    Agree. Even asking it can anchor your thinking.
  - jillesvangurp 10mo ago
    I do care about it; kind of my duty as a co-founder. Which is why I'm spending double digit percentages of my time doing this stuff. But I absolutely could use some tools to cut down on a lot of the drudgery that is involved with this. And me reading through 40 pages of dense legal German isn't one of my strengths since I 1) do not speak German 2) am not a lawyer and 3) am not necessarily deeply familiar with all the bureaucracy, laws, etc. But I can ask intelligent questions about that contract from an LLM (in English) and shoot back and forth a few things, come up with some kind of action plan, and then run it by our laywers and other advisors. That's not some kind of hypothetical thing. That's something that happened multiple times in our company in the last few months. LLMs are very empowering for dealing with this sort of thing. You still need experts for some stuff. But you can do a lot more yourself now. And as we've found out, some of the "experts" that we relied on in the past actually did a pretty shoddy job. A lot of this stuff was about picking apart the mess they made and fixing it. As soon as you start drafting contracts, it gets a lot harder. I just went through a process like that as well. It involves a lot of manual work that is basically about formatting documents, drafting text, running pdfs and text snippets through chat gpt for feedback, sparring, criticism, etc. and iterating on that. This is not about vibe coding some contract but making sure every letter of a contract is right. That ultimately involves lawyers and negotiating with other stakeholders but it helps if you come prepared with a more or less ready to sign off on document. It's not about handing stuff off but about making LLMs work for you. Just like with coding tools. I care about code quality as well. But I still use the tools to save me a lot of time.
pjc50 10mo ago
Investing a trillion dollars for a revenue of a billion dollars doesn't sound great yet.
- steveBK123 10mo ago
  Indeed, its the old Uber playbook at nearly two extra orders of magnitude. It is a large enough number to simply run out of private capital to consume before it turns cash flow positive. Lots of things sell well if sold at such a loss. I’d take a new Ferrari for $2500 if it was on offer.
  - derwiki 10mo ago
    Uber’s playbook worked for Uber
  - aoeusnth1 10mo ago
    You say that as if Uber's playbook didn't work. Try this: https://www.google.com/finance/quote/UBER:NYSE https://www.google.com/finance/quote/UBER:NYSE
  - pjc50 10mo ago
    Did Uber actually do a lot of capital investment? They don't own the cars, for example.
    - simonw 10mo ago
      I believe they spent a huge amount of money on incentives to help sign up drivers, and discounts to help attract customers.
      - pjc50 10mo ago
        Yes, but that's loss leader rather than capital investment. You can't put a customer on the balance sheet and depreciate them. Once you've paid for a free ride, you own nothing tangible.
    - 10mo ago
tstrimple 10mo ago
[flagged]
- ACCount37 10mo ago
  Honestly, I wouldn't be surprised if a system that's an LLM at its core can attain AGI. With nothing but incremental advances in architecture, scaffolding, training and raw scale. Mostly the training. I put less and less weight on "LLMs are fundamentally flawed" and more and more of it on "you're training them wrong". Too many "fundamental limitations" of LLMs are ones you can move the needle on with better training alone. The foundation of LLM is flexible and capable, and the list of "capabilities that are exclusive to human mind" is ever shrinking.
  - tim333 10mo ago
    They seem to be missing a bit on learning as you go and thinking about things and getting new insights.
    - ACCount37 10mo ago
      In-context learning and reasoning cover that already, and you could expand on that. Nothing prevents an LLM from fine-tuning itself either, other than its own questionable fine tuning skills and the compute budget.
  - HarHarVeryFunny 10mo ago
    That depends on how you define AGI - it's a meaningless term to use since everyone uses it to mean different things. What exactly do you mean ?! Yes, there is a lot that can be improved via different training, but at what point is it no longer a language model (i.e. something that auto-regressively predicts language continuations)? I like to use an analogy to the children's "Stone Soup" story whereby a "stone soup" (starting off as a stone in a pot of boiling water) gets transformed into a tasty soup/stew by strangers incrementally adding extra ingredients to "improve the flavor" - first a carrot, then a bit of beef, etc. At what point do you accept that the resulting tasty soup is not in fact stone soup?! It's like taking an auto-regressively SGD-trained Transformer, and incrementally tweaking the architecture, training algorithm, training objective, etc, etc. At some point it becomes a bit perverse to choose to still call it a language model Some of the "it's just training" changes that would be needed to make today's LLMs more brain-like may be things like changing the training objective completely from auto-regressive to predicting external events (with the goal of having it be able to learn the outcomes of it's own actions, in order to be able to plan them), which to be useful would require the "LLM" to then be autonomous and act in some (real/virtual) world in order to learn. Another "it's just training" change would be to replace pre/mid/post-training with continual/incremental runtime learning to again make the model more brain-like and able to learn from it's own autonomous exploration of behavior/action and environment. This is a far more profound, and ambitious, change than just fudging incremental knowledge acquisition for some semblance of "on the job" learning (which is what the AI companies are currently working on). If you put these two "it's just training/learning" enhancements together then you've now got something much more animal/human-like, and much more capable than an LLM, but it's already far from a language model - something that passively predicts next word every time you push the "generate next word" button. This would now be an autonomous agent, learning how to act and control/exploit the world around it. The whole pre-trained, same-for-everyone, model running in the cloud, would then be radically different - every model instance is then more like an individual learning based on it's own experience, and maybe you're now paying for compute for the continual learning compute rather than just "LLM tokens generated". These are "just" training (and deployment!) changes, but to more closely approach human capability (but again, what to you mean by "AGI"?) there would also need to be architectural changes and additions to the "Transformer" architecture (add looping, internal memory, etc), depending on exactly how close you want to get to human/animal capability.
ACCount37 10mo ago
Is the AI progress in 2025 an outstanding breakthrough? Not really. It's impressive but incremental. Still, the gap between the capabilities of a cutting edge LLM and that of a human is only this wide. There are only this many increments it takes to cross it.
wpietri 10mo ago
This is not a great argument: > But it is hard to argue against the value of current AI [...] it is getting $1B dollar runway already. The psychic services industry makes over $2 billion a year in the US [1], with about a quarter of the population being actual believers. [2]. [1] The https://www.ibisworld.com/united-states/industry/psychic-services/4413/ https://www.ibisworld.com/united-states/industry/psychic-ser... [2] https://news.gallup.com/poll/692738/paranormal-phenomena-met-skepticism.aspx https://news.gallup.com/poll/692738/paranormal-phenomena-met...
- apexalpha 10mo ago
  What if these provide actual value through placebo-effect?
  - recursive 10mo ago
    You talking about psychics or LLMs?
    - grosswait 10mo ago
      Yes
  - wpietri 10mo ago
    I think we have different definitions of "actual value". But even if I pick the flaccid definition, that isn't proof of value of the thing itself, but of any placebo. In which case we can focus on the cheapest/least harmful placebo. Or, better, solving the underlying problem that the placebo "helps".
    - computably 10mo ago
      I'll preface by saying I fully agree that psychics aren't providing any non-placebo value to believers, although I think it's fine to provide entertainment for non-believers. > Or, better, solving the underlying problem that the placebo "helps". The underlying problems are often a lack of a decent education and a generally difficult/unsatisfying life. Systemic issues which can't be meaningfully "solved" without massive resources and political will.
Atomic_Torrfisk 10mo ago
> People are willing to pay $200 per month Some people are of course, but how many? > ... People are willing to pay $200 per month This is just low-key hype. Careful with your portfolio...
HumblyTossed 10mo ago
It's a great tool, but right now it's only being used to feed the greed. >> Again, I guess no one knew AI would be as big as it is today, and it is only just started. People have been saying similar about self driving cars for years now. "AI" is another one of those expensive ideas that we'll get 85% of the way there and then to get the other 15% will be way more expensive than anyone will want to pay for. It's already happening - HW prices and electricity - people are starting to ask, "if I put more $ into this machine, when am I actually going to start getting money out?" The "true believers" are like, soon! But people are right to be hugely skeptical.
- jliptzin 10mo ago
  There are some things it's really great at. For example, handling a css layout. If we have to spend trillions of dollars and get nothing else out of it other than being able to vertically center a <div> without wrestling with css and wanting to smash the keyboard in the process, it will all have been worth it.
  - falkensmaize 10mo ago
    Not to be cheeky, but isn’t this just display: flex; align-items:center; now?
- aspenmartin 10mo ago
  I agree -- skepticism is totally healthy. And there are so many great ways to poke holes in the true underlying narratives (not the headlines that people seem to pull from). E.g. evaluation science is a wasteland (not for wont of very smart people trying very hard to get them right). How do we tackle the power requirements in a way that is sustainable? Etc. etc. But stuff like this im not sure I understand: > It's a great tool, but right now it's only being used to feed the greed. if its a great tool, then how is it _only_ being used to "feed the greed" and what do you mean by that? Also I think folks are quick to make analogies to other points in history: "AI is like the dot com boom we're going to crash and burn" and "AI is like {self driving cars, crypto, etc} and the promises will all be broken, its all hype" but this removes the nuance: all of these things are extremely different with very specific dynamics that in _some_ ways may be similar but in many crucial and important ways are completely different.
belter 10mo ago
>> But it is hard to argue against the value of current AI, which many of the vocal critics on HN seems to have the opinion of. What is the concrete business case? Can anyone point to a revenue producing company using AI in production, and where AI is a material driver of profits? Tool vendors don’t count. I’m not interested in how much money is being made selling shovels...show me a miner who actually struck gold please.
- tim333 10mo ago
  A lot of programmers seem willing to pay for the likes of Claude Code, presumably because it helps them get more done. Programmers cost money so that's a potential cost saving?
layer8 10mo ago
> Every single part of the hardware stack are being fused with money and demand. The last time we have this was Post-PC / Smartphone era which drove the hardware industry forward for 10 - 15 years. The current AI can at least push hardware for another 5 - 6 years while pulling forward tech that was initially 8 - 10 years away. It’s very unclear how much end-consumer hardware and DIY builders will benefit from that, as opposed to server-grade hardware that only makes sense for the enterprise marker. It could have the opposite effect, like hardware manufacturers leaving the consumer market (as in the case of Micron), because there’s just not that much money in it.

by ksec 10mo ago

coffeebeqn 10mo ago

Seems like Nvidia will be focusing on the super beefy GPUs and leaving the consumer market to a smaller player

_s 10mo ago
AMD owns a lot of the consumer market already; handhelds, consoles, desktop rigs and mobile ... they are not a small player.
- utopiah 10mo ago
  They said "smaller" not small.
- ac29 10mo ago
  Intel's client computing revenue was greater than AMD's entire revenue last quarter
Flow 10mo ago
I don't get why Nvidia can't do both? Is it because of the limited production capabilities of the factories?
- ACCount37 10mo ago
  Yes. If you're bottlenecked on silicon and secondaries like memory, why would you want to put more of those resources into lower margin consumer products if you could use those very resources to make and sell more high margin AI accelerators instead? From a business standpoint, it makes some sense to throttle the gaming supply some. Not to the point of surrendering the market to someone else probably, but to a measurable degree.
  - ksec 10mo ago
    We will have to wait and see but my bet is that Nvidia will move to Leading Edge node N2 earlier now they have the Margin to work with. Both Hopper and Blackwell were too late in the design cycle. The AI hype and continue to buy the latest and great leaving Gaming at a mainstream node. Nvidia using Mainstream node has always been the norm considering most Fab capacity always goes to Mobile SoC first. But I expect the internet / gamers will be angry anyway because Nvidia does not provide them with the latest and greatest. In reality the extra R&D cost for designing with leading edge will be amortised by all the AI order which give Nvidia competitive advantage at the consumer level when they compete. That is assuming there are competition because most recent data have shown Nvidia owning 90%+ of discreet market share, 9% for AMD and 1% for Intel.

utopiah 10mo ago

> All these improvement in a single year > hard to argue against the value of current AI > People are willing to pay $200 per month, and it is getting $1B dollar runway already. Those are 3 different things. There can be a LOT of fast and significant improvements but still remain extremely far from the actual goal, so far it looks like actually little progress. People pay for a lot of things, including snake oil, so convincing a lot of people to pay a bit is not in itself a proof of value, especially when some people are basically coerced into this, see how many companies changed their "strategy" to mandating AI usage internally, or integration for a captive audience e.g. Copilot. Finally yes, $1B is a LOT of money for you and I... but for the largest corporations it's actually not a lot. For reference Google earned that in revenue... per day in 2023. Anyway that's still a big number BUT it still has to be compared with, well how much does OpenAI burn. I don't have any public number on that but I believe the consensus is that it's a lot. So until we know that number we can't talk about an actual runway.

aspenmartin 10mo ago
> People pay for a lot of things, including snake oil, so convincing a lot of people to pay a bit is not in itself a proof of value But do you really believe e.g. Claude code is snake oil? I pay $200 / month for Claude, which is something I would have thought monumentally insane maybe 1-2 years ago (e.g. when ChatGPT came out with their premium subscription price I thought that seemed so out of touch). I don't think we would be seeing the subscription rates and the retention numbers if it really was snake oil. > Finally yes, $1B is a LOT of money for you and I... but for the largest corporations it's actually not a lot. For reference Google earned that in revenue... per day in 2023. Anyway that's still a big number BUT it still has to be compared with, well how much does OpenAI burn. I don't have any public number on that but I believe the consensus is that it's a lot. So until we know that number we can't talk about an actual runway. this gets brought up a lot but I'm not sure I understand why folks on a forum called YCombinator, a startup accelerator, would make this sound like an obvious sign of charlatanism; operating at a loss is nothing new and anthropic / openAI strategy seems perfectly rational: they are scaling and capturing market share, and TAM is insane.
jimmaswell 10mo ago

chias 10mo ago

These are not all improvements. Listed: * The year of YOLO and the Normalization of Deviance * The year that Llama lost its way * The year of alarmingly AI-enabled browsers * The year of the lethal trifecta * The year of slop * The year that data centers got extremely unpopular

steveBK123 10mo ago
> * The year that data centers got extremely unpopular I was discussing the political angle with a friend recently. I think Big Tech Bro / VC complex has done themselves a big disservice by aligning so tightly with MAGA to the point AI will be a political issue in 2026 & 2028. Think about the message they’ve inadvertently created themselves - AI is going to replace jobs, it’s pushing electric prices up, we need the government to bail us out AND give us a regulatory light touch. Super easy campaign for Dems - big tech trumpers are taking your money, your jobs, causing inflation, and now they want bailouts !!
mbesto 10mo ago
Said differently - the year we start to see all of the externalities of a globally scaled hyped tech trend.
Y_Y 10mo ago
Not that YOLO, PJ Reddie released that in 2015

jillesvangurp 10mo ago

2025 was the year of development tool using AI agents. I think we'll shift attention to non development tool using AI agents. Most business users are still stuck using chat gpt as some kind of grand oracle that will write their email or powerpoint slides. There are bits and pieces of mostly technology demo level solutions but nothing that is widely used like AI coding tools are so far. I don't think this is bottle necked on model quality. I don't need an AGI. I do need a secretary type agent that deals with all the simple but yet laborious non technical tasks that keep infringing on my quality engineering time. I'm CTO for a small startup and the amount of non technical bullshit that I need to deal with is enormous. Some examples of random crap I deal with: figuring out contracts, their meaning/implication to situations, and deciding on a course of action; Customer offers, price calculations, scraping invoices from emails and online SAAS accounts, formulating detailed replies to customer requests, HR legal work, corporate bureaucracy, financial planning, etc. A lot of this stuff can be AI assisted (and we get a lot of value out of ai tools for this) but context engineering is taking up a non trivial amount of my time. Also most tools are completely useless at modifying structured documents. Refactoring a big code base, no problem. Adding structured text to an existing structured document, hardest thing ever. The state of the art here is an ff-ing sidebar that will suggest you a markdown formatted text that you might copy/paste. Tool quality is very primitive. And then you find yourself just stripping all formatting and reformatting it manually. Because the tools really suck at this.

arcatech 10mo ago
> Some examples of random crap I deal with: figuring out contracts, their meaning/implication to situations, and deciding on a course of action This doesn’t sound like bullshit you should hand off to an AI. It sounds like stuff you would care about.
- nrclark 10mo ago
  Agree. Even asking it can anchor your thinking.
- jillesvangurp 10mo ago
  I do care about it; kind of my duty as a co-founder. Which is why I'm spending double digit percentages of my time doing this stuff. But I absolutely could use some tools to cut down on a lot of the drudgery that is involved with this. And me reading through 40 pages of dense legal German isn't one of my strengths since I 1) do not speak German 2) am not a lawyer and 3) am not necessarily deeply familiar with all the bureaucracy, laws, etc. But I can ask intelligent questions about that contract from an LLM (in English) and shoot back and forth a few things, come up with some kind of action plan, and then run it by our laywers and other advisors. That's not some kind of hypothetical thing. That's something that happened multiple times in our company in the last few months. LLMs are very empowering for dealing with this sort of thing. You still need experts for some stuff. But you can do a lot more yourself now. And as we've found out, some of the "experts" that we relied on in the past actually did a pretty shoddy job. A lot of this stuff was about picking apart the mess they made and fixing it. As soon as you start drafting contracts, it gets a lot harder. I just went through a process like that as well. It involves a lot of manual work that is basically about formatting documents, drafting text, running pdfs and text snippets through chat gpt for feedback, sparring, criticism, etc. and iterating on that. This is not about vibe coding some contract but making sure every letter of a contract is right. That ultimately involves lawyers and negotiating with other stakeholders but it helps if you come prepared with a more or less ready to sign off on document. It's not about handing stuff off but about making LLMs work for you. Just like with coding tools. I care about code quality as well. But I still use the tools to save me a lot of time.

pjc50 10mo ago

Investing a trillion dollars for a revenue of a billion dollars doesn't sound great yet.

steveBK123 10mo ago
Indeed, its the old Uber playbook at nearly two extra orders of magnitude. It is a large enough number to simply run out of private capital to consume before it turns cash flow positive. Lots of things sell well if sold at such a loss. I’d take a new Ferrari for $2500 if it was on offer.
- derwiki 10mo ago
  Uber’s playbook worked for Uber
- aoeusnth1 10mo ago
  You say that as if Uber's playbook didn't work. Try this: https://www.google.com/finance/quote/UBER:NYSE https://www.google.com/finance/quote/UBER:NYSE
- pjc50 10mo ago
  Did Uber actually do a lot of capital investment? They don't own the cars, for example.
  - simonw 10mo ago
    I believe they spent a huge amount of money on incentives to help sign up drivers, and discounts to help attract customers.
    - pjc50 10mo ago
      Yes, but that's loss leader rather than capital investment. You can't put a customer on the balance sheet and depreciate them. Once you've paid for a free ride, you own nothing tangible.
  - 10mo ago

tstrimple 10mo ago

[flagged]

ACCount37 10mo ago
Honestly, I wouldn't be surprised if a system that's an LLM at its core can attain AGI. With nothing but incremental advances in architecture, scaffolding, training and raw scale. Mostly the training. I put less and less weight on "LLMs are fundamentally flawed" and more and more of it on "you're training them wrong". Too many "fundamental limitations" of LLMs are ones you can move the needle on with better training alone. The foundation of LLM is flexible and capable, and the list of "capabilities that are exclusive to human mind" is ever shrinking.
- tim333 10mo ago
  They seem to be missing a bit on learning as you go and thinking about things and getting new insights.
  - ACCount37 10mo ago
    In-context learning and reasoning cover that already, and you could expand on that. Nothing prevents an LLM from fine-tuning itself either, other than its own questionable fine tuning skills and the compute budget.
- HarHarVeryFunny 10mo ago
  That depends on how you define AGI - it's a meaningless term to use since everyone uses it to mean different things. What exactly do you mean ?! Yes, there is a lot that can be improved via different training, but at what point is it no longer a language model (i.e. something that auto-regressively predicts language continuations)? I like to use an analogy to the children's "Stone Soup" story whereby a "stone soup" (starting off as a stone in a pot of boiling water) gets transformed into a tasty soup/stew by strangers incrementally adding extra ingredients to "improve the flavor" - first a carrot, then a bit of beef, etc. At what point do you accept that the resulting tasty soup is not in fact stone soup?! It's like taking an auto-regressively SGD-trained Transformer, and incrementally tweaking the architecture, training algorithm, training objective, etc, etc. At some point it becomes a bit perverse to choose to still call it a language model Some of the "it's just training" changes that would be needed to make today's LLMs more brain-like may be things like changing the training objective completely from auto-regressive to predicting external events (with the goal of having it be able to learn the outcomes of it's own actions, in order to be able to plan them), which to be useful would require the "LLM" to then be autonomous and act in some (real/virtual) world in order to learn. Another "it's just training" change would be to replace pre/mid/post-training with continual/incremental runtime learning to again make the model more brain-like and able to learn from it's own autonomous exploration of behavior/action and environment. This is a far more profound, and ambitious, change than just fudging incremental knowledge acquisition for some semblance of "on the job" learning (which is what the AI companies are currently working on). If you put these two "it's just training/learning" enhancements together then you've now got something much more animal/human-like, and much more capable than an LLM, but it's already far from a language model - something that passively predicts next word every time you push the "generate next word" button. This would now be an autonomous agent, learning how to act and control/exploit the world around it. The whole pre-trained, same-for-everyone, model running in the cloud, would then be radically different - every model instance is then more like an individual learning based on it's own experience, and maybe you're now paying for compute for the continual learning compute rather than just "LLM tokens generated". These are "just" training (and deployment!) changes, but to more closely approach human capability (but again, what to you mean by "AGI"?) there would also need to be architectural changes and additions to the "Transformer" architecture (add looping, internal memory, etc), depending on exactly how close you want to get to human/animal capability.

ACCount37 10mo ago

Is the AI progress in 2025 an outstanding breakthrough? Not really. It's impressive but incremental. Still, the gap between the capabilities of a cutting edge LLM and that of a human is only this wide. There are only this many increments it takes to cross it.

wpietri 10mo ago

This is not a great argument: > But it is hard to argue against the value of current AI [...] it is getting $1B dollar runway already. The psychic services industry makes over $2 billion a year in the US [1], with about a quarter of the population being actual believers. [2]. [1] The https://www.ibisworld.com/united-states/industry/psychic-services/4413/ https://www.ibisworld.com/united-states/industry/psychic-ser... [2] https://news.gallup.com/poll/692738/paranormal-phenomena-met-skepticism.aspx https://news.gallup.com/poll/692738/paranormal-phenomena-met...

apexalpha 10mo ago
What if these provide actual value through placebo-effect?
- recursive 10mo ago
  You talking about psychics or LLMs?
  - grosswait 10mo ago
    Yes
- wpietri 10mo ago
  I think we have different definitions of "actual value". But even if I pick the flaccid definition, that isn't proof of value of the thing itself, but of any placebo. In which case we can focus on the cheapest/least harmful placebo. Or, better, solving the underlying problem that the placebo "helps".
  - computably 10mo ago
    I'll preface by saying I fully agree that psychics aren't providing any non-placebo value to believers, although I think it's fine to provide entertainment for non-believers. > Or, better, solving the underlying problem that the placebo "helps". The underlying problems are often a lack of a decent education and a generally difficult/unsatisfying life. Systemic issues which can't be meaningfully "solved" without massive resources and political will.

Atomic_Torrfisk 10mo ago

> People are willing to pay $200 per month Some people are of course, but how many? > ... People are willing to pay $200 per month This is just low-key hype. Careful with your portfolio...

HumblyTossed 10mo ago

It's a great tool, but right now it's only being used to feed the greed. >> Again, I guess no one knew AI would be as big as it is today, and it is only just started. People have been saying similar about self driving cars for years now. "AI" is another one of those expensive ideas that we'll get 85% of the way there and then to get the other 15% will be way more expensive than anyone will want to pay for. It's already happening - HW prices and electricity - people are starting to ask, "if I put more $ into this machine, when am I actually going to start getting money out?" The "true believers" are like, soon! But people are right to be hugely skeptical.

jliptzin 10mo ago
There are some things it's really great at. For example, handling a css layout. If we have to spend trillions of dollars and get nothing else out of it other than being able to vertically center a <div> without wrestling with css and wanting to smash the keyboard in the process, it will all have been worth it.
- falkensmaize 10mo ago
  Not to be cheeky, but isn’t this just display: flex; align-items:center; now?
aspenmartin 10mo ago
I agree -- skepticism is totally healthy. And there are so many great ways to poke holes in the true underlying narratives (not the headlines that people seem to pull from). E.g. evaluation science is a wasteland (not for wont of very smart people trying very hard to get them right). How do we tackle the power requirements in a way that is sustainable? Etc. etc. But stuff like this im not sure I understand: > It's a great tool, but right now it's only being used to feed the greed. if its a great tool, then how is it _only_ being used to "feed the greed" and what do you mean by that? Also I think folks are quick to make analogies to other points in history: "AI is like the dot com boom we're going to crash and burn" and "AI is like {self driving cars, crypto, etc} and the promises will all be broken, its all hype" but this removes the nuance: all of these things are extremely different with very specific dynamics that in _some_ ways may be similar but in many crucial and important ways are completely different.

belter 10mo ago

>> But it is hard to argue against the value of current AI, which many of the vocal critics on HN seems to have the opinion of. What is the concrete business case? Can anyone point to a revenue producing company using AI in production, and where AI is a material driver of profits? Tool vendors don’t count. I’m not interested in how much money is being made selling shovels...show me a miner who actually struck gold please.

tim333 10mo ago
A lot of programmers seem willing to pay for the likes of Claude Code, presumably because it helps them get more done. Programmers cost money so that's a potential cost saving?

layer8 10mo ago

> Every single part of the hardware stack are being fused with money and demand. The last time we have this was Post-PC / Smartphone era which drove the hardware industry forward for 10 - 15 years. The current AI can at least push hardware for another 5 - 6 years while pulling forward tech that was initially 8 - 10 years away. It’s very unclear how much end-consumer hardware and DIY builders will benefit from that, as opposed to server-grade hardware that only makes sense for the enterprise marker. It could have the opposite effect, like hardware manufacturers leaving the consumer market (as in the case of Micron), because there’s just not that much money in it.