6 ms·

I'm sure you are right. At some point, the bubble will crash. The question remains is when the bubble will crash. We could be in the 1995 equivalent of the dot

I'm sure you are right. At some point, the bubble will crash. The question remains is when the bubble will crash. We could be in the 1995 equivalent of the dotcom boom and not 1999. If so, we have 4 more years of high growth and even after the crash, the market will still be much bigger in 2029 than in 2024. Cisco was still 4x bigger in 2001 than in 1995. One thing that is slightly different from past bubbles is that the more compute you have, the smarter and more capable AI. One gauge I use to determine if we are still at the beginning of the boom is this: Does Slack sell an LLM chatbot solution that is able to give me reliable answers to business/technical decisions made over the last 2 years in chat? We don't have this yet - most likely because it's probably still too expensive to do this much inference with such high context window. We still need a lot more compute and better models. Because of the above, I'm in the camp that believe we are actually closer to the beginning of the bubble than at the end. Another thing I would watch closely to see when the bubble might pop is if LLM scaling laws are quickly breaking down and that more compute no longer yields more intelligence in an economical way. If so, I think the bubble would pop. All eyes are on GPT5-class models for signs.

vladgur 2y ago
Re: Slack chat: Glean.com does it for the enterprise I work at: It consumes all of our knowledge sources including Slack, Google docs, wiki, source code and provides answers to complex specific questions in a way that’s downright magical. I was converted into a believer when I described an issue to it, pointers to a source file in online git repo and it pointed me to another repository that my team did not own that controlled DNS configs that we were not aware about. These configs were the reason our code did not behave as we expected.
- aurareturn 2y ago
  Thanks. I didn't know that existed. But does it scale? Would it still work if large companies with many millions of Slack messages? I suppose one reason Slack doesn't have a solution yet is because they're having a hard time getting it to work for large companies.
  - deleted 2y ago
    [deleted]
  - hn_throwaway_99 2y ago
    Yeah, Glean does this and there are a bunch of other competitors that do it as well. I think you may be confused about the length of the context window. These tools don't pull all of your Slack history into the context window. They use a RAG approach to index all of your content into a vector DB, then when you make a query only the relevant document snippets are pulled into the context window. It's similar for example to how Cursor implements repository-wide AI queries.
    - aurareturn 2y ago
      I'm aware that one can't feed millions of messages into an LLM all at once. The only way to do this now is to use a RAG approach. But RAG approach has pros and cons and can miss crucial information. I think context window still matters a lot. The bigger the window, the more information you can feed in and the quality of answer should increase. The point I'm trying to make is that increase context window will require more compute. Hence, we could still just be in the beginning of the compute/AI boom.
      - reissbaker 2y ago
        We might be even earlier — the 90s was a famous boom with a fast bust, but to me this feels closer to the dawn of the personal computer in the late 70s and early 80s: we can automate things now that were impossible to automate before. We might have a long time before seeing diminishing returns.
- _huayra_ 2y ago
  This is the main "killer feature" I've personally experienced from GPT things: a much better contextual "search engine-ish" tool for combing through and correlating different internal data sources (slack, wiki, jira, github branches, etc). AI code assistants have been a net neutral for me (they get enough idioms in C++ slightly incorrect that I have to spend a lot of time just reading the generated code thoroughly), but being able to say "tell me what the timeline for feature X is" and have it comb through a bunch of internal docs / tickets / git commit messages, etc, and give me a coherent answer with links is amazing.
  - aurareturn 2y ago
    This is partly why I believe OS makers, Apple, Microsoft, Google, have a huge advantage in the future when it comes to LLMs. They control the OS so they can combine and feed all your digital information to an LLM in a seamless way. However, in the very long term, I think their advantage will go away because at some point, LLMs could get so good that you don't need an OS like iOS anymore. An LLM could simply become standalone - and function without a traditional OS. Therefore, I think the advantage for iOS, Android, Windows will increase in the next few years, but less powerful after that.
    - thwarted 2y ago
      An LLM is an application that runs on an operating system like any other application. That the vendor of the operating system has tied it to the operating system is purely a marketing/force-it-onto-your-device/force-it-in-front-of-your-face play. It's forced bundling, just like Microsoft did with Internet Explorer 20 years ago.
      - aurareturn 2y ago
        I predict that OpenAI will try to circumvent iOS and Android by making their own device. I think it will be similar to Rabbit R1, but not a scam, and a lot more capable. They recently hired Jony Ive on a project - it could be this. I think it'll be a long term goal - maybe in 3-4 years, a device similar to the Rabbit R1 would be viable. It's far too early right now.
- mvdtnz 2y ago
  My workpalce uses Glean and since it was connected to Slack it has become significantly worse. It routinely gives incorrect or VERY incomplete information, misattributes work to developers who may have casually mentioned a project at some time and worst of all presents jokes or sarcastic responses as fact. Not only is it an extremely poor source of information, it has ruined the company's Slack culture as people are no longer willing to (for lack of a better term) shitpost knowing that their goofy sarcasm will now be presented to Glean users as fact.
  - dcsan 2y ago
    Maybe have some off limits to glean shit posting channels?
  - rendang 2y ago
    Interesting. I still find it to be a net positive, but it is amusing when I ask it about a project and the source cited is a Slack thread I wrote 2 days prior
- sofixa 2y ago
  > Glean.com does it for the enterprise I work at: It consumes all of our knowledge sources including Slack, Google docs, wiki, source code and provides answers to complex specific questions in a way that’s downright magical There are a few other companies in this space (and it's not something that complex to DIY either); the issue is data quality. If your Google Docs and wikis contain obsolete information (because nobody updated them), it's just going to be shit in, shit out. Curating the input data is the challenging part.
HarHarVeryFunny 2y ago
> the more compute you have, the smarter and more capable AI Well, this is taken on faith by OpenAI/etc, but obviously the curve has to flatten at some point, and appears to already be doing so. OpenAI are now experimenting with scaling inference-time compute (GPT-O1), but have said that it takes exponential increases in compute to produce linear gains in performance, so it remains to be seen if customers find this a worthwhile value.
- aurareturn 2y ago
  GPT-o1 does demonstrate my point: the more compute you have, the smarter the AI. If you run chain of thoughts on an 8B model, it becomes a lot smarter too. GPT-o1 isn't GPT5 though. I think OpenAI will have a chain of thoughts model for GPT5-class models as well. They're separate from normal models.
  - HarHarVeryFunny 2y ago
    There is only so much that an approach like O1 can do, but anyways in terms of AI boom/bust the relevant question is whether this is a viable product. All sorts of consumer products could be improved by making them a lot more expensive, but there are cost/benefit limits to everything. GPT-5 and Claude-4 will be interesting, assuming these are both pure transformer models (not COT), as they will be a measure how much benefit remains to be had from training set scaling. I'd expect gains will be more against narrow benchmarks, than in the overall feel of intelligence (LLM arena score?) one gets from the model.
    - aurareturn 2y ago
      I think OpenAI has already proven that it's a viable product. Their gross margins must be decent. I doubt they're making a loss for every token they inference.
      - HarHarVeryFunny 2y ago
        I don't think they've broken out O1 revenue, but it must be very small at the moment since it was only just introduced. Their O1-preview pricing doesn't seem to reflect the exponential compute cost, so perhaps it is not currently priced to be profitable. Overall, across all models and revenue streams, their revenue does exceed inference costs ($4B vs $2B), but they still are projected to lose $5B this year, $14B next year, and not make a profit until 2029 (and only then if they've increased revenue by 100x ...). Training costs are killing them, and it's obviously not sustainable to keep spending more on research and training than the revenue generated. Training costs are expected to keep growing fast, while revenue per token in/out is plummeting - they need massive inference volume to turn this into a profitable business, and need to pray that this doesn't turn into a commodity business where they are not the low cost producer. https://x.com/ayooshveda/status/1847352974831489321 https://x.com/ayooshveda/status/1847352974831489321 https://x.com/Gloraaa_/status/1847872986260341224 https://x.com/Gloraaa_/status/1847872986260341224
jackcosgrove 2y ago
> One gauge I use to determine if we are still at the beginning of the boom is this Has your barber/hairdresser recommended you buy NVDA?
- arach 2y ago
  There was an NVDA earnings watch party in NY this summer and Jensen signed some boobs earlier this year. There are some signs but still room to run
- JKCalhoun 2y ago
  Doesn't that say more about Nvidia than it does about AI in general? But I see your point. And yes, I think it has trickled down to the mainstream. (By that metric, I guess Bitcoin crashed a few years ago.)
tim333 2y ago
You can never really tell, though following some market tea leaf readers they seem to think a few months from now, after a bit of a run up in the market. Here's one random datapoint, on mentions of "soft landing" in Bloomberg https://x.com/bravosresearch/status/1848047330794385494 https://x.com/bravosresearch/status/1848047330794385494
- aurareturn 2y ago
  I read through a few pages of tweets from this author and it looks just like another perpetual doomsday pundit akin to Zerohedge.
  - tim333 2y ago
    Well yeah there may be a bit of that. I find them quite interesting for the data they bring up like the linked tweet but I don't really have an opinion as to whether they are any good at predicting things. I was thinking re the data in the tweet, that there were a lot of mentions of "soft landing" before the dot com crash, before the 2006 property crash and now, it is quite likely there was an easy money policy preceding them and then government policy mostly focuses on consumer price inflation and unemployment, so they relax when those are both low and then hit the brakes when inflation goes up and then it moderates and things look good similar to now. But that ignores that easy money can also inflate asset prices, eg dot com stocks, houses in 06, or money losing AI companies like now. And then at some point that ends and the speculative asset prices go down rather than up, leaving people thinking oh dear we've borrowed to put loads of money into that dotcom/house/ai thing and now it's not worth much and we still have the debts... At least that's my guess.
    - throwaway2037 2y ago
      > 2006 property crash Assuming you are talking about the US financial crisis, do you mean 2008, instead of 2006? As I recall, easy money (via mortgages) was still sloshing about, well into 2007.
      - 2y ago
Terr_ 2y ago
> Does Slack sell an LLM chatbot solution that is able to give me reliable answers to business/technical decisions made over the last 2 years in chat? Note that the presence of such a feature isn't the same as whether it's secure enough for normal use. In particular, anything anyone said in the last 2 years in chat could poison the LLM into exfiltrating your data or giving false results chosen by the attacker, because of the fundamental problems of LLMs. https://promptarmor.substack.com/p/data-exfiltration-from-slack-ai-via https://promptarmor.substack.com/p/data-exfiltration-from-sl...
rsynnott 2y ago
> Does Slack sell an LLM chatbot solution that is able to give me reliable answers to business/technical decisions made over the last 2 years in chat? We don't have this yet - most likely because it's probably still too expensive to do this much inference with such high context window. So, your problem there is 'reliable'. LLMs, fairly fundamentally, cannot do 'reliable'. If you're looking for reliable, you likely are looking at a different tech entirely.
- williamcotton 2y ago
  If an LLM is tasked with translating a full text to a summary of that text then it is very reliable. This is akin to an analytic statement, eg, "all bachelors are married". The truth is completely within the definition of the statement. Compare this to a synthetic statement such as "it is raining outside". In this case the truth is contingent on facts outside of the statement itself. When LLMs are faced with an analytic statement they are more reliable. When they are faced with a synthetic statement they are prone to confabulate and are unreliable.
  - rsynnott 2y ago
    > If an LLM is tasked with translating a full text to a summary of that text then it is very reliable. Hrm. I've found LLM summaries to be of... dubious reliability. When someone posts an article on this here orange website, these days someone will sometimes 'helpfully' post a summary generated by a magic robot. Have a look at these, sometime. They _often_ leave out key details, and sometimes outright make shit up. Interesting article on someones' experiences with this recently: https://ea.rna.nl/2024/05/27/when-chatgpt-summarises-it-actually-does-nothing-of-the-kind/ https://ea.rna.nl/2024/05/27/when-chatgpt-summarises-it-actu...
    - williamcotton 2y ago
      Sure, anecdotal evidence. Here's another anecdote: Original article: https://osa1.net/posts/2024-10-09-oop-good.html https://osa1.net/posts/2024-10-09-oop-good.html LLM (ChatGPT o1-preview) results: https://chatgpt.com/share/67166301-00d8-8013-9cf5-e8a980aca775 https://chatgpt.com/share/67166301-00d8-8013-9cf5-e8a980aca7... LGTM! I'd like to know which model is used in the article you've referenced as well as the prompt. I also suspect that 50 pages is pushing up to the limits of the context window and has an impact on the results. --- As for the article itself... Use a language like F# or OCaml and you get a functional-first language that also supports OOP!
n_ary 2y ago
I believe, what we will see in new few years is the complete(or nearly) abolision of all human friendly customer support and everything will be ChatBot or voice-chat-bot based support to reduce cost of service. We will also get some nice things, like more intelligent IDE at affordable cost, think CursorAi costs $20/month(240/year), while whole JetBrain's package costs only 25/month(290/year). However, I am a bit worried about all these data center and AI and energy use/scaling. While consumers are being pushed to more and more efficient energy usage and energy prices are definitely high(to what I would expect with massive renewable energy production), large corps and such will continue scaling energy usage higher and higher. Also, the AI fad will eventually spook out a lot of free knowledge sharing on the open and everything will get behind paywall, so random poor kid in some poor country will no longer have access to some nice tutorial or documentations online to learn cool stuff because in some countries, what we call a price of "morning coffee" is actually could be a day's earning of an adult but not for non-privileged people. Without ability to pay for AI services, no more access to knowledge. Search engines will eventually drown in slop, I mean even google now frequently gives me "no resoult found" page and I need to use ddg/brave/bing to fish out some results still.
sixhobbits 2y ago
I've been calling a crash for far too long so take with a pinch of salt BUT I think another four years of this is very unlikely. 1996 - Cisco was 23.4B or 0.3% of US GDP 2000 - Cisco peaked at 536B or 5.2% of US GDP 2020 - Nvidia was 144B or 0.7% of US GDP 2024 - Nvidia is 3.4T or 11.9% of US GDP Numbers very rough and from different sources, but I'd be surprised if Nvidia doesn't pop within 1-2 years at most.
- dragontamer 2y ago
  Rumor is that there just isn't enough power to turn on all those fancy AI accelerators or Datacenters. There's a reason Microsoft just outright purchased the entire output of 3-mile island (a full sized nuclear power plant). At some point, people will stop buying GPUs because we've simply run out of power. My only hope is that we don't suffer some kind of ill effect (ex: double the cost of consumer electricity or something, or local municipalities going bankrupt due to rising energy costs). The AI boom has so much money in it we need to account for tail wags dog effects.
  - shikon7 2y ago
    In a working market, we won't run out of power, but power becomes so expensive that it's no longer viable to use most of the GPUs. The boom shifts to power generation instead, and we will have a similar article "The power investment boom", where people will debate that we will stop building power plants because we've simply run out of GPUs to use that power.
    - SoftTalker 2y ago
      Fortunately we're also electrifying transportation so there will be no shortage of demand for electrical power generation.
- syndicatedjelly 2y ago
  Comparing a company’s market cap to the US GDP makes no sense to me. The former is the product of shares and stock price. The latter is total financial output of a country. What intuition is that supposed to provide?

vladgur 2y ago

Re: Slack chat: Glean.com does it for the enterprise I work at: It consumes all of our knowledge sources including Slack, Google docs, wiki, source code and provides answers to complex specific questions in a way that’s downright magical. I was converted into a believer when I described an issue to it, pointers to a source file in online git repo and it pointed me to another repository that my team did not own that controlled DNS configs that we were not aware about. These configs were the reason our code did not behave as we expected.

aurareturn 2y ago
Thanks. I didn't know that existed. But does it scale? Would it still work if large companies with many millions of Slack messages? I suppose one reason Slack doesn't have a solution yet is because they're having a hard time getting it to work for large companies.
- deleted 2y ago
  [deleted]
- hn_throwaway_99 2y ago
  Yeah, Glean does this and there are a bunch of other competitors that do it as well. I think you may be confused about the length of the context window. These tools don't pull all of your Slack history into the context window. They use a RAG approach to index all of your content into a vector DB, then when you make a query only the relevant document snippets are pulled into the context window. It's similar for example to how Cursor implements repository-wide AI queries.
  - aurareturn 2y ago
    I'm aware that one can't feed millions of messages into an LLM all at once. The only way to do this now is to use a RAG approach. But RAG approach has pros and cons and can miss crucial information. I think context window still matters a lot. The bigger the window, the more information you can feed in and the quality of answer should increase. The point I'm trying to make is that increase context window will require more compute. Hence, we could still just be in the beginning of the compute/AI boom.
    - reissbaker 2y ago
      We might be even earlier — the 90s was a famous boom with a fast bust, but to me this feels closer to the dawn of the personal computer in the late 70s and early 80s: we can automate things now that were impossible to automate before. We might have a long time before seeing diminishing returns.
_huayra_ 2y ago
This is the main "killer feature" I've personally experienced from GPT things: a much better contextual "search engine-ish" tool for combing through and correlating different internal data sources (slack, wiki, jira, github branches, etc). AI code assistants have been a net neutral for me (they get enough idioms in C++ slightly incorrect that I have to spend a lot of time just reading the generated code thoroughly), but being able to say "tell me what the timeline for feature X is" and have it comb through a bunch of internal docs / tickets / git commit messages, etc, and give me a coherent answer with links is amazing.
- aurareturn 2y ago
  This is partly why I believe OS makers, Apple, Microsoft, Google, have a huge advantage in the future when it comes to LLMs. They control the OS so they can combine and feed all your digital information to an LLM in a seamless way. However, in the very long term, I think their advantage will go away because at some point, LLMs could get so good that you don't need an OS like iOS anymore. An LLM could simply become standalone - and function without a traditional OS. Therefore, I think the advantage for iOS, Android, Windows will increase in the next few years, but less powerful after that.
  - thwarted 2y ago
    An LLM is an application that runs on an operating system like any other application. That the vendor of the operating system has tied it to the operating system is purely a marketing/force-it-onto-your-device/force-it-in-front-of-your-face play. It's forced bundling, just like Microsoft did with Internet Explorer 20 years ago.
    - aurareturn 2y ago
      I predict that OpenAI will try to circumvent iOS and Android by making their own device. I think it will be similar to Rabbit R1, but not a scam, and a lot more capable. They recently hired Jony Ive on a project - it could be this. I think it'll be a long term goal - maybe in 3-4 years, a device similar to the Rabbit R1 would be viable. It's far too early right now.
mvdtnz 2y ago
My workpalce uses Glean and since it was connected to Slack it has become significantly worse. It routinely gives incorrect or VERY incomplete information, misattributes work to developers who may have casually mentioned a project at some time and worst of all presents jokes or sarcastic responses as fact. Not only is it an extremely poor source of information, it has ruined the company's Slack culture as people are no longer willing to (for lack of a better term) shitpost knowing that their goofy sarcasm will now be presented to Glean users as fact.
- dcsan 2y ago
  Maybe have some off limits to glean shit posting channels?
- rendang 2y ago
  Interesting. I still find it to be a net positive, but it is amusing when I ask it about a project and the source cited is a Slack thread I wrote 2 days prior
sofixa 2y ago
> Glean.com does it for the enterprise I work at: It consumes all of our knowledge sources including Slack, Google docs, wiki, source code and provides answers to complex specific questions in a way that’s downright magical There are a few other companies in this space (and it's not something that complex to DIY either); the issue is data quality. If your Google Docs and wikis contain obsolete information (because nobody updated them), it's just going to be shit in, shit out. Curating the input data is the challenging part.

HarHarVeryFunny 2y ago

> the more compute you have, the smarter and more capable AI Well, this is taken on faith by OpenAI/etc, but obviously the curve has to flatten at some point, and appears to already be doing so. OpenAI are now experimenting with scaling inference-time compute (GPT-O1), but have said that it takes exponential increases in compute to produce linear gains in performance, so it remains to be seen if customers find this a worthwhile value.

aurareturn 2y ago
GPT-o1 does demonstrate my point: the more compute you have, the smarter the AI. If you run chain of thoughts on an 8B model, it becomes a lot smarter too. GPT-o1 isn't GPT5 though. I think OpenAI will have a chain of thoughts model for GPT5-class models as well. They're separate from normal models.
- HarHarVeryFunny 2y ago
  There is only so much that an approach like O1 can do, but anyways in terms of AI boom/bust the relevant question is whether this is a viable product. All sorts of consumer products could be improved by making them a lot more expensive, but there are cost/benefit limits to everything. GPT-5 and Claude-4 will be interesting, assuming these are both pure transformer models (not COT), as they will be a measure how much benefit remains to be had from training set scaling. I'd expect gains will be more against narrow benchmarks, than in the overall feel of intelligence (LLM arena score?) one gets from the model.
  - aurareturn 2y ago
    I think OpenAI has already proven that it's a viable product. Their gross margins must be decent. I doubt they're making a loss for every token they inference.
    - HarHarVeryFunny 2y ago
      I don't think they've broken out O1 revenue, but it must be very small at the moment since it was only just introduced. Their O1-preview pricing doesn't seem to reflect the exponential compute cost, so perhaps it is not currently priced to be profitable. Overall, across all models and revenue streams, their revenue does exceed inference costs ($4B vs $2B), but they still are projected to lose $5B this year, $14B next year, and not make a profit until 2029 (and only then if they've increased revenue by 100x ...). Training costs are killing them, and it's obviously not sustainable to keep spending more on research and training than the revenue generated. Training costs are expected to keep growing fast, while revenue per token in/out is plummeting - they need massive inference volume to turn this into a profitable business, and need to pray that this doesn't turn into a commodity business where they are not the low cost producer. https://x.com/ayooshveda/status/1847352974831489321 https://x.com/ayooshveda/status/1847352974831489321 https://x.com/Gloraaa_/status/1847872986260341224 https://x.com/Gloraaa_/status/1847872986260341224

jackcosgrove 2y ago

> One gauge I use to determine if we are still at the beginning of the boom is this Has your barber/hairdresser recommended you buy NVDA?

arach 2y ago
There was an NVDA earnings watch party in NY this summer and Jensen signed some boobs earlier this year. There are some signs but still room to run
JKCalhoun 2y ago
Doesn't that say more about Nvidia than it does about AI in general? But I see your point. And yes, I think it has trickled down to the mainstream. (By that metric, I guess Bitcoin crashed a few years ago.)

tim333 2y ago

You can never really tell, though following some market tea leaf readers they seem to think a few months from now, after a bit of a run up in the market. Here's one random datapoint, on mentions of "soft landing" in Bloomberg https://x.com/bravosresearch/status/1848047330794385494 https://x.com/bravosresearch/status/1848047330794385494

aurareturn 2y ago
I read through a few pages of tweets from this author and it looks just like another perpetual doomsday pundit akin to Zerohedge.
- tim333 2y ago
  Well yeah there may be a bit of that. I find them quite interesting for the data they bring up like the linked tweet but I don't really have an opinion as to whether they are any good at predicting things. I was thinking re the data in the tweet, that there were a lot of mentions of "soft landing" before the dot com crash, before the 2006 property crash and now, it is quite likely there was an easy money policy preceding them and then government policy mostly focuses on consumer price inflation and unemployment, so they relax when those are both low and then hit the brakes when inflation goes up and then it moderates and things look good similar to now. But that ignores that easy money can also inflate asset prices, eg dot com stocks, houses in 06, or money losing AI companies like now. And then at some point that ends and the speculative asset prices go down rather than up, leaving people thinking oh dear we've borrowed to put loads of money into that dotcom/house/ai thing and now it's not worth much and we still have the debts... At least that's my guess.
  - throwaway2037 2y ago
    > 2006 property crash Assuming you are talking about the US financial crisis, do you mean 2008, instead of 2006? As I recall, easy money (via mortgages) was still sloshing about, well into 2007.
    - 2y ago

Terr_ 2y ago

> Does Slack sell an LLM chatbot solution that is able to give me reliable answers to business/technical decisions made over the last 2 years in chat? Note that the presence of such a feature isn't the same as whether it's secure enough for normal use. In particular, anything anyone said in the last 2 years in chat could poison the LLM into exfiltrating your data or giving false results chosen by the attacker, because of the fundamental problems of LLMs. https://promptarmor.substack.com/p/data-exfiltration-from-slack-ai-via https://promptarmor.substack.com/p/data-exfiltration-from-sl...

rsynnott 2y ago

> Does Slack sell an LLM chatbot solution that is able to give me reliable answers to business/technical decisions made over the last 2 years in chat? We don't have this yet - most likely because it's probably still too expensive to do this much inference with such high context window. So, your problem there is 'reliable'. LLMs, fairly fundamentally, cannot do 'reliable'. If you're looking for reliable, you likely are looking at a different tech entirely.

williamcotton 2y ago
If an LLM is tasked with translating a full text to a summary of that text then it is very reliable. This is akin to an analytic statement, eg, "all bachelors are married". The truth is completely within the definition of the statement. Compare this to a synthetic statement such as "it is raining outside". In this case the truth is contingent on facts outside of the statement itself. When LLMs are faced with an analytic statement they are more reliable. When they are faced with a synthetic statement they are prone to confabulate and are unreliable.
- rsynnott 2y ago
  > If an LLM is tasked with translating a full text to a summary of that text then it is very reliable. Hrm. I've found LLM summaries to be of... dubious reliability. When someone posts an article on this here orange website, these days someone will sometimes 'helpfully' post a summary generated by a magic robot. Have a look at these, sometime. They _often_ leave out key details, and sometimes outright make shit up. Interesting article on someones' experiences with this recently: https://ea.rna.nl/2024/05/27/when-chatgpt-summarises-it-actually-does-nothing-of-the-kind/ https://ea.rna.nl/2024/05/27/when-chatgpt-summarises-it-actu...
  - williamcotton 2y ago
    Sure, anecdotal evidence. Here's another anecdote: Original article: https://osa1.net/posts/2024-10-09-oop-good.html https://osa1.net/posts/2024-10-09-oop-good.html LLM (ChatGPT o1-preview) results: https://chatgpt.com/share/67166301-00d8-8013-9cf5-e8a980aca775 https://chatgpt.com/share/67166301-00d8-8013-9cf5-e8a980aca7... LGTM! I'd like to know which model is used in the article you've referenced as well as the prompt. I also suspect that 50 pages is pushing up to the limits of the context window and has an impact on the results. --- As for the article itself... Use a language like F# or OCaml and you get a functional-first language that also supports OOP!

n_ary 2y ago

I believe, what we will see in new few years is the complete(or nearly) abolision of all human friendly customer support and everything will be ChatBot or voice-chat-bot based support to reduce cost of service. We will also get some nice things, like more intelligent IDE at affordable cost, think CursorAi costs $20/month(240/year), while whole JetBrain's package costs only 25/month(290/year). However, I am a bit worried about all these data center and AI and energy use/scaling. While consumers are being pushed to more and more efficient energy usage and energy prices are definitely high(to what I would expect with massive renewable energy production), large corps and such will continue scaling energy usage higher and higher. Also, the AI fad will eventually spook out a lot of free knowledge sharing on the open and everything will get behind paywall, so random poor kid in some poor country will no longer have access to some nice tutorial or documentations online to learn cool stuff because in some countries, what we call a price of "morning coffee" is actually could be a day's earning of an adult but not for non-privileged people. Without ability to pay for AI services, no more access to knowledge. Search engines will eventually drown in slop, I mean even google now frequently gives me "no resoult found" page and I need to use ddg/brave/bing to fish out some results still.

sixhobbits 2y ago

I've been calling a crash for far too long so take with a pinch of salt BUT I think another four years of this is very unlikely. 1996 - Cisco was 23.4B or 0.3% of US GDP 2000 - Cisco peaked at 536B or 5.2% of US GDP 2020 - Nvidia was 144B or 0.7% of US GDP 2024 - Nvidia is 3.4T or 11.9% of US GDP Numbers very rough and from different sources, but I'd be surprised if Nvidia doesn't pop within 1-2 years at most.

dragontamer 2y ago
Rumor is that there just isn't enough power to turn on all those fancy AI accelerators or Datacenters. There's a reason Microsoft just outright purchased the entire output of 3-mile island (a full sized nuclear power plant). At some point, people will stop buying GPUs because we've simply run out of power. My only hope is that we don't suffer some kind of ill effect (ex: double the cost of consumer electricity or something, or local municipalities going bankrupt due to rising energy costs). The AI boom has so much money in it we need to account for tail wags dog effects.
- shikon7 2y ago
  In a working market, we won't run out of power, but power becomes so expensive that it's no longer viable to use most of the GPUs. The boom shifts to power generation instead, and we will have a similar article "The power investment boom", where people will debate that we will stop building power plants because we've simply run out of GPUs to use that power.
  - SoftTalker 2y ago
    Fortunately we're also electrifying transportation so there will be no shortage of demand for electrical power generation.
syndicatedjelly 2y ago
Comparing a company’s market cap to the US GDP makes no sense to me. The former is the product of shares and stock price. The latter is total financial output of a country. What intuition is that supposed to provide?