10 ms·

What if A.I. doesn't get better than this?

90 points by sundache 1y ago

latexr 1y ago
https://archive.ph/20250813061454/https://www.newyorker.com/culture/open-questions/what-if-ai-doesnt-get-much-better-than-this https://archive.ph/20250813061454/https://www.newyorker.com/...
- Ekshef 1y ago
  Thank you for that!
fuzzfactor 1y ago
>What If A.I. Doesn't Get Better Than This? What if it does? There's a certain type of fear . . . "It's the fear . . . they're gonna take my job away . . . " It's the fear . . . I'll be working here the rest of my days . . . " -- David Fahl Same fear, different day.
- piskov 1y ago
  Because every s-curve looks like an exponent for those in the start. I mean look at the first plane, then first air-jets: it’s understandable to assume we would travel the galaxy in something like 2050. Meanwhile planes are basically the same last 60 years. LLMs are great but I firmly believe that in 2100 all is basically the same as in 2020: no free energy (fusion), no AGI.
  - dr-detroit 1y ago
    [dead]
  - marviio 1y ago
    OTOH: First flight: 1903. Moon landing: 1969. Humanity went from ”Look, we’re 3 meters off the ground!” to “We just parked on the Moon” in barely a lifetime. 66 years.
    - CagedCoder 1y ago
      And how much further have we gotten past the moon since then? This isn't an OTOH, it's just another example of looking at the exponential part of the S-curve.
    - Yossarrian22 1y ago
      And has gone no further in almost 60 years
  - SAI_Peregrinus 1y ago
    Nature abhors an exponential. They all seem to either turn out to be sigmoid or collapse entirely.
    - IAmGraydon 1y ago
      Well...everything has a limit, so yes every example of exponential growth gets capped somewhere and becomes a sigmoid. Viral spread, population growth, radioactive decay, tree branching, etc. are all exponential until they hit their limit. Otherwise each process would quickly proceed to infinity. That doesn't really tell us much about where the ceiling is for LLMs, however.
  - olddustytrail 1y ago
    > Because every s-curve looks like an exponent for those in the start. No, it looks like an exponential all the way to the top of the curve. And the natural reaction when you consider it an s-curve is to think you're near the top. Unfortunately near the top looks exactly the same as near the bottom, so you might consider that you're nowhere near the top and that there's no reason you should be. Then you go on to speculate about tech that didn't exist 3 years ago and extrapolate 75 years in the future. No one has any idea what we'll have in 10 years time never mind 75. Even linearly, it's like someone from 1950 trying to guess about 2025.
  - II2II 1y ago
    > I mean look at the first plane, then first air-jets: it’s understandable to assume we would travel the galaxy in something like 2050. To someone who did not understand what flight is, perhaps. For anyone who understood the laws of physics, no. A similar thing can be said of Moore's Law. The main difference is we likely exceeded the rational expectations derived from Moore's Law (though that was based more on computer performance, rather than the actual expression of Moore's Law in terms of transistor count) while the more rational expectations of flight (routine supersonic, perhaps even suborbital) are still flights of fancy. But that is more a product of cost than technical ability. Simply put, we figured out how to make semiconductors extraordinarily inexpensive. Flight is still expensive.
- lenerdenator 1y ago
  Nah, I'm not afraid of working here the rest of my days. Consistent paycheck, benefits, challenging-but-rewarding work. If you provide people with that they typically shut up and stay out of the way. Everyone should be more afraid of the former than the latter.
  - izzydata 1y ago
    There is no plan in place at all for the outcome of most work becoming redundant. At least in the US I highly doubt we will be capable of implementing some system such as UBI for the benefit of all citizens so everyone can take advantage of most work being automated. Everyone will be left to pick up scraps and barely survive. But I am extremely skeptical that current "AI" will be capable of eliminating so much of the modern workforce any time soon if ever. I can see it becoming a common place tool, maybe it already has, but not as a human replacement.
    - disgruntledphd2 1y ago
      > There is no plan in place at all for the outcome of most work becoming redundant. At least in the US I highly doubt we will be capable of implementing some system such as UBI for the benefit of all citizens so everyone can take advantage of most work being automated. Everyone will be left to pick up scraps and barely survive. If 80% of US citizens lose their jobs, I assure you that there will be a political response. It might not be one you (or I) like, but it will happen and it will be a big deal.
      - lenerdenator 1y ago
        > If 80% of US citizens lose their jobs, I assure you that there will be a political response. It might not be one you (or I) like, but it will happen and it will be a big deal. In societies where 80% of people are not able to draw an income and there are more firearms than there are people allowed to own them, that political response is revolution.
        disgruntledphd2 1y ago
- AtlasBarfed 1y ago
  Wow did you encapsulate millenia of management-labor disputes by saying don't worry be happy? Let's play the same game with totalitarianism! It's the fear they are watching everything It's the fear nobody is watching at all Oh wow, I totally understand the threat of totalitarianism from that. And I bring up totalitarianism quite in particular, because aside from vastly empowering the elites in the war against labor, AI vastly empowers the elites for totalitarian monitoring and control.
  - fuzzfactor 1y ago
    >Wow did you encapsulate millenia of management-labor disputes by saying don't worry be happy? Nope, sorry to disappoint. That would be quite an accomplishment though, but I can't take credit for any progress in that direction no matter how far others have gone :) Not trying to hurt any feelings. I probably should have kept it simple and not included the sample of vastly pre-AI lyrics from Fahl. Just trying to emphasize that the fear of AI getting better, is very similar to the fear of it not getting better. Like a number of other unrelated things. Which are nothing new at all. I guess more often I've got to expect the unexpected with such a short comment, when I don't even try to explain very effectively, that some are going to read between the lines in some of the most unrelated ways I can not always anticipate. If I may ask, what made you such a fan of totalitarianism anyway, I know it's more popular than ever but is that all there is?
qcnguy 1y ago
> You didn’t need a bar chart to recognize that GPT-4 had leaped ahead of anything that had come before. You did though. I remember when GPT-4 was announced, OpenAI downplayed it and Altman said the difference was subtle and wouldn't be immediately apparent. For a lot of the stuff ChatGPT was being used for the gap between 3 and 4 wasn't going to really leap out at you. https://fortune.com/2023/03/14/openai-releases-gpt-4-improved-performance-still-limitations/ https://fortune.com/2023/03/14/openai-releases-gpt-4-improve... In the lead up to the announcement, Altman has set the bar low by suggesting people will be disappointed and telling his Twitter followers that “we really appreciate feedback on its shortcomings.” OpenAI described the distinction between GPT-3.5—the previous version of the technology—and GPT 4, as subtle in situations when users are having a “casual conversation” with the technology. “The difference comes out when the complexity of the task reaches a sufficient threshold—GPT-4 is more reliable, creative, and able to handle much more nuanced instructions than GPT-3.5,” a research blog post read. In the years since we got a lot more demanding of our models. Back then people were happy if they got models to write a small simple function and it worked. Now they expect models to manipulate large production codebases and get it right first time. So, the difference between GPT-3 and GPT-4 would be more apparent. But at the time, the reaction was somewhat muted.
- supriyo-biswas 1y ago
  > Back then people were happy if they got models to write a small simple function and it worked. Now they expect models to manipulate large production codebases and get it right first time. This push is mostly coming from the C-level and the hustler types, both of which need this to work out in order for their employeeless corporation fantasy to work out.
  - delusional 1y ago
    I'm not going to say that nobody is expecting it to do these things, but I don't think they should. It's still unable to write a simple function. What we've seen isn't a reasonable increase in expectations based upon validation of previous experiments. Instead it's racking up of expectations by all the signals of success. When they time and time again take in more VC cash at ever greater valuations, we are forced to assume they want to do something more, and since they get the cash we have to assume somebody believes them. Its a pyramid scheme, but instead of paying out earlier investors with the later investors cash its a confidence pyramid scheme. They obsolete the previous investors valuations by making bigger claims with larger expectations. Then they use those larger expectations as proof they already fulfilled the previous expectations.
brainwipe 1y ago
The title is irritating, conflating AI with LLMs. LLMs are a subset of AI. I expect future systems will be mobs of expert AI agents rather than relying on LLMs to do everything. An LLM will likely be in the mix for at least the natural language processing but I wouldn't bet the farm on them alone.
- DanielHB 1y ago
  The computing power alone of all these gpus would bring a revolution in simulation software. I mean 0 AI/machine-learning, just being able to simulate much more things than we can. Most industry-specific simulation software is REALLY crap, most from the 90s and 80s and barely evolved since then. Many stuck on single core CPUs.
  - bee_rider 1y ago
    It could be a nice side-effect of having all this “LLM hardware” built into everything, nice little throughput focused accelerators in everybody’s computers. I think if I were starting grad school now and wanted some easy points, I’d be looking at mixed precision numerical algorithms. Either coming up with new ones, or applying them in the sciences.
- DanHulton 1y ago
  That battle was long-ago lost when the leading LLM companies and organizations insisted on referring to their products and models solely as "AI", not the more-specific "LLMs". Implementers of that technology followed suit, and that's just what it means now. You can't blame the New Yorker for using the term in its modern, common parlance.
  - dasil003 1y ago
    Agreed, and ultimately it's fine because they're talking about products not technology. If these products go in a completely different direction and LLMs become obsolete the AI label will adapt just fine. Once these things hit common parlance there's no point in arguing technical specificity as 99.99% of the people using the term don't care, will never care, and language will follow their usage not the angry pedant.
bbqfog 1y ago
AI is so new and so powerful, that we don't really know how to use it yet. The next step is orchestration. LLMs are already powerful but they need to be scaled horizontally. "One shotting" something with a single call to an LLM should never be expected to work. That's not how the human brain works. We iterate, we collaborate with others, we reflect... We've already unlocked the hard and "mysterious" part, now we just need time to orchestrate and network it.
- monkpit 1y ago
  I think you’re right - even if we accept the premise that there’s only room for minor marginal improvements, there’s vast amounts of room for improvement with integrations, mcp, orchestration, prompting, etc. I’m talking mostly about coding agents here but it applies more widely. It’s a completely new tool, it’s like inventing the internal combustion engine and then going, “well, I guess that’s it, it’s kinda neat I guess.”
- kbelder 1y ago
  I think that's it. Even if there were no improvements with LLMs as they exist today, the integration and usage can still be vastly improved. Right now, we don't have multiple LLM-aware systems communicating, with a standardized information repository. Right now we have the technology to have an AI observe a room, count the people in it, see what they're doing, observe their mood, and set the lighting to the appropriate level. We just don't have all the sensors and integrations and protocols to manage that. The LLM interfaces with email, your bank, your phone, etc., is crude and clunky. So much more could be done with the LLMs we have now. (And just to be clear, most of those integrations sound horrible and dystopian. But they're examples.)
- player1234 1y ago
  Powerful but we don't know how to use it? If it is as powerful as all you true believers spout the usefulness would be self evident and that would be the display of its power. But apparently it is powerful just because you say so, and then something, something ... business model ...
  - 1y ago
EcommerceFlow 1y ago
OpenAi has 700+ million users. Sam recently said only 7% of Plus users were using thinking (o3)!!! That means 93% of their users were using nothing but 4o! Clearly the OpenAi leadership saw these stats and understood the main initial goal of GPT5 is to introduce this auto-router, and not go all in on intelligence for the 3-7% who care to use it. This is a genius move IMO, and will get tons of users to flood to ChatGPT over competitors. Grok, Gemini, etc are now fighting over scraps of the top 1% while OpenAi is going after the blue ocean of users.
- SideburnsOfDoom 1y ago
  If they're not paying users then they're just a liability.
  - jcfrei 1y ago
    monetizing those will come eventually - it's just hard to get right
    - SideburnsOfDoom 1y ago
      > (monetizing LLMs is) just hard to get right Time will tell if that's just a euphemism for "there is no business model here".
  - EcommerceFlow 1y ago
    How so? You capture the market first, then you turn on paid ads and reap benefits for decades like Google.
    - SideburnsOfDoom 1y ago
      Even when a business makes a profit, then the costs are still technically liabilities. And you're assuming that ad revenues or whatever will be high enough to cover the costs. And that's not a given.
- throwaway0123_5 1y ago
rossdavidh 1y ago
What happens is they go out of business: "these firms spent five hundred and sixty billion dollars on A.I.-related capital expenditures in the past eighteen months, while their A.I. revenues were only about thirty-five billion." DeepSeek (and the like) will prevent the kind of price increases necessary for them to pay back hundreds of billions of dollars already spent, much less pay for more. If they don't find a way to make LLMs do significantly more than they do thus far, and a market willing to pay hundreds of billions of dollars for them to do it, and some kind of "moat" to prevent DeepSeek and the like from undercutting them, they will collapse under the weight of their own expenses.
- mgfist 1y ago
  DeepSeek is also undercutting itself. No one is making a profit here, everyone is trying to gobble market share. Even if you have the best model and don't care to make a dime, inference is very expensive.
  - disgruntledphd2 1y ago
    I'd be surprised if Google weren't closer to profitability than basically anyone else, as they have their own hardware and have been running these kinds of applications for much longer than anyone else.
    - tonyedgecombe 1y ago
      Google is still investing heavily in data centres. Presumably without the AI they could lift their foot off that throttle.
    - tim333 1y ago
      Google are well set up to monetize compared to the others. They just need people to see their ads.
      - owebmaster 1y ago
        They are also the ones with more to lose to LLMs
scotty79 1y ago
I predict that the article of roughly the same title will be popping up on him every couple of years.
stephc_int13 1y ago
My current intuition on this topic is that they are right about scaling but they are training on the wrong data. LLMs were not intended to be the core foundation of artificial intelligence but an experiment around deep learning and language. Its success was an almost accidental byproduct of the availability of large amount of structured data to train from and the natural human bias to be tricked by language (Eliza effect). But human language itself is quite weak from a cognitive perspective and we end up with an extremely broad but shallow and brittle model. The recent and extremely costly attempts to build reasoning around don't seem much more promising than using a lot of hardcoded heuristics, basically ignoring the bitter lesson. I've seen many argue that a real human level AI should be trained from real-world experience, I am not sure this is true, but training should likely start from lower-level data than language, still using tokens and huge scale, and probably deeper networks.
- reactordev 1y ago
  Not all AI is LLMs. That's just what's most prevalent right now. There's still great work being done by models that don't "speak" but "perform". The issue is they need to be trained to perform like you said. The more tools like Claude Code are used, the more training they receive as well. I do think we'll see a plateau (if we haven't reached it already) of diminishing returns and we'll seek out new algorithms to improve it. Never underestimate the will of someone determined to gain an extra 10% performance or accuracy. It's the last 1% I worry about. 99.99% uptime is great until it isn't. 99% accuracy is great until it isn't. These things could be mitigated by running inference on different quantinizations of a model tree but ultimately we're going to have to triple check the work somehow.
  - eddythompson80 1y ago
    > The more tools like Claude Code are used, the more training they receive as well. What do you mean? A model doesn't improve because it's being used more. Are you saying Anthropic invests more into Claude Code the more people use it? Or are you saying they collect its output and train it on it?
kerblang 1y ago
They didn't answer much of the "What if," though... Am just imagining the massive financial losses taken by so many, and if a bailout becomes necessary, because too-big-to-fail now means Microsoft, Google, Facebook et al since we transferred so much of financial engineering economics onto them since '08.
- danjl 1y ago
  Those three companies have products outside AI and won't die quickly. The ones that will collapse are betting exclusively on improvements in AI. It will be fun to watch the VC money burn.
- woodpanel 1y ago
  Last time I've checked each of these companies were still hugely profitable. So it's not going to be your average FANG in trouble here, but rather VCs and others who've jumped onto the AI-craze
- BeFlatXIII 1y ago
  I hope the necessary bailout fails due to political fighting in Congress.
Mistletoe 1y ago
The bear market decade the stock market has been putting off since 2021 with this AI gasping phase happens. https://www.currentmarketvaluation.com/models/s&p500-mean-reversion.php https://www.currentmarketvaluation.com/models/s&p500-mean-re... https://www.cell.com/fulltext/S0092-8674(00)80089-6 https://www.cell.com/fulltext/S0092-8674(00)80089-6
zahirbmirza 1y ago
AI doesn't need to get better than this. It is already saving millions of hours of previously wasted human productivity. The biggest threat to these companies if their products do not improve is the local running of LLMS. That would finally justify consumers buying more memory and processor speed.
energy123 1y ago
How can you say progress has stalled two weeks after LLMs won gold medals at IOI and IMO? How can you say progress has stalled without having visibility on the compute costs of gpt-5 relative to o3? How can you say progress has stalled by referring to changes in benchmarks at the frontier over just 3.5 months?
- rudedogg 1y ago
  All that feels like specialized stunts like IBM’s Watson beating Ken Jennings at Jeopardy. The rate of improvement has slowed significantly. And chasing benchmarks is making everything worse IMO. Opus 4.1 is worse than Sonnet 3.7 to me :/. I think the future will be: 1. Ads and quantization/routing to chase profits 2. Local models start taking over. New companies will slide in without the huge losses and provide what Claude/OpenAI do today at reasonable margins 3. Apple/Google eat up lots of the market by shipping good-enough models with iOS/Android
- svara 1y ago
  You can't say that with any certainty, but I personally share the impression that growth has not kept up with the hype of 2023. Take the following for example. That's an article from April 2023, that strongly implies that the next version of GPT would be so much more powerful than the current one that it would be dangerous to work on or even release. Altman specifically used the version number "GPT5" back then. GPT5 is quite good, but is it the kind of technology that requires a word-wide moratorium on its development, lest it make humanity redundant? """ (Friedman) asked Altman for his thoughts on the recently released and widely circulated open letter demanding an AI pause. In response, the OpenAI founder shared some of his critiques. “An earlier version of the letter claimed OpenAI is training GPT-5 right now. We are not, and won’t for some time,” Altman noted. “So in that sense, [the letter] was sort of silly.” But, GPT-5 or not, Altman’s statement isn’t likely to be particularly reassuring to AI’s critiques, as first pointed out in a report from the Verge. The tech founder followed up his “no GPT-5″ announcement by immediately clarifying that upgrades and updates are in the works for GPT-4. There are ways to increase a technologies’ capacity beyond releasing an official, higher-number version of it. """ (from: https://gizmodo.com/sam-altman-open-ai-chatbot-gpt4-gpt5-1850337299 https://gizmodo.com/sam-altman-open-ai-chatbot-gpt4-gpt5-185...)
varelse 1y ago
[dead]
danjl 1y ago
Investors are betting on growth. The public loves the hype. As a user, I already have something useful. Schadenfreude.
k__ 1y ago
Good question. In the one side I read stuff about exponential gains with every new model. On the other side, the coding improvements look logarithmic to me.
- AtlasBarfed 1y ago
  Which to me means, what's the Big o of this entire venture? Ultimately, what they need to do is add nines of reliability. I guess I could argue that what they are producing now is like two nines: 99% accuracy. Of course, that depends on how you measure it and yada yada yada. So for things like self-driving, I could see how people could argue that the accuracy rate is 99.9% on a minute by minute basis. But how many nines do you need? Especially for self-driving five more? What's the computational cost to achieve that? Is it just five times? Is it 25 times? Is it two to the five power?
- microtonal 1y ago
  That's completely possible if the development of LLMs follow an S-curve (sigmoidal). At the beginning of the curve it will look exponential, then linear, and finally logarithmic. For different tasks, LLMs could be on different points on the curve, which would explain why some people perceive the improvements as exponential and others perceive them as logarithmic - they are simply working on different things and so experience different gradients.
behole 1y ago
https://web.archive.org/web/20250813134114/https://www.newyorker.com/culture/open-questions/what-if-ai-doesnt-get-much-better-than-this https://web.archive.org/web/20250813134114/https://www.newyo...
garyrob 1y ago
It appears that Cal Newport has decided to be the one to most publicly initiate the inevitable Trough Of Disillusionment stage of the Hype Cycle. I'm not sure it'll last very long, though, considering (for starters) Google DeepMind's gold medal at the recent International Math Olympiad. Also, while he criticizes the cost-cutting measure which is ChatGPT 5, he doesn't even mention ChatGPT 5 Pro, which is performing excellently.
puppycodes 1y ago
AI getting better is like maybe 50% or less of the equation. The other part is the infrastructure supporting AI applications. The infrastructure and interfaces that need to be built to fully take advantage of whats already here already has a long way to catch up.
varelse 1y ago
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KevinMS 1y ago
I always expect things like this to eventually deliver about 90% of what they promise, which turns out to be 100% for some niche uses, and for the rest it just gets abandoned because 90% isn't good enough. Like when voice recognition became super hyped in the late 90's, it was going to change how the world interacts with machines, and eventually it turned into "Hey Siri"
ml_more 1y ago
We did a test of GPT5 yesterday. We asked it to generate a synopsis of a scientific topic and cite sources. We then checked those sources. GPT5 still hallucinated 65% of the citations. It did things like: Make up the paper title Make up the authors for a real paper title Mix a real title and a real journal If it can't even reference real papers it certainly can't be trusted to match up claims of fact with real sources. Current AI tools generate citations that LOOK real but ARE fake. This might not be solvable inside the LLM. If anyone could do it, it'd be OpenAI. (OK maybe I'm giving them too much credit, but they have a crap-ton of money and seem to show a real interest in making their AI better) If it can't be done in the LLM we can't trust LLMs basically ever. I suppose there's a pretty big loophole here. Doing it outside the LLM but INSIDE the LLM product would be good enough. The first AI tool to incorporate that (internal citation and claim checking) will win because if the AI can check itself and prevent hallucinated garbage from ever reaching the user we can start to trust them and then they can do everything we've been promised. Until that day comes we can't trust them for anything.
- beacon294 1y ago
  Google already did this, give free gemini deepresearch a spin. It's not perfect, but I have a feeling you'll be surprised if this is your honest impression.
alecco 1y ago
LLMs will sure hit a dead end. But I think they will be a major stepping stone to help figure out and write the next generation.

90 points by sundache 1y ago

https://archive.ph/20250813061454/https://www.newyorker.com/culture/open-questions/what-if-ai-doesnt-get-much-better-than-this https://archive.ph/20250813061454/https://www.newyorker.com/...

Ekshef 1y ago
Thank you for that!

fuzzfactor 1y ago

>What If A.I. Doesn't Get Better Than This? What if it does? There's a certain type of fear . . . "It's the fear . . . they're gonna take my job away . . . " It's the fear . . . I'll be working here the rest of my days . . . " -- David Fahl Same fear, different day.

piskov 1y ago
Because every s-curve looks like an exponent for those in the start. I mean look at the first plane, then first air-jets: it’s understandable to assume we would travel the galaxy in something like 2050. Meanwhile planes are basically the same last 60 years. LLMs are great but I firmly believe that in 2100 all is basically the same as in 2020: no free energy (fusion), no AGI.
- dr-detroit 1y ago
  [dead]
- marviio 1y ago
  OTOH: First flight: 1903. Moon landing: 1969. Humanity went from ”Look, we’re 3 meters off the ground!” to “We just parked on the Moon” in barely a lifetime. 66 years.
  - CagedCoder 1y ago
    And how much further have we gotten past the moon since then? This isn't an OTOH, it's just another example of looking at the exponential part of the S-curve.
  - Yossarrian22 1y ago
    And has gone no further in almost 60 years
- SAI_Peregrinus 1y ago
  Nature abhors an exponential. They all seem to either turn out to be sigmoid or collapse entirely.
  - IAmGraydon 1y ago
    Well...everything has a limit, so yes every example of exponential growth gets capped somewhere and becomes a sigmoid. Viral spread, population growth, radioactive decay, tree branching, etc. are all exponential until they hit their limit. Otherwise each process would quickly proceed to infinity. That doesn't really tell us much about where the ceiling is for LLMs, however.
- olddustytrail 1y ago
  > Because every s-curve looks like an exponent for those in the start. No, it looks like an exponential all the way to the top of the curve. And the natural reaction when you consider it an s-curve is to think you're near the top. Unfortunately near the top looks exactly the same as near the bottom, so you might consider that you're nowhere near the top and that there's no reason you should be. Then you go on to speculate about tech that didn't exist 3 years ago and extrapolate 75 years in the future. No one has any idea what we'll have in 10 years time never mind 75. Even linearly, it's like someone from 1950 trying to guess about 2025.
- II2II 1y ago
  > I mean look at the first plane, then first air-jets: it’s understandable to assume we would travel the galaxy in something like 2050. To someone who did not understand what flight is, perhaps. For anyone who understood the laws of physics, no. A similar thing can be said of Moore's Law. The main difference is we likely exceeded the rational expectations derived from Moore's Law (though that was based more on computer performance, rather than the actual expression of Moore's Law in terms of transistor count) while the more rational expectations of flight (routine supersonic, perhaps even suborbital) are still flights of fancy. But that is more a product of cost than technical ability. Simply put, we figured out how to make semiconductors extraordinarily inexpensive. Flight is still expensive.
lenerdenator 1y ago
Nah, I'm not afraid of working here the rest of my days. Consistent paycheck, benefits, challenging-but-rewarding work. If you provide people with that they typically shut up and stay out of the way. Everyone should be more afraid of the former than the latter.
- izzydata 1y ago
  There is no plan in place at all for the outcome of most work becoming redundant. At least in the US I highly doubt we will be capable of implementing some system such as UBI for the benefit of all citizens so everyone can take advantage of most work being automated. Everyone will be left to pick up scraps and barely survive. But I am extremely skeptical that current "AI" will be capable of eliminating so much of the modern workforce any time soon if ever. I can see it becoming a common place tool, maybe it already has, but not as a human replacement.
  - disgruntledphd2 1y ago
    > There is no plan in place at all for the outcome of most work becoming redundant. At least in the US I highly doubt we will be capable of implementing some system such as UBI for the benefit of all citizens so everyone can take advantage of most work being automated. Everyone will be left to pick up scraps and barely survive. If 80% of US citizens lose their jobs, I assure you that there will be a political response. It might not be one you (or I) like, but it will happen and it will be a big deal.
    - lenerdenator 1y ago
      > If 80% of US citizens lose their jobs, I assure you that there will be a political response. It might not be one you (or I) like, but it will happen and it will be a big deal. In societies where 80% of people are not able to draw an income and there are more firearms than there are people allowed to own them, that political response is revolution.
      - disgruntledphd2 1y ago
AtlasBarfed 1y ago
Wow did you encapsulate millenia of management-labor disputes by saying don't worry be happy? Let's play the same game with totalitarianism! It's the fear they are watching everything It's the fear nobody is watching at all Oh wow, I totally understand the threat of totalitarianism from that. And I bring up totalitarianism quite in particular, because aside from vastly empowering the elites in the war against labor, AI vastly empowers the elites for totalitarian monitoring and control.
- fuzzfactor 1y ago
  >Wow did you encapsulate millenia of management-labor disputes by saying don't worry be happy? Nope, sorry to disappoint. That would be quite an accomplishment though, but I can't take credit for any progress in that direction no matter how far others have gone :) Not trying to hurt any feelings. I probably should have kept it simple and not included the sample of vastly pre-AI lyrics from Fahl. Just trying to emphasize that the fear of AI getting better, is very similar to the fear of it not getting better. Like a number of other unrelated things. Which are nothing new at all. I guess more often I've got to expect the unexpected with such a short comment, when I don't even try to explain very effectively, that some are going to read between the lines in some of the most unrelated ways I can not always anticipate. If I may ask, what made you such a fan of totalitarianism anyway, I know it's more popular than ever but is that all there is?

qcnguy 1y ago

> You didn’t need a bar chart to recognize that GPT-4 had leaped ahead of anything that had come before. You did though. I remember when GPT-4 was announced, OpenAI downplayed it and Altman said the difference was subtle and wouldn't be immediately apparent. For a lot of the stuff ChatGPT was being used for the gap between 3 and 4 wasn't going to really leap out at you. https://fortune.com/2023/03/14/openai-releases-gpt-4-improved-performance-still-limitations/ https://fortune.com/2023/03/14/openai-releases-gpt-4-improve... In the lead up to the announcement, Altman has set the bar low by suggesting people will be disappointed and telling his Twitter followers that “we really appreciate feedback on its shortcomings.” OpenAI described the distinction between GPT-3.5—the previous version of the technology—and GPT 4, as subtle in situations when users are having a “casual conversation” with the technology. “The difference comes out when the complexity of the task reaches a sufficient threshold—GPT-4 is more reliable, creative, and able to handle much more nuanced instructions than GPT-3.5,” a research blog post read. In the years since we got a lot more demanding of our models. Back then people were happy if they got models to write a small simple function and it worked. Now they expect models to manipulate large production codebases and get it right first time. So, the difference between GPT-3 and GPT-4 would be more apparent. But at the time, the reaction was somewhat muted.

supriyo-biswas 1y ago
> Back then people were happy if they got models to write a small simple function and it worked. Now they expect models to manipulate large production codebases and get it right first time. This push is mostly coming from the C-level and the hustler types, both of which need this to work out in order for their employeeless corporation fantasy to work out.
- delusional 1y ago
  I'm not going to say that nobody is expecting it to do these things, but I don't think they should. It's still unable to write a simple function. What we've seen isn't a reasonable increase in expectations based upon validation of previous experiments. Instead it's racking up of expectations by all the signals of success. When they time and time again take in more VC cash at ever greater valuations, we are forced to assume they want to do something more, and since they get the cash we have to assume somebody believes them. Its a pyramid scheme, but instead of paying out earlier investors with the later investors cash its a confidence pyramid scheme. They obsolete the previous investors valuations by making bigger claims with larger expectations. Then they use those larger expectations as proof they already fulfilled the previous expectations.

brainwipe 1y ago

The title is irritating, conflating AI with LLMs. LLMs are a subset of AI. I expect future systems will be mobs of expert AI agents rather than relying on LLMs to do everything. An LLM will likely be in the mix for at least the natural language processing but I wouldn't bet the farm on them alone.

DanielHB 1y ago
The computing power alone of all these gpus would bring a revolution in simulation software. I mean 0 AI/machine-learning, just being able to simulate much more things than we can. Most industry-specific simulation software is REALLY crap, most from the 90s and 80s and barely evolved since then. Many stuck on single core CPUs.
- bee_rider 1y ago
  It could be a nice side-effect of having all this “LLM hardware” built into everything, nice little throughput focused accelerators in everybody’s computers. I think if I were starting grad school now and wanted some easy points, I’d be looking at mixed precision numerical algorithms. Either coming up with new ones, or applying them in the sciences.
DanHulton 1y ago
That battle was long-ago lost when the leading LLM companies and organizations insisted on referring to their products and models solely as "AI", not the more-specific "LLMs". Implementers of that technology followed suit, and that's just what it means now. You can't blame the New Yorker for using the term in its modern, common parlance.
- dasil003 1y ago
  Agreed, and ultimately it's fine because they're talking about products not technology. If these products go in a completely different direction and LLMs become obsolete the AI label will adapt just fine. Once these things hit common parlance there's no point in arguing technical specificity as 99.99% of the people using the term don't care, will never care, and language will follow their usage not the angry pedant.

bbqfog 1y ago

AI is so new and so powerful, that we don't really know how to use it yet. The next step is orchestration. LLMs are already powerful but they need to be scaled horizontally. "One shotting" something with a single call to an LLM should never be expected to work. That's not how the human brain works. We iterate, we collaborate with others, we reflect... We've already unlocked the hard and "mysterious" part, now we just need time to orchestrate and network it.

monkpit 1y ago
I think you’re right - even if we accept the premise that there’s only room for minor marginal improvements, there’s vast amounts of room for improvement with integrations, mcp, orchestration, prompting, etc. I’m talking mostly about coding agents here but it applies more widely. It’s a completely new tool, it’s like inventing the internal combustion engine and then going, “well, I guess that’s it, it’s kinda neat I guess.”
kbelder 1y ago
I think that's it. Even if there were no improvements with LLMs as they exist today, the integration and usage can still be vastly improved. Right now, we don't have multiple LLM-aware systems communicating, with a standardized information repository. Right now we have the technology to have an AI observe a room, count the people in it, see what they're doing, observe their mood, and set the lighting to the appropriate level. We just don't have all the sensors and integrations and protocols to manage that. The LLM interfaces with email, your bank, your phone, etc., is crude and clunky. So much more could be done with the LLMs we have now. (And just to be clear, most of those integrations sound horrible and dystopian. But they're examples.)
player1234 1y ago
Powerful but we don't know how to use it? If it is as powerful as all you true believers spout the usefulness would be self evident and that would be the display of its power. But apparently it is powerful just because you say so, and then something, something ... business model ...
- 1y ago

EcommerceFlow 1y ago

OpenAi has 700+ million users. Sam recently said only 7% of Plus users were using thinking (o3)!!! That means 93% of their users were using nothing but 4o! Clearly the OpenAi leadership saw these stats and understood the main initial goal of GPT5 is to introduce this auto-router, and not go all in on intelligence for the 3-7% who care to use it. This is a genius move IMO, and will get tons of users to flood to ChatGPT over competitors. Grok, Gemini, etc are now fighting over scraps of the top 1% while OpenAi is going after the blue ocean of users.

SideburnsOfDoom 1y ago
If they're not paying users then they're just a liability.
- jcfrei 1y ago
  monetizing those will come eventually - it's just hard to get right
  - SideburnsOfDoom 1y ago
    > (monetizing LLMs is) just hard to get right Time will tell if that's just a euphemism for "there is no business model here".
- EcommerceFlow 1y ago
  How so? You capture the market first, then you turn on paid ads and reap benefits for decades like Google.
  - SideburnsOfDoom 1y ago
    Even when a business makes a profit, then the costs are still technically liabilities. And you're assuming that ad revenues or whatever will be high enough to cover the costs. And that's not a given.
throwaway0123_5 1y ago

rossdavidh 1y ago

What happens is they go out of business: "these firms spent five hundred and sixty billion dollars on A.I.-related capital expenditures in the past eighteen months, while their A.I. revenues were only about thirty-five billion." DeepSeek (and the like) will prevent the kind of price increases necessary for them to pay back hundreds of billions of dollars already spent, much less pay for more. If they don't find a way to make LLMs do significantly more than they do thus far, and a market willing to pay hundreds of billions of dollars for them to do it, and some kind of "moat" to prevent DeepSeek and the like from undercutting them, they will collapse under the weight of their own expenses.

mgfist 1y ago
DeepSeek is also undercutting itself. No one is making a profit here, everyone is trying to gobble market share. Even if you have the best model and don't care to make a dime, inference is very expensive.
- disgruntledphd2 1y ago
  I'd be surprised if Google weren't closer to profitability than basically anyone else, as they have their own hardware and have been running these kinds of applications for much longer than anyone else.
  - tonyedgecombe 1y ago
    Google is still investing heavily in data centres. Presumably without the AI they could lift their foot off that throttle.
  - tim333 1y ago
    Google are well set up to monetize compared to the others. They just need people to see their ads.
    - owebmaster 1y ago
      They are also the ones with more to lose to LLMs

scotty79 1y ago

I predict that the article of roughly the same title will be popping up on him every couple of years.

stephc_int13 1y ago

My current intuition on this topic is that they are right about scaling but they are training on the wrong data. LLMs were not intended to be the core foundation of artificial intelligence but an experiment around deep learning and language. Its success was an almost accidental byproduct of the availability of large amount of structured data to train from and the natural human bias to be tricked by language (Eliza effect). But human language itself is quite weak from a cognitive perspective and we end up with an extremely broad but shallow and brittle model. The recent and extremely costly attempts to build reasoning around don't seem much more promising than using a lot of hardcoded heuristics, basically ignoring the bitter lesson. I've seen many argue that a real human level AI should be trained from real-world experience, I am not sure this is true, but training should likely start from lower-level data than language, still using tokens and huge scale, and probably deeper networks.

reactordev 1y ago
Not all AI is LLMs. That's just what's most prevalent right now. There's still great work being done by models that don't "speak" but "perform". The issue is they need to be trained to perform like you said. The more tools like Claude Code are used, the more training they receive as well. I do think we'll see a plateau (if we haven't reached it already) of diminishing returns and we'll seek out new algorithms to improve it. Never underestimate the will of someone determined to gain an extra 10% performance or accuracy. It's the last 1% I worry about. 99.99% uptime is great until it isn't. 99% accuracy is great until it isn't. These things could be mitigated by running inference on different quantinizations of a model tree but ultimately we're going to have to triple check the work somehow.
- eddythompson80 1y ago
  > The more tools like Claude Code are used, the more training they receive as well. What do you mean? A model doesn't improve because it's being used more. Are you saying Anthropic invests more into Claude Code the more people use it? Or are you saying they collect its output and train it on it?

kerblang 1y ago

They didn't answer much of the "What if," though... Am just imagining the massive financial losses taken by so many, and if a bailout becomes necessary, because too-big-to-fail now means Microsoft, Google, Facebook et al since we transferred so much of financial engineering economics onto them since '08.

danjl 1y ago
Those three companies have products outside AI and won't die quickly. The ones that will collapse are betting exclusively on improvements in AI. It will be fun to watch the VC money burn.
woodpanel 1y ago
Last time I've checked each of these companies were still hugely profitable. So it's not going to be your average FANG in trouble here, but rather VCs and others who've jumped onto the AI-craze
BeFlatXIII 1y ago
I hope the necessary bailout fails due to political fighting in Congress.

Mistletoe 1y ago

The bear market decade the stock market has been putting off since 2021 with this AI gasping phase happens. https://www.currentmarketvaluation.com/models/s&p500-mean-reversion.php https://www.currentmarketvaluation.com/models/s&p500-mean-re... https://www.cell.com/fulltext/S0092-8674(00)80089-6 https://www.cell.com/fulltext/S0092-8674(00)80089-6

zahirbmirza 1y ago

AI doesn't need to get better than this. It is already saving millions of hours of previously wasted human productivity. The biggest threat to these companies if their products do not improve is the local running of LLMS. That would finally justify consumers buying more memory and processor speed.

energy123 1y ago

How can you say progress has stalled two weeks after LLMs won gold medals at IOI and IMO? How can you say progress has stalled without having visibility on the compute costs of gpt-5 relative to o3? How can you say progress has stalled by referring to changes in benchmarks at the frontier over just 3.5 months?

rudedogg 1y ago
All that feels like specialized stunts like IBM’s Watson beating Ken Jennings at Jeopardy. The rate of improvement has slowed significantly. And chasing benchmarks is making everything worse IMO. Opus 4.1 is worse than Sonnet 3.7 to me :/. I think the future will be: 1. Ads and quantization/routing to chase profits 2. Local models start taking over. New companies will slide in without the huge losses and provide what Claude/OpenAI do today at reasonable margins 3. Apple/Google eat up lots of the market by shipping good-enough models with iOS/Android
svara 1y ago
You can't say that with any certainty, but I personally share the impression that growth has not kept up with the hype of 2023. Take the following for example. That's an article from April 2023, that strongly implies that the next version of GPT would be so much more powerful than the current one that it would be dangerous to work on or even release. Altman specifically used the version number "GPT5" back then. GPT5 is quite good, but is it the kind of technology that requires a word-wide moratorium on its development, lest it make humanity redundant? """ (Friedman) asked Altman for his thoughts on the recently released and widely circulated open letter demanding an AI pause. In response, the OpenAI founder shared some of his critiques. “An earlier version of the letter claimed OpenAI is training GPT-5 right now. We are not, and won’t for some time,” Altman noted. “So in that sense, [the letter] was sort of silly.” But, GPT-5 or not, Altman’s statement isn’t likely to be particularly reassuring to AI’s critiques, as first pointed out in a report from the Verge. The tech founder followed up his “no GPT-5″ announcement by immediately clarifying that upgrades and updates are in the works for GPT-4. There are ways to increase a technologies’ capacity beyond releasing an official, higher-number version of it. """ (from: https://gizmodo.com/sam-altman-open-ai-chatbot-gpt4-gpt5-1850337299 https://gizmodo.com/sam-altman-open-ai-chatbot-gpt4-gpt5-185...)

varelse 1y ago

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danjl 1y ago

Investors are betting on growth. The public loves the hype. As a user, I already have something useful. Schadenfreude.

k__ 1y ago

Good question. In the one side I read stuff about exponential gains with every new model. On the other side, the coding improvements look logarithmic to me.

AtlasBarfed 1y ago
Which to me means, what's the Big o of this entire venture? Ultimately, what they need to do is add nines of reliability. I guess I could argue that what they are producing now is like two nines: 99% accuracy. Of course, that depends on how you measure it and yada yada yada. So for things like self-driving, I could see how people could argue that the accuracy rate is 99.9% on a minute by minute basis. But how many nines do you need? Especially for self-driving five more? What's the computational cost to achieve that? Is it just five times? Is it 25 times? Is it two to the five power?
microtonal 1y ago
That's completely possible if the development of LLMs follow an S-curve (sigmoidal). At the beginning of the curve it will look exponential, then linear, and finally logarithmic. For different tasks, LLMs could be on different points on the curve, which would explain why some people perceive the improvements as exponential and others perceive them as logarithmic - they are simply working on different things and so experience different gradients.

behole 1y ago

https://web.archive.org/web/20250813134114/https://www.newyorker.com/culture/open-questions/what-if-ai-doesnt-get-much-better-than-this https://web.archive.org/web/20250813134114/https://www.newyo...

garyrob 1y ago

It appears that Cal Newport has decided to be the one to most publicly initiate the inevitable Trough Of Disillusionment stage of the Hype Cycle. I'm not sure it'll last very long, though, considering (for starters) Google DeepMind's gold medal at the recent International Math Olympiad. Also, while he criticizes the cost-cutting measure which is ChatGPT 5, he doesn't even mention ChatGPT 5 Pro, which is performing excellently.

puppycodes 1y ago

AI getting better is like maybe 50% or less of the equation. The other part is the infrastructure supporting AI applications. The infrastructure and interfaces that need to be built to fully take advantage of whats already here already has a long way to catch up.

varelse 1y ago

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KevinMS 1y ago

I always expect things like this to eventually deliver about 90% of what they promise, which turns out to be 100% for some niche uses, and for the rest it just gets abandoned because 90% isn't good enough. Like when voice recognition became super hyped in the late 90's, it was going to change how the world interacts with machines, and eventually it turned into "Hey Siri"

ml_more 1y ago

We did a test of GPT5 yesterday. We asked it to generate a synopsis of a scientific topic and cite sources. We then checked those sources. GPT5 still hallucinated 65% of the citations. It did things like: Make up the paper title Make up the authors for a real paper title Mix a real title and a real journal If it can't even reference real papers it certainly can't be trusted to match up claims of fact with real sources. Current AI tools generate citations that LOOK real but ARE fake. This might not be solvable inside the LLM. If anyone could do it, it'd be OpenAI. (OK maybe I'm giving them too much credit, but they have a crap-ton of money and seem to show a real interest in making their AI better) If it can't be done in the LLM we can't trust LLMs basically ever. I suppose there's a pretty big loophole here. Doing it outside the LLM but INSIDE the LLM product would be good enough. The first AI tool to incorporate that (internal citation and claim checking) will win because if the AI can check itself and prevent hallucinated garbage from ever reaching the user we can start to trust them and then they can do everything we've been promised. Until that day comes we can't trust them for anything.

beacon294 1y ago
Google already did this, give free gemini deepresearch a spin. It's not perfect, but I have a feeling you'll be surprised if this is your honest impression.

alecco 1y ago

LLMs will sure hit a dead end. But I think they will be a major stepping stone to help figure out and write the next generation.