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> When do you expect that impact? I think the models seem smarter than their economic impact would imply. > Yeah. This is one of the very confusing things abou

> When do you expect that impact? I think the models seem smarter than their economic impact would imply. > Yeah. This is one of the very confusing things about the models right now. As someone who's been integrating "AI" and algorithms into people's workflows for twenty years, the answer is actually simple. It takes time to figure out how exactly to use these tools, and integrate them into existing tooling and workflows. Even if the models don't get any smarter, just give it a few more years and we'll see a strong impact. We're just starting to figure things out.

otabdeveloper4 11mo ago
> the models seem smarter than their economic impact would imply Key word is "seem".
- zeroonetwothree 11mo ago
  Kind of like how some humans seem smart during the interview but then are incapable of actually doing anything properly.
coder-3 11mo ago
As someone who is building an LLM-powered product on the side, using AI coding agents to help with development of said LLM-powered product and for my day job, and has a long-tail of miscellaneous uses for AI, I suspect you're right.
tim333 11mo ago
Beyond that the smartness is very patchy. They can do math problems beyond 99% of humans but lack the common sense understanding to take over most jobs.
- ACCount37 11mo ago
  Most jobs involve complex long term tasks - which isn't something that's natural to LLMs.
- reeredfdfdf 11mo ago
  Yep, the lack of commons sense is sometimes very evident. For instance, one of these popular generative AI services refused to remove copyright watermark from an image when asked directly. Then I told it that the image has weird text artifacts on it, and asked it to remove them. That worked perfectly.
jiriknesl 11mo ago
Oh yes, this is 100% accurate. Very often, when designing ERP, or other system, people think: "This is easy, I just this XYZ I am done." Then, you find that there are many corner use-cases. XYZ can be split to phases, you might need to add approvals, logging, data integrations... and what was a simple task, becomes 10 tasks. In the first year of CompSci uni, our teacher told us a thing I remember: Every system is 90% finished 90% of time. He was right.
weatherlite 11mo ago
> Even if the models don't get any smarter, just give it a few more years and we'll see a strong impact. We're just starting to figure things out. 2 years ? 15 years ? It matters a lot for people, the stock market and governments
myrmidon 11mo ago
Another limitation that I see right now is that for "economic impact" you want the things to have initiative and some agency, and there is well-justified hesitancy in providing that even where possible. Having a bunch of smart developers that are not allowed to do anything on their own and have to be prompted for every single action is not too advantageous if everyone is human, either ;)
- kanwisher 11mo ago
  Screw driver doesnt have agency but it certainly helps me get tasks done faster. AIs don't need agency to accelerate a ton of work
  - myrmidon 11mo ago
    I did not mean to imply that AI isn't helpful already. But a screw-driving assistant is more useful if he drives in screws on his own than if you have to prompt his every action. I'm not saying that a "dumb" assistant does not help at all.
jmaker 11mo ago
That’s likely exactly how I feel about it. In the end the product companies like OpenAI will harness the monetary benefits of the academic advances. You integrate, you build the product, you win, you don’t need to understand anything in terms of academic disciplines, you need the connections and the business smarts. In the end the majority of the population will be much more familiar with the terms ChatGPT and Copilot than with the names behind it, even if the academic behemoths such as Ilya and Andrej, who are quite prominent in their public appearance. For the major population, I believe it all began with search over knowledge graphs. Wikipedia presented a dynamic and vibrant corpus. Some NLP began to become more prominent. With OCR, more and more printed works had begun to get digitalized. The corpus had been growing. With opening the gates of scientific publishers, the quality might have also improved. All of it was part of the grunt work to make today’s LLMs capable. The growth of the Cloud DCs and compute advancements have been making deep nets more and more feasible. This is just an arbitrary observation on the surface of the pieces that fell into place. And LLMs are likely just another composite piece for something bigger yet to come. To me, that’s the fascination of how scientific theory and business applications live in symbiosis.
vidarh 11mo ago
Yeah, I spend most of my days keeping up with current AI development these days, and I'm only scratching the surface of how to integrate it in my own business. For people for whom it's not their actual job, it will take a lot more time to figure out even which questions to ask about where it makes sense to integrate in their workflows.
gnfargbl 11mo ago
Could this be a problem not with AI, but with our understanding of how modern economies work? The assumption here is that employees are already tuned so be efficient, so if you help them complete tasks more quickly then productivity improves. A slightly cynical alternate hypothesis could be that employees are generally already massively over-provisioned, because an individual leader's organisational power is proportional to the number of people working under them. If most workers are already spending most of their time doing busy-work to pad the day, then reducing the amount of time spent on actual work won't change the overall output levels.
- jmcgough 11mo ago
  Varies depending on the field and company. Sounds like you may be speaking from your own experiences? In medicine, we're already seeing productivity gains from AI charting leading to an expectation that providers will see more patients per hour.
  - magicalist 11mo ago
    > In medicine, we're already seeing productivity gains from AI charting leading to an expectation that providers will see more patients per hour. And not, of course, an expectation of more minutes of contact per patient, which would be the better outcome optimization for both provider and patient. Gotta pump those numbers until everyone but the execs are an assembly line worker in activity and pay.
    - fireant 11mo ago
      I don't think that more minutes of contact is better for anybody. As a patient, I want to spend as little time with a doctor as possible and still receive maximally useful treatment. As a doctor, I would want to extract maximal comp from insurance which I don't think is tied time spent with the patient, rather to a number of different treatments given. Also please note that in most western world medical personnel is currently massively overprovisioned and so reducing their overall workload would likely lead to better result per treatment given.
conartist6 11mo ago
Yeah but it's just one model. Call it Dave. Now Microsoft hires Dave and Open AI hires Dave. And Meta hires Dave and Oracle hires Dave and the US govt hires Dave. And soon each of those had hired not just one Dave but 50 identical copies of Dave. It doesn't matter if Dave is a smart-ish ok guy. That's not the problem with this scenario. The problem is the the only thing on the market is Dave and people who think exactly like Dave thinks
- ozgung 11mo ago
  That seems like a valid problem that was also mentioned in the podcast. 50 copies of Ilya, Dave or Einstein will have diminishing returns. I think the proposed solution is ongoing training and making them individuals. MS Dave will be a different individual than Dave.gov. But then why don't we just train humans in the first place.
ZaoLahma 11mo ago
AI makes the parts of my work that I spend the least time on a whole lot quicker, but (so far / still) has negligible effects on the parts of my work that I spend the most time on. I'm still not sure if this is due to a technological limitation or an organizational one. Most of my time is not spent on solving tech problems but rather solving "human-to-human" problems (prioritization between things that need doing, reaching consensus in large groups of people of how to do things that need doing, ...)
cowsandmilk 11mo ago
We’re also still at a point where security is a big question mark. My employer won’t let us hook GenAI up to office 365 or slack, so any project or product management use of GenAI first requires manually importing docs into a database and pointing to that. Efficiency gains are hard to come by when you don’t meet people where their “knowledge” is already stored.
HarHarVeryFunny 11mo ago
No doubt LLMs and tooling will continue to improve, and best use cases for them better understood, but what Ilya seems to be referring to is the massive disconnect between the headline-grabbing benchmarks such as "AI performs at PhD level on math", etc, and the real-world stupidity of these models such as his example of a coding agent toggling between generating bug #1 vs bug #2, which in fact largely explains why the current economic and visible impact is much less than if the "AI is PhD level" benchmark narrative was actually true. Calling LLMs "AI" makes them sound much more futuristic and capable than they actually are, and being such a meaningless term invites extrapolation to equally meaningless terms like AGI and visions of human-level capability. Let's call LLMs what they are - language models - tools for language-based task automation. Of course we eventually will do this. Fuzzy meaningless names like AI/AGI will always be reserved for the cutting edge technology du jour, and older tech that is realized in hindsight to be much more limited will revert to being called by more specific names such as "expert system", "language model", etc.
- riku_iki 11mo ago
  > the real-world stupidity of these models such as his example of a coding agent toggling between generating bug #1 vs bug #2, which in fact largely explains why the current economic and visible impact is much less than if the "AI is PhD level" benchmark narrative was actually true. this could be true in the past, but in recent weeks I started more and more trust top AI models and less PhDs I work with. Quality jump is very real imo.
  - nateglims 11mo ago
    Are you a mathematician? I’m not an expert on the math field but it seems like they are hitting the same issues everyone else has: current LLMs still more or less need to be supervised by an expert and struggle to do something actually novel or build out a complicated proof correctly.
    - riku_iki 11mo ago
      I work in math heavy applied setting. Randomly hired PhDs are also need to be supervised, end results being monitored, code be reviewed or they will make lots of mistakes, and my view is if you throw some problem like: build optimization model for this kind of problem on this kind of data, LLMs may produce better results.

otabdeveloper4 11mo ago

> the models seem smarter than their economic impact would imply Key word is "seem".

zeroonetwothree 11mo ago
Kind of like how some humans seem smart during the interview but then are incapable of actually doing anything properly.

coder-3 11mo ago

As someone who is building an LLM-powered product on the side, using AI coding agents to help with development of said LLM-powered product and for my day job, and has a long-tail of miscellaneous uses for AI, I suspect you're right.

tim333 11mo ago

Beyond that the smartness is very patchy. They can do math problems beyond 99% of humans but lack the common sense understanding to take over most jobs.

ACCount37 11mo ago
Most jobs involve complex long term tasks - which isn't something that's natural to LLMs.
reeredfdfdf 11mo ago
Yep, the lack of commons sense is sometimes very evident. For instance, one of these popular generative AI services refused to remove copyright watermark from an image when asked directly. Then I told it that the image has weird text artifacts on it, and asked it to remove them. That worked perfectly.

jiriknesl 11mo ago

Oh yes, this is 100% accurate. Very often, when designing ERP, or other system, people think: "This is easy, I just this XYZ I am done." Then, you find that there are many corner use-cases. XYZ can be split to phases, you might need to add approvals, logging, data integrations... and what was a simple task, becomes 10 tasks. In the first year of CompSci uni, our teacher told us a thing I remember: Every system is 90% finished 90% of time. He was right.

weatherlite 11mo ago

> Even if the models don't get any smarter, just give it a few more years and we'll see a strong impact. We're just starting to figure things out. 2 years ? 15 years ? It matters a lot for people, the stock market and governments

myrmidon 11mo ago

Another limitation that I see right now is that for "economic impact" you want the things to have initiative and some agency, and there is well-justified hesitancy in providing that even where possible. Having a bunch of smart developers that are not allowed to do anything on their own and have to be prompted for every single action is not too advantageous if everyone is human, either ;)

kanwisher 11mo ago
Screw driver doesnt have agency but it certainly helps me get tasks done faster. AIs don't need agency to accelerate a ton of work
- myrmidon 11mo ago
  I did not mean to imply that AI isn't helpful already. But a screw-driving assistant is more useful if he drives in screws on his own than if you have to prompt his every action. I'm not saying that a "dumb" assistant does not help at all.

jmaker 11mo ago

That’s likely exactly how I feel about it. In the end the product companies like OpenAI will harness the monetary benefits of the academic advances. You integrate, you build the product, you win, you don’t need to understand anything in terms of academic disciplines, you need the connections and the business smarts. In the end the majority of the population will be much more familiar with the terms ChatGPT and Copilot than with the names behind it, even if the academic behemoths such as Ilya and Andrej, who are quite prominent in their public appearance. For the major population, I believe it all began with search over knowledge graphs. Wikipedia presented a dynamic and vibrant corpus. Some NLP began to become more prominent. With OCR, more and more printed works had begun to get digitalized. The corpus had been growing. With opening the gates of scientific publishers, the quality might have also improved. All of it was part of the grunt work to make today’s LLMs capable. The growth of the Cloud DCs and compute advancements have been making deep nets more and more feasible. This is just an arbitrary observation on the surface of the pieces that fell into place. And LLMs are likely just another composite piece for something bigger yet to come. To me, that’s the fascination of how scientific theory and business applications live in symbiosis.

vidarh 11mo ago

Yeah, I spend most of my days keeping up with current AI development these days, and I'm only scratching the surface of how to integrate it in my own business. For people for whom it's not their actual job, it will take a lot more time to figure out even which questions to ask about where it makes sense to integrate in their workflows.

gnfargbl 11mo ago

Could this be a problem not with AI, but with our understanding of how modern economies work? The assumption here is that employees are already tuned so be efficient, so if you help them complete tasks more quickly then productivity improves. A slightly cynical alternate hypothesis could be that employees are generally already massively over-provisioned, because an individual leader's organisational power is proportional to the number of people working under them. If most workers are already spending most of their time doing busy-work to pad the day, then reducing the amount of time spent on actual work won't change the overall output levels.

jmcgough 11mo ago
Varies depending on the field and company. Sounds like you may be speaking from your own experiences? In medicine, we're already seeing productivity gains from AI charting leading to an expectation that providers will see more patients per hour.
- magicalist 11mo ago
  > In medicine, we're already seeing productivity gains from AI charting leading to an expectation that providers will see more patients per hour. And not, of course, an expectation of more minutes of contact per patient, which would be the better outcome optimization for both provider and patient. Gotta pump those numbers until everyone but the execs are an assembly line worker in activity and pay.
  - fireant 11mo ago
    I don't think that more minutes of contact is better for anybody. As a patient, I want to spend as little time with a doctor as possible and still receive maximally useful treatment. As a doctor, I would want to extract maximal comp from insurance which I don't think is tied time spent with the patient, rather to a number of different treatments given. Also please note that in most western world medical personnel is currently massively overprovisioned and so reducing their overall workload would likely lead to better result per treatment given.

conartist6 11mo ago

Yeah but it's just one model. Call it Dave. Now Microsoft hires Dave and Open AI hires Dave. And Meta hires Dave and Oracle hires Dave and the US govt hires Dave. And soon each of those had hired not just one Dave but 50 identical copies of Dave. It doesn't matter if Dave is a smart-ish ok guy. That's not the problem with this scenario. The problem is the the only thing on the market is Dave and people who think exactly like Dave thinks

ozgung 11mo ago
That seems like a valid problem that was also mentioned in the podcast. 50 copies of Ilya, Dave or Einstein will have diminishing returns. I think the proposed solution is ongoing training and making them individuals. MS Dave will be a different individual than Dave.gov. But then why don't we just train humans in the first place.

ZaoLahma 11mo ago

AI makes the parts of my work that I spend the least time on a whole lot quicker, but (so far / still) has negligible effects on the parts of my work that I spend the most time on. I'm still not sure if this is due to a technological limitation or an organizational one. Most of my time is not spent on solving tech problems but rather solving "human-to-human" problems (prioritization between things that need doing, reaching consensus in large groups of people of how to do things that need doing, ...)

cowsandmilk 11mo ago

We’re also still at a point where security is a big question mark. My employer won’t let us hook GenAI up to office 365 or slack, so any project or product management use of GenAI first requires manually importing docs into a database and pointing to that. Efficiency gains are hard to come by when you don’t meet people where their “knowledge” is already stored.

HarHarVeryFunny 11mo ago

No doubt LLMs and tooling will continue to improve, and best use cases for them better understood, but what Ilya seems to be referring to is the massive disconnect between the headline-grabbing benchmarks such as "AI performs at PhD level on math", etc, and the real-world stupidity of these models such as his example of a coding agent toggling between generating bug #1 vs bug #2, which in fact largely explains why the current economic and visible impact is much less than if the "AI is PhD level" benchmark narrative was actually true. Calling LLMs "AI" makes them sound much more futuristic and capable than they actually are, and being such a meaningless term invites extrapolation to equally meaningless terms like AGI and visions of human-level capability. Let's call LLMs what they are - language models - tools for language-based task automation. Of course we eventually will do this. Fuzzy meaningless names like AI/AGI will always be reserved for the cutting edge technology du jour, and older tech that is realized in hindsight to be much more limited will revert to being called by more specific names such as "expert system", "language model", etc.

riku_iki 11mo ago
> the real-world stupidity of these models such as his example of a coding agent toggling between generating bug #1 vs bug #2, which in fact largely explains why the current economic and visible impact is much less than if the "AI is PhD level" benchmark narrative was actually true. this could be true in the past, but in recent weeks I started more and more trust top AI models and less PhDs I work with. Quality jump is very real imo.
- nateglims 11mo ago
  Are you a mathematician? I’m not an expert on the math field but it seems like they are hitting the same issues everyone else has: current LLMs still more or less need to be supervised by an expert and struggle to do something actually novel or build out a complicated proof correctly.
  - riku_iki 11mo ago
    I work in math heavy applied setting. Randomly hired PhDs are also need to be supervised, end results being monitored, code be reviewed or they will make lots of mistakes, and my view is if you throw some problem like: build optimization model for this kind of problem on this kind of data, LLMs may produce better results.