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2024 is going to shift into a tough year for AI. Business minded folks are already starting to deeply question where the value is relative to the amount of mone

by JCM9 3y ago

2024 is going to shift into a tough year for AI. Business minded folks are already starting to deeply question where the value is relative to the amount of money spent on training. Many/most of the GenAI companies have interesting ideas but no real business plan. Many of the larger AI companies look very shaky in terms of their governance and long term stability. Stability is showing itself to be very unstable, OpenAI has its mess that still seems not fully resolved, Inflection had a bunch of strange stuff go down earlier this week, and more to come. I’m a huge fan of the tech, but as reality sets in things are gonna get quite rough and there will need to be a painful culling of the AI space before sustainable and long term value materializes.

samstave 3y ago
>Many/most of the GenAI companies have interesting ideas but no real business plan - This is most likely the reason being the SAMA firing, to be able to re-align to the MIC without terrible consequence, from a PR perspective. no criticism here aside from the fact that we will see the AI killer fully autonomous robots will be here and unfettered by 'alignments' much sooner than we expected... And the MIC is where all the unscrupulous monies without audits will come from.
- MyFirstSass 3y ago
  > no criticism here aside from the fact that we will see the AI killer fully autonomous robots will be here and unfettered by 'alignments' much sooner than we expected... What exactly do you mean with this sentence? That less woke/regulated companies will suddenly leapfrog the giants now? What timeframe are we talking here? And do you mean for example non public mil applications from US/China or whatever or private models from unknown players? One thing i've been wondering is that if GPT-4 was 100 mil to train, then there's really a lot of plutocrats, despots, private companies and states for that matter that could in principle 10x that amount if they really wanted to go all in, and maybe they are right now? The bottleneck is the talent pool out there though, but i'm sure there's a lot people out there from libertarians to nation states that don't care about alignment at all, which is potentially pretty crazy / exciting / worrying depending on view.
  - anamax 3y ago
    > That less woke/regulated companies will suddenly leapfrog the giants now? What kind of "leapfrog" do you think is necessary to produce a "killer fully autonomous robot"? We've actually had "autonomous killer robots," machines that kill based on the outcome of some sensor plus processing, for centuries, and fairly sophisticated ones have been feasible for decades. (For example it's trivial to build a "killer robot" that triggers off of face or voice recognition.) The only thing that's changed recently is the kind of decisions and how effectively the robot can go looking for someone to kill.
    - MyFirstSass 3y ago
      Of course we're there already sadly. I was just confused by the sentence structure. This video is from 7 years ago: https://www.youtube.com/watch?v=ecClODh4zYk https://www.youtube.com/watch?v=ecClODh4zYk The interesting thing is who's actively working on this, any despots, private companies, foreign nations, who from the talent pool, criminal orgs or even western military which mostly works for the western elite classes.
  - wruza 3y ago
    maybe they are right now? Aren’t they? Pretty sure that most tactical and strategical decisions are automated to the bottom. Drones and cameras with all sorts of CV and ML features. You don’t need scary walking talking androids with red eyes to control battlefields and streets. The idea of “Terminator” is similar to a mailman on an antigrav bicycle.
  - yunwal 3y ago
    I don’t think the bottleneck is the talent pool. There are plenty of folks smart enough to do what OpenAI did, the question is, did they have enough access to capital?
alephnerd 3y ago
> Business minded folks are already starting to deeply question where the value is relative to the amount of money spent on training Kinda. In my experience, the bigger issue is the skillset has largely been diffused. Overtraining on internal corpora has been more than enough to enable automation benefits, and the ecosystem around ML is very robust now - 10 years ago SDKs like Scikit-learn or PyTorch were much less robust than they are now. Implementing commercial grade SVM or <insert_model_here> is fairly straightforward now. ML models have largely been commodified, and for most usecases, the process of implementing models fairly straightforward internally. IMO, the real value will be on the infrastructural side of ML - how to simply and enhance deployment, how to manage API security, how to manage multiple concurrent deployments, how to maximize performance, etc. And I have put my money where my mouth is for this thesis, as it is one that has been validated by every peer of mine as well.
- JCM9 3y ago
  I agree with you. My reference in business questioning value was on the flood of money going into building new models. That’s where we will see a significant and painful culling soon as it’s all becoming quite commoditized and there’s only so much room in the market when that happens. Tooling, security services and other things to build on top of a commoditized generative AI market opens up other doors for products to be built.
  - alephnerd 3y ago
    > flood of money going into building new models In my experience, there really hasn't been that significant of a flood of money in this space for several years now, or at least not to the level I've seen based on discussion here on HN. I think HN tends to skew towards conversations around models for some reason, but almost all my peers are either funding or working on either tooling or ML driven applications since 2021. ------- I've found HN to have a horrible noise to value signal nowadays, and people with experience (eg. My friends in the YC community) deviating towards Bookface or in person meetups instead now. There was a flood of new accounts in the 2020-22 period (my hunch is it's LessWrong, SSC, and Reddit driven based on the inside jokes and posts I've been seeing recently on HN) but they don't reflect the actual reality of the industry.
ASinclair 3y ago
> Business minded folks are already starting to deeply question where the value is relative to the amount of money spent on training. Not just training but inference too, right? They have to make money off each query.
- keyringlight 3y ago
  I'm fairly sure this is why there's a rush to get AI related capabilities into processors for consumer devices, to offload that computing so they don't bear the ongoing cost and it'll probably be more responsive to the user.
- JCM9 3y ago
  Yes. Each query needs to generate enough revenue to pay for the cost of running the query, a proportional cost of the cost to train the original model, overhead, SG&A, etc. just to break even. Few have shown a plan to do that or explained in a defensible way how they’re going to get there. A challenge at the moment is a lot of the AI movement is led by folks that are brilliant technologists but have little to no experience running viable businesses or building a viable business plan. That was clearly part of why OpenAI has its turmoil in that some where trying to be tech purists where others knew the whole AI space will implode if it’s not a viable business. To some degree that seems to behind a lot of the recent chaos inside these larger AI companies.
  - financltravsty 3y ago
    It's always fascinating to see. The business portions are much easier to solve for with an engineering mindset, but it seems to be a common issue that engineers never take it into account. This is saying nothing about "technologists" (or as they're starting to become derided as "wordcels": people that communicate well, but cannot execute anything themselves). It would be... not trivial, but straightforward to map out the finances on everything involved, and see if there is room from any standpoint (engineering, financial, product, etc.) to get queries to breakeven, or even profitable. But at that point, I believe the answer will be "no, it's not possible at the moment." So it becomes a game of stalling, and burning more money until R&D finds something new that may change that answer (a big if).
deleted 3y ago
[deleted]

by JCM9 3y ago

samstave 3y ago

>Many/most of the GenAI companies have interesting ideas but no real business plan - This is most likely the reason being the SAMA firing, to be able to re-align to the MIC without terrible consequence, from a PR perspective. no criticism here aside from the fact that we will see the AI killer fully autonomous robots will be here and unfettered by 'alignments' much sooner than we expected... And the MIC is where all the unscrupulous monies without audits will come from.

MyFirstSass 3y ago
> no criticism here aside from the fact that we will see the AI killer fully autonomous robots will be here and unfettered by 'alignments' much sooner than we expected... What exactly do you mean with this sentence? That less woke/regulated companies will suddenly leapfrog the giants now? What timeframe are we talking here? And do you mean for example non public mil applications from US/China or whatever or private models from unknown players? One thing i've been wondering is that if GPT-4 was 100 mil to train, then there's really a lot of plutocrats, despots, private companies and states for that matter that could in principle 10x that amount if they really wanted to go all in, and maybe they are right now? The bottleneck is the talent pool out there though, but i'm sure there's a lot people out there from libertarians to nation states that don't care about alignment at all, which is potentially pretty crazy / exciting / worrying depending on view.
- anamax 3y ago
  > That less woke/regulated companies will suddenly leapfrog the giants now? What kind of "leapfrog" do you think is necessary to produce a "killer fully autonomous robot"? We've actually had "autonomous killer robots," machines that kill based on the outcome of some sensor plus processing, for centuries, and fairly sophisticated ones have been feasible for decades. (For example it's trivial to build a "killer robot" that triggers off of face or voice recognition.) The only thing that's changed recently is the kind of decisions and how effectively the robot can go looking for someone to kill.
  - MyFirstSass 3y ago
    Of course we're there already sadly. I was just confused by the sentence structure. This video is from 7 years ago: https://www.youtube.com/watch?v=ecClODh4zYk https://www.youtube.com/watch?v=ecClODh4zYk The interesting thing is who's actively working on this, any despots, private companies, foreign nations, who from the talent pool, criminal orgs or even western military which mostly works for the western elite classes.
- wruza 3y ago
  maybe they are right now? Aren’t they? Pretty sure that most tactical and strategical decisions are automated to the bottom. Drones and cameras with all sorts of CV and ML features. You don’t need scary walking talking androids with red eyes to control battlefields and streets. The idea of “Terminator” is similar to a mailman on an antigrav bicycle.
- yunwal 3y ago
  I don’t think the bottleneck is the talent pool. There are plenty of folks smart enough to do what OpenAI did, the question is, did they have enough access to capital?

alephnerd 3y ago

> Business minded folks are already starting to deeply question where the value is relative to the amount of money spent on training Kinda. In my experience, the bigger issue is the skillset has largely been diffused. Overtraining on internal corpora has been more than enough to enable automation benefits, and the ecosystem around ML is very robust now - 10 years ago SDKs like Scikit-learn or PyTorch were much less robust than they are now. Implementing commercial grade SVM or <insert_model_here> is fairly straightforward now. ML models have largely been commodified, and for most usecases, the process of implementing models fairly straightforward internally. IMO, the real value will be on the infrastructural side of ML - how to simply and enhance deployment, how to manage API security, how to manage multiple concurrent deployments, how to maximize performance, etc. And I have put my money where my mouth is for this thesis, as it is one that has been validated by every peer of mine as well.

JCM9 3y ago
I agree with you. My reference in business questioning value was on the flood of money going into building new models. That’s where we will see a significant and painful culling soon as it’s all becoming quite commoditized and there’s only so much room in the market when that happens. Tooling, security services and other things to build on top of a commoditized generative AI market opens up other doors for products to be built.
- alephnerd 3y ago
  > flood of money going into building new models In my experience, there really hasn't been that significant of a flood of money in this space for several years now, or at least not to the level I've seen based on discussion here on HN. I think HN tends to skew towards conversations around models for some reason, but almost all my peers are either funding or working on either tooling or ML driven applications since 2021. ------- I've found HN to have a horrible noise to value signal nowadays, and people with experience (eg. My friends in the YC community) deviating towards Bookface or in person meetups instead now. There was a flood of new accounts in the 2020-22 period (my hunch is it's LessWrong, SSC, and Reddit driven based on the inside jokes and posts I've been seeing recently on HN) but they don't reflect the actual reality of the industry.

ASinclair 3y ago

> Business minded folks are already starting to deeply question where the value is relative to the amount of money spent on training. Not just training but inference too, right? They have to make money off each query.

keyringlight 3y ago
I'm fairly sure this is why there's a rush to get AI related capabilities into processors for consumer devices, to offload that computing so they don't bear the ongoing cost and it'll probably be more responsive to the user.
JCM9 3y ago
Yes. Each query needs to generate enough revenue to pay for the cost of running the query, a proportional cost of the cost to train the original model, overhead, SG&A, etc. just to break even. Few have shown a plan to do that or explained in a defensible way how they’re going to get there. A challenge at the moment is a lot of the AI movement is led by folks that are brilliant technologists but have little to no experience running viable businesses or building a viable business plan. That was clearly part of why OpenAI has its turmoil in that some where trying to be tech purists where others knew the whole AI space will implode if it’s not a viable business. To some degree that seems to behind a lot of the recent chaos inside these larger AI companies.
- financltravsty 3y ago
  It's always fascinating to see. The business portions are much easier to solve for with an engineering mindset, but it seems to be a common issue that engineers never take it into account. This is saying nothing about "technologists" (or as they're starting to become derided as "wordcels": people that communicate well, but cannot execute anything themselves). It would be... not trivial, but straightforward to map out the finances on everything involved, and see if there is room from any standpoint (engineering, financial, product, etc.) to get queries to breakeven, or even profitable. But at that point, I believe the answer will be "no, it's not possible at the moment." So it becomes a game of stalling, and burning more money until R&D finds something new that may change that answer (a big if).

deleted 3y ago

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