This is a bit of a doomer article, but quite honestly, 200 billion dollars a year on a 30 billion dollar a year business that is growing does not really sound as bad as the author makes it out to be, especially when that business consists of growth startups that are currently primarily concerned with completely automating your current revenue stream.
The obvious way to recoup spend is to grow, rugpull by cranking up costs 6x and reducing inference cost by half (highly achievable with improved silicon and technology), then simply fire a large percentage of software engineers. From that perspective the current behavior is a bit wicked but downright logical.
And having two whale customers is not really out of the ordinary for any software company... it's just the scale that is staggering.
The actual risk that the author does not even broach upon for investors... the thing that will actually torpedo this massive investment are the open source open weight chinese models that commoditize the entire endeavor. If you don't have a monopoly, you cannot rugpull and 6x the costs on the consumer.
Ironically, the actual thing that will likely kill OpenAI is ACTUAL OPEN AI.
Which makes me cynically believe that all this capital being burned that has made memory, storage and GPUs in some cases 5x is a feature, not a bug. "Can't use open weight models if you can't find or afford the hardware to run them on!"
I'm seeing this guy everywhere. He was on Bloomberg a day ago and then on another channel and now here. I'm curious why there are not more people like him voicing their concerns. Makes you wonder if he is completely wrong.
Has ed zitron ever accounted for the fact that his predictions literally never come true? In most jobs such a poor track record would be disqualifying.
This guy has zero zip nada null AI background. He is a videogame reviewer and PR guy. He is a pure influencer feeding on the AI backlash he helped to create.
He has been predicting a crash for how many years now? And while I can totally see Anthropic and OpenAI going through some things on the way to post-IPO FMV, those things do not include AI going away. It truly doesn't matter whether closed source Frontier lab models are spewing tokens or large foreign open weight models are doing it, the token factories will be just fine, and that's really all I care about.
The question to me is why the media favors influencers like this over practitioners.
And it's not like there aren't more balanced takes out there, here's just one...
100% of my income in the "employment" segment is coming from a single employer. That's an unsustainable level of concentration, showing there just isn't sufficient demand for my skills. Bubble!
Several top level comments are attacks on the author’s credibility that do not engage the substance of the piece at all. I think the fundamental problem for people on all sides of the various debates surrounding AI is that “AI is a powerful, transformative technology” and “Anthropic and OpenAI are both doomed, and this poses risks to the broader economy” are compatible statements of fact. Just because you believe (1) does not justify dismissing (2) out of hand.
(2) is the point of this article. OAI and Anthropic are spending by far the most money of anyone in the space, as the article rightly notes, but they have no path to becoming profitable, meaning they cannot occupy that position forever. The other entities that rely on their spending to support their own margins - in this case the major cloud providers - are vulnerable to revenue collapse if OAI and Anthropic fail.
The premise most would disagree with is that the labs have no path to profitability. Two points support this: demand for inference is functionally infinite, or at least is so great that it is not meaningful to discuss its limits; and the labs are profitable on inference and are only taking losses to compete with one another. Some would extend this further and say that once the tech is good enough it will be able to drastically reduce their costs by some combination of speeding up research and creating efficiencies to reduce compute spend.
These are valid criticisms. But “AI has gotten better since he started saying ‘AI bad’” is not a reason to ignore the fact that major cloud computing providers are taking on massive new debt while becoming increasingly dependent on only two customers who face meaningful margin pressures. Unless OAI and Anthropic can find a durable moat and a means to exert pricing power, this is a serious issue going forward. That is true whether we wind up with a machine god (although we might have bigger problems in that case) or if we plateau at current capabilities.
He's saying the demand is artificial because it's all from Anthropic and OpenAI but they have revenue from paying customers of about $74 bn and $49 bn annualized respectively. If those went under the customer demand would still presumably be there.
And it's not like those numbers are static - they are probably up several times on a year earlier.
In July 2024 Anthropic's annualized revenues were ~$0.5 bn when Ed wrote
>And yes, that sound you hear is the slow deflation of the bubble I've been warning you about since March...
>How does GPT – a transformer-based model that generates answers probabilistically (as in what the next part of the generation is most likely to be the correct one) based entirely on training data – do anything more than generate paragraphs of occasionally-accurate text?
which illustrates how spot on he is with understanding AI.
First thing a blog does is ask me to subscribe. Second thing it asks for is to subscribe to their premium blog. There are 7 separate requests on one page for my information to subscribe to their blog and newsletter.
The first 2 page lengths are 70% taken by subscription and premium callouts.
I didn't read the article. Was too distracted and annoyed.
17 comments
[ 0.22 ms ] story [ 17.0 ms ] threadThe obvious way to recoup spend is to grow, rugpull by cranking up costs 6x and reducing inference cost by half (highly achievable with improved silicon and technology), then simply fire a large percentage of software engineers. From that perspective the current behavior is a bit wicked but downright logical.
And having two whale customers is not really out of the ordinary for any software company... it's just the scale that is staggering.
The actual risk that the author does not even broach upon for investors... the thing that will actually torpedo this massive investment are the open source open weight chinese models that commoditize the entire endeavor. If you don't have a monopoly, you cannot rugpull and 6x the costs on the consumer.
Ironically, the actual thing that will likely kill OpenAI is ACTUAL OPEN AI.
He has been predicting a crash for how many years now? And while I can totally see Anthropic and OpenAI going through some things on the way to post-IPO FMV, those things do not include AI going away. It truly doesn't matter whether closed source Frontier lab models are spewing tokens or large foreign open weight models are doing it, the token factories will be just fine, and that's really all I care about.
The question to me is why the media favors influencers like this over practitioners.
And it's not like there aren't more balanced takes out there, here's just one...
https://overweightskepticism.substack.com/p/ais-cash-cushion...
Gotcha.
- Huge subscribe CTA at top
- In-text subscribe CTA
- scroll through that get pop up in your face full page subscribe CTA
- close that and continue scrolling to yet another subscribe CTA
was enough to make me close the page and ask my agent for a summary rather.
> What is it you think you’ve gotten yourself into? Because I think you’re being sold a lie.
Hundreds of thousands of dollars of realized gains.
And the question I want to ask to anyone paying $70/year for Ed's newsletter: what has throwing the AI baby out with the bathwater gotten you?
(2) is the point of this article. OAI and Anthropic are spending by far the most money of anyone in the space, as the article rightly notes, but they have no path to becoming profitable, meaning they cannot occupy that position forever. The other entities that rely on their spending to support their own margins - in this case the major cloud providers - are vulnerable to revenue collapse if OAI and Anthropic fail.
The premise most would disagree with is that the labs have no path to profitability. Two points support this: demand for inference is functionally infinite, or at least is so great that it is not meaningful to discuss its limits; and the labs are profitable on inference and are only taking losses to compete with one another. Some would extend this further and say that once the tech is good enough it will be able to drastically reduce their costs by some combination of speeding up research and creating efficiencies to reduce compute spend.
These are valid criticisms. But “AI has gotten better since he started saying ‘AI bad’” is not a reason to ignore the fact that major cloud computing providers are taking on massive new debt while becoming increasingly dependent on only two customers who face meaningful margin pressures. Unless OAI and Anthropic can find a durable moat and a means to exert pricing power, this is a serious issue going forward. That is true whether we wind up with a machine god (although we might have bigger problems in that case) or if we plateau at current capabilities.
And it's not like those numbers are static - they are probably up several times on a year earlier.
In July 2024 Anthropic's annualized revenues were ~$0.5 bn when Ed wrote
>And yes, that sound you hear is the slow deflation of the bubble I've been warning you about since March...
>How does GPT – a transformer-based model that generates answers probabilistically (as in what the next part of the generation is most likely to be the correct one) based entirely on training data – do anything more than generate paragraphs of occasionally-accurate text?
which illustrates how spot on he is with understanding AI.
The first 2 page lengths are 70% taken by subscription and premium callouts.
I didn't read the article. Was too distracted and annoyed.
Does this strategy actually work on people?