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thewayneI'm just going to copy the Slashdot summary, then comment on it: Fast Company ran a contrarian take about AI from entrepreneur/thought leader Faisal Hoque, who argues there's three AI bubbles.
The first is a classic speculative bubble, with asset prices soaring above their fundamental values (like the 17th century's Dutch "tulip mania"). "The chances of this not being a bubble are between slim and none..."
Second, AI is also arguably in what we might call an infrastructure bubble, with huge amounts being invested in infrastructure without any certainty that it will be used at full capacity in the future. This happened multiple times in the later 1800s, as railroad investors built thousands of miles of unneeded track to serve future demand that never materialized. More recently, it happened in the late '90s with the rollout of huge amount of fiber optic cable in anticipation of internet traffic demand that didn't turn up until decades later. Companies are pouring billions into GPUs, power systems, and cooling infrastructure, betting that demand will eventually justify the capacity. McKinsey analysts talk of a $7 trillion "race to scale data centers" for AI, and just eight projects in 2025 already represent commitments of over $1 trillion in AI infrastructure investment. Will this be like the railroad booms and busts of the late 1800s? It is impossible to say with any kind of certainty, but it is not unreasonable to think so.
Third, AI is certainly in a hype bubble, which is where the promise claimed for a new technology exceeds reality, and the discussion around that technology becomes increasingly detached from likely future outcomes. Remember the hype around NFTs? That was a classic hype bubble. And AI has been in a similar moment for a while. All kinds of media — social, print, and web — are filled with AI-related content, while AI boosterism has been the mood music of the corporate world for the last few years. Meanwhile, a recent MIT study reported that 95% of AI pilot projects fail to generate any returns at all.
But the article ultimately argues there's lessons in the 1990s dotcom boom: that "a thing can be hyped beyond its actual capabilities while still being important... When valuations correct — and they will — the same pattern will emerge: companies that focus on solving real problems with available technology will extract value before, during, and after the crash." The winners will be companies with systematic approaches to extracting value — adopting mixed portfolios with different time horizons and risk levels, while recognizing organizational friction points for a purposeful (and holistic) integration.
"The louder the bubble talk, the more space opens for those willing to take a methodical approach to building value."
The first bubble is obvious. Huge amounts of money is being 'invested' in AI/LLMs and the returns have been dubious and amusing, and sometimes lethal. Children and teens taking their own lives, a formerly well-behaved autistic child becoming violent, etc. The valuation of Tesla going up while its sales sales plunge is always an amusing example. The infrastructure bubble is tragic: coal and offline nuclear power plants are being planned to power data centers exclusively for these things, and along with them are their water requirements. And that is a really big problem with increasing climate change. I read an article that I'll post if I can find it that said that each simple AI query is the equivalent of the use of a small bottle of water. The ecological cost is really quite, quite staggering. The eco cost of bitcoin and its kin is trivial compared to this.
The third bubble is interesting. They've demonstrated that LLMs can do some very cool things when tasked into specific purposes and trained in specific bodies of knowledge, like researching new antibiotics or metal alloys with new properties that are needed.
I think the thing that I'm the most curios about is when the corrections/collapses will start taking place. Considering the valuations involved, the financial quake will make the Dot Com crash look like the merest tremor.
The author, Faisal Hoque, is a lot more optimistic about AI than I. He compares its development to such as Amazon and Google during the Dot Com era of the 90s. They had very long-term development timelines ('Moon Shots') that they were quietly pursuing that achieved their long-term survival. And while not all current AI companies are going to achieve those and remain largely in their current form, some may. He talks about Pets.com burning through $300mil before collapsing, which we now see as a trivially small amount of money in today's tech market.
Curious times. We shall see how things shake out.
https://www.fastcompany.com/91400857/there-isnt-an-ai-bubble-there-are-three-ai-bu
https://slashdot.org/story/25/09/20/1847246/there-isnt-an-ai-bubble---there-are-three
The first is a classic speculative bubble, with asset prices soaring above their fundamental values (like the 17th century's Dutch "tulip mania"). "The chances of this not being a bubble are between slim and none..."
Second, AI is also arguably in what we might call an infrastructure bubble, with huge amounts being invested in infrastructure without any certainty that it will be used at full capacity in the future. This happened multiple times in the later 1800s, as railroad investors built thousands of miles of unneeded track to serve future demand that never materialized. More recently, it happened in the late '90s with the rollout of huge amount of fiber optic cable in anticipation of internet traffic demand that didn't turn up until decades later. Companies are pouring billions into GPUs, power systems, and cooling infrastructure, betting that demand will eventually justify the capacity. McKinsey analysts talk of a $7 trillion "race to scale data centers" for AI, and just eight projects in 2025 already represent commitments of over $1 trillion in AI infrastructure investment. Will this be like the railroad booms and busts of the late 1800s? It is impossible to say with any kind of certainty, but it is not unreasonable to think so.
Third, AI is certainly in a hype bubble, which is where the promise claimed for a new technology exceeds reality, and the discussion around that technology becomes increasingly detached from likely future outcomes. Remember the hype around NFTs? That was a classic hype bubble. And AI has been in a similar moment for a while. All kinds of media — social, print, and web — are filled with AI-related content, while AI boosterism has been the mood music of the corporate world for the last few years. Meanwhile, a recent MIT study reported that 95% of AI pilot projects fail to generate any returns at all.
But the article ultimately argues there's lessons in the 1990s dotcom boom: that "a thing can be hyped beyond its actual capabilities while still being important... When valuations correct — and they will — the same pattern will emerge: companies that focus on solving real problems with available technology will extract value before, during, and after the crash." The winners will be companies with systematic approaches to extracting value — adopting mixed portfolios with different time horizons and risk levels, while recognizing organizational friction points for a purposeful (and holistic) integration.
"The louder the bubble talk, the more space opens for those willing to take a methodical approach to building value."
The first bubble is obvious. Huge amounts of money is being 'invested' in AI/LLMs and the returns have been dubious and amusing, and sometimes lethal. Children and teens taking their own lives, a formerly well-behaved autistic child becoming violent, etc. The valuation of Tesla going up while its sales sales plunge is always an amusing example. The infrastructure bubble is tragic: coal and offline nuclear power plants are being planned to power data centers exclusively for these things, and along with them are their water requirements. And that is a really big problem with increasing climate change. I read an article that I'll post if I can find it that said that each simple AI query is the equivalent of the use of a small bottle of water. The ecological cost is really quite, quite staggering. The eco cost of bitcoin and its kin is trivial compared to this.
The third bubble is interesting. They've demonstrated that LLMs can do some very cool things when tasked into specific purposes and trained in specific bodies of knowledge, like researching new antibiotics or metal alloys with new properties that are needed.
I think the thing that I'm the most curios about is when the corrections/collapses will start taking place. Considering the valuations involved, the financial quake will make the Dot Com crash look like the merest tremor.
The author, Faisal Hoque, is a lot more optimistic about AI than I. He compares its development to such as Amazon and Google during the Dot Com era of the 90s. They had very long-term development timelines ('Moon Shots') that they were quietly pursuing that achieved their long-term survival. And while not all current AI companies are going to achieve those and remain largely in their current form, some may. He talks about Pets.com burning through $300mil before collapsing, which we now see as a trivially small amount of money in today's tech market.
Curious times. We shall see how things shake out.
https://www.fastcompany.com/91400857/there-isnt-an-ai-bubble-there-are-three-ai-bu
https://slashdot.org/story/25/09/20/1847246/there-isnt-an-ai-bubble---there-are-three
no subject
Date: 2025-09-27 09:10 pm (UTC)It's going to be good for those who are professional debuggers and rewriters of code, certainly, but I'm not sure it's going to go over well with the people who have been replaced by a sub-par chatbot, or who are playing the part of the Mechanical Turk to those chatbots.
no subject
Date: 2025-09-28 01:15 am (UTC)I saw an article today about the energy cost of LLM-generated video. It doesn't scale linearly. "...a six-second AI video clip consumes four times as much energy as a three-second clip." Yeah, I agree. Data centers are the only recoverable asset in this tri-boom. In other reporting, the adoption rate of LLMs in corporate America seems to be slowing, which can indicate saturation or acknowledgement that it isn't doing much to improve productivity or profits. With the growth of 'vibe-code fixer shops', I'm suspecting more of the latter.
no subject
Date: 2025-09-28 05:37 am (UTC)Also, O^2 or worse time for running tasks also makes sense, since each pixel of reach frame has to go through the entire guessing and comparing procedure.