Last month, I bought a new Apple iPad to use for travel. Since I bought the last one in the mid-2010s, I thought the $450 price pretty cheap for a very powerful tool. But I was also aware that the Erudite Partner had two weeks before bought the same iPad for notably less. What led to the price increase?
Generative AI Is an Engineering Disaster: A shockingly inefficient trillion-dollar project
... The prices of some laptops have gone up as much as 50 percent, and low-cost computers are being hit the hardest. Affordable entry-level computers may “disappear by 2028” according to one forecast. And the memory shortage is expected to continue for years.
The memory is being put into data centers, which tech firms are expanding at incredible speed. They are planning to multiply total U.S.-data-center capacity by a factor of eight over the next few years. The demand for electricity at these sites is already so great that some companies are repurposing jet engines to power them. ...
He thinks the tech moguls who are building the AI future have blithely launched themselves into a morass.
... The problem with generative AI, in the industry’s own jargon, is that it does not scale. The cost of growing from, say, a thousand users to a million is a key factor that venture capitalists examine when they evaluate start-ups. They want to see that the cost of adding each new user decreases over time, so that the company can support millions of users and make increasing profits. ...
With generative AI, the work of building efficient, scalable systems has not been done. And the problem is exacerbated by the ever-larger generative-AI models, which have grown from 175 billion parameters in 2020 to more than 1 trillion today, according to independent estimates (the actual sizes of the models powering products such as Claude and ChatGPT are secret).
The large in large language model [LLM] should not be a selling point. But the industry’s observation that bigger models tend to outperform smaller ones has given rise to a totemic belief in “scaling laws” that suggest any problem can be solved by simply making models bigger.
Yet the returns are diminishing. The bigger an AI model is, the less it improves with each added parameter, and so it must be made bigger at a faster rate just to sustain steady progress. I asked a few AI researchers whether they could name any other real-world software that scales so poorly. None of them could think of any. ... By economic and engineering measures, generative AI might be the worst technology ever deployed.
But, Reisner concludes, the tech barons are enthralled by their messianic delusions.
Chatbot companies are aware that their products are inefficient. ... we seem stuck with LLMs, perhaps because they have been so aggressively marketed. They are now being added to everything, whether you want them or not. In 2024 and 2025, they were integrated into both Windows and MacOS, which means that more computing power is now needed to run a basic personal computer. Smartphones are also being sold with upgraded hardware as companies anticipate new AI features. Inefficient AI is also being added to common programs such as Adobe Photoshop and Microsoft Word, meaning that computers need to be more powerful to run this software.
This is all especially bad because computers are no longer improving at the rate they used to. Since the 1950s, manufacturers have learned to make microchips steadily faster, smaller, and cheaper, a trend known colloquially as Moore’s Law. But in the past few years, components have gotten so small that manufacturers have run into molecular-level limitations on shrinking them any further, which has slowed progress significantly.
Ultimately, inefficiency may be of little concern to the people within the tech industry who believe that they are replicating intelligence itself. There is an almost-religious conviction among many in Silicon Valley that something mindlike could arise from LLMs, which are ultimately just statistical language-generating software—this, despite the software’s inability to recall basic facts, its lack of common sense, and its complete dissimilarity to a biological brain. ... But the mythological lure of AI is so strong that many engineers believe that nothing should stand in their way. Not even the basic task of writing efficient software.
Is Reisner correct that AI is powering toward a messy, expensive, debilitating dead end? I'm certainly not qualified to evaluate, but I'm glad to have read his case. You can do that yourself at this gift link.
What makes Reisner's case highly plausible to me is where it touches on an area in which I do think I have the capacity for discernment. (And you, the reader, likely do too.) In the US political arena, these tech boys act like intoxicated teenagers with unmerited riches to throw around. Nobody can directly gainsay them; with their kind of money, they are surrounded by supplicants, flatterers, and, of course, politicians eager for a piece. An experienced con man like the Orange Toddler may seem to them a mark, but he's the true professional swindler.
Scientific and business "geniuses" have lent themselves to fascist regimes in the past. The results were not to their liking; the Trial of the Industrialists at Nuremberg exposed cooperation with Nazi war crimes and mass murders. (Yes -- many escaped justice become "the West" decided they were useful to the Cold War... but that's another story.) Some of these guys are making themselves targets for a future fall.
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