See Is Artificial Intelligence Really Worth the Hype? After the arrival of a less costly A.I. model from China, U.S. markets and academics are wrestling with the ultimate economic value of the technology by Jeff Sommer of The NY Times. Excerpts:
"Is the main approach to developing A.I. in the United States — pouring billions of dollars into chips and infrastructure — worth the expenditure for all companies if similar results can be achieved far more cheaply? DeepSeek’s lower-cost innovations add urgency to bigger, longstanding financial questions: How much are artificial intelligence companies really worth, and what will the broader economic value of A.I. ultimately be?"
"Daron Acemoglu, a winner of the 2024 Nobel in economic science," [said] "many financial and economic calculations were being based on mere “projections into the future that are sometimes exaggerated.”"
[Acemoglu] "is skeptical about the more fervent A.I. claims. He ranks A.I. as a significant advance, perhaps with a macroeconomic effect akin to the telephone, which was no small thing."
"But don’t get carried away, he said, at least not yet. He doubts that full, advanced artificial general intelligence “that can do anything a human can do, but more,” will be achieved. Therefore, over the next decade, he estimated, increased productivity from the diffusion of impressive, but limited, A.I. engines will increase the size of the U.S. economy by only about 1 percent, or roughly 0.1 percent a year."
"if one or more companies achieve true, complete, artificial general intelligence within the next several years, then his estimates will turn out to be far too low."
"From Jan. 24, when DeepSeek’s A.I. innovation began to roil the market, Tesla shares have fallen 11 percent.
Other A.I. companies fared nearly as badly.
Shares of Nvidia, whose chips run much advanced A.I., have dropped 9 percent. Aswath Damodaran, a New York University finance professor who has evaluated many tech companies, said DeepSeek’s efficiency implied that fewer and less-advanced chips would be needed for many A.I. functions. As a result, he wrote recently, the market for Nvidia’s high-end chips isn’t likely to grow as rapidly as expected. So, he said, Nvidia shares will be worth less than anticipated, even after the recent price decline.
In addition, shares of nuclear-powered electricity providers like the utilities Constellation and Vistra, which had soared in the expectation that A.I. data factories would need ever-increasing quantities of power, sank on reduced projections of the required electricity.
Meta, Alphabet and Microsoft, which have invested billions in A.I. development, have had mixed performances since DeepSeek’s arrival. Alphabet and Microsoft have fallen, while Meta has risen 11 percent."
See The Dangerous A.I. Nonsense That Trump and Biden Fell For by Zeynep Tufekci of The NY Times. Excerpts:
"there have been a lot of frantic attempts to figure out how DeepSeek did it and whether it was all above board. Those are not the most important questions, and the excessive focus on them is an example of precisely how we got caught off guard in the first place.
The real lesson of DeepSeek is that America’s approach to A.I. safety and regulations — the concerns espoused by both the Biden and Trump administrations, as well as by many A.I. companies — was largely nonsense. It was never going to be possible to contain the spread of this powerful emergent technology, and certainly not just by placing trade restrictions on components like graphics chips. That was a self-serving fiction, foisted on out-of-touch leaders by an industry that wanted the government to kneecap its competitors."
"The core idea that powers the artificial intelligence revolution, on the other hand, has been around since the 1940s. What opened the floodgates was the arrival first of vast data sets (via the internet and other digital technologies) and then of powerful graphics processors (like the ones from Nvidia), which can compute A.I. models from those data troves."
"Some A.I. models . . . can fit on a USB stick, and can be endlessly replicated and built upon just by plugging that stick into new laptops
Initially developing a new model, like ChatGPT, is a very costly process, but it’s the output, known as the model weights, that are so valuable, and so replicable. Companies like OpenAI, which has loudly proclaimed that A.I. poses an existential threat to humanity, kept these model weights to themselves, lest others piggyback on all that expensive development work to produce something even more powerful.
And if those protection-minded companies made a lot of money because of the U.S. government’s defensive measures? Well, that’s just the price of keeping humanity safe, right?
Those companies had an ally in President Joe Biden — especially, said his deputy chief of staff for policy, Bruce Reed, after he watched “Mission: Impossible — Dead Reckoning Part One,” a story of A.I. gone rogue. Having already signed one executive order restricting the sale of those crucial chips to China, Biden signed another to establish safety and security mandates.
The Trump administration is operating under the same faulty logic."
"DeepSeek . . . says it spent little of what OpenAI and others spent, because it was able to optimize its software and train its model more efficiently."
"Still, not everyone believes that account, especially given questions about China’s respect for intellectual property rights and trade restrictions. Could the company have amassed a forbidden stash of Nvidia chips? Maybe. Could the cost of developing the model have been higher than was disclosed? Some estimates suggest so. OpenAI says that DeepSeek may have stolen some of its work."
"Within the industry, there’s a popular trope that the real turning point will be the development of A.G.I., or artificial general intelligence, when A.I. reaches human-level intelligence and potentially becomes autonomous."
"We have reached the other A.G.I. turning point: artificial good-enough intelligence — A.I. that is fast, cheap, scalable and useful for a wide range of purposes"
"America can’t re-establish its dominance over the most advanced A.I. because the technology, the data and the expertise that created it are already distributed all around the world."
See Professor’s perceptron paved the way for AI – 60 years too soon by Melanie Lefkowitz of The Cornell Chronicle. This is an article linked above the core idea of A.I. having been around along time. Excerpt:
"In July 1958, the U.S. Office of Naval Research unveiled a remarkable invention.
An IBM 704 – a 5-ton computer the size of a room – was fed a series of punch cards. After 50 trials, the computer taught itself to distinguish cards marked on the left from cards marked on the right.
It was a demonstration of the “perceptron” – “the first machine which is capable of having an original idea,” according to its creator, Frank Rosenblatt ’50, Ph.D. ’56.
At the time, Rosenblatt – who later became an associate professor of neurobiology and behavior in Cornell’s Division of Biological Sciences – was a research psychologist and project engineer at the Cornell Aeronautical Laboratory in Buffalo, New York.
“Stories about the creation of machines having human qualities have long been a fascinating province in the realm of science fiction,” Rosenblatt wrote in 1958. “Yet we are about to witness the birth of such a machine – a machine capable of perceiving, recognizing and identifying its surroundings without any human training or control.”
He was right – but it took half a century to prove it."
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“Why did the human stare at the glass of orange juice?” “They were trying to concentrate.” (2023) (Partly about AI being used to tell jokes)
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