AI is normal technology
Nvidia CEO replies to shots across the bow
Nvidia’s CEO offers advice for Quantum industry
AI is normal technology
The statement “AI is normal technology” is three things: a description of current AI, a prediction about the foreseeable future of AI, and a prescription about how we should treat it. We view AI as a tool that we can and should remain in control of, and we argue that this goal does not require drastic policy interventions or technical breakthroughs. We do not think that viewing AI as a humanlike intelligence is currently accurate or useful for understanding its societal impacts, nor is it likely to be in our vision of the future. The normal technology frame is about the relationship between technology and society. It rejects technological determinism, especially the notion of AI itself as an agent in determining its future. It is guided by lessons from past technological revolutions, such as the slow and uncertain nature of technology adoption and diffusion.
We explain why we think that transformative economic and societal impacts will be slow (on the timescale of decades), making a critical distinction between AI methods, AI applications, and AI adoption, arguing that the three happen at different timescales. We discuss a potential division of labor between humans and AI in a world with advanced AI (but not “superintelligent” AI, which we view as incoherent as usually conceptualized). In this world, control is primarily in the hands of people and organizations; indeed, a greater and greater proportion of what people do in their jobs is AI control.
We argue that drastic interventions premised on the difficulty of controlling superintelligent AI will, in fact, make things much worse if AI turns out to be normal technology— the downsides of which will be likely to mirror those of previous technologies that are deployed in capitalistic societies, such as inequality. Consider this analogy: At the dawn of the first Industrial Revolution, it would have been useful to try to think about what an industrial world would look like and how to prepare for it, but it would have been futile to try to predict electricity or computers. Our exercise here is similar. Since we reject “fast takeoff” scenarios, we do not see it as necessary or useful to envision a world further ahead than we have attempted to. Diffusion is limited by the speed of human, organizational, and institutional change. The External world puts a speed limit on AI innovation. Benchmarks do not measure real-world utility. Economic impacts are likely to be gradual. There are speed limits to progress in AI methods. Human ability is not constrained by biology. Games provide misleading intuitions about the possibility of superintelligence….
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Nvidia CEO replies to shots across the bow
Nvidia's CEO Huang fielded a question about the company’s many partners and whether it was putting itself in a difficult position by competing in some instances with them. It is a common dynamic and source of contention for companies in tech and beyond. Amazon, for example, offers in-house brands that can compete with other sellers on its platform, much to those merchants’ displeasure.
“Obviously we did a very poor job explaining what we do,” Huang responded. He acknowledged that Nvidia provides technology across AI infrastructure, networking, switches, storage and of course computing of every size, shape and form. “However—this is the however—we build everything, but we offer it to the world however they would like to take it,” he said. “And the reason for that is we are not a solutions company.” Instead of selling its customers or partners finished solutions, Nvidia typically leaves them to decide how much of the last 50% of value creation they want to develop on their own. That makes it easier for enterprise tech companies to work with Nvidia, according to Huang. In theory, it also makes it easier for multiple companies in a single industry such as automobiles to build on top of Nvidia platforms and still differentiate themselves.
Nvidia’s discipline pays off internally as well. Its 36,000 employees make it much smaller than other big tech companies in Silicon Valley, and Huang wants to put that “very scarce energy” to best use, he said this month as he received an Edison Achievement Award for innovation and innovators. That means focusing on work that Nvidia’s people consider worthy of their time, which is a strong motivator, he said. Take the architecture of data centers. In recent decades, the idea was to link together huge numbers of cheap, commodity servers. But Nvidia believes in scaling up before scaling out—making a rack of computers as powerful as possible before they are yoked together in a vast infrastructure.
The risk to Nvidia at the strategic level is that another innovator, such as an open-source rival, calls its “scale up-scale out” equation into question. The rise of DeepSeek’s R1 model did just that, at least for a moment, because it seemed to have been trained using older infrastructure. (Huang said that the proliferation of such reasoning models actually means the world needs 100 times more computing power than it thought it needed last year.) “Notice, when you come talk to us, not one employee ever says, ‘We fight for share,’” Huang said at the Edison ceremony. “Why fight for share? Create something new.”….
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Nvidia’s CEO offers advice for Quantum industry
Quantum computing stocks sank, pausing a year-long rally, after Nvidia CEO Jensen Huang said the technology's practical use was likely two decades away. The long wait outlined by Huang for "very useful quantum computers" throws cold water on a sector that was already expected to spend millions more on the technology, which can only perform niche calculations so far. "If you kind of said 15 years... that'd probably be on the early side. If you said 30, it's probably on the late side. But if you picked 20, I think a whole bunch of us would believe it," he said.
The four quantum computing stocks rose at least threefold last year and outperformed a more than twofold rise in Nvidia shares, thanks to a high-profile breakthrough in the technology at Google in December. The technology is seen as a key national security undertaking, with countries counting on it to drive decryption for military purposes. Still, the revenue of these companies remains small. "If investors are worried about minimal revenues that will require dilution, they are missing a key part of the equation." Quantum computing "will be disruptive to parts of the classical compute business, of which Nvidia is a chief beneficiary," Shannon said.
The old, but still nascent, quantum computing industry got some help from arguably the most highly paid consultant in the technology business. But even if the field takes Nvidia Corp. Chief Executive Officer Jensen Huang’s advice, it’s got a long way to go. Huang hosted back-to-back panel sessions with a total of 12 quantum company leaders at his GTC conference earlier this month. He said he was giving them the stage by way of an apology — as well as an opportunity to change his mind — after earlier skeptical comments he made about the near-term prospects of the industry. His prognosis for when quantum might be useful crashed the stocks of the sector’s publicly traded companies. Huang allowed his panelists to introduce their work and pitch the audience on why their companies matter. Most argued they’ll make quantum impactful quicker than the 15 to 20 years Nvidia’s leader originally projected. Huang promised to let them all come back in a year’s time to showcase the progress they’ve made but, as is his way, didn’t have much patience for the lack of focus. Nvidia’s CEO interrupted them with sharp questions and then moved on to give them some homework.
The first thing they need to do is settle on a couple of techniques, at most, and put their combined energy into working on those areas to make quantum practical, Huang advised. They need to stop talking about quantum as an alternative to conventional computing. It will never catch up and quantum machines will never replace standard computers in the work they do. Quantum computers will never beat a PC at running Excel and they shouldn’t try, he said. With that in mind, they need to identify the problems that quantum can address and develop software that will allow that to happen. Chemistry, biology and material science were three areas that cropped up often in the conversations. Some of his panelists agreed with his points, and some stuck to their insistence that breakthroughs are just around the corner….