Tracking down those in the technology industry cautious about artificial intelligence (AI) is much like looking for Republicans in San Francisco: There are plenty of them out there, if you would care to ask. And lately, they seem to be growing in number.
On the one hand, it is an optimistic time. Encouraging numbers published last week showed the level of start-up investing in the April-June quarter had increased 57 percent compared with the level in the period a year earlier, with more than half of it going to AI companies. The trend has proved meaty enough to fuel talk of a “great reawakening” in the sector — a welcome turnaround from a year earlier, when start-ups were told to hunker down for a “mass extinction event.”
It turned out to be more of an Ozempic-speed slimming down of costs and workforce.
The AI hype has made that period of relative sobriety rather short-lived. As just about every tech commentator has observed, AI is a wave unlike anything seen since the advent of the Internet. The early big winners have been companies such as Nvidia Corp (stock up 213 percent in the past 12 months) and Taiwan Semiconductor Manufacturing Co, which recently joined the US$1 trillion valuation club for a short period.
Although there is some nervousness around how long soaring demand can last, no one doubts the business models for those at the foundations of the AI stack. Companies need the chips and manufacturing that they, and they alone, offer. Other winners are the cloud companies that provide data centers.
APPLICATION
However, further up the ecosystem, the questions become more interesting. That is where the likes of OpenAI, Anthropic and many other burgeoning AI start-ups are engaged in the much harder job of finding business or consumer uses for this new technology, which has gained a reputation for being unreliable and erratic. Even if these flaws can be ironed out, there is growing worry about a perennial mismatch between the cost of creating and running AI and what people are prepared to pay to use it.
The promise that AI could revolutionize every facet of life and business is offset by the chance that it will not.
While venture capitalists’ Web sites like to talk about investing in “disruptive ideas” and “changing the world,” it is more accurate to say these funding sources now exist primarily to foot the astronomical bills for cloud computing and energy. This is not necessarily bad — you could argue that it is not much different from covering other costs, like marketing or real estate.
However, the dizzying figures and the speed at which that money has to be spent have at least some starting to wonder whether this outlay will be worth it.
Sequoia Capital’s David Cahn is one of those at least pointing to the alarm, if not going as far as raising it. He is confident that AI will live up to the hype, but warns that many will lose tremendous amounts of money along the way.
Cahn argues that while some have compared those building AI to the railroad barons, there are important differences. The “railroads” of AI — the chips and data centers — will depreciate as quickly as smartphones as new chips are developed, and computing needs and expectations evolve. The H100 Nvidia GPU that start-ups have spent the past year or so scrambling to obtain are about to be replaced by the more capable B100. And while the first company to lay down tracks connecting San Francisco to Los Angeles locked up a monopoly over train journeys up and down the US west coast, there is no such constraint on how many companies can offer competing AI systems that do much the same thing, driving down prices.
REVENUES
Using Nvidia’s revenue as an informal, but plausible indication of sector-wide spending, Cahn said that actual revenues at AI companies — those selling AI to people and businesses — are well short of the US$600 billion or so a year required to pay back the likely continual infrastructure spending.
How short?
About US$500 billion, he estimated.
This should improve. OpenAI has gone from US$1.6 billion in annualized revenue at the end of last year to US$3.4 billion today, according to tech news site The Information.
However, OpenAI is so far the standout success of the frontline AI companies. Whether its many competitors can sell enough subscriptions or Application Programming Interface access to return investors’ money remains to be seen. A notable OpenAI rival, Anthropic, had forecast revenue this year of less than US$1 billion.
One canary in the coal mine might have been Inflection AI, which, facing mounting costs, ended up being gobbled up by Microsoft Corp in a curious non-acquisitiony acquisition, leaving investors with a “modest” return on investment, Bloomberg News reported.
Inflection was backed by about US$1.3 billion in funding — “modest” was not exactly what those investors had in mind when they hitched themselves to what they thought was an AI rocket ship.
Another big red flag, economist Daron Acemoglu warns, lies in the shared thesis that by crunching more data and engaging more computing power, generative AI tools will become more intelligent and more accurate, fulfilling their potential as predicted.
‘LEAP OF FAITH’
His comments were shared in a recent Goldman Sachs report titled Gen AI: Too Much Spend, Too Little Benefit?
“Large language models today have proven more impressive than many people would have predicted, but a big leap of faith is still required to believe that the architecture of predicting the next word in a sentence will achieve capabilities as smart as HAL 9000 in 2001: A Space Odyssey,” Acemoglu said.
What the skeptics (or realists) are ultimately warning is that AI’s journey from “pretty good” to “perfect” could be as long, if not longer, than the journey from “nothing” to “pretty good.” Even if artificial general intelligence (AGI) does reach perfection, or something acceptably and reliably close to it, the energy burden might just topple the US power grid, which, as a text message from investor-owned energy company Consolidated Edison Inc reminded me this week, currently struggles with summer.
The loudest voices suggesting that AGI — HAL — is around the corner are those who stand to benefit most from the hype. Trillions of dollars in shareholder value depends on believing.
Consider one cheeky comparison made by tech analyst Benedict Evans: At US$3.7 billion in annualized revenue for its AI business, Accenture is making more money from consulting companies on AI than OpenAI is from creating it.
Maybe some restraint is in order.
Dave Lee is Bloomberg Opinion’s US technology columnist. He was previously a correspondent for the Financial Times and BBC News. This column does not necessarily reflect the opinion of the editorial board or Bloomberg LP and its owners.
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