Representative image of artificial intelligence.
Credit: iStock Photo
By Dave Lee
Tracking down those in the technology industry cautious about artificial intelligence is much like looking for Republicans in San Francisco: There’s plenty of them out there, if you’d care to ask. And lately, they seem to be growing in number.
On the one hand, it’s an optimistic time. Encouraging numbers published last week showed the level of startup investing in the April-June quarter had increased 57 per cent 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 ago when startups 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 like Nvidia Corp. (stock up 213 per cent in the past 12 months) and Taiwan Semiconductor Manufacturing Co., which briefly joined the $1 trillion valuation club on Monday.
Though 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 they, and they alone, offer. Other winners are the cloud companies that provide data centers.
But further up the ecosystem, the questions become more interesting. That’s where the likes of OpenAI, Anthropic and many other burgeoning AI startups 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 (more on that in a moment), 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 revolutionise every facet of life and business is offset by the chance that it, well, won’t.
While venture capitalists’ websites like to talk about investing in “disruptive ideas” and “changing the world,” it’s more accurate to say these funding sources now exist primarily to foot the astronomical bills for cloud computing and energy. This isn’t necessarily bad — you could argue it’s not much different from covering other costs, like marketing or real estate. But 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’s confident AI will live up to the hype but warns many will lose tremendous amounts of money along the way).
He 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 startups have spent the last 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 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.
Using Nvidia’s revenue as an informal but plausible indication of sector-wide spending, Cahn observes that actual revenues at AI companies — those selling AI to people and businesses — are currently well short of the $600 billion or so a year required to pay back the likely continual infrastructure spending. How short? About $500 billion, he estimates.
This should improve. OpenAI has managed to go from $1.6 billion in annualized revenue at the end of last year to $3.4 billion today, according to tech news site The Information. But OpenAI is so far the standout success of the frontline AI companies. Whether its many competitors can sell enough subscriptions or API access to return investors’ money remains to be seen — a notable OpenAI rival, Anthropic, had forecast revenue this year of less than $1 billion. One canary in the coal mine may 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 some $1.3 billion in funding — “modest” wasn’t 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. 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,” he said. “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.”
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 does reach perfection, or something acceptably and reliably close to it, the energy burden may just topple the US power grid, which, as a text message from Con Edison 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 the tech analyst Benedict Evans: At $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.