The promise of generative AI technology to transform
companies, industries, and societies continues to be touted,
leading tech giants, other companies, and utilities to spend an
estimated $1tn on capex in coming years, including significant
investments in data centers, chips, other AI infrastructure, and
the power grid. But this spending has little to show for it so far
beyond reports of efficiency gains among developers. And even
the stock of the company reaping the most benefits to date—
Nvidia—has sharply corrected. We ask industry and economy
specialists whether this large spend will ever pay off in terms
of AI benefits and returns, and explore the implications for
economies, companies, and markets if it does, or if it doesn’t.
We first speak with Daron Acemoglu, Institute Professor at
MIT, who’s skeptical. He estimates that only a quarter of AI-
exposed tasks will be cost-effective to automate within the
next 10 years, implying that AI will impact less than 5% of all
tasks. And he doesn’t take much comfort from history that
shows technologies improving and becoming less costly over
time, arguing that AI model advances likely won’t occur nearly
as quickly—or be nearly as impressive—as many believe. He
also questions whether AI adoption will create new tasks and
products, saying these impacts are “not a law of nature.” So,
he forecasts AI will increase US productivity by only 0.5% and
GDP growth by only 0.9% cumulatively over the next decade.
GS Head of Global Equity Research Jim Covello goes a step
further, arguing that to earn an adequate return on the $1tn
estimated cost of developing and running AI technology, it
must be able to solve complex problems, which, he says, it
isn’t built to do. He points out that truly life-changing inventions
like the internet enabled low-cost solutions to disrupt high-cost
solutions even in its infancy, unlike costly AI tech today. And
he’s skeptical that AI’s costs will ever decline enough to make
automating a large share of tasks affordable given the high
starting point as well as the complexity of building critical
inputs—like GPU chips—which may prevent competition. He’s
also doubtful that AI will boost the valuation of companies that
use the tech, as any efficiency gains would likely be competed
away, and the path to actually boosting revenues is unclear, in
his view. And he questions whether models trained on
historical data will ever be able to replicate humans’ most
valuable capabilities.
But GS senior global economist Joseph Briggs is more
optimistic. He estimates that gen AI will ultimately automate
25% of all work tasks and raise US productivity by 9% and GDP
growth by 6.1% cumulatively over the next decade. While
Briggs acknowledges that automating many AI-exposed tasks
isn’t cost-effective today, he argues that the large potential for
cost savings and likelihood that costs will decline over the long
run—as is often, if not always, the case with new
technologies—should eventually lead to more AI automation.
And, unlike Acemoglu, Briggs incorporates both the potential
for labor reallocation and new task creation into his productivity
estimates, consistent with the strong and long historical record
of technological innovation driving new opportunities.
GS US software analyst Kash Rangan and internet analyst Eric
Sheridan also remain enthusiastic about generative AI’s long-
term transformative and returns potential even as AI’s “killer
application” has yet to emerge. Despite big tech’s large spending on AI infrastructure, they don’t see signs of irrational
exuberance. Indeed, Sheridan notes that current capex spend
as a share of revenues doesn’t look markedly different from
prior tech investment cycles, and that investors are rewarding
only those companies that can tie a dollar of AI spending back
to revenues. Rangan, for his part, argues that the potential for
returns from this capex cycle seems more promising than even
previous cycles given that incumbents with low costs of capital
and massive distribution networks and customer bases are
leading it. So, both Sheridan and Rangan are optimistic that the
huge AI spend will eventually pay off.
But even if AI could potentially generate significant benefits for
economies and returns for companies, could shortages of key
inputs—namely, chips and power—keep the technology from
delivering on this promise? GS US semiconductor analysts
Toshiya Hari, Anmol Makkar, and David Balaban argue that
chips will indeed constrain AI growth over the next few years,
with demand for chips outstripping supply owing to shortages
in High-Bandwidth Memory technology and Chip-on-Wafer-on-
Substrate packaging—two critical chip components.
But the bigger question seems to be whether power supply
can keep up. GS US and European utilities analysts Carly
Davenport and Alberto Gandolfi, respectively, expect the
proliferation of AI technology, and the data centers necessary
to feed it, to drive an increase in power demand the likes of
which hasn’t been seen in a generation (which GS commodities
strategist Hongcen Wei finds early evidence of in Virginia, a
hotbed for US data center growth).
Brian Janous, Co-founder of Cloverleaf Infrastructure and
former VP of Energy at Microsoft, believes that US utilities—
which haven’t experienced electricity consumption growth in
nearly two decades and are contending with an already aged
US power grid—aren’t prepared for this coming demand surge.
He and Davenport agree that the required substantial
investments in power infrastructure won’t happen quickly or
easily given the highly regulated nature of the utilities industry
and supply chain constraints, with Janous warning that a painful
power crunch that could constrain AI’s growth likely lies ahead.
So, what does this all mean for markets? Although Covello
believes AI’s fundamental story is unlikely to hold up, he
cautions that the AI bubble could take a long time to burst, with
the “picks and shovels” AI infrastructure providers continuing
to benefit in the meantime. GS senior US equity strategist Ryan
Hammond also sees more room for the AI theme to run and
expects AI beneficiaries to broaden out beyond just Nvidia, and
particularly to what looks set to be the next big winner: Utilities.
That said, looking at the bigger picture, GS senior multi-asset
strategist Christian Mueller-Glissmann finds that only the most
favorable AI scenario, in which AI significantly boosts trend
growth and corporate profitability without raising inflation, would
result in above-average long-term S&P 500 returns, making AI’s
ability to deliver on its oft-touted potential even more crucial.
Allison Nathan, Editor
Email: allison.nathan@gs.com
Tel: 212-357-7504
Goldman Sachs & Co. LLC
Bankwatch 