Our Chief Fixed Income Strategist Vishy Tirupattur thinks that
efficiency gains from Chinese AI startup DeepSeek may drive
incremental demand for AI.
----- Transcript -----
Welcome to Thoughts on the Market. I’m Vishy Tirupattur, Morgan
Stanley’s Chief Fixed Income Strategist. Today I’ll be talking
about the macro implications of the DeepSeek development.
It's Friday February 7th at 9 am, and I’m on the road in Riyadh,
Saudi Arabia.
Recently we learned that DeepSeek, a Chinese AI startup, has
developed two open-source large language models – LLMs – that can
perform at levels comparable to models from American counterparts
at a substantially lower cost. This news set off shockwaves in
the equity markets that wiped out nearly a trillion dollars in
the market cap of listed US technology companies on January 27.
While the market has recouped some of these losses, their
magnitude raises questions for investors about AI. My equity
research colleagues have addressed a range of stock-specific
issues in their work. Today we step back and consider the broader
implications for the economy in terms of productivity growth and
investment spending on AI infrastructure.
First thing. While this is an important milestone and a
significant development in the evolution of LLMs, it doesn’t come
entirely as a shock. The history of computing is replete with
examples of dramatic efficiency gains. The DeepSeek development
is precisely that – a dramatic efficiency improvement which, in
our view, drives incremental demand for AI. Rapid declines in the
cost of computing during the 1990s provide a useful parallel to
what we are seeing now. As Michael Gapen, our US chief economist,
has noted, the investment boom during the 1990s was really driven
by the pace at which firms replaced depreciated capital and a
sharp and persistent decline in the price of computing capital
relative to the price of output. If efficiency gains from
DeepSeek reflect a similar phenomenon, we may be seeing early
signs [that] the cost of AI capital is coming down – and coming
down rapidly. In turn, that should support the outlook for
business spending pertaining to AI.
In the last few weeks, we have heard a lot of reference to the
Jevons paradox – which really dates from 1865 – and it states
that as technological advancements reduce the cost of using a
resource, the overall demand for the resource increases, causing
the total resource consumption to rise. In other words, cheaper
and more ubiquitous technology will increase its consumption.
This enables AI to transition from innovators to more generalized
adoption and opens the door for faster LLM-enabled product
innovation. That means wider and faster consumer and enterprise
adoption. Over time, this should result in greater increases in
productivity and faster realization of AI’s transformational
promise.
From a micro perspective, our equity research colleagues, who are
experts in covering stocks in these sectors, come to a very
similar conclusion. They think it’s unlikely that the DeepSeek
development will meaningfully reduce CapEx related to AI
infrastructure. From a macroeconomic perspective, there is a good
case to be made for higher business spending related to AI, as
well as productivity growth from AI.
Obviously, it is still early days, and we will see leaders and
laggards at the stock level. But the economy as a whole we think
will emerge as a winner. DeepSeek illustrates the potential for
efficiency gains, which in turn foster greater competition and
drive wider adoption of AI. With that premise, we remain
constructive on AI’s transformational promise.
Thanks for listening. If you enjoy the podcast, leave us a review
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friend or colleague today.
DISCLAIMER
In the last few weeks… (Laughs) It’s almost like the birds are
waiting for me to start speaking.
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