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market commentary from Tal Elyashiv, Founder of
SPiCE VC
Two data points came out of Morgan Stanley’s AI market analysis
recently that I keep coming back to.
First, $3 trillion in AI-related infrastructure investment will flow
through the global economy by 2028, with more than 80% of that still
ahead.
Second, 21% of S&P 500 companies now cite AI benefits in their
results. The companies delivering measurable returns are seeing cash
flow margin expansion at roughly 2x the global average. The
companies in the second group, the ones reporting AI benefits
without margin expansion, are absorbing the cost without capturing
the upside.
Those two facts together tell a more interesting story than either
one does alone.
When a technology becomes a macro variable
For the past three years, AI has been covered as a tech sector
story, with the focus on which AI companies are winning, which
models are strongest and which stocks to own.
That framing is now outdated. Morgan Stanley’s conclusion, which
BlackRock echoed in its Q2 outlook, is that AI has crossed into
macro territory. The capital expenditure decisions of a handful of
companies are, in Morgan Stanley’s words, “so large that the micro
is macro.” That language describes spending plans large enough to
move GDP. Tech themes do not move GDP. Structural economic forces
do.
When I wrote Investing in Revolutions, I described what
happens when a transformational technology reaches the phase where
it stops being a sector story and becomes a force field, where the
question shifts from “which companies are building this” to “which
businesses are positioned to thrive in a world where this is how
things work.” We are at that transition point now.
The investment logic changes when a technology becomes structural.
The work is no longer about picking the winners of a race. It is
about understanding which businesses have the architecture to
capture value in a world where AI is ambient, where AI lives inside
every workflow, every pricing model and every competitive dynamic.
Some companies are built for that world. Many are not. The gap
between the two is beginning to show up in the numbers.
The 2x split
The Morgan Stanley data on cash flow margin expansion is the most
important data point in any AI investment discussion right now, and
it gets far less attention than the total spending figures.
21% of S&P 500 companies cite AI benefits. That sounds like
progress, and it is. Inside that group, the divide is sharp. The
companies seeing measurable margin expansion have demonstrated
pricing power. The companies without pricing power are adopting AI
and absorbing the infrastructure cost without capturing the
economics.
The distinction between companies that adopt AI and companies that
profit from AI is one I have written about under a different name.
In Investing in Revolutions, I call them Beneficiaries and
Casualties. The Beneficiary integrates a new technology at scale in
a way that extends its competitive position. The Casualty adopts the
tool, pays for the infrastructure and watches the value flow to
someone else, a competitor with a stronger market position, or the
technology vendor charging for the capability.
The Morgan Stanley data shows that the Casualty risk in AI is real
and visible right now. Adopting AI without the underlying pricing
power or structural advantage to monetize it can land as a margin
headwind. The infrastructure cost shows up on the income statement.
Customers will not pay more for it. Competitors are doing the same
thing, so the expected competitive advantage does not arrive.
The 2x split is the market sorting Beneficiaries from Casualties in
real time.
What determines which side a company lands on
Three characteristics define the companies capturing value from AI.
The first is pricing power in the underlying business. If customers
will pay more for a better, faster or more personalized product or
service, and if AI helps deliver that, the economics work. If the
product is commoditized and customers are already price-sensitive,
AI reduces costs, but competitive pressure passes the savings to the
customer rather than the income statement.
The second is data specificity. The companies winning with AI have
proprietary data, customer behavior, operational history and
domain-specific knowledge that a general AI model does not have.
That data makes their AI more accurate, more useful and harder to
replicate. It is a moat. Companies deploying generic AI on top of
public data do not have that moat.
The third, and the one most often underestimated, is organizational
capability. An AI system is only as good as the context it operates
on. Context means the right data flowing into the right workflows
with the right human oversight. The companies that have invested in
their data architecture, their integration layers and their change
management can make AI work at scale. The companies that have not
are learning that bolting AI onto broken infrastructure does not fix
the infrastructure.
The $3 trillion question
$3 trillion in AI infrastructure investment by 2028, with more than
80% still ahead. The scale describes an industrial buildout on the
order of the railroad era or the electrification of American
industry.
I have spent years studying how transformational technology waves
create and destroy value. The consistent pattern: the largest wealth
creation does not happen at the beginning, when attention is focused
on the technology pioneers. It happens in the middle, when the
infrastructure is built and the question shifts to which businesses
use it most effectively. We are in that middle phase now.
The $3 trillion will flow. The companies receiving it will build.
Some of what gets built will be genuinely transformative. The value
created by that transformation will accumulate heavily in the
businesses with the structural characteristics to capture it:
pricing power, proprietary data and organizational capability.
The 2x margin differential already visible in the data will widen as
AI becomes more embedded and structural advantages compound.
The relevant question for any investor right now is whether a given
business sits in the layer of AI that captures value, or the layer
that pays for infrastructure another business profits from.
What this means for how I am investing
I am writing this partly because of a decision I made recently that
reflects this thesis directly.
I have joined True Global Ventures as a General Partner, alongside
my continuing role as Managing Partner at SPiCE VC. TGV Fund 6
focuses on AI-first vertical companies: businesses built from the
ground up around AI for specific domains, where the data is
proprietary, the workflow integration is deep and the pricing power
is real.
That decision is not a coincidence. It is where the Investing in
Revolutions framework points when I ask the question: at this stage
of the AI cycle, where does the value accumulate?
The Originator phase, investing in the foundational AI models and
infrastructure, is largely played. The Beneficiary phase,
identifying the businesses that use AI to build durable competitive
advantage, is where the most interesting opportunity sits right now.
The 2x split in the Morgan Stanley data is the evidence.
Tal Elyashiv is the founder and Managing Partner of SPiCE VC,
General Partner of True Global Ventures, co-founder of Securitize
and author of Investing in Revolutions and Blockchain
Prophecies.
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