Entropy. Stasis. Death.
But tonight, we dance!
From Forbes, March 16:
Earlier today Chamath Palihapitiya, the founder of Social Capital and
co-host of the All-In Podcast, published a lengthy thought experiment
on X (formerly Twitter)
that has since drawn nearly 800,000 views and more than 3,200 likes.
The post, titled "The Collapse of Terminal Value," discusses how we
might value markets in a post AI era. It poses a unsettling question:
what if artificial intelligence erodes competitive advantages and moats
so quickly that markets can no longer rationally assign value to what
companies might earn in year ten or beyond?
The answer,
Palihapitiya suggests, could require a fundamental re-pricing of equity
markets at a scale that would make the 2008 financial crisis look
modest.
The Argument From First Principles
Palihapitiya
grounds his thesis in the basic mechanics of equity valuation. Modern
capital markets assign value to companies not only on the basis of what
they earn today, but on discounted projections of what they will earn in
the future. This "terminal value," the sum of all projected cash flows
beyond a forecast period, accounts for a substantial portion of any
company's stock price. For high-growth technology companies, that figure
is especially large.
In his post, Palihapitiya writes that the S&P 500 currently
trades at roughly 22 times earnings, with top technology companies at 30
to 60 times. For most of these businesses, he estimates that 60 to 80
percent of their equity value is embedded in terminal value rather than
near-term cash generation. That figure is consistent with broader
analysis of large-cap technology valuations. Goldman Sachs Research
has noted that the S&P 500's price-to-earnings multiple ranked at
the 93rd historical percentile in late 2024, a level that embeds
substantial assumptions about future earnings growth.
Palihapitiya's disruption repricing framework begins with a simple
variable: the annual probability that any given business is rendered
obsolete by AI. Using a 9 percent cost of equity as a baseline, he
calculates that a company facing a 20 percent annual probability of AI
disruption has an expected lifespan of roughly five years. Discounted
over that window, its rational free cash flow multiple drops to
approximately 3.9 times. At a 30 percent disruption probability, the
multiple falls further to 2.8 times. At 10 percent, it rises to 6.5
times. The implied range, 2 to 7 times free cash flow, is not the result
of panic but of straightforward duration math applied to a world where
competitive advantages compound for years rather than decades.
He asks investors a direct question: what annual probability of AI
disruption would you honestly assign to the most important holding in
your portfolio? He suggests that any number below 10 percent is
difficult to defend given what the industry itself says about the pace
of change.
Historical Precedents for Duration Discounting
Palihapitiya’s
framework is not new. He draws on four industries where markets
previously applied steep duration discounts to businesses generating
real cash flows, and where those discounts proved to be correct.
The
newspaper industry between 2005 and 2015 is his first example. As
digital advertising destroyed the print revenue model, companies that
had traded at 12 to 15 times EBITDA compressed to 2 to 4 times. Tribune
Company and the Philadelphia Inquirer, among others, eventually filed
for bankruptcy. Cash flows were real in year one; they were gone before
year seven. Retail experienced a comparable repricing between 2016 and
2020 as Amazon dismantled the economics of brick-and-mortar stores.
Department stores and specialty retailers compressed to 3 to 6 times
free cash flow even while generating significant cash. The market was
pricing duration risk, not current earnings.
Energy companies between 2019 and 2021 saw a similar valuation
decline. Major oil producers with decades of proven reserves traded at 4
to 6 times free cash flow as markets priced in the possibility that
falling demand for fossil fuels would strand those assets before they
could be fully monetized.
The most extreme case, Palihapitiya
argues, was the taxi market. Medallion Financial, which provided loans
against New York City taxi medallions, watched its collateral collapse
from over one million dollars per medallion to under one hundred
thousand. These assets were cash-flowing assets with decades of
operating history yet the market repriced them to near zero once it
became apparent that Uber had made the endpoint of their cash flows
visible, even if Uber had not yet finished the job.
The Scale of a Generalized Repricing
What
makes Palihapitiya's thesis novel is the proposition that this kind of
duration discounting, historically applied one sector at a time, could
now be applied across the economy simultaneously. The aggregate market
capitalization of the S&P 500 currently sits at approximately $58
trillion. Corporate free cash flow from index constituents runs at
roughly $2.8 trillion annually. At a 5 times free cash flow multiple,
the midpoint of Palihapitiya's disruption range, the index would be
worth approximately $14 trillion. That represents a 75 percent drawdown
from current levels. First Trust Advisors' analysis
has already flagged that the "Buffett Indicator," which compares total
market capitalization to GDP, reached an all-time high of 167 percent of
GDP in late 2024, a level Buffett himself originally cited as a warning
sign before the dot-com crash.
Palihapitiya is careful to frame
this as a thought experiment rather than a forecast. He describes the
equilibrium as likely self-defeating. If markets repriced to 2 to 7
times free cash flow, the capital expenditure that drives AI disruption
would dry up. AI development would slow. Moats would begin to look
durable again. The fear would fade and the cycle would reverse. His more
considered conclusion is not a permanent regime change but an
oscillating transition: shorter innovation cycles, higher volatility,
periodic crises of confidence in long-duration equity valuations, and a
structural rise in the equity risk premium.
The Venture Capital Question...
....MUCH MORE
There is no way I would ever invest in one of his deals but he raises a good point about knowing how your discounted-cash-flow model works.
If interested see:
May 2025 - We've Entered The Predation Phase Of The A.I. Boom: Chamath Palihapitiya Edition
From September 2022's "A Look At Chamath Palihapitiya's SPAC's":
Anyone in the media who gave this guy any oxygen, at all, is an idiot.
(after typing that I thought, "maybe one should check the archive before, rather than after, using the word 'Idiot').*
Virgin Galactic
Open Door
*And our mentions of Mr. Palihapitiya? There were three or four, here's another one on the SPACs
May 11, 2021 I'm A SPAC Cowboy.... Bet you weren't ready for that.
Because shorting stocks based
on valuation (vs fraud) in a bull market is so dangerous, we don't talk
about it all that much. There have been a few, the Great Kinder Morgan
short of '14* being a wonderful
memory, along with a few tactical i.e. quick shorts of Tesla over the
years (in direct violation of the decade-long "Don't short TSLA"
admonition), but as a general rule, on the blog we only short frauds in a
bull. In a bear, "Short 'em all" as one of my mentors used to say.
The
fact we don't put every last thought that pops into our collective
heads out in public actually benefits our readers as a couple of things
that hurt results in 2020 never made it to the blog.
But I don't like blind pools.
And I especially don't like SPAC's with PIPES
And with that confessional we'll turn the narrative over to the professional....
"The short-term, dopamine-driven feedback loops we've created are destroying how society works.
No civil discourse, no cooperation; misinformation, mistruth. And it's
not an American problem — this is not about Russians ads. This is a
global problem."
—Former Facebook Vice President for Addicting Users, Chamath Palihapitiya
