The
budget of the federal government is the largest of any organization in
human history. It’s larger than the entire economy of every foreign
country except China. And it mostly grows automatically, without regular
input from elected officials.
In
the past, huge surges in the national debt were largely caused by wars
or recessions. They were temporary. When the war concluded or economic
growth resumed, debt as a share of the economy would fall.
That’s
not how it works anymore. The debt increases due to demographic trends
as the bulk of spending goes to programs that serve an aging population.
The budget deficit as a share of the economy in 2025, during a
peacetime expansion, was larger than any year of the 1930s, the decade of the Great Depression.
Around
2030, the United States is projected to surpass three milestones that
illustrate the lack of precedent for the government’s fiscal
predicament.
According
to the Congressional Budget Office, 2030 is the year when federal debt
held by the public as a share of the economy will exceed the record set
by World War II. Unlike in the ’40s, this debt shows no signs of ever
declining....
That statement seems carefully worded, it's obvious she's not
running. And it is not exactly General Sherman's "I will not accept if
nominated and will not serve if elected."
It's also not another Sherman quote (my fave) regarding his friend and superior officer General Grant:
"Grant
stood by me when I was crazy, and I stood by him when he was drunk, and
now we stand by each other."
but then again the former First Lady
probably wouldn't say something like that regarding President Biden.
I wonder though if she would accept her party's nomination at the convention in Chicago. President Biden would have a whole bunch of delegates he could release if he were to retire from the field.
On
the other hand, I'm not sure you would want to be President during the
next four years, there are so many problems that have been growing and
metastasizing just beneath the surface of the daily news that the person
in the hot seat could end up just plain reviled....
...On
the other hand, I'm not sure you would want to be President during the
next four years, there are so many problems that have been growing and
metastasizing just beneath the surface of the daily news that the person
in the hot seat could end up just plain reviled.
If
I were a Democrat strategist I would propose letting Donald Trump win a
second term while concentrating on House and especially Senate (to
bottle up judicial, including Supreme Court, nominees) races.
A
Trump win would give an excuse for riots (for the visuals) and if he is
handcuffed by the Legislative branch to limit the range of possible
responses, you go beyond polycrisis to the omnicrisis. Throw in a bit of
Frances Fox Piven with her "overwhelm the system" and "motor voter"
strategies and you could see one-party rule for thirty years.
There
are probably a dozen ways things could come to a head, Joe steps down,
Kamala is elevated, appoints Gavin or Michelle as Veep, steps down
herself etc.
After Nixon resigned the country ended up with Gerald
Ford and Nelson Rockefeller in the top two spots, with neither of them
having run for their respective position. So all sorts of possibilities.
Italy’s $4.7 billion cheese economy is feeling the heat as climate change threatens its cheese banks that hold Parmigiano wheels as loan collateral
In the hills of Emilia-Romagna, a bank vault holds more than half a
million wheels of Parmigiano Reggiano, worth well over 300 million
euros.
The vault belongs to the bank Credito
Emiliano, known colloquially as Credem, which has long accepted young
wheels of Parmigiano Reggiano as collateral for loans to local dairy
farms since 1953.
But now, extreme heat is threatening
Italy’s “cheese banks,” and economists who study heat’s effect on growth
say the exposure runs well beyond a single vault but into the country’s
vineyards, its olive groves, and its broader economy.
A blockchain-backed cheese loan collateral program After receiving the wheels of cheese from dairy farmers, a Credem subsidiary, Magazzini Generali delle Tagliate, ages the wheels in two warehouses in Reggio Emilia and Modena. Producers typically receive 60% to 80% of a wheel’s value upfront.
But the process has come a long way from
the 1950s, as blockchain technology now lets farmers pledge wheels even
while the cheese stays in their own facilities, doubling Credem’s
lending capacity. The arrangement solves a real problem: Parmigiano
needs at least 12 months to age, often 24 or 36, and small family farms
can’t easily keep that much inventory tied up for that long without
generating some cash. So the bank provides some before any sales are
made.
The scale of that arrangement is bigger
than the vault itself. Italy produces about 4 million wheels of
Parmigiano Reggiano a year, and the cheese banks hold about 500,000 of
them, Giancarlo Ravanetti, who runs the bank’s cheese warehouse
business, told CNN. His warehouses handle about 2.3 million wheels a year in total.
Meanwhile, Parmigiano Reggiano is a 4
billion-euro ($4.7 billion) industry sustained by roughly 300 certified
dairies, and keeping that much cheese at the right temperature has
gotten more expensive. Thanks to this year’s record heat waves in Europe,
daily energy consumption rose about 30%, forcing the bank to upgrade
cooling systems and boilers, add insulation, and expand renewable power
generation.
Climate change is affecting dairy farmers’
milk supply as well. Because it’s so hot outside, cows lie down more
and eat less, reducing milk production by up to 10% a year. As longer
and more intense heat events become all the more common, they hit both
the quantity and quality of milk, ultimately driving up costs.
Climate change hits the vineyard The same climate pressure is showing up on a similar timeline in Italy’s vineyards. In Lombardy’s Franciacorta sparkling-wine region, the 2026 harvest began July 30, the earliest start on record, after budbreak came more than a week ahead of the historical average. In Sicily, the harvest has stretched into what growers describe as a hundred-day picking season across the island’s microclimates, as producers time each variety’s picking to stay ahead of the heat.
Coldiretti, Italy’s largest farmers’ association, has called
2026 one of the earliest harvests on record nationally, citing record
temperatures and drought that are pushing sugar into the grapes faster
than their flavor can develop, a mismatch that’s especially hard on
late-ripening reds like the Nebbiolo grape behind Barolo....
....Years ago I heard of a Chicago company that made a whey-based artificial cheese. Apparently the operation was headed by a mad scientist type who had come up with the formula but had no marketing ability.
He
was producing the stuff and not selling any, converting all the
investors cash into this "analog" goop and storing it in Chicago area
warehouses.
Then the Chernobyl reactor blew, the price of whey
skyrocketed, I've no idea what the connection was, the company went
broke and the receivers opened the warehouses to find tons of this
'cheeze', semi-molten in the summer heat.
That's what I thought
of when I saw this story, tons of the stuff oozing out of bonded
warehouses. No connection of course, just a visual.
In the past the PE shops have sold their investee companies to other PE shops or even between their own sponsored funds. Marking the valuation higher with each flip and using the higher mark for additional dividend recaps. Maybe they should do that again.
From the New York Times, August 10:
The long-awaited deal-making boom has finally arrived. SpaceX set a record for the world’s largest initial public offering. David Ellison is pursuing a $110 billion deal linking Paramount with Warner Bros. The utility firm NextEra Energy has struck a deal to buy Dominion Energy that values it at more than $120 billion.
But
private equity — a deal-making machine for decades — is largely sitting
on the sidelines. For the third consecutive year, private equity firms
are saddled with a rapidly increasing number of companies that they
cannot sell or take public at the returns their investors expect.
As
of June 30, private equity firms had 33,575 unsold companies in their
portfolios, according to PitchBook, an industry data firm. That’s up
from 32,451 companies at the end of last year and 15,923 companies a
decade ago.
The
growing backlog is a challenge for private equity’s core business
model. Typically, such firms aim to buy a company, often add large
amounts of debt to its balance sheet, improve its financial performance
and then sell it for a profit, usually within five to seven years.
The
state of limbo has been difficult for large investors like pension
funds and endowments that have spent decades paying steep fees to
private equity firms promising market-beating returns. Some investors
and industry professionals are worried that the firms won’t be able to
sell companies without taking big losses.
“Private
equity is stuck because those companies have failed to fulfill their
value promise,” said Andrew Milgram, a managing partner and chief
investment officer at Marblegate Asset Management, an investment firm.
As
the backlog grows, private equity firms continue to underperform the
broader stock market. From July 1, 2022 to March 31, 2026, U.S. private
equity firms generated annualized returns of 6.4 percent, according to
the most recent data from MSCI, an index firm. That’s far below the 15.2
percent annualized returns of the S&P 500 and the 19.3 percent of
the Nasdaq during the same time period.
There
are several reasons for the logjam. Higher interest rates have made it
difficult for private equity firms to find buyers that rely on cheap
debt to finance acquisitions.
Another
challenge is the weakness in the software sector, where there is a
heavy concentration of private equity-owned companies. The value of many
software firms has declined, as investors worry that artificial
intelligence will cut into future earnings.
Historically,
many private equity-owned companies have been sold to other private
equity firms. But that important source of demand has also largely dried
up.
When
the Federal Reserve held interest rates low and debt was cheap, it was
relatively easy for a private equity firm to write small checks and
borrow heavily to purchase a company. But since the Fed began raising
interest rates in 2022, private equity firms have needed more cash to
pay down debt at the higher rates. That means the firms are not willing
to spend as much to purchase companies, which drives down their
valuations.
Last
week, the investment behemoth Apollo Global Management reported weak
quarterly results in its private equity division. The firm specifically
pointed to a tricky market for company sales and I.P.O.s as a reason for
its lower performance returns in that part of the business, saying
exits were being “prudently delayed.”
“Buyers
and sellers still have too big of a valuation gap,” said John
Maldonado, managing partner at Advent International, a private equity
firm.
The
delays are becoming the new normal. For years now, private equity firms
and their bankers have promised a rebound in either selling the
businesses they own or taking them public. Such an outcome has proved
elusive.
Elizabeth
Cooper, global head of private equity at the law firm Simpson Thacher
& Bartlett, said that going into this year, she had “a whole stable
of companies” she had thought would be sold in the early part of the
year. They’re still waiting.
“Everything
basically got reset,” Ms. Cooper said, pointing to continued high
interest rates and volatility in the stock market and politics.
Some
private equity executives say the backlog may not end up being a big
problem, because some companies that have taken longer to sell could
ultimately generate large returns.
Still,
the private equity struggles contrast sharply with the investments that
venture firms — another giant source of money in private companies —
have made in A.I. pioneers like OpenAI, Anthropic and SpaceX, which are
generating seemingly once-in-a-lifetime returns.
The first half of 2026 was the second highest-volume period for initial public offerings in more than a decade.
But for many private equity firms, I.P.O.s have not been a viable path to selling their businesses.
Since
2022, only 70 private equity-backed companies have gone public on U.S.
exchanges, according to the data firm Dealogic. From 2017 to 2021, 424
private equity-backed companies did so.
Software companies are some of the most troubled parts of the pipeline....
The Colorado River is approaching thresholds its dams were never meant to cross. What happens next could transform how water and electricity move across the American Southwest.
FROM THE RIM
of Lake Powell, you can’t see the danger line. There isn’t a flashing
gauge or a chart on the canyon wall that warns visitors when one of the
most important machines in America begins to fail. There’s only an
invisible number: 3,490 feet above sea level.
Engineers
call it “minimum power pool.” Above that elevation, Lake Powell can
still push water through Glen Canyon Dam’s massive penstocks, spin its
turbines, and send electricity across the Southwest with no trouble.
Below it, the dam loses the function that helped justify its existence.
The river still wants to move, but the machine starts running out of
ways to move it.
Glen
Canyon Dam and Lake Powell sit near the center of the Colorado River
system, upstream from Hoover Dam and Lake Mead. Together those
reservoirs store the water that cities, farms, and power plants across
the Southwest depend on. Now the entire system is being pushed past its
limits, and Lake Powell is where the cracks are really starting to show.
Since 2000, the Southwest has been caught in a megadrought that has
steadily pulled down water levels in both Lake Powell and Lake Mead.
Climate change has made that trend hotter, drier, and harder to reverse.
In 2026, a punishingly weak snowpack and an extreme heat wave pushed
the basin deeper into emergency. The Bureau of Reclamation began taking
steps to keep Lake Powell’s water levels high enough for electricity
generation; Hoover Dam’s hydropower output is expected to drop by 40
percent of its maximum capacity this year....
...Donald Coxe, chief strategist of Harris Investment Management and one
of my favorite analysts, spoke at my recent Strategic Investment
Conference. He shared a statistic that has given me pause for concern as
I watch food prices shoot up all over the world.
North America
has experienced great weather for the last 18 consecutive years, which,
combined with other improvements in agriculture, has resulted in
abundant crops. According to Don, you have to go back 800 years to find a
period of such favorable weather for so long a time.
Well we are now at 26 years of near perfect weather for row crops.
Finally, from March 2015:
That little red blip at the far right side of the timeline is the current drought.
You could make a reasonable argument that for the last 150 years Californians have been living in a fool's paradise.
Nvidia signed memorandums of understanding with Apollo Global Management, Blackstone, BlackRock, Brookfield Asset Management, Goldman Sachs and KKR to establish financing platforms for Nvidia’s customers
The effort aims to mobilize more than $500 billion in third-party capital for hyperscalers, frontier AI labs and enterprises to build out data centers and acquire Nvidia hardware.
Executives from the seven companies joined CNBC’s Becky Quick in a rare, live joint interview to discuss the announcement.
Nvidia
is attempting to turn its artificial intelligence chips into Wall
Street’s newest asset class, partnering with six large asset managers on
a $500 billion financing push designed to treat compute infrastructure
much like commercial real estate, toll roads or other assets to borrow
against.
Executives from the seven companies joined CNBC’s Becky Quick in a rare, live joint interview to discuss the announcement.
The
effort aims to mobilize more than $500 billion in third-party capital
for hyperscalers, frontier AI labs and enterprises to build out data
centers and acquire Nvidia hardware, marking a potentially important
shift in how AI infrastructure is funded. By using institutional credit,
insurance funds and private capital to underwrite GPUs and data
centers, Nvidia is helping its end users secure financing without
tapping their own balance sheets.
“This is really the first time
that technology chips have become an investable asset class,” Nvidia
founder and CEO Jensen Huang told CNBC. “These are revenue-generating
assets now. They’re productive, they’re long-lived, they’re fungible,
they’re flexible.”
Huang argued that because Nvidia’s hardware is
broadly adopted and transferable across customers, lenders can reliably
underwrite compute as a revenue-generating asset with an extended life.
Historically, GPUs have been viewed as rapidly depreciating hardware.
Nvidia’s effort challenges that assumption, transforming AI compute
capacity into long-term, bankable infrastructure, though skeptics may
question whether AI chips can retain their value as newer generations
emerge....
As Grandmother used to say, if it's not one tham ding it's another.
From The South China Morning Post, August 8:
The global supply of high-quality, publicly available human-generated text could be fully exhausted within the next six years
China’s high-stakes race to build next-generation artificial
intelligence models is entering a critical new phase, where a less
visible yet far more existential threat is coming into view: a severe
shortage of high-quality training data.
While the US chokehold on
advanced computing chips has dominated headlines, Chinese AI experts
increasingly warn that running out of quality data could prove to be the
next major bottleneck to the nation’s tech ambitions – and one that
hardware workarounds cannot easily solve.
It is a challenge
confronting AI giants on both sides of the Pacific – and some US
companies are already resorting to aggressive measures to stay ahead.
The
global supply of high-quality, publicly available human-generated text
could be fully exhausted within the next six years, according to
US-based research institute Epoch AI.
OpenAI co-founder Andrej
Karpathy has also warned of a looming “data wall” by the end of this
decade, beyond which model capabilities could hit a plateau unless they
were fed fresh, reliable information.
Top American labs are spending lavishly to mine offline human knowledge, igniting a fierce ethical debate in the process.
Under
an internal initiative code-named Project Panama, Amazon.com-backed
Anthropic spent tens of millions of dollars acquiring millions of
physical books, severing their bindings and scanning every page into
digital form before discarding the originals, according to court filings
reported by The Washington Post in January.
The disclosures drew fierce condemnation from authors, archivists and
preservationists who accused tech firms of treating human heritage as
disposable raw material. “None of our data-acquisition programmes buy
and destroy ‘rare’ or ‘antiquarian’ books,” an Anthropic representative
told fact-checking site Snopes earlier this month.
Still, the
controversy highlighted how desperate frontier AI developers have become
to secure vast volumes of human-written text as online web data runs
dry.
For China, however, the impending data wall poses a unique threat.
While
English accounts for nearly half of all content on the global web as of
this month, Chinese represents a mere 1.3 per cent, according to
internet tracker W3Techs, placing it far behind languages like Spanish
at 6 per cent, German at 5.9 per cent and Japanese at 5 per cent.
In response, Beijing is moving aggressively to treat data as a core
strategic asset. In June, the National Data Administration unveiled a
sweeping nationwide plan to boost the supply, circulation and
commercialisation of high-quality AI training data....
Companies buying their own stock was illegal until 1982 when the SEC adopted Rule 10b-18 which created a safe harbor exemption to the anti-market-manipulation rules of the Securities Exchange Act.*
Goldman Sachs expects corporate demand for U.S. equities to outweigh
supply this year, even as follow-on equity issuance climbs to its
highest level at this point of the calendar year since 2021.
“Follow-on equity issuance is increasing but represents a
return to normal rather than a boom,” the bank’s strategists led by Ben
Snider said in a note.
U.S. corporates have raised $105 billion via follow-on
offerings year-to-date through July, while total equity
issuance—including IPOs, follow-ons, converts and SPACs—hit a record
$252 billion in the second quarter, surpassing the previous high of $234
billion set in the first quarter of 2021, Goldman noted.
Still, strategists said that both the number of offerings
and issuance relative to equity market capitalization remain below
long-term averages, with activity concentrated in a handful of large
deals.
Financing needs for AI investment are a key driver behind
the increase. AI-related issuance has accounted for roughly 40% of U.S.
equity follow-on volume this year, and Goldman’s strategists said this
trend "will continue to increase going forward."
Consensus estimates imply hyperscaler capital expenditures
of $1.1 trillion will exceed operating cash flow by $150 billion in
2027, before turning free-cash-flow positive in 2028. “While recent
earnings reports signal upside risk to estimates for hyperscaler
revenues, many investors believe capex will register well above
consensus forecasts,” the strategists wrote.
*We've looked at Rule 10b-18 a few times but first one of the foundational truths of human creations, March 2024:
The Purpose Of A System Is What It Does, Not What It Claims To Do
Stafford Beer, British theorist, consultant, and professor at the
Manchester Business School, coined and frequently used the phrase “The purpose of a system is what it does”
(POSIWID) to explain that the observed purpose of a system is often at
odds with the intentions of those who design, operate, and promote it.
For example, applying POSIWID, one might ask if the purpose of an
education system is to help children grow into well-rounded individuals,
or is it to train them to pass tests? “There is after all,” Beer observed,“no point in claiming that the purpose of a system is to do what it constantly fails to do.”
POSIWID stands above judgement and partisan opinion when considering
any system - all one has to do is take note of its actions and outputs.
And when those actions and outputs don’t align with what the system
claims as its purpose, it jeopardizes the trust, confidence, and loyalty
of those who work inside the system and those whom the system purports
to serve....
Using
this heuristic to look at systems like education or government helps
focus on the fact that in a system, as opposed, possibly, to a one-off
event, the result is the reality to focus upon.
Reality is not
the intentions of the systems designers and the systems implementers and
reality is surely not the protestations or explanations, excuses or
justifications that surround most human endeavors.
The end result of a system, is what the system is meant to do. For the rest it is hard to put it better than:
"Ils ne se servent de la pensée que pour autoriser leurs injustices, et emploient les paroles que pour déguiser leurs pensées"
I've mentioned SEC Rule 10b-18 a few times, some links after the
jump. A lifetime of looking at this stuff has led me to the conclusion
that in the U.S. stock buybacks are nothing more than a tax-avoidance
scam with the added benefit of rewarding managers for things they didn't
do by, well, managing the company rather than the stock price.... (there's an icebreaker for tonight's festivities: "Say, what's your take on SEC Rule 10b-18?")
As we've said over the years, stock buybacks are nothing more than a
tax dodge, magically turning cash flows that would otherwise be taxed at
ordinary income rates as dividends into higher stock prices due to
monotonic and incessant buying pressure from corporations, which cap
gains are taxed at much-lower capital gains rates.
The buybacks
have an added bonus, at least from management's perspective, of
dramatically increasing the value of shares based compensation by using
corporate assets—the cash flows that otherwise would be distributed as
dividends— to boost a company's stock price, as opposed to the whims of
an actual market.
The flaw has two parts, 1) The November 17, 1982 SEC ruling on Rule 10b-18
which opened the floodgates of kleptocratic value extraction of
American businesses by giving corporations a safe harbor against charges
of stock manipulation when buying their own shares and 2) The smart
kids, members of Phi Scamma Jamma, are still pitching a
differential between tax on earned income and tax on capital gains even
though the efficacy of capital gains tax breaks in performing their
original purposes, investment and job creation, has been declining since
the 1970's and is now just an excuse for a loophole. See "TAXES, CAPITAL AND JOBS" for an exceptionally lucid discussion, again, if interested.
The
new law, and the Administration's proposed increase, are a fig leaf
slapped over the naughty bits, designed to give the appearance of doing
something while having no discernible real-world effect on behavior....
"The Real Reason Stock Buybacks Are a Problem" This
argument is a corollary of the fact that the preferential taxation of
capital does not seem to deliver on the policy goals with which it is
rationalized. More on that after the jump. (I'm going to get kicked out of the club aren't I?)
...II. Overview of Current Rule 10b-18 A. Rule 10b-18 as a "Safe Harbor"
In 1982, the Commission adopted Rule 10b-18,4
which provides that an issuer will not be deemed to have violated
Sections 9(a)(2) and 10(b) of the Exchange Act, and Rule 10b-5 under the
Exchange Act, solely by reason of the manner, timing, price, or volume
of its repurchases, if the issuer repurchases its common stock in the
market in accordance with the safe harbor conditions.5
Rule 10b-18's safe harbor conditions are designed to minimize the
market impact of the issuer's repurchases, thereby allowing the market
to establish a security's price based on independent market forces
without undue influence by the issuer....
For many, many years corporations have been the marginal buyer, meaning their actions are what sets stock prices, which is directly at odds with the original intent of the rule change.
From the SEC, December 10, 2002, comments on the proposed amendment to 10b-18 which was adopted in 2003.
Proposed Rule Rule 10b-18 and Purchases of Certain Equity Securities by the Issuer and Others
....At the same time, an issuer has a strong interest in the market
performance of its securities. Among other things, its securities may be
the consideration in an acquisition, or serve as collateral for
financing. The market price also determines the price of offerings of
additional securities. Therefore, at various times, the issuer may have
an incentive to manipulate the price of its securities. One way to
positively affect the price is to purchase the securities in the open
market. Because repurchases of its securities could affect the market
price of an issuer's stock, this may expose the issuer to claims that
the repurchases were made in a manipulative manner even when they were
done in a manner not intended to move market prices.
Rule 10b-18 addresses this problem. In 1982, the Commission adopted Rule 10b-18,2 which provides issuers3
with a safe harbor from liability for manipulation under Sections
9(a)(2) and 10(b) of the Exchange Act, and Rule 10b-5 under the Exchange
Act, when they repurchase their common stock in the market in
accordance with the rule's manner, timing, price, and volume conditions.4
Rule 10b-18's safe harbor conditions are designed to minimize the
market impact of the issuer's repurchases, thereby allowing the market
to establish a security's price based on independent market forces
without undue influence by the issuer.5
See, here's the thing, AI is effectively a last chance effort to revive developed-nation economies and stave off stasis/entropy/death. It's a gamble. And the best shot we have.
But if we fail, then the whole world, including the United States,
including all that we have known and cared for, will sink into the abyss
of a new Dark Age made more sinister, and perhaps more protracted, by
the lights of perverted science.
Oops, sorry, I don't know why Churchill is popping in. That's the penultimate sentence of his "Their finest hour" speech. It was immediately preceded by:
Hitler knows that he will have to break us in this Island or lose the
war. If we can stand up to him, all Europe may be free and the life of
the world may move forward into broad, sunlit uplands.
Which reminds me there is one other option to goose developed economies short of hyper-inflationary money-printing; the one Germany and the EU have chosen, massive investment in arms and armaments.
From the University of Chicago, Booth School of Business, Chicago Booth Review, August 7:
Investors should look past software to the financing of AI infrastructure.
For much of the past year, alarm about the credit taken on to fund
the boom in artificial intelligence has centered on software—a concern I
examined in June.
The focus is understandable. Private-credit lending to software
companies had exceeded $500 billion by the end of 2025, and fears that
AI-related debt could crowd out other borrowers have pressured
valuations and software-heavy business development companies alike.
This exposure is relatively visible because many private-credit
vehicles disclose their portfolios. But other AI infrastructure
financing, however, is much harder to observe.
The physical build-out of AI—chips, data centers, power connections,
servers, and specialized compute capacity—has mostly been financed off
the consolidated balance sheets of the largest technology companies via
project and construction financing, equipment-backed lending, leases,
asset-backed securities, and, yes, private credit.
There are even loans secured by GPUs, with the chips that run AI
themselves pledged as collateral. As it works with a mortgage secured by
a house, if a borrower stops paying, its lender takes the chips and
sells them. But while most houses hold their value for decades, GPUs are
being displaced every two or three years as faster ones are developed,
and nobody yet knows what a used one is worth.
Investors commonly measure AI-related leverage by looking at the
balance sheets of the largest cloud-computing companies, the five big
hyperscalers: Alphabet, Amazon, Meta, Microsoft, and Oracle. For these
companies, conventional leverage remains manageable, but that is only
one part of the financing system. An institution may hold technology
equities, infrastructure funds, private-credit vehicles, securities
issued by insurers, and real estate debt, and regard those exposures as
diversified. Legally, they are separate investments, but economically,
they may share risks related to future AI demand.
In a working paper, I mapped this financing system and performed a
stress-test on it. The question I had is how much financing depends on
the same underlying cash flows—and where the associated risk ultimately
resides.
The results of this test suggest that today’s situation is not a
rerun of the 2008–09 financial crisis: First-loss positions sit mostly
outside the regulated banking system. But losses could still reach $140
billion and could grow as AI-infrastructure credit expands.
Are the hyperscalers overextended? Through 2024, the largest technology companies financed most of their aggregate capital spending from operating cash flow. That has changed. The big hyperscalers undertook approximately $380 billion of capital spending in 2025 and are expected to spend roughly double that this year, putting capital spending on course to overtake operating cash flow.
Debt issuance has risen in step. The hyperscalers issued
approximately $120 billion of corporate bonds last year, compared to an
average of about $28 billion annually between 2020 and 2024. Issuance in
the first half of 2026 has already exceeded the 2025 amount.
Let’s be clear: The hyperscalers aren’t in financial difficulty. But
the marginal financing of AI infrastructure has begun to extend well
beyond their own balance sheets.
A hyperscaler may lease a data center from a developer that has
financed the property, construction, and equipment separately, borrowing
from a range of private-credit funds, insurers, pension funds, and
retail-oriented vehicles.
Commitments also extend beyond funded debt: S&P Global Ratings,
the credit rating agency, has identified about $675 billion of signed
but not yet commenced lease obligations across the hyperscalers—an
amount that’s excluded from the funded-debt totals in my analysis but
that demonstrates why reported corporate debt is an incomplete measure.
News you can use for a few more years at any rate.
From The Conversation, August 6:
Taxi drivers rarely die of Alzheimer’s – how complex mental maps and spatial reasoning protect your brain
Taxi and ambulance drivers are less likely than workers in almost any other job to die of Alzheimer’s disease. That was the surprising result of a 2024 study examining the death certificates of nearly 9 million people in the U.S.
These findings stopped me in my tracks because those two jobs rely on the same thing as my own work: maps.
I have spent more than two decades staring at maps. Not paper maps on
a wall, but digital ones with multiple layers: flood boundaries draped
over census blocks, car crash hot spots plotted against road geometry,
and satellite readings of rainfall stitched across river basins. Much of
my work as a civil and environmental engineer is done through GIS – that is, geographic information systems.
Engineers like me hold several spatial relationships in their minds at
once, reasoning about where things sit relative to one another across
scales ranging from a city block to a whole watershed.
I always assumed that spatial reasoning across map layers was purely
professional. But that study on taxi and ambulance drivers made me
wonder whether all that mental work might be doing something good to the
brain.
Taxi driver brains Of the 9 million death certificates from January 2020 to December 2022 that researchers examined, taxi and ambulance drivers had the lowest risk of dying from Alzheimer’s disease out of 443 occupations. After adjusting for age, sex, race, ethnicity and education, roughly 1 in 100 taxi and ambulance drivers died of Alzheimer’s, compared with 1 in 60 people overall.
This pattern did not extend to other driving jobs. The researchers
concluded that the key to reducing the risk of Alzheimer’s was not
driving itself but continuous real-time navigation: the constant work of
locating yourself in space, tracking a destination and updating a mental map as conditions change. Drivers whose jobs relied on fixed or predetermined routes, like bus drivers and aircraft pilots, didn’t seem to experience a similar advantage....
A London Taxi Family Confronts the Driverless Future
With more than a century of combined experience behind the wheel, the Gouldings now face their greatest challenge yet: Waymo, Wayve and Baidu.
I
wasn’t sure at first whether the taxi driver who arrived for my usual
predawn work pickup had also driven me a few days before. The name was
different, but Ron looked curiously similar to my driver from last week.
Time
and light banter eventually cleared things up. I hadn’t been
hallucinating before my caffeine hit — my two recent drivers were
identical twins. More interesting still, the twins were the sons of a
taxi driver whose own mother kept cabbies fueled with tea, coffee and
sandwiches from one of London’s traditional Cabmen’s Shelters. Two other brothers had also followed their father into the trade. Together, they form a remarkable London taxi family.
The
black cab business has been good to the Gouldings. It hasn’t made them
rich, but it provides a very comfortable living — without a boss to
answer to. This proud clan’s heart is David, 70, the family patriarch,
who took up the trade 42 years ago to support his young family. With his
twin sons Ron and Billy, 44, following in their father’s footsteps —
and their younger brothers, David, 40, and Harry, 38, close behind — the
Goulding tradition spans more than a century of combined experience
behind the wheel.
Like many long-standing traditions, the world in which they operate — London’s taxi trade — keeps confronting new threats. The centuries-old
industry has survived wars, the onslaught of Uber, and Covid. Black
cabs remain one of London’s defining symbols for many, alongside
Buckingham Palace, Sunday roast lunches and Paddington Bear.
But
the trade faces perhaps its greatest disruption yet: competition from
vehicles with no driver at all. Still in testing mode, autonomous taxis
could, pending government approval, offer driverless rides to Londoners
by the end of the year. Their arrival could arguably prove a greater
shock in the city than elsewhere. London is, after all, home to the
world’s most thoroughly trained cabbies, who spend up to four years
completing “the Knowledge” — a famously rigorous memorization of the
city’s street plan.
The Map in Their Minds
Introduced
in 1865, the Knowledge remains one of the world’s toughest professional
qualifications. Candidates master the city’s streets through intensive
visualization, training so rigorous that studies have shown it can
physically reshape the brain. One study even found that London cabbies outperform GPS navigation apps in some respects.
Ron
would agree. While he occasionally uses navigation apps to verify
obscure addresses, he says GPS often misses taxi-only routes. He recalls
winning over one skeptical passenger by ignoring the satnav and taking a
faster route using bus lanes which are open to licensed taxis in the
UK. The passenger was so impressed that he left a tip and said, “I trust
you.”....
A month ago This Is Money was reporting
that the number of drivers taking "The Knowledge", the notoriously
difficult geographical test required to become a London black-cab
driver, had dropped by two thirds as new aspirants hooked up with Uber
and GPS rather than the black cabs.
Well now Uber has rolled out a video game version of The Knowledge for its San Francisco drivers, call it "Grand Theft Uber"....
The most important development today is the yen’s weakness. The dollar has approached JPY158.90 in the European morning, a new high for the month. Despite a hawkish sounding record of last month’s Bank of Japan meeting, the swaps market shaved the risks of a rate hike at next month’s meeting. The market is challenging the resolve of Japanese and US officials. The greenback is more broadly narrowly mixed and the yen’s roughly 0.65% loss stands out. A record jump in Swedish industrial orders (32% month-over-month, seasonally adjusted), led by export orders for transport equipment is lifting the krona by about 0.25% to top the G10 leaders’ board. The Middle East is the other major story. Iran and its allies continued to press. Tehran has demanded that US lift its naval blockade, withdraw forces, lift sanctions, release frozen assets, and pay reparations before the Strait of Hormuz will be allowed to open again. President Trump has suggested that the US may rely on the economic chokehold on Iran to pressure the regime and that the US was only “semi-negotiating” with Iran. This follows reports that the US has depleted much of its defense weapons, and the Saudi-Pakistan-Türkiye defense treaty struck last week. Oil prices are firm near four-day highs....
From American Affairs Journal, Volume X, Number 2, Summer 2026:
America has no peer competitor in
artificial intelligence outside of China. Several years ago, when the
trajectory of AI model development was uncertain enough to allow middle
powers like the United Kingdom and France to nurse hopes of remaining
competitive, this may have sounded like an overconfident pronouncement.
Today, it is well-earned conventional wisdom. Competing at the frontier
of AI requires coordinating talent, data, energy, and compute
infrastructure at unprecedented scales—scales
that only the United States and China are realistically capable of
delivering on, although each in their own distinctive ways.
Understanding China’s approach to developing and diffusing AI is thus of
existential importance to understanding America’s relative position in
the world to come.
America has been surprised by China’s AI prowess before. In January
2025, the release of DeepSeek was widely described as a Sputnik moment
by policymakers and business commentators. While DeepSeek’s technical
advances were overstated, the media’s reaction revealed the extent to
which many in the United States had become complacent about China’s lag
in AI capabilities.1Against
available evidence, too many American observers believed that China was
incapable of discovering AI breakthroughs on its own, whether because
of constraints on their access to advanced semiconductors, or the
persistent myth that Chinese companies can only copy but not innovate.
Even now, many still seem to believe that Chinese AI models will remain
behind American models in perpetuity, offering lesser capabilities but
at a fraction of the price. Yet offering a good enough product at
ultra-low prices and thereby cornering the market on less exquisite
technologies and manufacturing inputs is exactly how China became a peer
competitor to the United States in the first place. In AI, we are thus
primed to be surprised once again.
The pervasive indifference that characterizes America’s overconfident
view of its place in the AI race stems from grading the Sino-American
AI race against our own preferred rubric: frontier model benchmarks, the
scale of the data center buildout, and timelines to artificial general
intelligence (AGI). Rarely do we measure American performance against
the categories that the Chinese themselves choose to emphasize. The
party-state and various Chinese companies are clearly trying to unleash
AI capabilities, and Beijing’s desire for international AI leadership is
beyond dispute. But their methods and benchmarks of success are
different from ours, evincing a fundamentally distinct understanding of
the nature of the competition.
American readers inclined to dismiss China’s focus on open source AI
diffusion and applications as a case of settling for less than the
frontier should consider an alternative interpretation: that the Chinese
state has made a sincere and potentially well-founded judgment about
where the benefits from AI development will accrue in the medium- to
long-term horizon, and its leaders are organizing the many arms of the
state to support their industry ecosystem accordingly. The primary
questions explored by this essay are (1) how that AI-focused industrial
policy is orchestrated, and (2) what the subnational dynamics between
China’s provinces, municipalities, and central government reveal about
their model of AI development, for which America has no equivalent.
China’s AI Division of Labor
Before describing Chinese industrial policy for AI, it is necessary
to explain two paradigmatic differences in the ways that the Chinese and
American governments perceive AI development and diffusion, as well as
how those different perspectives influence tangible policy outcomes.
The first is the difference between how the two countries approach
hardware versus software. The United States has myriad regulatory
barriers to physical infrastructure buildouts that coexist with an
engrained hesitation to regulate algorithms and models. China is almost
the inverse. The PRC actively regulates the algorithmic layer of AI,
requiring registries of proprietary data and, in some cases, imposing
“ethical committees” to oversee algorithmic usage.2 At
the same time, the Chinese state aggressively organizes and subsidizes
physical infrastructure and deployment: data centers, compute vouchers,
industry funds, procurement mandates, start-up incubators, and more.
In contrast, the U.S. federal government maintains a relatively
light-touch approach to software but is mired by legal and regulatory
constraints on physical infrastructure, including power generation,
transmission lines, data centers, heavy industries, and until recently,
chip fabrication. It is too simple to frame this as American lawyers
litigating the physical buildout of AI while skilled Chinese engineers
speed ahead at constructing power generation, transmission, and
roboticized factories. Nevertheless, this asymmetry is a good starting
place for understanding how the respective AI strategies of the United
States and China diverge.
Second, high-level discourse in China concerning AI’s technological
potential is conceptualized quite differently relative to the
English-language AI community. In the United States, the AI race is
widely seen as a sprint to AGI, an autonomous system capable of
outperforming expert humans in virtually every domain. Developing AGI in
a way that benefits humanity is the explicit mission of OpenAI, for
instance, reflecting the influence of early AI safety thinkers from the
Effective Altruist (EA) and rationalist communities, in particular.
While AI development is proceeding along a continuous spectrum, AGI is
considered a particularly momentous threshold, beyond which progress
rapidly accelerates toward superintelligent systems with the potential
to transform every aspect of our economy and society, while giving the
first company or country to achieve AGI decisive economic and military
advantages.
While the discourse in America regarding the correct path for AI development is uniform—AGI is brought into existence as an emergent property of scaling LLMs and related infrastructure—the same cannot be said for China. To be sure, some Chinese researchers and thinkers do share this perspective.3
Especially at model-developing start-ups, such as Moonshot and Zhipu,
where each company’s CEO has explicitly stated that his mission is to
achieve AGI through LLM scaling, there is a symmetry between the
American and Chinese perspectives. Another perspective is the idea of
Embodied AI (EAI), a viewpoint which has been mentioned in recent
high-level national documents, such as the Fifteenth Five-Year Plan.
Chinese proponents of the EAI perspective tend to believe that scaling
LLMs is not the path to AGI.4
Instead, EAI advocates see integrating model development with physical
applications, such as robotics and self-driving cars that have
self-improving capacities from interactions with the tangible world, as
the best path toward AGI.5
But in practice, as well as in policy, perhaps the most influential
Chinese perspective is the one that treats AI as a general purpose
technology, much like electricity. This view is best articulated in
Taiwanese scientist and entrepreneur Kai-Fu Lee’s popular 2018 book AI Superpowers,
where he describes AI in these terms: “Often, once a fundamental
breakthrough has been achieved, the center of gravity quickly shifts
from a handful of elite researchers to an army of tinkerers—engineers with just enough expertise to apply the technology to different problems.”6
In this case, the power of AI will accrue not to the most sophisticated
model developers but instead to those who find novel applications for
the technology. This is a view that explicitly rejects imminent AGI. Lee
himself describes it as decades or centuries away, if it is feasible at
all.7
It should not be assumed that the Chinese government agrees with this
assessment, and there are new reasons to believe that party officials
are taking the prospect of superintelligence seriously.8....
Talk about your "big if true"/"big if, true". The numbers are just so mindbending.
From SemiAnalysis, August 7:
SpaceX 10GW in 2027 – Why It’s Real, Will Drive $300B ARR for SpaceX, and Why Microsoft Will Be the Largest Offtaker Inference at 100B/GW/year, SpaceX's stellar pace, Microsoft's 10GW 2026 Awakening, Azure Can Grow Triple-Digits
Elon Musk shocked the world, once again, when he announced on
SpaceX’s first earnings his Gigawatt ambitions for next year. He
“conservatively” aims to build & deliver an incremental 6-8GW in
2027 alone, with potential for that number to be well above +10GW. At
50B per GW, that’s $300-500B in capex in 2027, on par with what we
expect from AWS and Google – an unbelievable number for a company
significantly less profitable than rival hyperscalers.
Yet, we believe that the number is real. We see SpaceX on track to build about 10GW by year-end 2027. We’ve evaluated all sites suitable for SpaceX and provided the list to our Datacenter Model subscribers. Our Energy Model subscribers
also have the precise list of gas generation equipment available,
quarter by quarter, by 30+ turbine, engine, fuel cell suppliers. We
provided much of this data, before the market woke up to it. Below, we
discuss how Elon bypasses typical datacenter construction constraints.
As
explained in our Meta Compute deep dive, large-scale + near-term
compute is a remarkably scarce combination, and it’s priced at a huge
premium – up to $50B/GW/year. However, AI labs can handle it and make a
good living off it.
Our Tokenomics Model and our Inference Simulator demonstrate that at realistic performance levels (e.g. tokens/sec per GPU), both OpenAI and Anthropic can generate over $100B/GW/year of revenue when selling API inference on a GB300 cluster. This is significantly more than the costs of renting a GB300 cluster for a year at current neocloud prices.
Serving inference tokens is unbelievably profitable for the frontier model companies.
We
assume around $12B/GW/year of cost per year, using a conservative
rental pricing rate of $3/GPU-hr, and make a token production estimate
using our Inference Simulator with a frontier-class model architecture and our agentic coding benchmark, AgentX (part of InferenceX),
which is built by collecting real production coding traces. We blend
that token production rate between input, cache-read, cache-write, and
output token costs at our real workload ratios, and produce the final
estimate, exceeding $100B/GW/year.
For background, our Inference Simulator
is built from the ground up with a fundamental understanding of how
modern AI accelerators work. We build a roofline and realistic
performance model for how frontier models work during inference, with
timings for every operation and a real trace output. It is an end-to-end
simulation of the actual workload executing on the actual silicon. We
have validated the simulators fidelity on a wide range of accelerators
and workloads and continue to improve its ability to accurately forecast
performance of future accelerators based on design specifications....
*****
...Please reach out to sales@semianalysis.com for more information on how we apply the Inference Simulator for custom research and analysis.
Beyond OpenAI and Anthropic, there is actually a third company in the world capable of printing such economics per GW: Microsoft. Having full access to OpenAI models, they can generate the exact same revenue and margin per MW, while paying none of the training costs. Satya nailed the negotiations with OpenAI: the deal reworked in April 2026 dropped the old 20% revenue share from the equation. Put
simply, Microsoft has a giant incentive to procure as many MWs as
possible, as fast as possible. While much of their datacenter capacity
currently goes to OpenAI at ~14M/MW/year, they have the opportunity to
improve that mix. The potential impact is Microsoft Azure accelerating
revenue growth from ~42% to over 100% by next year. A
once-in-a-generation opportunity, that SpaceX is incredibly well
positioned to serve.
While Microsoft signing 3GW with SpaceX for 50B/GW/year sounds insane, we view it as possible for two reasons:
1/
Microsoft is already preparing for an epic datacenter ramp. As
discussed below, they’ve signed 10GW of contracts year-to-date, for over
$300B of total contract value (not including the GPU cost). We expect
much more to be signed. Caveat: these contracts contribute to late 2027
and 2028 capacity. There is a near-term gap to fill.
2/
With a 90-day cancellation policy, akin to the SpaceX deals with
Anthropic and Google, there is zero balance sheet risk. This is
remarkably easy for Amy Hood to sign off, given the revenue opportunity.
For SpaceX, the next natural question is financing.
How can Elon afford to pay so much CapEx without the balance sheet of
the leading hyperscalers? We expect a combination of the two following
items:
1/ Support from Nvidia, in the form of vendor
financing to lower the upfront cash cost. This is likely why Elon
declared to be Nvidia exclusive on the earnings call! As our Accelerator
Model has repeatedly explained, xAI/SpaceX have actively evaluated
alternatives like TPU and AMD – so the financial argument likely made
them abandon these and focus on Nvidia.
2/
Operating cash-flow financing led by industry-high pricing, enabled by
fastest timelines: SpaceX will continue to sell large-scale compute with
3-5 months lead time, an unbeatable offering, and price it accordingly
at 30-50M/MW/year. That pays back the capex in less than a year. We
dived into this in our Meta Compute article.
The
implications of this are a path to $300B of ARR by the end of 2027 for
SpaceX. This assumes only 50% of their 2027 incremental compute is
monetized, the reminder being for the Grok & Cursor teams for
training (no inference revenue modelled)....
We've chronicled much of the Elon - Jensen frenemy relationship in real-time for over a decade. On August 4 Musk said SPCX would use NVDA's platforms exclusively.
Before we get to the headline story, some background. Tesla and Nvidia have a history.
In
2015 - 2016 when everyone thought that autonomous driving was just
around the corner, the challenge was seen as both a sensor issue, for
example: LIDAR vs cameras, and a machine learning/artificial
intelligence problem which boils down to training the AI 'puters with as
much data as you can so that out in the real world the autonomous
vehicle can say to itself: "Yeah, I've seen this situation before,
here's the response that worked best. Both the training and the
on-the-road-recall, if they are to be anywhere near efficient, require
the fastest chips you can find. Tesla had a whole bunch of data from a
few billion miles of actual driving for computers to train on, and,
combined with Nvidia's fastest-in-the-world GPU chips, it was a match
made in heaven.
Except it wasn't.
The challenge of
autonomous driving on open roads alongside non-autonomous vehicles was
bigger than anyone in that simple, optimistic time ever envisioned, even
in their nightmares. Here's one example about Waymo from a 2017 post:
"When Google was training its self-driving car on the streets of Mountain View, California, the car rounded a corner and encountered a woman in a wheelchair, waving a broom, chasing a duck. The car hadn’t encountered this before so it stopped and waited."
"Tesla says it’s dumping Nvidia chips for a homebrew alternative" (TSLA) The
only reason for Tesla to do this is that NVIDIA's chips are general
purpose whereas specialized chips are making inroads in stuff like
crypto mining (ASICs), Google's Tensor Processing Units (TPUs) for
machine learning and Facebook's hardware efforts.
And now on to the headliner, from Observer, June 8:
Elon Musk Predicts Nvidia’s Monopoly in A.I. Chips Won’t Last....
And possibly related July 13: Elon Musk's x.AI Launches The company was formed in March so it's valuation is probably around a hundred billion or so.
Just
kidding. I have no idea what sort of valuation it has been assigned.
x.AI is a Nevada corporation which, as our corporate attorney readers
well know, is handy as hell for a privately-held stealth company. As
part of the company's coming-out I think they dropped the period in the
name on the original incorporation papers.
Mr. Musk was one of
the founder/funders ($100 million gift not equity) of ChatGPT parent
OpenAI when it was a .org (non-profit) and seemed a bit miffed when Sam
Alman hooked up with Microsoft to the tune of $10 billion.
So Elon went out and bought a garage-full of GPUs.
Here's a twofer, first up TechCrunch, July 12:....
As the old-time traders used to say: Pay attention or pay the offer.
From the South China Morning Post, May 8, 2026:
Imagine China applying the same industry policy playbook for electric
cars, solar panels and AI to agriculture and food self-sufficiency
Even
as “China shock 2.0” is roiling Western manufacturers, we must, it
seems, brace for “China shock 3.0” – to the global food economy – as
President Xi Jinping doubles down on the imperative that has obsessed
Beijing for decades: food security.
“China’s
Food Future”, a consultation paper by Systemiq funded by the
California-based Gordon and Betty Moore Foundation, warns that China is
poised to “reshape global agricultural commodity supply chains”.
It suggests China is set to apply to agriculture the industry policy playbook
that has lifted it to manufacturing dominance in sectors ranging from
cars, batteries and rare earths, to solar and wind power, semiconductors
and artificial intelligence.
It sees Beijing’s “ability to
coordinate policy, scale production and mobilise capital at
unprecedented speed” driving development of a wide range of food-related
technologies intended to ensure food security and reduce reliance on
imports. These range from synthetic biology and new protein sources,
genetically engineered farm products, seed development and fermentation-derived ingredients, all built around new domestic innovation clusters.
It expects China to reduce reliance on soy imports, improve farmland, build aquaculture, spawn vertical livestock farms and develop integrated farm-sector infrastructure.
The
paper warns: “Producer countries who are dependent on China as a
destination for soy, beef and dairy need to build alternative market
relationships now.” That means the United States, Brazil and New Zealand in particular, though where they will turn to replace such a large market is a moot point.
China’s
food security imperative has been front and centre of national policy
since 1949. Back then, the battle was against brute poverty. Since 1982,
this core priority has been laid out annually in “No 1 document”,
the first policy document released by the Chinese government each year,
and traditionally focused on agriculture, rural development and
farmers.
According to Systemiq, the food security imperative is
being reasserted – not as part of the geopolitical arm-wrestling we will
see next week at the Xi-Trump summit
in Beijing, but in response to flaring anxiety over food security and a
sense of acute vulnerability despite striking success in lifting
hundreds of millions of rural Chinese out of poverty.
The sense of
precarity has its roots in demographics: 15 per cent of the world’s
population needing to feed itself on 8 per cent of the world’s arable
land. It has been made worse by hectic urbanisation, acute water
shortages, pollution and climate change. The Covid-19 disruption of
global supply chains and Trump tariff wars have aggravated the sense of
insecurity and boosted the imperative to “de-risk”.
As China’s
leaders clawed away from grinding poverty, they have also facilitated
what may be the biggest dietary transformation in human history. The
country’s 1.4 billion people have converted from diets dominated by
starchy roots and pulses to animal protein, processed foods and convenience products.
In
the process, China has become the world’s largest food producer by far –
7.5 billion tonnes in 2022, compared with second-placed India with
around 4.5 billion tonnes. But it also lost contact with self-reliance
and became the world’s largest food importer. Despite a record trade
surplus, China’s import deficit in agricultural commodities rose to
US$124.5 billion in 2024.
Up
to 2002, China remained largely self-sufficient in food, but since
2003, as diets have become more varied and protein-rich, it has become
increasingly import-reliant. Today, China is the world’s largest
consumer of meat and fish, accounting for more than 25 per cent of
demand. And it relies on imports to meet about 30 per cent of its food
needs, heading towards 40 per cent by 2030.
While it remains
largely self-sufficient in grains, it imports over 84 per cent of the
soybeans it needs, 67 per cent of its oils and nuts, and almost 25 per
cent of its beef. Despite having the world’s largest food stockpiles,
China is paranoid about its burgeoning trade deficit in foods, and the
fertilisers and pesticides needed to sustain yields....
