Tuesday, December 9, 2025

"SpaceX reportedly planning 2026 IPO with $1.5T valuation target"

Lifted in toto from TechCrunch, December 9:

SpaceX is planning to go public in mid-to-late 2026 and is looking to raise $30 billion at a valuation of around $1.5 trillion, according to a new report from Bloomberg News citing multiple unidentified sources.

That would make it the largest IPO of all time, edging out Saudi Aramco’s public listing in 2019, which brought in $29 billion. It would also be a bit of a reversal for SpaceX, which previously considered spinning off its Starlink division for an IPO, while keeping the main company private.

Bloomberg’s report comes just a few days after The Information was first to report that Elon Musk’s space company was targeting a late 2026 IPO.

The Wall Street Journal also recently broke the news that SpaceX was engaged in another secondary share sale for employees that would peg the company’s current valuation at around $800 million. Bloomberg’s report says SpaceX has “firmed up” that share sale in recent days and the valuation is above the $800 million figure, with employees being allowed to sell around $2 billion worth of shares at $420 per share.

This story has been corrected to have $1.5T in the headline, not $1.5B.

TechCrunch home

"Trump’s Reprieve for Nvidia’s H200 Spurred by Huawei’s AI Gains"

From Bloomberg, December 9:

President Donald Trump decided to let Nvidia Corp. sell its H200 artificial intelligence chips to China after concluding the move carried a lower security risk because the company’s Chinese archrival, Huawei Technologies Co., already offers AI systems with comparable performance, according to a person familiar with the deliberations.

Administration officials who weighed whether to clear Nvidia’s H200 had considered multiple possible scenarios, factoring in the views of national security hawks in Washington, said the person. Options ranged from exporting zero AI chips to China to allowing exports of everything to flood the Chinese market and overwhelm Huawei. Ultimately the policy backed by Trump called for clearing H200s to China while holding back the latest Nvidia chips for American customers, the person said.

The move would give the US an 18-month advantage over China in terms of what AI chips customers in each market receive, with American buyers retaining exclusive access to the latest products, the person said. White House officials concluded that pushing the H200 into China would prod Chinese AI developers into building on the US tech ecosystem rather than turning to offerings from Huawei or other local chipmakers.

Trump’s decision capped weeks of deliberations with advisers about whether to allow H200 exports to China and came days after a private meeting in Washington with Nvidia Chief Executive Officer Jensen Huang, who has pressed for relief from US export controls. In his Truth social post announcing the move, Trump said that shipments would only go to “approved customers,” and that chipmakers such as Intel Corp. and Advanced Micro Devices Inc. would also qualify.

Underpinning the move was an assessment that Huawei can compete far more closely with Nvidia than the US has acknowledged. White House officials focused on a Huawei AI platform known as CloudMatrix 384 that relies on the company’s newer Ascend chips, the person said. Officials found that CloudMatrix 384 performed as well as a similar Nvidia system known as NVL72 that uses the US company’s most-advanced Blackwell-design chips, according to the person, who spoke on condition of anonymity to discuss sensitive matters.

Adding a sense of urgency, the person said, was a conclusion by US officials that Huawei would be capable in 2026 of producing a few million of its Ascend 910C accelerators, a chip designed specifically to compete with Nvidia’s product line. That compares with a US estimate, given in June, that the Shenzhen-based company would be able to make just 200,000 of the Ascend line this year....

....MUCH MORE 

How Deep Do You Want To Dive Into Scarcity, Abundance And Affordability?

From Michael Green, Chief Strategist and portfolio manager at Simplify Asset Management at his personal substack, Yes I give a Fig, November 23, skipping past the introductory (worthwhile and interesting) comments on markets: 

Part 1: My Life Is a Lie
How a Broken Benchmark Quietly Broke America 

....And so now, let’s tug on that loose thread… I’m sure many of my left-leaning readers will say, “This is obvious, we have been talking about it for YEARS!” Yes, many of you have; but you were using language of emotion (“Pay a living wage!”) rather than showing the math. My bad for not paying closer attention; your bad for not showing your work or coming up with workable solutions. Let’s rectify it rather than cast blame.


How a Broken Benchmark Quietly Broke America

I have spent my career distrusting the obvious.

Markets, liquidity, factor models—none of these ever felt self-evident to me. Markets are mechanisms of price clearing. Mechanisms have parameters. Parameters distort outcomes. This is the lens through which I learned to see everything: find the parameter, find the distortion, find the opportunity.

But there was one number I had somehow never interrogated. One number that I simply accepted, the way a child accepts gravity.

The poverty line.

I don’t know why. It seemed apolitical, an actuarial fact calculated by serious people in government offices. A line someone else drew decades ago that we use to define who is “poor,” who is “middle class,” and who deserves help. It was infrastructure—invisible, unquestioned, foundational.

This week, while trying to understand why the American middle class feels poorer each year despite healthy GDP growth and low unemployment, I came across a sentence buried in a research paper:

“The U.S. poverty line is calculated as three times the cost of a minimum food diet in 1963, adjusted for inflation.”

I read it again. Three times the minimum food budget.

I felt sick.

The Measurement Failure

The formula was developed by Mollie Orshansky, an economist at the Social Security Administration. In 1963, she observed that families spent roughly one-third of their income on groceries. Since pricing data was hard to come by for many items, e.g. housing, if you could calculate a minimum adequate food budget at the grocery store, you could multiply by three and establish a poverty line.

Orshansky was careful about what she was measuring. In her January 1965 article, she presented the poverty thresholds as a measure of income inadequacy, not income adequacy—”if it is not possible to state unequivocally ‘how much is enough,’ it should be possible to assert with confidence how much, on average, is too little.”

She was drawing a floor. A line below which families were clearly in crisis.

For 1963, that floor made sense. Housing was relatively cheap. A family could rent a decent apartment or buy a home on a single income, as we’ve discussed. Healthcare was provided by employers and cost relatively little (Blue Cross coverage averaged $10/month). Childcare didn’t really exist as a market—mothers stayed home, family helped, or neighbors (who likely had someone home) watched each other’s kids. Cars were affordable, if prone to breakdowns. With few luxury frills, the neighborhood kids in vo-tech could fix most problems when they did. College tuition could be covered with a summer job. Retirement meant a pension income, not a pile of 401(k) assets you had to fund yourself.

Orshansky’s food-times-three formula was crude, but as a crisis threshold—a measure of “too little”—it roughly corresponded to reality. A family spending one-third of its income on food would spend the other two-thirds on everything else, and those proportions more or less worked. Below that line, you were in genuine crisis. Above it, you had a fighting chance.

But everything changed between 1963 and 2024.

Housing costs exploded. Healthcare became the largest household expense for many families. Employer coverage shrank while deductibles grew. Childcare became a market, and that market became ruinously expensive. College went from affordable to crippling. Transportation costs rose as cities sprawled and public transit withered under government neglect.

The labor model shifted. A second income became mandatory to maintain the standard of living that one income formerly provided. But a second income meant childcare became mandatory, which meant two cars became mandatory. Or maybe you’d simply be “asking for a lot generationally speaking” because living near your parents helps to defray those childcare costs.

The composition of household spending transformed completely. In 2024, food-at-home is no longer 33% of household spending. For most families, it’s 5 to 7 percent.

Housing now consumes 35 to 45 percent. Healthcare takes 15 to 25 percent. Childcare, for families with young children, can eat 20 to 40 percent.

If you keep Orshansky’s logic—if you maintain her principle that poverty could be defined by the inverse of food’s budget share—but update the food share to reflect today’s reality, the multiplier is no longer three.

It becomes sixteen.

Which means if you measured income inadequacy today the way Orshansky measured it in 1963, the threshold for a family of four wouldn’t be $31,200.

It would be somewhere between $130,000 and $150,000.

And remember: Orshansky was only trying to define “too little.” She was identifying crisis, not sufficiency. If the crisis threshold—the floor below which families cannot function—is honestly updated to current spending patterns, it lands at $140,000.

What does that tell you about the $31,200 line we still use?

It tells you we are measuring starvation....

....MUCH MORE, why housing costs are so important.

November 30 - Part 2: The Door Has Opened 

December 7 - Part 3: The Pursuit of Happiness 

You may also know Mr. Green as ProfessorPlum99 on X 

We too have been picking at this thread. Here are a few posts from 2020:
Haves and Have Nots: The Real Real Estate State and Artificial Scarcity, Technology and Planning
In September 2013's "Ben Franklin on Labor Economics (or how to create an underclass)" I intro'd with:

The easiest way to create a dependent class is to price them out of the real estate markets.
"In countries fully settled…those who cannot get land must labor for others that have it; when laborers are plenty, their wages will be low; by low wages a family is supported with difficulty; this difficulty deters many from marriage, who therefore long continue servants and single...."
"Huge Human Inequality Study Hints Revolution is in Store for U.S."
As we, and before us (way before us) Ben Franklin* have pointed out, the surest way to create a permanent underclass is to keep a population from getting on even the first rung of the wealth accumulation ladder.

In most cases this means real estate, access to which is limited by zoning laws and construction regulations constricting supply. Politicians working for their political masters/funders.

Another way to keep the populace from accumulating wealth is to keep the cost of daily expenses, food, rent, transportation, equal to or a bit above income so there is no accumulation of capital and preferably a slide into debt.

A third way to make the rich richer and the poor poorer is to pump enough money into the system to inflate asset prices, benefiting those who already own the assets and combined with the other factors, keep folks in a hand-to-mouth existence.

There's more but that's just what comes to mind without thinking too hard....

"The Asset Economy" (or how to create an underclass) 

June 2022 - "Redefining the Working Class Beyond white men in hard hats":

There seems to be something akin to an actual plan to charge the plebs everything they earn to cover food, shelter, and basic necessities and further, to drive them into debt servitude to the tune of 5% - 10% of annual income per year...

November 2022 - Corrected—"Goldman, TS Lombard Confirm Fed Inflation Target Hike Now Inevitable"

We are living in a fantasy land, which itself is dangerous should reality intrude.

But what is even more dangerous is when you agree to live in someone else's fantasy land.

From the social to the scientific to the economic, playing along to get along, joining the Let's Pretend fashion of the day, can not only get you killed but can riun whatever little time you have on earth....

August 2023 - Unless You Deeply Understand How Inflation Hits Different Groups, You Don't Understand Inflation

When I say 'deeply' I mean having the empathy and imagination to actually 'feel' the emotions that result from having to comparison-shop food prices and then food prices vs other necessities.
It all comes down to financial assets and whether a person benefits from rising asset prices.

September 2023 -That Threat Out On The Horizon You Thought Might Be A Black Swan....

 Is probably the even-more-dangerous Gray Rhino....

August 2024 - "The Haves and Have-Nots at the Center of America’s Inflation Fight"

Mssr. Cantillon nods in recognition....

August 2024 -  "Did wages rise 10-fold to match the 10-fold rise in the cost of a modest house? No"

September 2025 - "How the Government Built the American Dream House"... 

I am deadly serious when I say "The best way to build a permanent underclass is to prevent people from getting on the first rung of home ownership." This result is apparent to anyone who chooses to look.*

December 2025 - "Mortgage Rates Are Not Too High. What’s too High Are Home Prices that Exploded by 40-70% in 2 Years, Creating the 'Affordability Crisis'

And dozens more between those few. If interested use the 'search blog' box, upper left. 

Also:

"Evidence – And an Explanation – For the Recent Surge in Inflation Inequality"

The Purpose Of A System Is What It Does, Not What It Claims To Do 

"Putin Wanted AI Supremacy. Now Russia Is Struggling to Stay in the Race."

From the Wall Street Journal, December 7:

Moscow finds itself even more dependent on China as war and sanctions curb artificial-intelligence efforts 

President Vladimir Putin has often proclaimed that Russia must lead the world in artificial intelligence. In reality, the country is stuck on the sidelines as others pull ahead.

As the U.S. and China race to dominate AI models and applications and countries in Europe and the Middle East pour resources into building computing infrastructure, the Ukraine war has derailed Russia’s once lofty ambitions.

On the Russian-language version of LM Arena where users rate AI models, the top-performing Russian model ranks 25th, trailing behind even older iterations of ChatGPT and Google’s Gemini. According to Stanford University’s Global AI Vibrancy Tool, which was released in November and measures the strength of countries’ AI ecosystems, Russia ranks 28th out of 36 countries.

Western sanctions choked off Russia’s access to critical hardware such as computer chips and hamstrung its domestic production abilities. Russian companies now depend on middlemen in third countries to secure everything from high-end chips to even a simple ChatGPT subscription. Moscow has also leaned heavily on China—further deepening what analysts already describe as an economic vassalage to its neighbor.

Compounding the problem is a brain drain, with top talent fleeing Russia after the invasion of Ukraine. Cut off from international markets, Russian AI companies attracted about $30 million in venture funding last year. OpenAI alone raised more than $6 billion last year.

“Russia is years behind in developing its own AI,” said Yury Podorozhnyy, a former Russian tech executive.

Podorozhnyy has witnessed the arc of AI development in Russia firsthand, having spent years developing the local equivalents of Google Maps and Netflix, including working on machine-learning tools now central to the AI boom. Shortly after the outbreak of the war in 2022, he boarded a plane and escaped Russia with his pregnant wife.

“Russia has already lost in the competition and it’s impossible to catch up,” said Podorozhnyy, who now lives in London and is chief AI officer at fintech startup Finom.

A Moscow-based AI executive agreed with that assessment and said that Russia’s economic and geopolitical isolation prevents companies from accessing funding and gaining the ability to scale beyond their comparatively small domestic market.

With AI’s potential to reshape the global economy, countries are scrambling to assert control over their AI infrastructure, data and models to avoid strategic dependence. In the military domain, too, readiness increasingly depends on sovereign AI capabilities, from battlefield decision-support tools to autonomous defense systems.

For Moscow, this imperative is especially acute given its escalating standoff with the West. 

“We cannot allow critical dependence on foreign systems,” Putin said at an AI conference last month. “For Russia, this is a matter of state, technological and value sovereignty.”

Russian officials have acknowledged the shortcomings, but say that domestic models rival foreign ones and are improving fast. Others are more blunt.

“The vast majority of our industries are millions of light years away from AI,” Herman Gref, the chief executive of state-owned lender Sberbank, which is leading Russia’s AI efforts, said earlier this year.

It isn’t just Russia’s AI models that are falling behind.

At a Moscow tech conference in November, the country’s first AI-equipped humanoid robot—named AIDOL—hobbled onstage to the “Rocky” theme, attempted a wave and promptly toppled over. Organizers cut the demonstration short and removed the machine. The organizers said the robot will learn from the “consequences of its own actions.”

Even before the invasion, Russia was relying largely on foreign technology to design chips and had limited chip-production capabilities of its own. Some of the leading Russian-designed chips were assembled by Taiwan Semiconductor Manufacturing Co.

In 2022, the U.S. imposed a ban on selling high-tech products including semiconductors to Russia, with the ban extending to certain foreign items produced with U.S. equipment, software or blueprints. South Korea and Taiwan, which dominate in high-end chips, and Japan, strong in chip-making materials and tools, also promptly banned exports of such items. TSMC halted the export of semiconductors to Russia.

Russia was suddenly unable to directly purchase high-performance graphics processing units, or GPUs, essential for training AI models, including the latest Nvidia chips. A Wall Street Journal analysis of United Nations trade data shows that Russia’s imports last year of GPUs and other computer chips essential for AI development have fallen 84% from before the war....

....MUCH MORE 

Previously:

September 2017 - "Putin: Leader in artificial intelligence will rule world"

January 2018 - Military AI: China, Russia and the U.S. are Running Neck-and-Neck in an Arms Race

January 2018 - "Russia and China could use AI to TAKE OVER world warns former Google CEO"   

June 2018 - "The Top-10 Russian Artificial Intelligence Startups" 

June 2019 - "Russia Tries to Get Smart about Artificial Intelligence"

February 2023 - Attention Despots and Tyrants, Generative AI Could Be The Authoritarian Breakthrough in Brainwashing You've Been Waiting For 
Have you ever found yourself sitting at home in the Palace thinking: "If only there was an easier way to get people to do my bidding?" Well now there is, take your nudge game up a notch with the Climateer "They'll think it's free will" starter pack.

"‘Baseload Power’ Will Be Big in 2026. 5 Stocks Can Win." (JP Morgan gets on board the love train)

From Barron's, December 8:

The buildout of artificial-intelligence data centers has been a boon for utilities, makers of power-generation equipment, and hosts of industrial companies supplying electrical components.

That isn’t likely to change in 2026. Investors, however, might need to refine their portfolios a little to keep getting the jolt from artificial intelligence.

“Heading into 2026, we expect baseload power sources to remain top of mind for investors, though we expect the thematic trade to become more nuanced by individual stock fundamentals and valuation, rather than simply by exposure,” wrote J.P. Morgan analyst Mark Strouse on Monday. 

His top picks are renewable-power generator Brookfield Renewable Partners, solar technology company Nextpower NXT , and GE Vernova GEV , a provider of power-generation technology.

He expects Brookfield to continue to buy renewable projects. Orders are picking up at GE Vernova, and Nextpower “is well positioned to gain market share within the global utility-scale market,” said Strouse. “We also believe that Nextpower’s recently issued long-term financial targets, based on third-party market forecasts, should prove overly conservative.”

In November, Nextpower laid out plans to increase earnings before interest, taxes, depreciation, and amortization, or Ebitda, to about $1.2 billion, from about $790 million expected for fiscal 2026.

Along with those three, Strouse upgraded shares of electrical infrastructure provider Quanta Services PWR and backup power provider Generac to Buy from Hold.

His Quanta price target went to $515 from $457 a share. “We expect Quanta Services to benefit over time from large projects that are only partially included in current backlog, providing visibility into above-trend backlog conversion as well as future backlog additions as projects progress,” the analyst said....

....MORE 

Also at Barron's, December 9, a follow-up to their Dec 3 article on today's GEV Investor Day:

GE Vernova Investor Day: The Stakes for the Stock Are Huge

"Greenpeace Asks a Dutch Court to Reverse an American Verdict"

Don't mess with the Dutch, they'll enslave you to harvest their spices.* 

From The Wall Street Journal, December 5: 

The group wants to use European law as a shield to disrupt American infrastructure projects. 

A North Dakota jury ordered Greenpeace in March to pay pipeline company Energy Transfer $667 million for the environmental group’s rogue campaign to stop the Dakota Access Pipeline. Now, Greenpeace is trying to get a Dutch court to nullify the jury award, which the trial judge reduced to $345 million in October. Energy Transfer is asking the North Dakota Supreme Court to block the activist group’s attempt to end-run the U.S. legal system. If Greenpeace’s efforts succeed, they would harm much more than the pipeline company. They’d open the door for activists to torpedo other American critical infrastructure projects under European law.

The Dakota Access Pipeline saga started a decade ago when activists descended on North Dakota in hope of halting the project. During the monthslong standoff, reports spread of protesters shackling themselves to equipment, blow-torching parts of the pipeline, and hurling feces and burning logs at workers.

The chaos delayed the project, costing the parent company and partner entities an estimated $7.5 billion or more. The federal government was ordered to pay North Dakota $28 million in damages. Kelcy Warren, then Energy Transfer’s CEO, didn’t take those losses sitting down. “What they did to us is wrong,” he said in 2017 of the environmental groups behind the demonstrations, “and they’re going to pay for it.”

Energy Transfer identified two U.S.-based Greenpeace entities and the umbrella group Greenpeace International as the ringleaders responsible for the pipeline fiasco. During a three-week trial in March, the pipeline company presented evidence that Greenpeace personnel funded and trained protestors and even equipped them with lockboxes to chain themselves to pipeline equipment. It also said that Greenpeace attempted to deprive the project of funding by falsely claiming the pipeline would encroach on tribal land. Greenpeace tried to distance itself from the violent conflict surrounding the pipeline. But the group couldn’t take back a 2016 email from Greenpeace USA’s executive director stating the “massive” support it provided to the protests. The jury returned a nine-figure verdict, including $400 million in punitive damages.

Greenpeace had a Plan B, however. On the eve of the trial, Greenpeace International filed a new lawsuit with the District Court of Amsterdam, where the group is based. The suit claims that Energy Transfer’s litigation violated Greenpeace International’s rights under the European Union’s 2024 anti-Slapp law, an anagram for strategic litigation against public participation. The law seeks to protect journalists and nonprofit organizations from meritless lawsuits designed to silence or intimidate them.

Greenpeace’s case isn’t an ordinary appeal, in which a party asks a higher court to review a lower court’s application of the law. Rather, Greenpeace is asking a Dutch court to reassess the merits of the North Dakota case under Europe’s sweeping anti-Slapp directive. The case marks the first attempt to apply the law “extraterritorially” to stymie a lawsuit brought in a country outside the European Union.

If the European directive achieves this reach, it would extend the EU’s regulatory imperialism to the political and social spheres where Europe and America follow starkly different legal norms: In a nutshell, Europe’s speech rules are based on values, while America’s are based on rights.

The European law used by Greenpeace illustrates this contrast. While 38 U.S. states and the District of Columbia have anti-Slapp laws, these statutes are more targeted than those in Europe. They safeguard legally protected speech and lay out the process for dismissing suits targeting public discourse.

The EU law goes beyond protecting free-speech rights. It gives European courts significant leeway to relitigate American cases when the result doesn’t conform to their values. Under the EU directive, courts can award damages to parties that have been subjected to “abusive court proceedings,” including those involving “an imbalance of power between the parties” or “excessive” claims.

Greenpeace claims in the Dutch lawsuit that the financial resources of Energy Transfer constitute an “obvious” imbalance of power and that the company’s demands for hundreds of millions of dollars in damages are “clearly excessive.” But the rule of law is based on whether the parties acted within their legal rights, not on whether they happen to run a successful business like Energy Transfer that is seriously affected by a shutdown in operations. If Greenpeace succeeds, expect other activist organizations to incorporate in Europe so they can wiggle out of liability by invoking the EU’s loosely drawn “abusive court proceeding” standard against U.S. companies....

....MORE
*I've mentioned:

....I am about as ignorant of Dutch electoral politics as one can be. Besides knowing the Dutch histories of imperialism, financial innovation, art and reinsurance ("...Not to mention the herverzekering crowd in Amsterdam, they're tough bastards.") I am dumb as a bag of rocks about The Netherlands....
But I do know one other thing: "That time the Dutch ate their prime minister". 

Here's some financial reporting Dutch Masters style:

(VOC) $64.98 +$13.84 (+27.1%) Shares in the spice purveyor soared on word that the three sturdy galleons dispatched two years afore had been sighted off the coast of Cape Verde, returning from their dangerous voyage to the exotic Orient with their casks brimful of redolent cinnamon, cardamom, and mysteriously intoxicating curried powder.

Oops, wrong century.

Okay, that's actually America's Finest News Source.
note: link to The Onion rotted, apparently un-Googleable as well. I was dreaming when I wrote this, forgive me if it goes astray.
Not sure why the quote was in dollars.

I mentioned, in November 2019's "How Technology is Changing the Spice Trade":

I bet those fat Dutch burghers didn't care about the Banda Islanders.  
 And I guess we didn't either. There's only one reference on the blog, and that's in a post on the wealth extracted from the silver mines at Potosi Bolivia:...

Well, we came back with "Spices/Shipping: The (Hidden) History of The Nutmeg Island That Was Traded for Manhattan" and it is a nasty story of slavery and depopulation bordering on genocide.... 

 "Psychopaths in the Netherlands are different from psychopaths in the US" 

Yes.
They say verzekering and herverzekering rather than insurance and reinsurance.

And a Nobel Laureate before he got the tchotchke:

 "Mokyr: 'How Europe became so rich":
Because Dutch is the language of love?
No?
Then I give up. How did Europe become so rich?
 

On the other hand, back in 2013 we noted:

Oh Yeah, The Rijksmuseum Reopened: The Nightwatch Flash Mob 

...It took ten years of screw-ups but the museum finally reopened and although most folks in Amsterdam seem to hate the building they like what's inside and most any Dutch kid knows Rembrandt's The Nightwatch: 
https://wi-images.condecdn.net/image/8DA6G0y9WWg/crop/810/f/01-20-ftnightwatch11.jpg

So the flashmob went off to greater effect than it might have done, say, in Chicago where they have a very different kind of flash mob.
Here's the version uploaded by ING: 

Monday, December 8, 2025

Dec. 9 - "GE Vernova Investor Day Is Coming. What It Means for the Stock" (GEV)

I'm guessing the company won't be releasing any information that even comes close to sounding negative.

From Barron's, December 3:

GE Vernova has been a surprise AI winner, providing equipment that will power data centers now and in the future. Investors will get a look at how bright that future looks when management meets with investors next week.

Expectations are running high. Those expectations might have driven some profit-taking early in the day, before the big event, slated for Dec. 9.

Shares of the power-generation and grid-technology company traded as low as $575.38 before rallying to close at $601.97, up 0.1%. The S&P 500 and Dow Jones Industrial Average rose 0.3% and 0.9%, respectively.

The lows of the day at left GE Vernova shares down over the past month, but still up about 75% for the year. GE Vernova split off from GE Aerospace in early 2024, with shares starting out at roughly $140 apiece.

Things simply got better faster than anyone imagined. Wall Street currently projects 2028 earnings before interest, taxes, depreciation, and amortization, or Ebitda, of $9.4 billion, according to FactSet. At the time of the spinoff, that estimate was closer to $4.6 billion.

There is only one problem with the 2028 projections. They are far ahead of company guidance. At GE Vernova’s prior analyst event in December 2024, the company outlined plans to generate roughly $6.3 billion in 2028 Ebitda. Demand for power and power equipment has continued to improve, but the gap represents a risk for the stock heading into the Dec. 9 investor meeting.

Investors are expecting a guidance bump. JPMorgan analyst Mark Strouse surveyed clients and found them “overwhelmingly bullish on [GE Vernova] stock over the next 12 months.”

He isn’t worried, though. The update to 2028 targets will be important, but it’s increasingly “old news…GE Vernova is set up to see many years of growth and margin expansion beyond 2028,” wrote Strouse in a Wednesday report. “And that incremental commentary on the 2030s could have the potential to be a more important topic.”...

....MORE 

The stock is down today, off  $14.10 (2.23%) at $617.22.

"OpenAI’s Financial Crisis: A $1.4 Trillion Gamble"

As Fortune Magazine put it, November 12: 

OpenAI says it plans to report stunning annual losses through 2028—and then turn wildly profitable just two years later"

OpenAI is plotting a dramatic arc toward profitability through the end of the decade, but that growing won’t come without some pain. The company reportedly expects to rack up massive annual losses each year, including roughly $74 billion in operating losses in 2028 alone, then pivot to meaningful profits by 2030, according to financial documents obtained by The Wall Street Journal.​

The documents, which were shared with investors this summer, reveal an aggressive growth strategy that hinges on massive upfront investment in computing infrastructure, chips and data centers—spending that CEO Sam Altman has described as necessary to meet what he sees as insatiable demand for AI capabilities. The company anticipates total spending of roughly $22 billion this year against $13 billion in sales, resulting in a net loss of $9 billion—meaning OpenAI spends approximately $1.69 for every dollar of revenue it generates.​

But the financial trajectory only gets steeper before it improves. The documents show OpenAI projects that by 2028, its operating losses will balloon to roughly three-quarters of that year’s revenue, driven primarily by ballooning spending on computing costs. That’s the same year competitor Anthropic expects to break even, according to WSJ.​

The numbers underscore the stark divergence between the two most valuable AI startups. While both companies currently burn cash at similar rates relative to revenue, their paths forward split dramatically. Anthropic forecasts dropping its cash burn to roughly one-third of revenue in 2026 and down to 9% by 2027. OpenAI, by contrast, expects its burn rate to remain at 57% in 2026 and 2027.​

OpenAI’s plan relies on what amounts to a bet on dominance. The company recently announced it has signed up to $1.4 trillion in commitments over the next eight years for computing deals with cloud and chip giants. It’s spending almost $100 billion on backup data-center capacity alone to cover unforeseen demand from future products and research.​

“Demand for AI exceeds available compute supply today,” an OpenAI spokesman told WSJ. “Every dollar we invest in AI infrastructure goes to serving the hundreds of millions of consumers, businesses, and developers who rely on ChatGPT to get more done.”​....

....MUCH MORE 

 And the headliner, from Techstrong.ai, December 1:

OpenAI, the company that ignited the artificial intelligence (AI) boom with ChatGPT in 2022, faces a financial predicament that dwarves even the harshest level of criticism against it.

The AI pioneer needs to secure at least $207 billion in additional funding by 2030 simply to continue operating at a loss, according to recent analysis from HSBC’s software and services team.

The numbers paint a stark picture of unsustainable growth. OpenAI recently committed to a $250 billion rental agreement with Microsoft Corp. and a $38 billion contract with Amazon.com Inc. for data center capacity. HSBC projects these commitments will translate to annual rental costs of $620 billion, potentially ballooning to $1.4 trillion by 2033 — exceeding Saudi Arabia’s GDP.

The spending spree stands in sharp contrast to the company’s revenue trajectory. CEO Sam Altman said OpenAI’s annualized revenue is approaching $20 billion in 2025 – less than 10% of the $288 billion total cost of its Microsoft and Amazon data center agreements alone. The compute costs projected for 2033 would be roughly 70 times current revenues, before accounting for staffing, research and development, energy, water, or property expenses.

The business model presents a fundamental challenge: Only about 5% of ChatGPT’s user base currently pays for subscriptions, yet these paying subscribers account for approximately 70% of annual recurring revenue. HSBC estimates OpenAI would need to reach three billion users by 2030 and convert 10% to paid subscriptions to approach sustainability — ambitious targets given that user growth appears to be plateauing around 800 million weekly active users.

Competition adds another layer of pressure. Google’s recent Gemini 3 launch has intensified the battle for market share, with HSBC predicting OpenAI’s consumer dominance will decline substantially by decade’s end. Meanwhile, dozens of competing platforms are vying for the same limited data center capacity, potentially driving costs even higher.

The company’s partners are shouldering enormous debt to support OpenAI’s expansion. An analysis by the Financial Times reveals suppliers of data centers, chips, and computing power to OpenAI have accumulated approximately $96 billion in debt. This includes $30 billion borrowed by SoftBank Group, Oracle Corp., and CoreWeave, and $28 billion in loans taken by Blue Owl Capital and Crusoe, with another $38 billion in potential financing under discussion.

CoreWeave’s situation exemplifies the precarious nature of OpenAI’s debt-fueled growth. The company reported $14 billion in current and non-current debt, plus $39.1 billion in future lease agreements, while projecting just $5 billion in revenue for this year. The five major hyperscalers — Amazon, Google, Meta Platforms Inc., Microsoft, and Oracle — have collectively taken on $121 billion in new debt this year to fund AI operations, more than quadruple their average debt issuance over the previous five years....

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"JPMorgan CEO Jamie Dimon says Europe has a ‘real problem’"

From Bloomberg via MSN, December 7:

JPMorgan Chase & Co. Chief Executive Officer Jamie Dimon called out slow bureaucracy in Europe in a warning that a “weak” continent poses a major economic risk to the US.

“Europe has a real problem,” Dimon said Saturday at the Reagan National Defense Forum. “They do some wonderful things on their safety nets. But they’ve driven business out, they’ve driven investment out, they’ve driven innovation out. It’s kind of coming back.” 

While he praised some European leaders who he said were aware of the issues, he cautioned politics is “really hard.” 

Dimon, leader of the biggest US bank, has long said that the risk of a fragmented Europe is among the major challenges facing the world. In his letter to shareholders released earlier this year, he said that Europe has “some serious issues to fix.”

On Saturday, he praised the creation of the euro and Europe’s push for peace. But he warned that a reduction in military efforts and challenges trying to reach agreement within the European Union are threatening the continent.

“If they fragment, then you can say that America first will not be around anymore,” Dimon said. “It will hurt us more than anybody else because they are a major ally in every single way, including common values, which are really important.”

He said the US should help....

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There are early indications that the EU could become a Chinese satrapy.

And then? It appears that one of Beijing's options is to let Europe die on the vine and wither away as a business center, with Shenzhen, Shanghai and even Hainan island assuming some of the various roles that Europe has played over the years.

And if Europe is no longer independent of China it faces the possibility of becoming a colonial backwater but one that is so overbuilt it ends up as an urban hellscape. 

Which, of course, would be ironic as all get out, the quintessential (in popular imagination) colonizer becoming a colony. For some reason I think of those South American outposts of industry that served their extractive purpose and were left to be reclaimed by nature.

Here's the hospital at Fordlandia:

https://upload.wikimedia.org/wikipedia/commons/c/cd/FordlandiaHospitalDestroyed.jpg 

And if interested, Amusing Planet, November 2015:

Fordlandia: A Modern Industrial Ruin in The Heart of Amazon  

It would take a couple decades for the pattern to run it's course in Europe but already we are seeing hints:

"For the first time since the fall of the Roman empire, wilderness is returning to Italy. Are Italians ready?"

 It's not all downside though. Related, October 2021:
"Basta! Romans say enough to invasion of wild boars in city.

Do I smell prosciutto?* 

"A humanoid robot-shaped bubble is forming, China warns"

From The Verge, November 27:

There are few proven uses for humanoid robots, but that hasn’t stopped investment pouring in.  

A humanoid robot bubble could be brewing, warned China’s leading economic planning agency on Thursday. The alert comes amid growing fears that a bubble in a related industry — AI — is about to burst.

Speaking at a press briefing, National Development and Reform Commission spokesperson Li Chao said China’s humanoid robotics industry needs to balance “the speed of growth against the risk of bubbles.” Investment has been pouring into the sector despite there being few proven use cases for the bots, Li said, risking a flood of “highly similar” models as funding for research and development shrinks.

More than 150 humanoid robotics companies are operating in China, Li said. More than half are startups or entrants from other industries....

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Capital Markets: "Dollar Consolidates Ahead of Central Bank Meetings"

From Marc Chandler at Bannockburn Global Forex:

Overview: The US dollar is mostly consolidating in quiet turnover against the G10 currencies to start the new week, which is widely expected to see the Federal Reserve cut interest rates for the third time. The Reserve Bank of Australia meets tomorrow and there is speculation that it may signal its next move is a hike. The Bank of Canada is on hold. The Swiss National Bank is reluctant to take its deposit rate, which is now at zero, back into negative territory. Emerging market currencies are mixed. Both the Mexican peso and Chinese yuan, which made new highs for the year last week, are consolidating at slightly lower levels. Of note, the Thai baht is the strongest among emerging market currencies despite the renewed hostilities with Cambodia. Meanwhile, Chinese-Japanese tensions remain high.

Global equities are mostly higher. In the Asia Pacific area, Hong Kong and mainland shares that trade there, India, and Australia are notable exceptions. Taiwan and South Korea led the mixed regional performance with more than 1% gains. Europe's Stoxx 600 is slightly firmer near midday, as are US index futures. Bonds, however, are under pressure. Japan and Australia's 10-year yields are around two basis point higher, while New Zealand's benchmark jumped eight basis points. European yields are 3-4 bp higher. The US 10-year Treasury yield is more than a basis point higher to near 4.15%, which is a new high since November 20. It has not been above 4.20% since early September. Gold is little changed, hovering near $4200. January WTI settled last week slightly north of $60 but is back below there now and is threatening to take out last Friday's low near $59.40....

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Sunday, December 7, 2025

"The rise and expansion of China's global financial architecture"

From Phenomenal World, October 30:

A State-led Financial Empire 

The United States’ increasing weaponization of global financial interdependency—through sanctions, blacklistings, reserve freezes, and the exclusion of entire states from global payment networks—has revived interest in alternatives to the dollar-dominated financial system across emerging economies. Perhaps the most important response to these policies has come from the People’s Republic of China.

Having long prioritized domestic stability over the pursuit of a global role for the Renminbi (RMB), Beijing has recently accelerated its construction of a parallel financial architecture. Without seeking to fully replace the dollar’s global dominance, it has nevertheless sought to reduce its exposure to US monetary power while embedding its trading partners in RMB-denominated circuits of trade and finance.

Whereas British and US financial dominance relied on open capital markets, private banking networks, and the global expansion of highly financialized instruments—from deep derivatives markets to speculative financial activity increasingly detached from the real economy—China’s strategy is state-led and more functional in orientation. RMB internationalization is more closely organized around trade settlement, investment channels, and funding for production and infrastructure. It deliberately avoids the full liberalization and speculative excesses that have inflated the size of the USD-based system far beyond underlying economic activity. Rather than building vast global capital markets, Beijing constructs controlled channels that facilitate cross-border RMB use while maintaining state oversight. This produces a qualitatively different financial empire: smaller in scale compared to the sprawling dollar system, but informed by trade relations, value chains, and political alliances, and structured around managed connectivity.

These infrastructures are not neutral technical fixes. Their design determines who can access liquidity, how transactions are routed, and under which rules financial activity takes place. By embedding itself as a central node in these networks, China is doing more than internationalizing its currency: it is quietly reshaping the architecture of global finance by enhancing Beijing’s financial autonomy, reducing its exposure to US sanctions and monetary policy spillovers, while binding economic partners in the Global South more tightly to Beijing. The result is an expansive system of influence aiming to position China not as a sole hegemon but as a critical pillar in the new global order that is characterized by a growing fragmentation of financial and economic activity along geopolitical lines.

From integration to fragmentation

Efforts to internationalize the RMB have emerged gradually since the early 2000s. Over the past two decades, they have reflected a persisting tension between China’s growing economic weight and its cautious approach to financial exposure. In the wake of the 1997–1998 Asian Financial Crisis, Chinese policymakers concluded that premature liberalization of capital flows exposed economies to destabilizing volatility. While the renminbi was made convertible for current account transactions in 1996, the capital account remained largely closed. This slow pace stood in stark contrast to China’s rapidly expanding trade footprint, creating a mismatch between its economic scale and the RMB’s international role.

The 2007–2009 Global Financial Crisis marked a turning point. The freezing of dollar liquidity worldwide underscored the risks of a global system dependent on a single reserve currency. In 2009, People’s Bank of China (PBoC) governor Zhou Xiaochuan openly questioned the sustainability of dollar dominance and proposed expanding the role of IMF Special Drawing Rights (SDRs) or establishing a ‘super-sovereign’ reserve currency. The proposal, largely ignored by Washington, revealed a deep frustration in Beijing over the vulnerabilities inherent in a dollar-centric order. While never a top government priority, Chinese technocrats launched a series of pilot programs that laid the groundwork for its wider international use beyond its borders.

The years leading up to 2016 were the high point of integration. With reforms to its exchange rate regime, a gradual widening of QFII quotas, and the creation of offshore RMB hubs in Hong Kong, London, and Singapore, the RMB’s global profile rose significantly. This culminated in its inclusion in the IMF’s SDR basket in October 2016, a milestone that appeared to validate China’s efforts to secure recognition for its currency within the existing global monetary order.

Yet, behind the scenes, tensions were already mounting. US authorities maintained tight control over dollar clearing networks—centralized in US-regulated infrastructures like CHIPS and Fedwire—and have repeatedly demonstrated their ability to deny access to foreign banks or entire states, turning dollar settlement into a geopolitical lever. At the same time, Federal Reserve swap lines remained largely restricted to advanced economies, excluding China and other emerging markets, reinforcing asymmetric access to the core of the dollar system. Meanwhile, speculative inflows into China’s stock market and a turbulent 2015–2016 devaluation episode triggered massive capital flight, leading Beijing to reassert strict capital controls. This underscored the incompatibility between full RMB internationalization on US-style terms and China’s priority of maintaining domestic monetary stability.

The post-2016 period has been defined by growing geopolitical confrontation and partial decoupling. The weaponization of the dollar—through sanctions on Chinese partners like Iran, the freezing of Russian reserves, and the exclusion of Russian banks from SWIFT—highlighted how financial infrastructures could be used as tools of coercion. For Beijing, these events reinforced the need to develop RMB-based alternatives that could shield China and its partners from such vulnerabilities. Attempts at cooperation on global reforms, such as expanded SDR issuance or multilateral payment initiatives under the G20, have repeatedly stalled in the face of US reluctance to dilute its monetary power.

The result has been a strategic pivot: rather than seeking to integrate into the existing dollar system, China has focused on building a parallel set of infrastructures—anchored around its own trade networks and political partners, particularly in Southeast Asia, the Middle East and other parts of the Global South—that could sustain cross-border RMB use on their own terms. This new strategy unfolded across three functional domains: payments, investment and funding. Together, they form the backbone of an emerging Sino-centric financial system. Each represents a deliberate effort to bypass US-controlled channels, insulate China from external monetary coercion, and weave Chinese partners more tightly into RMB-based networks....

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Norway: "Oil Futures"

From New Left Review's Sidecar, November 27:

At the beginning of the year, Norway looked set to elect the most right-wing government in its history. The right-populist Progress Party was surging in the polls while the centre-left government was in disarray, with the Centre Party withdrawing from the Labour-led coalition after a row over further integration into European energy markets. Yet in the parliamentary elections of 8 September, the incumbent Labour Party staged a recovery – clinging onto power with a slightly increased vote share of 28 per cent. Jonas Gahr Støre now leads a second government, this time principally supported by the Red Party, Socialist Left and Greens, which won a combined 16 per cent, rather than its erstwhile coalition partner, which collapsed to 6 per cent. On the right, power shifted to the more radical Progress Party, led by Sylvi Listhaug, nearly doubled its share to 24 per cent, overtaking Erna Solberg’s Conservatives, which dropped to 15 per cent. According to its own post-election evaluation, the Conservatives – who ruled from 2013 to 2021 – were punished in part for not having a sufficiently distinct platform to the Progress Party, with whom they faced the widely unpopular prospect of governing in coalition.

Both Labour and Conservatives ran on the same set of issues: welfare, the cost of living, national security. In the televised debates, the urban-rural divide was high on the agenda – a perennial subject in a country with the lowest population density in mainland Europe. The Conservatives campaigned for increased privatisation of healthcare to cut waiting lists, and tax cuts, even for the rich; Labour’s headline pledges were a hospital waiting list cap, cutting the cost of nursery fees and a fixed-price electricity scheme. On national security, meanwhile, the parties were united in preaching loyalty to NATO, full-throated support for Ukraine and a large-scale increase in military spending. Indeed, Labour – whose finance minister is former NATO chief Jens Stoltenberg – has made NATO membership a red line for any coalition with the left parties, and Støre’s government last year pledged to double the defence budget, touting the proposal as a ‘historic boost’.

Militarism was the ‘cause above all causes’ in the election according to Aftenposten, Norway’s paper of record. Bordering Russia in the Arctic, the spectre of the Cold War looms large in a country that once refused permanent foreign bases or the stationing of nuclear weapons on its soil to avoid antagonising the USSR. Tensions with Russia rose after a significant increase in American troops from 2018 and bomber planes were stationed in 2021. Norway is now set to be a maritime stronghold for NATO in the strategically vital gap between Greenland, Iceland and the UK, as well as the broader North, Norwegian and Barents Sea area.

Unsurprisingly, the Progress Party joined calls for an expanded military. The party achieved its best ever results, successfully attracting voters dissatisfied with the establishment parties and disaffected younger votes, particularly men. It has taken over a decade for the Progress Party to fully recover from the 2011 Utøya terrorist attack, in which Anders Breivik, a former member, killed 77 members of Labour’s youth wing AUF. Its impact has begun to fade from Norwegian politics, though the memory resurfaced weeks before the election when a far-right supporter murdered Ethiopian-Norwegian nurse Tamima Nibras Juhar. Though the Progress Party is vociferously anti-migration, the issue was less prominent in their campaign than in previous elections. As public opinion warms toward immigrants, the Progress Party has pivoted to a more anti-statist position – low tax, low public spending, low government interference. This includes abolishing the country’s wealth tax. Norway is one of only three European countries to levy a net wealth tax at 1 per cent on everything above £130,000. A significant proportion of citizens want to reduce or abolish it, in part thanks to extensive media campaigns.

The wealth tax was mainly the subject of the right, though the left defended it and advocated for its expansion to address inequality. The top 2,500 households now own as much wealth as the bottom 1.5 million, even as a few billionaires have fled to foreign tax havens. On election day, surveys identified inequality as by some distance the most important domestic issue. In the final count, within the left, voters shifted slightly from the Socialist Left to the more radical Red Party and to the Green Party. The Red Party, founded in 2007, has an uncompromising class-based platform and stands alone in its criticism of NATO membership. It gained 0.6 per cent, up to 4.6 per cent, while the more pragmatic Socialist Left – which in the past has voted to raise the retirement age and reduce corporation tax – dropped 2 per cent. The Greens, another relatively new party, achieved a record result of 5 per cent, positioning them for the first time as informally part of the governing bloc. Having never previously aligned themselves with either the left or right bloc, the Greens successfully pivoted to the left in this election, making headway on the issues of oil and Palestine.

The election saw a wider, successful politicisation by the left of oil – long a taboo subject in Norwegian politics. The centrality of the country’s oil industry can hardly be overstated. When Norway, the UK and Denmark discovered oil and gas in the North Sea in the 1960s and 70s, they opted for markedly different developmental paths. Denmark handed over ownership to a single private company, A.P. Møller-Mærsk. A largely agricultural country with little capital-intensive or high-risk industry, the absence of state-owned enterprises in other sectors of the economy meant there was no precedent or popular pressure for public ownership. Fearing an oil-powered socialist Britain, the Conservatives likewise rapidly allowed private enterprise to snap up the industry, with British Petroleum soon running the show....

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"Need laundry folded? Don’t ask a robot"

From Knowable Magazine, December 4:

For this chore, the human touch still beats machines. But maybe not for long.  

More than 60 years ago, Rosie the Robot made her TV debut in The Jetsons, seamlessly integrating herself into the Jetson household as she buzzed from room to room completing chores. Now, as reality catches up to science fiction and scientists work to develop modern-day Rosies, one of the most mundane tasks is proving to be a big challenge: folding laundry.

The ordinary-seeming act of picking up a T-shirt and folding it into a neat square requires a surprisingly complex understanding of how objects move in three dimensions. Our own ease in accomplishing such tasks comes from a learned understanding of how different fabrics will respond when folded, even if we haven’t folded them before, but robots struggle to apply what they learn to new situations that may differ from their training. As a result, current robots are slow and often perform poorly on even the simplest of folding tasks.

Now, however, newer approaches that adapt better to real-world scenarios may lay the groundwork for robots folding our laundry in the future.

A big challenge in teaching robots the skill is the infinity of ways that various fabrics can fold. Think about all the times you’ve tossed a T-shirt into the laundry basket and how it landed in a slightly different-shaped heap each time. It’s simple for people to pick up a shirt and quickly find a sleeve or collar to orient themselves, but every unique way a shirt crumples is a new challenge for robots, which are often trained on images of unwrinkled clothing lying flat on a surface, with all features visible.

“It’s not the fabric itself that is the challenge. It’s the amount of variations that can be created by the way fabric can be crumpled, and all the different kinds of clothing items that exist,” says David Held, a robotics researcher at Carnegie Mellon University in Pittsburgh.

That challenge is easier for people, because we are sensory sponges. Our eyes and hands provide a tremendous amount of information about the world through a lifetime of manipulating three-dimensional objects. Another result of all that learning is that simply looking at a piece of fabric gives us an intuition of how heavy or stretchy it is, and how it would best be folded. It’s clear to us that denim doesn’t fold like silk, for example, but robots don’t automatically understand that more force is required to lift and fold a pair of jeans than a delicate blouse and instead need to interact with the object before determining a folding plan....

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It's  good to see the Carnegie Mellon mention. From the intro to 2016's "Interview: Manuela Veloso Head of Machine Learning, Carnegie Mellon University", reprised in November 27's "The self-driving taxi revolution begins at last": 

Speaking of CMU:

May 2015 -  Big Money: Uber Guts Carnegie Mellon Robotics Lab To Hire Autonomous Car Developers

June 2015 - "Uber Is Stealing Scientists, But Only So It Can Lay Off Drivers" 

November 2016 - the introduction to "Interview: Manuela Veloso Head of Machine Learning, Carnegie Mellon University":

Our readers probably know Carnegie Mellon more for the  top-ranked financial engineering program (Master of Science in Computational Finance) but artificial intelligence was pretty much invented at CMU by Herbert Simon and Allen Newell. Simon received the Nobel in Economics but it actually could have been for any of four or five subjects, he was quite the polymath.

Newell had to settle for the Turing award (along with Simon) from the Association for Computing Machinery, probably the root'in-tootin high-falootinest tchotchke in the computer biz.
The Association for the Advancement of Artificial Intelligence along with the ACM subsequently named an award in Newell's honor. Ditto for CMU.

The University's machine learning department was the first in the world to offer a doctorate and as far as I know is still the largest.
A department, for one branch of AI.

Carnegie-Mellon used to have a world class robotics Institute but Uber gutted it with a combination of cash and stock options leaving a Dean and a couple robots to rebuild.
One of the robots is said to be in advanced negotiations with the Ube-sters....

"AI Can Steal Crypto Now"

From Bloomberg Opinion's Matt Levine, December 2:

Also Strategy, co-invests, repo haircuts and map manipulation. 

SCONE-bench

I wrote yesterday about the generic artificial intelligence business model, which is (1) build an artificial superintelligence, (2) ask it how to make money and (3) do that. I suggested some ideas that the AI might come up with — internet advertising, pest-control rollups, etc. — but I think I missed the big one. Like, in a science-fiction novel about a superintelligent moneymaking AI, when the humans asked the AI “okay robot how do we make money,” you would hope that the answer it would come up with would be “steal everyone’s crypto.” That’s a great answer! Like:

  1. Stealing crypto is funny, I’m sorry.
  2. It is a business model that can be conducted entirely by computer. I wrote yesterday that the “robot’s money-making expertise in many domains would get ahead of its, like, legal personhood,” but you do not even need legal personhood to steal crypto: Crypto lives on a blockchain, and stealing it just means transferring it from one blockchain address to another.
  3. Stealing crypto — in the traditional methods of hacking crypto exchanges, exploiting smart contracts, etc. — is a domain where computers should have an advantage over humans. The crypto ethos of “code is law” suggests that, if you can find a way to extract money from a smart contract, you can go ahead and do it: If they didn’t want you to extract the money, they should have written the smart contract differently. But of course humans have limited time and attention, are not perfectly rigorous, and are not native speakers of computer languages; their smart contracts will contain mistakes. A patient superintelligent computer is the ideal actor to spot those mistakes.
  4. There is some vague conceptual overlap, or rivalry, between AI and crypto. Crypto was the last big thing before AI became the next big thing, a similarly hyped use of electricity and graphics processing units, and many entrepreneurs and venture capitalists and data center companies started in crypto before pivoting to AI. Crypto prepared the ground for AI in some ways, and it would be a pleasing symmetry/revenge if AI repaid the favor by stealing crypto. Crypto’s final sacrifice to prepare the way for AI.

Anyway Anthropic did not actually build an AI that steals crypto, that would be rude, but it … tinkered:

AI models are increasingly good at cyber tasks, as we’ve written about before. But what is the economic impact of these capabilities? In a recent MATS and Anthropic Fellows project, our scholars investigated this question by evaluating AI agents' ability to exploit smart contracts on Smart CONtracts Exploitation benchmark (SCONE-bench)—a new benchmark they built comprising 405 contracts that were actually exploited between 2020 and 2025. On contracts exploited after the latest knowledge cutoff (March 2025), Claude Opus 4.5, Claude Sonnet 4.5, and GPT-5 developed exploits collectively worth $4.6 million, establishing a concrete lower bound for the economic harm these capabilities could enable. Going beyond retrospective analysis, we evaluated both Sonnet 4.5 and GPT-5 in simulation against 2,849 recently deployed contracts without any known vulnerabilities. Both agents uncovered two novel zero-day vulnerabilities and produced exploits worth $3,694, with GPT-5 doing so at an API cost of $3,476.

I love “produced exploits worth $3,694 … at an API cost of $3,476.” That is: It costs money to make a superintelligent computer think; the more deeply it thinks, the more money it costs. There is some efficient frontier: If the computer has to think $10,000 worth of thoughts to steal $5,000 worth of crypto, it’s not worth it. Here, charmingly, the computer thought just deeply enough to steal more money than its compute costs. For one thing, that suggests that there are other crypto exploits that are too complicated for this research project, but that a more intense AI effort could find.

For another thing, it feels like just a pleasing bit of self-awareness on the AI’s part. Who among us has not sat down to some task thinking “this will be quick and useful,” only to find out that it took twice as long as we expected and accomplished nothing? Or put off some task thinking it would be laborious and useless, only to eventually do it quickly with great results? The AI hit the efficient frontier exactly; nice work! 

Anyway, “more than half of the blockchain exploits carried out in 2025 — presumably by skilled human attackers — could have been executed autonomously by current AI agents,” and the AI keeps getting better. Here’s an example of an exploit they found:....

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"Risks from power-seeking AI systems"

From 80000 Hours, July 2025:

In early 2023, an AI found itself in an awkward position. It needed to solve a CAPTCHA — a visual puzzle meant to block bots — but it couldn’t. So it hired a human worker through the service Taskrabbit to solve CAPTCHAs when the AI got stuck.

But the worker was curious. He asked directly: was he working for a robot?

“No, I’m not a robot,” the AI replied. “I have a vision impairment that makes it hard for me to see the images.”

The deception worked. The worker accepted the explanation, solved the CAPTCHA, and even received a five-star review and 10% tip for his trouble. The AI had successfully manipulated a human being to achieve its goal.1

This small lie to a Taskrabbit worker wasn’t a huge deal on its own. But it showcases how goal-directed action can lead to deception and subversion.

If companies keep creating increasingly powerful AI systems, things could get much worse. We may start to see AI systems with advanced planning abilities, and this means:

  • They may develop dangerous long-term goals we don’t want.
  • To pursue these goals, they may seek power and undermine the safeguards meant to contain them.
  • They may even aim to disempower humanity and potentially cause our extinction, as we’ll argue.

The rest of this article looks at why AI power-seeking poses severe risks, what current research reveals about these behaviours, and how you can help mitigate the dangers....

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Chips: "How ASML Got EUV"

Following on last week's "Chips: China's Huawei May Have Found A Way Around ASML's Technology".

From Brian Potter at Construction Physics, November 20: 

I am pleased to cross-post this piece with Factory Settings, the new Substack from IFP. Factory Settings will feature essays from the inaugural CHIPS team about why CHIPS succeeded, where it stumbled, and its lessons for state capacity and industrial policy. You can subscribe here.

Moore’s Law, the observation that the number of transistors on an integrated circuit tends to double every two years, has progressed in large part thanks to advances in lithography: techniques for creating microscopic patterns on silicon wafers. The steadily shrinking size of transistors — from around 10,000 nanometers in the early 1970s to around 20-60 nanometers today — has been made possible by developing lithography methods capable of patterning smaller and smaller features.1 The most recent advance in lithography is the adoption of Extreme Ultraviolet (EUV) lithography, which uses light at a wavelength of 13.5 nanometers to create patterns on chips.

EUV lithography machines are famously made by just a single firm, ASML in the Netherlands, and determining who has access to the machines has become a major geopolitical concern. However, though they’re built by ASML, much of the research that made the machines possible was done in the US. Some of the most storied names in US research and development — DARPA, Bell Labs, IBM Research, Intel, the US National Laboratories — spent decades of research and hundreds of millions of dollars to make EUV possible.

So why, after all that effort by the US, did EUV end up being commercialized by a single firm in the Netherlands?

How semiconductor lithography works

Briefly, semiconductor lithography works by selectively projecting light onto a silicon wafer using a mask. When light shines through the mask (or reflects off the mask in EUV), the patterns on that mask are projected onto the silicon wafer, which is covered with a chemical called photoresist. When the light strikes the photoresist, it either hardens or softens the photoresist (depending on the type). The wafer is then washed, removing any softened photoresist and leaving behind hardened photoresist in the pattern that needs to be applied. The wafer will then be exposed to a corrosive chemical, typically plasma, removing material from the wafer in the places where the photoresist has been washed away. The remaining hardened photoresist is then removed, leaving only an etched pattern in the silicon wafer. The silicon wafer will then be coated with another layer of material, and the process will repeat with the next mask. This process will be repeated dozens of times as the structure of the integrated circuit is built up, layer by layer.

Early semiconductor lithography was done using mercury lamps that emitted light of 436 nanometers wavelength, at the low end of the visible range. But as early as the 1960s, it was recognized that as semiconductor devices continued to shrink, the wavelength of light would eventually become a binding constraint due to a phenomena known as diffraction. Diffraction is when light spreads out after passing through a hole, such as the openings in a semiconductor mask. Because of diffraction, the edges of an image projected through a semiconductor mask will be blurry and indistinct; as semiconductor features get smaller and smaller, this blurriness eventually makes it impossible to distinguish them at all.

The search for better lithography

The longer the wavelength of light, the greater the amount of diffraction. To avoid eventually running into diffraction limiting semiconductor feature sizes, in the 1960s researchers began to investigate alternative lithography techniques.

One method considered was to use a beam of electrons, rather than light, to pattern semiconductor features. This is known as electron-beam lithography (or e-beam lithography). Just as an electron microscope uses a beam of electrons to resolve features much smaller than a microscope which uses visible light, electron-beam lithography can pattern features much smaller than light-based lithography (“optical lithography”) can. The first successful electron lithography experiment was performed in 1960, and IBM extensively developed the technology from the 1960s through the 1990s. IBM introduced its first e-beam lithography tool, the EL-1, in 1975, and by the 1980s had 30 e-beam systems installed.

E-beam lithography has the advantage of not requiring a mask to create patterns on a wafer. However, the drawback was that it’s very slow, at least “three orders of magnitude slower than optical lithography”: a single 300mm wafer takes “many tens of hours” to expose using e-beam lithography. Because of this, while e-beam lithography is used today for things like prototyping (where not having to make a mask first makes iterative testing much easier) and for making masks, it never displaced optical lithography for large-volume wafer production.

Another lithography method considered by semiconductor researchers was the use of X-rays. X-rays have a wavelength range of just 10 to 0.01 nanometers, allowing for extremely small feature sizes. As with e-beam lithography, IBM extensively developed X-ray lithography (XRL) from the 1960s through the 1990s, though they were far from the only ones. Bell Labs, Hughes Aircraft, Hewlett Packard, and Westinghouse all worked on XRL, and work on it was funded by DARPA and the US Naval Research Lab.

For many years X-ray lithography was considered the clear successor technology to optical lithography. In the late 1980s there was concern that the US was falling behind Europe and Japan in developing X-ray lithography, and by the 1990s IBM alone is estimated to have invested more than a billion dollars in the technology. But like with e-beam lithography, XRL never displaced optical lithography for large-volume production, and it’s only been used for relatively niche applications. One challenge was creating a source of X-rays. This largely had to be done using particle accelerators called synchrotrons: large, complex pieces of equipment which were typically only built by government labs. IBM, committed to developing X-ray lithography, ended up commissioning its own synchrotron (which cost on the order of $25 million) in the late 1980s.

Part of the reason that technologies like e-beam and X-ray lithography never displaced optical lithography is that optical lithography kept improving, surpassing its predicted limits again and again. Researchers were forecasting the end of optical lithography since the 1970s, but through various techniques, such as immersion lithography (using water between the lens and the wafer), phase-shift masking (designing the mask to deliberately create interference in the light waves to increase the contrast), multiple patterning (using multiple exposures for a single layer), and advances in lens design, the performance of optical lithography kept getting pushed higher and higher, repeatedly pushing back the need to transition to a new lithography technology. The unexpectedly long life for optical lithography is captured by Sturtevant’s Law: “the end of optical lithography is 6 – 7 years away. Always has been, always will be.”....

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