And Now, A Marxist Perspective: "AI Shock and Central Bank Trap"

From one of our three go-to Marxists. Two are economists* and then there's Fabio. 

As noted in the introduction to June 2024's "The Enemy and the Libidinal Economy of the Apocalypse":

Professor Fabio Vighi (Critical Theory and Italian at Cardiff Uni.) can get heavy/borderline tedious but I think he's on to something. Plus, where else are you going to find sentences like:

"At the heart of this process is the reliance on the toxic fetish of the speculative bubble: trillions (quadrillions if we count derivatives) of insubstantial money orbiting above our heads at the speed of light."

There is a good chance that I will purloin "...the toxic fetish of the speculative bubble"

From Professor Vighi's substack, July 28:

A week of living dangerously 

Moonshot AI's Kimi K3 Becomes World's First Open-Source Model in 3-Trillion-Parameter Class

The system always looks most stable just before it starts to crack—a mixture of vanity and method. That is why the present moment seems significant: the launch of a Chinese open-weight AI model, a global bond sell-off, and a round of central bank meetings are not separate stories but connected symptoms of the same exhausted order. Grinding in the background is the debt machine, which is now accelerating into its own limits.

This is a showdown. On one side stands Moonshot AI’s Kimi K3, released yesterday (July 27th), as the largest open-weight model yet—free to use, modify, and run privately, provided you have the computer power to handle it—with company claims of strong coding performance at a lower list price than leading US rivals. On the other side stand the Federal Reserve, the Bank of England, and the Bank of Japan, meeting this Wednesday, Thursday and Friday respectively in a market environment already strained by rising yields and a global bond sell-off. The coincidence is not accidental. It is a concentration of pressures that capitalism can no longer keep politely separated.

The Illusion of the Moat

The AI industry likes to speak the language of moats, frontier advantages, and scale. But what is a moat in a sector whose core asset is not a patentable machine but a rapidly replicable architecture? The fantasy of permanent technical superiority is already dissipating. If open-weight Chinese models can deliver comparable performance at lower cost, then the rent structure supporting the US AI bubble becomes much less secure.

Yesterday’s release confirms the pattern. In developer tests, K3 scored strongly at a cost that undercuts its US rivals on many important workloads. K3 does not need to win every comparison: it only needs to be good enough to make the price difference impossible to ignore. Chinese models have now moved from being dismissed as imitators to serious price-setters, and that alone is enough to force a repricing—while the economics may improve further as external developers optimise inference, quantise the model, and create specialised versions.

This is where the surface meets the deeper turbulence. Capital wants to convert technological advance into private monopoly, yet the very spread of AI undermines exclusivity and erodes control over access. The more essential the technology becomes to coding, research, administration, and decision-making, the more obvious it is that the real struggle is not over “intelligence” as such, but over control of the conditions under which the artificial intelligence is commodified.

And here lies the constitutive, inescapable contradiction. AI is celebrated as a productivity revolution; in truth, it intensifies a formidable crisis of valorisation. Capital seeks to replace living labour with machine intelligence, but commodity-producing living labour remains the only source of economic value. The more capital automates, the more it corrodes the basis on which profit is supposed to rest.

The capital-technology contradiction is now being dramatised at a scale that demands more than conventional economic cheerleading; it demands self-deception, mythology, and increasingly baroque financial engineering.

The Financial Trap

The monetary side of the story is just as unstable. The AI boom has been financed through pandemic-era cheap money, speculative expectation, and the capitalisation of future earnings that will never arrive as investors imagine. That is fictitious capital in its purest form: claims on the future priced as if they were already secure.

Now the conditions that sustained this arrangement are changing very rapidly. Higher policy rates devalue future earnings, while they also raise refinancing costs and expose overleveraged firms to a harsher arithmetic. You can think of it this way: if the government guarantees you 5% on a bond, why would you pay a sky-high price for a tech stock that might only deliver that return years from now—if it delivers at all? The same companies that were rewarded for growth at any price are now being asked to explain how that growth will be funded when money stays expensive. The answer, more often than not, is that they won’t.

The current bond sell-off makes the situation worse. When even sovereign debt is under pressure, the old assumption that capital can flee risk into safety becomes less convincing. Investors are dumping bonds because they realise that inflation is here to stay, inducing central banks to keep rates high for longer than markets had hoped. The immediate result is likely to be a broad repricing of assets built on the promise of distant returns. AI stocks are especially vulnerable because so much of their valuation depends on future margins that are increasingly uncertain.

The arithmetic, therefore, is brutal: higher rates plus lower projected earnings equals violent repricing—and that repricing is less a temporary glitch than the logical consequence of a grotesquely delusional structure.

The Energy Reality Check....

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We don't always agree with his conclusions but the arguments he makes along the way can be insightful/enlightening/borderline brilliant.

Our other go-to Marxists are Michael Roberts, formerly an economist in the City (Londres, not Nieuw Amsterdam) and Professor Michael Hudson about whom we once wrote:

May 1, 2024 
"Germany as Collateral Damage in America’s New Cold War"

Happy May Day!

From one of our two favorite Marxist economists. But first the introduction to 2022's "America’s real adversaries are its European and other allies":

The author of this essay, Michael Hudson, is a Marxist economist.

But not just any Marxist economist. Leon Trotsky was his godfather.

And in addition to his professorship at the University of Missouri - Kansas City he teaches at Beijing's School of Marxist Studies, Peking University.

Yves Smith at naked capitalism seems to like him.

And he almost has me convinced that the only way to clear the sclerotic arteries of American capitalism is to declare Jubilee on all debts. He may have gotten attracted to this ancient idea during his time on Harvard's archaeology faculty at the Peabody Museum as a research fellow in Babylonian economics. (Wiki) Not to be confused with The Babylon Bee's 2019 piece "Modernized Year Of Jubilee Will Forgive Everyone For Their Old Tweets". [rather ironic in light of the Bee's being kicked off Twitter, inciting Elon Musk and setting that whole train in motion]

Anyhoo, from Professor Hudson's website, February 8, 2022 i.e. sixteen days before Russia invaded:

The U.S. aim is to keep them from trading with China and Russia 

And the headline essay, from Professor Hudson's personal website, March 29, 2024:

As published in Berliner Zeitung.... 

More On Physical AI: "How world models became AI's next frontier"

From The Deep View, July 3:

Large language models can do a lot of things, as long as those things exist on a screen.

Having consumed practically all of the data on the internet, these models can tell you something about practically anything. They can analyze thousands of documents and pull out the most important parts. They can plan your trips, write poems, and mimic therapists in times of need. They can help researchers understand anything from protein structures to ancient history. And they’re the backbone of millions of agents that are the hottest ticket in tech right now.

But the world is bigger than a screen, and understanding it requires more than words.

That's why Nvidia’s Jensen Huang has said more than once that physical AI is due for its "ChatGPT moment."

It’s also the reason that world models, or AI models capable of understanding the physical environment, have gained significant momentum in 2026. Along with a flock of young startups entering the space, some of AI’s most prominent figures have homed in on the concept.

But as the momentum around world models grows, in tandem with physical AI and robotics, some leaders have begun to question their impact on LLMs, and ultimately, the ever-elusive path to artificial general intelligence (AGI).

"Seeing the world in a profound way, in a way that you participate in your movement, your interaction, in your communication, is critical for intelligence," said Dr. Fei-Fei Li, largely considered the godmother of AI, while also being the founder and CEO of World Labs, in a panel at the HumanX conference in April. "Not having that is intelligence in the dark."

Investors take notice

As world models catch the attention of some of AI’s biggest tastemakers, investors have become captivated. World model startups have been raking in billions in funding, some at incredibly early stages:

• Advanced Machine Intelligence, or AMI Labs, a startup founded by AI godfather and former Meta AI chief Yann LeCun, raised a $1 billion seed funding round at a $3.5 billion pre-money valuation in March, after launching in late 2025.
• World Labs, Li’s startup founded in 2024, announced its own $1 billion funding round in February at a valuation of $5 billion.
• Runway, a company focused on scaling video models as a means of creating world models, raised $315 million in Series E funding at a valuation of $5.3 billion.
• Luma, an AI video startup that focuses on creating multimodal reasoning models, raised a $900 million Series C in November at a valuation of $4 billion.
• General Intuition, a lab dedicated to building foundation models for environments that require "deep spatial and temporal reasoning," raised $320 million in Series A funding at a $2.3 billion valuation in June.

Beyond startups, several companies have pivoted into world models from one industry in particular: gaming.

Niantic, the maker of the beloved Pokémon Go app, sold its suite of mobile games to Scopely for $3.5 billion and spun out a lab last March called Niantic Spatial, focused on developing what it calls a "Large Geospatial Model," a world model that enhances spatial reasoning in LLMs.

And Roblox, the online gaming platform with more than 150 million daily active users, is developing its own version of a world model that it calls "real-time dreaming," that allows creators to generate and iterate on virtual environments through language prompts. In a panel at the HumanX conference in April, David Baszucki, Roblox founder and CEO, said that he envisions the company’s world models "not just as a play technology, but as a creation technology as well."

In January, Google DeepMind released Project Genie, an "experimental research prototype" that marks the latest iteration of its work in the world model space. The project is powered by its flagship Gemini model, its Nano Banana Pro image model, and Genie 3, its most powerful world model yet.

Nvidia, meanwhile, unveiled its Cosmos model at CES 2025, a world foundation model that’s aimed at accelerating the development and deployment of autonomous vehicles and robots. Since then, the company has expanded Cosmos further, including debuting the third generation of the Cosmos family and releasing world-generation models, controllable simulations for synthetic data generation, and multimodal reasoning models for physical AI.

Many bets, same problems....

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August 14 - "World Models Are AI’s Next Frontier" 

"Japan’s Underground Lab Wants to Solve the World’s Nuclear Waste Problem"

From The Diplomat, August 13:

A rare visit to the Horonobe Underground Research Laboratory, where Japanese researchers are testing options for storing radioactive waste. 

Reindeer pastures and dairy farms stretch across the flat, windswept plain of Japan’s northernmost prefecture, giving little hint of what lies roughly 300 meters below. Inside the Horonobe Underground Research Laboratory, teams of engineers are trying to answer a question no country has fully solved: how to keep highly radioactive waste isolated from the environment for periods measured not in decades, but in tens of thousands of years.

In May, I gained special access to the facility through the Japan Atomic Energy Agency (JAEA), touring the underground galleries and observing several of the experiments currently underway.

A Rare Laboratory in Sedimentary Rock

Horonobe is one of only two underground research laboratories Japan has built to study geological disposal of high-level radioactive waste, and the only one still operating. Its counterpart, the Mizunami Underground Research Laboratory in Gifu Prefecture, was built in crystalline rock and was decommissioned in 2022 after its land lease expired. Horonobe, by contrast, sits in Neogene-era sedimentary rock, giving the JAEA a very different and, officials argue, complementary geological environment to study.

The project traces back to November 2000, when the governor of Hokkaido, the mayor of Horonobe town, and the president of the JAEA signed an agreement to permit research on the deep geological environment at the site. Construction and excavation followed over the next two decades, and the laboratory now includes shafts and galleries used to test how engineered barriers, groundwater, and surrounding rock interact over time – the same combination of natural and artificial safeguards that would, in theory, protect a real repository.

Two of the central experiments underway involve what the JAEA calls the “full-scale engineered barrier system performance experiment” and a “solute transport experiment,” both designed to test, at real scale, how bentonite buffers, canisters, and cement-based materials behave once water and pressure are introduced over years rather than months. The JAEA has also been testing how the surrounding sedimentary rock buffers natural disturbances, earthquakes, and groundwater shifts that could compromise a repository’s isolation over the long term....

 

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They have very nice tunnels. That's what Iran should have done.

Instead of "Death to America", repeating it over and over for 47 years until people start to think you mean it or something. 

"Nvidia’s new $500B plan is risky but brilliant, especially for aging GPUs" (NVDA)

From TechCrunch, August 13:

Nvidia announced this week that Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR were willing to commit up to $500 billion to build AI data centers. That eye-popping figure got a lot of the attention, but the bigger story is Nvidia’s effort to create a secondary market for aging GPUs.

To convince those big-name financial companies, Nvidia has agreed to guarantee, with its own money, that its chips used as collateral in these deals will retain their value.

Many have now commented on how unusual, smart, and dangerous this plan is. It is all of those things. The bond markets got so spooked that Nvidia CEO Jensen Huang took to X and business TV to better explain how Nvidia’s risk would be limited.

But underneath the financial maneuvering to fund AI data centers (and keep revenue for Nvidia flowing), is something, perhaps, far more interesting for startups and enterprises: Huang wants to ensure an ecosystem of used AI hardware flourishes, helping sustain demand for Nvidia hardware as it ages.

Specifically, Nvidia is promising that if GPUs used as collateral don’t retain their value as expected, the company will cover up to 25% of the difference. So, if a data center owner defaults on a loan and the lender must liquidate, but the chips can’t command the price the books say they should, Nvidia will chip in.

The dangerous part for Nvidia is that this creates something financiers call “wrong way” risk. That is, Nvidia’s obligations will grow as demand weakens. Should that happen, its revenues will likely be squeezed as well.

Still, the scheme is deliberately unlike the comparison to Lucent Technologies that some have been making. Lucent was the telecommunications equipment provider that rose and crashed with the dotcom bubble after lending its customers money to buy its wares.

The Lucent comparison is a shadow over Nvidia, Huang knows. And not an unfair one. Nvidia definitely has committed billions toward those who buy its chips, including frontier AI labs OpenAI and Anthropic, neoclouds like CoreWeave (the originator of using Nvidia chips as collateral), as well as Nebius, Firmus, and Lambda. And it has been working on another $750 billion worth of circular deals this summer, Bloomberg has calculated....

....MUCH MORE 

Recently: 

August 10 - "Nvidia lines up $500 billion in financing as CEO Jensen Huang tells CNBC his chips are ‘investable asset’" (NVDA)

August 12 - "AI computing power is becoming a tradable asset class as CME launches futures contracts" (tied to H100 and B200 GPU rental costs)

If interested see also:

September 2025 -  "OpenAI in talks to lease Nvidia chips instead of buying them, Information says"

September 2025 - "The $4trn accounting puzzle at the heart of the AI cloud"

Nvidia's development cycle is currently around eighteen months, shorter than the magic number that Moore's  Law observed for the number of transistors in an integrated circuit, two years.

I don't know how long Nvidia can maintain that pace but Mr. Huang is pushing to shorten the development cycle further, thus making earlier generations of the company's GPUs obsolete even faster, a point we were pitching as a positive earlier this year regarding leakage of state-of-the art chips from the Middle Eastern buyers to China:

As noted exiting from May 18's "U.S. Still Mulling Whether To Allow Nvidia To Sell One Million+ Most Powerful Chips To UAE": 

On the one hand with that many chips floating around that part of the world there is no way to keep a bunch of them from ending up in China. On the other hand, Nvidia's development cycle is focused on releasing new, more powerful chips every 12 -14 months meaning the current smoking hot H100 chips will have been superseded by two cycles at the end of the contract period. 

The first point, that chips will get to China is borne out by the recent news that $1 billion worth of chips had been smuggled into China in three months after the export ban on the more powerful Nvidia chips. 

note: the smuggled chips were not the ones destined for the UAE.

The second point is that the real technology transfer deterrent is in the pace of NVDA's development cycle. 

Although there are hiccups—most recently server racks overheating from the amazing amount of electricity flowing through the systems—the overarching goal is an almost metronomic rhythm to the development of new chips such that the H20s will be out-dated in under 2 1/2 years.

If interested in a deeper dive into the pace of development see also:

Nvidia Earnings Call Transcript: Q2 2026 (August 27, 2025)  

May 6 - Goldman Sachs: "Tracking Trillions: The Assumptions Shaping the Scale of the AI Build-Out"

"The Plague That Broke English (And Why the Founding Fathers Didn’t Sound British)"

From the Past Life, Present Cleavage substack, April 3:

So. Did the founding fathers sound British.

The short answer is no. The longer answer is going to make you rethink every movie and every mental image you’ve ever had of anyone who lived before 1800. I need you to be prepared for that.

The founding fathers did not sound like modern British people. But they also didn’t sound like modern Americans. Both accents have changed since 1776, which makes sense because accents are living things and 250 years is a long time. But here’s the part that’s going to ruin you: modern American accents are actually closer to what English sounded like in the 18th century than modern British accents are. The British changed more than we did. Americans are the ones who kept the old sounds. I am not ok about this.

But to explain why, I need to take you somewhere you’re not expecting. I need to take you to a plague.

In the 1300s the Black Death swept through England and killed somewhere between a third and half of the entire population. Entire villages emptied. Labor shortages reshaped the economy. And the survivors started moving. Massive waves of migration poured into London and the southeast from every corner of the country, bringing with them dozens of regional dialects that had never been in the same room before.

And somewhere in that collision of accents and dialects, something shifted. Literally. The vowels started moving.

Between roughly 1400 and 1700 every single long vowel in the English language changed how it was pronounced. It’s called the Great Vowel Shift. Before it happened, English vowels worked the way vowels work in Italian or Spanish. The letters did what they looked like they should do. “Bite” was pronounced “beet.” “House” was pronounced “hoos.” “Name” was pronounced “nahm.” “Knight” was pronounced “k-nicht” with a guttural sound at the end like you were clearing your throat. English was phonetic. The spelling matched the sounds. It made SENSE.

And then it didn’t.

All the long vowels began migrating upward in the mouth. The tongue positions shifted higher and higher over the course of about 300 years. And the vowels that were already at the top had nowhere left to go so they broke apart into two sounds. “Ee” became “eye.” “Oo” became “ow.” A 300-year linguistic pileup that nobody voted on and nobody agreed to. It just happened. And it happened during the worst possible window of time.

William Caxton brought the printing press to England in 1476. One of the most important technological developments in the history of communication. And it arrived RIGHT IN THE MIDDLE of the Great Vowel Shift.

Caxton started standardizing English spelling based on how words sounded at that exact moment. Printers needed consistency. They needed to pick a spelling and commit. So they did. But the sounds kept changing after the spelling was already locked in. The vowels were still moving. The consonants were still dropping. And the printed page couldn’t keep up.

We had ONE CHANCE to get English spelling right and we fumbled it at the worst possible moment.

This is why English spelling is a disaster. It’s not random. It’s not lazy. It’s a snapshot of a language mid-transformation that got frozen in place by a new technology and was never corrected. Every word you have ever misspelled, every silent letter that made you feel stupid, every time you stared at “knight” and thought why is there a K in this. You used to pronounce that K. You used to pronounce that GH. Those sounds died 500 years ago and the letters are still standing there like gravestones. English spelling is full of ghosts.

That’s why “food” and “good” and “blood” are spelled almost identically but sound completely different. In Chaucer’s time around the 1390s they all rhymed.....

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Okay, but why does the word Queue have four silent vowels? 

Followup: "Tapes Hidden in an Ice Cream Box Lay Bare a Maltese Journalist’s Assassination"

From the Organized Crime and Corruption Reporting Project, August 13:

In the trial of business magnate Yorgen Fenech, a middleman’s secret recordings and auto-deleting texts exposed the chilling mechanics behind the 2017 car bombing—and the political leaks that shielded the killers. 

Secret audio recordings captured on a device hidden in a sock, along with text messages presented in a Maltese courtroom, laid bare the mechanics behind the 2017 assassination of journalist Daphne Caruana Galizia—linking a prominent tycoon to hitmen, cash payoffs, and high-level government leaks.

The recordings were played at the trial of Yorgen Fenech, the businessman accused of ordering the assassination. Caruana Galizia, 53, was killed by a car bomb placed beneath the driver’s seat of her vehicle and detonated remotely via SMS on the outskirts of Mosta.

Five men—George Degiorgio, Alfred Degiorgio, Vince Muscat, Robert Agius, and Jamie Vella—have all been convicted for their roles in the murder. Fenech denies any involvement and has pleaded not guilty.

The tapes were recorded by Melvin Theuma, a confessed middleman who was granted a presidential pardon in exchange for evidence. Theuma hid the recorder in his sock and began taping Fenech in April 2018, months after the hitmen were arrested.

“I started recording Yorgen because I needed the proof,” he told jurors. “I knew they were going to betray me either by killing me or throwing me in jail.”....

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Previously:

September 2018 - "Who Killed Investigative Journalist Daphne Caruana Galizia?"

Pilatus: A Private Bank for Azerbaijan’s Ruling Elite...

October 2018 - "Malta, A Modern Smugglers’ Hideout"

October 2018 - "Death in a Smugglers’ Paradise"

December 2019 - Was 17 Black the Motive Behind the Assassination of Maltese Reporter Caruana Galizia? 

March 2021 - "Hitman Pleads Guilty in Maltese Journalist’s Murder"

I missed this when it first came out. There are people on Malta and on the mainland who think this goes a lot deeper than the three arrested....

May 2021 - Murder on Malta: "New evidence links former police officer to Daphne murder leaks"

When all is said and done I would not be surprised if Italian mobsters, Azerbaijani kleptocrats and that Josef Mifsud guy all make an appearance.

Previously on Malta:

How Malta Became A Global Online Gambling Giant

Grand Theft Europe: Malta’s role in a scam worth €50 billion annually"
Combining three of our favorite topics: skullduggery, Malta, and skullduggery on Malta.

GFI Names the #1 Money Laundering Facility of 2018
After Reporter's Murder: "Malta's Pilatus Bank has European licence withdrawn"

"2019 Man Of The Year In Organized Crime And Corruption: Malta's Joseph Muscat"
It was a weaker than average field but still a deserved win for the former MEP and soon-to-be ex-Prime Minister.
Here's hoping that Africa and the 'Stans up their game in the New Year to compete with the Westerners who will be in the running in 2020....

In other mysteries, whatever became of Josef Mifsud? 

Singapore Sovereign Wealth Fund, Temasek, Plans Further AI Investment Including South Korean Memory Stocks

If I were running some money at Temasek I would not be pleased that someone in South Korea was blabbing to the media. Not at all. Shades of BYD's CEO saying, back in the day, that Berkshire Hathaway was planning to up their stake in his company.*

From Bloomberg, August 11/12:

South Korean chipmakers rallied as risk appetite returned after last month’s rout and traders weighed a local media report that Singapore’s Temasek Holdings Pte plans to invest in Samsung Electronics Co. and SK Hynix Inc.

Shares of the chip duo briefly extended gains to more than 8% after Asia Business Daily Temasek Contacts S. Korea to Invest in Samsung, Hynix: Daily that the state fund plans to invest directly in the two chipmakers through its internal investment team, and is mulling timing. Samsung ended the day up 6.7% while SK Hynix rose 5.5%. The benchmark Kospi gained 3.7%.

Temasek said in response to queries from Bloomberg News that it didn’t seek advice from the Korean government on the timing of investments in either SK Hynix or Samsung, and that it first invested in both companies more than two years ago.

The chipmakers saw a brutal selloff in July as doubts grew over the pace of rapid AI infrastructure buildout, leading to an unwinding of leveraged positions. The shares have been rebounding recently, amid growing expectations the companies will bolster shareholder returns with buybacks and more dividends.

“It is a positive confidence signal, but I would not see it as a game changer or the main driver of the Kospi rebound,” said Albert Yong, managing partner at hedge fund Petra Capital Management, referring to the local report. “Samsung and SK Hynix had become technically oversold despite still-strong fundamentals.”....

 

....MUCH MORE 

Now back in July the fund said they would double their AI exposure by 2031, noteworthy not because they named names but because they take a fairly conservative approach (growth-at-a-reasonable-price+some moonshots) to investing:

Temasek’s Net Portfolio Value Grows to S$518 billion, up S$49 billion from Last Year 

There are other reports that this would be the big (401 billion USD) fund's first foray into Korea, which seems a bit surprising.**
*August 2009 - China's BYD says Buffett wants to raise stake (BRK.A: 1211.HK)

[this comment did not please Buffet or Munger - Don't be tellin' people to go frontrun our buying] 

**Singapore's other SWF, GIC is even more conservative and has $936 billion AUM.

"Why Didn’t India Build Its Own Silicon Valley?"

From the Los Angeles Review of Books, August 5:

Dwaipayan Banerjee’s new book investigates India’s role in the history of 20th-century tech development, and what caused it always to seem a step behind the West. 

Computing in the Age of Decolonization: India’s Lost Technological Revolution by Dwaipayan Banerjee. Princeton University Press, 2026. 296 pages.

IN THE 1970S, my father was among thousands of science and engineering graduates who left India for the West. The United States and Britain had flung open their doors to Asian immigrants. Leaving India made sense for the superfluously educated, whom India was ambitious enough to train even while lacking the infrastructure to employ them. The West, however, wasn’t always what they had hoped for. Like my dad, many ended up running small businesses or finding quiet jobs in research or manufacturing, hitting glass ceilings if they tried to move any higher. But as their numbers grew and discrimination softened, Indian-born engineers would eventually take over the biggest technology firms in the world, from Google (Sundar Pichai) to Microsoft (Satya Nadella) and Adobe (Shantanu Narayen). In Fremont, the closest major East Bay city to California’s Silicon Valley, nearly a third of residents are Indian Americans.

Their success leaves us to ask why India never built a Silicon Valley of its own.

It is more of a puzzle than it seems. While it’s true that India is best known for backend IT services, it lags so far behind China and the United States in scientific research that it’s scarcely mentioned in the same breath anymore. For decades, however, the outcome of the global technological arms race was far from certain. For a while, India was a contender. After wresting independence from the British in 1947 (when my father was two years old), Prime Minister Jawaharlal Nehru bet big on science and technology, nurturing what he termed India’s “scientific temper,” a quality that in late antiquity had led its thinkers to discover “the zero,” a foundational concept in modern mathematics. India started its own space program as early as 1962. It launched its first satellite in 1975. It established the Indian Institutes of Technology to rival the technical colleges of the West.

“In the 1950s, 1960s, and 1970s, policymakers across the Global South adopted these radical economic ideas,” writes MIT anthropologist Dwaipayan Banerjee in his new book Computing in the Age of Decolonization: India’s Lost Technological Revolution, which traces the nation’s early computing successes and failures. For some Asian nations, these bets paid off. South Korea and Taiwan bolstered their manufacturing bases and offered incentives to companies that carried out innovative research. Today, the two countries together are the world’s largest net exporters of computer chips. In India, the ingredients were present, but somehow the bread didn’t rise. India retreated into low-cost outsourcing and offshoring services, becoming more of a servant than a competitor to Silicon Valley. Unlike South Korea and Taiwan, India imports far more computer chips than it makes.

“This was not the future that Indian policymakers hoped for,” Banerjee observes. The postindependence dream had been to harness high technology to repair the damage of colonial underdevelopment, perhaps even to leapfrog the nations that had once ruled the country. As the historian S. Irfan Habib has noted, “for Nehru, the solution to India’s problems lay in a combination of socialism and science, of technology and heavy industrialisation.” Instead, notes Banerjee, “India provides the raw labor that drives Global North research and manufacturing.”

Indeed, he adds, American computing dominance is premised on underdevelopment elsewhere. If it had not depressed the technological ascent of countries like India and absorbed their engineering talent, the United States would never have become the computing power it is now.

This is more than a story of bad decisions and lost opportunities. For Banerjee, it’s a cautionary tale about the enduring effects of empire: the long psychological reach of providing cheap labor and raw materials for two centuries while the colonizers reaped the real profits. In the 18th century, India had been one of the world’s biggest exporters of fine cotton textiles. By the early 19th century, its own cotton was being processed at an industrial scale by English textile mills, making Britain unimaginably rich while India languished.

“Computing has become the new cotton,” he argues.

Banerjee’s case study comes by way of a single machine: the Tata Institute of Fundamental Research (TIFR) Automatic Calculator. Developed in Bombay in the 1950s, it was India’s first homegrown digital computer. Having witnessed how foreign powers kept India industrially stagnant by rationing access to technology, scientists and engineers at TIFR resolved to build a device sans foreign parts or input. In this way, they hoped to show that they could be self-reliant, in true Gandhian style.

The internationally celebrated physicist Homi J. Bhabha, who was the founding director of TIFR and the driving force behind India’s nuclear program, recognized that Indian science would be lost in a backwater without fast computers. Paper and pencil were no longer enough to get Nobel Prize–winning research done. The future lay in processing power. The ENIAC at the University of Pennsylvania (the first general-purpose electronic computer, announced in 1946), mathematician John von Neumann’s IAS machine at Princeton (operational by 1952), and the IBM 701 (the company’s first commercial digital electronic computer, introduced in 1952) were turning the United States into the center of the scientific world.

Bhabha became a power broker, furiously working to ensure that scientists at TIFR had the equipment they needed. Leveraging his personal contacts and considerable charisma, he earned both the government’s backing and the support of left-leaning Western researchers who felt that formerly colonized countries had been deliberately overshadowed by “a kind of apartheid in global scientific expertise,” Banerjee writes. Although India was officially nonaligned, the Cold War played out behind the scenes....

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"Russian Strikes on Dozens of Grain Ships Threaten Global Food Supplies"

The wheat section of August 13's "U.S. Drought Monitor: Continued Slow Spread/Increase in Severity Levels" looked at the Ukrainian attacks on Russian ports and shipping. Here the Times goes the other way round.

From the New York Times, August 13:

The deadly strikes are part of a long-running maritime battle with Ukraine and echo battles over other key shipping routes around the world. 

As families strolled along a promenade in the Ukrainian port city of Odesa recently, a lone cargo ship sailed in the distance. Suddenly, a drone dropped out of the clear blue sky and slammed into the vessel, sending smoke billowing across the horizon.

The attack, which I witnessed in late July, was part of a Russian campaign targeting cargo vessels that has halted nearly all shipping in and out of Odesa, Ukraine’s most crucial link to the world economy.

Coming at the start of the harvest season, the Russian campaign in Ukraine, which is a major producer of grains like wheat, corn and barley, could throw global food supply chains into turmoil. In 2022, during the first months of the full-scale war, Russia established a blockade of Ukrainian ports, causing a spike in global food prices and placing an estimated 70 million people at increased risk of acute food insecurity.

“Russia is hunting these vessels,” Dmytro Barinov, president of the Ukrainian Ports Association, said in an interview. “It’s a professional hunting for civilian vessels.”

The Russian strikes are the latest chapter in a long-running battle over the Black Sea and the adjacent Sea of Azov. New weapons that allow for precision targeting at great distances have made slow-moving cargo vessels easy targets. In June and July, Russian forces attacked at least 50 cargo ships, according to Ukrainian officials, killing more than 30 civilians.

Ukraine has also systematically targeted Russian-linked tankers and cargo vessels. Moscow’s maritime campaign appears to be a response to Ukraine’s decimation of the Russian fleet of river-sea oil tankers in the Sea of Azov and to Kyiv’s continued targeting of ships transporting Russian goods, which has left occupied Crimea isolated and starved for fuel.

Ukraine’s latest major assault took place before dawn on Wednesday, when the country’s forces attacked the Russian port of Novorossiysk, on the Black Sea, with jet-powered drones, missiles and unmanned naval systems, President Volodymyr Zelensky said. Air defense positions, piers and seaport infrastructure were struck, he said.

Three people died, including an 8-year-old child, and about 60 residential buildings were damaged, Veniamin Kondratyev, the governor of the Krasnodar region, which includes Novorossiysk, said on social media. Andrei Kravchenko, the mayor of the city, which is a critical hub for grain exports, declared a state of emergency. He said the city’s water supply had been suspended after two mains were damaged.

The escalating battle between Russia and Ukraine is a flashpoint in a larger global phenomenon that threatens one of the foundational tenets of the international order: freedom of navigation.

That maritime system, which underpins modern global commerce, is under strain as state and nonstate actors increasingly use drones, anti-ship missiles and mines to disrupt routes, particularly in places like the Red Sea and the Strait of Hormuz. The strait, a key conduit for oil and gas, has effectively been closed since the United States and Israel attacked Iran in February.

At the port in Odesa and two others nearby, the Russian blockade could trap millions of tons of grain. The three ports account for about 60 percent of Ukraine’s total monthly export revenue of $3.5 billion. The Ukrainian Agri Council, a trade group, has warned of a potential wave of bankruptcies and has pleaded for emergency loans and state guarantees....

....MUCH MORE 

And year-to-date of the soft red winter wheat futures traded in Chicago via TradingView: 

ZW1! 676'4 +23'6 (+3.64%)

  

"World Models Are AI’s Next Frontier"

One of the overarching themes in the arc of AI development: Large Language Models and especially chatbots are not the be-all and end-all of artificial intelligence.

The writer,  Celine Herweijer, is Visiting Professor in Energy and Geopolitics at LSE and former Group Chief Sustainability Officer at HSBC.

From Time Magazine, July 15:

Inside the labs building the next generation of AI, a phrase has been gaining weight: world models. A large language model like ChatGPT, Claude, or Gemini predicts what comes next in text. World models, in contrast, learn dynamics from observation, then simulate forward to test what happens next. They model the world itself, rather than just descriptions. 

Yann LeCun, who left Meta in late 2025 to launch Advanced Machine Intelligence Labs, has built his research program around it. Demis Hassabis, who runs Google DeepMind, has made world models central to its push toward more general AI. Sam Altman has called OpenAI’s Sora a world simulator, a claim that is contested. Fei-Fei Li raised a billion dollars for her company World Labs to pursue what she calls “spatial intelligence.” Jensen Huang, meanwhile, is building the simulation platforms and compute behind the next wave of AI, as NVIDIA did for large language models.

The term is used loosely; not everything marketed as a “world model” qualifies in the strict architectural sense.

The bet, and the reason LeCun rejects video-generators like Sora, is that a model trained on how a system behaves rather than how it looks, an architecture he calls JEPA, will generalize better to the physical world. It is not a product category but an architecture that could take AI from fluent at language but with no real model of the physical world, to a grounded understanding of how that world behaves. 

If they are right, this is more than another commercial AI cycle. It is the period in which the substrate of the next AI gets built, and what gets built now, by whom and on what data, will shape what AI can do for years. The potential for solving problems in climate, oceans, the biosphere, and the biology of disease is vast. 

What AI has already accomplished for the Earth

I started my career as a climate scientist at NASA running ocean-atmosphere simulations on supercomputers. I later co-wrote, with the World Economic Forum and Microsoft’s chief environmental officer, two of the earliest reports on AI and the Earth system. A lot of what we predicted has happened. 

AI now spots wildfires and methane leaks from orbit. Today’s weather forecasts are unrecognizably better. Google’s flood forecasting runs in over 150 countries. Neural weather models, from DeepMind’s GraphCast to systems now run by the public forecasting agencies themselves, have matched or beaten the best physics-based forecasts at a fraction of the compute cost, though they still trail on extremes and tail risk.

These are real gains. But the hardest problems have barely moved: what a hurricane will do at landfall, when the next drought breaks, how ocean circulation behaves as the ice melts. 

Sub-seasonal weather forecasts—the window that drives water, energy, and agricultural planning—remain weak. The forests, soils, and vegetation that absorb roughly a third of our emissions carry the largest uncertainty in the entire carbon budget. Unlike fossil fuel emissions or atmospheric carbon dioxide, this land carbon sink cannot be measured directly at the global scale; it has to be inferred. And tropical convection, the storm systems that deliver rainfall for billions, unfolds at scales too small for global models to capture, so they fall back on rough approximations that scientists have tried to improve for decades.... 

....MUCH MORE

If interested see also November 2025's "He’s Been Right About AI for 40 Years. Now He Thinks Everyone Is Wrong.". 

"Great disappointment will follow the Great Wealth Transfer as baby boomers pass on just a fraction of their fortune to millennials and Gen Xers"

I suppose you could call this progress, going from the Great Depression to the Great Recession to the Great Disappointment. 

From Fortune, August 8:

Baby boomers are sitting on immense wealth after benefiting from a historic era of economic growth and financial gains, but heirs shouldn’t get their hopes up too much. 

Estimates of the so-called Great Wealth Transfer vary, reaching as high as $124 trillion. A report last month from Visa Business and Economic Insights put the number at a more modest $93 trillion, though that’s still three times the size of U.S. GDP.

But millennials and Gen Xers with great expectations for inheriting that wealth may be setting themselves up for great disappointment. Visa likened it to winning the lottery, then seeing the eventual check whittled down drastically.

“You hit the jackpot, but you immediately lose half by—smartly—taking the lump sum,” the report said. “Next, you lose another 30–40% through taxes and fees. The advertised jackpot is enormous, but after the lump-sum haircut, taxes, and fees, the take‑home number is much lower. A similar dynamic applies to the great wealth transfer.” 

For the Great Wealth Transfer, Visa calculated that boomers will pass on $36 trillion of their $93 trillion in wealth, translating to about $515,000 per inheriting household.

That’s after excluding wealth from the top 1% of households, subtracting debts and other liabilities, as well as deducting retirement spending, charitable donations, taxes, and fees.

Despite being the wealthiest generation, boomers are still burdened by significant debt. In fact, 41% of homeowners ages 65 to 79 and 31% who are 80 and older still carry mortgage debt, according to Visa.

Their other liabilities include credit cards and auto loans, borrowing against brokerage accounts and other investments, as well as personal and business loans.

“Taken together, the high share of cost-burdened older homeowners and substantial non-mortgage debt indicate that many baby boomers have far less financial flexibility—and potentially less wealth to pass on—than headline figures might suggest,” the report said.

After backing out all that debt, about $88 trillion remains. A third of that belongs to the top 1%, so excluding them leaves $60 trillion. But the top 2% to 10% owns $44 trillion, meaning the bottom 90% of boomer households hold the remainder—just $16 trillion.

Boomers will also spend a chunk of their wealth, roughly $16 trillion, during retirement. Think housing, food, health care, prescription drugs, and other essentials. And of course, they must also pay taxes....

....MUCH MORE 

So in forty years Gen-Xers will be humblebragging to Gens Alpha and Beta: "Yeah, well my generation lived through the Great Disappointment and let me tell you..." 

"PPI Inflation Is in the Revisions? Prior Month Services PPI & Core PPI Massively Revised Higher Today"

Following on yesterday's "Inflation: Producer Price Index UNCH in July; UP 4.7% Over The last Twelve Months". 

From Wolf Street, August 13:

In July, the plunge in energy prices migrated into services via truck transportation services and others. 

The Producer Price Index final demand, which tracks broad inflation in prices that companies pay each other, edged down 0.03% in July from June (annualized -0.3%, blue in the chart), held down in part by the PPI for energy, which plunged for the second month in a row in July. These energy prices are part of the input costs for companies, and it spread across industries, including the services PPI via truck transportation services and other services, where energy costs weigh heavily.

But June was substantially revised higher today, to -0.1% today from -0.28% reported originally a month ago, on a massive up-revision of the services PPI.

Year-over-year, the overall PPI rose by 4.7%, still a lot of inflation, but lower than the multi-year highs in the prior three months of 5.5% to 5.9% (red). The PPI has been zigzagging higher ever since the low point in mid-2023.

 

The services PPI rose by 0.20% in July from June, held down by prices of truck transportation of freight, which plunged by 1.8% month to month, due to the plunge in energy prices.

The services PPI accounts for 68% of the overall PPI final demand. It’s the biggie.

But inflation was in the revision. A month ago, the BLS reported that the June services PPI rose by 0.21% in June from May. Today, it more than doubled that inflation for June to +0.47%.

Year-over-year, the services PPI rose by 3.9%, a deceleration from the upwardly revised June reading (red line). That’s a lot of inflation in services. It has been zigzagging higher since the December 2023 low....

....MUCH MORE 

 Also at Wolf Street:

The Fed Cuts its Reserve Management Purchases (RMPs) to Zero, Starting August 14  

AI: China's DeepSeek Is Raising Prices 50% To 1000%

This at the same time OpenAI is lowering prices.*

From CryptoBriefing, August 13: 

DeepSeek raises API prices by up to 1,100% starting Aug. 16  

The Chinese AI startup's rock-bottom pricing era appears to be over as surging demand forces a dramatic rate restructuring across its V4 model lineup. 

DeepSeek, the Hangzhou-based AI company that rattled Silicon Valley with absurdly cheap language models, is about to get a lot less cheap. Starting August 16 at 16:00 UTC, the company will raise API prices across its V4-Flash and V4-Pro models by anywhere from 50% to more than 1,100%, depending on token type and time of day.

The increases come with a new peak and off-peak pricing structure. Using the API during busy hours will cost twice as much as using it during off-peak windows....

....MUCH MORE
*CNBC, July 30:

OpenAI cuts prices for two of its GPT-5.6 AI models as companies grow sensitive to costs 

• OpenAI announced it is slashing the price of two of its latest artificial intelligence models, GPT-5.6 Terra and GPT-5.6 Luna.
• The company said it is reducing the price of Terra by 20% and the cost of Luna by 80%.
• The company is facing pressure to cater to a more cost-sensitive customer base and fend off competition from Chinese startups and other tech giants. 

....MUCH MORE 

Fuel: "Diesel Crack Spread Explodes To Record As Wall Street Warns Of Refined-Products "Perfect Storm""

From ZeroHedge, August 13:

Wall Street Warns About "Perfect Storm" Diesel Crunch: 

• Goldman's Daan Struyven Shows Global Diesel Exports Crashing
• Citi's Anthony Yuen Warns: Global Diesel Inventories "Below 5YR Minimum"
• BofA's Francisco Blanch Warns: "Diesel's Perfect Summer Storm" Unfolding 
• Jefferies' Sam Burwell Warns: Hormuz Shock "Manifesting Itself In Cracks, Not Crude" 

Brent crude remains hostage to daily geopolitical developments in the Gulf region more than five months into the conflict, with muted traffic through the Strait of Hormuz (read the latest US-Iran wrap) constraining tanker flows and driving refined-product markets to new, dire extremes as they become the focal point of the energy crisis.

Brent briefly fell below $80 a barrel last week as prospects improved for an Iran-Oman deal to reopen the maritime chokepoint, before rebounding toward $90 as negotiations stalled this week.

Hormuz traffic has stabilized at about 10 crossings a day, down from 30 to 40 before the latest escalation. Liquids flows are averaging roughly 4 million barrels a day, well below public estimates of 9 million, according to HSBC analysts.

We earlier cited Jefferies analyst Sam Burwell, who warned clients:

"What this all shows is that global oil-market tightness is manifesting itself in cracks, not crude, at least for now. Wide cracks suggest refining runs should remain strong, however, which is positive for crude. 

By lunchtime Thursday, the front-month US diesel crack spread (HOCL1 on the Terminal) had exceeded the $97 level reached in mid-March, when the US-Iran conflict was just three weeks old, and was closing in on $100. That signals extreme tightness in diesel....

***

...Francisco Blanch, head of commodities at Bank of America, warned clients in a note earlier titled "Diesel's Perfect Summer Storm" that the industrial fuel is "materially disrupted in 3 of 4 major regions" around the world.

As we recently warned (see report: The crude reality of oil markets), supply disruptions are amplifying the squeeze on petroleum markets.

Three of the world's four major refining hubs remain impaired for one reason or another.

First, the closure of the Strait of Hormuz and adjacent military activity has reduced Middle East fuel exports, with the recent Houthi strike on Saudi Arabia's Jazan refinery being the latest example.

Second, record Russian refining disruptions following Ukrainian strikes have removed significant volumes from the global diesel pool. 

Third, fearful of potential domestic shortages, China has yet to restart petroleum product exports to the Asia region. As such, Europe has increasingly relied on record US exports to fill the gap.

Yet those flows are drawing down already tight US inventories, the only major hub open for business, creating a global competition for fuel that is pushing diesel cracks back toward record seasonal highs.

Beyond Ukraine drone-striking Russian energy assets, Moscow has decided to ban diesel exports; yet again, more evidence of dwindling global supplies:...

....MUCH MORE (chart mania) 

Previously:

March 11 - "Diesel markets, upended by Middle East conflict, threaten global economic slowdown"
Diesel, very important.... 

March 2 - Fuel: More On Saudi Arabia's Giant Ras Tanura Refinery

March 5 - Diesel Prices Are Rising Fast

March 8 -  Diesel equals Soybeans; Soybeans equal Diesel (in more ways than one) reprised 

We will see a double whammy in the ag sector. Fertilizer prices, including ammonia which is made from natural gas, are rising fast. Additionally, with the Northern Hemisphere planting season approaching, the cost of diesel fuel for the tractors and later this year for combines and other harvesting implements will of necessity pass through, eventually, to the end user....

April 10 - A Look At The Chevron Refinery Built To Process Venezuelan Crude Oil (plus a look at Venezuela's economic uptick)

April 14 - "‘It’s killing everything.’ California’s truckers are buckling under country’s priciest diesel"

July 12 - The World Runs On Diesel and Diesel Is Running out

With Russian shipping and refining capacity getting whacked we're seeing crack spreads greater than the price of crude itself.

July 15 - "JPMorgan shifts focus from Hormuz oil chokepoint to Russian refining crisis"

Or a picture that's worth a thousand words, the stock price chart of independent refining behemoth (revenue [ttm] $139.40B) Valero:

 

Year-to-date up 108%; past twelve months up 154%

U.S. Drought Monitor: Continued Slow Spread/Increase in Severity Levels

From the University of Nebraska-Lincoln, August 13:

 

A different presentation this week, showing change in classification. The yellow and a small patch of orange in Oklahoma are areas of concern. The experimental Drought Severity and Coverage Index (DSCI) was up for the third consecutive week, from 159 to 165 on a 500-is-bone-dry-from-coast-to-coast scale. 

Week	None	D0-D4	D1-D4	D2-D4	D3-D4	D4	DSCI
2026-08-11	26.38	73.62	50.38	29.50	10.27	1.04	165
2026-08-04	29.05	70.95	48.54	28.57	9.51	0.95	159
2026-07-28	32.08	67.92	47.89	28.30	10.00	0.86	155
2026-07-21	34.76	65.24	45.77	29.14	10.71	0.93	152

From  Kansas Wheat (the state wheat commission + the growers association), what's what in wheat: 

Which Wheat for What? 

Six classes of U.S. wheat

 

So far this year the action in Chicago SRW wheat (ZW) has been driven by the wars in Iran (fertilizer - fuel; Feb. - May) and in Ukraine, rather than U.S. weather with the latest upticks occurring after heavy Ukrainian attacks in the Black Sea: 

 

FinViz 

Ukrainian drone strikes halt Novorossiysk grain terminals, sparking wheat futures rally

• Chicago SRW wheat (ZW) surged 3.61% to 653.00 as of Aug 12, 2026, erasing prior losses.
• Overnight Ukrainian drone and missile attacks targeted Russia's Novorossiysk port, halting operations at two major grain terminals.
• Novorossiysk is a primary Black Sea export hub; Russia is the world's top wheat exporter with most shipments via these ports.
• Analysts expect Russian August wheat shipments at decade lows; IKAR cut 2026/27 export forecast to 44.5M tonnes.
• Ukraine lowered its 2026/27 grain export outlook by up to 12%; Turkey suspended Black Sea vessel transits over security risks.
• USDA's August WASDE (released same day) showed tight US stocks at 717M bu for 2026/27 with steady production at ~1.53B bu amid Northern Plains dryness.
• Global supply concerns from EU heat/drought, Russian harvest/shipping issues, and Black Sea disruptions amplified the move. 

Inflation: Producer Price Index UNCH in July; UP 4.7% Over The last Twelve Months

 From the Bureau of Labor Statistics, August 13:

PRODUCER PRICE INDEXES - JULY 2026 

The Producer Price Index for final demand was unchanged in July, seasonally adjusted, the U.S. Bureau of Labor Statistics reported today. Final demand prices edged down 0.1 percent in June and rose 0.5 percent in May. (See table A.) On an unadjusted basis, the index for final demand increased 4.7 percent for the 12 months ended in July.

In July, a 0.2-percent increase in the index for final demand services and a 2.2-percent advance in prices for final demand construction offset a 0.7-percent decrease in the index for final demand goods.

Prices for final demand less foods, energy, and trade services rose 0.4 percent in July after inching up 0.1 percent in June. For the 12 months ended in July, the index for final demand less foods, energy, and trade services advanced 4.7 percent.

Final Demand Final demand services: Prices for final demand services advanced 0.2 percent in July after rising 0.5 percent in June. The July increase can be traced to the index for final demand services less trade, transportation, and warehousing, which moved up 0.6 percent. Conversely, the indexes for final demand transportation and warehousing services and for final demand trade services decreased 1.8 percent and 0.1 percent, respectively.

Product detail: Leading the July increase in prices for final demand services, the index for portfolio management advanced 6.5 percent. Margins for health, beauty, and optical goods retailing; automobiles and automobile parts retailing; lawn, garden, and farm equipment and supplies retailing; food and alcohol retailing; and food and alcohol wholesaling also moved higher. In contrast, prices for truck transportation of freight fell 1.8 percent. The indexes for machinery and vehicle wholesaling and for securities brokerage, dealing, and investment advice also decreased. (See table 2.)

Final demand goods: The index for final demand goods fell 0.7 percent in July after moving down 1.4 percent in June. A major factor in the July decrease was a 3.1-percent decline in prices for final demand energy. The index for final demand foods moved down 0.9 percent. Conversely, prices for final demand goods less foods and energy increased 0.1 percent.

Product detail: More than half of the July decrease in the index for final demand goods can be attributed to a 5.7-percent decline in prices for gasoline. The indexes for fresh and dry vegetables, diesel fuel, jet fuel, residual fuels, and thermoplastic resins and materials also fell. In contrast, prices for motor vehicles and equipment moved up 0.3 percent. The indexes for electric power and for grains also increased....

....MUCH MORE 

Capital Markets: "Becalmed FX Market"

From Marc to Market:

The US dollar is mixed against the G10 currencies.  Despite yesterday’s constructive price action, there has been little follow-through buying today.  Mostly narrow ranges prevail.  While the Japanese government reported signaled acceptance of faster rate hikes, the greenback has held above JPY159 and the 10-year JGB yield edged higher. It has not fallen since last Thursday. 

US Energy Secretary Wright claimed that almost 9 mln barrels of oil a day transited the Strait of Hormuz over the past week, which is well above the 4-5 mln barrels estimated by those tracking flows.  Still, the actual amount is indeterminate as many vessels are tuning off transponders and GPS.  Satellite-imagery vendors have reportedly ceased sales of high-resolution pictures....

....MUCH MORE  

It's quiet out there. Too quiet. 

"AI computing power is becoming a tradable asset class as CME launches futures contracts" (tied to H100 and B200 GPU rental costs)

Speaking of financialization....* 

From CNBC, August 11: 

• CME Group is partnering with Silicon Data to introduce two compute futures contracts on Oct. 5, pending regulatory approval.
• The new asset class will give companies and investors an avenue to trade and hedge the price of AI computing capacity, much as they do oil, electricity and other commodities. 

Computing power is emerging as a new tradable asset class, with CME Group set to launch the first futures contracts tied to the cost of running the chips that power artificial intelligence.

The exchange is partnering with Silicon Data to introduce two compute futures contracts on Oct. 5, pending regulatory approval, giving companies and investors a way to trade and hedge the price of AI computing capacity much as they do oil, electricity and other commodities.

“For years, two companies buying the exact same GPU capacity could pay wildly different prices with no way to know who got the better deal. They will now have a benchmark to check that against,” Carmen Li, CEO of Silicon Data, said in a statement. “Compute futures give the market something it’s never had: a public, tradable reference price for the resource every AI system runs on.”

The contracts will allow buyers and sellers to trade against the rental cost of Nvidia’s H100 and newer Blackwell B200 graphics processing units and will be based on Silicon Data indexes that track hourly GPU rental prices. Each contract will represent a month’s rent for the Nvidia H100....

....MORE 

Here's the Chicago Merc: "Trading the infrastructure of tomorrow"

*Post immediately below:

"Foot Soldiers of Financialization: When New York City Was Occupied By Yuppies"