"How to Keep Global Agricultural Output Rising"

From Timothy Taylor at his The Conversable Economist blog, October 16:

Global population is expected to keep rising for the next 50-60 years, because it will take awhile for the demographic force of declining birthrates to overcome the life expectancies. In addition, the world still has lots of very poor people: for example, about 800 million people around the world live below the World Bank extreme poverty line of $3/day, and of course hundreds of millions more are not far above that line. With a rising world population, and one where we hope the standard of living for the poorest will be rising, more food production will be needed. So how is the world doing in this area?

The Global Agricultural Productivity initiative at Virgina Tech University,tackles the big questions of global agricultural productivity in the 2026 GAP Report, Restarting Growth: The Role of Regenerative Agriculture in Productivity Growth and Profitability, with a team effort led by Chellie Maples. Here’s a picture to set the stage:

Growth in total agricultural output can come from several sources: more land, more irrigation, more intensive use of inputs (labor, materials, and capital), and increased productivity given the same amounts of land, irrigation, and inputs (say, from improved farming practices, better weather information, improved seeds, and so on). To do these calculations, gains in agricultural output that cannot be attributed to measurable factors like land, irrigation, and inputs are then ascribed to productivity gains.

In the 1960s, most growth of agriculture came from more intensive use of inputs. By the 1990s and early 2000s, most of the growth of agricultural output was coming from producitvity gains, with very little coming from land expansion or additional inputs. But since about 2011, growth of agricultural output has slumped. Productivity gains are way down, although expansion of land and inputs have made up some of the difference. In a nutshell, this is global challenge of finding ecologically-sound ways of increasing agricultural output.

Of course, these factors vary by region. But even in the areas with relatively rapid growth in agricultural output, that growth is being driven by additional inputs (like South Asia) or by additional land (sub-Saharan Africa)....

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Previous visits to The Conversable Economist (there are many) including such hits as:

2013's A Fine Addition to The Economist's Big Mac Index: The McWage Ratio (MCD)

2021's "Some Economics of Sawdust"

2022's  “Economics is the Social Science of Love”

2026's "The Austin Experience: More Housing, Lower Rents"

Andreessen Horowitz Has Raised $1.1 Billion For "The Machine Age Fund"

 From Ben Horowitz et al. at a16z, August 28:

We’ve raised $1.1B for a16z’s newest fund: the Machine Age Fund. It’s time to open the throttle and accelerate the physical buildout of AI: the strongest tool ever developed for solving problems and bestowing abundance. It is our social and national imperative.

For anyone paying attention, this shouldn’t be a surprise. We’re seeing an upward inflection point in utility and variety of work that AI can do, just over the past year. And as we evolve from chat to reasoning to coding and other forms of knowledge work, both the demand for work and the token intensity of the work increase by orders of magnitude. Machine intelligence is going vertical.

This fund will invest into all of the computer infrastructure on which AI runs, including chips, memory, networking, and storage. It also includes full systems for running AI: from data centers to robotics to home AI appliances. The common thread among all of these layers of the AI stack is that they are all hitting the wall of today’s supply chain capability, and the limits of physics and computer science. So there is an urgent need for innovation and investment, and a once-in-a-generation opportunity to rearchitect them as platforms, all the way down to the electricity.

Reinventions like this happen at each epoch of compute, from the shift from mainframe to client server, to the Internet, cloud and mobile. However, what is different this time is the magnitude and breadth of change needed to keep up with demand, and the speed required to do so. Everything needs an upgrade, now:

• Compute density per rack increased by 28X from an H100 rack to a Rubin rack.
• Networking within a rack has grown similarly, hitting the limits of copper cabling
• Rack power moved from roughly 5-10 kW to 100-250 kW to support today’s systems and will increase to 1MW over the next 3 years.
• Data center scale is moving from tens to hundreds of MW and, in some cases, GW-scale campuses.
• Power is increasingly supplied not only from grid-only to grid plus behind-the-meter or captive sources.

We need faster, more efficient systems. We need cheaper and higher-bandwidth memory across the memory hierarchy. We need faster and more scalable interconnects between nodes and systems. We need power efficient edge devices for AI to explore and interact with the world. And of course we need all the cooling, materials, electrical, and real estate build out to support them. The hardware industry supply side is used to growing 20% to 30% per year at most; not the triple-digit growth that’s needed to catch up with demand. This will change, quickly.

Founders understand the opportunity. Over the last couple of years we’ve seen hardware startups grow from a small amount of deal flow to now over 20%. While we at a16z are generally known as software investors, we have always paid attention to hardware. Recently we’ve backed a number of hardware companies including Unconventional AI, Nexthop, Volta, Atoms, Heron Power and Mind Robotics. And we have selectively invested in hardware companies since our earliest days. In 2016, we led the series A in Skydio; we invested in SpaceX; we wrote our first check into Anduril in 2019, and were among the first venture investors in Waymo’s 2020 raise....

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"An Abecedary Of Scientific Serendipity"

The author of this excursion, Sheldon Lee Glashow, is old 
(emeritus professor at both Harvard and Boston University)
He also has one of those Nobel thingies. (physics, 1979)

From the (possibly) resurrected Inference Review:

The most exciting phrase to hear in science, the one that heralds new discoveries, is not ‘Eureka!’ but: ‘That’s funny...’

—Isaac Asimov

Horace Walpole coined the word serendipity in a letter to Horace Mann. The two men had become friends during Walpole’s Grand Tour of Europe in 1739. Walpole returned to England a year later while Mann remained in Florence as Britain’s permanent envoy, a position he secured with assistance from Walpole’s father. The two men never met again but corresponded for forty-six years.

In one of his letters, Walpole wrote:

I once read a silly fairy tale, The Three Princes of Serendip. As their highnesses traveled they were always making discoveries, by accidents and sagacity, of things they were not in quest for: for instance, one of them discovered that a mule blind of the right eye had traveled the same road lately, because the grass was eaten only on the left side where it was worse than on the right — now do you understand serendipity?

The Travels and Adventures of the Three Princes of Serendip first appeared in a fourteenth century Persian poem by Amir Khusrau. The mule that Walpole recalled from childhood had actually been a one-eyed lame camel, driven by a woman and laden with bags of butter and honey. Given these clues, the prince deduced the path the lost camel had taken. The camel was found, the princes richly rewarded. The tale was translated from Persian to Italian in 1557, from Italian to French in 1619, and then to English in 1721. The story of the wayward one-eyed camel may be traced back to the sixth century Talmud, the princes replaced by Jewish slaves, the butter and honey, by wine and oil.

The Abecedary

A is for the first aniline dye, stumbled upon in 1856 by the 18-year-old English chemist Henry Perkin whose German tutor asked him to synthesize quinine from coal tar. Perkin ended up with a dirty brown sludge. After cleaning his beaker with alcohol, he noticed that the sludge had turned purple. Patented and marketed as Perkin’s Mauve, it was exhibited publicly by Empress Eugénie thereby becoming highly fashionable and making Perkin rich. His tutor synthesized another aniline dye, magenta, and helped to create the huge German dye industry. Sir William Henry Perkin was knighted in 1906 and quinine itself was first synthesized in 1944, just in time for its use in World War II. Mauve was not alone among serendipitous synthetic dyes: Prussian blue was accidentally hit upon in 1706, Scheele’s Green in 1775, Monastral blue in 1927, Pyrrole red in 1974, and Yin Mn blue in 2009. Serendipity comes in many colors.

B is for Botox. The powerful toxin produced by botulism bacteria was introduced to medicine by Alan B. Scott. His experimental drug, a form of the toxin later called Botox, was approved in 1989 for treating eye muscle problems, but often found off-label targets such as migraine headache or urinary incontinence. Its bonanza application arose by chance when Jean Carruthers saw her patient’s facial wrinkles vanish after treating her with Botox. For years she and her husband explored the many cosmetic applications of Botox. Once approved for such purposes, Botox became a multi-billion dollar drug, but the Carrothers had neglected to patent its cosmetic use.

C is for Cisplatin, the trade name for a chemical first synthesized in 1844 and called Peyrone’s salt. A century later, the American chemist Barnett Rosenberg set out to explore the effect of electricity on bacterial growth by sending electric currents through media containing live bacteria. Noticing that the cells stopped dividing, Rosenberg hoped that electricity might be used to treat cancer, but the suppression of cell division had resulted from a compound produced at their platinum electrodes. Rosenberg identified Peyrone’s salt as the most effective such platinum compound. Cisplatin arrived at clinics in 1978 to treat testicular and ovarian cancers. It is often used off label for other cancers. Cisplatin, along with several other platinum-based drugs, account for about forty percent of all effective chemotherapeutic agents.

D is for Dynamite, devised by Alfred Nobel in 1867. Two decades earlier, Ascanio Sobrero had created the liquid explosive nitroglycerine. Due to its instability, he could find no practical use for it. Nobel sought a material that could absorb nitroglycerine thereby yielding a stable solid that could safely be shaped, transported and triggered. He tried many materials – sawdust, cement, charcoal – but with no success. The serendipity of Nobel’s discovery lay in his fortuitous use of sand from dunes near his lab as his absorbent material – not the sand of ocean beaches nor that used to make concrete or fill children’s sandboxes, but kieselguhr, a diatomaceous earth made of ancient fossilized organisms. You can buy it as a health food or an insecticide and it gave rise to Nobel’s prize-creating discovery. Incidentally, symptoms often reported by nitroglycerine workers led to its serendipitous use to treat angina. Late in life Nobel was prescribed nitroglycerine pills for heart disease.

E is for chemical elements discovered more or less by chance. Nine of the prenuclear ninety-two elements qualify: Helium, Phosphorus, Oxygen, Argon, Iodine, Yttrium, Lanthanum, Praseodymium and Neodymium. Three more are found among the twenty-six transuranic elements: Neptunium, Einsteinium and Fermium. Phosphorus was discovered by alchemist Hennig Brand in 1669. He found it by distilling urine in quest of the Philosopher’s Stone. Three centuries later phosphorus bombs helped destroy his hometown of Hamburg. Lanthanum and didymium were chanced upon in 1840 when Carl Mosander tried to purify cerium. Both were regarded as elements and included in Mendeleev’s Table, but forty-five years later Carl von Weisbach was amazed to find didymium to be an alloy of two new elements, which he named Praseodymium and Neodymium....

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Previously from Inference Review:

Disease, Famine, Drudgery, Bondage: A Lively Look at the Birth of the Modern 
Not that kind of bondage. Not that lively a look, sorry.
Refunds available at the entrance.

"Reviewing Mervyn King's The End of Alchemy: Money, Banking, and the Future of the Global Economy"

"The Euro Is Doomed"

Lo and Behold: Andrew Lo's Adaptive Markets

"The Subjective Statistician"

Review: "The Great Leveler: Violence and the History of Inequality from the Stone Age to the Twenty-First Century"

One Euro, One Europe (One Love)

When France Invented The Internet

Tribes and States: Human Self-Organization

Emanuel Derman: "Trading Volatility"

The BIS and the Rise of Secular Stagnation 

The Great Transition: Climate, Disease and Society in the Late-Medieval World 

Economics: "The Miracle of General Equilibrium"

In response to 'At Lunch with Freeman Dyson': "Escape from the Prisoner’s Dilemma"

Vaclav Smil Is A Party Pooper: "History and Risk" 

And many more. Use the 'search blog' box upper left, if interested. 

"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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"Inside the Race to Make AI Build Itself"

From Time magazine, August 7:

Jack Clark, Anthropic’s co-founder, left on paternity leave last November. When he returned in February, he was surprised to learn that colleagues hardly wrote code anymore. They managed five or six copies of the company’s AI, Claude, which sometimes managed several more Claudes. 

To Clark, this looked like an early form of something the field has anticipated and feared for decades: recursive self-improvement, or the point at which AI begins to accelerate its own development. First, the thinking goes, models make researchers faster, but as each improvement feeds the next, the models take over more of the research cycle. Years of progress compress into months—leaving society with little time to absorb the consequences, from job disruption to engineered pathogens. Taken to its limit, AI could improve itself without humans, triggering a runaway loop long known as an "intelligence explosion," where machines rapidly advance beyond human understanding, and possibly beyond human control.

Clark believed the world needed to confront this prospect. He posted a flurry of blog posts on recursive self-improvement and flew home to England to deliver a talk on the topic in May, then led an Anthropic report in June titled “When AI Builds Itself,” arguing the technology is already accelerating its development. The volume of code produced per person at Anthropic has increased eight-fold, with Claude writing 80%, the report noted. “We're trying to help substantiate this concept now ... before it becomes something that is politicized or otherwise gains some valence that makes talking about it difficult,” Clark says.

It worked out as Clark feared. “Anthropic is trying to strike terror into everyone’s hearts,” wrote AI skeptic Gary Marcus, adding “all they have really shown is just faster coding.” Skeptics point out that technological progress has always compounded. Oil is used to drill oil. Why, in AI’s case, should the curve suddenly bend upward? For a company betting on continued advances, they argued, the claim is plainly self-serving.

Even Clark concedes coding volume is a crude yardstick. Claude’s code can be long-winded. But the trouble runs deeper. Neither Claude nor any large language model is written in code at all. Researchers set growth conditions—deciding the size and shape of a neural network, then pour an internet’s worth of text through it, letting it adjust itself billions of times until abilities to answer questions, write code, and hold a conversation emerge. They are cultivated, the way one grows a plant by tending the soil and the light without ever deciding where a single leaf will go. 

Progress, therefore, depends on trial and error. If Claude could take over that cycle, designing, running, and analyzing experiments, would progress accelerate gradually… or suddenly explode? And if it did, could anyone pump the brakes? The uncomfortable truth is that the people building the technology are nearly as much in the dark as everyone else.

Claude began beating the benchmarks
The change Clark had walked into was not entirely unexpected. Fellow Anthropic co-founder and chief science officer Jared Kaplan had long feared that AI would eventually accelerate research, perhaps outpacing safety efforts. In early 2025, he folded a warning into the company’s Responsible Scaling Policy, its plan for managing AI’s growing dangers. Back then, Claude was no good at running experiments. But he believed that would change one day, and Anthropic would need to be ready.

To find out whether that day was coming, Anthropic built a series of tests—tasks that would take a human expert hours. Could Claude train a smaller AI model from scratch? Could it program a virtual robot dog? No single measure would settle it, but together they offered a snapshot. 

In one test, Claude had to rewrite a piece of code to use GPUs—the chips for training AI—more efficiently. In spring, it momentarily got them running seven times faster, then broke the code. By summer, a newer Claude pushed the same speedup from seven times faster to 73 without introducing errors. “We started seeing these tasks fall over,” says Daniel Freeman, a member of Anthropic’s frontier red team who designed the evaluations....

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This is what the former Google employees are up to. With Google's acceptance if not its blessing.

From the New York Times, August 5:

Four Top Google A.I. Researchers Form New Start-Up
Jeff Dean, who for years was one of Google’s most important executives, is leading the new artificial intelligence company with the backing of Google.

Jeff Dean, Google’s chief scientist, and three other artificial intelligence researchers are leaving the company to form their own start-up, a move that exposes the changing power structure inside Google and shows there is still plenty of venture money willing to back new A.I. ideas.

Dr. Dean, Google’s 30th employee, is widely regarded as one of the most important figures in the history of the company. He played a key role in creating the global computer network powering its search business and was one of the company’s early leaders in A.I. research.

His profile inside Google appeared to wane in recent years as other executives, including the co-founder Sergey Brin, asserted control over Google’s all-in bet on A.I. But Dr. Dean remained one of the key engineers driving the creation of the company’s most important technologies.

His new company, called Discovery Loop, joins a growing list of prominent start-ups chasing a goal that has obsessed Silicon Valley researchers for decades. Dr. Dean and his collaborators want to build A.I. that can improve itself with little or no help from humans.

They believe that this mind-bending process, called recursive self-improvement, can significantly accelerate the development of A.I. The same techniques, they say, can then help design new kinds of computer hardware, discover new drugs, create new materials and speed other forms of scientific discovery.

“We think there is opportunity for A.I. to more fully automate what has traditionally been a very human-intensive experimental loop,” Dr. Dean said in an interview at his home in Silicon Valley. “You will get both a higher quantity and a higher quality of experiments, and that will lead to scientific breakthroughs and advances.”

As Google battles to match the A.I. delivered by smaller companies like OpenAI and Anthropic, prominent researchers, lured by a deep pool of venture funding and the chance to run their own companies, continue to leave the tech giant.

Over the past year, Google lost several top researchers, including Peter Norvig, who spent 25 years as Google’s head of research, to A.I. start-ups such as Recursive Superintelligence, one of the many other companies focused on recursive self-improvement.

“It is just a very different environment inside a start-up. It is much more fast-paced and focused,” said Tim Rocktäschel, who left Google earlier this year to help found Recursive Superintelligence. “You are a smaller team. There is much less bureaucracy and politics.”

Dr. Dean, 58, expected to leave Google in the next few days for his new job as the start-up’s chief executive. He will be joined by Sanjay Ghemawat, his close collaborator of more than two decades; Quoc Le, whose research helped inspire the creation of today’s A.I. chatbots; and Oriol Vinyals, who had overseen the design of Google’s chatbot technologies with Dr. Dean since 2023.

The new company is backed by seed funding from Radical Ventures, Khosla Ventures and several Silicon Valley investors, including Google’s parent company. The departing Google researchers declined to reveal how much money their start-up has raised, but they appear to be leaving Google on good terms. Google’s parent company, Alphabet, has also agreed to provide the computing power they need to build their A.I. technologies for at least the next year, Dr. Dean said.

“Over 27 years, Jeff and Sanjay helped to drive some of the most significant technology transitions, from our early search infrastructure to the neural networks that helped create the modern A.I. era,” Google’s chief executive, Sundar Pichai, said in a statement. “On a personal note, it’s been a privilege to work alongside Jeff and Sanjay.”

Google will collaborate with the new start-up, Mr. Pichai said.

Dr. Vinyals said he believed it was important to have the freedom to shape their own company. “Having extreme focus is very powerful,” he said in an interview. “The ultimate form of extreme focus is when it is the only thing that matters — when it aligns with the mission of the company.”

Dr. Dean joined Google in 1999. Along with Dr. Ghemawat and others, he designed the enormously complex software systems that allowed Google’s search engine to reliably and efficiently respond to search queries from billions of people across the world....

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If interested see also:

January 28 -  So it Begins: "Silicon Valley Wants to Build A.I. That Can Improve A.I. on Its Own"

The headline at TechCrunch was "AI chip startup Ricursive hits $4B valuation two months after launch" 

Serious money believes these women are on to something. 

April 26 - U.S. Treasury Secretary Bessent On A.I.: "'a year, maybe 18 months,' before the new technology defines our lives across the board." 

May 8 - AI: "Are we just 18 months away from everything changing?"

May 12 - "AI Is Starting to Build Better AI"

Not there yet but some very smart people think it's close.

May 16 - Recursive Artificial Intelligence: The Coming Acceleration (plus Recursive, the company, raises $650 million)

June 5 - Anthropic Warns Fully Recursive AI Is Coming Faster Than Expected, Humans May Lose Control

In other news...

August 6 - "AI's architects say the next era of human history is here" 

And related:

December 2025 - Introducing Unified Model Collapse

Possibly also of interest:

May 2025 - News You Can Use: "....How AI-enabled coups could allow a tiny group to seize power"

Ahead of July 29's Fauci Hearing Senator Rand Paul Has Released 1100 Pages of Fauci's Diary.

Although it is easy to get caught up in the "Dear Diary, I am so pretty. And popular!" stuff (hundreds of the things), on his tweet announcing the drop the Senator leads with one of the more disgusting entries:

1/9 — My investigation uncovered that Anthony Fauci kept a diary.
What he wrote privately and what he told the country are two different stories.

Today I'm releasing his entries from December 2019 through December 2022. 🧵 pic.twitter.com/58LvmeYkCN

— Senator Rand Paul (@SenRandPaul) July 25, 2026

2/9 Jan. 26, 2020. Fauci writes in his diary: "Now we know the market was not the source, it was the amplifier."

That is January of 2020. The wet market was already off the table in his own private notes. pic.twitter.com/mz0OzcPHgg

— Senator Rand Paul (@SenRandPaul) July 25, 2026

3/9 Feb. 1, 2020. The call that produced Proximal Origin. Twelve scientists.

Only 2 concluded it was natural. Per Fauci's own notes, "the rest felt that deliberate insertion was possible."
This was never a 50/50 room. pic.twitter.com/vFg79if4oB

— Senator Rand Paul (@SenRandPaul) July 25, 2026

....MORE (9 tweet thread at Threadreader) 

And at the Reading Room: 

New Documents

Tony's Diary: What He Wrote Privately vs. What He Said Publicly

July 25, 2026 – Fauci’s personal diary reveals that as early as January 31, 2020, he was told by top virologists that the virus’s furin cleavage site raised real doubts about a natural origin, with about half the scientists on that initial call believing it looked “constructed.” Despite this early private uncertainty, Fauci spent the following years publicly and confidently dismissing the lab leak theory while insisting the science was settled. The diary also documents his private frustration and defensiveness over gain-of-function funding questions tied to EcoHealth Alliance and the Wuhan Institute of Virology — even as he told Congress under oath that no such research had occurred.

Read Tony's Diary 

Also new since our last visit:

New Documents

Fauci's Side Hustle: How NIH Lined Dr. Anthony Fauci's Pockets with Prize Money

July 24, 2026 – Records released today reveal that Dr. Anthony Fauci, the highest-paid federal official in the U.S. government, stacked up cash prizes worth hundreds of thousands of dollars during COVID, with NIH’s number two official serving as his personal awards manager and his taxpayer-funded staff writing the nominations for him. Some of the prizes came from American institutions, but the largest came from a foreign institution that wired him $900,000.

Read the Documents 

New Documents

NEW: Federal Officials Intercepted Undeclared Biological Materials Tied to a U.S. – China Coronavirus-Research Network as Early as 2018

July 24, 2026 – Undeclared coronavirus materials moved between U.S. and Chinese labs as far back as 2018 — years before COVID-19. A courier was caught twice, three years apart…first with a few hidden vials, then with 132 more and a laptop that wasn’t his.

Read the Documents 

Previously:

July 23 - Ahead Of Next Week's Fauci Hearing, Senator Rand Paul Has Opened A Reading Room Stocked With Documents

Following on July 21's "Heads Up From Senator Rand Paul". 

Senator Paul, in his position as the Chairman of the U.S. Senate Committee on Homeland Security and Governmental Affairs will be convening the hearing to take Fauci's testimony on  Wednesday July 29 at 8:30AM ET.

Here are the additions to the background information/documents

"Bezos Backs AI Startup to Discover New Materials for Chipmaking" (NVDA)

From Bloomberg, July 19/20:

Producing the world’s most in-demand semiconductors requires enormous amounts of energy and access to rare minerals. CuspAI, a two-year-old British startup, has raised nearly half a billion dollars on its bet that artificial intelligence can improve that process.

On Monday, CuspAI launched the AI Materials Foundry, a coalition of more than 48 technology giants, industrial firms and research facilities. The goal of this body — whose members include Nvidia Corp., Meta Platforms Inc., and Hyundai Motor Group — is to pool computing and scientific resources with the aim of building software that can help researchers develop new materials for chipmakers and other industries at a faster pace and for a lower cost than today’s methods.

To make this work, the Cambridge, UK-based company has raised $450 million in Series B financing from investors including Jeff Bezos’s fund. Chad Edwards, CuspAI’s co-founder and chief executive, said much of that funding will go towards supporting labs with foundry partners in Cambridge, Singapore and the San Francisco Bay area.

On the research front, he noted, 80% of its efforts this year will go toward work on semiconductors. One potential focus, he added, is to reduce or eliminate the use in chipmaking of rare metals with supply-chain risks, such as ruthenium and iridium.

The new funding — led by venture firms Kleiner Perkins and NEA, with “significant participation” from Bezos Expeditions, according to CuspAI — gives the startup a $2.6 billion valuation, up from $520 million last September. Bloomberg and the Financial Times previously reported on elements of the deal.

CuspAI initially started out with a focus on finding new materials for carbon capture and water purification. But Edwards said that the company decided to pivot over the past year in response to intense demand from the chip manufacturing supply chain, whose products are critical to powering artificial intelligence. Severe shortages of semiconductors, particularly in memory chips, sank shares last week across a range of tech companies dependent on the hardware. Chipmakers and semiconductor suppliers are “frantically searching for new materials,” Edwards said in an interview. “We’ve been literally pulled by all four limbs.”

Geetika Gupta, a senior director at Nvidia, said the chipmaker is seeing huge demand for new materials across its entire market, from energy storage systems to data center cooling. Through the Foundry, Gupta said Nvidia will most likely work on novel materials in “three-way collaborations” between CuspAI and a third partner. “All of these companies, they treat a lot of this information as a secret sauce,” she said.

CuspAI is among the growing number of AI developers turning to science to open new markets. Earlier AI systems have shown they can learn from biological data, like protein shapes, to potentially discover new drugs. Newer models can, in theory, sift through huge swaths of molecular data to generate designs for materials that could be useful for manufacturing and renewable energy. Investors have piled into the field to back efforts led by former OpenAI and DeepMind researchers, as well as Prometheus, a $41 billion new company launched by Bezos. Google DeepMind has also indicated that it will soon begin work on material discovery for AI.

CuspAI is trying to set itself apart with talent. Max Welling, its other co-founder, is a respected AI researcher. Yann LeCun and Geoffrey Hinton, two pioneers of modern AI techniques, are on the startup’s advisory board. On Monday, the company also named Abhi Talwalkar, a semiconductor veteran and the director of Advanced Micro Devices Inc to the board. AMD’s venture unit joined in the latest round of CuspAI financing. CuspAI moreover said it hired John Giannandrea, a former Google and Apple executive, to work part-time setting up the startup’s California outpost....

....MUCH MORE 

Chilling warning from Nobel physicist as date is set for humanity's final destruction

Bummer kids.

Buy short-dated paper.

From The Daily Mail, April 20:

The winner of the Nobel Prize in Physics has issued a stark warning to humanity, saying it could face an existential catastrophe within roughly 35 years.

David Gross, who shared the 2004 Nobel Prize in Physics, warned that 'due to the danger of nuclear war,' humankind may have just a little more than three decades left.

'Even after the Cold War ended, when we had strategic arms control treaties, all of which have disappeared, there were estimates that there was a one percent chance of nuclear war every year,' he told Live Science.

'I feel it's not a rigorous estimate that the chances are more likely two percent. So that's a one-in-50 chance every year.

'The expected lifetime, in the case of two percent per year, is about 35 years.'

The calculation is based on equations similar to those used to estimate the half-life of radioactive materials, which model the probability of an event occurring over time.

'Things have gotten so much worse in the last 30 years, as you can see every time you read the newspaper,' Gross added, pointing to renewed nuclear threats, the war in Europe, escalating tensions involving Iran and recent near-war conditions between India and Pakistan.

Gross won the Nobel Prize for discovering 'asymptotic freedom,' the idea that the strong nuclear force holding atoms together weakens as quarks, tiny subatomic particles, move closer together, much like a rubber band that tightens only when pulled apart.

In the interview with Live Science, Gross also highlighted that there have been no major nuclear arms-control treaties signed in the past 10 years.

'There are now nine nuclear powers. Even three is infinitely more complicated than two,' he said.

The last surviving US-Russia nuclear treaty expired on February 5, 2026. 

The New Strategic Arms Reduction Treaty (New START), signed in 2010, is due to end on February 5. It marks the eighth agreement between the two nations since the 1963 treaty that banned nuclear tests in the atmosphere, outer space and underwater.  

Gross also pointed to the rise of AI, which adds more risks to humanity's existence. 

'The agreements, the norms between countries, are all falling apart,' he said.

'Weapons are getting crazier. Automation, and perhaps even AI, will be in control of those instruments pretty soon....

....MUCH MORE 

The LiveScience article is headlined "'The chances of you living 50 years are very small': Theoretical physicist explains why humanity likely won't survive to see all the forces unified" so I went with the more reserved Daily Mail. 

"Why Swedish Schools Are Bringing Back Books"

From UnDark, April 1:

Amid declining test scores, the country has pivoted away from screens and invested in back-to-basics school materials. 

In 2023, the Swedish government announced that the country’s schools would be going back to basics, emphasizing skills such as reading and writing, particularly in early grades. After mostly being sidelined, physical books are now being reintroduced into classrooms, and students are learning to write the old-fashioned way: by hand, with a pencil or pen, on sheets of paper. The Swedish government also plans to make schools cellphone-free throughout the country.

Educational authorities have been investing heavily. Last year alone, the education ministry allocated $83 million to purchase textbooks and teachers’ guides. In a country with about 11 million people, the aim is for every student to have a physical textbook for each subject. The government also put $54 million towards the purchase of fiction and non-fiction books for students.

These moves represent a dramatic pivot from previous decades, during which Sweden — and many other nations — moving away from physical books in favor of tablets and digital resources in an effort to prepare students for life in an online world. Perhaps unsurprisingly, the Nordic country’s efforts have sparked a debate on the role of digital technology in education, one that extends well beyond the country’s borders. U.S. parents in districts that have adopted digital technology to a great extent may be wondering if educators will reverse course, too.

So why did Sweden pivot? In an email to Undark, Linda Fälth, a researcher in teacher education at Linnaeus University, wrote that the “decision to reinvest in physical textbooks and reduce the emphasis on digital devices” was prompted by several factors, including questions around whether the digitalization of classrooms had been evidence-based. “There was also a broader cultural reassessment,” Fälth wrote. “Sweden had positioned itself as a frontrunner in digital education, but over time concerns emerged about screen time, distraction, reduced deep reading, and the erosion of foundational skills such as sustained attention and handwriting.”

Fälth noted that proponents of reform believe that “basic skills — especially reading, writing, and numeracy — must be firmly established first, and that physical textbooks are often better suited for that purpose.”

In a country with about 11 million people, the aim is for every student to have a physical textbook for each subject.

Between 2000 and 2012, Swedish students’ scores on standardized tests steadily declined in reading, math, and science. Though they recovered ground between 2012 and 2018, those scores had dropped again by 2022.

Though it’s unclear precisely how much of the decline is due to digitization, there is some evidence that analog teaching materials for reading may be superior to screen learning. However, this applies to expository as opposed to narrative texts. Narrative texts tell a story, whether fiction or non-fiction, while expository texts are designed to inform, describe, or explain a topic in a logical, factual manner.

Swedish officials emphasize that digital technology isn’t being removed from schools altogether. Rather, digital aids “should only be introduced in teaching at an age when they encourage, rather than hinder, pupils’ learning.” Achieving digital competence remains an important objective, particularly in higher grades.

Historically, the technology industry has pushed for more use of digital learning, seeing itself as a transformer of education.....

....MUCH MORE 

"Electrical current might be the key to a better cup of coffee"

Electricity, is there anything it can't do?

From ArsTechnica, April 28:

University of Oregon scientists repurposed battery-testing tool to better measure coffee’s flavor profile

University of Oregon chemist Christopher Hendon loves his coffee—so much so that studying all the factors that go into creating the perfect cuppa constitutes a significant area of research for him. His latest project: discovering a novel means of measuring the flavor profile of coffee simply by sending an electrical current through a sample beverage. The results appear in a new paper published in the journal Nature Communications.

We’ve been following Hendon’s work for several years now. For instance, in 2020, Hendon’s lab helped devise a mathematical model for brewing the perfect cup of espresso, over and over, while minimizing waste. The flavors in espresso derive from roughly 2,000 different compounds that are extracted from the coffee grounds during brewing. So it can be challenging for baristas to reproduce the same perfect cup over and over again.

That’s why Hendon and his colleagues built their model for a more easily measurable property known as the extraction yield (EY): the fraction of coffee that dissolves into the final beverage. That, in turn, depends on controlling water flow and pressure as the liquid percolates through the coffee grounds. The model is based on how lithium ions propagate through a battery’s electrodes, similar to how caffeine molecules dissolve from coffee grounds.

Three years later, Hendon’s team turned their attention to studying why the microscopic clumps form in the first place, particularly at very fine grind levels. The culprit is static electricity arising from the fracturing and friction between the beans during grinding. Hendon thought reducing that static would be a good way to eliminate those clumps. The technical term is triboelectricity, which arises from the accumulation of opposite electric charges on the surfaces of two different materials due to contact with each other.

A similar charge build-up also occurs during volcanic eruptions. So Hendon collaborated with volcanologists Josef Dufek and Joshua Méndez Harper, who were regulars at the same local coffee house and had noted striking similarities between the science of coffee and plumes of volcanic ash, magma, and water....

....MUCH MORE  

 If interested see also:

Quanta Services

GE Vernova 

Marko Papic, BCA Research: «If This War Is Not Over in Three Weeks, Things Will Start Breaking Down Rapidly»

From Neue Zürcher Zeitung's TheMarket.ch, April 17: 

Marko Papic, Chief Strategist for GeoMacro at BCA Research, talks about his scenarios on how the war in Iran will play out and what the long term consequences for the world economy and for investors will be. 

Deutsche Version

The war in the Persian Gulf is entering its eighth week. Although a fragile ceasefire is currently in place, the supply of raw materials to the global economy – oil, gas, petroleum products, fertilizers, helium – has by no means returned to normal. The Strait of Hormuz remains largely blocked; only a handful of tankers pass through it each day. The global economy is drawing on its reserves.

Equity markets, however, are back in high spirits. The correction suffered in mid-March has already been absorbed; the S&P 500 is trading at an all-time high, as is the Nikkei 225 in Tokyo.

How does that fit together? «The Spice must flow,» says Marko Papic, referencing the science fiction novel and movie Dune. The chief GeoMacro strategist at BCA Research assumes that the flow of resources from the Persian Gulf will soon normalize. «Both history and game theory teach that we’ll have this outcome. It’s my base case too», he says.

But Papic also warns: «If this is not over in three weeks, things are going to start breaking down rapidly.» In an in-depth conversation with The Market NZZ, Papic explains how he views the chokepoint of Hormuz, how China sees the war, and what longer-term consequences investors need to prepare for: «We will see a global capex boom, and that means a global commodities bull market.»

«We need to rebuild the physical infrastructure for a multipolar world. 

This means a lot of capital investment. We’ll see capex everywhere»: Marko Papic. 

What’s your base case scenario on how the war in Iran and the crisis around the Strait of Hormuz will play out?

I see three scenarios. My base case, to which I assign a 35 to 40 percent probability, goes like this: Iran, Israel and the US find what I would call a kinetic equilibrium, where they can continue to have their enmity, while the world gets its oil and other resources from the Persian Gulf. This would mean a continued state of war, where America and Israel come back and bomb Iran every time they think they do nuclear stuff. But they stop for now and somehow both sides find a way to claim victory. The concept of a kinetic equilibrium might be difficult for many people to process, but that is the history of the Middle East. Throughout the last 80 years or so, wars happened in the region – but those wars did not shut off the flow of oil. Think of the war between Iran and Iraq, a war that lasted for eight years, involving a brutal ground invasion, claiming millions of lives. And yet, for most of the time, oil continued to flow out of the Persian Gulf. That’s not supernatural, it’s simply a function of the fact that too many countries will become involved, because they need those resources.

Is it really an apt parallel to compare today’s war with the war between Iran and Iraq?

Why not? The Gulf War of 1980 to 1988 was infinitely worse than what’s going on today. Most clients, when I tell them that, they bring up Israel and claim it’s different this time. To which I counter that Saddam Hussein was the original Benjamin Netanyahu. Iraq back then was a proxy of the west, armed by France, given intelligence by the Americans, and financed by Gulf petro monarchies. Iran sees both Israel in 2026 and Saddam in 1980 similarly as a proxy of America.

Let’s assume that we’ll have that kinetic equilibrium scenario: What makes you so sure that the flow of oil and other resources will resume? Hasn’t Iran shown that they can effectively close the Strait of Hormuz at will?

One might say that Iran has a lot of leverage with their ability to threaten shipping around Hormuz. But they can’t overplay that leverage without coming under immense pressure. I’m not talking about pressure from American bombs or financial sanctions. I’m talking about the fact that Iran completely depends on China for all of its imports. Imagine a development where eventually Iran’s leverage over Hormuz angers Beijing. In that scenario, China will stop sending Iran the semiconductors that it needs for their Shahed drones. All drones need Chinese components. Eventually, it is difficult to maintain this kind of aggressive composure towards commodities that are required for the rest of the world to operate – including people in South Asia that depend on resources from the Gulf to feed themselves.

What are the other two scenarios?

The best case, to which I would assign a probability of 30 to 35 percent, is a comprehensive peace deal and a lasting ceasefire. In this case, the flow of commodities, be it crude, LNG, refined crude products, fertilizers or helium, will revert back to normal.

And the third?

That’s when it all goes terribly wrong in many different ways. A large scale escalatory spiral where physical infrastructure for the production and shipment of oil and gas in the Gulf region will be attacked and destroyed. It’s clear that both sides have refrained from that kind of escalation so far, but it’s not impossible. This would be an absolute calamity for the world. We’d have shortages everywhere. The world economy would fall into a violent recession. That scenario perhaps has a probability of 30 percent.

So all in all, scenarios one and two, with a combined probability of 70 percent, assume that the resources – oil, gas, fertilizer, helium – will soon flow again and the world economy will get what it needs. While in scenario three, all bets will be off and the world economy will fall off a cliff. Correct?

Yes. But my concern is that financial markets completely agree with the combined two scenarios of a sanguine outcome. The market has not panicked. It agrees with me that there is a 70 percent probability that everything will eventually be okay. That’s a problem for investors.

How so?

The danger is that the market has not left any room for error. It assumes the Spice will flow. That assumption is not wrong per se. History teaches that we’ll have this outcome, and game theory teaches it too. It’s my base case too. But that doesn’t make it a certainty. There is a non-trivial probability that we fall into a calamitous recession, which would be a shock for financial markets.

Right now, we’re still in a situation where none of these scenarios have played out. The Strait of Hormuz is not completely blocked, a handful of ships get through every day, but nowhere near enough to keep the world economy afloat. How much longer can that continue?

We are at a point where, depending on the commodity, it’s between three weeks and three months before things have to get shut down. Then we have a big problem. That’s why I don’t agree with commentators who say that China is ambivalent or even cheerleading to what’s happening in the Gulf. Sure, China may be happy that America looks like an unreliable ally and is depleting its stock of offensive weaponry. But if semiconductor production in Taiwan has to stop because there is no helium or LNG, then Chinese AI ambitions, which is a national security priority, are going to have to be put on ice. And if food prices go up, that’s not something that Chinese policymakers will be comfortable with. That’s why I think China will lean on Iran to reach some form of solution where both sides can declare victory.

President Trump is scheduled to visit Beijing in mid May, less than a month from now. Does this fact increase the chance of a solution before then?

President Trump generally tends to have only one crisis going on at any time. So yes, I do think that he would like to wrap this up by then. China can help by pressuring Iran, and I think it will, because at the end of the day China doesn’t want 100 $ oil any more than Trump does. And again, if this is not over in three weeks, things are going to start breaking down rapidly.

Some commentators suggest that the war in Iran is part of a grand strategy by the Trump Administration to weaken China, as was the intervention in Venezuela. What do you make of that?

That’s nonsense. White House officials will tell you it is, to kind of connect the dots post facto, but America just kind of stumbled backwards into this crisis. It’s absolute nonsense that this is designed to hurt China. Look at the math: China gets less than 20% of its crude imports from Venezuela and Iran. Until recently, it was able to purchase that crude at discounts to market prices of 7 to 9 $ per barrel. So China was saving 7 to 9 bucks per barrel on less than 20% of its oil imports. Big deal. If anyone thinks that China is a global power because of that discount, then I’m Queen Elizabeth. Do people really think that this was part of a brilliant plan, that the US just depleted its entire stock of offensive weaponry, so that China can pay more on less than a fifth of its oil imports? Plus, what would the proponents of this ‹It’s All About China› theory suggest America will do if Saudi Arabia stepped in to replace Iran as China’s main Middle East partner? Bomb Riyadh? That doesn't make much sense.

If we take a longer term perspective: What do you see as the most important consequences of this war?....

....MUCH MORE 

"The AI water issue is fake"

There are any number of reasons to argue against the current infrastructure build-out and the eventual draw on resources that may, or may not, be better used elsewhere but the water issues, except for draining aquifers, don't really matter.

Draining aquifers for pretty much anything other than drinking water is dumb. Draining aquifers for agriculture is insanity. And we do so much of it that the result can actually be measured as a contributor to sea level rise.*

From Andy Masley's substack, October 11, 2025:

AI data centers use water. Like any other industry that uses water, they require careful planning. If an electric car factory opens near you, that factory may use just as much water as a data center. The factory also requires careful planning. But the idea that either the factory or AI is using an inordinate amount of water that merits any kind of boycott or national attention as a unique serious environmental issue is innumerate. Individual data centers can sometimes stress local water systems in the way other industries do, but when you use AI, you are not contributing to a significant problem for water management compared to most other things you do in your day to day life. On the national, local, and personal level, AI is barely using any water, and unless it grows 50 times faster than forecasts predict, this won’t change. I’m writing from an American context and don’t know as much about other countries. But at least in America, the numbers are clear and decisive.

The idea that AI’s water usage is a serious national emergency caught on for three reasons:

• People get upset at the idea of a physical resource like water being spent on a digital product, especially one they don’t see value in, and don’t factor in just how often this happens everywhere.

• People haven’t internalized how many other people are using AI. AI’s water use looks ridiculous if you think of it as a small marginal new thing. It looks tiny when you divide it by the hundreds of millions of people using AI every day.

• People are easily alarmed by contextless large numbers, like the number of gallons of water a data center is using. They compare these large numbers to other regular things they do, not to other normal industries and processes in society. They aren’t aware of how much water society uses on other normal industries.

Together, these create the impression that AI water use is a problem. It is not. Regardless of whether you love or hate AI, it is not possible to actually look at the numbers involved without coming to the conclusion that this is a fake problem. This problem’s hyped up for clicks by a lot of scary articles that completely fall apart when you look at the simple easy-to-access facts on the ground. These articles have contributed to establishing fake “common wisdom” among everyday people that AI uses a lot of water.

This post is not at all about other issues related to AI, especially the very real problems with electricity use. I want to give you a complete picture of the issue. I think AI and the national water system are both so wildly interesting that they can be really fun to read about even if you’re not invested in the problem.

Importantly, I am not saying that it’s impossible for data centers to ever cause any problems with water. They require careful planning in the same ways other large industrial buildings do. What I am saying is that right now no reasonable forecasts imply that data centers will rise to become a significant problem for water access in the US. Almost all complaints about their national water use are basically just saying “We should not have a new large industry in America using water.” The tax revenue per gallon from data centers is just so high that in many places they are among the best new buildings possible to benefit a community experiencing water scarcity, because any other industries using the same amount of water would generate way less tax revenue. Critics of data centers need to carefully weigh their water costs against the massive amounts of revenue they can bring in for everyday people, not just look at the water costs alone. The debate about building data centers should involve reasonable conversations between ecologists, economists, and city officials, not everyday voters shouting down local meetings with misleading statistics.

Contents 

• A few important definitions

• AI water use isn’t an issue on the national, local, or personal level

  • National

  • Local

  • Personal

• How big of a deal is it that data centers use potable water?

• Using AI can save way more water than is used in data centers

• It’s okay to use water on a digital product

• The social value or harm of a tool isn’t the final word on how harmful it is to the environment

• There’s a trade-off between water and energy for data center cooling systems. For the climate, water’s often preferable

• What about all those news stories about AI harming local water access?

  • Every popular article about how AI’s water use is bad for the environment in the last year has had a wildly misleading framing

  • 5 common misleading ways of reporting AI water usage statistics

  • Some examples of great news coverage

• Further reading

A few important definitions 

Suppose I take a cup of water from a lake, and then immediately dump it back in. That doesn’t seem bad. Now I take a cup from the lake, and this time I evaporate it. That seems worse. Now I take a cup and spend some resources on making it drinkable. These all have very different costs and effects on the water system. We need words to describe it.

Water is complicated, but not too complicated. There are a few key definitions to understand. First:

• Consumptive use removes water from a local system. Taking the cup of water and evaporating it is consumptive use. Evaporated water mostly does not return to its original source.

• Non-consumptive use temporarily takes water from a local system, and returns it later unaffected. Taking the cup of water from the stream and pouring it back in is non-consumptive use.

Growing food is an example of consumptive use. Some of the water becomes part of the food itself. When the food is shipped away, the water leaves the local system.

Many data centers rely on evaporative cooling. This is the way water is consumptively used in data centers. They do not immediately evaporate all the water they are using. Most of it circulates through the cooling system repeatedly before evaporation.

Many reports on AI’s water use do not only include water in data centers, they also include the water consumed by the power plants data centers draw from. This leads to a second important distinction....

....MUCH MORE 

 HT: UnDark's "How Much Water Do AI Data Centers Really Use?", December 18.

And the aquifers? Last seen in May 2024's "Over a third of urban Chinese live in sinking cities":

....In fact people are depleting aquifers at such a rate that the H₂O that moves through the water cycle actually ends up in the oceans, raising sea levels. Not huge but measurable.

From the U.S. Geological Survey:

...Estimated global groundwater depletion during 1900–2008 totals ~4,500 km³, equivalent to a sea-level rise of 12.6 mm (>6% of the total). Furthermore, the rate of groundwater depletion has increased markedly since about 1950, with maximum rates occurring during the most recent period (2000–2008), when it averaged ~145 km³/yr (equivalent to 0.40 mm/yr of sea-level rise, or 13% of the reported rate of 3.1 mm/yr during this recent period)....

—Contribution of global groundwater depletion since 1900 to sea-level rise

We pay pretty close attention to water and in the decade since we started pitching Nvidia we've had only two "water and AI" posts 

August 2025 - "AI Data Centers in Texas Used 463 Million Gallons of Water, Residents Told to Take Shorter Showers" 

And October 2025 - "Opportunity: "Next-Gen AI Needs Liquid Cooling"  which has such tidbits as:

....“The average power density in a rack was around 8 kW,” says Josh Claman, CEO of the startup Accelsius. “For AI, that’s growing to 100 kW per rack. That’s an order of magnitude. It’s really AI adoption that’s creating this real urgency” to figure out a better way to cool data centers.

Specifically, the urgency is to move away from fans and toward some sort of liquid cooling. For example, water has roughly four times the specific heat of air and is about 800 times as dense, meaning it can absorb around 3,200 times as much heat as a comparable volume of air can. What’s more, the thermal conductivity of water is 23.5 times as high as that of air, meaning that heat transfers to water much more readily.

“You can stick your hand into a hot oven and you won’t get burned. You stick your hand into a pot of boiling water and you can instantly get third-degree burns,” says Seamus Egan, general manager of immersion cooling at Airedale by Modine. “That’s because the liquid transfers heat much, much, much, much more quickly.”....

On the other hand we have dozens of posts on H₂O. 

From trying to figure out various bets on water to: September 2019's - Water and Its Mysteries:

Isaac Asimov was fascinated with water and among his non-fiction books is The Left Hand of the Electron which references water over 200 times. And via Inference Review someone else with the fascination. Marc Henry, Professor of Chemistry, Materials Science, and Quantum Physics at the University of Strasbourg:

There are very few things that modern science does not understand. One of them is consciousness; the other is water.¹ In the case of consciousness, the hard problem is designing good experiments; in the case of water, finding a theory that explains its properties.

Thermodynamic theories assume that any substance may be characterized by its internal energy U(S,V), where S and V are extensive variables, S is a measure of the number of accessible quantum states at a given temperature T, and V is a measure of the spatial extension of the system at a given pressure p. By the first law of thermodynamics dU(S,V) = T·dS – p·dV. Every substance has an equation of state (EoS) p = k_BT·[1/V + B₂(T)/V² + …], where k_B = 0.0138 zJ·K^–1 is a universal constant. B₂(T) is negative at low temperature and positive at high temperatures. At B₂(T) = 0, attractive and repulsive forces are in balance. For an ideal gas, B₂(T) = 0; for a system of hard spheres of diameter σ, B₂(T) = 2π·σ³/3; for a van der Waals gas, B₂(T) = b – a/k_BT, with b and a measuring the repulsive and attractive forces between molecules.

Let MW designate molecular weight. The substance displaying the greatest ratio T_B/MW = 80(8) K·Da^–1 is water (H₂O), followed by hydrogen fluoride (HF), and ammonia (NH₃). T_B/MW thus corresponds to the density of cohesive energy. The fact that water is an elixir for life is perfectly justified from a thermodynamic point of view. In chemistry, such high densities are usually associated with abnormally high ebullition points.

A phase diagram in the plane represents the domains where a substance may exist as a solid, liquid, or gas. Domain boundaries are marked by coexistence lines. It is there that two phases coexist. Substances undergo first-order transitions at coexistence lines; the system absorbs or releases energy, giving rise to discontinuous changes in volume and density. The sublimation line is the locus of points where a solid coexists with a gas and may extend from zero to the triple-point temperature T₃. In the case of water, this triple point is observed at T₃ = 0.01°C = 273.16 K and p₃ = 611 Pa = 0.006 atm. The melting line is the locus of points from T₃ to an infinite temperature. These phases have different symmetries. Full rotational invariance in the liquid phase turns into discrete symmetries in the crystal phase. The condensation line is the locus of points where a gas coexists with a liquid and extends from T₃ to a critical point C. Above C, only a single-phase homogenous system exists. In the case of water, this critical point is observed at T_C = 373.946°C = 647.096 K and p_C = 22.06 MPa = 217.7 atm. This critical point occurs as soon as two phases share the same symmetry group.....MUCH MORE

*Archive.org: The Left Hand of the Electron - Isaac Asimov

Regarding Asimov, though he made his money writing science fiction he was a trained chemist: PhD, Columbia, post doc, taught biochem at Boston Uni's Med school etc.

And he was fascinated by water. See after the jump. 

That's from December 30, 2020's "Hydrogen Storage: A New Form Of Ice" which has this outro:

One of Asimov's riffs on water, "Not as We Know it: The Chemistry of Life" he uses this little paragraph as a jumping-off point:

Water is an amazing substance with a whole set of unusual properties which are ideal for life-as-we-know-it. So well fitted for life is it, in fact, that some people have seen in the nature of water a sure sign of Divine providence. This, however, is a false argument, since life has evolved to fit the watery medium in which it developed. Life fits water, rather than the reverse.

In his book "The Left Hand Of the Electron" Dr. A gathers essays he had written over the years:

The Thalassogens

Subject: common liquids
First Published In: Dec-70, The Magazine of Fantasy and Science Fiction
Collection(s):

• 1972 The Left Hand of the Electron

Cold Water

Subject: freezing of water
First Published In: Feb-71, The Magazine of Fantasy and Science Fiction
Collection(s):

• 1972 The Left Hand of the Electron

Hot Water

Subject: boiling points
First Published In: Jan-71, The Magazine of Fantasy and Science Fiction
Collection(s):

• 1972 The Left Hand of the Electron

If interested, here's the book at Archive.org. I did a quick search and saw 114 hits for 'water'.

So yeah, we pay attention to water.