I have started to use a few of the major AI platforms, mostly for literature searches. So far I like Grok best, which is Elon Musk’s platform. I learned recently that Grok has decided to compete with Wikipedia by creating Grokipedia. I learned about it by reading a Substack entry from David Henderson saying that Grokipedia’s page on David Henderson was better than Wikipedia’s page on David Henderson. Curious, I logged onto Grokipedia, looked for a page on me, didn’t find one, and then requested that they create one. They did and I (like David Henderson) came away impressed with what they created. (My opinion is as of 3/18/26.) If you feel like checking it out, the link is: https://grokipedia.com/page/Arthur_M_Diamond_Jr
Category: Technology
Let Parents Decide if Child Has Cell Phone
In the media and the academy I sense growing agreement that children and adolescents should have their cell phones restricted or even taken away. I disagree. Parents within a very wide range should be free to parent. I wanted our child to have a cell phone when she was in school, partly to co-ordinate logistics, but mostly to be able to contact her in emergencies. Many girls died in the flooding at Camp Mystic last summer. Camp Mystic, apparently like many camps, did not allow the girls to have cell phones. With cell phones some of the girls might have received timely warnings from their parents, and still be alive today.
(p. A3) In the wake of revelations by Kerr County officials that they didn’t have a flood warning system, an online petition has been started to get one set up for any such future disasters. “This is not just a wish—it is a necessary investment in public safety,” said the Change.org petition signed by more than 100 people since going up Friday [July 4, 2025]. “Early warning sirens have saved thousands of lives in other communities by giving clear, unmistakable alerts day or night, even when cell phone service or electricity fails.”
Nicole Wilson, a resident of nearby San Antonio who started the petition, said she was moved into action after seeing friends nearly lose their children to the floods while at Mystic and other camps along the river and knowing that most, like one her daughters go to near New Braunfels, Texas, don’t allow cellphones or other electronic devices. She said outdoor warning sirens, such as the one she grew up with in Kentucky to seek shelter from tornadoes, could give lifesaving advance notice.
“You are going to hear the sirens, and you are going to know what the sirens mean,” said Wilson, 42, an Army veteran. “I have no doubt if they had five minutes warning they would have had opportunity to get uphill, and they would have had a chance.”
“They had no chance,” she added, her voice breaking. “They had no warning.”
For the full story, see:
(Note: bracketed date added.)
(Note: the online version of the story was updated July 6, 2025, and has the title “Escalating Alerts of Dangerous Flooding Arrived When People Were Sleeping.”)
Technology Was Democratized When Standardization of Parts Enabled Simplification of Manufacture and Maintenance
There’s a lot to like about Steward Brand. His Whole Earth Catalog was quirky unpretentious fun. His How Buildings Learn, has a wonderful chapter on the ramshackle, unnamed, disrespected building on the MIT campus where quirky innovators were given space to create. His essay on Xerox Parc explained how the technology being developed there could liberate individual creativity. When Steve Jobs at Stanford delivered what is widely believed to be the best commencement address in history, he ended by quoting Stewart Brand’s final message in the 1974 Whole Earth Catalog: “Stay hungry, stay foolish.”
In the review quoted below, highlights that the simplification of production enabled by standardization of parts promoted the democratization of technology maintenance (and we might add, helped to democratize innovation too). Major simplification goes against the Theory of the Adjacent Possible which claims that technology develops toward greater and greater complexity.
(p. C7) Read front to back, “Maintenance” tells a coherent story of civilizational progress. Prior to the Industrial Revolution, most machines were one-off creations, built by artisans to their own quirky specifications. But the technological age increasingly demanded standardization. Weapons led the way. If a cannonball jammed in an imprecisely bored barrel, the cannon might explode, killing its crew. On the other hand, if the parts of a flintlock rifle were interchangeable, a soldier could repair his weapon without the need for a gunsmith.
The manufacturing techniques that enabled this kind of precision gradually spread to other technologies. The same tools developed to bore cannon barrels were then used to improve steam engines. But standardization had its enemies, Mr. Brand notes, especially among gunsmiths and other artisans. During the French Revolution, the sansculottes rebelled against the new industrial techniques. “Craft was extolled; uniformity was deplored,” Mr. Brand writes. France’s technical progress was set back 50 years.
A century later, the early automobile industry faced a similar split. The original Rolls-Royce Silver Ghost, Mr. Brand writes, “was manufactured as a bespoke, unique vehicle, meticulously crafted by a dedicated team.” Henry Ford’s Model T, by contrast, was a crude but ingeniously simple machine. Ford made sure each part was manufactured to unvarying specifications, “perfect enough” that it could be installed by a moderately skilled worker on a moving assembly line. No fine-tuning needed.
Ford’s embrace of standardization allowed his Model T to be built quickly and inexpensively. But standardization had another, paradoxical effect: It allowed nonexperts to repair their own vehicles and other equipment. A farmer who owned a Model T didn’t need a forge or metal lathe to fix his engine; he could simply order a replacement part—or cannibalize one from a wrecked car in a junkyard.
The French revolutionaries feared industrialization would depersonalize society by marginalizing skilled artisans. Mr. Brand shows that, instead, standardization democratized access to technology. With a few tools and a little gumption, anyone could learn to maintain and repair the machinery of daily life.
For the full review see:
James B. Meigs. “Fixing the Future.” The Wall Street Journal (Sat., Dec. 6, 2025): C7.
(Note: the online version of the review has the date December 5, 2025, and has the title “‘Maintenance: Of Everything, Part One’: Making the Future.”)
The book under review is:
Brand, Stewart. Maintenance of Everything: Part One. South San Francisco, CA: Stripe Press, 2026.
An earlier Brand book that I praised in my opening comments is:
Brand, Stewart. How Buildings Learn: What Happens after They’re Built. New York: Viking Adult, 1994.
AI Cannot Know What People Think “At the Very Edge of Their Experience”
The passages quoted below mention “the advent of generative A.I.” From previous reading, I had the impression that “generative A.I” meant A.I. that had reached human level cognition. But when I looked up the meaning of the phrase, I found that it means A.I. that can generate new content. Then I smiled. I was at Wabash College as an undergraduate from 1971-1974 (I graduated in three years). Sometime during those years, Wabash acquired its first minicomputer, and I took a course in BASIC computer programming. I distinctly remember programming a template for a brief poem where at key locations I inserted a random word variable. Where the random word variable occurred, the program randomly selected from one of a number of rhyming words. So each time the program was run, a new rhyming poem would be “generated.” That was new content, and sometimes it was even amusing. But it wasn’t any good, and it did not have deep meaning, and if what it generated was true, it was only by accident. So I guess “the advent of generative A.I.” goes back at least to the early 1970s when Art Diamond messed around with a DEC.
This is not the main point of the passages quoted below. The main point is that the frontiers of human thought are not on the internet, and so cannot be part of the training of A.I. So whatever A.I. can do, it can’t think at the human “edge.”
(p. B3) Dan Shipper, the founder of the media start-up Every, says he gets asked a lot whether he thinks robots will replace writers. He swears they won’t, at least not at his company.
. . .
Mr. Shipper argues that the advent of generative A.I. is merely the latest step in a centuries-long technological march that has brought writers closer to their own ideas. Along the way, most typesetters and scriveners have been erased. But the part of writing that most requires humans remains intact: a perspective and taste, and A.I. can help form both even though it doesn’t have either on its own, he said.
“One example of a thing that journalists do that language models cannot is come and have this conversation with me,” Mr. Shipper said. “You’re going out and talking to people every day at the very edge of their experience. That’s always changing. And language models just don’t have access to that, because it’s not on the internet.”
For the full story see:
(Note: ellipsis added.)
(Note: the online version of the story has the date May 21, 2025, and has the title “Will Writing Survive A.I.? This Media Company Is Betting on It.”)
If AI Takes Some Jobs, New Human Jobs Will Be Created
In the passage quoted below, Atkinson makes a sound general case for optimism on the effects of AI on the labor market. I would add to that case that many are currently overestimating the potential cognitive effectiveness of AI. Humans have a vast reservoir of unarticulated common sense knowledge that is not accessible to AI training. In addition AI cannot innovate at the frontiers of knowledge, not yet posted to the internet.
(p. A15) AI doomsayers frequently succumb to what economists call the “lump of labor” fallacy: the idea that there is a limited amount of work to be done, and if a job is eliminated, it’s gone for good. This fails to account for second-order effects, whereby the saving from increased productivity is recycled back into the economy in the form of higher wages, higher profits and reduced prices. This creates new demand that in turn creates new jobs. Some of these are entirely new occupations, such as “content creator assistant,” but others are existing jobs that are in higher demand now that people have more money to spend—for example, personal trainers.
Suppose an insurance firm uses AI to handle many of the customer-service functions that humans used to perform. Assume the technology allows the firm to do the same amount of work with 50% less labor. Some workers would lose their jobs, but lower labor costs would decrease insurance premiums. Customers would then be able to spend less money on insurance and more on other things, such as vacations, restaurants or gym memberships.
In other words, the savings don’t get stuffed under a mattress; they get spent, thereby creating more jobs.
For the full commentary, see:
(Note: the online version of the commentary has the date June 5, 2025, and has the same title as the print version.)
Do Not Ridicule Those Who Know How to Sew
15 years ago I ran a blog entry quoting Brian Fagan’s theory that we Homo sapiens (aka Cro-Magnons) outlasted the Neanderthals because we developed the sewing needle technology that allowed us to sew tighter fitting garments against the cold. Now added evidence elaborates and supports Fagan’s theory. Near the time when Neanderthals became extinct, the magnetic poles of the earth shifted over a few hundred years, allowing substantially more ultraviolet radiation to hit the earth than usual. With better-filling garments, due to sewing needles, Homo sapiens were better protected against that radiation.
The WSJ article summarizing the new research is:
(Note: the online version of the NYT article has the date August 6, 2025, and has the title “Did UV Rays Doom Neanderthals?”)
The published academic paper summarized in The Wall Street Journal article mentioned and cited above is:
Substrate Startup Develops Less Complex and Cheaper Way to Etch Computer Chips
To prepare for a workshop next week I have been reading a lot about Stuart Kauffman and Roger Koppl’s theory of the adjacent possible (TAP), as it is applied to the growth of technology. One of the implications of TAP is the that new technology gets progressively more complex, in the sense of using an ever larger number of components. I think that is often true but I can think of a couple of counter-examples. So I was interested to read yesterday that the production of computer chips may provide another counter-example.
(p. B1) In March [2025], James Proud, an unassuming British-born American without a college degree, sat in Vice President JD Vance’s office and explained how his Silicon Valley start-up, Substrate, had developed an alternative manufacturing process for semiconductors, one of the most fundamental and difficult challenges in tech.
For the past decade, semiconductors have been manufactured by a school-bus-size machine that uses light to etch patterns onto silicon wafers inside sterile, $25 billion factories. The machine, from the Dutch company ASML, is so critical to the chips in smartphones, A.I. systems and weaponry that Washington has effectively blocked sales of it to China.
But Mr. Proud said his company, which has received more than $100 million from investors, had developed a solution that would cut the manufacturing cost in half by channeling light from a giant instrument known as a particle accelerator through a tool the size of a car. The technique had allowed Substrate to print a high-resolution microchip layer comparable to images produced by the world’s leading semiconductor plants.
. . .
(p. B4) Mr. Proud moved to San Francisco from London in 2011 as a member of the first Thiel Fellowship class, a college alternative for aspiring founders created by Peter Thiel, the venture capitalist.
. . .
After the Trump administration persuaded TSMC to build a plant in Arizona, Mr. Proud decided to build his own company. He and his brother Oliver, 25, started reading books and academic papers on semiconductor lithography. They questioned why the process had become so complex and expensive.
One of the major costs in modern lithography machines, which have more than 100,000 parts, is how they use high-powered lasers to turn droplets of molten tin into a burst of extreme ultraviolet light. The machines use the light to etch a wafer of silicon in a process known as EUV lithography.
. . .
The team spent much of 2023 building a custom lithography tool. It featured thousands of parts and was small enough to fit in the back of a U-Haul. They tested it in computer simulations.
In early 2024, Substrate reserved a Bay Area particle accelerator for a make-or-break test. The company ran into problems when vibrations near the particle accelerator caused the tool to gyrate and blur the image, Mr. Proud said.
A frantic, daylong search found that the air-conditioning system was causing the vibration. Substrate adjusted the fan speed until the process printed “very beautiful and tiny things repeatedly” on a silicon wafer, Mr. Proud said.
For the full story see:
(Note: ellipses, and bracketed year, added.)
(Note: the online version of the story has the date Oct. 28, 2025, and has the title “Can a Start-Up Make Computer Chips Cheaper Than the Industry’s Giants?”)
We Need to “Tolerate Heterodox Smart People” if We Want to Achieve Big Things
Peter Thiel is often quoted as having said many years ago that “We wanted flying cars, instead we got 140 characters” (as quoted in Lewis-Kraus 2024), a reference to the original limit to the length of a tweet on Twitter. The quotations below are all from the more recent Peter Thiel, who was having a conversation with NYT columnist Ross Douthat. He still believes that we are not boldly pursuing big goals, the only exception being A.I. Is the constraint that big goals are impossible to achieve, or do we lack people smart enough or motivated enough to pursue them, or do we regulate motivated smart people into discouraged despair?
(p. 9) One question we can frame is: Just how big a thing do I think A.I. is? And my stupid answer is: It’s more than a nothing burger, and it’s less than the total transformation of our society. My place holder is that it’s roughly on the scale of the internet in the late ’90s. I’m not sure it’s enough to really end the stagnation. It might be enough to create some great companies. And the internet added maybe a few percentage points to the G.D.P., maybe 1 percent to G.D.P. growth every year for 10, 15 years. It added some to productivity. So that’s roughly my place holder for A.I.
It’s the only thing we have. It’s a little bit unhealthy that it’s so unbalanced. This is the only thing we have. I’d like to have more multidimensional progress. I’d like us to be going to Mars. I’d like us to be having cures for dementia. If all we have is A.I., I will take it.
. . .
And so maybe the problems are unsolvable, which is the pessimistic view. Maybe there is no cure for dementia at all, and it’s a deeply unsolvable problem. There’s no cure for mortality. Maybe it’s an unsolvable problem.
Or maybe it’s these cultural things. So it’s not the individually smart person, but it’s how this fits into our society. Do we tolerate heterodox smart people? Maybe you need heterodox smart people to do crazy experiments.
. . .
I had a conversation with Elon a few weeks ago about this. He said we’re going to have a billion humanoid robots in the U.S. in 10 years. And I said: Well, if that’s true, you don’t need to worry about the budget deficits because we’re going to have so much growth, the growth will take care of this. And then — well, he’s still worried about the budget deficits. This doesn’t prove that he doesn’t believe in the billion robots, but it suggests that maybe he hasn’t thought it through or that he doesn’t think it’s going to be as transformative economically, or that there are big error bars around it. But yeah, there’s some way in which these things are not quite thought through.
For the full interview, see:
(Note: ellipses added.)
(Note: the online version of the interview has the date June 26, 2025, and has the title “Peter Thiel and the Antichrist.”)
Peter Thiel’s yearning many years ago for flying cars was quoted more recently in:
Lewis-Kraus, Gideon. “Flight of Fancy.” The New Yorker, April 22, 2024, 28-39.
Latest “So-Called Reasoning Systems” Hallucinate MORE Than Earlier A.I. Systems
Since more sophisticated “reasoning” A.I. systems are increasingly inaccurate on the facts, it is unlikely that such systems will threaten any job where job performance depends on getting the facts right. Wouldn’t that include most jobs? The article quoted below suggests it would most clearly include jobs working with “court documents, medical information or sensitive business data.”
(p. B1) The newest and most powerful technologies — so-called reasoning systems from companies like OpenAI, Google and the Chinese start-up DeepSeek — are generating more errors, not fewer. As their math skills have notably improved, their handle on facts has gotten shakier. It is not entirely clear why.
Today’s A.I. bots are based on complex mathematical systems that learn their skills by analyzing enormous amounts of digital data. They do not — and cannot — decide what (p. B6) is true and what is false. Sometimes, they just make stuff up, a phenomenon some A.I. researchers call hallucinations. On one test, the hallucination rates of newer A.I. systems were as high as 79 percent.
. . .
The A.I. bots tied to search engines like Google and Bing sometimes generate search results that are laughably wrong. If you ask them for a good marathon on the West Coast, they might suggest a race in Philadelphia. If they tell you the number of households in Illinois, they might cite a source that does not include that information.
Those hallucinations may not be a big problem for many people, but it is a serious issue for anyone using the technology with court documents, medical information or sensitive business data.
“You spend a lot of time trying to figure out which responses are factual and which aren’t,” said Pratik Verma, co-founder and chief executive of Okahu, a company that helps businesses navigate the hallucination problem. “Not dealing with these errors properly basically eliminates the value of A.I. systems, which are supposed to automate tasks for you.”
. . .
For more than two years, companies like OpenAI and Google steadily improved their A.I. systems and reduced the frequency of these errors. But with the use of new reasoning systems, errors are rising. The latest OpenAI systems hallucinate at a higher rate than the company’s previous system, according to the company’s own tests.
The company found that o3 — its most powerful system — hallucinated 33 percent of the time when running its PersonQA benchmark test, which involves answering questions about public figures. That is more than twice the hallucination rate of OpenAI’s previous reasoning system, called o1. The new o4-mini hallucinated at an even higher rate: 48 percent.
When running another test called SimpleQA, which asks more general questions, the hallucination rates for o3 and o4-mini were 51 percent and 79 percent. The previous system, o1, hallucinated 44 percent of the time.
. . .
For years, companies like OpenAI relied on a simple concept: The more internet data they fed into their A.I. systems, the better those systems would perform. But they used up just about all the English text on the internet, which meant they needed a new way of improving their chatbots.
So these companies are leaning more heavily on a technique that scientists call reinforcement learning. With this process, a system can learn behavior through trial and error. It is working well in certain areas, like math and computer programming. But it is falling short in other areas.
For the full story see:
(Note: ellipses added.)
(Note: the online version of the story was updated May 6, 2025, and has the title “A.I. Is Getting More Powerful, but Its Hallucinations Are Getting Worse.”)
A.I. Only “Knows” What Has Been Published or Posted
A.I. “learns” by scouring language that has been published or posted. If outdated or never-true “facts” are posted on the web, A.I. may regurgitate them. It takes human eyes to check whether there really is a picnic table in a park.
(p. B1) Last week, I asked Google to help me plan my daughter’s birthday party by finding a park in Oakland, Calif., with picnic tables. The site generated a list of parks nearby, so I went to scout two of them out — only to find there were, in fact, no tables.
“I was just there,” I typed to Google. “I didn’t see wooden tables.”
Google acknowledged the mistake and produced another list, which again included one of the parks with no tables.
I repeated this experiment by asking Google to find an affordable carwash nearby. Google listed a service for $25, but when I arrived, a carwash cost $65.
I also asked Google to find a grocery store where I could buy an exotic pepper paste. Its list included a nearby Whole Foods, which didn’t carry the item.
For the full commentary see:
(Note: the online version of the commentary has the date May 29, 2024, and has the title “Google Introduced a New Way to Use Search. Proceed With Caution.”)
Development of IVF Took 10 Years of Trial and Error
If the Joy television movie accurately reflects the history of the development of IVF (in vitro fertilization) then it illustrates a couple of themes that are important. One is the frequent fruitfulness of trial-and-error experimentation. The other is that some medical entrepreneurs are motivated by having some form of ‘skin-in-the-game,’ in this case nurse Jean Purdy. (Support for the second theme is more speculative than for the first, since the evidence that the real Jane Purdy experienced endometriosis and infertility is circumstantial.)
(p. A10) “Joy,” . . . begins in 1968 and charts the 10-year journey of trial, error and more trial and error by an odd trio of pioneers: Bob Edwards (James Norton), a biologist and true-believer in the possibilities of IVF; Patrick Steptoe (Bill Nighy), a surgical obstetrician who is less than convinced but can be; and Jean Purdy (Thomasin McKenzie), a nurse who signs on as Bob’s assistant and, as we learn, has her own agenda regarding infertile women. (Edwards received the 2010 Nobel Prize in Medicine, his partners having passed away.)
Jack Thorne’s screenplay massages the IVF medical story into a personal one, mostly about Jean, who is portrayed as a critical member of the team and the one whose life reflects the social uproar over the mission—giving childless women a choice about becoming mothers.
For the full television review see:
(Note: ellipsis added.)
(Note: the online version of the television review has the date November 21, 2024, and has the title “‘Joy’ Review: The Birth of a Medical Miracle on Netflix.”)