How Artificial Intelligence Ran Up the Score the Past Ten Weeks in Its Own Unexpected Way

Behind the news of growing general dislike, gargantuan amounts of debt, and plummeting data center acceptance, AI has been accomplishing a lot. 

First, “How A.I. Might Change the Way Doctors Think” (Helen Ouyang, The New York Times, July 1^st).  One change is taking over the “clinical note,” which physicians have long written to document patient interactions and prospects, and, without expensive doctor’s time, “eavesdropping” and creating organized and clear information packages designed for reference on the patient’s electronic chart.  That is especially valuable when someone is moved from one physician to another, meaning minimal need to filter what the receiving one sees through the biases of the first.  Although this capability will have unknown and not always positive effects on how medical students learn to think, the efficiency gains are great.

In a lower-labor-cost setting, “Taco Bell Ramps Up Voice AI Use Across Nearly 900 Drive-Thrus” (Eric Revell, Fox Business, July 8^th).  The platform, using speech recognition instead of speaking itself, “helps automate the ordering process when a customer pulls up to a drive-thru speaker and is capable of adapting to an individual location’s menu, real-time stocking levels, as well as limited-time offers that are available, which can make the ordering process more consistent and efficient for customers.”  Transaction speed “is on a par with, and in some cases faster than traditional ordering methods.”

Back to medicine, a “Johns Hopkins surgeon highlights AI breakthrough that could spot pancreatic cancer before doctors” (Arabella Bennett, Fox Business, July 20^th).  “Artificial intelligence is rapidly changing cancer care, with researchers developing tools that could help identify some of the deadliest cancers much earlier than doctors can on their own.”  The technology is “helping researchers recognize patterns that would otherwise take physicians decades of experience to identify,” which means earlier detection and therefore many saved lives.

We have read about AI systems becoming “employees,” with varying degrees of success, but how about using them to simulate actual people?  In “To Know What Your Customers Think, Just Ask Their A.I. Twins” (The New York Times, July 30^th), Sri Muppidi described the work of “Simile, a year-old start-up,” which claims it “can predict human behavior by tapping into its growing database of A.I. agents” to get information “faster and cheaper than by running traditional market research studies.”  The company’s head of product says its “responses are now between 85 percent and 99 percent accurate, depending on the scenario and the population surveyed.”  Simile has competitors, but it also has large customers and hundreds of millions of dollars in funding.

One kind of virus is more akin to AI, but, in “This A.I. Just Created Viruses Not Found in Nature” (The New York Times, August 6^th), author Carl Zimmer told us about its work with the other.  When “scientists trained artificial intelligence on libraries of DNA and then asked the model to create recipes for viral genomes,” “sixteen of them were viable, yielding new viruses.”  When “researchers followed those recipes to create DNA molecules, which they inserted into bacteria,” the result was “viruses never seen in nature,” which could “infect other bacteria.”  The work was made possible through the software understanding DNA’s sequencing rules, “strikingly similar to a book in some ways” and thus well suited, when “trained… on genetic sequences drawn from millions of animals, plants, microbes and viruses” for large language models.  If it can be contained and not used for biological warfare, this ability could have many applications.

Staying in the same general area, which has seen more accomplishments than any other, “A.I. Is Finding Sperm Where Doctors Couldn’t” (Andrew Zaleski, again in the Times, August 11^th).  A fertility center director compared AI’s effectiveness at this task with seeing stars with a telescope instead of with a naked eye.  Specifically, men who seemingly have none at all may have “several “hidden” sperm” which cannot be seen by microscope users, and which can be “captured” and possibly used for fertilization.

The closest I have seen to AI bringing down an entire field was the subject of “As A.I. Makes Strides in Mathematics, Mathematicians Urge Caution” (Siobhan Roberts, The New York Times, June 2^nd).  Then, there were “A.I. models… making headlines with successful results in research-level mathematics,” along with “a flood of plausible seeming A.I.-generated papers and proofs that have turned out to be incorrect, and in ways that are difficult for mathematicians to discern.”  That precipitated “the Leiden Declaration on Artificial Intelligence and Mathematics,” which along with bad articles condemns not “keeping the field’s best interests in mind,” and attempts to preserve “the cultivation of ideas, understanding, judgment, and human insight.”  When asked if “the declaration might be seen as mathematics embarking on a futile effort - circling the wagons in order to save an outdated profession that A.I. is threatening with obsolescence,” Roberts’ panel made it plain that that is indeed the case.  Indeed, “OpenAI Says It Has Cracked One of Math’s ‘Millennium Problems’” (Cade Metz, once more in the Times, September 8^th), “another clear sign that A.I. is fundamentally changing the upper reaches of mathematics, which have long been viewed as a pinnacle of human achievement.”  Even though they have no chance of catching up to the best machines, there are still devoted chess experts - look for mathematicians to occupy the same spot.

Do you want more successes?  There are plenty more in “AI isn’t just a problem, it’s a solution.  Some people are already making the most of it” (Fox News, August 18^th).  In the last two printed pages. Len Khodorkovsky mentioned therapy and companionship, organizing lives, finding a purpose, medication and appointment schedules, job searching, advice for new businesses, and support for “democratic values.”  More and more things helping people in a variety of ways.  Could it be that “the real AI story isn’t about machines becoming omnipotent.  It’s humans becoming more capable”?  That’s truly one of the stories, and not the main one we anticipated.  It is not to pooh-pooh.  It is in place, it is valuable, and it is growing.  Now. 

Most Numbers Rebounded in This Morning’s Jobs Report - AJSN Shows Latent Demand Down 200,000 to 17.1 Million

This was not an unusually hotly anticipated Bureau of Labor Statistics Employment Situation Summary.  I had to dig a bit for estimates of how many net new nonfarm positions were added, and those, given last month’s loss, turned out to be cautiously low.

That figure rose 162,000 compared with 53,000 to 65,000 published approximations, much higher but closer than July’s loss.  Other winners were unadjusted unemployment, down 0.1% to 4.3%, labor force participation, up 0.2% to 61.6%, the employment-population ratio, up the same to 59.1%, the count of people working part-time for economic reasons or holding short-hours jobs while looking thus far unsuccessfully for full-time ones, off 400,000 to 4.4 million, and average hourly private nonfarm payroll earnings which edged out inflation by climbing 13 cents to $37.75.  Unadjusted unemployment held at 4.1%.  Losers included the adjusted number of jobless, up 100,000 to 7.0 million, and those out for 27 weeks or longer which also gained 100,000, to 1.9 million.

The American Job Shortage Number or AJSN, the Royal Flush Press metric showing how many additional positions could be filled if all knew they would be easy and routine to get, lost 227,000 to reach the following:

The largest gains were from people not wanting a job and those in school or training, adding 35,000 and 30,000.  The largest losses came from those not looking for the previous year and employment-discouraged people, which subtracted 100,000 and 83,000.  The share of the AJSN from those officially unemployed edged up 0.2% to 38.9%.

Compared with a year before the AJSN has fallen 700,000, mostly from those wanting work but not searching for it for a year or more, down 355,000, and those officially unemployed who removed 329,000 from the total.  The largest offsetter was people not wanting a job, which is up three million since August and thus took away 150,000.

What happened this time?  Simple.  Many jobseekers, after July’s loss, left the market, shown by those not in the labor force increasing 320,000 and those not interested jumping almost 700,000.  Those things made a good month look even better.  I am happy with the results here, but when evened out with what we saw last time, we have just completed two mediocre months.  Still, this one by itself was a winner.  The turtle took a modest but clear step forward.

Artificial Intelligence’s Effect on Getting Jobs - What’s Been Happening to the Extent That We Can Tell, and the Outlook

A hot topic ever since AI jumped up to the headlines, a few years ago, is still drawing ink.

“California’s Governor Signs A.I. Order Aimed at Protecting Workers” (Cecilia Kang, The New York Times, May 21^st) related not quite that, but one intended “to explore a broad overhaul of labor policies, an attempt to front run a potential mass job displacement caused by artificial intelligence.”  One thing it did nail down, though, was “an expansion of job training programs, particularly for white-collar workers like customer service representatives, software developers and marketing and sales people.”  With one overlap there are “The Hidden Workers Most Threatened by A.I.” (Ben Casselman, The New York Times, June 10^th,” which also included “bookkeepers, payroll clerks and human resources specialists who fly under the radar but collectively account for tens of millions of jobs.”  Although a researcher said she was concerned that “A.I. will be to high-school-educated women what deindustrialization was to high-school-educated men,” but, as “there is little firm evidence that A.I. has hurt the labor market as a whole,” “such an outcome is a fear, not a forecast.”

Parallel to the conflict above was that “53% of Americans fear AI could take their jobs, poll finds” (Rachel Barber, USA Today, June 10^th).  That number actually included worry about “someone in their household,” and was “consistent across age, gender and education levels, though Democrats were more likely than Republicans to express concern.”

Also, “Big Companies Aim to East A.I. Transition for American Workers” (Lydia DePillis, The New York Times, June 25^th).  Although “the White House, excited about the upside for stocks and investment, has downplayed the potential for widespread job losses,” “a group of employers, state governors and foundations has raised $500 million to” come up with conclusions about what AI regulation we need.  The consortium, Raise Us, “will work primarily with governors, starting with those in Utah, Arkansas, Maryland and Connecticut,” and “plans to furnish technical assistance for companies that want to retain workers as A.I. changes their roles, rather than eliminating them.”  Valuable, especially if others, as I noted recently, continue dragging their feet on this critical issue.

What is happening now?  Ben Casselman, in the New York Times, said that “A.I. Is Reshaping the Economy.  Good Luck Measuring How” (July 2^nd).  Although “pretty much everyone agrees that artificial intelligence has the potential to reshape the economy in the coming decades… no one is sure what effect the technology is having right now.”  “According to some measures, A.I. is contributing to high unemployment rates among new graduates and might already have destroyed tens of thousands of jobs.  Other sources suggest companies might actually be adding workers as a result of the technology.”  It “might be contributing to the U.S. inflation problem, or part of the solution to it.”  As well, “it might be responsible for a recent pickup in productivity growth, or might be playing virtually no role.”  And, if these possibilities seem easier to choose between, “researchers can’t even agree on basic questions like how many companies are using A.I. or which workers are most vulnerable to the disruptions it could cause.”  What a mess!  It is no surprise, then, that a think tank director “published a report documenting the challenge of A.I. measurement and proposing steps to improve it.”  In the meantime, per an economist, “it’s like going to the doctor and getting three different diagnoses for the same condition.”

What is “The Work of Helping A.I. Destroy Work” (Lora Kelley, also in the Times, July 10^th)?  “Every day, Mercor, a start-up that sells training data to artificial intelligence companies, pays 30,000 contractors more than $4 million to help make their jobs, and those of their colleagues, obsolete.”  The people Mercor needs are highly specific, such as “a voice actor able to maintain a customer service persona in fluent Hebrew,” “a Ph.D. physicist with a specialization in general relativity, astrophysics or cosmology,” and “a physician with more than three years of experience in the Rwandan primary care medical system.”  If the company gets the gig workers they need, there will be some obscure corners with remarkably high-quality AI interactions.

As expected, changes that affect people get reactions, such as what Jessica Grose wrote about:   “The Hunt for a Job Has Never Been Worse.  These Applicants Are Fighting Back” (The New York Times, July 18^th).  She called our current setup “the purgatory job market of 2026, in which potential employees are largely evaluated by automated systems, engage in chatbot interviews and, even then, often get no feedback.”  The problem is that “as employers continue to use A.I. to rationalize the process of identifying strong applicants, applicants have begun to use similar tools to game the systems evaluating them,” creating a “vicious cycle” of “a futile “Spy vs. Spy” showdown instead of a useful way to meet the ostensible goal of giving qualified people jobs.”  As laziness is a powerful force, I do not expect much relief through in-person involvement here, so we can expect more bad times for both jobseekers and employers, who “are still having trouble finding genuinely skilled and appropriate employees” while getting “way too many résumés to evaluate.”

The common thread, then, is that AI is rarely helping people get jobs.  Will that continue indefinitely?  Will it somehow improve?  I doubt it, and think we can expect this situation to further feed AI discontent.  So this is another thing artificial intelligence must improve on.  Don’t count on it.

USA vs. China: A Big Artificial Intelligence Showdown, With One Possible Newfangled Solution

Received just as I was starting to write this post, in Anna’s Daybreak News on August 20th:  “China is closing the AI gap with the U.S., and in some areas leads on usage and cost.  Chinese models now account for 41.4% of generative-model downloads on Hugging Face and over 60% of global market share on OpenRouter…  U.S. firms still hold a computing edge” (credited to Bloomberg).

Fine, but what does all that really mean?  And what’s been in the press since early May?

A viewpoint from Jacob Dreyer, in the May 9^th New York Times: “America’s A.I. is Futuristic.  China Is Just Making It Work.”  The author, living in Shanghai, said that ‘In China, surveillance technology and A.I. surround our everyday life,” from obtaining groceries from seeing pictures of his 3-year-old “at school,” in which “an artificial intelligence facial recognition feature puts a red square around his face.”  So are the Chinese superior at AI?  Not quite.  “Americans want to create the most powerful technology humans have ever known,” but “the Chinese want to make A.I. more practical and embedded in society,” resulting in “improving the country’s domestic quality of life” and “exporting Chinese influence.”  For better or worse, it is being used in ways that could not be implemented in America.  That means there are two races going on, each with its own leader.

Soon afterwards, we read that “China Seeks A.I. Independence, Weakening Trump’s Leverage” (Meaghan Tobin, The New York Times, May 12^th).  This was about Chinese company DeepSeek announcing that “its new model had been optimized to run on chips made by the Chinese giant Huawei,” removing American ability to control their processor supply.  The chips could also be made elsewhere, as “DeepSeek is calling out into the void to… other companies, ‘Please make these changes so we can get better performance out of your chips.’”

On technical quality, “Chinese A.I. Models Close the Gap With Anthropic and OpenAI” (Cade Metz, Karen Weise and Meaghan Tobin, The New York Times, June 25^th).  That’s a consensus of intuitive estimates, as though this piece printed out to ten pages it offered no substantive or quantitative justifications.  Not so different in “American A.I. Companies Say Chinese Copycats Are Quickly Catching Up (Cade Metz, July 6^th, also in the Times).  Both pieces name a presumably estimated six-month AI development difference and mention “distillation” in which “researchers… collect data from a particularly powerful system and use that data to build a system that can run on less expensive hardware” - the second one concedes that “it is… not completely clear what Chinese companies are doing.”

After that, two of these authors told us that “China’s Latest A.I. Breakthrough Threatens America’s Lead" (Meagan Tobin and Cade Metz, July 17^th, still the Times).  They perceived that “the Chinese startup Moonshot AI released a new A.I. model that appeared to narrow the lead held by well-funded American competitors.”  But here, “according to benchmarks run by Vals AI, an independent company that evaluates the performance of A.I. models, Kimi K3 performs just below Anthropic’s Fable 5 model while outperforming OpenAI’s flagship GPT-5.6 Sol model.”  The authors do not blame distillation, but, per “experts,” “years of operating under export controls have forced Chinese companies to innovate within tight computing constraints.”  In “Chinese A.I. Start-Up Shows the World What It Has Built” (July 27^th, again in the Times), Tobin related that Kimi K3 has now been internally revealed, and “the model’s release has also inflamed a fierce debate in Beijing, Silicon Valley and Washington about whether governments should take steps to limit access to Chinese open-source models.”  There is irony there, if not a true paradox.

If all this is as significant as these “experts” say, it was valuable for Jason Hsu to tell us, in the New York Times on July 23^rd, “This Is How America Trounces China in the A.I. Race.”  He said our government issuing “a ban of its own,” in response to proposed Chinese ones, would be “a mistake,” but that “American and its allies should build and deploy their own so-called open-weight models, which users can download, run and modify on their machines.”  That refers to “the billions of individual settings… that help determine how a model responds to prompts,” which most companies including OpenAI keep proprietary.  So, yes, if there is a way to stay ahead of China, which seems now a worthy goal even if our objectives are different, it’s even worth rolling out a new-to-me buzzword.  Fair is fair.

Artificial Intelligence Regulation - Three Months of… What?

A lot of small things have happened in this area since mid-May.  To what do they add up?

First, “Trump Cancels Signing of A.I. Executive Order” (Tripp Mickle and Sheera Frenkel, The New York Times, May 21^st).  Another flip-flop for our president, the kind of thing that happens when he finds out only after proposing something that it won’t work.  It was “an executive order that would give the government the power to evaluate artificial intelligence models before they were publicly released.”  The cancellation was the right decision, as nothing and no one in our government could possibly do that competently.

Next, “Elizabeth Warren calls for taxing AI industry to ‘invest in people’” (Alex Nitzberg, Fox Business, about May 28^th).  As I wrote in Work’s New Age 15 years ago, we may need to rearrange the sources of our tax revenues as they change in significance - for example, if the number of people working becomes greatly reduced, from efficiency, foreign competition, and automation, it would become unfair to count on them for the same share of tax collections they currently provide.  If, as Warren also said, “the tax system incentivizes replacing workers with AI,” that needs correction as soon as possible.  It is too soon for directly taxing AI to be noncontroversial, but, if AI shows clearer signs of replacing large numbers of workers, we will need to discuss that with rather more urgency.  Accordingly, Warren is right to bring up this idea now.

Moving along, per a “Fox News Poll:  Voters see AI regulation as urgent, rank safeguards ahead of innovation” (Victoria Balara, Fox News, May 28^th).  Innovation is great, but there are always guardrails, and, as we should have learned from dealing with the likes of Uber and Airbnb, using new technology should not exempt firms from governmental control.  Here, “nearly 8 in 10 think is it extremely (40%) or very (37%) urgent for the government to address AI regulations.”  There is no real partisan divide there, as 83% of Democrats, 77% of Republicans, and 82% of Independents want “to prioritize protecting the public interest.”  We’ll see how the other pieces in this post support this need.

Less than two weeks after the first article here came out, we saw as “Trump signs AI order that asks companies to give government early access” (Joey Garrison, USA Today, June 2^nd).  In it, the president “asks AI developers to voluntarily submit their models to the federal government to review for potential security risks.”  Although Washington “would have access to advanced AI models submitted for testing for up to 30 days - a shorter period than the 90-day window established in the draft order that Trump shelved,” that’s still a long time in the AI industry, and it is even less clear what our government can constructively accomplish.

Another effort from a different direction appeared as a “New Super PAC Aims to Rally Tech Workers to Help Limit A.I.” (Mike Isaac and Theodore Schleifer, The New York Times, June 18^th).  “Two Democratic operatives are aiming to leverage the unease within the tech industry over A.I. and harness an agitated work force into a political movement.”  We do have that.  The new PAC is smaller than its opposing one, Leading the Future, but “has started deploying” its funds, “buying ads for the Democratic party in New York City’s 12^th Congressional District to support Alex Bores, a former tech worker who has written A.I. safety legislation.”  No results here yet, but this organization will get support from Republicans as well.

Soon afterwards, “U.S. Presses Meta to Agree to A.I. Reviews as Security Concerns Rise
 (Tripp Mickle, Eli Tan and Sheera Frankel, The New York Times, June 23^rd).  Voluntary again, and “the latest example of the administration’s efforts to step up oversight of the A.I. industry after promoting a hands-off approach to” it.  Yet “Meta is the only major U.S. developer of A.I. technology that has not reached an agreement to voluntarily share its models with the federal government for review,” so also for another reason nothing is guaranteed.

Days later, “U.S. Loosens Restrictions on Anthropic’s Mythos A.I. Model” (Sheera Frenkel, The New York Times, June 26^th) was updated into “U.S. Lifts Restrictions on Anthropic’s Most Powerful A.I. Models” (Sheera Frankel and Ana Swanson, The New York Times, June 30^th).  The first “move de-escalates a clash between the Trump administration and the company,” and the second extended that to all the company’s strongest products.  It should surprise no one to read that “Trump officials are still working on a framework for how companies should formally submit new A.I. models for review, and what standards they would be held to.”

On a side not as opposite as it may seem, “AI could unleash ‘single greatest productivity revolution’ if Washington avoids overreach:  report” (Sophia Compton, Fox Business, June 28^th).  AI has great potential, we all can agree, but we don’t see eye-to-eye on what “overreach” would be - especially when it seems the potential overreachers are neither consistent nor effective.

Finally, something substantive!  “New York makes history with first-of-its-kind law regulating AI-powered commercials” (Julie Bonavita, Fox News, July 2^nd).  “The state’s synthetic performer disclosure law, signed by Gov. Kathy Hochul in December 2025, requires advertisements featuring an AI-generated person to include a clear label indicating the individual is not real.”

 That’s it.  I found nothing else.  Now, can we create boundaries for more things, and beyond just one state?  We are collectively floundering at that.  As the survey above shows, we need, somehow, to do better with artificial intelligence regulation - and, given recent intensified concerns, do it soon.  That is our bipartisan task.

July Jobs: A Blah, Flat Report, No Better but No Worse, with AJSN Down Somewhat to 17.3 Million

The first impression many will have of this morning’s Bureau of Labor Statistics Employment Situation Summary was that it was a loser, since total nonfarm payroll employment, with a 23,000 loss, fell short of published estimates by 103,000 to 118,000. 

That’s half true.  In support of that, the two measures of how common working and being officially unemployed, the labor force participation rate and the employment-population ratio, are, each dropped 0.1% to 61.4% and 58.9%.  The count of those working part-time for economic reasons, or holding part-time jobs while looking for full-time ones, gained 100,000 to 4.8 million, and average hourly private nonfarm wages lost two cents to $37.62.  Otherwise, though, unadjusted joblessness stayed at 4.4%, and other measures logged improvements: seasonally adjusted unemployment fell 0.1% to 4.1%, there were 200,000 fewer adjusted jobless (now 6.9 million), and 100,000 fewer, 1.8 million, who had been looking for 27 weeks or longer.

The American Job Shortage Number or AJSN, the metric showing how many more positions could be quickly filled if all knew they would be easy and routine to get, came in at 167,000 lower as follows:

The largest change was a 120,000 loss from those wanting work but not looking for it for at least a year - no other differences exceeded 37,000.   The share of the AJSN from those officially unemployed edged up 0.2% to 38.7%.  Compared with a year before, the AJSN has dropped just over half a million, with the largest subtractions from those jobless and those wanting but not seeking for a year or more.

What happened here?  Nothing.  The number of people not interested in work rose 284,000, which explains the lower unemployment rate.  Otherwise, it all looks light and variable, with no trends, no big areas of concern, and no real changes.  Likewise, no progress.  The turtle stayed right where he was.

The Problems with Artificial Intelligence Data Centers, At the Forefront Since March

Until recently, most of the articles I have referenced on the value of AI data facilities have been as positive as “Demand for AI Data Centers Sends Prospectors Hunting for Land and Power” (Tripp Mickle, The New York Times, March 5^th).  In this one, we learned how “a former Microsoft executive and his firm Cloverleaf have become modern-day land men, packaging electricity and land for data centers.”  He and his firm were portrayed as intrepid (“He’s a wildcatter”; “the team has faced hostile town halls… and also faces the risk that the A.I. boom fizzles out”), suitable (“they work at the intersection of utility companies and tech giants”), and timely (“their product - powered land, they call it - has become one of the nation’s most valuable commodities”).  Mickle seemed admiring, yet that may have been the last positive article on those facilities I have seen.

Later that month came out “Local Opposition Is Slowing A.I. Data Centers.  Wall Street Has Noticed” (Lydia DePillis, The New York Times, March 26^th).  It started with “The torrential wave of data center construction for artificial intelligence has seemed unstoppable,” and moved to “but lately, zoning commissions and county councils across the country have been resisting.”  Of “the top A.I. companies’” $710 billion 2026 facility-spending expectations, many have shifted their plans from resistive states such as Michigan, Oregon, and Wisconsin to more compliant Texas and New Mexico.  Yet “if America were to adopt A.I. at the scale that its boosters are banking on, data centers would need to be built closer to cities like Seattle, San Francisco and Chicago.”  The piece ended with doubt, specifically “if we assume tomorrow that data center construction stops… the ripple effects across the semiconductor industry would be pretty substantial.”

Next, we went as far as “Something Liberals and Conservatives Agree On:  Hatred of Data Centers” (Sabrina Tavernise, The New York Times, May 1^st).  “Early evidence suggests that Americans - once agnostic - are now souring on (data centers),” which a Milwaukee comedian called “the most bipartisan issue since beer.”  The refreshing anecdotals here featured liberals and conservatives finding common cause, when, for example, a Republican sportsman concerned about his fishing river joined with “a left-leaning musician and environmental activist.”  Although the article emphasized Michigan, it also mentioned related events in four other states.

Here, the June 11^th River Reporter led off with Patrick Kelly’s “Data centers delayed.”  “On Thursday, June 4, the New York State Legislature passed a moratorium on the approval of the development of large data centers for one year, among other regulations.”  Per “New York becomes first state to freeze new AI data centers in move critics warn could drive away jobs” (Bonny Chu, Fox Business, July 14^th), the state’s governor Kathy Hochul signed it, mandating “the nation’s first statewide temporary ban on new AI data centers.”  Per Hochul, who also wants to “repeal sales tax exemptions for large data centers,” “the initiative would require large data centers to shoulder more of the infrastructure costs they create and is intended to protect New Yorkers from rising utility bills and other financial risks associated with the industry’s rapid expansion.”  There will be more.

Here is also a political opportunity, as, per New York Times columnist Tressie McMillan Cottom on June 14^th, “Hating Data Centers Is a Winning Issue.”  She suggested it especially for Democrats, who could use that as a platform plank with more perceived sincerity than the Republicans would gain if they matched it.  As Cottom put it, “the voters showing up to fight data centers demonstrate that a lot of us want something different… Democrats… need a national message equal to the righteous rage driving millions of Americans to look up from their enemy and finally see, instead, a neighbor and future worth fighting for.”  Yes, “it’s simple.”

We know about these facilities’ impact on water and power, but how about auditory effects?  As Adeel Hassan explained in the New York Times on June 17^th, “The Cloud Has Sound:  The Unrelenting and Unseen Cost of A.I. Data Centers.”  They sound “like a low-frequency thrum of a neighbor’s central air-conditioning unit, an airplane flying overhead at high altitude or a truck engine idling down the road.  But it feels like the vibrating, rhythmic pulse of a subwoofer from a party that will never end… and some who live closest to data centers that emit the noise have reached their wit’s end trying to block it out.”  Unlike “many traditional community noise sources - like airports and freeways,” the disturbance comes out around the clock.  Although excessive sound in our country is usually unregulated, some have filed suit to change that.

Countries hoping for a US AI slowdown have been getting involved, as “China, Russia and Others Seek to Inflame Debate Over A.I. Data centers” (Steven Lee Myers and Dustin Volz, The New York Times, July 9^th).  They - one of the “others” was Iran - put up videos and a comic strip purported to be American-produced, “blaming data centers for soaring electricity bills” and saying they “posed a threat to Americans’ physical and financial well-being.”  These nations’ purpose was to “stoke the debate over data centers” and “deepen our divisions in order to dent our appeal and weaken us from within.”  It all sounds small-scale and unlikely to do real damage, especially since the side they are backing is now, as we will see, a majority position.

Next, “Data Centers to Add Billions in Power Costs in 13 States,” by Ivan Penn on July 14^th, also in the Times.  PJM, “the nation’s largest electrical grid operator,” held “an electricity auction that would add $6.3 billion in costs to the bills of millions of households and businesses within the next three years, an increase driven by the power demands of data centers.”  That’s supply and demand, but shows how those in one huge industry can massively increase the latter.  Appropriately, much of the rest of the piece emphasized supply growth as the solution, as state “governors have complained that PJM has been too slow to connect more power plants, solar and wind farms, batteries and other resources that would have helped lower prices and eased strain on the grid.”  That power network serves 13 states from Tennessee to Michigan and New Jersey.

Then, “US data center protests go national as backlash grows” (Valerie Volcovici and Lisa Baertlein, Reuters, July 18^th).  “Opponents of the rapid buildout of data centers,” namely the “grassroots group” HumansFirst, planned protests that day “in at least 125 locations across the United States, the first coordinated national effort to channel anger at the AI infrastructure expansion that has ramped up over the past year and roiled local politics.”  Participants were expected to “rally against what HumansFirst calls the “unaccountable” buildout of data centers and “unacceptable infringement on our liberty.”” Therefore, the movement now has a point of leadership.

So, “Is This the Fastest Opinion Shift in American Politics” (David Wallace-Wells and Robinson Meyer, The New York Times, July 15^th)?  The authors said that “seventy-one percent of Americans say they don’t want an A.I. data center built close to where they live,” changing from “last fall” when we were “almost exactly evenly split.”  Wallace-Wells and Meyer called it “shocking” and “absolutely crazy,” in “an insanely polarized country, which means it isn’t very often that you see a political supermajority on any issue, let alone one that emerges almost out of nowhere to announce itself as a major new front in our politics.” 

“So what’s actually going on here?”  The authors of the previous piece took a swing at that, and we can too.  It’s a rare combination of strongly valid local concerns, feelings of being pushed aside by stereotypically rapacious titanic corporations, failure of people to see any end in sight, a perception among at least half of Americans that the economy and the system in general are not working for them, and several more factors.  It will not end soon.  It will be hugely discouraging to companies.  But it will find balance.  There are places within our borders godforsaken enough to be considered by most to be suitable for such data centers.  When benefits from AI start making it to ordinary people, views will shift - slightly.  In the meantime, as I predicted on New Year’s Eve, in the artificial intelligence world a hard rain’s a-gonna fall.

Artificial Intelligence-Driven Robots: Moving Right Along, Literally

Ten of them in nine weeks, all provided by Fox News or Fox Business, and most by one of their leading technology writers.  Which ones do you like?

First, “AI robot changes your tires and balances them, too” (Kurt Knutsson, News, May 12^th).  The company Automated Tire, Inc. “has unveiled SmartBay, an AI-powered robotic tire change platform built for dealerships, tire shops and service centers.  The system handles tire changes, wheel balancing and vehicle inspections with minimal human intervention,” all that while leaving “the wheel on the car.”  These devices seem to be available now.

Next, “Rideable robot looks ready to stomp all over us” (Kurt Knutsson, News, May 19^th).  This one, from Unitree, costs $574,000, and “can carry a passenger, smash bricks and shift into a four-legged form.”  It looks like a real-life GoBot, and its “most likely uses seem to be entertainment, exhibitions, research, security demos or specialized industrial testing.”  You can probably buy one now - if you want to.

More practically, “AI robotic beehives installed in Florida community claim 70% reduction in colony collapse threatening crops” (Brittany Miller, News, May 21^st).  They are called BeeHome systems and are made by Beewise (note how these robot manufacturers are specializing around their offerings).  The product, now in use in Land O’ Lakes, “uses robotics, sensors and artificial intelligence to monitor hive health and protect colonies from environmental threats,” and “arrives as bee populations across the United States continue facing pressure from parasites, pesticides, disease and extreme weather conditions.”

Fourth, an improvement in an area getting automaton attention for not only years but decades: “Humanoid robots work nonstop in package test” (Kurt Knutsson, News, May 24^th).  This time, three of them, made by Figure AI and using its “in-house AI system” “without human control,” “crossed more than 24 hours of continuous autonomous operation after a test that was supposed to last only eight hours kept running.”  So, “if robots can keep working through long shifts, what happens to people who do this work today?”  Although “businesses… will want to know how often the robots fail, how much maintenance they need and whether they can handle messy conditions without slowing down the whole operation,” per Knutsson “if companies can make these robots reliable, safe and affordable, the warehouse floor could look very different in the years ahead.”

We used to hear how automata could not make up hotel rooms, which made it news when “Humanoid robot cleans first US apartment” (Kurt Knutsson, News, May 31^st).  This service, offered now by Gatsby only in San Francisco, costs $150 and involves one of them arriving as prescheduled “to clean your apartment,” including “dishes, surfaces, floors, making the bed and folding laundry.”  Although “routine work is autonomous,” and no person arrives with the robot, “harder tasks can be handled through remote human teleoperation.”  This service needs a longer success record, but as with the others here, the proof of concept has been completed.

Sixth, two other companies have made progress on a situation above, as “Warehouse robots move packages without human handoff” (Knutsson again, News, June 30^th).  The firms, Ambi Robotics and Pickle Robot Company (so much for focused names), have “announced a commercial integration that connects Pickle Robot’s trailer-unloading robots with Ambi Robotic’s AmbiStack pallet-building system.”  The author especially liked the handoff ability, something which is still almost always left to humans; that capability may often turn out to make the difference between worthwhile and doubtful automation.

Next, “Zoox robotaxi redesign brings big rider upgrades” (Knutsson, News, July 3^rd).  That “company has updated its custom-built electric robotaxi with new comfort and usability upgrades”; it “added more padding and ergonomic curves to the seats and headrests,” for comfort, and “updated the color, materials and finish,” which they maintain will make it seem “a calmer cabin.”  These are available in San Francisco and Las Vegas, with Miami and Austin “listed as “Now Arriving” on its ride pages.”  Zoox has a track record of success, and these improvements have no real chance of damaging that.

Eighth, “Starship delivery robots leave campuses for cities” (Knutsson, News, July 6^th).  Remember “those little white robots that once rolled across college sidewalks with lattes, fries and late-night snacks”?  They are “getting a new assignment,” as Starship “will wind down its U.S. university campus operations and redeploy more than 1,200 robots toward grocery chains and hot food delivery in cities across the United States and Europe.”  The choice to remove them was a matter of “focus,” and will not take effect until next year.  The move to city sidewalks is challenging, and may take a long time.

Next, “Hyundai Motor brings Boston Dynamics’ Atlas humanoid robot to FIFA World Cup in groundbreaking activation” (Scott Thompson, Business, July 6^th).  “A special guest delivered the match ball to the head referee on the pitch” before a game in East Rutherford, New Jersey, and you can probably guess who - or, rather what - that guest was.  Its product name was Atlas, “an advanced humanoid robot” that also “performed” for spectators in other ways, so Hyundai, per a top manager, could “demonstrate that the future isn’t something we imagine - it starts now.”  The article had no mention of malfunctions, which, despite the lack of practicality, was excellent for the company.

Last, “Humanoid robots perform live surgery in world first” (Jesse Watson, News, July 14^th).  “Surgeons remotely guided” two robots, which “copied the surgeons’ movements rather than making medical decisions” “through two gall bladder removal procedures” on pigs.  The test “marked the first time teleoperated humanoid robots,” as opposed to “surgical robots” which have been used before, “successfully completed live gallbladder surgeries.”  Another step.

All these developments are favorable, and point out how robots, especially in human form, are a fast-moving, effective area of artificial intelligence.  There will be many, many more, and most of these here will propagate, some dramatically.  That, without any real doubt, will be extremely positive.

Artificial Intelligence on the Job - What’s Happening?

Although AI has not clearly been eliminating jobs, a lot has been going on with it at work.

Its effect on the up-and-down popularity of working from home hasn’t been moving in the direction many would expect, as “Return to office gaining momentum as AI reshapes corporate strategy” (Arabelle Bennett, Fox Business, November 2nd).  Per Newmark, “a global commercial real estate advisory firm that counsels Fortune 500 occupiers and major landlords on leasing, strategy and transactions,” “artificial intelligence is driving a surprising surge in office demand.”  That company’s president says these companies are “making bold moves” to “re-skill” employees “and have them retrained in artificial intelligence.”

Speaking of up and down, we next got “More!  More!  More!  Tech Workers Max Out Their A.I. Use” (Kevin Roose, The New York Times, March 20th).  As “at tech companies like Meta and Shopify, managers have started to factor A.I. use into performance reviews, rewarding workers who make heavy use of A.I. tools and chastening those who don’t.”  “An engineer at Open AI processed 210 billion “tokens” - enough text to fill Wikipedia 33 times - through the company’s artificial intelligence models over the last week,” and “at Anthropic, a single user of the company’s A.I. coding system, Claude Code, racked up a bill of more than $150,000 in a month.”  “Some tech companies, including Meta and OpenAI” had “internal leaderboards that show how many tokens… each worker consumes,” and on which “employees compete(d).”  The logical worker response is for them to use AI nonconstructively, which would put an end to this rather shortsighted practice.  Indeed, on June 18th, less than three months later, the Times printed Eli Tan’s “Tech Workers Maxed Out Their A.I. Use.  Now They’re Trying to Minimize It.”; the change came from “the bills from companies, like Anthropic and OpenAI, that provide A.I. tools - and they were not cheap.”  Going from almost mandating it to putting “some monthly limits on A.I. coding tools” - was that embarrassing, or just the cost of learning?

In what might be another natural outcome of such maxing out, “Meta’s Embrace of A.I. Is Making Its Employees Miserable” (Kalley Huang, Eli Tan and Kate Conger, The New York Times again, May 8th).  In order for that organization’s management officially “to capture employee data so Meta’s artificial intelligence models could learn “how people actually complete everyday tasks using computers,”” it gathered “what employees typed into their computer, how they moved their mouse, where they clicked and what they saw on their screen”; as a result, “many workers immediately revolted” and “blasted the tracking as a privacy violation, calling it antisocial and callous.”  In conjunction with substantial announced upcoming layoffs, many workers there were showing “anger and anxiety.”

Specifically, “What Are A.I. Agents Actually Doing?” (Cade Metz, The New York Times, June 4th).  “A San Francisco start-up called Arena, which tracks hundreds of thousands of artificial intelligence users, is trying to take some of the mystery out of what” tasks it is performing.  It found that 17% in “agent mode” were “code-writing,” 10% were for “research,” and almost as many were for making images, creating “documents like graphs and spreadsheets,” and to “brainstorm ideas.”  Significant shares also were spent on “creative writing or tutoring and education,” along with “code debugging” and “chatting.”  A wide variety.

Yes, it seems clear that “We’re Only Starting to Grasp the Pitfalls of Using A.I. at Work” (Noam Scheiber, The New York Times, June 29th).  Although “at a conference where two human resources executives said that treating A.I. agents like real employees was a way to increase productivity and to put their companies on the cutting edge,” “in an experiment involving dozens of companies with A.I. employees, the researchers found that managers tended to vet documents less carefully when told an A.I. employee had produced them,” and they “missed errors that other managers caught when told they were vetting the work of a human.”  As AI models “tend to favor work produced by artificial intelligence” causing a “potentially consequential form of implicit ‘anti-human’ bias,” and AI models do not often “cooperate and seek win-win outcomes” as people do, we can question whether businesses are using too much AI.  

Should companies slow down?  That would be hard for many to do, but it might be the best choice.  As these stories show, the race, even when it is about implementing artificial intelligence, is not always to the swift.

June Jobs Report Slightly Warm, with AJSN Showing Latent Demand Up Seasonally to 17.5 Million

If you were looking for an exciting Bureau of Labor Statistics Employment Situation Summary this morning, you didn’t get one.  So I will parse what we got for you. 

Instead of double the predicted number of net new nonfarm payroll positions, we got half, 57,000 instead of a published 110,000 estimate.  Otherwise, most of the numbers were not disappointing.  Seasonally adjusted unemployment lost 0.1% to reach 4.2%, and the unadjusted variety stayed at 4.4%.  Measured adjustedly, the number of unemployed fell 200,000 to 7.1 million.  There were 100,000 fewer long-term jobless, out for 27 weeks or longer.  Those working part-time for economic reasons, or keeping such positions while looking unsuccessfully for full-time ones, also lost 100,000, to 4.7 million.  Average hourly private nonfarm payroll wages rose 11 cents, again close to inflation, to $37.64.  Two outcomes worsening were the two showing Americans’ connection to work, the labor force participation rate and the employment-population ratio, off 0.3% to 61.5% and down 0.2% to 59.0% respectively. 

The American Job Shortage Number or AJSN, which shows how many additional positions could be quickly filled if all knew they would be easy and routine to get, gained 310,000 to the following:

 

The largest shifts came from the count of those officially jobless, pushing the AJSN up 515,000, and those wanting work but not looking for it for the past year, moving it down 239,000 - no others were more than 55,000 either way.  Of the AJSN, 38.5% was from those employed, up 2.3% from last month.  Compared with a year before, the AJSN was almost unchanged, losing 52,000, with its largest input differences from those discouraged, down 139,000, and those saying they did not want a job, up 136,000. 

How, overall, did we do?  When taking the outcomes above, that those not in the labor force decreased almost 300,000, and remembering that fewer people work in June than in May, we did well.  It wasn’t huge, but it was positive and broad-based.  We cannot get discouraged about missing projections, as those do not affect anything except our perceptions.  The turtle took a small step forward.

Driverless Cars: A Sputtering Spring

Even disregarding the story about the Tesla vehicle questionably running in self-driving mode, hitting a house, and killing someone, we haven’t seen much to like here since April.

What is, or was, “Robotaxi’s single point of failure” (Tech Brew, April 2^nd)?  “A few days ago, over 100 Baidu robotaxis halted on highways in Wuhan, China.”  Attributed only to a “system malfunction,” they stopped where they were, even in fast expressway lanes.  “Some passengers reported that in-car SOS buttons didn’t work, and one college student told Wired it took 30 minutes to even connect to a customer service rep - and help never came.”  If vehicles are linked, a single cause can bring all of them down - a real exposure.

Speaking of “all of them,” we saw as “Waymo recalls massive autonomous fleet after incident flags major safety issue” (Bonny Chu, Fox Business, May 12^th). “A driverless vehicle failed to come to a complete stop after encountering flooded road conditions on a high-speed roadway,” a problem of “the company’s 5^th and 6^th generation Automated Driving Systems (ADS).”  The flooded area was “untraversable,” almost 3,800 cars were held back, and “that same day, Waymo implemented additional restrictions to reduce the risk of similar incidents in inclement weather.”

That company, long on the forefront of autonomous vehicle technology and rollouts, got hit again soon afterwards, as “Waymo pauses freeway robotaxi routes after safety and software concerns” (Michael Sinkewicz, Fox Business, May 21st).  It was dealing with “performance issues in construction zones” by “updating its software.”  Just what happened became clear in “Waymo recalls nearly 4,000 robotaxis after cars enter freeway work zones” (Brittany Miller, Fox Business again, June 18^th).  There were “more than a dozen” such “incidents,” caused by a “software defect.”

Overall, “Would you ride in Waymo’s new Ojai robotaxi” (Kurt Knutsson, Fox News, June 2^nd)?  “The first public Ojai rides,” the cars offering “more legroom, bigger screens and accessibility features,” “will begin in the coming weeks,” starting in San Francisco, Phoenix, and Los Angeles.  They will be “free for a limited time while Waymo gathers feedback and refines the experience.”  No mention of software problems appeared here.

A potential issue worth publicizing is “When Someone Else Owns the Car, They Can Dictate Where You Travel” (Donald Kendal, The Epoch Times, June 3-9).  Potentially an issue with free robotaxi rides, it is more a concern for people someday commissioning cars which offer them free or discounted service in exchange for the likes of advertising exposure, or even for customers charged monthly amounts for auto transportation.  There is potential for other factors to sneak in.  For example, “could people be denied access to transportation services based on their political beliefs or statements they have made on social media (which has happened already)?  Could access be limited to curtail climate change?  Could environmental, social, and governance principles or other corporate social credit systems encourage companies to restrict travel based on a user’s carbon footprint?  Could the political winds of the day lead platforms to restrict rides to a firearms store, a church, or a specific political rally?”  When such arrangements appear, there should be laws already in place preventing these sorts of things.

One city doesn’t look good for the most common autonomous vehicles, as David McCabe in the June 17^th New York Times told us “Why Waymo’s Driverless Taxis Won’t Be on Your Streets Anytime Soon.”  The main objection here was not from snow, traffic, or narrow streets, but “groups that represent drivers” such as the New York Taxi Workers Alliance.  State governor Kathy Hochul unsuccessfully “introduced a budget proposal in January that would have allowed Waymo to operate in much of the state,” outside the city, where “mayor Zohran Mamdani has said he would heavily weigh the interests of taxi drivers in deciding rules for the technology.”  Much the same happened in Illinois.  Although Waymo “floated the prospect of creating a fund for displaced workers,” after their experience with Uber and Lyft that may not be enough.

One story of the eight here, though, was favorable toward autonomous vehicles, as a “humanless big rig completes first US freight run” (Kurt Knutsson, Fox News, May 5^th).  The semitrailer truck “left Houston, Texas in the middle of the night with nobody inside,” and “by morning, it had completed a 230-mile delivery near Dallas right on schedule,” with “no driver, no backup operator, and no one stepping in remotely.”  Was it, as provider Bot Auto said, “the first fully humanless, over-the-road commercial truckload in the U.S.”?  I know such vehicles have done similar things, but perhaps this was the first complete unassisted run. 

Good news, but, going forward, will stories like this predominate over the other seven?  Regular readers know I hope so.  I hope you do too.

Specific Artificial Intelligence Achievements - What It Has Nailed Down

What new things has AI excelled at over the past five months?

First, coding, per “This A.I. Tool Is Going Viral.  Five Ways People Are Using It” (Natallie Rocha, The New York Times, January 23^rd).  The product is Anthropic’s Claude Code, which “can generate computer code when people type a prompt,” and “has shown record growth” after “people had time to experiment with Claude Code over the holidays… and users realized how capable it was.”  This year we have heard a lot about coding being an obsolescent profession, which may or not be true, and Claude Code is a major reason why.

Second, expanded use for existing medications, sometimes as the only choice patients have.  In “A.I. Saved His Life by Discovering New Uses for Old Drugs” (March 20^th, The New York Times), Kate Morgan, after describing one patient’s move from expected death to remission, told us about AI’s finding an increasing number of side effects and unknown properties and making new applications primary.  Sometimes repurposing can start with something as simple as asking “show us every proposed treatment there has ever been in the history of medicine for (a condition).”  We should expect, and hope with gratitude, that there will be vastly more.

Third, faster travel through the skies.  “AI air traffic system promises fewer flight delays” (Fox News, May 9^th) gave us Kurt Knutsson explaining that “the Federal Aviation Administration is testing a new system designed to predict congestion weeks before it happens,” allowing airlines to “fix the schedule early so fewer problems show up later.”  AI’s capabilities for checking billions of data points can help it, for example, decide to schedule “a flight five or 10 minutes earlier,” which in cases it has recognized could “reduce bottlenecks in busy airspace,” or if “it could identify that a specific route tends to clog up at certain times of year… it could adjust schedules before tickets are even sold.”  Considering the “ripple effect” one late flight has on others, small changes could lead to saving tens of thousands of hours of passenger time.  The key companies working with the FAA are Palantir Technologies, Thales SA, and Air Space Intelligence, and we hope the outcome will be as valuable as they expect.

Fourth, “From Cow-Milking Robots to Weed-Zapping Lasers, Farmers Are Embracing A.I.” (Coralie Kraft, The New York Times, June 5^th).  Although “you can’t digitize an ear of corn,” “the industry is in the midst of what some are calling the fourth agricultural revolution, as driverless tractors trundle through fields, drones map moisture levels in soil and cows are outfitted with Fitbit-like devices that track their eating patterns.”  There are a stunning number of AI-related farming developments already implemented described here, and they are already helping this once-ailing occupation.

Fifth, helping doctors “find answers to clinical questions,” as described in “Have a Thorny Medical Question?  Your Doctor May Be Using A.I. for That” (Steve Lohr, The New York Times, June 8^th).  There are many gaps in doctor-to-doctor communication, but there is a massive amount of available knowledge, making the situation natural for software that can perform gigantic searches.  The tool is OpenEvidence, which is “essentially a chatbot for medicine,” and “has become a viral hit with physicians,” as, now, “more than half of the nation’s physicians are regular users” to the level of “30 million questions and consultations” in May alone.  When using the product, doctors “can ask (it) specific questions or enter the characteristics and symptoms of a patient and ask for potential explanations.”  Other companies are working to enter this field, and we can see why.

Last, if you want to buy “items you can picture but can’t name,” you may have help, as described in “New Amazon AI search turns words into shoppable images” (Kurt Knutsson, Fox News, June 14^th).  It works when customers use “more descriptive language” about something they want, such as “green dress with puff sleeves” or “wood coffee table with rounded edges” instead of only the first two or three words.  “As you add details, AI-generated images appear below the search bar.  Those images update as you refine your wording.  When one looks close to what you imagined, you can tap on it and shop for products with a similar look.”  It is working now, at least through “the Amazon Shopping app on your iPhone or Android phone,” and seems like a huge improvement over dealing with too many choices. 

What did I mean by “nailed down”?  I meant that even if there is a colossal AI bubble-bursting, these services will continue.  Companies, though we may not be able to choose which ones, will provide them.  They will be around in five, ten, or twenty years.  The controversy is over.  Artificial intelligence is here to stay.

Misuse of Artificial Intelligence - Real Problems, but What Stands Out?

 

Here are examples of how people have been using AI to deceive, defraud, and commit other sharp practices.

The pictures above the headline of “’A cat-and-mouse game’” (Sarah Kessler, The New York Times, September 6^th) show three receipts.  One is from the Midway Bar and Grill, giving the address, date, server, amount, tip, total, and credit card information.  Another says “FedEx Office” with a familiar logo, date and time, and information on a shipped package, including its cost.  The third is an itemized restaurant bill, with three courses and beverages.  All are apparently flawless, unimpeachable, and unremarkable, probably looking like millions of others submitted for expense reimbursement, but none are real.  Per the CEO of a company making “software used by finance teams to manage expenses,” producing such counterfeits is “too easy,” and employees often start with using AI to synthesize documentation for a legitimate expense for which they lost the receipt, and when they “don’t get caught, they do it again.”  Two other companies are involved in detecting this bogusness, but “A.I.-generated receipts will only get better from here,” and “to combat fraudulent A.I., we need to use A.I.”

We now have that technology built into web browsers.  I often pose questions to the pedestrian Google Chrome release I regularly use, and it may seem like little more than a convenience, but it’s natural to want to discover “How AI browsers open the door to new scams” (Kurt Knutsson, Fox News, September 20^th).  Such tools “can stumble into scams faster than humans ever could,” with a “dangerous mix of speed and trust.”  When people direct AI to buy things, it may “confidently” complete transactions on “fake” store sites.  “Old phishing tactics,” such as sending false bank-identified emails with destructive links, have worked smoothly with AI software.  Clearly, we’re not ready yet to delegate such sensitive activities.

What can happen when the roles are reversed?  We will need to learn “How to spot and stop AI phishing scams” (Kurt Knutsson again, Fox News, October 14^th).  Such things happen “when hackers use AI to make their scams more convincing,” by assembling “super-realistic emails, messages, voices and even videos.”  These communications rarely have the old-time tells of “typos and bad grammar,” and AI’s repertoire now includes “voice clone scams” and “deepfake video scams.”  Some of the red flags are unchanged, though, with Knutsson telling readers to beware of a “suspicious sender’s address,” “generic greetings like Dear Customer,” “unsolicited attachments” used to prompt action, email addresses with slight but real variations from official ones, and, perhaps most mentioned, a need for urgency.  With friends and family members, you can “set up and use a shared secret,” and with others you know, asking them something from the past not likely to be relevant now works well.  In a case I had, a Facebook friend and former high school football teammate asked me to participate in something believable but dicey, but when I asked him what position he played, “he” gave me the online equivalent of a blank stare.

How about misbehavior originating from AI providers themselves?  We think and hope there isn’t much of that anymore, but we had a case last fall where an “AI-enabled teddy bear… gave advice on B.D.S.M. sex and where to find knives.”  That was the springboard for “Public Shame Is the Most Effective Tool for Battling Big Tech” (Jessica Grose, The New York Times, January 14^th).  The author described how consumers successfully pressured company Mattel to pause “the release of any A.I.- powered” toys.  Yet many corporate responses to even sexual material have been weak, such as X-platform spokespeople saying that “its policy is to “take action”” against lewd deepfakes, and other events, such as the federal government asserting its ability to override state AI laws, may be creating a foundation for further wrongdoing.  So, per Grose, “negative publicity” is most effective in getting solutions for these problems to be reached.

Finally, there were “Lawyers Barred for A.I.-Generated Citations to Fake Cases” (Neil Vigdor, The New York Times, June 9^th).  When “all four lawyers on opposing sides in a civil trial” found themselves “removed from the case and fined” after “some of them, relying on artificial intelligence, cited fake legal cases in court filings,” one attorney said she had used “First Drafts, an A.I.-powered program for drafting legal documents.”  But the precedent here will be that due diligence with those tools will require more than was practiced this time.

What is noteworthy about these cases?  You may have been thinking that I picked only a few as samples for this post.  But, in nine months, this is all I saw.  For all of AI’s potential to do damage, it hasn’t been doing much.  Certainly, there has been more, especially in deepfakes, but it doesn’t look like a lot.  There is a message here - could it be that AI is not as compatible with crimes, and even vice, as we think?  Could it be that our laws and restrictions, as immature and makeshift as they seem to be, are working remarkably well?  It is time for us to consider these things, and, once again, look at how AI is actually turning out.

Jobs Report: More Work and More Not Looking, with the AJSN, Now 17.2 Million, Showing 700,000 Additional Latent Demand

This morning’s Bureau of Labor Statistics Employment Situation Summary was a mixed bag.  Once again, the number of net new nonfarm payroll positions beat up on the published estimates, more than doubling the one I saw to 172,000.  And once more, the rest of the report didn’t follow through.

Although seasonally unadjusted unemployment rose 0.1%, in a typically slightly lower employment month, to 4.1%, the adjusted variety held at 4.3%.  Long-term joblessness, or 27 weeks or longer, jumped 200,000 to 2 million, but the count of those working part-time for economic reasons, or keeping shorter-hours positions while continuing to look for longer-hours ones, lost 100,000 to 4.8 million.  The labor force participation rate sat at 61.8%, but the employment-population ratio, showing without embellishment how likely it is for Americans to be working, gained 0.1% to 59.1%.  Average hourly private nonfarm payroll earnings roughly matched inflation, up 12 cents to $37.53.  The adjusted number of unemployed dropped 100,000 to 7.3 million, while the unadjusted one rose 136,000 to 6.904 million.

The American Job Shortage Number or AJSN, the metric showing how many more positions could be quickly filled if all knew they were easy to get, was up 697,000 as follows:

The largest change came from, oddly enough, those wanting to work but not looking for it for a year or more - they were 835,000 more numerous, adding 668,000 to the calculation.  The second largest gainer was actual unemployment, which contributed 122,000 more.  The share of the AJSN from unemployment was 36.2%, down 0.8%.

Compared with a year before, the AJSN came out 278,000 higher, with most of the gains from those not looking for a year or more, those not wanting a job, those discouraged, and those unemployed.  The institutional, military, and off-the-grid category, down over one million since May 2025, provided the largest offset.

The other possible trend May provided was from the numbers of those not in the labor force, off 153,000 to 105,253,000, and not interested in working, which plunged 953,000 to 98,497,000.  Those can both be proxies for expected poor work prospects. 

What to make of this month?  Probably not a lot of lasting significance.  People left the labor force, but the new jobs were still there.  Per capita employment rose, but, at 4.8 million, too many people are being stopped from moving from part-time to full-time.  We’re not losing the employment battle now, but we’re not improving at it either.  Accordingly, the turtle stayed right where he was.