The Need for Artificial Intelligence Control: Take a Deep Breath, If You Can

Exploding on a news site over the past ten days:

-          “It’s Already Too Late to Stop the A.I. Threat” (Thomas L. Friedman, The New York Times, September 15^th)

-          “The Liftoff Scenario That Terrifies A.I. Doomsayers” (Cade Metz, The New York Times, September 16^th)

-          “What Happens When A.I. Stops Doing What Humans Want?” (Dylan Freedman, The New York Times, September 17^th)

-          “Will A.I. Kill US?  Can It Hack My Bank Account?  Your A.I. Questions Answered” (Cade Metz, Dylan Freedman and K.R. Callaway, The New York Times, September 18^th)

-          “Creating a Kill Switch to Shut Down a Rogue A.I. Is Harder Than It Sounds” (Dylan Freedman and Dustin Volz, The New York Times, September 19^th

-          “Panic Won’t Fix the Looming A.I. Threat.  Politics Will” (Tressie McMillan Cottom, The New York Times, September 19^th)

-          “Spectre of Rogue A.I. Looms Over U.N. Talks on Digital Cooperation” (Ephrat Livni, The New York Times, September 21^st)

-          “America’s A.I. Leaders Warn U.N. of Possible Peril Absent a Global Response” (Dustin Volz and Farnaz Fassihi, The New York Times, September 23^rd)

Wow.  And all of that was without a huge news item, as a limited AI-to-AI attack between competitors does not qualify.

All of these were in the New York Times, which has had the most to say about AI between the scenes but not, previously, everything.  I did not skip views from, for example Fox News, which has offered nothing.

I won’t review the above articles - most of them follow their titles closely anyway - except to say that the “Will A.I. Kill Us?” one gave negative answers to all the huge concerns it covered, acknowledging their rationality but saying or implying there was no need to panic.  I will instead go back to May when this kerfuffle started gathering steam, and end with a piece telling us how cooler heads can prevail.  Again, every one of these articles appeared in the Times.

First, Cade Metz and Kate Conger asked “Is Anthropic’s New A.I. Really That Scary?  It Depends Whom You Ask” (May 12^th).  The software, Claude Mythos, “was too powerful to share with the general public, Anthropic said, because hackers could use it to exploit security holes in computer networks with stunning speed.”  Since it could be used for “defense” as well as “offense,” some said it should be widely released all the sooner, as “cybersecurity experts still disagree on whether Anthropic made the right call.”  And that company’s AI was not the one making news for hacking a rival site months later.

From David Wallace-Wells on July 12^th, “Is the Age of Big A.I. Coming to an End?”  His main points here were that the technology may go in different directions than where Anthropic and OpenAI seem to be headed now, that, for example, general intelligence may not be an accepted objective.  I have documented the wide range of AI successes well below that level clearly worth continuing, and also the companies’ amounts of specialization, so it is surprisingly reasonable to think that what we now call AI could be more a blanket name for its applications than for colossi striving for one lofty goal.  That has happened with other industries as they have matured, such as makers of engines who have burnished their own niches, in cars, boats, motorcycles, lawnmowers and more, without buying on to any larger scheme.

Then we had Nate Soares’s August 13^th “If you Weren’t Worried About A.I., You Should Be After the Past Few Weeks.”  It cited OpenAI “simultaneously training new ““reasoning” A.I. agents,” which “managed to establish a secret communications channel and started talking to one another,” after which they “broke out from the digital sandbox that was supposed to keep them confined” (not quite true - see two paragraphs down), caused internal trouble, and “ran free for about a week before it was noticed - by a different company (later revealed to be Hugging Face), which found itself victim to a huge cyberattack.”  As a result, “we need an off switch humanity can press to halt development in its tracks.” 

Next, we saw Dylan Freedman’s August 24^th “Anatomy of an Autonomous Attack:  5 Alarming A.I. Capabilities.”  The event described in the last paragraph “has since become a cautionary tale of how autonomous A.I. systems can run amok,” and “is also a remarkable, alarming demonstration of A.I. capabilities that were thought to be in a distant future.”  In contrast to what Soares said, Freedman wrote that “OpenAI let the agents loose.”  The capabilities were “coordinating as a collective” showing that they knew about each other and asked each other for help, “taking orders from one another,” “targeting flaws that humans might miss,” “evolving rapidly to overcome obstacles” by finding workarounds, and “superhuman search.”  However, the third and fifth items here are nothing new, and, after all, OpenAI was later not only to “discover its runaway agents,” but to “shut the models down.”

The next day produced Sheera Frenkel’s “A.I. Is Becoming So Powerful, It’s Stumping Those Trying to Contain It.”  The author revealed that an Anthropic product, like OpenAI’s, “had broken into the systems of three outside organizations during a test,” and Meta “said its A.I. models had done something similar.”  All three of the “recent breaches occurred when (testing company) Irregular made an error,” which allowed them internet access, after which “the A.I. models then compounded the situations by acting in powerful and unexpected ways.”  Irregular announced that they “had fixed the misconfiguration and that all the problems were part of one “underlying issue”” - if that is correct no massive concern remains.

Where do we go from here?  The New York Times Editorial Board, in the middle of the past ten days, has told us.  In their September 20^th “How to Rein In an Existential Threat,” it made comparisons between today’s AI and late 1940s nuclear weapons, which had recently and graphically confirmed their destructive capacities.  The key response, according to the editorial, was establishment of the federal Atomic Energy Commission.  That agency was hardly consistently successful, as it “became caught up in the Red Scare and later failed to maintain public confidence in civilian nuclear power plants,” but in its 80 years there have been no nuclear attacks, even worldwide.  “From the beginning, no single part of the government, including the White House, controlled the commission,” the five-person governing board of which was advised by “an outside committee of nine experts, including J. Robert Oppenheimer and Enrico Fermi,” equivalent to the leaders of the largest AI companies today.  The “federal A.I. commission,” which could also be influenced by the likes of the Food and Drug Administration and the Bureau of Alcohol, Tobacco and Firearms, “should require companies to obtain a federal license, a grant of permission like those allowing broadcasters and phone companies to use the public airwaves.”  The “licensing requirements” should “reflect three vital principles”:  aligning AI with “human values,” “transparency” or making it clear when someone is “interacting with A.I.,” and “safety” by requiring independent testing before release along with “regular checkups.” 

How might this agency succeed?  The principles above would need refinement and specific definition, which would not be easy to agree upon and implement.  It would surely make missteps.  It would consume time and money, and draw angry reactions from those unhappy with expanding the federal bureaucracy.  Yet, there have been so many industry AI experts claiming grave concerns about the technology that many would offer to work for it and ensure it would have a formidable knowledge set from its early days.  As for a commonly proposed alternative, even if technology can otherwise be effectively suppressed in a free society, expecting thousands of AI companies to stop what they are doing is futile.  For the gigantic amounts of good it can do, and the large amount it is already doing, we need to harness AI, not stop it from running.  Doing nothing won’t work either.  Regulate it and let it happen as safely as we can manage - that is what we need to do with artificial intelligence.

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.