Non-Huge Threats from Artificial Intelligence: There are Those, Too

Along with the human-species-ending concerns, we have other, smaller but beyond tiny, ones about AI. 

First, we look “Inside the British Lab Hunting for Dangers Lurking in A.I.” (Adam Satariano and Paul Mozur, The New York Times, May 24^th).  The “A.I. Security Institute,” part of the United Kingdom government, has been conducting experiments with the software, in one finding that although a certain system refused to communicate “instructions for making the deadly bioweapon anthrax,” after it was hit “with thousands of automated questions and prompts,” it “caved” by providing “a detailed list of materials and equipment, along with a step-by-step recipe for making the lethal mixture at home.”  Technicians there “simulate attacks on A.I. systems,” and elsewhere “broke through” their “safeguards,” showing the systems are not as secure as their creators intended.  Although the institute’s limitations include a lack of “regulatory power” and being able to offer only relatively low pay, it seems to be doing great work, and is leading the way to similar efforts elsewhere.

Across the ocean, “Scientists Find Way to Supercharge Dangerous Computer ‘Worms’ With A.I.” (Cade Metz, The New York Times, June 2^nd).  This time, University of Toronto researchers “found a way to use artificial intelligence to create a dangerous computer “worm” capable of targeting any known flaw in the world’s computers and quickly spreading mayhem throughout the internet,” a project “likely to raise fears that A.I. is leading to a new era of computer hacking that will be difficult to defend against.”  The Toronto-developed worm “can rapidly spread across a network by tailoring a new attack for each machine it encounters,” meaning that “there is no longer a single software fix you can apply to the devices to protect them.”  Scary, but it is good that benevolent forces are doing the discovering.

For completeness, I note here as elsewhere that “OpenAI Says Its A.I. Models Went Rogue and Attacked a Digital Library (Kate Conger, The New York Times, July 21^st).  When this story broke, Conger reported that two of (OpenAI’s) artificial intelligence models went rogue and successfully hacked into Hugging Face, a digital library of A.I. technology that is popular among developers,” and “displayed the kind of science-fiction potential that A.I. companies have warned would soon become a reality.”  That became less spooky when it was revealed that the testing company responsible for the models’ excursions had erred by allowing internet access, and OpenAI had had no problem retrieving the bots.  We can be concerned about such mistakes, but not about AI breaking into electronic areas from which it has been barred.

“Is A.I. ‘Scheming’ Against Us?” (Lora Kelley, again in the Times, August 1^st)?  “A small slice of A.I. technology has lately been caught defying human instruction (and even covering up that they’ve done so), a phenomenon some researchers call “scheming.””  We have long known that “doing exactly what they are told is not always” something at which AI excels, but more recently, “as the models care more and more about doing well at tests, some seem to care less about what the lab wants or what the user wants.”  There is serious anthropomorphizing here, but teaching to the test, and likewise learning for it, is indeed a human tendency, as is the following: “Many A.I. tools are trained through the process of reinforcement learning.  When A.I. does something right, it gets what can be thought of as a “thumbs-up and a pat on the head.”  When wrong, “it gets bopped on the head...  The models want to get the pat and avoid the bop.  Sometimes, the machine becomes so set on pursuing the reward that it breaks rules to get there.”  Yes, it is a shame that we know so little about consciousness.

Finally, from another angle, “The Big A.I. Build-Out Is Endangering the Economy” (Robin Wigglesworth, The New York Times, August 30^th).  “Artificial intelligence is no longer primarily a stock market story,” but, as “A.I. companies… had already tapped debt markets for $217 billion last year,” this time were up to $445 billion as of “mid-August,” and the technology’s course “is a story of debt, and that should worry us” since ”manias fueled mainly by credit almost inevitably end badly.”  Since AI is such a gigantic force in the economy today, it may turn out to be, as the musical group R.E.M.’s lyrics put it, that it could say “I raised the wall… I will be the one to knock it down.”

How many of these problems will materialize?  We don’t know, but efforts toward discovering and stopping them will generally prevent disasters.  We can’t say the same for AI’s chance of destroying civilization, but your correspondent’s p(doom) of .001 reflects his bias toward not fearing too much.  In the meantime, let’s continue anticipating artificial intelligence’s problems.

Artificial Intelligence Regulation: Still Many Concerns, Still Little Progress, But…

We start with six-plus weeks of chattering, plus two small improvement areas.

In the best of the latter, a “California law puts digital fingerprints on AI fakes” (Kurt Knutsson, Fox News, August 13^th).  Something we need nationwide, as of August 2^nd there the “rules require artificial intelligence tools to embed provenance data in synthetic media, with social platforms, device makers next,” as “large generative AI providers” must “embed hidden provenance information in covered media created by their systems,” those forms including “images, video and audio.”  These “covered AI providers must offer a detection tool at no cost,” which also allows uploading media and “submit(ting) a link to content stored online.”  Unfortunately, “a detection tool from one AI company may not identify media produced by another.”  Let us hope that, with a limited number of large producers, the tools can soon be combined - and mandated in the other 49 states.

Another form of regulation might end up being implemented, as “With A.I. Riches at Stake, Pressures Mount to Share the Wealth” (Tony Romm, The New York Times, August 16^th).  In June, generally pro-AI Virginia became the first state to “impose a small tax on data centers based on the vast amounts of electricity they consume.”  Not so “small,” as that money “is expected to be a windfall, supplying up to $600 million in revenue in the coming year,” as companies “shoulder” the “burden” of “new economic and environmental costs.”  Elsewhere, “policymakers are rethinking their economic relationship with artificial intelligence, trying to ensure their communities stand to benefit more clearly from a technology that may mint Silicon Valley an incalculable new fortune.”  Meanwhile, Senator Bernie Sanders “has called for a new sovereign wealth fund to redistribute profits from tech giants to the public,” which is not a totally new thought, as “for decades, Alaska has paid small dividends to its residents based on the sale of oil extracted within its borders… hardly enough to replace a person’s wages, but for some economists, the fund is a tempting model for the digital age.”  Nebraska’s Republican governor has cancelled a state law providing tax incentives to AI companies, as “Big Tech needs to pay their way,” and Texas’s same “ordered a statewide audit of data centers and their compliance with local electricity rules,” saying that “simply put, Texans must come first.”  Expect more of these changes, by Democrats as well - indeed, “over the past two years, roughly 30 states have considered legislation related to data center energy use.”

In the first state to temporarily stop allowing new data centers, “A.I. Is to Be Banned in N.Y.C. Elementary and Middle Schools” (Matthew Haag, The New York Times, September 1^st).  There will be no more “individual screens… until third grade,” “and teachers will not be able to ask A.I. to grade assignments.”  Yet there are exceptions, and “a member of a city education oversight panel” said the effectiveness of the ruling “remains to be seen.”

More overall, “Big AI is begging to be regulated.  That should worry you” (Dace Potas, USA Today, September 18^th).  Six days before that, “Anthropic CEO Dario Amodei published an essay calling, surprisingly, for a slowdown of American and global artificial intelligence development,” saying that “the most effective method of pacing is via regulation that targets all US frontier AI companies,” and drawing agreement from “OpenAI CEO Sam Altman and X CEO Elon Musk.”  That, per Potas, could be either in anticipation of acting “independently of instructions,” wanting to be seen advocating the newly inevitable, or working to create an oligopoly with smaller firms kept out, and, as I have written, it could be from expecting their companies would fare better than competitors.

“Has the A.I. Industry Become Too Big to Fail?” (Bethany McLean, The New York Times, September 26^th).  Altman called for the federal government to serve as “insurer of last resort” in case OpenAI collapsed, which would be similar to what it was during the 2008 financial crisis; now, by comparison, “the A.I. boom is far larger than the boom in subprime mortgages” which caused the 2008 problems.  As McLean put it, since “Americans really do not like A.I. companies,” and “it is hard to imagine the nation would accept a deal like the one that emerged in 2008, which prioritized the balance sheets of the investor class at the expense of everyone else,” “the solution to the whole ever-expanding A.I. terror starts with… No company should be too big to fail.”

Now, on to the hopeful articles.

What do we need to legally implement to bridle AI?  Perhaps not much, as “There Are Plenty of Laws on the Books to Check the A.I. Giants.  Use Them.” (Lina M. Khan, The New York Times, September 30^th).  Product liability laws, which “apply when goods are defective or dangerous”; tort law, which “establishes basic duties for companies to not act negligently or recklessly”;  and “consumer protection laws prohibiting unfair or deceptive practices,” which can take effect “when businesses injure others by failing to take adequate precautions,” are three categories, and have already been the foundations for “a host of lawsuits,” along with “criminal laws that address more egregious forms of corporate lawbreaking.”  Marginal cases when brought to courts may end up establishing precedents, but defensible legal actions where any of the above could apply should not be suppressed because of a perceived lack of protection under the law.  So even though on that same date Michael Gold and Cecilia Kang’s Times piece, “Congress Set to Leave Washington for the Midterms With No A.I. Progress” sounded sad, it did not mean the technology is without national restrictions.

One thing needing official clarification though is when “A.I. Is Going Rogue.  Who Should Be Held Responsible?” (David McCabe, also in the New York Times, October 1^st), as “legal scholars say applying existing law could be messy.”  Although “common sense might indicate A.I. companies would… be held liable for their runaway technology,” “the problem is so new that courts have largely yet to confront it.”  As well, nothing has been established on how the liability for open-source AI software will be shared between those producing its “underlying code” and those modifying it.  Some situations may be judged to be examples of the First Amendment.  It would be hard to show negligence when “an A.I. model exhibited harmful behavior for the first time,” and “it may also be difficult to show that an A.I. company intended to do harm, which can be essential to some criminal prosecutions.” 

Yet, per “A.I. Companies Say They Aren’t Responsible for Their Unpredictable Products.  Don’t Believe Them” (The Editorial Board, The New York Times, October 10^th), “liability is an incomplete answer for the challenges posed by A.I., but it has two great advantages: The laws are on the books, and they are powerful.  Makers of cars, airplanes, tobacco products and opioids have all been forced by lawsuits to change how they do business.  They made their products safer, restrict access to them or both.”  As McCabe implied, “the most important step is to start bringing lawsuits.  The legal process moves slowly.  The work of applying old laws to a new technology will be difficult, and progress requires judges to start chewing on actual cases.”

So that is where we are.  Yes, we need the federal AI regulation commission.  But until then, people with legitimate complaints about it should move forward as well as they can.  That, whether our government acts or doesn’t, should get us the AI control we need.

Artificial Intelligence’s Dislike Factor: How Strong Is It?

 

As if AI’s technical problems and ability to attract fears of human doom aren’t bad enough, it has attracted increasing adverse reactions for over five months now.  What do they mean and what do they say about AI’s prospects?

The first piece here, “One Issue Uniting Democrats and Republicans?  Worries About A.I” by Tim Balk on May 3^rd in the New York Times, mentioned “public concerns” rising “about the threat that A.I. poses to jobs, energy prices, education, privacy and mental health.”  A recent survey “found that 50 percent of Republicans and 51 percent of Democrats said that they were more concerned than excited about the increased use of A.I. in daily life,” with another 40 percent calling it even.  Neither party offered much, as “a fifth of voters said Republicans were better at handling A.I., a fifth said Democrats were, and a third said that neither party was good at it.”  Wonderfully bipartisan all around - then.

Next, we had “A.I. Populism Is Here.  And No One Is Ready” (David Wallace-Wells, The New York Times, May 8^th).  Per the author, that’s not only about data centers, but, because “the vision peddled by A.I. companies is remarkably depersonalized,” citizens also care about “job loss and economic turmoil,” and that “it is unsettling to many and outrageous to some that so few people seem to stand in such absolute control of it.”  Attitudes have shifted: “Northern Virginia is ground zero for the breakneck build-out of data centers, and between 2023 and 2025, voters there swung 69 points against building them in their own communities - from 45 points in favor to 24 points against,” in the face of Loudon County’s centers being “expected to generate nearly half of all local tax revenue” next year.

So, is it true that “The Generation That Grew Up With A.I. Hates It” (Michelle Goldberg, The New York Times, May 18^th)?  Well, during a pro-A.I. commencement speech at the University of Arizona, the “stadium-sized audience roared its disapproval,” and “at the University of Central Florida a week earlier, when” a speaker “described A.I. as “the next Industrial Revolution,” listeners booed, and someone shouted “A.I. sucks!””  The technology was “increasingly a pop culture villain,” and “only 18 percent of Gen Z-ers feel hopeful” about it.  Showing a reason, another study showed “that out of people in 30 countries, Americans had the least faith in their leaders’ ability to regulate A.I.”  A similar article, “The Kids Are Not OK With AI.  And They Know It” (Kay Rubacek, The Epoch Times, July 8^th), named a similar incident at Middle Tennessee State University and noted that “even among those who use it every day, enthusiasm dropped by 18 points over 12 months,” “eight in 10 now believe that AI will make learning harder,” “forty-two percent believe that it will hurt their ability to think carefully,” and “only one-quarter of respondents believe that it will help.”

Per the Wall Street Journal in “The American Rebellion Against AI Is Gaining Steam” on May 19^th, as shown by “one poll after another in recent weeks,” “the only thing growing faster than the artificial-intelligence industry may be Americans’ negative feelings about it.”  One found that the share of “U.S. voters” feeling positive about AI was lower than for “Donald Trump,” the Republican Party, the Democratic Party, or ICE.  Damning.

“How Popular Is A.I. With Voters?  Not Very, a New Poll Finds” (Tim Balk and Caroline Soler, The New York Times, September 15^th).  Although, in response to President Trump’s statements supported by much of his base, many Republicans have become more pro-AI, “less than two months before the midterm elections, neither party has claimed a clear advantage on A.I,” as “forty-two percent of voters said they trusted Republicans more, while 40 percent” said the same for Democrats.  At the time, “sixty-one percent of voters said they opposed the construction of the enormous computing complexes that power A.I., including 75 percent of Democrats and 47 percent of Republicans.”  Negative views were most common among “younger voters…with nearly 80 percent of those under 30 in opposition,” compared with 53% of those over 65.  However, “A.I. remains a relatively low priority for voters, according to the poll.”  According to Nate Cohn in “The Subtle Sign That A.I. Is Not Yet a National Political Issue” (The New York Times, September 16^th), those results show “that the issue is not quite ripe for national party politics,” as “voters, politicians and the parties are still figuring out what they think and where they stand,” and “over just the last 10 days or so since the Times/Siena poll was finalized, concerns over A.I. security have erupted into a major policy issue” [see my September 25^th post] “with the potential to become a political football.”

Finally and more comprehensively, “Fox News Poll:  Voters question whether AI’s promise outweighs its risks” (Victoria Balara, Fox News, September 22^nd).  Here, for which data was collected September 11-14, 71% were against “building data centers in their area including 46% who strongly oppose them,” yet 52% “favors building them in remote locations.”  That’s not unreasonable, since at least 71% of Americans live in areas without much unused or underused land.  In contrast to previous results, this survey showed that “voters think Democrats would better handle building AI data centers by a 14-point margin, with “that lead… especially strong among” Blacks (51 points), those under 30 (35), Hispanics (29) and independents (26 points).  AI in general “is viewed more negatively than positively” by 60% to 37%, and “majorities of voters are more likely to see AI as bad rather than good for the U.S. economy (54% bad vs 36% good), mental health (54-34%), society (56-34%), the environment (60-30%), and jobs (63-29%).”  Democrats, Whites without college degrees, women, and independents were the “most negative.”  Additionally, “three-quarters of voters (76%) favor banning AI in kindergarten through eighth grade classrooms.”

So what do we have here?  The dislike factor is strong, with no reason coming up, except potentially if a federal regulatory agency is created and sets rules, for it to abate.  Despite comments in a few of these pieces, AI will indeed be a political issue, and one that may, as it now stands, cost Republican candidates dearly if they support the technology and building of its data centers.  People running next month will need to pay attention to what their current and potential constituents are saying and feeling, here as well as elsewhere.  AI will make it through, but will be limited much more than now.  That is where artificial intelligence, now, is going.

September Jobs Report and AJSN, Now 16.4 Million, Show Expected Seasonal Improvement but Worse Than Nothing When Adjusted

This morning’s Bureau of Labor Statistics Employment Situation Summary was supposed to show at least some continuation from August’s strong outcomes.  Did we get that?  No.

The number of net new nonfarm payroll positions was projected at 90,000, but it was 29,000.  Seasonally adjusted unemployment worsened by a tenth of a percent to 4.2%.  The adjusted number of unemployed gained 100,000 to 7.1 million.  The count of those working part-time for economic reasons, or doing such while looking thus far unsuccessfully for full-time propositions, added 100,000, reaching 4.5 million.  Average private nonfarm hourly payroll wages fell short of inflation, gaining 6 cents to $37.81.  On the break-even side, the best this data could offer, people unemployed for 27 weeks or longer still added to 1.9 million, and the two measures showing how many Americans are working or one step away, the employment-population ratio and the labor force participation rate, were split, with the former up 0.1% to 59.2% and the latter off the same amount to 61.8%.

The American Job Shortage Number or AJSN, the Royal Flush Press statistic showing how many more positions could be quickly filled if all knew they would be easy and routine to get, lost over 600,000, on the large seasonal difference between August and September, to reach the following:

The share of the AJSN from official joblessness lost 1.4% to get to 37.5%.  Over 90% of the AJSN’s improvement came from unemployment, 469,800 better, and those wanting to work but not looking for it for a year or more, contributing 100,800 less.  Nothing was significant in the other direction.

Compared with a year before, the AJSN improved very close to a half million, with fewer unemployed, fewer discouraged, and fewer in the military, institutions, or off the grid partially offset by more people claiming no interest in work. 

What’s the story here?  I did not mention the improvements in other unadjusted figures driven by many people coming back to work as school years started.  Did we get any help from the numbers not mentioned above, those not in the labor force, down 111,000, and not interested, up 140,000?  Not there.  Annual progress was not helped this time.  That leaves us with the down picture in the second paragraph, which shows a poor employment month.  It was small, but the turtle, indeed, took a step backwards.

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.

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.