Artificial Intelligence in 2026: A Hard Rain’s A‐Gonna Fall

For many reasons, AI may be heading for a storm.

This was a great year for the technology.  It absorbed tens of billions of dollars in spending, in the process accounting for, according to one estimate, fully half of the nation’s Gross Domestic Product increase.  The NASDAQ index, heavy on technology stocks and reacting greatly to AI events, rose 20% during the 52 weeks ending on December 29^th’s early morning.  A large string of niche successes, from health care to robotics to shopping aids, have put AI in the news and in people’s lives.  Companies have generally done well and acted in good faith when problems with their products have materialized.  Press coverage was copious and predominantly positive, with a big drop in the number of stories about how and whether it endangers humankind. 

Yet in some ways, 2025 was more of a getting-into-position year than one of overwhelming success.  The most profitable AI-related companies, starting with Nvidia, were not producing AI tools but providing chips and other resources to those that are.  That firm’s market capitalization, along with that of others, reflects mostly expected future income, dwarfing how much it has had so far.

There are unresolved problems looming.  Many communities have recently said they do not want data centers, which have pushed up water and electricity prices, the latter nationwide.  Chinese competition, from an unfree state which need not reveal the practices it fosters and condones, greatly strengthened this year.  The American people bifurcated, into one group containing about the bottom two-thirds of families by earnings and another with more, and AI has helped the first cohort little while hurting them proportionally more with the higher utility rates.  A variety of lawsuits against these corporations are in progress and have begun to be resolved, starting with the first of many large ones from those owning the rights to books and other material used by AI model builders without authorization.  The emergence of “artificial general intelligence,” not pegged to specific tasks, even in a recently shortened estimate, is expected no sooner than 2029.

What does all that mean?  First, what was accomplished with AI this year does not require huge data centers for improved versions, as it was with largely limited if well-focused applications.  That will also be true for the vast majority of 2026 successes.  Second, if current market valuations are to be maintained, firms selling the software itself will need to start getting amounts consistent with the cost of the chips it requires.  Third, it needs to be perceived as benefiting most Americans, else it may be taken to symbolize the richer-poorer split above.  Fourth, we want to see major-publication articles with titles and contents more positive and less demanding than “A 1 Percent Solution to the Looming A.I. Job Apocalypse” (Sal Khan, December 27^th) and “An Anti-A.I. Movement Is Coming. Which Party Will Lead It?” (Michelle Goldberg, December 29^th, both in the New York Times).  Fifth, it is time for the industry to integrate its opposite communication pairs, of potential and present, 2022 views and 2025 views, and niches and humanity-shaking feats.

For 2026, I predict continuing AI specific application success, but problems of financing, earnings, and public support causing industry concern and even panic.  It may be time for some companies to expensively exit the scene, which many will interpret as a crash or bubble.  Data center construction will level off near the middle of the year and be greatly reduced by Christmas.  Overall, artificial intelligence will end shaky, but in 2027 we will learn with much more accuracy where it is going – and not going.  For now, the people are many and their hands are all empty – as always, we pays our money and takes our chances.

Artificial Intelligence Regulation Since April, and Why It Won’t Be Settled Soon

Not a lot has changed this year in the laws around AI, but we’ve spent eight months getting into position for what could be a big year for that. 

First, a look at “Where the legal battle stands around copyright and AI training” (Patrick Kulp, Emerging Tech Brew, April 21^st).  The short answer is unsettled, as although the Supreme Court will probably eventually hear a related case, “intellectual property lawyers say there aren’t yet many signs of where courts will land.”  As Anthropic seems to have used at least one of my books without permission, I was offered membership in a group to be compensated in a “$1.5 Billion Proposed Class Action Settlement.”  This may go through, and there may be similar resolutions offered by other AI companies.

Next, “Why the A.I. Race Could be Upended by a Judge’s Decision on Google” (David McCabe, The New York Times, May 1^st).  Although “a federal judge issued a landmark ruling last year, saying that Google had become a monopolist in internet search,” that did not resolve whether it “could use its search monopoly to become the dominant player in A.I.”  A hearing had started in April 2025 to settle that issue; at its conclusion four months later, in the views of Kate Brennan of Tech Policy Press, the “Decision in US vs. Google Gets it Wrong on Generative AI” (September 11^th).  The judge of the hearing considered AI, unlike search engines, to be a competitive field, and rejected “many of the Department of Justice’s bold, structural remedies to unseat Google’s search monopoly position.”  That could be a problem, as “Google maintains control over key structural chokepoints, from AI infrastructure to pathways to the consumer.”  This conflict, though, may not be completely settled, as the extent to which that company can absorb more of the AI field with its Gemini product is unknown.

In “Trump Wants to Let A.I. Run Wild.  This Might Stop Him” (Anu Bradford, The New York Times, August 18^th), we see that our presidential administration produced an “A.I. Action Plan, which looks to roll back red tape and onerous regulations that it says paralyze A.I. development.”  The piece says that while “Washington may be able to eliminate the rules of the road at home… it can’t do so for the rest of the world.”  That includes the European Union, which follows its “A.I. Act,” which “establishes guardrails against the possible risks of artificial intelligence, such as the loss of privacy, discrimination, disinformation and A.I. systems that could endanger human life if left unchecked.”  If Europe “will take a leading role in shaping the technology of the future” by “standing firm,” it could effectively limit AI companies around the world.

From there, “Status of statutes” (Patrick Kulp, Jordyn Grzelewski, and Annie Sanders, Tech Brew, October 3^rd) told us that that week California passed “major AI legislation… establishing some of the country’s strongest safety regulations” there, which “will require developers of the most advanced AI models to publish more details about safety steps taken in development and create more protections for whistleblowers at AI companies.”  Comments, both ways, were that the law “is a good start,” “doesn’t necessarily go far enough,” and “is too focused on large companies.”  It may, indeed, be changed, and other states considering such efforts will learn from California’s experience.

Weeks later, “N.Y. Law Could Set Stage for A.I. Regulation’s Next ‘Big Battleground’” (Tim Balk, The New York Times, November 29^th).  It “became the first state to enact a law targeting a practice, typically called personalized pricing or surveillance pricing, in which retailers use artificial intelligence and customers’ personal data to set prices online.”  Companies using such in New York will now need to post “THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA.”  As of article time, there were “bills pending in at least 10 states that would either ban personalized pricing outright or require disclosures.”  Expect more.

After a “federal attempt to halt state AI regulations,” “State-level AI rules survive – for now – as Senate sinks moratorium despite White House pressure” (Alex Miller, Fox News, December 6^th).  Although “the issue of a blanket AI moratorium, which would have halted states from crafting their own AI regulations, was thought to have been put to bed over the summer,” it “was again revived by House Republicans.”  Would this be constitutional, as AI is not mentioned as an area to be overseen by the federal government?  Or would it just be another power grab?

The latest article here, fittingly, is “Fox News Poll:  Voters say go slow on AI development – but don’t know who should steer” (Victoria Balara, Fox News, December 18^th).  “Eight in ten voters favor a careful approach to developing AI,” but “voters are divided over who should oversee the new technology, splitting between the tech industry itself (28%), state governments (26%), and Congress (24%).” Additionally, 11% “think the president should regulate it… while about 1 in 10 don’t think it should be regulated at all.”  That points up how contentious the artificial intelligence regulation issue is – and tells us that, urgent need or not, it may take longer to be resolved than we may think.  We will do what we can, but once again it won’t be easy.

Merry Christmas, happy Hanukkah, happy Kwanzaa, and happy new year.  I’ll see you again on January 2^nd.

November’s Employment Data Sluggish and Worse – AJSN Shows Latent Demand Up 110,000 to 17 Million

Here we are with the first Bureau of Labor Statistics Employment Summary since last month, the first data in two, and the first timely information in three.  Was it worth the wait?

The headline number, the count of net new nonfarm payroll positions, exceeded its 45,000 estimate but not by much – 64,000.  Seasonally unadjusted unemployment stayed at 4.3% but the adjusted figure gained 0.2% to 4.6%, since September.  The unadjusted number of unemployed rose 200,000 to 7.8 million, of which 1.9 million were designated as long-term or out for 27 weeks or longer, up 100,000.  The labor force participation rate gained 0.1% to 62.5%, but the employment-population ratio, best showing how common it is for Americans to be working, lost the same amount to 59.6%.  Average private nonfarm payroll earnings grew 19 cents, less than inflation, since September, to reach $36.86.  The alarming change was to the count of those working part-time for economic reasons, or holding on to less than full-time opportunities while looking thus far unsuccessfully for full-time ones.  That soared 900,000, to 5.5 million.

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

The largest change came from those discouraged, adding almost 130,000 to the metric, followed by unemployment itself which contributed 69,000 more.  The main subtraction came from people wanting to work but not searching for it for a year or more, which took away 62,000.  Thirty-nine point one percent of the AJSN came from those officially jobless, up 0.2% from September.

Compared with November 2024, the AJSN rose 1.2 million, half of that from official unemployment, and most of the rest from those discouraged and those not looking for at least a year. 

What does all that add up to?  Adding the modest increases in unadjusted unemployment (+172,000), those not in the labor force (+156,000), and those claiming no interest in work (+175,000) does not change anything much.  It was a torpid month or two, with few positive outcomes.  The fair number of new jobs was offset by what we hope is not a new level for those working part-time for economic reasons.  We also don’t like the rising unemployment rate, the highest in four years and in need of more front-line attention.  Without knowing what he did in October, we saw the turtle stay just where he was the month after.

Two Months of Driverless Cars, With Progress and Cogent Observations

This area has been heating up lately.  It’s been a good but limited year for autonomous vehicles, with those offering them mainly building on their robotaxi success.  What’s been happening?

On that, “Way more” (The Economist, October 4^th) discussed “the peculiar economics of self-driving taxis,” claiming that “the rise of autonomy has played out in two different ways.  First it has raised overall taxi demand in San Francisco.  Second, it has catered to a lucrative corner of the market.”  The number of rides in cabs with drivers stayed the same, and from 2023 to 2024 the count of people working in “taxi and limousine service” increased, 7%, leading Lyft’s CEO to say that autonomous taxis will “actually expand the market.” 

Moving along, “Could a driverless car deliver your next DoorDash?  New collab announced” (Michelle Del Rey, USA Today, October 16^th).  That company is combining with Waymo, to “launch the testing phase of an autonomous delivery service in the Phoenix metro area, with plans to expand it more broadly this year.”  Customers, if they are “in an eligible area,” can use the Waymo app’s “Autonomous Delivery Platform.”  As well as human “dashers,” DoorDash is already also making at least some deliveries with robots and drones.

On the other size end, an “AI truck system matches top human drivers in massive safety showdown with perfect scores” (Kurt Knutsson, Fox News, October 29^th).  Autonomous system Kodiak Driver’s rating, described as 98 on a 1-100 scale on the industry assessment VERA, “placed it beside the safest human fleets.”  The self-driving trucks have “advanced monitoring and hazard detection systems,” and have eliminated many human problems, such as “distraction, fatigue and delayed reaction.”  Nothing was provided, though, on how many of these trucks are running now, and whether they are being used for true production – but see three paragraphs below for one modest data point.

Now we can expect “Waymo to launch robotaxi service in Las Vegas, San Diego and Detroit in 2026” (Akash Sriram, USA Today, November 4^th).  The first two cities aren’t surprising, but can such vehicles deal with snow?  “In Detroit, the company said its winter-weather testing in Michigan’s Upper Peninsula has strengthened its ability to operate year-round.”  We will see if that place can really join “Phoenix, San Francisco, Los Angeles, and Austin,” where it “has completed more than 10 million trips.”

Miami is not in that group, but there, a “Sheriff’s office tests America’s first self-driving police SUV” (Kurt Knutsson, Fox News, November 6^th).  This “bold experiment” is a “year-long pilot program” of “the Police Unmanned Ground Vehicle Patrol Partner, or PUG,” which “is packed with high-tech features” including interfaces “with police databases, license plate readers and crime analytics software in real time,” and “can drive itself, detect suspicious activity through artificial intelligence-powered cameras and even deploy drones for aerial surveillance.”  A massive, if scary, potential help for law enforcement forces.

Are “Self-driving cars still out of reach despite years of industry promises” (Jackie Charniga, USA Today, November 25^th)?  Although “driverless semitrucks have traveled more than 1,000 miles hauling cargo between Dallas and Houston,” and robotaxis are established as above, “the unmanned vehicles circulating on American highways and side streets are a fraction of what executives promised in the giddy early days.”  We know that, though, and progress, on a more specialized and certainly slower track, is still real.  Don’t bet anything you don’t want to lose against improvement continuing indefinitely.

On the other hand, autonomous vehicles are still embarrassing themselves.  We now have “US investigating Waymo after footage captures self-driving cars illegally moving past school buses in Texas” (Bonny Cho, Fox Business, December 4^th).  Driverless technology has struggled mightily with understanding on a detailed level how human drivers think, and have not been able to quantify some great pieces of that, but why weren’t school buses, with telltale flashing lights and capability of being tagged in some way, long since identified and understood?  Were there none of them in the mile-square testing grounds where base autonomous software was developed?  This is the kind of thing which causes people to be overly fearful, and, if there are many more problems remaining at this stage, that’s justified.  I hope there are no more humiliations as low-level as this yet to emerge.

Ever since I first wrote about driverless technology, close to ten years ago, I have been making points about how beneficial it would be.  Avoiding the tens of thousands of annual deaths caused by human driver error was the main benefit, followed by higher general prosperity and allowing easier transportation of older children, those impaired, and others unable to drive.  As with when our current cars became the norm, we would not know all of self-driving’s effects, but many, such as reduced smoking as people would eventually not need to stop at gas stations where many now buy cigarettes, would be both probable and valuable.  I have been disappointed by overreactions to autonomous vehicles’ tiny amounts of fatalities, governmental unwillingness to allow the technology to progress, and general lacks of will and ability to see how many lives could be saved, but there has lately been, in two places, at least a small advance.

The first opinion piece was “Auto injuries are my job.  I want Waymo to put me out of work” (Marc Lamber, USA Today, November 21^st).  The author, with “a 34-year career as a plaintiff personal injury lawyer,” said his “calls have been heartbreakingly familiar:  a parent and spouse is paralyzed because someone was texting; a pedestrian on a sidewalk is killed because a driver had “just two drinks”; a family is shattered by speeding, fatigue or road rage.”  He pointed out that “autonomous driving technology doesn’t get drunk, distracted, tired or tempted to speed,” and that “a rare autonomous vehicle mistake dominates headlines while the daily toll of human driving error goes underreported.”  He mentioned that Waymo, “across 96 million miles without a human driver,” had “91% fewer serious injury crashes, 79% less airbag deployment crashes,” and “92% fewer injury-resulting pedestrian collisions,” along with “89% less injury-causing motorcycle collisions and 78% fewer injury-related cyclist crashes.”  Overall, “that is not perfection.  That is progress worth protecting.”

The second piece, published in the New York Times website on December 2^nd and in the Sunday print edition December 7^th, by Jonathan Slotkin, a neurosurgeon, was, in the latter, “The Human Driver Is a Failed Experiment.”  He made many of the same points Lamber did, adding that “more than 39,000 Americans died in motor vehicle crashes last year,” of which “the combined economic and quality-of-life toll exceeds $1 trillion annually, more than the entire U.S. military or Medicare budget.”  He said that “if 30 percent of cars were fully automated, it might prevent 40 percent of crashes,” and that “insurance markets will accelerate this transition, as premiums start to favor autonomous vehicles,” but “many cities are erecting roadblocks,” and “in a future where manual driving becomes uncommon, perhaps even quaint, like riding horses is today… we no longer accept thousands of deaths and tens of thousands of broken spines as the price of mobility.”  Ending, “it’s time to stop treating this like a tech moonshot and start treating it like a public health intervention.” 

Do we want this outcome?  If not, why not?

Artificial Intelligence Going Right Means No Total Crash is Possible

There’s been ever-increasing talk about an “AI bubble,” perhaps meaning a business shakeout but to some ways of thinking, concern that it will all prove illusory.  It may well fall short of being a massive, overarching technological change, but over 2025, and especially over the past three months, it has produced a steady flow of valuable applications.  Here are some worthy of your attention.

To stanch a problem that had been causing deaths and threatened huge lawsuit settlements, we saw as “OpenAI announces measures to protect teens using ChatGPT” (Stephen Sorace, Fox Business, September 16^th).  These “strengthened protections for teens will allow parents to link their ChatGPT account with their teen’s account, control how ChatGPT responds to their teen with age-appropriate model behavior rules and manage which features to disable, including memory and chat history.”  It is now in place, and is at least a commendable start.

On another gigantic corporate side, “Elon Musk Gambles on Sexy A.I. Companions” (Kate Conger, The New York Times, October 6^th).  And they are certainly trying to be.  Musk’s firm xAI offered “cartoonish personas” which “resemble anime characters and offer a gamelike function:  As users progress through “levels” of conversation, they unlock more raunchy content, like the ability to strip (them) down to lacy lingerie.” They would also talk about sex, and have kindled romantic, as opposed to pornographic, user interest.  As for the latter, “ChatGPT to allow ‘erotica for verified adults,’ Altman says” (Anders Hagstrom, Fox Business, October 15^th).  Their CEO Sam claimed he implemented this capability partly as a response to successfully limiting teens as above, and expected that “In December, as we roll out age-gating more fully and as part of our ‘treat adult users like adults’ principle, we will allow even more.”

In a rather unrelated achievement, “Researchers create revolutionary AI fabric that predicts road damage before it happens” (Kurt Knutsson, Fox News, October 15^th).  “Researchers at Germany’s Fraunhofer Institute have developed a fabric embedded with sensors and AI algorithms that can monitor road conditions from beneath the surface,” which would “make costly, disruptive road repairs far more efficient and sustainable” by assessing “cracks and wear in the layers below the asphalt.”  The fabric “continuously collects data,” and “a connected unit on the roadside stores and transmits this data to an AI system that analyzes it for early warning signs.”  Seems conceptually solid, and is now being tested.

If you want more than just hot other-sex representations, now “People are talking with ‘AI Jesus.’  But do they have a prayer?” (Scott Gunn, Fox News, October 26^th).  The author named concerns with that app, some from his Christian perspective, such as “your conversation might take a strange turn when “Jesus” says something that’s just not true or makes up a Bible verse or reference that doesn’t exist,” and that using it constitutes “replacing the living and true God with a false God.” He also noted that “people in church… will answer your questions and support you through uncertain times.”  This program could be used as an attempt to learn Christian teachings, and end up helping people “grow in faith and love,” but, per Gunn, it’s no substitute for the old-fashioned means.

Medical-related AI uses have been growing exponentially, and, in the October 30^th New York Times, Simar Bajaj gave us “5 Tips When Consulting ‘Dr.’ ChatGPT.”  Although “ChatGPT can pass medical licensing exams and solve clinical cases more accurately than humans can,” and “are great at creating a list of questions to ask your doctor, simplifying jargon in medical records and walking you through your diagnosis or treatment plan,” they “are also notorious for making things up, and their faulty medical advice seems to have also caused real harm.”  The pieces of advice are “practice when the stakes are low,” “share context – within reason,” “check in during long chats” by asking it to summarize what it “knows,” “invite more questions,” and “pit your chatbot against itself” by requesting and verifying sources. 

Back to romantic uses with “How A.I. Is Transforming Dating Apps” (Eli Tan, The New York Times, November 3^rd).  The area of online dating, per a mountain of articles and anecdotal reports, is now a disaster zone of dissatisfaction, so the appearance of “artificial intelligence matchmakers” must at least have potential.  People are entering information about what kind of partner they want, the tool distills them down to one candidate, and the user pays individually for that.  I don’t think this is really anything new, just an adjustment from providing a smaller number of recommendations to just one, but perceptions are powerful, and sending $25 for a crack at meeting “the one” may turn out to have great emotional, and even logistical, appeal.

Another personal thing AI has been doing is counseling.  But “Are A.I. Therapy Chatbots Safe to Use?” (Cade Metz, The New York Times, November 6^th).  The question here is not whether the products are useful, but if they “should be regulated as medical devices.”  The day this article was published, as “how well therapy chatbots work is unclear,” “the Food and Drug Administration held its first public hearing to explore that issue.”  At the least, such programs will be usable only unofficially for psychiatric counseling; at best, certain ones will be formally, and perhaps legally, approved.

The other side of one of the technology’s most-established setting came out in “I’m a Professor.  A.I. Has Changed My Classroom, but Not for the Worse” (Carlo Rotella, also in the Times, November 25^th).  The author, a Boston College English instructor, related how his students “want to be capable humans” and “independent thinkers,” and “the A.I. apocalypse that was expected to arrive in full force in higher education has not come to pass just yet.”  He had told his learners that “reading is thinking and writing is thinking,” “using A.I. to do your thinking for you is like joining the track team and doing your laps on an electric scooter,” and “you’re paying $5 a minute for college classes; don’t spend your time here practicing to be replaceable by A.I.”  Those things, and the “three main elements” of “an A.I.- resistant English course,” “pen-and-paper and oral testing, teaching the process of writing rather than just assigning papers, and greater emphasis on what happens in the classroom” have seen this contributor through well.

In the same publication on the same day, Gabe Castro-Root asked us “What Is Agentic A.I., and Would You Trust It to Book a Flight?”  Although not ready now, its developers claim it “will be able to find and pay for reservations with limited human involvement,” once the customer provides his or her credit card data and “parameters like dates and a price range for their travel plans.”  For now, agentic A.I. can “offer users a much finer level of detail than searches using generative tools.”  One study found that earlier this year, “just 2 percent of travelers were ready to give A.I. autonomy to book or modify plans after receiving human guidance.”  If hallucinated flights, hotels, and availability prove to be a problem, that may not get much higher.

Another not here now but perhaps on the way is “Another Use for A.I. – Talking to Whales” (David Gruber, again in the Times, November 30^th).  Although the hard part of understanding whale sounds is only in the future, AI has proved handy in anticipating “word patterns” as it does with human language, and can also “accurately predict” the clicks they make “while socializing,” “the whale’s vocal clan, and the individual whale with over 90 percent accuracy.”  We don’t know how long it will take for humans to decode this information, but AI is helping to clear conceptual problems in advance.

Once more in the November 25^th New York Times was the revelation that “A.I. Can Do More of Your Shopping This Holiday Season” (Natalie Rocha and Kailyn Rhone).  Firms providing “chatbots that act as conversational stylists and shopping assistants” include Ralph Lauren, Target, and Walmart.  Customers with ChatGPT can use an “instant checkout feature” so they “can buy items from stores such as Etsy without leaving the chat.”  Google’s product “can call local stores to check if an item is in stock,” and “Amazon rolled out an A.I. feature that tracks price drops and automatically buys an item if it falls within someone’s budget.”  While “many of the A.I. tools are still experimental and unproven,” per a Harris poll “roughly 42 percent of shoppers are already using A.I. tools for their holiday shopping.” 

And so it is going.  Most of these innovations don’t require more expensively expanded large language models.  Why would people stop using them?  Why would companies stop improving them in other ways?  They are here to stay, and so, it must be, is artificial intelligence.

September’s Jobs Report – Months Ago Now, with Mild Changes – AJSN Now 16.9 Million

Between the government shutdown and my own outage, we’re about eight weeks later for this one than we usually are, but it still has something meaningful to say.  What?

The number of net new nonfarm payroll positions in the Bureau of Labor Statistics Employment Situation Summary came in at 119,000, not huge but strongly positive and exceeding a few estimates.  Seasonally adjusted unemployment was 4.4%, up 0.1%, and the unadjusted variety, reflecting work increases in September, fell from 4.5% to 4.3%, with the unadjusted count of those with jobs up 606,000, just more than last time’s loss, similarly moving to 163,894,000.  The two measures showing how many Americans are working or only one step away, the employment-population ratio and the labor force participation rate, each gained 0.1% to 59.7% and 62.4%.  The count of those working part-time for economic reasons, or looking thus far unsuccessfully for full-time labor while keeping at least one part-time proposition, was down 100,000 to 4.8 million, as was the number of people officially unemployed for 27 weeks or longer, reaching 1.8 million.  Average private hourly nonfarm payroll earnings rose 14 cents, a bit more than inflation, to $36.67.

The American Job Shortage Number or AJSN, the Royal Flush Press statistic showing how many additional positions could be quickly filled if all knew they would be easy to get, lost 844,000, mostly seasonally, to get to the following:

 

Less than half of the drop was from lower unemployment – more was from a large cut in those reporting they wanted to work but had not looked for it during the previous year.  The other factors changed little.  Year-over-year, the AJSN increased 316,000, with unemployment up since September 2024 and those not wanting work adding 115,000.  The share of the AJSN from official joblessness shrank 0.3% to 38.9%.

What happened this time?  Not a great deal, and barely better than neutral.  Those not interested in work rose 750,000, which with August’s 860,000 meant over 1.6 million over two months, which is a lot.  Otherwise, everything reasonably hung on.  There will be no October AJSN or Employment Situation Summary, but you can expect November’s writeup to appear here on the next jobs report’s December 16^th release date.  For now, the turtle managed only a tiny step forward.

Artificial Intelligence Going Wrong: Eleven Weeks of Real or Questionable Problems

Somewhere between AI’s accomplishments and its postulated threats to humanity are things with it that have gone wrong, and concerns that something might.  Here are nine – almost one per week since the end of August.

A cuddly danger?  In “Experts warn AI stuffed animals could ‘fundamentally change’ human brain wiring in kids” (Fox News, August 31^st), Kurt Knutsson reported that “pediatric experts warn these toys could trade human connection for machine conversation.”  Although television has been doing that for generations, some think that with AI playthings, “kids may learn to trust machines more than people,” which could damage “how kids build empathy, learn to question, and develop critical thinking.”  All of this is possible, but speculative, and nothing in this piece convinced me AI toys’ effect would be much more profound than TV’s.

A good if preliminary company reaction was the subject of “OpenAI rolls out ChatGPT parental controls with help of mental health experts” (Rachel Wolf, Fox Business, September 2^nd).  In response to a ChatGPT-facilitated suicide earlier this year, “over the next 120 days… parents will be able to link their accounts with their teens’ accounts, control how ChatGPT responds to their teen, manage memory and chat history features and receive notifications if their child is using the technology in a moment of acute distress.”  That will be valuable from the beginning, and will improve from there.

On another problem front, “Teen sues AI tool maker over fake nude images” (Kurt Knutsson, Fox News, October 25^th).  The defendant, AI/Robotics Venture Strategy 3 Ltd., makes a product named ClothOff, which can turn a photo into a simulated nude, keeping the original face.  A plaintiff’s classmate did that to one of hers, shared it, and “the fake image quickly spread through group chats and social media.”  As of the article’s press time, “more than 45 states have passed or proposed laws to make deepfakes without consent a crime,” and “in New Jersey,” where this teenager was living, “creating or sharing deceptive AI media can lead to prison time and fines.”  Still, “legal experts say this case could set a national precedent, as “judges must decide whether AI developers are responsible when people misuse their tools” and “need to consider whether the software itself can be an instrument of harm.”  The legal focus here may need to be on sharing such things, not just creating or possessing them, which will prove to be impossible to stop.

In a Maryland high school, “Police swarm student after AI security system mistakes bag of chips for gun” (Bonny Chu, Fox News, October 26^th).  Oops!  This was perpetrated by “an artificial intelligence gun detection system,” which ended up “leaving officials and students shaken,” as, per the student, “police showed up, like eight cop cars, and they all came out with guns pointed.”  I advise IT tool companies to do their beta testing in their labs, not in live high school parking lots.

Was the action taken by the firm in the third paragraph above sufficient?  No, Steven Adler said, in “I Worked at OpenAI.  It’s Not Doing Enough to Protect People” (The New York Times, October 28^th).  Although the company “ultimately prohibited (its) models from being used for erotic purposes,” and its CEO claimed about the parental-control feature above that it “had been able to “mitigate” these issues,” per Adler it “has a history of paying too little attention to established risks,” and that it needs to use “sycophancy tests” and “commit to a consistent schedule of publicly reporting its metrics for tracking mental health issues.”  I expect that the AI-producing firms will increasingly do such things.  And more are in progress, such as “Leading AI company to ban kids from chatbots after lawsuit blames app for child’s death” (Bonny Chu, Fox Business, October 30^th).  The firm here, Character.ai, which is “widely used for role-playing and creative storytelling with virtual characters,” said that “users under 18 will no longer be able to engage in open-ended conversations with its virtual companions starting Nov. 24.”  They will also restrict minors from having more than 2 daily hours of “chat time.”

In the October 29^th New York Times, Anastasia Berg tried to show us “Why Even Basic A.I. Use Is So Bad for Students.”  Beyond academic cheating, “seemingly benign functions” such as AI-generated summaries, “are the most pernicious for developing minds,” as that stunts the meta-skill of being able to summarize things themselves.  Yet the piece contains its own refutation, as “Plato warned against writing,” since “literate human beings… would not use their memories.”  Technology, from 500 BC to 2025 AD, has always brought tradeoffs.  As calculators have made some arithmetic unnecessary but have hardly extinguished the need to know and use it, while people may indeed be weaker at summarizing formal material, they will continue to have no choice but to do that while living the rest of their lives.

We’re getting more legal action than that mentioned above, as “Lawsuits Blame ChatGPT for Suicides and Harmful Delusions” (Kashmir Hill, The New York Times, November 6^th).  Seven cases were filed that day alone, three on behalf of users who killed themselves after extensive ChatGPT involvement, another with suicide plans, two with mental breakdowns, and one saying the software had encouraged him to be delusional.  As before, this company will need to ongoingly refine its safeguards, or it may not survive at all.                  

I end with another loud allegation, this one from Brian X. Chen, who told us, also in the November 6^th New York Times, “How A.I. and Social Media Contribute to ‘Brain Rot.’”   He started noting that “using A.I.-generated summaries” got less specific information than through “traditional Google” searches, and continued to say that those who used “chatbots and A.I. search tools for tasks like writing essays and research” were “generally performing worse than people who don’t use them.”  All of that, though, when it means using AI as a substitute for personal work, is obvious, and not “brain rot.”  This article leaves open the question of whether the technology hurts when it is being used to help, not to write.

Three conclusions on the above jump out.  First, as AI progresses it will also bring along problems.  Second, legally and socially acceptable AI considerations are continuing to be defined and to evolve, and we’re nowhere near done yet.  Third, fears of adverse mental and cognitive effects from general use are, thus far, unsubstantiated.  Artificial intelligence will bring us a lot, both good and bad, and we will, most likely, excel at profiting from the former and stopping the latter.

Artificial Intelligence’s Power, Water, and Land Uses, What’s Coming Next, and What Might Remain After a Business Bloodbath

How big has the AI buildup been?  What major problem with that is on the way?  If AI proves to be a bubble, what of value would stay? 

The oldest piece here, “AI energy demand in US will surge but also provide opportunity to manage energy” (Aislinn Murphy, Fox Business, April 18^th) told us that “the world, particularly the United States, is projected to see a massive jump in data center and artificial intelligence demand for electricity by 2030, per a recently released International Energy Agency (IEA) report.”  That happened not only in five years but within six months, though we can’t yet vouch for the prediction that “renewable energy sources will meet nearly half of the additional demand, followed by natural gas and coal, with nuclear starting to play an increasing important role.”

With that, let’s look at “What AI’s insatiable appetite for power means for our future” (Kurt Knutsson, Fox News, June 20^th).  Even less than five months ago, “the modern AI boom” was “pushing our power grid to its limits,” as “the energy needed to support artificial intelligence is rising so quickly that it has already delayed the retirement of several coal plants in the U.S., with more delays expected,” and “energy is becoming the next major bottleneck.”  As the previous author also wrote, power is going for “running” it “at scale,” for current use of the technology, not for creating models for future releases.  Perhaps unexpectedly, 30% to 55% “of a data center’s total power use” goes to “keeping AI servers from overheating,” and, overall, “the demand for AI is growing faster than the energy grid can adapt.”  Despite pledges to use renewable energy, much of that may be nuclear instead of wind, solar, or hydro, and even if not, “because the grid is shared, fossil fuels often fill the gap when renewables aren’t available.”

In “At Amazon’s Biggest Data Center, Everything Is Supersized for A.I.” (June 24^th, The New York Times), Karen Weise and Cade Metz reported that “a year ago, a 1,200-acre stretch of farmland outside New Carlisle, Ind., was an empty cornfield.  Now, seven Amazon data centers rise up from the rich soil, each larger than a football stadium.”  The company plans to build about 23 more there “over the next several years,” which “will consume 2.2 gigawatts of electricity – enough to power a million homes,” along with “millions of gallons of water to keep the chips from overheating.”  When fully constructed, this facility “will be the largest power user in the state of Indiana by a country mile.”

People connected with rural areas may not mind the jobs and money such projects bring, but per Ivan Penn and Karen Weise in the August 14^th New York Times, “Big Tech’s A.I. Data Centers Are Driving Up Electricity Bills for Everyone.”  Even though “Amazon, Google, Microsoft and other technology companies” are moving “into the energy business,” “the average electricity rate for residents has risen more than 30 percent since 2020,” and as “recent reports expect data centers will require expensive upgrades to the electric grid,” “A.I. could turbocharge those increases,” “unless state regulators and lawmakers force tech companies to cover those expenses.” 

Similarly, “AI Isn’t Free.  The First Costs Are on Your Bill, and More Are Coming” (Kay Rubacek, The Epoch Times, September 24^th).  With rising electric costs common nationwide, “despite the technological advancements, computing power is not getting more efficient in terms of power usage.  It is becoming ever more energy-hungry.”  As such, “the Department of Energy now warns of a hundred-fold increase in blackout risk by 2030 if data center growth continues and plants keep closing on schedule,” yet “experts cannot accurately predict (AI’s) future costs because the technology is changing too fast.”

General-public reactions to AI power and water use are coming in.  They are often not positive, as “AI Data Centers Create Fury from Mexico to Ireland” (Paul Mozur et al., The New York Times, October 20^th).  “In country after country, activists, residents and environmental organizations have banded together to oppose data centers,” but “there are few signs of a slowdown,” as, per bank UBS, “companies are expected to spend $375 billion on data centers globally this year and $500 billion in 2026.”  In Ireland in particular, where “a third of the country’s electricity is expected to go to data centers in the next few years, up from 5 percent in 2015,” the “welcoming mood has soured,” and it has now “become one of the clearest examples of the transnational backlash against data centers,” as “a protest movement has grown.”  “Impoverished small towns” in Mexico near where data centers have appeared have “began experiencing longer water shortages and more blackouts.”

It is clear from all this that the rubber of increased AI infrastructure is meeting the road of damage to residents.  There will be vastly more conflict next year, much of it, even in the United States as protests multiply, preventing data centers from being built.  That will become yet another problem for the technology to overcome, and will push costs even higher.

I have been reading about the possibility of a severe artificial intelligence downturn, and comparisons and contrasts with what happened almost 200 years ago with railroads.  Then, the failed companies left behind track, bridges, and stations that were later used when the industry reconstructed itself.  What would AI abandon?  Failed companies’ data center buildings would remain, but the chips would, as now, be worthless well under a decade later.  While the news that it is not upgrades driving current resource usage is heartening, and the chance of what is now a vast number of profitable and worthwhile applications disappearing is almost nonexistent, companies going bust could mean the end of tens of trillions in market capitalization.  It’s easy to imagine effects such as a 50% NASDAQ-index fall.  Yet those gigantic physical structures will still be useful.  How, we don’t know, but they will be, one way or another.

Still No AJSN

 Until data from the Bureau of Labor Statistics becomes available, there will be no further editions of the American Job Shortage Number.  If the November data is available by December 4th, the November version will be posted December 5th as previously expected.  I will put together and release the September and October editions, on dates to be determined, if the BLS publishes back data supporting them.

Scary Monsters, aka Physical Artificial Intelligence: Five Months’ Progress with Robots

This AI subarea is not only one of the oldest, but the most graphically visible.  And, in honor of our publication date, the closest to monsters.  But how good are they really?

We start with “Delivery robot autonomously lifts, transports heavy cargo” (Kurt Knutsson, Fox News, May 26^th).  So you don’t want to pay those mid-six-figure incomes to union dock workers, or even way over $100k to UPS package handlers?  This technology, LEVA, can “load and unload cargo boxes without any human help” by “securing the box,” then lifting “itself back up and” carrying “the load to its destination,” handling up to 187 pounds and dealing with stairs and “rough terrain” on the way.  Alas, nothing here about availability.

Dealing with a current problem, “John Deere addresses farm labor shortages with autonomous tractors” (Sophia Compton, Fox Business, also May 26^th).  Although they have been made in at least prototype form for almost four years, we can’t tell from this article if, or when, you can buy one.

More clearly in the present tense is “Humanoid robots handle quality checks and assembly at auto plant” (Kurt Knutsson, Fox News, June 24^th).  “Kepler Robotics has officially introduced its Forerunner K2 “Bumblebee” humanoid robot at the SAIC-GM automotive plant in Shanghai… in a recently released video, the K2 is seen moving confidently through the plant, performing detailed quality checks, and handling assembly operations that demand both strength and precision.”  It “can load stamped parts, manipulate mechanical fixtures, and adapt to new tasks using a combination of imitation and reinforcement learning.”  In addition to “tactile manipulators with an impressive 11 degrees of freedom per hand, and flexible fingertip sensors that boost its dexterity,” it “leverages a cloud-based cognitive system that enables it to learn new tasks quickly and coordinate its movements with full-body awareness.”  Fundamentally, largely because of AI, better than the industrial robots of decades ago.

Continuing along more general but similar lines, from the same author and source, “Job-killing robot learns at work, and it’s coming to the factory floor” (July 5^th).  Although the previous example would match the title, this time it’s Hexagon’s AEON, also “humanoid” and designed for “handling repetitive and error-prone tasks,” which allows “raising the bar for productivity and workplace safety.”  It uses Microsoft Azure and “Maxon’s advanced actuators” to get “remarkable agility and dexterity,” along with “spatial awareness,” and its “intelligence grows over time thanks to a self-learning loop.”  But no availability information, and described by Knutsson as “new.”

Did you know that “There Are More Robots Working in China Than the Rest of the World Combined” (Meaghan Tobin and Keith Bradsher, The New York Times, September 25^th)?  That was “more than two million… in Chinese factories last year,” per the International Federation of Robotics, with 300,000 “new” ones.  Charts of “annual installations of industrial robots,” one for China and one for “rest of world,” show the second one way ahead from 2015 to 2019, somewhat higher in 2020, and behind from 2021 to 2024.  Because of a “national push,” “over the past decade, China has embarked on a broad campaign to use more robots in its factories, become a major maker of robots and combine the industry with advances in artificial intelligence,” and now has “five times as many robots working in its factories as the United States.”

This time’s stunning speed achievement is from a product that “is a collaboration between Crest Robotics and Earthbuilt Technology, “Australian construction robot Charlotte can 3D print 2,150-sq-ft home in one day using sustainable materials” (Kurt Knutsson, Fox News, October 12^th).  The author described that as “the speed of more than 100 bricklayers working simultaneously.”  The material it uses “comes from sand, crushed brick and recycled glass,” forming “a structure that’s fireproof, floodproof and created with a far smaller carbon footprint than traditional building methods.”  Its “future versions” could even build “moon bases for research and exploration.”  Until then, though, it needs to become available, as it, representations to the contrary, “may be years away from building its first full-scale home.”

Apparently in progress now, though, are “The Robots Fueling Amazon’s Automation” (Karen Weise, The New York Times, October 21^st).  In that company’s “most advanced warehouse in Shreveport, La., employees touch products at just a few stages, such as taking them out of shipping boxes and placing them in bins,” whereupon “the Sparrow robotic arm looks into a bin of items, picks the one it wants and puts it in another bin,” sometime after which “the robotic arm called Robin places packed packages on a small robot called Pegasus, which shuttles packages to drop down specific chutes depending on where they will be shipped.”  After that, robots sort them and “autonomously” take “them to shipping docks.”  No doubt there will be further improvements, and perhaps more steps automated.

How can it be that “Robots power breakthrough in pregnancy research, boosting IVF success rates” (Angelica Stabile, Fox News, October 23^rd)?  The automata “assist in the (in-vitro fertilization) lab,” which incorporates a great deal of other AI, by “preparing specialized plates to sustain embryos,” at which they are “10 times more precise in preparing (them) than humans.”

These are wonderful developments.  My only gripe is that, as so often happens in the information technology field, the difference between current and projected future obtainability is often blurred.  Is it fair to credit a product with being able to do something, if it has not been publicly rolled out?  What steps remain for the manufacturers of, in the cases above, LEVA, robotic tractors, AEON, and Charlotte to sell their products commercially?  How much low-error time after that would it take for the makers, and us, to declare their product productively deployed?  We don’t have much for answers to any of these.  Until we do, or see successful robotic sales and deployment, we should limit credit to the Amazons, Bumblebees, and IVF robots.  The other companies can see why – if they can get there, we’ll do the same for them.

Electric Vehicles – Almost One Year’s Telling Stories

It’s been the quietest year of several for electric vehicles.  Are they settling down, or just reacting to changing governmental policies?  How predominant, if at all, will they be late this decade and beyond?

To set the tone for 2025, we saw “Tesla Annual Sales Slip for First Time as Competition Grows” (Jack Ewing, The New York Times, January 2^nd).  They “fell slightly in 2024” for “the first annual decline in the company’s history, as rivals in China, Europe and the United States introduced dozens of competing electric models.”  Total Tesla deliveries were off about 1% to 1.789 million, and, as of press time, “still accounts for nearly half of all electric cars sold in” America. 

The next was even gloomier, as Ivan Penn asked if “Electric Vehicles Died a Century Ago.  Could That Happen Again?” (The New York Times, May 26^th).  The author’s reasons for concerns were that “The Trump administration and Republicans in Congress are working to undercut the growth of electric vehicles, impose a new tax on them and swing federal policy sharply in favor of oil and gasoline.”  He mentioned that “the oil industry has enjoyed numerous tax breaks,” but electric cars, starting with their now-discontinued buyer subsidies, have as well.  He focused on EV’s being less “macho,” but there has been much more than that to why “electric cars may be                                                                         in trouble, at least in the United States.”

“The EVs We’ve Lost” (Wired.com, July 19^th) told us that “shifts in economic policy and manufacturing have led major automakers to cancel upcoming electric vehicle launches in the US.”  Whatever it is, consulting company AlixPartners “dropped its 2030 sales predictions for battery-electric and hybrid card by a whopping 46 percent compared to last year’s projections.”  With that, the following, many of which have been absorbing money for several years, will not be produced: Ford Three-Row EV SUV, Honda Five and Seven-Seat EV SUV, Mercedes-Benz MB.EA-Large Platform, Nissan and Infiniti EV Sedans, Volvo All-EV Lineup, Maserati MC20 Folgore, Apple Car, and Fisker Pear.  These are still in progress, but believed delayed: Buick EV, Ferrari EV 2, Lamborghini Lanzador, Lamborghini Urus, Porsche 718 EV, and Tesla Model 2.  That’s a lot.

Something healthy, and good for electric car buyers, is that we are seeing “Used E.V. Sales Take Off as Prices Plummet” (Jack Ewing, The New York Times, September 13).  In contrast to delivery numbers above, “sales of used electric vehicles rose 40 percent in July from a year earlier, according to Cox Automotive, a research firm.”  Those too, though, were subsidized, with customers “rushing to take advantage of a $4,000 tax credit that can be applied to used electric vehicles that sell for $25,000 or less.”  Used sales prices will be a good indicator of how highly EVs are desired by people who don’t already have one, which is perhaps obvious but reveals valuable information about the extent of their market.

As September rolled to a close, we got the judgment that “Electric Vehicles Face a ‘Pretty Dreadful Year’ in the U.S.” (Neal E. Boudette, The New York Times, September 29^th).  The author, backed up by analysts, expected that the end of various federal tax credits that month would cause sales figures “to plummet in the last three months of the year and then remain sluggish for some time,” as that and other industry developments show “a stark turnaround from the heady days a few years ago when many automakers believed electric vehicles were poised to take off.”  Additional manufacturer cancellations named here included Honda’s electric Acura, Stellantis’s “battery-powered” Ram pickup, and importing of Nissan’s Japanese Ariya electric SUV.

It was time for another writeup on “How Much It Costs to Drive an E.V. and a Gas Car in Every State,” and, courtesy of Francesca Paris and the October 8^th New York Times, we got one.  It, however, considered only fuel cost, so anyone serious about this issue will need to assemble and properly interpret data on depreciation and other expenses.  This study, though, found that charging or filling up for 100 miles ran averages of $5.26 for home electricity, $6.15 for hybrids, $12.80 for “standard” gas cars, and $15.62 for “fast charging.”  Factors mentioned for possible individual consideration were “cheaper electricity rates at night or for E.V.’s,” home charging when power comes from solar panels, regional electricity-cost differences favoring some west-of-the-Mississippi states, different gasoline prices, and differing fast-charging rates.  The states with the cheapest, relative to gas, home-charging prices were all in the West, with the most expensive five all in New England.  When gas was compared with fast charging, the most favorable to EVs were Florida and four in the Pacific, while the worst were scattered: Arkansas, Wyoming, the District of Columbia, Vermont, and Maine.  It is noteworthy that New England, which culturally is one of the areas most favorable to electric cars, has the most expensive electricity, and the mountain states of the West are opposite in both ways.

What overall?  Even without the subsidy losses, electric vehicles, in the United States, were not poised to become the norm.  They seem solid as a minority preference, but that’s all we, automakers, legislators, and presidential hopefuls should expect.  They have more gyrations to go through before we know just how large a share they will command, but it won’t be a majority.  On that the data, tangled though it may seem, can agree.

What’s Happening with Driverless Cars, Good and Bad

Although from a late-teens perspective autonomous vehicles haven’t done much of anything, as I have reported they are succeeding in several cities.  What else has been going on with them?

In what should be good news, “GM restarts driverless car program more than a year after Cruise robotaxi incident” (Greg Wehner, Fox Business, August 11^th).  In a one-off event well before their decision to leave, “a Cruise Origin robotaxi… struck and dragged a woman about 20 feet.”  The automaker claimed here they’re “accelerating the development of autonomous driving technology capable of operating without human oversight,” and per Bloomberg will “be focusing on developing driverless cars for personal use instead of for a robotaxi service.”  As the firm’s “sources… reportedly” said, “the first steps should be to develop hands-free and eyes-free driving with a human inside the vehicle, but ultimately the company is working to have a car that can drive without anyone at the wheel.”  That sounds like returning to where they were, if probably incorporating improvements the taxis have discovered.

Soon thereafter, we watched as “Stellantis hits the brakes on Level 3 autonomous driving tech over soaring costs” (Nora Eckert, USA Today, August 26^th).  That wasn’t defined in the article, except that it “enables drivers to have their hands off the wheel and eyes off the road under certain conditions,” which “would allow them to temporarily watch movies, catch up on emails, or read books.”  That sort of thing seems scarier than it did even years ago, and indeed was “never launched,” but the company “stopped short of saying that the program was canceled.”  Clearly an indefinite delay.

Per Charlemagne in the September 6^th Economist, such technology is important enough that we can call the continent’s slow pace with it “Europe’s Sputnik Moment.”  Robotaxis, which are “starting to feel humdrum in Guangzhou or Phoenix” remain perceived as “science fiction in Warsaw or Rome,” as they are “barely being tested” there, and exemplify “how far the continent has fallen behind” and how “Europe has become too dependent on China and America.”  However, this interpretation is unfair, since cities with robotaxis have new road and highway systems and generally fine weather.  I have heard nothing about them being planned for New York or Boston, in which self-driving vehicles would fare little better than in the much older cities across the Atlantic, and as well have more people walking and using public transportation.

“The one thing that’s free in Las Vegas – but it requires taking a gamble” (Deirdre Bardolf, Fox News, September 21^st) is a ride on a Zoox robotaxi, provided by Amazon.  The vehicles, which distinctively look like “toaster(s) on wheels,” have been available for just over five weeks, before which they progressed from serving “company employees” to helping “friends and family members,” before opening to “anyone with the Zoox app.”  At press time, Zoox was “collecting rider feedback, testing its user interface, refining its pickup and drop-off infrastructure and working to gain the public trust in driverless transportation.”  All strongly positive, even in a city with conditions, as above, unusually well suited to robotaxis.

Forbes, though, printed something called “Tesla’s Full-Self Driving Software is A Mess.  Should It Be Legal?” (Alan Ohnsman, September 23^rd).  In order for company CEO Elon Musk to get “his jaw-dropping $1 trillion pay package,” he must put “1 million Tesla robotaxis on the road and 10 million active (full self-driving) users over the next decade” – a tall order for technology described as “error-prone,” as during an hour-and-a-half Los Angeles test it “ignored some standard traffic signs and posted speed limits, didn’t slow at a pedestrian crossing with a flashing sign and people present, made pointless lane changes and accelerated at odd times, such as while exiting a crowded freeway with a red light at the end of the ramp.”  One observer called it ”just a prototype” and said “it’s not a product,” yet it stays as “driving-assist systems are unregulated.”  The previous paragraph gave; this one took away.  A week later, we saw “Two US senators urge probe of Tesla’s Full Self Driving response to rail crossings” (David Shepardson, Reuters), in response to “a growing number of reported near-collisions.”

“When a Driverless Car Makes an Illegal U-Turn, Who Gets the Ticket?” (Michael Levenson and Laurel Rosenhall, The New York Times, October 1^st).  Two policemen in San Bruno, California, “saw a car make an illegal U-turn right in front of them,” but “a ticket couldn’t be issued,” since, although “California approved a law last year allowing the police to cite autonomous vehicles,” it isn’t in force yet, “did not specify any penalties,” and “citation books don’t have a box for ‘robot.’”  Indeed, “there are no clear rules in California,” although “Arizona has a state law that allows the police to issue traffic citations to driverless vehicles, just as they would to regular drivers.”

What can we make of this motley collection?  One takeaway is that robotaxis, when in their carefully chosen environments, are doing superbly.  Another is that elsewhere they are not, erring with such as obeying signs that I would have thought the software’s 2010s closed-course training would have long resolved.  A third is that those programming and implementing autonomous vehicle technology need to change some things they are doing.  Until the results improve, driverless software will be limited to robotaxis and, with warnings to them to never stop paying attention, in cars with drivers.  Could all that substantially improve?  Maybe in a year, maybe not in ten.  Don’t bet on it – unless the odds you get are good enough.

Artificial Intelligence’s Gigantic Financials, and the Effects They are Having on Others

A great deal has recently been announced about the often-staggering sums of money associated with AI.  What have we heard, and what does it mean?

On August 27^th, Tripp Mickle saw “Nvidia Sales Jump 56%, a Sign the A.I. Boom Isn’t Slowing Down” (The New York Times).  What was, at least as of that date, “the most valuable public company in the world,” and had the month before reached $4 trillion market value, sold $46.74 billion from May through July with “profit” of $26.42 billion, the latter up 59%. 

On the same date and in the same publication, we read that “The A.I. Spending Frenzy Is Propping Up the Real Economy, Too” (Lydia DePillis).  “Companies will spend $375 billion globally in 2025 on A.I. infrastructure, the investment bank UBS estimates,” and “that is projected to rise to $500 billion next year.”  Per the Commerce Department, “investment in software and computer equipment, not counting the data center buildings which have “overtaken office construction,” accounted for a quarter of all economic growth this past quarter.”  The boom has especially helped building materials companies, along with “electricians, engineers and heavy-equipment operators.”  The sector is so strong that “the most significant constraint on data center growth is more likely to be supply:  The energy, water, workers and technical equipment required to construct and run them are all getting more expensive.”

The Economist, on September 13^th, took a warning stance with “The $3trn bet on AI,” saying that “even if the technology achieves its potential, some people will lose their shirts.”  The piece, though, did not focus on the near-certainty of some companies losing their market share or their perceived potential, but asserted that even “in the rosiest scenario,” many shareholders “would face big losses,”; if worse, “the flow of capital could slow; some startups, struggling under the weight of losses, could fold altogether,” and “a lot of today’s spending could prove worthless,” as “more than half the capex splurge has been on servers and specialized chips that become obsolete in a few years.”

One area less considered is “How Wall Street’s Big Bets on A.I. Are Driving Interest in Huge Parking Lots” (Patrick Sisson, The New York Times, September 16^th).  A company, Gray, “building 22 data centers,” reports that each one requires “a space nearby that’s large enough to store millions of dollars’ worth of tools, generators, and tractors and trailers, in other words “a couple of acres of gravel or asphalt near highways, ports and other shipping infrastructure.”  That is nothing completely new, as e-commerce already “requires huge spaces to park inventory, shipping containers and the vehicles used in last-mile delivery services,” meaning that rents for suitable industrial outdoor storage facilities are up, an average of 123% in the past five years, and “owners of anything that resembles” such properties, such as “old truck stops and auto repair yards,” “have been hearing more from brokers.”

In response to differing perceptions, Cade Metz and Karen Weise told us, in the New York Times on September 16^th, “What Exactly Are A.I. Companies Trying to Build?  Here’s a Guide.”  Those mentioned in the article, which prints out to 13 pages, are “A Better Search Engine,” “Tools That Make Office Workers More Productive (and Maybe Replace Them),” “An Everything Assistant,” “A.I. Friends,” “Scientific Breakthroughs,” and “A.I. That’s as Smart as a Human, or Smarter.”  As I have documented, instances of these objectives have already succeeded, and companies expect many more.

Have you wondered “What Wall Street Sees in the Data Center Boom” (Ian Frisch, September 20^th, additionally in the Times)?  Analysts, right or wrong, consider that “data center capacity has become a barometer for both the health of the tech market and the risk of an A.I. bubble,” so they have been enthused about AI for most of this year.  Yet there are causes for concern, namely that “even if A.I. proliferates, demand for processing power may not,” “some worry that costs will always be too high for profits,” “it’s not just Silicon Valley with skin in the game,” and, as we will see, “the stakes extend beyond finance.”

As Schumpeter revealed in “AI’s $4trn accounting puzzle” (The Economist, September 20^th), depreciation rates have an enormous impact on industry profitability.  Not all companies agree on “the longevity of all those fancy AI chips they are installing,” especially now that Nvidia has “said it would unveil a fresh AI chip every year rather than every couple of years,” and the useful lives of servers have proven controversial.  Using reasonable assumptions, the author estimated that if “the entire AI big five” set server depreciation at three years, “their combined annual pre-tax profit would fall by $26bn, or 8% of last year’s total,” which could “amount to a $780bn knock to their combined value.”  Serious business those accountants are doing.

Since the most effective way for businesses to assure that their customer relationships will stay good is to buy those they sell to, it was no surprise that we can expect “Nvidia to Invest $100 Billion in OpenAI” (Tripp Mickle and Cade Metz, The New York Times, September 22^nd).  That will be “part of a wider effort among tech companies to spend hundreds of billions of dollars on A.I. data centers around the world.”  Yet that raised the question “Is A.I. Investment Getting Too Circular?” (Andrew Ross Sorkin et al., The New York Times, October 7^th).  Do such deals
“raise questions about the robustness of the artificial intelligence boom”?  Possibly, if, as a “prominent short seller” put it, “Don’t you think it’s a bit odd that when the narrative is ‘demand for compute is infinite,’ the sellers keep subsidizing the buyers?”  That is a good question, and the answer could be that nobody else, even in 2025, has enough money.  After all, when “deals with AMD, Nvidia, Oracle, CoreWeave and others promise to give the ChatGPT maker more than 20 gigawatts of computing power over the next decade, roughly equivalent to that of 20 nuclear reactors,” and “the electricity needed to support that compute could cost about $1 trillion,” they’re getting way up there.

An idea to consider from a Yale Law School professor and Budget Lab president there is if “There are Two Economies: A.I. and Everything Else” (Natasha Sarin, October 6^th, also in the Times).  In a year when the American population has been bifurcated into rich and not rich, and split ever more sharply into pro-Donald Trump and anti-Trump sections, it is nothing too strange to suggest that related economic forces have done the same.  Money spent on AI-benefiting capital “may reach 2 percent of the gross domestic product in 2025,” a 20-fold three-year increase, and it alone may be pushing national economic growth from 1% to “almost twice that.”  “There are signs that the non-A.I. economy is under duress,” shown by problems with inflation and jobs, and “it’s possible that other parts of the economy are being held back by A.I.’s dominance,” as opportunities in that area may be hogging capital.  If indeed “the A.I. boom is masking Trump’s policy blunders,” we may need it to succeed even more than we think.

Overall, artificial intelligence is cutting new channels in our economy wider than any before.  The courses they take will be different.  Its outcomes and major impacts inside and outside that field are still very much unknown.  Sometimes, all we can do in such circumstances is to hang on for the ride and stay away from the banks as much as we can.  That is where we are now.