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.

No AJSN Today Per Government Shutdown

 I will publish September's American Job Shortage Number within a couple of days of receiving the Bureau of Labor Statistics' Employment Situation Summary and its supporting tables.  

Artificial Intelligence in General – Hopes and Expectations Both Behind and Ahead Of Reality

In some ways, AI is excelling, with a shoulder-high pile of successful specific applications.  Yet in others, such as matching human cognitive abilities, it seems to be motionless.  What do I mean?

First, “It’s Smart, But for Now, People Are Still Smarter” (Cade Metz, The New York Times, May 25^th).  This Sunday piece by this paper’s most prominent AI writer, subtitled “The titans of the tech industry say artificial intelligence will soon match the powers of human brains.  Are they underestimating us?,” addressed the coming of artificial general intelligence (AGI), which OpenAI CEO Sam Altman had told President Donald Trump “would arrive before the end of his administration,” and Elon Musk “said it could be here before the end of the year.”  This AGI “has served as shorthand for a future technology that achieves human-level intelligence,” but has “no settled definition,” meaning that “identifying A.G.I. is essentially a matter of opinion.”  As of the article’s time, “according to various benchmark tests, today’s technologies are improving at a consistent rate in some notable areas, like math and computer programming,” yet ”these tests describe only a small part of what people can do,” such as knowing “how to deal with a chaotic and constantly changing world,” at which AI has not excelled.  There is no clear reason why it should be able to jump from huge specific competences to matching overall human intelligence, which “is tied to the physical world,” and “that is why many… scientists say no one will reach A.G.I. without a new idea – something beyond the neural networks that merely find patterns in data.”  In the four months since this article came out, I have seen no signs of any such notion. 

On a related point, De Kai wrote on “Why AI today is more toddler than Terminator” (freethink.com, June 9^th).  That scarily predictive 1980s movie series provided the blueprint in many people’s minds for how AI could be relentlessly effective at what it called “autonomous goal-seeking,” at which it was amoral and lapsed into evil, and has underlaid people’s fears about it ever since.  Yet AI “relies less on human labor to write out digital logic and much more on automatic machine learning, which is analog,” meaning that “today’s AIs are much more like us than we want to think they are,” and “are already integral, active, influential, learning, imitative, and creative members of our society.”  Overall, “AIs are our children.  And the crucial question is:  How is our – your – parenting?” 

A new major ChatGPT release is gigantic news in the AI industry.  Soon after the last one, we read “How GPT-5 caused hype and heartbreak” (Alex Hern, Simply Science, The Economist, August 13^th).  It did well in some ways, “hitting new highs” by “showing improvements over its predecessors in areas including coding ability, STEM knowledge and the quality of its health advice,” but some users of its older 40 system “have bonded with what they perceived as its friendly and encouraging personality” which GPT-5 did not share, making the change they were steered to “a very personal loss.”  Apparently, the company did not expect that.

A wider-scope issue appeared in “MIT report: 95% of generative AI pilots at companies are failing” (Sheryl Estrada, Fortune, August 18^th).  “Despite the rush to integrate powerful new models, about 5% of AI pilot programs achieve rapid revenue acceleration; the vast majority stall, delivering little to no measurable impact on P&L.”  The study blamed “flawed enterprise integration,” as the software doesn’t “learn from or adapt to workflows.”  Working with vendors functioned better than “internal builds,” and “back-office automation,” such as “eliminating business process outsourcing, cutting external agency costs, and streamlining operations,” fared better than “sales and marketing tools,” which were absorbing half of generative AI budget money.

How reasonable is it to consider that “A.I. May Just Be Kind of Ordinary” (David Wallace-Wells, The New York Times, August 20^th)?  The author contrasted 2023 thoughts by “one-third to one-half of top A.I. researchers” that “there was at least a 10 percent chance the technology could lead to human extinction or some equally bad outcome,” with “A.I. hype… passing out of its prophetic phase into something more quotidian,” similar to what happened with “other charismatic megatraumas” such as “nuclear proliferation, climate change and pandemic risk.”  An April paper by two “Princeton-affiliated computer scientists and skeptical Substackers” claimed that we should see artificial intelligence “as a tool that we can and should remain in control of, and… that this goal does not require drastic policy interventions or technical breakthroughs.”  Given what it has done already, though, “the A.I. future we were promised, in other words, is both farther off and already here.”  With so much available right now and so much going nowhere, that is a better summary of this post than I can write, so I’ll stop there.

Another Two Months of Artificial Intelligence Accomplishments and Uses

For all the controversy and problems about AI, it’s building up its repertoire of ways of being useful.  Which have been in the news the past nine weeks?

According to “More Americans are turning to AI for health advice” (Kurt Knutsson, Fox News, July 31^st), 35% of US adults “report already relying on AI to understand and manage aspects of their well-being.  From planning meals to getting fitness advice, AI is quickly moving from a futuristic concept to a daily health tool.”  As “trust in AI is climbing fast,” from 20% to 31% are using it to “explore specific medical concerns,” provide “meal planning and recipes,” get them “new workout routines,” and give “emotional or therapeutic support.”  All of that is constructive, unless people treat it as equivalent to a professional’s service, and do not get that level of help when they need it.

On the business side, “Delta moves toward eliminating set prices in favor of AI that determines how much you personally will pay for a ticket” (Irina Ivanova, Fortune, July 16^th).  The airline used it for “3% of fares,” but they called its results “amazingly favorable.”  The article wasn’t clear about how Delta accomplished that, and reactions outside its industry will be negative, with one “surveillance pricing” tracker calling it “trying to see into people’s heads.”  Airline tickets have already been the strangest priced consumer product for many decades – what other can be priced higher if you buy less of it, necessitating rules against leaving multistep itineraries early?  I don’t know if this will work for the company, but they are vulnerable to people choosing their competitors instead, and good consumer relations are more than important.

Another frequently disturbing idea is in “Where Human Labor Meets ‘Digital Labor’” (Lora Kelley, The New York Times, August 1^st).  “A digital native is a person raised on the internet.  A digital nomad is a person who moves around doing their computer job.  And a digital laborer is not a person at all.”  Say what?  It’s sort of an electronic-only robot that works “independently with a bit of management,” which can “grow and mature with its own data.”  Such things “are not really in wide use yet,” but the borders between them and people will take a while to firm up.  At Salesforce, an early proponent, “customers unhappy with a digital agent can escalate to a human,” sort of like getting out of phone-mail jail, but if the devices are going to be on “mainstream org charts,” they may need at least to be untouted (un-outed?) as automata.

How about “21 Ways People Are Using A.I. at Work” (Larry Buchanan and Francesca Paris, The New York Times, August 11^th)?  “Almost one in five U.S. workers say they use it at least semi-regularly for work.”  They can get it to, among many other tasks, “select wines for restaurant menus,” “digitize a herbarium,” “make everything look better,” “create lesson plans that meet educational standards,” “make a bibliography,” “write up therapy plans,” act “as a ‘muse,’” “detect leaks in a water system,” “just write code,” “type up medical notes,” “run experiments to figure out how the brain encodes language,” “help get pets adopted,” “check legal documents in a D.A.’s office,” “get the busywork done,” “review medical literature,” “pick a needle and thread,” “(More politely) let band students know they didn’t make the cut,” “help humans answer more calls at a call center,” “help translate lyrics from the 17^th and 18^th centuries,” “explain my ‘legalese’ back to me,” and, fittingly, “detect if students are using A.I.”  The last one and many of the others are not new, but the list gives a good picture of how the technology is now being used in off-the-radar, pedestrian settings.

As of the turn of the century anyway, almost all credit reports had incorrect information, so it looks good to see that an “AI credit disputing tool launches for consumers nationwide to correct credit report errors” (Pilar Arias, Fox Business, August 20^th).  It has already “been used tens of thousands of times by consumers.”  AI Credit Dispute, “in the Kikoff app,” can assist users to “spot errors, send disputes and move forward.”  Worthwhile.

Is it any surprise that “Madison Avenue Is Starting to Love A.I.” (Emmett Lindner, The New York Times, August 18^th)?  It “can sharply lower production costs,” and can easily change any “number of different elements” in a commercial or print ad.  AI use can be anywhere between “easy to spot” and “difficult to discern,” and, although “generational divides inform how much A.I. will be tolerated,” “there is no doubt the technology is changing advertising.”

Finally, we got word that “Amazon backs AI startup that lets you make TV shows” (Kurt Knutsson, Fox News, September 12^th).  Fable’s “artificial intelligence platform,” Showrunner, intends to let people put together their “own episode of a hit show without a crew or cameras, only a prompt.”  Sort of like writing fanzines in the old days, which were authors’ independent looks at what characters in novels, comic books, or movies might do, this effort would, at least, make a splendid toy.  And not all such products would need to be G or PG-rated.  For now, Showrunner is “focused entirely on animated content,” but it’s certain it could eventually handle realistic looking humans, and its work could be easily edited. 

We have a lot of good things here.  The few detrimental ones may not be viably continued, as standards for AI use are in their childhood if not infancy.  Applications such as these are evidence of why artificial intelligence, even if it falls way short of its loftiest expectations, will still be valuable.  And there will be many, many more.

Artificial Intelligence’s Effect on Getting Jobs: Different Perspectives, Different Results

On the issue of how AI is doing at helping or hindering our employment efforts, there are several things to consider.  Here they are, with looks at how it has been doing.

On the effect of its abilities to compete with employees, we saw “Which Workers Will A.I. Hurt Most:  The Young or the Experienced?” (Noam Scheiber, The New York Times, July 7^th).  That’s a matter of controversy, on whether “younger workers are likely to benefit from A.I.,” or will it “cannibalize half of all entry-level white-collar roles within five years.”  Or, could it instead, “untether valuable skills from the humans.”?  The outcome so far is split, with entry-level candidates having the most difficulty in today’s job market, but there is a lot to say for plans to “take the cheapest employee,” use AI to help them, “and make them worth the expensive employee.”

What is the technology doing to employment now?  Not much, per Walter Frick on August 10^th in Bloomberg Weekend’s “AI Is Everywhere But the Jobs Data.”  Per an Economic Innovation Group study, “US workers whose jobs involve tasks that AI can do are actually much less likely than other workers to be unemployed,” and are “much less likely to be leaving the labor force.”  Other factors are at work here, but clearly little in AI job killing has happened.

On the downside was “The 1970s Gave Us Industrial Decline.  A.I Could Bring Something Worse.” (Carl Benedikt Frey, The New York Times, August 19^th).  The author started by saying “a silent recession has arrived for recent college graduates,” and after acknowledging the results in the previous paragraph compared our current situation to what happened with Pittsburgh’s steel and Detroit’s cars in the 1960s.  He called for areas to reinvent themselves and foster innovation by paying for “amenities that attract and retain talented residents:  public spaces, fast and affordable transit, top-tier schools” and “museums and theaters.”  Those sorts of investments would not get political approval in many places, but if widespread industry damage from AI becomes obvious, they might.

A comprehensive view was the topic of “Jobs that are most at risk from AI, according to Microsoft” (Kurt Knutsson, Fox News, August 28^th).  The “Top Jobs Most at Risk From AI” turned out to be “technical writers, ticket agents and travel clerks, editors, telemarketers, broadcast announcers and radio DJs, mathematicians, political scientists, interpreters and translators, advertising sales agents, CNC tool programmers, news analysts reporters and journalists, customer service representatives, historians, farm and home management educators, business teachers postsecondary, hosts and hostesses, public relations specialists, concierges, brokerage clerks, proofreaders and copy markers, writers and authors, sales representatives (services), telephone operators, demonstrators and product promoters, passenger attendants, data scientists, market research analysts, web developers,” and “management analysts.”

The piece also included the “Jobs Least Likely to be Replaced by AI Right Now,” which were “medical equipment preparers, surgical assistants, dishwashers, roofers, massage therapists, cement masons and concrete finishers, motorboat operators, orderlies, floor sanders and finishers, bridge and lock tenders, industrial truck and tractor operators, gas compressor and pumping station operators, helpers-roofers, roustabouts, oil and gas, ophthalmic medical technicians, packaging and filling machine operators, logging equipment operators, dredge operators, pile driver operators, water treatment plant and system operators, foundry mold and coremakers, machine feeders and offbearers, rail-track maintenance equipment operators, supervisors of firefighters,” and “tire builders.”  Note that the positions here tend heavily to be lower-paying, blue-collar, and requiring less education.

I end with two articles concerning job seeking itself, which nobody can claim has been unaffected.  “Hidden risks of AI in hiring: 4 traps to avoid” (Pilar Arias, Fox Business, August 23^rd) was for potential employees, of whom “forty percent… are using artificial intelligence to improve their chances of getting hired, according to a recent report by Jobseeker.”  Destructive things they may have in cover letters include a “manufactured feel” or “stiff, formal language patterns when describing career history,” ”missing concrete examples of success” which have long been critical to the process and “AI simply can’t invent,” “unusual formatting patterns” such as “odd spacing between paragraphs, weird alignment issues or random font changes,” and “too perfect, no human touch,” clarified to mean “perfect sentences with little variation in length and structure.”

“Can AI make the job search less grueling?” Per Patrick Kulp on September 7^th in Tech Brew and interviewee Tomer Cohen of LinkedIn, it can.  As Cohen put it, “if I know more about your skill set, your aspirations, there might be a job that actually not many have applied to, but is a great opportunity for you.  So instead of a lot of job seekers applying to a few jobs… you’re able to actually spread out the supply and demand.”  As well, such a program can get the most valuable and appropriate work from AI, such as “interview prep one on one with an AI coach,” identifying missing skills which the applicant can learn, and helping less completely with cover letters. 

More than anything else, artificial intelligence’s role with jobs is evolving.  I don’t expect Microsoft’s list of jobs to change much, but other aspects of AI and employment searches may differ even from month to month.  So it is valuable to stay current as much as possible, but look for principles which are good through this area’s evolution.  That’s the best we can do.

Jobs Report: Small Changes Here and There, But Basically We’re Going Nowhere

This morning’s Bureau of Labor Statistics Employment Situation Summary was supposed to be critically important – as if some of them lately haven’t been.  How did we do?

The number of net new nonfarm payroll positions failed to reach even its modest published estimates of 75,000 and 54,000, and came in at 22,000.  Seasonally unadjusted unemployment fell to 4.5%, down 0.1%, and the adjusted variety, reflecting more people usually working in August than in July, increased the same to 4.3%.  The adjusted count of unemployed gained another 200,000 to 7.4 million, and that of long-term unemployed, looking for 27 weeks or longer, rose 100,000 to 1.9 million.  The number of people working part-time for economic reasons, or looking for full-time work while maintaining part-time labor, stayed at 4.7 million.  The measures showing how common it is for Americans to be working or officially jobless, the labor force participation rate and the employment-population ratio, gained 0.1% and broke even to get to 62.3% and 59.6%.  Unadjusted unemployment was off just over 500,000 to end at 163,288,000.  The unadjusted counts of those not in the labor force and not interested in working each fell over 800,000, reaching 102,966,000 and 96,167,000.  Average private nonfarm payroll wages were up 10 cents per hour, close to our inflation rate, to $36.53.

The American Job Shortage Number or AJSN, the seasonally unadjusted metric showing how many new positions could be quickly filled if all knew they would be easy and routine to get, was down 48,000 as follows:

None of the components changed as much as 100,000, with the effect of the drop in employment subtracting 90,000 and people discouraged and not wanting a job adding 43,000 and 31,500.  The share of the AJSN from official unemployment was down 0.4% to 39.2%. 

Compared with a year before, the AJSN showed a noteworthy pattern, as although it only increased 179,000, all the factors above except the last were higher this time.  The largest gains were from those unemployed, those who wanted work but did not look for it over the past year, those discouraged, and those who did not want a job.

How can I summarize this report?  I think you can guess what the turtle did from the title, but a few other things happened that we should notice.  First, people are now reacting more by leaving the labor force than by trying when they don’t think their chances are good.  Second, as I had been saying for years, monthly job gains of 100,000 to 200,000, though they were the norm before and ever since the pandemic, were nothing to take for granted, and we’re solidly out of that territory now.  Third, the smaller categories of marginal attachment, the second through sixth and eighth rows above, are showing their capacity to absorb generally unsatisfied jobseekers and should not be ignored.  While there are plenty of differences below the surface, our employment situation, overall, is at a standstill, with virtually no growth.  The chances are good that tariffs are having a real effect.  The turtle did not move.