Kamala Harris for President

After most of this one-of-a-kind campaign, reminiscent of 1968 but hardly the same, we’re 18 days away from making our presidential decisions.  The right choice is no foggier than it was last time.

The Republican nominee in 2020 and election winner in 2016 runs again as the first major-party choice to stand for a third time since Franklin D. Roosevelt in 1940.  That’s not the problem, and his age, 78, which would make him older on Inauguration Day than any other president, isn’t the main one either.  Donald Trump has more flaws and disqualifying characteristics than any Democratic or Republican nominee I have seen since I started following campaigns with Nixon-Humphrey.  He is a convicted felon who has consistently shown he does not want to follow our laws and Constitution.  He has a bizarre, apparently insatiable sweet tooth for lying, way beyond any of his competitors even in this often-sordid profession.  He has shown affinity with the world’s dictators, while saying many things indicating he would strive to be one himself.  He has threatened legal and even military retribution against those taking lawful measures to stop him.  His attitudes toward women, over a wide spectrum of areas, are disastrous.  His delusions, such as him being the true 2020 winner, which he has often insisted be supported by those working with him, have persisted.  And on jobs and the economy, his proposed extensive and expensive tariffs would drastically worsen both.

Nothing reported in news sources has been able to reverse the Trump tide.  His advocates have remained impervious to these issues, even when they are shown to be the truth.  And others have expressed a willingness to pollsters to join his side.  The reasons for his popularity will be discussed for decades or centuries to come, but common sense or prudent judgment will not be among them.  Some of his heavy contributors are extremely wealthy, expecting to save tens or hundreds or millions of dollars on his likelihood of taxing them less, but that does not make them worthy of emulation by the rest of us.  Perhaps the largest lesson of the 20^th century was that those who people find charismatic may lead us in devastatingly wrong directions, and caution about Trump seems a clear response.

His opponent, Kamala Harris, is a former district attorney who is currently the vice president.  She has shortcomings, but has shown in public appearances to be sober, reasonable, lawful, and almost always truthful.  We don’t know exactly how well she would work out, but it is obvious that her downside is vastly smaller.  As contrasted with her opponent, who has been described as a weak man’s idea of a strong man, Harris is forceful without being abrasive, and will work with politicians on both sides.  That is what we need in 2025 and beyond.

The best justification for a Trump vote I have heard was from one who said he was a reprehensible person, but she was not choosing a friend.  I don’t buy that, since the world is too dangerous, and our allies too valuable, for us to pick someone who needs to be contained.  And we still have a large nuclear arsenal with which the president would have great scope.  Junkyard dogs can be mean, but presidents need not be.

I have not mentioned Harris’s or Trump’s running mates, but both seem fair choices.  Either Tim Walz or J.D. Vance would rate to be adequate if circumstances put them into the top job, and, in the case of Vance, would get the presidency in steadier hands.  There is also little here about either candidate’s meager list of proposed policies, since that is not what this election is about. 

As of yesterday morning, the PredictIt site showed its contributors giving a combined five-point winning-percentage advantage to Trump.  We can do better, and massive amounts of safety and prosperity may depend on whether we do.  We need look only at what news and information sources, even including those generally favorable to his cause, say the realities are.  If this registered Republican who might have chosen a conservative nominee from that party can avoid him, so can you.  And please vote. 

Royal Flush Press endorses Kamala Harris for president.

A Strong Jobs Report Gathered Before the Interest Rate Cut, with AJSN Showing Latent Demand Almost a Million Lower

Commentary I read before this morning’s Bureau of Labor Statistics Employment Situation Summary’s release said that it would be a critical installment, mainly because of the effect it would have on the Federal Reserve’s two remaining 2024 interest rate decisions.

It turned out to show real improvement.  The number of net new nonfarm payroll positions exceeded its 150,000 consensus estimate with 254,000.  Seasonally adjusted unemployment dropped another 0.1% to 4.1%, the same place it was three months before.  There were 6.8 million unemployed people, down 300,000, and the unadjusted rate fell from 4.4% to 3.9%, some but not all due to seasonality.  The count of people working part-time for economic reasons, or keeping such jobs while thus far unsuccessfully seeking longer-hours ones, erased the last report’s 200,000 gain, going back to 4.6 million.  Those officially unemployed and looking for work for 27 weeks or longer, though, gained 100,000 to 1.6 million.  The unadjusted number of employed grew 700,000 to 162,046,000.  The two best measures of how many people are working or one step away, the employment-population ratio and the labor force participation rate, gained 0.2% and stayed the same to reach 60.2% and 62.7%.  Average private nonfarm payroll earnings increased 15 cents, almost double the effect of inflation, to $35.36.  More people continued to leave the labor force, with those claiming no interest gaining almost 600,000 to add to last time’s 1.3 million, reaching 94,920,000.

The American Job Shortage Number or AJSN, the Royal Flush Press measure showing how many additional positions could be quickly filled if all knew they would be easy to get, lost 980,000, as follows:

The effect of fewer people officially jobless was responsible for 800,000 of the drop, and those interested but not looking for a year or more cut off another 340,000.  Gains in the second through sixth categories above offset that by 150,000.  The share of the AJSN from those unemployed fell 2.6% to 35.3%.  Compared with a year before, the AJSN has increased 433,000, almost exactly that amount from those officially unemployed. 

What happened here?  Still many more new positions than we can expect, and that along with continued workforce departures assured our unemployment-rate’s lowering.  The job market is healthy, but hardly overheated.  That means the Federal Reserve ball will go back to the inflation court, and then back to the next jobs report on November 1st, five days before the next Fed meeting starts.  We are very much in the hunt for another quarter-point decrease, but more than that, considering the progress above, is less likely.  The turtle did, this time, take a moderate step forward.

Remote Work: The Pendulum Has Swung Back to the Office

As I have written repeatedly before, employer attitudes on working from home have oscillated back and forth over the past three-plus decades.  In the 2010s, hybrid labor, or putting in time in some combination between in the office and elsewhere, was getting reestablished, with glowing reviews of home productivity gains as well as work-life balance encouraging organizations to allow a large portion of time to be spent out of sight, and, as always, greatly out of mind.  By early 2020 the pendulum was moving toward not allowing that, but the pandemic necessitated it, with not only physical proximity issues but a greatly tightening labor market facilitating too many people to leave if they did not get the schedules they wanted.

Now, with Covid-19 almost no factor and unemployment, especially for information technology positions, growing, opposition to non-office work is again becoming entrenched.  What is the evidence of that?

One piece is the emerging of a new expression, as featured in “No more “coffee badging”” (Business Insider, July 21^st).  The term applies to “employees who badge in, get coffee, and leave shortly after to satisfy their (return to office) requirements.”  As of just before this date, Amazon was “getting serious” about ending this custom, and, as we shall see, there was more to come.

Especially in transition times, private organizations have varied greatly in what they allow.  One large public one, capable of setting national, multi-installation rules, is the subject of “To be remote or not to be?  That is the burning federal workplace question” (Gleb Tsipursky, Fox News, August 16^th).  While “many federal agencies have implemented hybrid work models, allowing leaders to refine strategies to adapt to evolving employee needs and mission-driven objectives,” “there is tension between this flexible approach and congressional legislative efforts such as the Back to Work Act of 2024… a bipartisan bill that seeks to limit telework for federal employees to no more than 40% of their workdays per pay period.”  That is broad-based and specific, and nothing that would have been taken seriously in 2014.

As well as the stick, businesses are also using the carrot.  “The Hotelification of Offices, With Signature Scents and Saltwater Spas” (Stacey Freed, The New York Times, August 18^th).  Such things have been controversial since they began appearing around the turn of the century, especially when remote work has been unfashionable.  This case is “the Springline complex in Menlo Park, Calif.,” where employees and others “are surrounded by a sense of comfort and luxury often found at high-end hotels:  off-white walls with a Roman clay finish, a gray-and-white marble coffee table and a white leather bench beneath an 8-by-4 resin canvas etched with the words “Hello, tomorrow,” and “hints of salty sea air, white water lily, dry musk and honeydew melon linger in the air.”  You get the idea.  While “companies have over the years improved their spaces in the hopes of getting more out of employees,” this kind of thing is now transparently designed to make people happier about reporting in person, and will not be immune to backlashes as they figure that out.

Another change many companies are making turned up in “Downtown’s lost prestige” (Bloomberg, August 27^th).  “The US office market is splitting in two:  Investors are writing off the value of older buildings downtown as newer developments outside traditional business hubs become prestige destinations,” resulting in “more than half-a-trillion dollars of value” being “erased from US offices from 2019 through 2023.”  Suburban desk farms are nothing new – I started my AT&T cubicle career at one 35 years ago – but employers are now motivated by “trying to get employees back to their desks” by moving to “low-crime neighborhoods with plenty of shopping and parking.”

The big story here was, though, “Bosses Rejoice!  Amazon Delivers the End of Hybrid Work” (Vanessa Fuhrmans, Katherine Bindley and Chip Cutter, The Wall Street Journal, September 21).  This article, on the front page of the Exchange section and embellished with a picture of an Amazon shipping box containing someone at a plain-looking office desk, was subtitled “If you thought your two days a week of work-from-home were safe, think again.  The CEO of one of America’s largest employers just called everyone back to the office full-time,” effective January 2^nd. 

It was clearly an overreaction – Amazon does not set national workplace policy – but documented a remarkably firm and all-encompassing decision.  Per the first story above, “until (the CEO’s) memo, 4½ years after the Covid-19 pandemic sent everyone home, bosses and employees had largely reached a truce on part-time remote work,” as, while “many company leaders looked out at their substantially empty offices in quiet exasperation,” they feared top-performer departure.  Amazon’s pronouncement, “the talk of the town” in Seattle, was publicized as something “that will help both the company and its employees,” as in offices “we’ve observed that it’s easier for our teammates to learn, model, practice, and strengthen our culture,” as, in person, “collaborating, brainstorming, and inventing are simpler and more effective;  teaching and learning from one another are more seamless; and teams tend to be better connected to one another.” 

Beyond Amazon, a survey showed that while about 30% of CEOs said they “expect workers to be back in the office full-time within three years” in April.  Earlier this month that had become almost 80%.  That stunning shift gives Amazon’s decision at least the appearance of spearheading a widespread change.  There will be exceptions, but many more companies will follow, and, for now, we will hear little about the good side of remote work.

That will come back in the 2030s.  Count on it.

The Jobs Report Tells Only One Story; Consistently, AJSN Shows Latent Demand Down 250,000 to 17.6 Million

This morning’s was supposed to be a critically important Bureau of Labor Statistics Employment Situation Summary.  How did it turn out?

The headline figure, the number of net new nonfarm payroll positions, fell a small amount short of published 160,000 and 161,000 estimates at 142,000.  Seasonally adjusted unemployment ended its monthly march upwards, falling back 0.1% to 4.2%.  Unadjusted unemployment lost the same amount, from 4.5% to 4.4%.  There were 7.1 million officially jobless people, 100,000 better.  The number of long-term unemployed, out 27 weeks or longer, was 1.5 million for the third straight month.  The two measures showing most clearly the share of people actually working or that plus officially jobless, the employment-population ratio and the labor force participation rate, held at 60.0% and 62.7%.  Average hourly private nonfarm payroll earnings gained 14 cents, more than inflation, to $35.21.  Trailing the rest was the count of people working part-time for economic reasons, or keeping such employment while thus far unsuccessfully seeking a full-time proposition, up 200,000 to 4.8 million.

The American Job Shortage Number or AJSN, our long-standing statistic showing how many positions, in addition to those now available, could be quickly filled if all knew they would be easy and routine to get, lost 258,000 as follows:

  

The fall from July’s result was almost exactly the amount from unemployment, with no other change more than 100,000.  The share of the AJSN from that, at 37.9%, was 0.8% lower. 

Compared with a year before, the AJSN was about 800,000 higher, with 713,000 added from official joblessness, 288,000 from more people wanting work but not looking for it for a year or more, and 200,000 less from a smaller number of American expatriates.  None of the other factors increased or decreased over 50,000. 

What was the one thing which happened?  People left the labor force.  Remember that last month the boost in joblessness came from those jumping back into the working pool without finding it – well, this time, they got out.  Evidence of that was the count of those not interested leaping 1.3 million, the unadjusted number of employed despite the unemployment rate’s fall losing 690,000, and the numbers above of marginal attachment – those wanting work but stopped now for family responsibilities, being in school or training, with ill health or disability, the “other” category, and especially, with a 24% reduction, discouraged – all down.  The AJSN’s drop came from the same place, as people moved to the status with the lowest latent demand.  With this event factored out, despite the 142,000 gain the American employment situation stayed right where it was.  Interest rate decisions should be unchanged from yesterday.  As for the turtle, he did not budge either.

Artificial Intelligence’s Limitations and Clear Current Problems

We’re marching through months and years since last year’s AI awakening.  We can’t fairly say that the shortcomings it has are permanent, but, as of now, what are they?

First, although computer applications have excelled at many games, such as chess, where they are vastly better than any human ever, and checkers, which was electronically solved 17 years ago, they have not done the same with bridge.  Per BBO Weekly News on July 21^st, Bill Gates said, correctly, that “bridge is one of the last games in which the computer is not better.”  Artificial intelligence progress has done nothing, so far, to change that, and it is noteworthy that even in a closed system with completely defined rules, objectives, and scoring, it has not been able to take over. 

Not only has it not replaced huge numbers of jobs, but “77% Of Employees Report AI Has Increased Workloads And Hampered Productivity, Study Finds” (Bryan Robinson, Forbes, July 23^rd).  The effort, “in partnership with The Upwork Research Institute, interviewed 2,500 global C-suite executives, full-time employees and freelancers.”  It found that “the optimistic expectations about AI’s impact are not aligning with the reality faced by many employees,” to the point where, in contrast with 96% of C-suite executives expecting AI to boost productivity… 77% of employees using AI say it has added to their workload and created challenges,” and has been “contributing to employee burnout.”  Also, 47% “of employees using AI say they don’t know how to achieve the expected productivity gains their employers expect, and 40% feel their company is asking too much of them when it comes to AI.”  This is what we used to call a disconnect.  The author recommended employers get outside help with AI efforts and measuring productivity differently, and workers to generally “embrace outside expertise.”

A similarly negative view was the subject of “Machines and the meaning of work” (Bartleby, The Economist, July 27^th).  The pseudonymous writer cited a paper claiming that although “in theory, machines can free up time for more interesting tasks; in practice, they seem to have had the opposite effect.”  Although in health care, automation can allow more time with patients, in others, as “the number of tasks that remain open to humans dwindles, hurting both the variety of work and people’s understanding of the production process,” “work becomes more routine, not less.”  Overall, “it matters whether new technologies are introduced in collaboration with employees or imposed from above, and whether they enhance or sap their sense of competence.”

Similarly, Emma Goldberg, in the New York Times on August 3^rd, asked “Will A.I. Kill Meaningless Jobs?”  If it does, it would make workers happier in the short run, but it could also contribute to “the hollowing out of the middle class.”  Although the positions that AI could absorb might be lacking in true significance, many “have traditionally opened up these white-collar fields to people who need opportunities and training, serving as accelerants for class mobility:  paralegals, secretaries, assistants.”  These roles could be replaced by ones with “lower pay, fewer opportunities to professionally ascend, and – even less meaning.”  Additionally, “while technology will transform work, it can’t displace people’s complicated feelings toward it.”  So we don’t know – but breaking even is not good enough for what is often predicted to be a trillion-dollar industry.

Back to the issue of perceived AI value is “A lack-of-demand problem” (Dan DeFrancesco, Insider Today, August 8^th).  “A chief marketing officer” may have been justified in expecting that the Google AI tools it introduced would “be an easy win,” as “in the pantheon of industries set to be upended by AI, marketing is somewhere near the top,” as the technology could “supercharge a company’s marketing department in plenty of ways,” such as by providing “personalized emails” and “determining where ads should run.” Unfortunately, per the CMO, “it hasn’t yet,” as “one tool disrupted its advertising strategy so much they stopped using it,” “another was no better at the job than a human,” and one more “was only successful about 60% of the time.”  Similar claims appear here from Morgan Stanley and “a pharma company.”  In all, “while it’s only fair to give the industry time to work out the kinks, the bills aren’t going to slow down anytime soon.”

In the meantime, per “What Teachers Told Me About A.I. in School” (Jessica Grose, The New York Times, August 14^th), AI is causing problems there, per examples of middle school students, lacking “the background knowledge or… intellectual stamina to question unlikely responses,” turning in assignments including the likes of “the Christian prophet Moses got chocolate stains out of T-shirts.”  Teachers are describing AI-based cheating as “rampant,” but are more concerned about students not learning how to successfully struggle through challenging problems.  Accordingly, they are “unconvinced of its transformative properties and aware of its pitfalls,” and “only 6 percent of American public school teachers think that A.I. tools produce more benefit than harm.”

I do not know how long these AI failings will continue.  With massive spending on the technology by its providers continuing, they will be under increasing pressure to deliver useful and accurate products.  How customers react, and how patient they will be, will eventually determine how successful artificial intelligence, as a line of business, will be over the next several years.  After some length of time, future promises will no longer pacify those now dissatisfied.  When will we reach that point?

Seven Weeks on Artificial Intelligence Progress: Real, Questioned, Disappointing, and Baked into the Investment Cake

What recent substantial contributions has AI recently made?  What big weakness does it still have?  What has happened to its great prospects?  Can we know its true inherent advancement?  And what forecasts do today’s AI-related stock prices include?

The first report is “A sequence of zeroes” (The Economist, July 6^th), subtitled “What happened to the artificial-intelligence revolution?”  “Move to San Francisco and it is hard not to be swept up by mania over artificial intelligence… The five big tech firms – Alphabet, Amazon, Apple, Meta and Microsoft… this year… are budgeting an estimated $400bn for capital expenditures, mostly on AI-related hardware.”  However, “for AI to fulfil its potential, firms everywhere  need to buy the technology, shape it to their needs and become more productive as a result,” and although “investors have added more than $2trn to the market value of the five big tech firms in the past year… beyond America’s west coast, there is little sign that AI is having much of an effect on anything.”  One reason for the non-progress is that “concerns about data security, biased algorithms and hallucinations are slowing the roll-out” – an example here is that “McDonald’s… recently canned a trial that used AI to take customers’ drive-through orders after the system started making errors, such as adding $222 worth of chicken nuggets to one diner’s bill.”  Charts here show that the portion of American jobs that are “white collar” has still been marching steadily upward, and, disturbingly, that share prices of “AI beneficiaries” have stayed about even since the beginning of 2019 while others have on average risen more than 50%.  Now, “investors anticipate that almost all of big tech’s earnings will arrive after 2032.”

“What if the A.I. Boosters Are Wrong?” (Bernhard Warner and Sarah Kessler, The New York Times, July 13^th), and not even premature?  MIT labor economist Daron Acemoglu’s “especially skeptical paper” described how “A.I. would contribute only “modest” improvement to worker productivity, and that it would add no more than 1 percent to U.S. economic output over the next decade.”  The economist “sees A.I. as a tool that can automate routine tasks… but he questioned whether the technology alone can help workers “be better at problem solving, or take on more complex tasks.””  Indeed, AI may fall victim to the same problem which got 3D printing out of the headlines in the 2010s – lack of a massively beneficial, large-scale application.

In real contrast to common concerns, especially from last year, “People aren’t afraid of A.I. these days.  They’re annoyed by it” (David Wallace-Wells, The New York Times, July 24^th).  One issue “has inspired a… neologistic term of revulsion, “AI slop”: often uncanny, frequently misleading material, now flooding web browsers and social-media platforms like spam in old inboxes.”  Some delightful examples cited here are X’s and Google’s pronouncements that “it was Kamala Harris who had been shot… that only 17 American presidents were white… that Andrew Johnson, who became president in 1865 and died in 1875, earned 13 college degrees between 1947 and 2012… that geologists advise eating at least one rock a day,” and “that Elmer’s glue should be added to pizza sauce for thickening.”  Such “A.I. “pollution”” is causing “plenty of good news from A.I.” to be “drowned out.”  With Google’s CEO admitting “that hallucinations are “inherent” to the technology,” they don’t look like they’ll be going away soon.

Even given the disappointments above, “Getting the measure of AI” (Tom Standage, The Economist, July 31^st) is not easy.  One way “is to look at how many new models score on benchmarks, which are essentially standardised exams that assess an AI model’s capabilities.”  One such metric is “MMLU, which stands for “massive multi-task language understanding,”” contains “15,908 multiple-choice questions, each with four possible answers, across 57 topics including maths, American history, science and law,” and has been giving scores “between 88% and 90%” to “today’s best models,” compared with barely better than the pure-chance 25% in 2020.  There will be more, and it will be useful to see how they improve from here.

On the constructive side, “A.I. Is Helping to Launch New Businesses (and Not Just A.I. Businesses)” (Sydney Ember, The New York Times, August 18^th).  A Carnegie Mellon University professor who for 14 years has been having “groups of mostly graduate students start businesses from scratch,” said, after advising the use of generative AI extensively, that he’d “never seen students make the kind of progress that they made this year.”  The technology helped them to “write intricate code, understand complex legal documents, create posts on social media, edit copy and even answer payroll questions.” As well, one budding entrepreneur said, “I feel like I can ask the stupid questions of the chat tool without being embarrassed.”  That counts also, and while none of these are, as a Goldman Sachs researcher quoted in the Wallace-Wells article asked about, a $1 trillion problem that AI could solve, they collectively are of real value.

Is it reasonable to think that AI stocks will roughly break even from here if lofty expectations go unrealized?  No, according to Emily Dattilo, in Barron’s on August 19th: “Apple Is Set to Win in AI.  How That’s ‘Already Priced In.’”  Analysts at Moffett Nathanson, for example, pronounced that, although Apple was “on track to win in artificial intelligence,” the “bad news” was “that’s exactly what’s already priced in.”  I suspect that’s happening with the other AI stocks as well.  If the technology not only grows in scope but does so more than currently expected, share prices may rise, but if it only gets moderately larger, they could drop.  That can be called another problem with artificial intelligence – if enough investors realize this situation, the big five companies above, Nvidia, and others may have already seen their peaks.  Small-scale achievements such as startup business help will not be enough to sustain tremendous financial performance.  What goes up does not always come down, but here it just might.  And the same thing goes for AI hopes.

Artificial Intelligence’s Data Needs: Can They Be Met Legally and Logistically?

Three of the problems I identified with AI in previous posts concern the data used to train its large language models.  One is the sheer volume of information it needs to create more advanced capabilities.  Second is data’s legal status, which has caused several large lawsuits, and doubtless many more small ones, charging copyright infringement.  The third is distortion from chatbots taking in output from themselves or others.  What has been in the press lately about these issues, and what does it mean not only about this aspect of AI but about AI in general?

Apparently, “Apple, Nvidia, Anthropic Used Thousands of Swiped YouTube Videos to Train AI” (Annie Gilbertson, WIRED, July 16^th).  The problem has been that “tech companies are turning to controversial tactics to feed their data-hungry artificial intelligence models, vacuuming up books, websites, photos, and social media posts, often unbeknownst to their creators.”  Everyone anywhere near the field, let alone companies’ legal personnel, should know that electronic versions of books and published articles are as subject to copyright laws as hardcopy editions, long documented in statements such as “no part of this book may be reproduced in any form or by any means without the prior written permission of the Publisher, excepting brief quotes…” which I got from a random 1968 paperback – but it is understandable for lay people not to know if that also applies to the likes of videos and other less formally protected online material.  It also may be difficult, in these data-absorbing efforts, to avoid off-limits products, but the problem still must be solved.

That’s why, at least per Nico Grant and Cade Metz, in the New York Times on July 19th, we are seeing or should see “The Push to Develop Generative A.I. Without All the Lawsuits.” The partial copyrighted-information solution here is those owning the rights to data “building A.I. image generators with their own data,” and then selling AI-development access.  Two companies already starting that are “the major stock photo suppliers Getty Images and Shutterstock,” which will pay photographers when their work is thus used.  Fair play, or so it seems.

Otherwise, “The Data That Powers A.I. Is Disappearing Fast” (Kevin Roose, The New York Times, July 19^th).  Although, per research “by the Data Provenance Initiative, an M.I.T.-led research group,” “three commonly used A.I. training data sets” had restricted only 5% of their data (though “25 percent… from the highest-quality sources”), but the operation is in progress.  Conclusive definition of legal information use is not here yet, as “A.I. companies have claimed that their use of public web data is legally protected under fair use.”  Perhaps, per the author, “if you take advantage of the web, the web will start shutting its doors.”

Another way out was described in Forbes Daily on July 24^th: “The Internet Isn’t Big Enough To Train AI.  One Fix?  Fake Data.”  “OpenAI’s ChatGPT, the chatbot that helped mainstream AI, has already been trained on the entire public internet, roughly 300 billion words including all of Wikipedia and Reddit” (italics in original), meaning that “at some point, there will be nothing left.”  A company, Gretel, wants to provide AI firms with “fake data made from scratch,” which is not totally new, as “Anthropic, Meta, Microsoft and Google have all used synthetic data in some capacity to train their models.”  Two issues with it are that “it can exaggerate biases in an original dataset and fail to include outliers,” which “could make AI’s tendency to hallucinate even worse.”  If, that is, it does not “simply fail to produce anything new.”  We will find out, probably within the year, if artificial data is a worthwhile partial or complete substitute.

To the point of the final first-paragraph problem is “What happens when you feed AI-generated content back into an AI model?  Put simply:  absolute chaos” (Maggie Harrison Dupre, Futurism.com, July 26^th).  Per a recent study, “AI models trained on AI-generated material will experience rapid “model collapse” … as an AI model cannibalizes AI-generated data, its outputs become increasingly bizarre, garbled, and nonsensical.”  The problem is out there now, as “there are thousands of AI-powered spammy “news” sites cropping up in Google; Facebook is quickly filling with bizarre AI imagery… Very little of this content is marked as AI-generated, meaning that web scraping, should AI companies continue to attempt to gather their data from the digital wilds, is  becoming a progressively dubious means of collecting AI training data.”

Despite the hope in the second story above, none of this looks good for future AI releases.  These problems will not be easy to solve.  We already have the issue that AI is nowhere near ready to produce even page-length writing releasable without human scrutiny – the concerns here will, most likely, keep that capability at bay.  Until then, AI will fail to even approximate the utility expected by its customers and backers.  That means, even without regard to other obstacles such as insufficient power for fundamentally more advanced releases, that artificial intelligence is in deep trouble.  All should govern themselves accordingly.

Artificial Intelligence Investments and Revenue – A Month’s Worth

AI, you’ve done it to me.

Even when eliminating most of the news items about it, those focusing on the future instead of the present and on other factors irrelevant to what is happening with the technology itself, there are too many for even weekly posts.  So I have divided it into subtopics, of which this is the first to get publication. 

The oldest here is “Microsoft’s Steep AI Investments Raise Questions About Returns” (Patrick Seitz, Investor’s Business Daily, July 10^th).  As crisply put by a Morgan Stanley analyst, “with Microsoft’s capital expenditures poised to nearly double from $32 billion in fiscal 2023 to our forecasted $63 billion in fiscal 2025, the question of monetization against these investments rises to the forefront for many investors.”  The analyst still rated the stock highly, but, per the author, “after digging into the question of return on investment,” “was left with more questions than answers” because of a “lack of visibility.”

The first item in the July 19^th Goldman Sachs Briefings was “Will the $1 trillion in AI spending pay off?.”  The determination here was “AI may prove far less promising than many business leaders and investors expect,” per an MIT professor who “estimates that only a quarter of AI-exposed tasks will be cost-effective to automate within the next 10 years – which means that AI will impact less than 5% of all tasks and boost US productivity by only 0.5% and GDP growth by 0.9% cumulatively over the next decade.”  A Goldman Sachs research head went “a step further” by saying that “there’s not a single thing that this is being used for that’s cost-effective at this point.”

On the other hand, “Alphabet Reports 20% Jump in Profit as A.I. Efforts Begin to Pay Off” (Nico Grant, The New York Times, July 23^rd).  “The company has incorporated generative A.I. into all of its products and increased its spending on data centers and associated hardware to underpin the technology.  Now, Google executives said, those investments have started to bear fruit,” with second-quarter profit of $23.6 billion.  However, Victor Tangermann, in “Investors Are Suddenly Getting Very Concerned That AI Isn’t Making Any Serious Money” in Futurism.com on July 27^th, reported that “investment bankers are… starting to become wary of Big Tech’s ability to actually turn the tech into a profitable business," saying that Google’s earning report was “failing to impress investors with razor-thin profit margins and surging costs related to training AI models.”  Still pertinently, a “tech stock analyst” wrote back in March that “capital continues to pour into the AI sector with very little attention being paid to company fundamentals… a sure sign that when the music stops there will not be many chairs available.” 

Perhaps in reaction to these pieces and the thoughts behind them, “Tech Bosses Preach Patience as They Spend and Spend on A.I.” (Karen Weise, The New York Times, August 2^nd).  “The tech industry’s biggest companies have made it clear over the last week that they have no intention of throttling their stunning levels of spending on artificial intelligence, even though investors are getting worried that a big payoff is further down the line than once thought.”  On a week ago Thursday, Mark Zuckerberg of Meta said he would spend “at least $37 billion” on “new tech infrastructure,” and “would spend even more next year.”  “In the last quarter alone, Apple, Amazon, Meta, Microsoft, and Google’s parent company Alphabet spent a combined $59 billion on capital expenses, 63 percent more than a year earlier and 161 percent more than four years ago.”  Another Goldman Sachs executive asked “What $1 trillion problem with A.I. solve?,” and implied that “replacing low wage jobs with tremendously costly technology” was not sufficient justification.  Yet leaders of the other largest IT firms also emphasized the need to be at the front of AI progress, and that true business success there might take a long time.

Is it true that “The AI supply chain is in jeopardy” (Guy Scriven, The Economist, August 3^rd)?  “About a year ago,” he “started to ask analysts what it would take to stop the… (AI) bull run.  Most suggested it would end if big tech firms failed to deliver meaningful AI-related sales for a couple of quarters in a row.  It took more than a couple of quarters, but over the past two weeks, as the tech giants reported their earnings, that scenario has started to play out.”  Adding to general murkiness is that Microsoft, where “growth in this revenue stream has been slowing,” “is still the only big tech firm that puts a figure to its AI sales.”  He echoed that “scepticism has set in among investors,” and opined that “the boom looks somewhat precarious,” as “the larger risk is that demand peters out.”

Will it, or won’t it?

July Jobs Report: Downward Trend Continues – AJSN 600,000 Higher, With Latent Demand Over 17.8 Million

 I saw three published estimates of the number of net new nonfarm payroll positions in this morning’s Bureau of Labor Statistics Employment Situation Summary.  All three were 175,000. 

It didn’t make it – the number was 114,000.  That’s still decent, as American population increased only 163,000, but was the worst in months. Even poorer in relation to recent outcomes was the seasonally adjusted unemployment rate, up from 4.1% to 4.3%.  Other results inferior to June’s were unadjusted joblessness at 4.5% instead of 4.3%, the number of unemployed up 400,000 to 7.2 million, the employment-population ratio from 60.1% to 60.0%, and the count of those working part-time for economic reasons, or thus far unsuccessfully seeking full-time positions while keeping shorter-hours ones, up a disturbing 400,000 to 4.6 million.  Average hourly nonfarm private payroll earnings again roughly matched inflation, up 8 cents to $35.07, and the number of long-term unemployed, those jobless for 27 weeks or longer, remained at 1.5 million.  On the good side, the number claiming no interest in work lost 804,000 for a 1.41 million drop over two months, to reach 92,972,000, and the labor force participation rate gained 0.1% to get to 62.7%.

The American Job Shortage Number or AJSN, the measure showing how many additional positions could be quickly filled if all knew they would be as easy to get as a lottery ticket, gained 602,000 as follows:

The share of the AJSN from those officially jobless was 38.7%, up from 37.7%.  Compared with a year before, the metric is almost 1.2 million higher, almost matching the difference from more unemployment but also reflecting gains in those discouraged and those wanting work but not looking for it for a year or more. 

Some jobs reports are confusing and contradictory, but this one was not.  People are still joining the labor force in large numbers, shown by the 1.41 million above and by the count of those employed actually going up, 264,000 to 162,038,000.  When you look at the discouraged and did-not search outcomes, along with the unemployment results, you can see that many Americans are either losing work or not getting it.  The labor force is growing faster than the number of positions.  Inflation is now at 2.5%, reasonable in every way except in relation to the Federal Reserve’s target, but we clearly need more jobs – especially full-time jobs.  The Fed missed a chance to cut interest rates this week; at next month’s meeting, they shouldn’t, and a nominal quarter point, unless the data above substantially improves, will not be enough.  For now, the turtle stayed right where he was.

Electric Vehicles are Going Nowhere Special

In my December 15^th post, I wrote “fundamental sale price drops may require more to be sold than the market can support.  I predict that by November 2024 the share of electrics as American vehicles will be higher than it is now, but only about 10%, with a growing consensus that under current conditions they will not be completely or even largely taking over.”  Seven-plus months out, how are those forecasts looking?

On May 26^th in Fox Business, we saw how “Buttigieg defends Biden’s EV strategy after question on how only 8 federal charging stations have been built.”  The unfortunately-placed transportation secretary was asked why, when two years ago President Biden signed a bill authorizing 500,000 such facilities to be built by 2030 and after one-quarter of the time since has elapsed, the number completed stands at .0016% of that; he reiterated the half-million goal and said there was “utility work” to be done at each, under “a new category of federal investment.”  How many have been started?  I could find information on ports, overall stations, and groups of stations with at least one under construction, but not that.

Another thing Buttigieg mentioned in the interview above was the necessity of lower electric vehicle prices.  Soon afterward, on June 3^rd, the New York Times published “Electric Cars Are Suddenly Becoming Affordable” (Jack Ewing).”  The reason, though, was not higher sales providing manufacturing economies of scale, but that “customers have been snapping up used Teslas for a little over $20,000, after applying a $4,000 federal tax credit.”  That trend started with Hertz’s January-announced sale of 20,000 electric rental cars, which the market has not yet absorbed.  Ewing also mentioned, for new cars, the effect of “increased competition, lower raw-material costs and more efficient manufacturing,” but companies choosing to cut prices were pushed by Hertz, as “electric cars still cost more to manufacture than cars with internal combustion engines,” and sales would further drop without continuing subsidies.

The lack of growth has also pushed companies to build “More Gas Cars and Trucks, Fewer E.V.s as Automakers Change Plans” (Neal E. Boudette, The New York Times, July 18^th).  Ford Motor Company “said it would retool a plant in Canada to produce large pickup trucks rather than the electric sport-utility vehicles it had previously planned to make there,” and the day before that, “General Motors said it expected to make 200,000 to 250,000 battery-powered cars and trucks this year, about 50,000 fewer than it had previously forecast.”  Tesla, as well, “has changed its plans because it no longer expects sales to grow 50 percent a year,” as “its global sales fell 6.6 percent in the first six months of the year.” 

And speaking of the last company mentioned, “Tesla’s Profit Fell 45% in the Second Quarter on Weak E.V. Sales” (Neal E. Boudette, The New York Times, July 23^rd).  Its second-quarter year-over-year revenue increased from $24.9 billion to $25.5 billion, but its net profit fell from $2.7 billion to $1.5 billion.  That seems to confirm that its shortfall from lower selling prices is not from lower costs.  For the same interval, its number of EV’s sold dropped 4.8% and their production 14%. 

As of an Experian Automotive Market Trends fourth quarter 2023 report, only 3.3 million out of 288.8 million American vehicles were electric.  Per Edmunds, 6.9% of sales from January to May this year were the same.  Therefore, it is almost impossible to imagine the year-end share will be anywhere near 10%, let alone higher.  It looks also very doubtful that, given the news above, EV’s will be “completely or even largely taking over” – even if a Democrat wins the November presidential election.  Their percentages may substantially increase, but until then, we must project electric vehicles, with some geographical exceptions, to remain a small minority indefinitely.  If their manufacturers cut back and switch to others, and subsidies continue to prop them up, it will mean little if those in the press say they will predominate.  The lack of federal charging stations may, in the end, be just fine, and customers will get the vehicles they want.  That will work.

Four Areas of the Economy – Four Views on Them

What American economic segments have been analyzed over the past four months?

One thing dragging us down over the past several years is tariffs.  About that, David Wallace-Wells asked, in the New York Times on May 22^nd, “Can Biden Actually Win This Trade War?”  I mentioned this massive increase, which was actually “a unilateral quadrupling” on Chinese electric vehicle duties, on a post about EV’s.  It seemed destructive for a president who seemed to badly want Americans to get away from gasoline and diesel cars, and, per the author here, “it’s not just EVs.  Five years after blasting (then-President Donald) Trump for imposing tariffs on Chinese exports, Biden raised them – on aluminum, steel, lithium batteries, solar cells and semiconductors, among other products.”  The damage to other environmentally favorable efforts is also great, as China’s production shares of related items include “84 percent of the world’s solar modules… 89 percent of the world's solar cells… 97 percent of its solar wafers and ingots, 86 percent each of its polysilicon and battery cells, 87 percent of its battery cathodes, 96 percent of its battery anodes, 91 percent of its battery electrodes and 85 percent of its battery separators.”  Although various “Democratic policymakers” say we should “avoid growing entirely dependent on China for clean energy,” “industrial policy isn’t guaranteed to work,” and “Trump’s imposition of tariffs on Chinese solar-panel exports in 2018 may have meaningfully slowed American renewable rollout.”  The extra charges have real potential to continue doing that.

Something around for many decades and worsening every year gets concern of various strengths every so often, but there are reasons, per Paul Krugman on June 6^th in the New York Times, “Why You Shouldn’t Obsess About the National Debt.”  Although, per usdebtclock.org, it is now almost $35 trillion, or over $100,000 per citizen, Krugman calls it “a lot less scary than many imagine if you put it in historical and international context,” and making it “sustainable wouldn’t be at all hard in terms of the straight economics; it’s almost entirely a political problem.”  As a share of GDP, the author said “it’s roughly the same as it was at the end of World War II,” and is now “considerably lower” than Japan’s.  We could “stabilize debt as a percentage of G.D.P. for the next 30 years” by raising taxes or reducing spending only 2.1%.  So, the money we owe is real, but it may be dormant instead of a looming threat.

Do we have “A towering real estate crisis” (Andrew Ross Sorkin, The New York Times DealBook, June 12^th)?  “The $2.4 trillion office building sector” has been hit by “sky-high interest rates and a pandemic-induced shift to remote work,” as “tenants are reducing or vacating office space to landlords at a record clip.”  As a result, “developers are looking to repurpose empty offices,” although “conversions are expensive, and not all buildings can be easily retrofitted.”  This problem may be worse than it seems, as the pendulum I have documented which swings back and forth between businesses favoring remote and in-office work is approaching the latter maximum, so it will be almost one complete cycle before office demand increases substantially, if it does at all.  Accordingly, a surplus of commercial space will seem to this generation like a permanent problem, so it needs permanent solutions.

I’m used to issuing the AJSN and basing my views using information I trust, so was not happy to see “Reliability of U.S. Economic Data Is in Jeopardy, Study Finds” (Ben Casselman, The New York Times, July 9^th).  Per an American Statistical Association report, while “government statistics are reliable right now,” “that could soon change,” due to “shrinking budgets, falling survey response rates and the potential for political interference.”  Responses to the Current Population Survey, one of two providing the foundation for “the unemployment rate and other labor force statistics,” have dropped from “nearly 90 percent” ten years ago to “about 70 percent in recent months,” and those to “other government surveys” are down also.  As for the third problem, “there are few legal provisions ensuring that the statistical agencies can operate independently,” and an author of the report called for ““legislation to make this issue of professional autonomy statutory.””  We will see, and weakness here will depend on the result of the upcoming presidential election.  The same will affect the previous three items as well – along with a great deal more. 

A Directionless Month for AI – What Do Its Scattered Reports Say?

Artificial intelligence seems to be moving into another phase, or subphase.  There have never been so many concerns, not about the technology causing a major disaster, but about it being adequate and worthwhile at all.  At the same time, there are new perceptions and more progress in related employment, and, if I am perceiving it correctly, growing acceptance that where AI is now might be where it is for months or even years, so stakeholders are realizing they cannot just wait for the next big eruption. 

A look at one chronic AI problem is the subject of “How Game Theory Can Make AI More Reliable” (Steve Nadis, Wired.com, June 9^th).  The author bemoaned the tendency of large language models to give different answers, not all correct, to generative (open-ended) and discriminative (choice of options) questions.  Researchers have invented a game of sorts where “two modes” of LLMs are asked to agree on answers.  Excellence at games is a long-time AI strength, so engineers have reason to be optimistic such inconsistencies will go away.

Top AI workers are scarce, and many, soon after starting work with an organization, move on to another for more pay.  That is why, on one level, “Retention is all you need” (The Economist, June 15^th).  As, per a market analyst, “20,000 companies in the West are hiring AI experts,” “AI talent, previously hoarded at tech giants, is becoming more distributed.”  Indeed, AI hiring at Amazon, Apple, Google, Meta, and Microsoft has only about broken even since the beginning of 2023.  Advertisements for many more software positions now mention AI, as do those for math, scientific research and development, and information design.

Defining another clear problem, Sherin Shibu’s “How Can AI Help Small Businesses?  It’s A Matter of Trust, According to a New Report” (Entrepreneur, June 17^th), tells us that “just 7% of U.S. desk workers see AI answers as completely trustworthy,” the share of American businesses as of June was only 5% although 96% of “surveyed executives felt pressure to bring AI into their business,” and “privacy” and “data quality” were the other two top issues.  These tell AI companies where they should focus, perhaps above all other considerations.

Unsurprisingly, more universities and technical schools are preparing students for jobs using the technology.  A Brock Dumas June 17^th Fox Business piece, “Want an AI career?  These colleges offer degrees for the best chance” provides a current look.  “A new study by software development firm Vention shows which U.S. colleges are offering bachelor’s degrees with the best chance of landing a job fresh out of school – and the potential for the biggest paychecks.”  The top five were California Polytechnic State University, Wake Forest University in North Carolina, Trinity University in Texas, Clarkson University in New York state, and Knox College in Illinois.  There are more, and this list will change, but these may be the ones currently most worth investigating.

Also, for now, “We’re Still Waiting for the Next Big Leap in AI” (Will Knight, Wired.com, June 20^th).  Knight wrote that “the world is still waiting for another AI leap forward… akin to that delivered by GPT-4” 16 months ago.  He described possible contenders, but then concluded “it’s unclear how long the world must wait for that next big leap in AI.  OpenAI has said it has started training its next big model.  In the meantime, we will need to figure out new ways to measure how useful the technology really is.”

On June 27^th, Fox News released “US tops world ratings for AI preparedness:  China, Russia and Iran lag in key measures, report finds” (Peter Aitken).  The rankings were on countries’ “ability to immediately adopt artificial intelligence… into their economies.”  The American “value of preparedness” tied the Netherlands, and was followed by Finland, Estonia, New Zealand, Germany, Sweden, Australia, Japan, and Israel.  That may or may not prove to be valuable information.

There must be something noteworthy about a source called Futurism.com having the most consistently negative AI articles of anyone.  Here are two more.  The first is “There’s a Small Problem with the AI Industry:  It’s Making Absolutely No Money” (July 4^th).  Author Sharon Adarlo noted that “Goldman Sachs analysts have concluded… that AI just isn’t making any serious money yet,” and that “Goldman found that companies that hoped to profit from using AI to boost productivity – ranging from H&R Block to Walmart – have seen their shares vastly underperform the broader stock market since the tail end of 2022.”  She also found that “the only companies making much actual revenue off AI are the ones selling the hardware it needs, like Nvidia.”  While “companies using it want to see major returns,” “so far, it sounds like they aren’t.  Maybe the real question is how much runway the AI industry has before business leaders move onto the next thing.”  This piece is consistent with my previous comments.

The second Futurism.com piece was “Expert Warns That AI Industry Due for Huge Collapse” (Victor Tangermann, July 9^th).  He named a “founding partner” at a “macroeconomic research firm” calling AI “completely unproven,” saying that its hallucinations may never go away and that it was “too energy hungry,” along with a previous Stability AI CEO telling bankers that “this will be the biggest bubble of all time.”  Hardly universal views, but with merit.

Overall, is it true that, per New York Times columnist Thomas Friedman on July 9^th, “the artificial intelligence revolution of the past four years is widely expected to slam into the white-collar job market in the next four like a Category 5 hurricane”?  We don’t know that.  As with actual storms, we will benefit from tracking them and projecting their courses and times of landfall, but this one is way too weak and distant to fear.  We don’t need to buy flashlights, batteries, and water, and may never need to.  The future of artificial intelligence is still up in the air – and it may never come down.

Jobs in June: Seasonal Worsenings Mostly Offset Elsewhere, with AJSN Showing Latent Demand Up Almost Half a Million

The published projections I saw for this morning's Bureau of Labor Statistics Employment Situation Summary was that it would be worse than for May’s data a month ago.  The two estimates of net new nonfarm payroll positions were 190,000 and 200,000, and unemployment might be going up again.  So what happened?

Employment as above, at plus 206,000, was quite close to the predictions.  Seasonally adjusted joblessness had its third straight 0.1% gain, to 4.1%, with the corresponding total of people up 200,000 to 6.8 million.  (We now can ignore when the BLS says something “changed little.”)  Long-term unemployed, for 27 weeks or longer, gained 100,000 to 1.5 million, up 36% from June 2023, with so many people joining the labor force that its participation rate increased, 0.1% to 62.6%.  The measure of how many Americans are actually working, the employment-population ratio, stayed at 60.1%.  The count of those working part-time for economic reasons, or holding onto shorter-hours positions while looking for full-time ones, shed 200,000 to get to 4.2 million.  Average private nonfarm payroll earnings rose 9 cents per hour, close to the inflation rate, to $35.00.

Since May and June have different employment characteristics, the seasonally unadjusted figures did not match the others.  Unemployment that way jumped 0.6% to 4.3%.  The count of those not interested in working lost 637,000 to 93,776,000.  Those employed rose 433,000 to 161,774,000.

The American Job Shortage Number or AJSN, the statistic showing how many additional positions could be quickly filled if all knew they would be easy and routine to get, was up 478,000, as follows:

The share of the AJSN from those officially jobless was 4.3% higher at 37.7%.  Compared with a year earlier, the AJSN grew 504,000, with almost 800,000 more from unemployment partially equalized by, among others, 174,000 from fewer people wanting work but not looking for it for a year or more, and 200,000 fewer from expatriates. 

What patterns can we get from this report?  The new jobs, once again plentiful and nothing to take for granted, went largely to people with statuses other than simple unemployment.  Many more people returned to the labor market, and enough were unsuccessful to bring overall joblessness up.  A goodly number of those without work are not finding it, even after six months away.  Latent demand is not only alive and well but increasing.  Still, June is a tougher month than May, and the smaller, marginal categories show that this was, overall, a good one.  The turtle took a moderate step forward.

Driverless Cars in Mid-2024: A Niche, A Great Future, or Stalling Out?

Autonomous vehicles, through thrown off their horse (strange pun intended) years ago, have not gone away, and aren’t even out of the news as artificial hearts have long been.  What has been happening with them?

The largest recent news item was “Feds are investigating Waymo driverless cars after reports of crashes, traffic violations” (Corina Vanek Natalie Neysa Alund, USA Today, May 16^th).  The National Highway Safety Administration got “reports of nearly two dozen incidents where a Waymo vehicle was the sole vehicle operating during a collision or the driving system allegedly violated traffic laws.”  There were no injuries, but “17 involved crashes or fires,” and the automated driving system “was either engaged through the incident, or, in certain cases when supervised by an in-vehicle test driver,” it “disengaged in the moments just before an incident occurred.”  Waymo gave itself a vote of confidence, with a spokesperson saying “we are proud of our performance and safety record over tens of millions of autonomous miles driven”; additionally, “according to data released by Waymo in December 2023… which was peer-reviewed by experts outside the company, Waymo vehicles were involved in 0.4 collisions with injuries per million miles driven, compared with humans who were involved in 2.78.”  This story graphically shows how autonomous vehicles are being held to vastly higher standards.

Travelers are showing an interest in “San Francisco’s Hot Tourist Attraction:  Driverless Cars” (Lauren Sloss, The New York Times, May 22^nd).  There they “have been operating commercially since August,” though only through Waymo, as “popular pickup and drop-off locations” include “the Ferry Building, Pier 39, Coit Tower, and the Japantown Peace Plaza.”  They are “all-electric Jaguar I PACEs,” and are accessed through an app.  Trips are remotely monitored.  Although different in some ways, “perhaps the most noteworthy aspect of a first-time Waymo ride is how quickly it feels normal.”

A company not doing as well is the subject of “The Very Slow Restart of G.M.’s Cruise Driverless Car Business” (Yiwen Lu, The New York Times, May 30^th).  General Motors is still using its “sprawling complex in Warren, Mich.,” but “G.M.’s driverless future looks a lot further away today than it did a year ago,” before “a Cruise driverless car hit and dragged a pedestrian for 20 feet on a San Francisco street, causing severe injuries.”  Since then, it has “slowed its breakneck development to a crawl,” and, per a consultant, “catching up with Waymo technologically is going to take three to five years at best.”  Yet GM’s CEO said the subsidiary “has made tangible progress.”

Meanwhile, we saw “Waymo, Zoox expand autonomous ride-hailing operations despite recent AV setbacks” (Jordyn Grzelewski, Emerging Tech Brew, June 11^th).  Zoox is moving from three cities to five, but is only testing; Waymo “revealed that it expanded its ride-hailing service area in Metro Phoenix by 90 square miles, bringing its total service area to 315,” and as well as San Francisco, “operates… in Los Angeles, and is testing in Austin.”

For now, Waymo is the only normally available option.  But another competitor, nation-sized, is emerging, as “China Is Testing More Driverless Cars Than Any Other Country” (Keith Bradsher, The New York Times, June 13^th).  In the city of Wuhan, “a fleet of 500 taxis navigated by computers, often with no safety drivers in them for backup, buzz around,” operated by “tech giant Baidu.”  No mention here, though, of a date when paying customers can ride in them.  That seems better though, than another major country, as, although resumed in March, “last fall, Japan suspended its test of driverless golf carts that travel seven miles per hour after one of them hit the pedal of a parked bicycle,” causing no injuries. 

All of this is much the same as 2023’s reports, and largely like the past five years’ worth.  While Waymo is piling up miles and a record, the others are too often stopped by small mishaps.  Companies’ levels of caution are based on the correct perception that such blips unduly scare people.  However, as before, we are paying too little attention to the upside of driverless technology.  Over 40,000 died in American car crashes last year alone, compared with zero in the accidents above.  A tenuous niche has been established – a great future autonomous vehicles still have, if we allow that.  Will we get to the point where extensive testing efforts are not halted for months by the likes of hitting a bicycle pedal?  The answer to that question is more important than any possible driverless technology improvement.  The choice, once again, is ours.

Five Weeks of Artificial Intelligence – Where Is It Now?

A lot has happened with AI over the past several weeks.  I’m not talking about projections, assumptions, justified or other worries, 100-to-1 price-to-earnings ratio stock run-ups, market capitalizations, self-serving representations, CEO hijinks, and other things at the fringes of substantive news making up the great bulk of writing on the technology.  There is real stuff here, so much that I’m calling this piece an expanded edition, appropriate since I won’t be posting next week.

The oldest article is “The great AI power grab” (The Economist, May 11^th).  It addresses the “awful lot of electricity” the software will need, and asks “where will it come from?.”  With “Dominion Energy, one of America’s biggest utilities” being “frequently” asked for “several gigawatts,” when the company has only 34 installed, it’s getting up there.  That power is consumed “at a steady rate,” regardless of sunlight and wind conditions.  It has already started affecting AI companies’ choices of location, and will do so more as long as anyone anywhere has what they need.

It took some digging to show, on my last AI post, what actual sales were, but how about number of transactions?  That is the metric used in “The 10 most popular AI companies businesses are paying for” (Jordan Hart, Business Insider, May 12^th).  The list, which includes “specialized tools” as well as “generative AI,” in order, are OpenAI, Midjourney, Anthropic, Firefiies.ai, ElevenLabs, Perplexity AI, Instill AI, Instantly.ai, Beautiful.ai, and Pinecone.  I found it noteworthy how many are not household words, even in my house.  It shows not only that there are firms being quietly effective, but that some with the noisiest press releases aren’t selling to many people at all.

“As A.I. search ramps up, publishers worry” (Andrew Ross Sorkin, New York Times DealBook, May 15^th) shows cause for concern among those using an “ad-focused business model.”  They are fearful, as “AI Overviews will give more prominence to A.I.-generated results, essentially pushing website links farther down the page, and potentially depriving those non-Google sites of traffic.”  They will need to work this out, as the presence of AI does not please everyone.

Something remarkably lost in the outpouring of manufacturer claims got its own article: “Silicon Valley’s A.I. Hype Machine” (Julia Angwin, The New York Times, May 19^th).  Although in early 2023 “leading researchers asked for a six-month pause in the development of larger systems of artificial intelligence, fearing that the systems would become too powerful,” now “the question is… whether A.I. is too stupid and unreliable to be useful.”  Results have lagged the previous intensity, but corporate statements haven’t – for example, OpenAI CEO Sam Altman, the week before, had “promised he would unveil “new stuff” that “feels like magic to me,”” but delivered only “a rather routine update.”  One “cryptocurrency researcher” asserted that AI companies “do a poor job of much of what people try to do with them” and “can’t do the things their creators claim they one day might.”  The author agreed that “some of A.I.’s greatest accomplishments,” such as its 2023 law bar exam performance critical to perception of AI being amazingly high quality turning out to be in the 48^th percentile instead of the 90th as stated, “seem inflated.”  The technology, per Angwin, “is feared as an all-powerful being,” but now seems “more like a bad intern.”  There will be growing discontent about blatant exaggerations as products fail to meet the stunning standards we were told to expect by now.

The big May story, which many probably confused with higher AI sales, was “Nvidia, Powered by A.I. Boom, Reports Soaring Revenues and Profits” (Don Clark, The New York Times, May 22^nd).  For this leading supplier, “revenue was $26 billion for the three months that ended in April, surpassing its $24 billion estimate in February and tripling sales from a year earlier for the third consecutive quarter.  Net income surged sevenfold to $5.98 billion.”  These numbers show how much more companies such as OpenAI have bought than they have sold.

A related area was the subject of “OpenAI Insiders Warn of a ‘Reckless’ Race for Dominance” (Kevin Roose, The New York Times, June 4^th).  Per “a group” there, the firm, “racing to build the most powerful A.I. systems ever created,” “published an open letter… calling for leading A.I. companies, including OpenAI, to establish greater transparency and more protections for whistle-blowers.”  That firm, “still recovering from an attempted coup last year” and “facing legal battles with content creators who have accused it of stealing copyrighted works to train its models,” has big issues to go with its big chip purchases.

For June, the largest news item so far has been “Apple Jumps Into A.I. Fray With Apple Intelligence” (Tripp Mickle, The New York Times, June 10^th).  This company, ancient and entrenched by the standards of its industry, “revealed plans to bring (AI) to more than a billion iPhone users around the world,” including “a major upgrade for Siri, Apple’s virtual assistant.”  This business decision has a huge possible upside for AI, as it could “add credibility to a technology that has more than a few critics, who worry that it is mistake-prone and could add to the flood of misinformation already on the internet.”  That would close some of OpenAI’s sales-to-purchases gap – I don’t say “will,” since, per Forbes Daily on June 12^th, “to utilize these AI features, iPhone users will have to wait until the iOS 18 operating system becomes available later this year,” which, per the Angwin story above, seems less than a sure thing.

It has been slow on the national regulatory front lately, so we are seeing “States Take Up A.I. Regulation Amid Federal Standstill” (Cecilia Kang, The New York Times, June 10^th).  Although, per the Institute for Technology Law and Policy’s director, “clearly there is a need for harmonized federal legislation,” current and anticipated violations are prodding other governments to quicker action.  “Lawmakers in California last month advanced about 30 new measures on artificial intelligence aimed at protecting consumers and jobs,” including “rules to prevent A.I. tools from discriminating in housing and health care services” and ones that “also aim to protect intellectual property and jobs.”  Legislation has already passed in Colorado and Tennessee, the first against “discrimination,” and the second, through the snappily named “ELVIS act,” guarding “musicians from having their voice and likenesses used in A.I.-generated content without their explicit consent.”

Two AI achievements have reached the present, as described in “This is, like, really nice” (Vlad Savov, Bloomberg Tech Daily, June 11^th).  Here, even though “the breathless bluster about AI changing industries, jobs and lifestyles has obviously not been met by reality,” it has come up with the “Descript editing tool” for audio files, which “eliminates pauses, verbal fillers like “like” and “um,” redundant retakes and anything else that’s not essential.”  Listening to its end results, the author “couldn’t tell where the seams were,” and noted that when “everything takes far longer to edit than its actual running time,” “automating the process is invaluable.”  The second was “AI noise cancelling” with “the Audeze Filter,” “a smartphone-sized Bluetooth conference speaker” that “effectively cancels even unpredictable and high-pitched noises, such as the crying of a baby,” and in a demonstration “a cacophonous café was made tranquil with the flip of a switch.”  Not world domination, but perhaps it can help with wedding audiotapes damaged by unwanted sounds.

To end a bit lighter, new technologies get us new word usages, with one of the latest in “First Came ‘Spam.’  Now, With A.I., We’ve Got ‘Slop’” (Benjamin Hoffman, The New York Times, June 11^th).  The author identified that as “a broad term that has developed some traction in reference to shoddy or unwanted A.I. content in social media, art, books, and, increasingly, in search results.”  As a two-years-ago “early adopter of the term” was quoted, “Society needs concise ways to talk about modern A.I. – both the positives and the negatives.  ‘Ignore that email, it’s spam,’ and ‘Ignore that article, it’s slop,’ are both useful lessons.”  And so it will be.  Beyond that, though, we have no idea.