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.