Misuse of Artificial Intelligence - Real Problems, but What Stands Out?

 

Here are examples of how people have been using AI to deceive, defraud, and commit other sharp practices.

The pictures above the headline of “’A cat-and-mouse game’” (Sarah Kessler, The New York Times, September 6^th) show three receipts.  One is from the Midway Bar and Grill, giving the address, date, server, amount, tip, total, and credit card information.  Another says “FedEx Office” with a familiar logo, date and time, and information on a shipped package, including its cost.  The third is an itemized restaurant bill, with three courses and beverages.  All are apparently flawless, unimpeachable, and unremarkable, probably looking like millions of others submitted for expense reimbursement, but none are real.  Per the CEO of a company making “software used by finance teams to manage expenses,” producing such counterfeits is “too easy,” and employees often start with using AI to synthesize documentation for a legitimate expense for which they lost the receipt, and when they “don’t get caught, they do it again.”  Two other companies are involved in detecting this bogusness, but “A.I.-generated receipts will only get better from here,” and “to combat fraudulent A.I., we need to use A.I.”

We now have that technology built into web browsers.  I often pose questions to the pedestrian Google Chrome release I regularly use, and it may seem like little more than a convenience, but it’s natural to want to discover “How AI browsers open the door to new scams” (Kurt Knutsson, Fox News, September 20^th).  Such tools “can stumble into scams faster than humans ever could,” with a “dangerous mix of speed and trust.”  When people direct AI to buy things, it may “confidently” complete transactions on “fake” store sites.  “Old phishing tactics,” such as sending false bank-identified emails with destructive links, have worked smoothly with AI software.  Clearly, we’re not ready yet to delegate such sensitive activities.

What can happen when the roles are reversed?  We will need to learn “How to spot and stop AI phishing scams” (Kurt Knutsson again, Fox News, October 14^th).  Such things happen “when hackers use AI to make their scams more convincing,” by assembling “super-realistic emails, messages, voices and even videos.”  These communications rarely have the old-time tells of “typos and bad grammar,” and AI’s repertoire now includes “voice clone scams” and “deepfake video scams.”  Some of the red flags are unchanged, though, with Knutsson telling readers to beware of a “suspicious sender’s address,” “generic greetings like Dear Customer,” “unsolicited attachments” used to prompt action, email addresses with slight but real variations from official ones, and, perhaps most mentioned, a need for urgency.  With friends and family members, you can “set up and use a shared secret,” and with others you know, asking them something from the past not likely to be relevant now works well.  In a case I had, a Facebook friend and former high school football teammate asked me to participate in something believable but dicey, but when I asked him what position he played, “he” gave me the online equivalent of a blank stare.

How about misbehavior originating from AI providers themselves?  We think and hope there isn’t much of that anymore, but we had a case last fall where an “AI-enabled teddy bear… gave advice on B.D.S.M. sex and where to find knives.”  That was the springboard for “Public Shame Is the Most Effective Tool for Battling Big Tech” (Jessica Grose, The New York Times, January 14^th).  The author described how consumers successfully pressured company Mattel to pause “the release of any A.I.- powered” toys.  Yet many corporate responses to even sexual material have been weak, such as X-platform spokespeople saying that “its policy is to “take action”” against lewd deepfakes, and other events, such as the federal government asserting its ability to override state AI laws, may be creating a foundation for further wrongdoing.  So, per Grose, “negative publicity” is most effective in getting solutions for these problems to be reached.

Finally, there were “Lawyers Barred for A.I.-Generated Citations to Fake Cases” (Neil Vigdor, The New York Times, June 9^th).  When “all four lawyers on opposing sides in a civil trial” found themselves “removed from the case and fined” after “some of them, relying on artificial intelligence, cited fake legal cases in court filings,” one attorney said she had used “First Drafts, an A.I.-powered program for drafting legal documents.”  But the precedent here will be that due diligence with those tools will require more than was practiced this time.

What is noteworthy about these cases?  You may have been thinking that I picked only a few as samples for this post.  But, in nine months, this is all I saw.  For all of AI’s potential to do damage, it hasn’t been doing much.  Certainly, there has been more, especially in deepfakes, but it doesn’t look like a lot.  There is a message here - could it be that AI is not as compatible with crimes, and even vice, as we think?  Could it be that our laws and restrictions, as immature and makeshift as they seem to be, are working remarkably well?  It is time for us to consider these things, and, once again, look at how AI is actually turning out.

Jobs Report: More Work and More Not Looking, with the AJSN, Now 17.2 Million, Showing 700,000 Additional Latent Demand

This morning’s Bureau of Labor Statistics Employment Situation Summary was a mixed bag.  Once again, the number of net new nonfarm payroll positions beat up on the published estimates, more than doubling the one I saw to 172,000.  And once more, the rest of the report didn’t follow through.

Although seasonally unadjusted unemployment rose 0.1%, in a typically slightly lower employment month, to 4.1%, the adjusted variety held at 4.3%.  Long-term joblessness, or 27 weeks or longer, jumped 200,000 to 2 million, but the count of those working part-time for economic reasons, or keeping shorter-hours positions while continuing to look for longer-hours ones, lost 100,000 to 4.8 million.  The labor force participation rate sat at 61.8%, but the employment-population ratio, showing without embellishment how likely it is for Americans to be working, gained 0.1% to 59.1%.  Average hourly private nonfarm payroll earnings roughly matched inflation, up 12 cents to $37.53.  The adjusted number of unemployed dropped 100,000 to 7.3 million, while the unadjusted one rose 136,000 to 6.904 million.

The American Job Shortage Number or AJSN, the metric showing how many more positions could be quickly filled if all knew they were easy to get, was up 697,000 as follows:

The largest change came from, oddly enough, those wanting to work but not looking for it for a year or more - they were 835,000 more numerous, adding 668,000 to the calculation.  The second largest gainer was actual unemployment, which contributed 122,000 more.  The share of the AJSN from unemployment was 36.2%, down 0.8%.

Compared with a year before, the AJSN came out 278,000 higher, with most of the gains from those not looking for a year or more, those not wanting a job, those discouraged, and those unemployed.  The institutional, military, and off-the-grid category, down over one million since May 2025, provided the largest offset.

The other possible trend May provided was from the numbers of those not in the labor force, off 153,000 to 105,253,000, and not interested in working, which plunged 953,000 to 98,497,000.  Those can both be proxies for expected poor work prospects. 

What to make of this month?  Probably not a lot of lasting significance.  People left the labor force, but the new jobs were still there.  Per capita employment rose, but, at 4.8 million, too many people are being stopped from moving from part-time to full-time.  We’re not losing the employment battle now, but we’re not improving at it either.  Accordingly, the turtle stayed right where he was.

Both Sides Now: Eye-Popping Artificial Intelligence Financials from Producers as Well as Suppliers, But…

Despite crashing public approval and incessant talk about a bubble, AI business results, and not only those from the gunrunners, are more stunning than ever.

In “Ads coming to ChatGPT for some US users as OpenAI seeks to generate new revenue” (Michael Sinkewicz, Fox Business, January 17^th), we saw something consistent with other online products, as “OpenAI plans to test advertisements in coming weeks for free and lower-tier subscribers ahead of (its) anticipated IPO.”  A company spokesperson said the pitches will appear “at the bottom of answers in ChatGPT when there’s a relevant sponsored product or service based on your current conversation,” they “will not be shown in accounts where the user is under the age of 18,” they “cannot appear near sensitive or regulated topics like health, mental health or politics,” and the company will “never sell your data to advertisers.”  Business as usual.

Riches for AI-making are relatively new, so there can still be headlines like “The A.I. Boom’s Uncertain Payoff” (Andrew Ross Sorkin et al., The New York Times, January 29^th), as “investors remain antsy about seeing results.”  However, “investors appeared worried” about the “payoff” from Microsoft spending “$37.5 billion in capital expenditures in its most recent quarter,” but less so about Meta planning on disbursing “$115 billion to $135 billion on capex this year,” and even Tesla, outside “making electric vehicles,” $20 billion.  The large questions still in the air are “how will investors feel if companies’ margins remain depressed for a long time amid all of their spending?” and “how will they ultimately measure success?”  Indeed, per “Microsoft Continues to Spend Big on A.I. While Profit Jumps 60%” (Natallie Rocha, The New York Times, January 28^th), despite the headline outcome along with a 17% quarterly revenue gain, its stock price later that day was “down more than 5 percent in after-hours trading.”

Next, we saw as “Nvidia’s Quarterly Profit Hits $43 Billion on Strong A.I. Chip Sales” (Tripp Mickle, The New York Times, February 25^th).  That was also $120 billion for the year.  The leading AI supplier - it “controls about 90 percent of the market for the cutting-edge semiconductors that power A.I. projects” - joined “only a handful of companies, including Alphabet, Microsoft and Apple” in profiting an annual $100 billion.  Yet, per the previous paragraph, its customers’ “spending is starting to unsettle Wall Street, and Nvidia’s share price has been relatively flat in recent months,” even if “demand for Nvidia’s chips was still growing at an astonishing rate.” 

Moving to this month and a new Nvidia competitor, “A.I. Chip Maker Soars 68% in Market Debut, as Tech I.P.O.s Ramp Up” (Natallie Rocha, also in the New York Times, May 14^th).  The company, Cerebras, which sold its first chips in 2019, made itself the largest IPO of the year so far, and closed that day with market capitalization of $67 billion.  It was profitable last year with $238 million net.  There will be more - which will survive?

We got another quarterly update, as “Nvidia’s Profit Hits $58.3 Billion as A.I. Boom Gathers More Steam” (Tripp Mickle, also in the Times, May 20^th).  That was over 35% higher, and as “Nvidia’s biggest problem appears to be meeting demand from its spendthrift tech industry customers,” there is plenty of room for the likes of Cerebras.  The stock market shrugged, though, as that day, Nvidia’s “share price fell 1 percent in after-hours trading, giving up most of its gains from earlier in the day.”

Since then, something has changed.  In “Why Memory Chips Are Dominating the A.I. Rally,” Andrew Ross Sorkin et al., still in the Times, reported that “Micron, Samsung and SK Hynix are now trillion-dollar companies,” all three of which among the firms that “dominate the memory chip supply chain.”  Those who invested in Micron or SK Hynix last year have been well rewarded, as their share values have increased threefold and tenfold thus far in 2026.  All of that meant “global stocks climbed to fresh records for a sixth straight session and US futures advanced as investors piled into tech shares” (Morning Briefing, Bloomberg, May 27^th).

We are now seeing companies creating AI products doing well, in some ways better than their suppliers.  That is a huge improvement.  Yet AI’s financial side, even if bilateral now, seems more like a protected cove than the state of the technology in general.  If American data center construction screeches to a halt or near-halt, and public dissatisfaction becomes more important in other ways, what will happen to AI profits, revenues, and market capitalization?  We will see, but it won’t be good.  AI may be soaring, but rough air may, as always, be just ahead.  Place your bets as you will, but don’t count on anything - we really don’t know artificial intelligence at all.

Artificial Intelligence Regulation - Activity on the Way to Something to Be Determined

How will AI be constrained?  That is the same open question it has been since the start of AI.  Yet things have been happening toward that.  What?

First, a late 2025 Donald Trump view: “Chasing an Economic Boom, White House Dismisses Risks of A.I.” (Tony Romm and Colby Smith, The New York Times, December 24^th).  At that point, the technology got “the administration’s unqualified support,” as that year “the president and his top aides have fully embraced A.I. and showered its leading corporate backers with money and regulatory” help.  Other major players, though, looking at possible lower product demand, job losses, or at least greatly decreased hiring, were not so sure.

In response to a widespread concern soon before then, a “Deepfake porn crackdown passes in Senate to allow people to sue” (Alex Miller, Fox News, January 13^th).  “The Senate quietly passed legislation… that would create stiffer penalties for explicit AI-manipulated images, known as deepfakes.”  The bill “is designed to beef up federal penalties against the creation, distribution or solicitation of “non-consensual digital forgeries,”” and is “geared to act as a companion to a previously passed bill targeting revenge porn.”  Even if passed into law, controversy on this topic remains, centering around the ease of producing such material, making it unlikely that here is the last word.

Do we want all AI-related laws to come from Washington?  That could be a presidential goal, as “White House Unveils A.I. Policy Aimed at Blocking State Laws” (Cecilia Kang, The New York Times, March 20^th).  “Dozens of states have passed laws in recent months to regulate A.I., which has created concerns about the technology’s potential to steal jobs, push up energy prices and threaten national security.  But President Trump has made clear U.S. companies should have mostly free rein in a global race to dominate the technology.”  That view is supported by the industry, as “Meta, OpenAI, Google and other A.I. giants have argued that a patchwork of state laws could slow down their progress” and “have repeatedly pointed to regulation as the biggest hindrance to the nation’s success in leading the world in A.I.”  Yet “the White House also called for provisions that protected children, including stronger parental controls and privacy protections.”

Appropriately, we also saw as “IBM CEO Arvind Krishna warns Washington must find ‘Goldilocks’ middle ground on AI regulation” (Kristen Altus, Fox Business, May 5^th).  Krishna said that, as “they were always going to regulate the use case of (AI),” “there is always a level of government oversight.”  However, “if it turns into a bloated bureaucracy, that would not be so good for us to win the AI race,” and “this is always the balance between innovation and safety.”  He thought it best with regulators “going to do their judgment quite quickly within a few days or a few weeks,” as that “serves everybody very well.”

With regulation in flux, it also makes sense that “Silicon Valley’s A.I. Lobbying Reaches a Fever Pitch” (Cecilia Kang, The New York Times, May 13^th).  As a component of “OpenAI’s increasingly aggressive push to sway A.I. policy,” the firm is opening “its first lobbying office in Washington,” which will be “part lab, part showroom.”  It joins Anthropic, which “opened its first office in Washington in April, as it battled with the Pentagon over the use of its technology.”  Already, “a quarter of the 13,000 federal lobbyists in Washington are involved in A.I. issues, up from 11 percent in 2023.”  The companies are hardly unified in their objectives, as while “OpenAI, Meta and Google have pushed for little or no regulation,” “Anthropic and others have supported new laws, pointing to the technology’s potential dangers.”  That same day, though, “OpenAI backs creation of global AI governance body led by the U.S. that would include China as a member” (Michael Sinkewicz, Fox Business).  Per an OpenAI vice president, “the proposed organization could resemble the International Atomic Energy Agency, which includes China and sets global safety standards for nuclear energy development.” 

A fine idea, and whether you consider that regulation or a device to keep regulation at bay is up to you.  So, control of artificial intelligence is still sketchy and makeshift, but, in the pieces above, we may be able to see more clearly what its future could resemble.  As always, stay tuned.

Different Views on Artificial Intelligence’s Effect on Getting Jobs, All From the Past Five Weeks

Within AI, the press subjects vary.  Lately, I’ve seen nothing about problems with ever-larger data feeds, and while a general backlash reaction to AI is gaining strength and attention, little specific has been out lately about its previous hot topic, data centers.  Even rundowns on specific AI achievements and its huge philosophical issues have been relatively scant.

The oldest here was “Time to ditch AI anxiety – experts say there’s a lot less to fear than we think” (Simon Constable, Fox Business, April 11^th).  As you will see, not all agreed with this thesis.  Constable started with acknowledging the amount of change, and concomitant tension, AI has wrought, and with “data from Challenger, Gray and Christmas” saying that “AI was directly involved in firing 54,000 people during 2025” (though that is hardly a huge number), moved to saying that “last year, approximately 280,000 new jobs in Gen-AI were created for people, according to Electro IQ Job Creation Stats,” that the “28.3% of the working-age population” that “used generative artificial intelligence” had become more productive, and that, ultimately, “creativity comes to life because people working with AI need to do the thinking.”  A short but clear argument.

As a solution to positions disappearing, “The AI revolution threatens office jobs, but revives demand for skilled trades” (James Altmire and Riley Burr, Fox News, April 12^th).  This is clearly happening, especially among younger career-choosers, who are also rightfully attracted to earlier paychecks, remarkably high compensation, modern-day work conditions, and, with all of that, higher prestige.  The subtitle “trade careers requiring manual dexterity, problem-solving and emotional intelligence remain beyond AI’s reach” describes the other side.  “Even if AI is able to automate some of the more routine tasks in the workplace, tradespeople are further insulated from AI-driven job displacements because of the unique need for human touch in these roles… AI algorithms may help diagnose issues, but human experts must then step in… with careful judgment, manual dexterity and complex problem-solving.”  There are reasons why trade positions have long had apprenticeships, which tells us that abstract learning is not sufficient preparation for them.  Nor will it be.

I didn’t like seeing the headline “Silicon Valley Is Bracing For a Permanent Underclass” on my May 3^rd New York Times, and that feeling didn’t improve as I read the story.  What was author Jasmine Sun trying to say?  “Most people I know in the artificial intelligence industry think the median person is screwed” – is that the median West Coast programmer, stuck in a career field endangered long before AI came to prominence?  “Anthropic chief executive Dario Amodei” made self-serving “pronouncements about a white-collar blood bath” – so what?  “You feel it in the fretting of recent college graduates who apply to hundreds of jobs without landing a single interview” – so what else is new?  From a quoted “23-year-old start-up founder and Stanford dropout… There’s only a matter of time before GPT-7 comes out and eats all software and you can no longer build a software company,” and another future tool “can perform all physical labor as well” – the first is called creative destruction, and the second can most charitably be called wildly unlikely.  It may well be that Silicon Valley will become less prominent – change does happen, and it is not all favorable to everyone – but the country in general will not succumb to a failure of “society’s ability to cushion A.I.'s disruption.”  We are long past any need for reality-unsupported, obsolete-anyway screeds like this.

Perhaps the New York Times editor had a sense of humor, when, on the same date as above, there also appeared Ezra Klein’s “Why the A.I. Job Apocalypse (Probably) Won’t Happen.”  Despite unexamined-sounding predictions such as Microsoft AI’s CEO saying “that most white-collar work will be fully automated by an A.I. within the next 12 to 18 months,” “the microdata isn’t matching the anecdata:  The unemployment rate was 4.3 percent in March 2026; in March of 2020, it was 4.4 percent.  Average hourly earnings are stable.  Claude Code is a marvel, yet demand for software engineers is booming.”  Additionally, “A.I. will make knowledge plentiful,” “the more automation there is, the more people value a human’s touch,” “computers can do much that humans once did, but they didn’t put humans out of work,” as “the ability to do more made people realize there was more to do,” and, personally, “the better my A.I. has gotten the more I’ve wanted from the human beings around me – and from myself.”  A future of mass idleness indeed does not seem reasonable, so this piece is much better.

More additive AI effects are described in “How AI exposure is reshaping jobs in creative fields” (Eric Revell, Fox Business, May 4^th).  The technology is integrating here, instead of taking over.  While some roles, such as dancing and acting, don’t work as much with AI, for music directors and composers “a substantial portion of their tasks involve composition or production that AI tools may draft or modify.”  A recent study “found little evidence that generative AI has broadly reduced artists’ earnings.”  There, though, could be jobs lost by people not able to keep up with those using AI for assistance.

So, should we be concerned if “Congress Is Doing Little to Prepare for Potential A.I. Job Losses” (Ben Casselman and Tony Romm, The New York Times, May 5^th)?  “The federal safety net isn’t ready for such a shock,” presuming that displacement would be sudden and severe, but would it be?  If “unemployment insurance and other safety net programs are long overdue for an overhaul,” it could be right to do that, but the authors’ comparison with when “over just a few weeks in the spring of 2020, more than 20 million Americans lost their jobs” is weak.  Accordingly, we can go from there.  Although evidence for expecting massive AI-caused cuts is missing, the chance is more than zero, and it may be time to prepare.  Let us consider that, while still not giving in to unjustified reactions in any artificial intelligence area. 

April’s Jobs Report No Success After Employment Gain – Latent Demand Little Dropped per AJSN of 16.5 Million

One of many problems of emphasizing a single result in the monthly Bureau of Labor Statistics Employment Situation Summary is that it may not be representative of the outcomes as a group.  April’s edition, which came out this morning, was a prime example.

The number of net new nonfarm payroll positions almost doubled the published estimate I saw, reaching 115,000 instead of 67,000.  From there, although seasonally unadjusted joblessness fell from 4.3% to 4.0%, the adjusted version did not worsen at 4.3%, unadjusted unemployment was seasonally off 465,000 to 6.77 million, and long-term unemployed held at 1.8 million, it was a negative month.  The count of adjusted jobless rose 200,000 to 7.4 million.  The two figures showing how common it was for Americans to be working or just short of that, the employment-population ratio and the labor force participation rate, each dropped 0.1% to reach 59.1% and 61.8%.  The number of people working part-time for economic reasons, or holding short-hours positions while seeking thus far unsuccessfully full-time ones, soared 400,000 to 4.9 million.  Average hourly private nonfarm payroll earnings again gained less than inflation, 3 cents per hour, to $37.41.  Despite April being a seasonally stronger month than March, the unadjusted count of employed moved up only 17,000, to 162,781,000. 

The American Job Shortage Number, the seasonally unadjusted metric showing how many new and unadvertised positions could be suddenly filled if all knew they would be easy to get, lost about one third of a million, less than seasonal expectations, to the following:

 

The inputs, except for classic unemployment which deducted over 500,000, offset one-third of the latter’s subtraction, with most coming from increased numbers of those not looking for the previous year, those wanting to work but unavailable for now, those not wanting a job, and those in school or training.  The share of the AJSN from this official joblessness shrank from 39.3% to 37.0%. 

We did not make progress in April.  As well as in the results above, the year-over-year comparison showed the AJSN up 423,000, with more from nearly all of the secondary categories above as well as 169,000 from unemployment, which was almost 200,000 lower in April 2025.  More people are falling behind in pay.  More people – 650,000 additional over the month – have left the labor force, with 422,000 more claiming no work interest.  The number of Americans going part-time for economic reasons, an indicator of hardship while working that is not reflected in other statistics, is again pushing post-pandemic highs.  It is good to get more jobs, but this time that wasn’t enough.  The turtle stayed right where he was.

Artificial Intelligence and China – Where Are We Now?

I can remember fears about China taking over the world, dominating certain industries, and becoming generally invincible since the 1970s.  Now, despite what has clearly been American supremacy in AI, events, those old thoughts keep popping up.  How valid is that concern now?  Here’s what I’ve seen over the past year.

Remember the kerfuffle about a Chinese company allegedly matching the best AI in the world for a tiny fraction of the resources?  The oldest article here, and the latest one I’ve seen about that product, is “China’s DeepSeek faces House probe over US data harvesting, CCP propaganda” (Morgan Phillips, Fox News, April 24^th).  The House’s Energy and Commerce Committee wrote that “DeepSeek admits to sending Americans’ personal information to servers in China, where it is undoubtedly accessed by officials connected to the Chinese Communist Party.”  That led to three states forbidding that company from using “government devices,” joining “Canada, Australia, South Korea, Taiwan and Italy.”  Adding that to “reports” which have “suggested that DeepSeek trained its R1 model by “distilling” outputs from American competitors,” we have the end of any concern, once widespread, that we have been leapfrogged.

Overrepresentation and posturing again appeared in “Does China Have a Robot Bubble” (Meaghan Tobin and Xinyun Wu, The New York Times, December 17^th).  “Robots made by Chinese start-ups have danced on television, staged boxing matches and run marathons,” but, while “the robots can mimic human movement and even complete basic tasks,” “they are not skilled enough to handle many tasks now done by people,” and “have a hard time reacting to events as they happen.”  Many humanoid robots are produced there and are priced lower, but, given their limitations, do not seem to have enough customers.

Maybe “Move Fast, but Obey the Rules:  China’s Vision for Dominating A.I.” (again Meagan Tobin and Xinyun Wu in the New York Times, February 2^nd) is an improvement.  Yet there is “a tension shaping China’s tech industry,” as while its “leadership has decided that A.I. will drive the country’s economic growth in the next decade,” “it cannot allow the new technology to disrupt the stability of Chinese society and the Communist Party’s hold over it.”  The resulting “increasingly complex set of rules” included each company acting “as a gatekeeper to prevent the spread of information that the Chinese government deemed illegal,” with violations possibly causing them “significant legal, financial and operational consequences.”  When “these systems learn by ingesting large amounts of data,” including “internet sources, like Reddit and Wikipedia, that contain information censored in China,” the choices of allowing or not allowing large language models to access such storehouses are both problematic.

On data center projects, is it fair to say that “America must power AI with speed and discipline – or China will dominate” (Jeff Kupfer and Brant Fewell, Fox News, March 3^rd)?  Here, “AI data center projects are encountering growing resistance,” as “in 2025 alone, at least 25 projects were canceled, four times more than the year before,” “nearly 100 projects nationwide are now contested,” and “in December, more than 230 environmental organizations urged Congress to impose a nationwide moratorium on new data center approvals.”  While more and more American areas do not want such construction, many still do, so even a temporary nationwide ban would be unreasonable, and yes, if we cannot within our borders “build the infrastructure to power it with speed and discipline…  China will.”

I had thought that the title of “China Is Embracing OpenClaw, a New A.I. Agent, and the Government is Wary” (once more Tobin and Wu in the Times, March 17^th) meant more Communist Party data-suppressing issues – but it did not.  This popular and “versatile” new product, which has gained admiration even from Nvidia’s CEO, has committed “leaks of personal information,” “errors in financial transactions,” and at least once, when it was “left… running with access to (a) credit card,” it “had run up the card to its limit.”  How quickly OpenClaw will stop these things will determine whether its introduction, when it was, will be seen as bold and powerful or irresponsibly premature.

Somewhat updating the second story above was “Humanoid robots hit mass production in China” (Kurt Knutsson, Fox News, April 9^th).  The subject was “a new factory in China” making “about 10,000 units a year,” with “24 precision assembly stages” and “77 inspection steps.”  Knutsson declared that “the robotics industry has reached a turning point,” as “it is no longer enough to show what a robot can do,” since “companies now need to prove they can build them at scale.”  Although such factory output “signals that a company can move beyond demos and into real deployment” with “confidence that there will be actual demand,” the pieces above say we shouldn’t take that for granted.  Indeed, per Knutsson, while “building the body is getting easier,” “teaching it how to function in the real world is still difficult.”  Still.  I acknowledge the manufacturing progress, but will need to see stories like this without text like that before I think humanoid robots are truly about to achieve widespread, routine success.

After the six articles above, I did not expect “I Went to China to See Its Progress on A.I.  We Can’t Beat It” (Sebastian Mallaby, The New York Times, April 13^th).  But this author of a pertinent-sounding book told us that “China has rolled out a series of excellent A.I. models,” building “copycat” versions of “cutting-edge” American products through distillation, and is now “leading” “on industrial applications,” and so, fearing not only for our safety but that China is ahead, we should seek “nonproliferation” agreements. 

Chinese AI certainly has its strengths, but its flaws seem greater, and while for some the future may be theirs, the past has not been.  Chinese culture and government have not historically rewarded independent thought nearly as much as in America, where eccentric geniuses, not fearing or even wanting homogeneity, have kept us on the forefront of innovation for centuries.  Now, while there are certainly orthodoxies in American thought, they have much milder effects on identifying reality than those in China, with greater chances of being discredited.  As well, American business transparency vastly exceeds the Chinese, and we simply cannot be sure if they are doing what they say they are.  We will never know what they are suppressing, and our companies do not enjoy potentially unlimited governmental support. 

So, I decline seriously worrying about Chinese AI leadership.  If OpenClaw reaches even ChatGPT and Claude security levels, and those 10,000 annually-built robots are as successful on jobsites as they seem when leaving the factory, I will take stock.  But for now, we still lead, and not by a small margin, the artificial intelligence world.