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Authored by Lipton Matthews via The Mises Institute,

The recent death of Jason Arday has been met with widespread grief, but also with a disturbing rush to assign blame.

Much of the media narrative has positioned Nathan Cofnas as the villain, the man who exposed plagiarism and, in doing so, supposedly hounded a vulnerable academic to his death.

This is a convenient story, but it is not the truth.

Let us be clear. Nathan Cofnas did nothing wrong. Yet he has been suspended from his post as a postdoctoral researcher in the Department of Philosophy and Moral Sciences at Ghent University. He brought to light legitimate concerns about Jason Arday’s academic record. That is the function of journalism and scholarly scrutiny. If the allegations were false, Arday would have defended himself more effectively. If they were true, then they deserved to be aired. The fact that Arday was mentally unwell is tragic, but it does not retroactively make Cofnas’s actions malicious. We do not hold journalists responsible for the pre-existing vulnerabilities of those they cover.

This is not the first time a scandal has broken around a prominent figure. Stephen Glass—once a star at the New Republic—saw his career implode when his fabrications were exposed. He did not retreat; he wrote a novel about his disgrace, turning infamy into profit. Jayson Blair—the New York Times plagiarist—did the same. Both men monetized their scandals. Jason Arday’s exposure came in the age of social media, so the venom was more intense, but the principle is unchanged. Public figures often exploit controversy for personal gain. That Arday could not do so is unfortunate, but it is not evidence of a uniquely cruel campaign against him.

What is striking about Arday’s case is the institutional support he received. Diane Abbott and prominent Cambridge academics rallied to his defence. Compare this to the treatment of Charles Negy, Linda Gottfredson, Arthur Jensen, Helmut Nyborg, and others who have been vilified as racists simply for engaging with research on intelligence and group differences. Gottfredson continues to be defamed by the disgraced Southern Poverty Law Center. Nyborg had to sue the Danish Committees for Scientific Dishonesty for falsely accusing him of scientific misconduct. Negy is not even a race researcher; he was penalized by his university for saying that black privilege is real. As Negy put it, “beyond affirmative action, special scholarships, and other set asides, being shielded from legitimate criticism is itself a form of privilege.” None of these scholars had the institutional backing that Arday enjoyed. He was given an opportunity on a platter of gold. However, he failed to distinguish himself and, when the scrutiny came, he could not withstand it.

The real lesson of this harrowing tale is not a racist media hounding a black academic to death. It is the intensity of what might reasonably be called black privilege—a system that elevates individuals to positions they are not prepared for, shields them from criticism, and then reacts with shock when reality intrudes. Mike Adams - a white academic - was badgered to suicide in 2020 by social media and his university simply because people found his tweets offensive. Unlike Arday, he received no institutional support from elite institutions, nor was there an outpouring of solidarity. The difference between his case and Arday’s is glaring.

Invariably, the hatred directed at Nathan Cofnas stems not from his role in the Arday affair, but from his wider writings on race and intelligence. Cofnas holds that racial differences in intelligence are partially genetic. This is controversial, but it is not unsupported. Psychologist Russell Warne has defended similar positions in his book In the Know: Debunking 35 Myths About Human Intelligence. Even if the hereditarian view is ultimately wrong, the environmentalist thesis has failed to produce a convincing alternative. Not much has changed since Arthur Jensen’s landmark 1969 report How Much Can We Boost IQ and Scholastic Achievement? Robert Plomin’s more recent text Blueprint: How DNA Makes Us Who We Are reinforces the point that parental influence is largely genetic. Cognitive gaps between blacks and whites persist even when both groups are similar in socioeconomic status, education, and other environmental measures. The reality is that—irrespective of the truth of hereditarianism—groups will differ in behavior and intelligence. If elites would simply accept this, we could stop obsessing over erasing every disparity and instead focus on helping people thrive where they are.

In an ordinary world, Jason Arday might have been a successful PE teacher, or even a comedian.

He had charm, energy, and a compelling personal story.

He died because Cambridge elevated him to a position for which he was not prepared, and the inevitable scrutiny crushed him.

When he was alive, Arday said he wanted the world to spin on an axis of love. But the truth is that the DEI fanaticism that elevated him, and ultimately destroyed him, was propelled by an excess of love for egalitarianism, a love so blind that it refused to see the human cost of its own ideology.

Jason Arday’s death is a tragedy. But it is not Nathan Cofnas’s fault. It is the fault of a system that prioritizes symbolism over substance, and then abandons its symbols when they fail.

If we want to honor Arday’s memory, we should begin by telling the truth about how he got there and who really put him in harm’s way.

Kiss those data center technician jobs goodbye...

Meta is testing robots that can swap network cables, restart servers, reseat components, and perform other physical tasks traditionally handled by data center technicians. The experiments come as the company rapidly expands its AI infrastructure and seeks to operate facilities more efficiently, according to Wired.

Some employees worry about job security. One Meta worker estimated that a successful cable swapping robot could eventually take over as much as 80 percent of certain workloads. “We thought those of us performing the physical tasks were safe for a while, but not anymore,” the worker said.

Meta says automation does not mean it needs fewer people. Spokesperson Francis Brennan cited a shortage of skilled tradespeople and the company’s investments in training and hiring workers. Meta has launched programs teaching electrical, mechanical, and plumbing skills and partnered with trade unions on apprenticeships.

Still, robotics is part of Meta’s longer term strategy. Robotics manager Eric Xu has said robots could eventually assist with incident response, environmental monitoring, and preventive maintenance.

The company is experimenting with equipment from several manufacturers. A Kinova Gen3 arm is being evaluated for power cycling servers, while other machines are being tested for cable replacement. Some facilities already use a simple remotely controlled device resembling a mechanical finger to press power buttons and reboot equipment.

At its Altoona, Iowa, campus, Meta is testing dual arm Watney robots for cabling. At its newer Prometheus campus in New Albany, Ohio, ABB robots mounted on four wheel platforms are being used to reseat components and could eventually perform more work with less human supervision.

These projects build on simpler automation already deployed in Meta facilities. Self driving tugger robots transport heavy server racks, while wheeled inventory robots scan equipment and assist with inspections. Microsoft, Google, and Amazon have also explored robotics for data center operations.

Wired writes that the economics are attractive. Robots could provide consistent labor where qualified technicians are scarce and handle repetitive or hazardous work. They could also operate in hotter, darker, or otherwise less hospitable environments.

But current systems remain far from replacing technicians entirely. Meta’s inventory robots struggle with cables and corners, require humans to move them between buildings, and cannot reliably interpret some equipment indicators. Other robots need substantial charging time and remain slower than people.

Data centers were also designed around human dexterity. Complex cabling, particularly around advanced AI systems such as Nvidia’s GB300, remains difficult for robots. As one former Meta employee put it, “Things have been designed for human hands forever to make everything a five-minute repair.”

Even so, the experiments are changing how some Meta workers view their future. Employees have reportedly discussed fears that automation could eliminate jobs or shift remaining positions toward lower paid workers who mainly follow AI generated instructions.

That could also affect the politics surrounding data centers. Communities often justify tax incentives partly through the jobs these facilities create. If robotics significantly reduces employment, governments may reconsider those economic tradeoffs.

For now, humans remain faster and more adaptable. But as robotic hardware gets cheaper and AI improves, Meta is preparing for a future where machines perform considerably more of the physical work inside its data centers.

Authored by Joe Bertolami via RealClearMarkets,

A recent report from Stanford reviewed the latest employment data and found that, so far, AI has not resulted in large scale job destruction. Meanwhile, new hiring data from the Economic Times reveals that AI is actively fueling unprecedented job creation, with AI skills now powering nearly two-thirds of new Global Capability Center hiring. Together, these recent dispatches from the front lines of the labor market point to a calming reality: the much-dreaded AI job apocalypse hasn't materialized as a sudden extinction event.

The (sometimes buried) lede: AI is delivering real impact, and it is broadly changing the nature of work. But disruption is not a new phenomenon. The economy has always dismantled old work to build new work. What determines whether this evolution feels like progress or collapse isn't just the number of jobs lost, it's the speed at which that loss hits the labor market.

In 1995, Bill Gates circulated a memo titled "The Internet Tidal Wave," calling the web the most important computing development since the IBM PC. If the internet was a tidal wave, artificial intelligence is a tsunami. It is arguably the biggest advancement in computing since the Turing machine. Yet, from a distance, it's difficult to appreciate the speed of this wave, leading many to wonder when the broader economy will truly feel its impact.

To put this in context, we must understand the historical pattern already visible in the labor market. Combining decades of data from the U.S. Bureau of Labor Statistics and the Federal Reserve yields a remarkably consistent story of overlapping curves: job loss and job creation. Over the last two decades, nearly 20 million U.S. jobs vanished in disrupted sectors. Over the same period, total payrolls grew by 25.7 million. That equates to roughly 1.3 new jobs for every one destroyed. Classic examples include jobs in video rentals (-98.9%) and word processing (-83%) which largely vanished, but new work sprung up at the same time in areas like data processing (+54%) and warehousing (+260%) to support the digital economy.

The data also reveals an early signal that separates an absorbable decline from a brutal collapse: the disruption half-life, or how long an occupation takes to lose half its peak employment. Across the largest technological disruptions of the last few decades, the median half-life is about 10 years. Fast disruptions, like photo processing, take one to five years. Typical disruptions take eight to 13 years. And time is the ultimate shock absorber. When the economy transitions over ten years it feels like progress rather than a fast collapse, because it gives older workers time to retire and younger workers time to prepare.

If we track the most AI-exposed occupations-customer-service reps, IT support, telemarketers-since modern LLMs arrived in 2022, the early data is measured. After three years the current disruption looks closer to "typical" than a fast collapse, even before discounting the effects of offshoring, automation, and post-COVID corrections. This is Amara's Law playing out in real time: we tend to overestimate the effect of technology in the short run and underestimate it in the long run. The dire early warnings have given way to more cautious rhetoric. In 2025, Anthropic's Dario Amodei warned AI could erase half of entry-level white-collar jobs within five years. By 2026, he and OpenAI's Sam Altman are emphasizing productivity, economic growth, and the continued demand for human labor.

However, looking solely at total employment numbers masks a dangerous structural threat. Current evidence does not foretell the end of human labor, but AI is quietly breaking the mechanism by which we create experienced workers.

Software engineering is the canary in the coal mine. By most aggregate measures, employment looks stable; unemployment held at 4.2% in June 2026, and groups like the Yale Budget Lab find no clear AI effect yet on exposed occupations' absolute job totals. But the composition is shifting underneath our feet. Per AP and Oxford Economics, junior developer postings are down roughly 40% in four years. Employment for 22-to-27-year-old computer and math grads has fallen 8% since 2022, even as older grads in the same fields have edged up. This same erosion is surfacing wherever entry-level work once meant routine tasks: paralegals, junior analysts, and first-line support.

The paradox is that these industries keep growing even as their entry-level doors narrow. The BLS still projects software developers and QA analysts to grow 15% through 2034. But that projection relies on a pipeline that turns juniors into senior talent-precisely the pipeline now being choked off.

The reason lies in the nature of the work. Software development is a process of judgement and accountability: deciding what to build, executing it, and owning the result. AI is fluent at the middle layer-the well-specified, routine coding that once served as a junior's apprenticeship. But it remains far weaker at the judgment required on either side. The tasks AI automates are precisely the ones juniors were hired to learn on.

This is not merely an academic concern; it is a capital allocation problem. Misjudge the speed of disruption and you risk premature layoffs followed by a scramble to rehire, or funding the transition years too late, leaving you with a critical talent shortage when the leadership pipeline runs dry.

The challenge of the next decade isn't surviving the end of work. It is training the next generation of experts when the traditional paths to apprenticeship no longer exist. And businesses are beginning to realize this new reality as demand for AI continues to grow. IBM is tripling its entry-level hiring, redesigning those roles around the oversight of AI and systems thinking rather than cutting them. Rebuilding the entry-level on-ramp is now a competitive imperative.

Junior roles are not charity; they are talent capex. If AI creates more work than it destroys, companies will still need people who know how to run it, judge it, and fix it. AI may be the broadest technology yet, but that breadth is its best reason for optimism. A general-purpose technology seeds new work across every sector. The firms that recognize this, protect their entry-level pipelines, and keep training now are the ones who will own the senior labor market later.

Joe Bertolami is the Co-Founder and CTO at Clifton AI, an agentic context engine for investment research. Previously at Snap, Google, and Microsoft, with a couple of startups in between. He holds an M.B.A. from the University of Washington and likes using AI to write code, stories, and music, which he posts at https://www.bertolami.com.

The Centers for Disease Control and Prevention on Friday said COVID-19 activity is "increasing" across the United States although its levels are still considered "very low" overall.

"As of August 26, 2026, we estimate that COVID-19 infections are growing or likely growing in 48 states, declining or likely declining in zero states, and not changing in two states," the CDC said on Friday.

Overall community viral activity levels, or a measurement of the virus in wastewater levels, for COVID-19 is considered by the CDC to be "very low." Emergency department visits were also considered "very low," which is the lowest ranking on the CDC's website, with "very high" being the top.

According to a Friday update on the CDC's website, wastewater data show that COVID-19 activity is "very high" in Texas and "high" in Mississippi.

"Moderate" levels were observed in California, Florida, Hawaii, Louisiana, Nevada, South Carolina, and West Virginia.

All other states were listed as either "very low," "low," or there was limited or no data.

As Jack Phillips reports for The Epoch Times, another map provided by the CDC on Friday shows that COVID-19 levels were either "growing" or "likely growing" in every state where data was available.

Influenza levels are also growing nationwide, according to the CDC. There was no change in levels for RSV, or respiratory syncytial virus, on a week-to-week basis.

"RSV activity is very low in most areas of the country," the CDC said on a webpage broadly dedicated to respiratory viruses in the United States, adding that "seasonal influenza activity is low."

The CDC said that rhinovirus and enterovirus, which are also respiratory viruses, are increasing around the country.

Meanwhile, infections caused by the Mycoplasma pneumoniae bacteria, sometimes called "walking pneumonia," are low in most areas across the United States, and infections caused by the pertussis bacteria, known as "whooping cough," are at lower levels than seen post-pandemic, the CDC said.

The latest figures and estimations published by the CDC come as the Food and Drug Administration approved Moderna, Novavax-Sanofi, and Pfizer-BioNTech's updated COVID-19 vaccines, the companies said on Thursday, after a CDC advisory panel recommended that the shots should target the dominant XFG variant.

Uptake of COVID-19 vaccines has dropped in recent years. Just 17.5 percent of adults and 10 percent of children received a shot in late 2025 and early 2026, according to figures from the CDC.

This month, the Chinese CDC reported more than half a million COVID-19 cases in July, a sharp increase from June's figures. In July, 522,000 cases were reported, up from the 443,000 cases that were reported in the previous month.

Experts who are familiar with local conditions in China told The Epoch Times they suspect there are far more cases of the virus, which is believed to have originated in or around the Chinese city of Wuhan in late 2019 before sparking a worldwide pandemic, than the Chinese regime is reporting publicly.