Apple has released security updates to fix a zero-day vulnerability that was exploited in an “extremely sophisticated attack” targeting specific individuals.

Tracked as CVE-2026-20700, the flaw is an arbitrary code execution vulnerability in dyld, the Dynamic Link Editor used by Apple operating systems, including iOS, iPadOS, macOS, tvOS, watchOS, and visionOS.

Apple’s security bulletin warns that an attacker with memory write capability may be able to execute arbitrary code on affected devices.

Apple says it is aware of reports that the flaw, along with the CVE-2025-14174 and CVE-2025-43529 flaws fixed in December, were exploited in the same incidents.

“An attacker with memory write capability may be able to execute arbitrary code,” reads Apple’s security bulletin.

“Apple is aware of a report that this issue may have been exploited in an extremely sophisticated attack against specific targeted individuals on versions of iOS before iOS 26. CVE-2025-14174 and CVE-2025-43529 were also issued in response to this report.”

Apple says Google’s Threat Analysis Group discovered CVE-2026-20700. The company did not provide any further details about how the vulnerability was exploited.

Affected devices include:

• iPhone 11 and later
• iPad Pro 12.9-inch (3rd generation and later)
• iPad Pro 11-inch (1st generation and later)
• iPad Air (3rd generation and later)
• iPad (8th generation and later)
• iPad mini (5th generation and later)
• Mac devices running macOS Tahoe

Apple fixed the vulnerability in iOS 18.7.5, iPadOS 18.7.5, macOS Tahoe 26.3, tvOS 26.3, watchOS 26.3, and visionOS 26.3.

While Apple says the flaw was exploited in targeted attacks, users are advised to install the latest updates to protect their devices.

This is the first Apple zero-day fixed in 2026, with the company fixing seven in 2025.

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If you’ve ever considered practicing meditation, you might believe you should relax, breathe, and empty your mind of distracting thoughts. Novices tend to think of meditation as the brain at rest, but a new international study concludes that this ancient practice is quite the opposite: Meditation is a state of heightened cerebral activity that profoundly alters brain dynamics.

Researchers from the University of Montreal and Italy’s National Research Council recruited 12 monks of the Thai Forest Tradition at Santacittārāma, a Buddhist monastery outside Rome. In a laboratory in Chieti-Pescara, scientists analyzed the brain activity of these meditation practitioners using magnetoencephalography (MEG), technology capable of recording with great precision the brain’s electrical signals.

The study focused on two classical forms of meditation: Samatha, a technique that focuses on sustained attention to a specific objective, often steady breathing, with the aim of stabilizing the mind and reaching a deep state of calm and concentration, and Vipassana, which is based on equanimous observation of sensations, thoughts, and emotions as they arise in order to develop mental clarity and a deeper understanding of the experience.

“With Samatha, you narrow your field of attention, somewhat like narrowing the beam of a flashlight; with Vipassana, on the contrary, you widen the beam,” explains Karim Jerbi, professor of psychology at the University of Montreal and one of the study’s coauthors. “Both practices actively engage attentional mechanisms. While Vipassana is more challenging for beginners, in mindfulness programs the two techniques are often practiced in alternation.”

The researchers recorded multiple indicators of brain dynamics, including neural oscillations, measures of signal complexity, and parameters related to so-called “criticality,” a concept borrowed from statistical physics that has been applied to neuroscience for 20 years. Criticality describes systems that operate efficiently on the border between order and chaos, and in neuroscience, it is considered a state optimal for processing information in a healthy brain.

“A brain that lacks flexibility adapts poorly, while too much chaos can lead to malfunction, as in epilepsy,” Jerbi explained in a press release. “At the critical point, neural networks are stable enough to transmit information reliably, yet flexible enough to adapt quickly to new situations. This balance optimizes the brain’s processing, learning, and response capacity.”

During the experiment, the monks’ brain activity was recorded by a high-resolution MEG system as they alternated from one type of meditation to the other with brief periods of rest in between. The data were then processed with advanced signal analysis and machine learning tools to extract different indicators of neural complexity and dynamics.

Striking a Balance

Results published in the journal Neuroscience of Consciousness show both forms of meditation increase the complexity of brain signals compared to a brain at rest. This finding suggests the brain in meditation does not simply calm down but rather enters a dynamic state rich with information. At the same time, the researchers observed widespread reductions in certain parameters linked to the global organization of neural activity.

One of the most striking findings in the analysis of the criticality deviation coefficient showed a clear distinction between Samatha and Vipassana. This indicates that, although both practices increase brain complexity, they do so through different dynamic configurations, consistent with their subjective experiences. In other words, Vipassana brings the practitioner closer to the balance of stability and flexibility, while Samatha produces a somewhat more stable and focused state. According to researchers, the closer the brain gets to this critical state of balance, the more responsively and efficiently it functions. This is reflected, for example, in a greater capacity to switch tasks or to store information.

from the historical-analogies dept

Artificial intelligence promises to change not just how Americans work, but how societies decide which kinds of work are worthwhile in the first place. When technological change outpaces social judgment, a major capacity of a sophisticated society comes under pressure: the ability to sustain forms of work whose value is not obvious in advance and cannot be justified by necessity alone.

As AI systems diffuse rapidly across the economy, questions about how societies legitimate such work, and how these activities can serve as a supplement to market-based job creation, have taken on a policy relevance that deserves serious attention.

From Prayer to Platforms

That capacity for legitimating work has historically depended in part on how societies deploy economic surplus: the share of resources that can be devoted to activities not strictly required for material survival. In late medieval England, for example, many in the orbit of the church made at least part of their living performing spiritual labor such as saying prayers for the dead and requesting intercessions for patrons. In a society where salvation was a widely shared concern, such activities were broadly accepted as legitimate ways to make a living.

William Langland was one such prayer-sayer. He is known to history only because, unlike nearly all others who did similar work, he left behind a long allegorical religious poem, Piers Plowman, which he composed and repeatedly revised alongside the devotional labor that sustained him. It emerged from the same moral and institutional world in which paid prayer could legitimately absorb time, effort, and resources.

In 21st-century America, Jenny Nicholson earns a sizeable income sitting alone in front of a camera, producing long-form video essays on theme parks, films, and internet subcultures. Yet her audience supports it willingly and few doubt that it creates value of a kind. Where Langland’s livelihood depended on shared theological and moral authority emanating from a Church that was the dominant institution of its day, Nicholson’s depends on a different but equally real form of judgment expressed by individual market participants. And she is just one example of a broader class of creators—streamers, influencers, and professional gamers—whose work would have been unintelligible as a profession until recently.

What links Langland and Nicholson is not the substance of their work or any claim of moral equivalence, but the shared social judgment that certain activities are legitimate uses of economic surplus. Such judgments do more than reflect cultural taste. Historically, they have also shaped how societies adjust to technological change, by determining which forms of work can plausibly claim support when productivity rises faster than what is considered a “necessity” by society.

How Change Gets Absorbed

Technological change has long been understood to generate economic adjustment through familiar mechanisms: by creating new tasks within firms, expanding demand for improved goods and services, and recombining labor in complementary ways. Often, these mechanisms alone can explain how economies create new jobs when technology renders others obsolete. Their operation is well documented, and policies that reduce frictions in these processes—encouraging retraining or easing the entry of innovative firms—remain important in any period of change.

That said, there is no general law guaranteeing that new technologies will create more jobs than they destroy through these mechanisms alone. Alongside labor-market adjustment, societies have also adapted by legitimating new forms of value—activities like those undertaken by Langland and Nicholson—that came to be supported as worthwhile uses of the surplus generated by rising productivity.

This process has typically been examined not as a mechanism of economic adjustment, but through a critical or moralizing lens. From Thorstein Veblen’s account of conspicuous consumption, which treats surplus-supported activity primarily as a vehicle for status competition, to Max Weber’s analysis of how moral and religious worldviews legitimate economic behavior, scholars have often emphasized the symbolic and ideological dimensions of non-essential work. Herbert Marcuse pushed this line of thinking further, arguing that capitalist societies manufacture “false needs” to absorb surplus and assure the continuation of power imbalances. These perspectives offer real insight: uses of surplus are not morally neutral, and new forms of value can be entangled with power, hierarchy, and exclusion.

What they often exclude, however, is the way legitimation of new forms of value can also function to allow societies to absorb technological change without requiring increases in productivity to be translated immediately into conventional employment or consumption. New and expanded ways of using surplus are, in this sense, a critical economic safety valve during periods of rapid change.

Skilled Labor Has Been Here Before

Fears that artificial intelligence is uniquely threatening simply because it reaches into professional or cognitive domains rest on a mistaken historical premise. Episodes of large-scale technological displacement have rarely spared skilled or high-paid forms of labor; often, such work has been among the first affected. The mechanization of craft production in the nineteenth century displaced skilled cobblers, coopers, and blacksmiths, replacing independent artisans with factory systems that required fewer skills, paid lower wages, and offered less autonomy even as new skilled jobs arose elsewhere. These changes were disruptive but they were absorbed largely through falling prices, rising consumption, and new patterns of employment. They did not require societies to reconsider what kinds of activity were worthy uses of surplus: the same things were still produced, just at scale.

Other episodes are more revealing for present purposes. Sometimes, social change has unsettled not just particular occupations but entire regimes through which uses of surplus become legitimate. In medieval Europe, the Church was the one of the largest economic institutions just about everywhere, clerical and quasi-clerical roles like Langland’s offered recognized paths to education, security, status, and even wealth. When those shared beliefs fractured, the Church’s economic role contracted sharply—not because productivity gains ceased but because its claim on so large a share of surplus lost legitimacy.

To date, artificial intelligence has not produced large-scale job displacement, and the limited disruptions that have occurred have largely been absorbed through familiar adjustment mechanisms. But if AI systems begin to substitute for work whose value is justified less by necessity than by judgment or cultural recognition, the more relevant historical analogue may be less the mechanization of craft than the narrowing or collapse of earlier surplus regimes. The central question such technologies raise is not whether skilled labor can be displaced or whether large-scale displacement is possible—both have occurred repeatedly in the historical record—but how quickly societies can renegotiate which activities they are prepared to treat as legitimate uses of surplus when change arrives at unusual speed.

Time Compression and its Stakes

In this respect, artificial intelligence does appear unusual. Generative AI tools such as ChatGPT have diffused through society at a pace far faster than most earlier general-purpose technologies. ChatGPT was widely reported to have reached roughly 100 million users within two months of its public release and similar tools have shown comparably rapid uptake.

That compression matters. Much surplus has historically flowed through familiar institutions—universities, churches, museums, and other cultural bodies—that legitimate activities whose value lies in learning, spiritual rewards or meaning rather than immediate output. Yet such institutions are not fixed. Periods of rapid technological change often place them under strain–something evident today for many–exposing disagreements about purpose and authority. Under these conditions, experimentation with new forms of surplus becomes more important, not less. Most proposed new forms of value fail, and attempts to predict which will succeed have a poor historical record—from the South Sea Bubble to more recent efforts to anoint digital assets like NFTs as durable sources of wealth. Experimentation is not a guarantee of success; it is a hedge. Not all claims on surplus are benign, and waste is not harmless. But when technological change moves faster than institutional consensus, the greater danger often lies not in tolerating too many experiments, but in foreclosing them too quickly.

Artificial intelligence does not require discarding all existing theories of change. What sets modern times apart is the speed with which new capabilities become widespread, shortening the interval in which those judgments are formed. In this context, surplus that once supported meaningful, if unconventional, work may instead be captured by grifters, legally barred from legitimacy (by say, outlawing a new art form) or funneled into bubbles. The risk is not waste alone, but the erosion of the cultural and institutional buffers that make adaptation possible.

The challenge for policymakers is not to pre-ordain which new forms of value deserve support but to protect the space in which judgment can evolve. They need to realize that they simply cannot make the world entirely safe, legible and predictable: whether they fear technology overall or simply seek to shape it in the “right” way, they will not be able to predict the future. That means tolerating ambiguity and accepting that many experiments will fail with negative consequences. In this context, broader social barriers that prevent innovation in any field–professional licensing, limits on free expression, overly zealous IP laws, regulatory bars on the entry to small firms–deserve a great deal of scrutiny. Even if the particular barriers in question have nothing to do with AI itself, they may retard the development of surplus sinks necessary to economic adjustment. In a period of compressed adjustment, the capacity to let surplus breathe and value be contested may well determine whether economies bend or break.

Eli Lehrer is the President of the R Street Institute.

Filed Under: ai, business models, jobs, labor

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Claude is Anthropic’s AI assistant, and one of the more popular GenerativeAI models out there. It offers Desktop Extensions – MCP servers packaged and distributed through Anthropic’s extension marketplace, which when installed appear similar to Chrome add-ons.