President Donald J. Trump holds a cabinet meeting in the Cabinet Room Wednesday, May 27, 2026. (Official White House photo by Daniel Torok)

President Donald J. Trump holds a cabinet meeting in the Cabinet Room Wednesday, May 27, 2026. (Official White House 

Transparency and Accountability Gaps in Trump's New AI Executive Order

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AI, Privacy, and the Hidden Architecture of Harm from Inference

AI Am Over It by Nadia Piet & Archival Images of AI + AIxDESIGN / 

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Toward Representative AI Governance

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Members of the European Parliament Arba Kokalari and Michael McNamra pictured during the AI omnibus interinstitutional negotiations. / Photo: EP

Members of the European Parliament and Arba Kokalari and Michael McNamra pictured during the AI omnibus interinstitutional negotiations. / Photo: EP

EU Lawmakers Move to Ban AI Nudifiers, but Enforcement Remains Unclear

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Luke Conroy and Anne Fehres & AI4Media / 

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BRUSSELS, BELGIUM—JUNE 3: EU Commissioner for Tech Sovereignty, Security and Democracy Henna Virkkunen introduces the European Technological Sovereignty Package, featuring the Chips Act 2.0, Cloud and AI Development Act, and related strategies to reduce the EU's dependence on non-European suppliers for critical digital technologies. (Photo by Thierry Monasse/Getty Images)

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How AI’s Labor Supply Chains Fail Workers

Varsha Bansal is a fellow at Tech Policy Press

A Meta sign at Meta Headquarters in Menlo Park, California, in January 2025. Shutterstock

On May 12, Meta employees in at least five US offices posted flyers asking workers to come together and sign a petition against the company’s new Model Capability Initiative (MCI)—which allows Meta to collect its employees’ computer use data to train AI models. Over 1,600 workers 

 the petition. Earlier in May, Google DeepMind workers in the UK 

 in a bid to fight against the company providing AI for military use. In April, over 600 of the laid off Oracle employees 

 asking the company for higher severance and longer healthcare — after they came together to discuss their issues. They claim that Oracle was using them to train AI systems, 

Since 2025, close to 400,000 tech workers 

, with several explicitly fired due their company’s increased focus on artificial intelligence. At the same time, many of these companies have reported 

. This paradox has shattered a long-held assumption: that white-collar tech workers don’t need unions.

There’s a tech worker movement brewing across companies and continents. As tech companies embrace AI — forcing workers to use it, using it to surveil them, or justify firing them because of it — more workers are coming together to organize and fight back as they feel they are losing influence over decisions that affect their jobs. This collective action is their way to get back the voice they used to have at their workplace, more than half a dozen tech workers told Tech Policy Press.

“Most people coming in right now are interested in unionizing,” said Kaitlin Cort, founder of a new organizing group for tech workers impacted by AI called What We Will. “That was not the case before.”

Meta, Alphabet and Oracle did not respond to a request for comment by Tech Policy Press.

The past few months have been demoralizing for Meta workers. First, they were told their computer usage including mouse movements and keystrokes 

 just before the company let go of around 10% of its workforce in May. The morale is so low, that some of the workers drafted to an AI team, which they say involves data labelling work for Meta’s AI model, exchanged their frustration in internal chat conversations with their colleagues that they’d rather be laid off. “Getting drafted, can I switch with someone getting laid off instead?” posted a Meta worker on this internal chat, viewed by Tech Policy Press.

It was around this sentiment that one Meta worker in London realized that they had two options: they could either quit or do something about this situation. They chose the latter.

Even until recently, most of their colleagues didn’t feel the need to organize. “They just felt like high pay, chance of layoff, fair deal, I'll take it,” said this worker, who requested anonymity due to fear of professional reprisal. “But when it’s high pay, chance of layoff, and all the other stuff, invasive monitoring, training these various questionable models, and being treated with absolute disrespect and no dignity — now people are ready.”

This worker decided to band together with others to organize. They changed their name on internal online platforms to “Union Rep” and started discussing with colleagues about the options they had to fight back, and that they only need 500 people to get signed up and get recognized as a union. “We have a path,” this worker said. “Let's drive towards it.”

They reached out to all the unions possible, and teamed up with United Tech and Allied Workers (UTAW), a branch of the Communications Workers Union. “We're a few weeks in, we’ve got a good number of sign ups,” said this Meta worker. “It’s looking positive. Our next stage really is trying to just go from hundreds to thousands. My goal is to build large support that can actually change what the company is doing.”

Meanwhile, Meta workers’ pushback against MCI seemed to have had some impact: earlier this month, Meta said they will allow employees to 

pause data collection for up to 30 minutes at a time

The different waves of tech worker organizing

There have been a few waves of tech workers organizing in the past, says Emily Mazo, a tech labor collective action researcher at Columbia University, with two distinct periods in recent years. One took place from roughly 2014 to 2019, when workers mostly organized around external issues. Their concerns ranged from immigration policy and government contracts, to race and gender discrimination. That wave, Mazo explains, led into a second wave of organizing from 2019 to 2022 which took on a more labor-oriented form. “Tech workers who had engaged in workplace protest on social issues had largely either experienced retaliation or been ignored,” said Mazo. “And this led to an understanding that tech workers and their bosses were not aligned, and did not have the same goals.” 

Mazo explains that armed with this new understanding, tech workers began taking actions aimed at improving their own working conditions or attempts at exerting more control over their workplaces, such as salary transparency projects, open letters demanding reinstatement for fired workers, or organizing unions. The first recognized majority union of software engineers in the US 

. Today, the largest tech worker unions in the US are the New York Times Tech Guild 

, and more recently, the tech workers of the University of California system, 

, who joined an existing university employees union.

This current wave of tech organizing is qualitatively different from the first two waves, according to Mazo. Even though there are similarities—with Google DeepMind unionization push being an effort to gain more control over the AI technology and Meta action being made up of technical workers standing together to protest changes to their working conditions, the current context of the tech labor market has changed significantly. “Because of the rolling layoffs since late 2022, and the permission structure for laying off workers created by AI companies promising productivity gains and automation of programmers, tech workers feel much more precarious now than they did five or ten years ago,” explains Mazo.

At Google, it’s this very precarity that has fueled workers to come together through the Alphabet Workers Union, which was formed in 2021.

According to Dan Freedman, a Google software engineer of 14 years and an AWU member, the company has changed dramatically: from being open to employee suggestions and questions, to now having an AI mandate that has dialed up the pressure immensely. “It just feels like it’s a vice from both sides of do more work and use this thing that doesn't work to do that work,” said Freedman about the pressure at work to use AI. What makes it worse for him is that their engineering culture that would pride in careful, deliberate work has now been replaced with this need for speed. “The guidance from leadership is: that doesn't matter anymore, ship it now.”

All this has led to feeling anxious about losing their job – even though it’s a job that doesn’t feel meaningful anymore. What makes this situation worse, Freedman explains, is the lack of a financial cushion because previously these workers had several job alternatives, but that’s not the same anymore. “Everything feels so much more fragile,” he said.

This kind of pressure at work has led to conversations around organizing double in the last year, said Freedman. The union has been focused on a 

, asking the company to offer voluntary exit packages before forcing workers to leave. He said they are also asking the company to end performance rating quotas and protections for workers on visas. “Once we feel like we have a solid understanding with the company of how our jobs are going to be handled,” he said, “then we can start making more powerful demands.”

In April, when Oracle’s laid off workers started looking at ways to organize, they were flooded with challenges due to a lack of proper infrastructure and playbook. Through What We Will’s Cort, these workers from different countries formed a Slack channel that grew to having hundreds of members. They collectively drafted a letter to Oracle asking for better severance, which, they said, was lower than the industry standard. But, the company 

 saying it would only accept individual letters. They sent those too, but nothing happened.

“You don't know who you're talking to that can actually make a decision,” said one organizer, who was laid off from Oracle, requesting anonymity. This worker explained that Oracle's internal system routes their requests through a ticketing portal and these workers would receive a reply from an anonymous HR representative with no explanation. “That's it,” he said.

Cort, who has been engaging with workers from different companies, including Oracle, says there is no established model for this kind of organizing. Each company presents a different set of issues and conditions. While Oracle workers are demanding better severance, Meta workers are fighting surveillance, and DeepMind workers are against AI military contracts. “Every company is kind of different,” Cort said, “and people are at very different stages.”

She talks to several workers each day. Some come to her with a single idea and no colleagues yet convinced; others have already quietly spoken to ten or more coworkers and are ready to move. “The materials that are out are often not specific to the tech worker context,” said Cort. “So we’re building materials that really help align with the broader movement in tech worker organizing.”

Mazo agrees. She explained that the private sector union membership in the tech industry hovers just under 6%. While self-organization is part of a healthy democracy, Mazo believes that organizing some policies can be implemented to improve the working conditions of these workers and create the guardrails against AI harms that they are protesting.

“Policies protecting workers from replacement from automation and policies preventing military use of AI would answer these workers’ demands,” she said, referring to Google DeepMind and Meta. “But we could go a step further, and implement policies requiring board seats filled by elected worker representatives, or other worker decision-making bodies inside of tech companies: these would also provide these workers the ability to make the changes they are demanding.”

Cort is hopeful about the future. “There’s just so many more people from various companies that are starting the process,” she said. “And we're hoping, a year from now, we'll have a very different looking industry.”

, "Promoting Advanced Artificial Intelligence Innovation and Security." On its face, the order is a cybersecurity measure: it directs federal agencies to harden their systems against AI-enabled threats, evaluate advanced cyber capabilities, and create a clearinghouse for sharing vulnerability information. And, it asks the United States Department of Justice to go after criminals who use AI for cybercrime. Few people would object to any of that in isolation. Cyber defense for national security systems, federal civilian systems, and critical infrastructure is important.

However, the EO should attract more critical attention due to its substantive misdirection and 

 of approach, and because of the primary agency it authorizes—the National Security Agency, an intelligence agency of the Department of Defense.

We should ask who gets to evaluate the most powerful AI systems in the country, on what terms, who gets to find out what those evaluations conclude, and how the opaqueness of the system undermines public trust in the US and abroad.

A classified yardstick for "covered frontier models"

The EO proposes a framework for assessing AI models judged to have advanced cyber capabilities, such as the ability to discover or exploit software vulnerabilities at scale, before the model’s release. The Treasury Department, the NSA (acting through the Secretary of Defense), and the Cybersecurity and Infrastructure Administration (CISA) are delegated to build a benchmarking process that determines the threshold at which a model earns "covered frontier model" designation. However, that benchmarking process will then be classified, and the NSA Director will make the final call on whether a given model crosses the line. The government will design the voluntary framework with input from industry players.

Classifying the benchmark prevents adversaries from reverse-engineering which exploits the government can and can't detect. But in practice, the EO creates a situation where companies that participate will presumably learn how their own models score, while the public will be kept in the dark. Possibly, even the developers of these models who do not carry security clearance may not know. Even many of the cybersecurity professionals working in major American companies providing critical infrastructure services will not know.

In effect, an intelligence agency is being positioned not just to assess frontier AI systems for cyber risk, but to help decide which outside organizations get privileged early access to the most capable models in the world.

In practice, the EO disregards transparency, due process and democratic accountability. Layered on top of this opaque process is a voluntary arrangement. There is no requirement for unclassified summary reporting to Congress, no role for the Government Accountability Office, no ability to acquire information through freedom of information requests, either of the benchmarking criteria or the results. Smaller AI companies, academic researchers, journalists, and civil society organizations who study AI risk will have no way to know what the threshold is, how it was set, or how consistently it is applied.

, “[g]oing forward, the gravest questions about AI’s military, cyber, and social power will be answered through a classified review and private collaboration.” Such disregard for transparency and due process also opens the door for the Trump administration to take interventionist steps toward companies.

Compare this to other domains where the government evaluates products for public safety (drug approvals, vehicle safety, environmental permitting), all of which involve some combination of published criteria, notice-and-comment processes, and records that can eventually be reviewed by courts, journalists, or independent experts. The EO places the most consequential judgments about powerful AI systems almost entirely inside the classified world of an intelligence agency whose core mission and culture are built around secrecy. Secrecy leads to decisions which can undermine the interests of the public. The EO does not simply create a democratic accountability problem; it also creates practical oversight problems. Blackbox decisions make it difficult to contest agency decisions.

If anyone thinks the EO’s transparency logic will not repeat itself, only days after the EO, 

 of the administration asking the Center for AI Standards and Innovation (CAISI) to “stop issuing public reports

 of its model assessments should be clear evidence. As I 

 elsewhere, the National Institute of Standards and Technology (NIST) and CAISI are civilian entities, with public-facing work, and with missions to advance “measurement science, standards, and technology” and “foster technological advancements that benefit society.” Yet, their work is now being censored.

"Unlawful access" versus the broader data problem

The EO asks prosecutors to treat AI-enabled hacking as a priority within existing laws. The order does address data, yet it says nothing about the mass scraping of personal information used to train frontier models, the profiling and inference capabilities those models enable, or the privacy implications for government access (for example the NSA). The government gains early, privileged access to systems trained on enormous quantities of data about ordinary citizens. Most concerning is that the EO focuses only on cyber risks, while leaving out any mention of privacy, civil liberties, or public safety. By framing "AI and data" almost entirely as a cybercrime issue, the order sidesteps the implications for surveillance, data brokerage, and behavioral profiling by both government and corporations. The government 

 align policy with civil liberties and democratic values.

Why this matters for trust at home and abroad

The EO pre-release model review framework is voluntary, and explicitly disclaims any “mandatory licensing, preclearance, or permitting requirement for the development, publication, release, or distribution of new AI models.” Voluntariness has obvious appeal in a political environment focused on avoiding regulation. But it also means the entire system depends on companies choosing to participate or move in tandem with administration’s goals. Through the powers bestowed by this EO, the administration can make winners and losers. Meanwhile, Defense Secretary Pete Hegseth 

 on social media that his department “kicked @AnthropicAI out of our building—forever.”

CAISI already has voluntary agreements with AI labs, runs pre-deployment evaluations and collaborates with US allies. The correct direction from the government should have been an EO establishing a robust and public evaluation standard for AI systems that would strengthen trust, enhance legal certainty and make American AI stack trustworthy. Instead, the current approach strengthens the position of intelligence agencies and a handful of powerful companies while leaving everyone else in the dark. What is at stake, right now, is replacing the public interest with the interests of the few, and dismantling existing public reporting infrastructures.

The strategy is also inconsistent with US efforts to promote US-made AI solutions. Secret and ad-hoc decisions create ambiguity and legal uncertainty. Very few foreign businesses will be comfortable depending heavily on AI models which can be unplugged overnight. Similarly, very few foreign governments will trust the NSA to certify AI systems for their use. In effect, the EO and future decisions dependent on it are more likely to alienate US allies and encourage countries to diversify their AI model options.

Kris Rose is a Georgetown McCourt School of Public Policy Tech & Policy Visiting Fellow. He is an AI transformation leader at IBM and previously held governance and policy roles at other technology companies and the US government. The views expressed here are his own and do not reflect those of his employer.

. It’s hard to miss Americans’ increasingly palpable anxiety about AI, even as it becomes more and more a part of daily life.

Why is this the case? I think the paradox is clear: we are building the future, but we are doing it without the people we are building it for.

The reality is that there are two crises of confidence surrounding artificial intelligence. The first, as noted above, is the profound public anxiety about the technology itself, which has 

 since ChatGPT's release in 2022. Hardly a day passes without a new poll pointing to the public’s concern about AI’s impact on things like job displacement, mental health, misinformation, and environmental impact. 

The second crisis is one of legitimacy facing the institutions that are responsible for developing and governing the technology. Americans' trust and confidence in 

 think it’s urgent for the government to pursue AI regulation, there is little consensus on how and who can be trusted to do so. One survey found that

 Americans trust independent experts over the federal government and industry

 to address AI guardrails – respondents identifying as Democratic or Independent were significantly 

less trusting of the US federal government’s ability to do so

, likely because of Republican majorities in DC. Taken together, Americans urgently want action, but there is little trust that our existing institutions are able or willing to manage AI effectively.

So, how are we to navigate the inevitable rise of a technology that people do not trust, managed by institutions in which they have lost faith?

We face a choice. We can repeat the mistakes of the social media era: political paralysis, rapid deployment, unanticipated consequences, public backlash. This path led to a deeper legitimacy crisis, constituents feeling ignored, the government looking incompetent, and companies operating with reduced social license.

It doesn’t have to be this way. Public concern is not an obstacle; it is the key to building a new form of representative AI governance. Instead of dismissing public anxiety, we can channel it to shape AI development, which will align it with society’s expectations, ease anxiety, and boost adoption. We can shift from a model where technology is imposed upon a skeptical public to one where it is co-created, better representing the people it is intended to serve.

More provocatively, AI itself can be a tool for this governance shift. AI-enabled experiments showcase how the technology can make public engagement more representative, sophisticated, and nuanced, giving way to new consultative methods capable of sensing, aggregating, and acting upon public opinion at a scale previously impossible.

By turning to the public, representative AI governance offers an off-ramp from the increasingly sour public discourse about AI and helps shape a more productive policy path. And, done right, it offers a broader blueprint for how governments and companies can rebuild legitimacy that has been lost.

Polling is clear that most people want to see policy action on AI, but few agree on what to prioritize first and who should be responsible for what. It’s easy to see how the sheer scale and complexity of issues surrounding AI—from data privacy, cybersecurity, existential risk, social harms, and economic disruption, just to name a few—combined with industry special interests and a lack of AI expertise in government, can quickly result in policy paralysis. Policymakers often seem locked in a game of cat and mouse with technology, as evidenced by Colorado’s 

Governments and companies need not do this alone. They should turn to the public—the very people affected by the technology yet often excluded from shaping it— to help guide the way forward. The public’s concerns about AI can offer a roadmap of sorts: helping policymakers spot, prioritize, and frame issues of societal consequence, as well as help inform solutions.

When ChatGPT burst into the public scene in early 2023, there were a few scattershot efforts to build public input into policy discussions. Yet, many of these pioneering initiatives were seen as “nice to have” and ultimately easily siloed away from core governance. What has changed since these early experiments is that public anxiety has reached a boiling point, amounting to what the 

. This concern is not just a public relations issue — it is a commercial risk for companies, a political liability for governments, and a threat to adoption.

Now, a growing set of efforts is already underway that offer a path for more systematic representative AI governance. From citizen assemblies like the 

 Collective Intelligence Project’s Alignment Assemblies

, different forms of public engagement around AI are emerging, all geared toward constructively channeling public input into policy. When paired with the growing ecosystem of AI tools–like 

, and a whole host of other platforms— they can make it easier for people to participate, bridge across even polarized community groups to find areas of consensus, and offer more insights than traditional town halls or focus groups.

This isn’t just about “feel-good” public engagement. Evidence shows that institutionalizing public input yields superior policy outcomes while systematically rebuilding fractured public confidence. For example, Taiwan’s former Digital Minister Audrey Tang found that their 

, which combined the launch of new digital tools with shifts in values and communication, 

boosted citizens’ trust from a low of 9% in 2014 to 70% in 2020

 that indicate that deliberative mechanisms like citizens’ assemblies have spillover effects that include improved trust in governing institutions. By turning to the public, policymakers can shape better policy and rebuild trust with their constituents.

This model need not be limited to government. During my time at Meta, we partnered with 

 to build one of the most ambitious public engagement programs by any private company – these deliberative engagements have expanded into 

 effort to ask people around the world how AI companies should design AI technologies. Anthropic has also engaged the public on projects like its 

Representative AI Governance is not a replacement for expertise, but instead channels experts to focus on the issues most critical to the public. In this governance arrangement, the public defines the values and the priorities and experts provide the technical implementation. For example, public input can help inform and scope legislation, but experts would then take that signal and implement it in a way that is technically feasible. By empowering the public to shape the policy agenda, we liberate experts to solve technical problems and manage issues beyond public view (e.g., national security) without constantly fighting political fires. In other words, representative governance does not undermine the role of experts but rather complements it by identifying and prioritizing issues of public concern.

Done right, over time, representative AI Governance could help lay the groundwork for a new and more holistic technology governance ecosystem. Over the past 6 years, Meta’s Oversight Board has served as a vital proof of concept in novel technology governance, demonstrating that independent, expert-led bodies can successfully adjudicate complex issues where companies struggle to self-regulate. Now, public consultation could help inform a new set of institutions, bridging the jurisdictional gap that currently separates government action from corporate implementation. One such idea could be the “

,” which could move from public input toward public oversight.

A former colleague once posed a question that has stayed with me: “What if AI happened with people, not to them?” I think this should be our guiding principle for the AI era.

Imagine a world in which our seemingly endless strife about the costs and benefits of technologies wasn’t about people imposing the technology on users. Instead, the public recognizes the very real tradeoffs associated with a technology’s development—and perhaps even embraces them—because they have participated in shaping the design.

Representative AI governance offers governments, companies, and society two benefits. First, it transforms AI governance itself, enabling us to move beyond policy paralysis and technocratic frameworks to input that brings AI alignment closer to the needs and values of real people. Second, it offers a path for broader democratic renewal by confronting what scholar Marjan Ehsassi has 

 "voice insecurity" –- the pervasive sense among citizens that their perspectives are no longer heard in policymaking. Institutionalizing deliberative and consultative processes would foster long-term public trust by involving citizens in shaping the technology that impacts their lives.

This vision is within reach and the tools already exist. From deliberative convenings to AI-enabled consensus-building platforms, the infrastructure for representative governance is ready for systematic use. The only missing element is the political will to trust the people. We must act now to ensure that the future of technology is shaped by the society it serves.

By renewing public input across government and industry, we don’t just make AI better – we transform one of the thorniest policy issues into a framework for the future of governance. This is the promise of representative AI governance: an approach that both empowers the public to be an architect in one of the world’s most transformative technologies and guides a broader democratic renewal.

 of student essays produced in collaboration with the Berkman Klein Center for Internet & Society at Harvard University.

AI Am Over It by Nadia Piet & Archival Images of AI + AIxDESIGN / Better Images of AI / CC by 4.0

In the beginning, the Bible says God created man in His own image. “And the LORD God formed man of the dust of the ground,” Genesis tells us, “and breathed into his nostrils the breath of life; and man became a living soul.” In 1818, the English novelist Mary Shelley wrote a new creation myth. “Accursed creator! Why did you form a monster so hideous that even you turned from me in disgust? God, in pity, made man beautiful and alluring, after his own image,” Shelley wrote, “but my form is a filthy type of yours, more horrid even from the very resemblance.”

 to the public. It purportedly “passed” the bar exam in the 

 higher on the SAT than most of the students. Now, AI writes code, argues philosophy, and generates publishable prose. In some fields, frontier models answer boundary-breaking questions in 

, not years. The gap between human and machine capability, which took centuries to close in physical labor, closed substantially in some forms of cognitive work within the span of a few years.

With generative AI, humanity has flipped the creation myth on its head. We've built something that, on certain kinds of problems, exceeds what individual humans can do not through size or strength, but through scale and pattern recognition.

While God obviously made man lesser than Himself, Frankenstein’s monster actually surpassed him in strength and prowess. While intelligent, however, the monster was never able to match Frankenstein’s scientific prowess; when it came to attaining his ultimate goal of companionship, the monster relied on Frankenstein to build his bride. In our new reality of creation, we appear to want to do the reverse. Instead of creating a more physically powerful being, humanity seeks to develop an intelligence that exceeds its own.

What that inversion may cost us is what this essay is about. Rather than ruining us with brutality, AI threatens humanity with temptation, offering an irrefusable fix-all that condemns human effort to obsolescence and denatures the bonds that spur learning and development.

Learning is often driven by inherently pure motives: curiosity, love of craft, or desire for meaning. But people also set out to learn in order to reap future earnings and increase their material utility. No matter the goal, learning is fundamentally an act of faith in the future. To spend three years mastering tax law or organic chemistry or the novels of Henry James is to make a wager: that the future will arrive, that it will resemble the present enough to reward you for acquiring knowledge, that the slow accumulation of competence will eventually be redeemed. Whether preparing for case interviews at McKinsey or bolstering their résumé for graduate school applications, many students today are at least partially motivated by the rewards that their learning might promise.

AI redefines the odds of that wager. It diminishes the economic premium associated with knowledge and skill by removing scarcity from the equation, dismantling the motivation architecture for learning. Beyond the economic consequences, however, AI erodes something harder to quantify: the identity that mastery once conferred. Long hours in the library or the time spent in an apprenticeship formed the foundation of cognitive identities that people would leverage, building their skillset by solving challenging problems and dedicating time and energy to learning. They used to define themselves by their passions, spending years reading, practicing and mastering their fields in order to answer difficult questions. When AI can produce competent analysis in seconds, learning begins to feel unnecessary.

Recent research has begun to describe this shift as “

”: the tendency to adopt AI-generated outputs without a second glance, bypassing the friction of reasoning in favor of outsourcing the work of deliberation. The concern is not that AI makes people less intelligent overnight, but that constant reliance on artificial cognition changes our relationship to effort itself. The student who once wrestled with a difficult text now asks Claude for a summary. The programmer who once debugged line by line now supervises generated code they barely understand. Over time, the muscle of sustained thought weakens through disuse.

This intellectual reality is already taking shape. In classrooms, students 

 question whether their work will benefit their futures. Every time that a philosophy student prompts Claude to write a term paper overnight or a computer science student asks Gemini to solve an entire problem set, the sense of futility spreads. I’ve witnessed classmates complete class projects in minutes with a few prompts, removing any struggle or challenge in schoolwork. Groupwork, which used to revolve around intellectual sparring and genuine debate, now boils down to asking ChatGPT for the best path forward. When I write code using AI tools like Gemini or Claude, the sheer volume and speed at which they program leaves me feeling empty and mindless, completely outclassed by these overwhelming models. My skills and insight have been taken out of the equation, making me no more than a supervisor to agents that have less and less need for me. The agency and pride that came from building up projects from scratch no longer exists.

This deterioration of intellectual faith does not occur in a vacuum. It lands on a generation that was already losing faith in the future for reasons that have nothing to do with AI. As the income required to afford a median-priced American home has jumped 

 since 2019, only about one in five Gen Z adults believe the American Dream is “very much alive.” A 

 study comparing 22-to-24-year-olds today with millennials at the same age found that Gen Z is earning less, carrying more debt, and experiencing higher delinquency rates, with 14% of Gen Zers describing themselves as “extremely stressed out” about money, compared to 8% of millennials at their age. Most critically, 

 American Opportunity Survey found that unlike previous generations, Gen Z workers are less likely to expect their period of financial insecurity to ever end, and harbor high levels of doubt about their eventual ability to buy homes or retire at all.

This is the context in which a striking behavioral shift is occurring. Rather than doubling down on conventional wealth-building, a significant portion of young people are making a different calculation entirely. According to Northwestern Mutual’s 2026 

, almost a third of Gen Z are risking money on sports betting, prediction markets, and crypto; of those, eight in ten believe these assets offer a faster path to their goals than traditional methods.

The economic theory underlying this behavior is straightforward, even if rarely stated plainly. People discount future rewards relative to present ones. The size of that discount reflects how much one trusts the future—the less they trust the future, the more they favor immediate payoff. A generation that grew up through a global pandemic, a student debt crisis, and a climate emergency has rational cause to apply a heavy discount to any future payoff that requires decades of patient accumulation to realize. The expected value of a small chance at fast wealth looks more attractive not because young people have become reckless, but because they have assessed their odds under the conventional path and found them wanting.

AI does not cause this dynamic, but it accelerates it, and adds a dimension the economic data cannot capture. The gambler still believes in the possibility of winning, betting on some outcome they can still imagine reaching. What AI introduces is something more total: the suspicion that self-investment is inherently a losing proposition. Why spend three years becoming a competent writer, coder, or analyst when the tool that outperforms you is already free, in your pocket, and improving faster than any human can match? In previous eras of technological disruption, competence still mattered because there were other avenues in which humans could pivot to and still contribute to society. When powerful agents are capable of acting faster and more precisely than humans in just about every field imaginable, however, it’s hard to justify developing skills and abilities that will rapidly fall into disuse.

 ends with the scientist’s destruction, as his body finally gives out on his quest to destroy his creation. In Shelley’s book, Victor finds reprieve from his inhumane creation in the natural world, seeking solace in the grandness of God’s creation that mitigates his guilt from his demonic offspring. I have found myself needing something similar: not faith exactly, but the suspicion that the chain of creation does not simply terminate with us handing dominion to our machines. “Wherefore I perceive that there is nothing better, than that a man should rejoice in his own works; for that is his portion” (Ecclesiastes 3:22). Perhaps what remains uniquely human is not intelligence, but the choice to find meaning beyond the material world in effort and work. ChatGPT can write this essay, but it cannot derive satisfaction from having written it. What Ecclesiastes terms as the portion, or the private, irreducible fact of having made something with the full weight of your attention and your struggle, remains, for now, ours alone.

Abhijeet Dipke, center, founder of the Cockroach Janta Party addresses supporters during a protest demonstration in New Delhi, India, Saturday, June 6, 2026. (AP Photo/Manish Swarup)

After Test Data Scandal, India’s Youth Stake Their Claim on the Digital Square

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