In an era of increasing social isolation, platforms like Facebook and Twitter are among the most important tools we have to understand each other. However, social media can also lead to polarization and misunderstanding as author Chris Bail discusses in this episode of Democracy Works.
Universal basic income would cost trillions. Wage insurance, a bigger child tax credit, and a tax break for work could help workers left behind by AI at a fraction of the price.
The age of AI will be a revolutionary age. In medicine, engineering, and more, there is the possibility of great benefit to humanity. Consider some Wall Street Journal headlines: “The Quest to Use AI to Help Find New Drugs,” and “Fusion Startup Gets an AI Boost.” Already, AI is a substantial portion of GDP growth.
Yet a rising tide will not lift the boats with holes in their hulls, as Jack Kemp once said. Artificial intelligence is challenging our leaders to think about those possibly left behind. Within the AI community itself, Anthropic CEO Dario Amodei expresses concern that we could see 5-10% GDP growth with 10% unemployment.
We cannot allow this concern about AI’s human costs to go totally unaddressed. “Pull yourself up by only your own bootstraps” is not an appropriate response when there is so much disruption throughout the entire economy—when the owners of capital will thrive as workers and would-be workers struggle. Ideas from both political parties are necessary.
A universal basic income is one possibility being pushed, but the cost may be prohibitive. A decent-sized UBI of $12,000 a year would cost trillions annually. A more realistic approach is needed.
There are two alternate proposals that have not passed Congress in the past, but they could be brought back up for consideration. First is the expansion of a concept called “wage insurance.” The second is an expanded child tax credit. I will contribute a third idea too.
Wage insurance was part of the Trade Adjustment Act’s safety net. A worker 50 or older who lost a job due to foreign trade qualified if the worker found another job paying less (but no more than $50,000). The benefit was half the difference in pay between the old and new jobs, up to $10,000 over two years.
The Obama administration proposed eliminating the trade-impacted requirement. Anyone over age 50 would qualify for wage insurance so long as they were in their previous job for at least three years. The cost was estimated at $3 billion a year. Unlike a universal basic income, Congress could find the savings to offset this spending.
Wage insurance directly addresses the immediate costs of an AI economy. The worker whose career is disrupted by what was only, until recently, science fiction finds a benefit other than only unemployment insurance. The worker may very well never have the old income level again and is approaching retirement. Help for such a person in the throes of capitalism’s “creative destruction” makes sense. We are addressing those in the spaces between what was and what is becoming.
I mentioned an expanded child tax credit as well. Former Sen. Mitt Romney proposed a kind of refundable and advanceable child tax credit—meaning paid even to those who do not owe taxes, and paid monthly before even filing a tax return—of $4,200 per child under age six and $3,000 for other children. This contrasts with the $2,200 per child, with less refundability, under President Donald Trump’s “one big beautiful bill.”
Sen. Romney proposed paying for the cost partly through tax reforms that largely, but not entirely, shift the tax code’s other credits and deductions in a more progressive direction. The “mostly” is why anti-poverty advocates seemed to give the bill two cheers instead of three. Overall, the proposal would net reduce childhood poverty but would cause some families lifted out of poverty to slip right back in.
Still, the Romney proposal could be a countervailing force against AI’s corrosive effects. Even more so if the bill’s pay-for is tipped in an even more progressive direction. We would have a kind of modest universal basic income for families with children. We would be addressing those of greatest need in the economy.
The third suggestion is this: Provide a tax break for work equivalent to the size of what we give to capital. The lower income tax rate for dividend income costs the federal government a little over $40 billion a year. Putting similarly-sized tax relief into workers’ pockets would produce an average tax savings of about $235 per worker.
An extra $235, or for a two-earner family, $470, is not a universal basic income, but it is affordable. The more progressive could very well connect the revenue that can be raised by eliminating the dividend preference to offsetting the cost of lowering the 10% bottom income tax rate, or some other way to put the money in pockets. A more ideologically conservative approach would seek the curtailment or elimination of tax breaks and government programs.
What is important is recognizing that the owners of capital will be great beneficiaries of the AI age. As a matter of public policy, we should look out for labor too. Patch the boat’s holes and let the tide rise. The human cost of not doing anything needs to be addressed along with the fiscal cost of doing something.
Scott Miller is a graduate of Widener School of Law, a former chief of staff in Congress, and the author of 'Christianity & Your Neighbor's Liberty.
Efficient AI may not mean less energy. The Jevons paradox shows why demand can grow, and why no one can say yet where AI’s real limits will be.
Artificial Intelligence is everywhere and can no longer be avoided. Will there ever be a ceiling for the expansion of this technology? AI companies are not just selling software. The story about the inevitability that AI will have in transforming work, education, medicine, art, government, and daily life is much more profitable. People must accept the transformation or be left behind. They are selling behind a mask of certainty.
The court of public opinion on AI has concluded that this technology is too energy-consuming for the benefits it allegedly brings and the ways many people use it. Therefore, companies have shifted their agendas to presenting efficiency as a solution to the technology’s enormous need for power. Smaller, better models and chips, and more efficient data centers may reduce the energy required for a single text-based request or generated image. If those improvements make AI cheaper to use, it will be invited into more daily life right?
In the late 19th century, a British economist named William Stanley Jevons wrote The Coal Question: An Inquiry Concerning the Progress of the Nation, and the Probable Exhaustion of Our Coal Mines. This was a revolutionary book for that time that explored the limits to growth and sustainability within an industrialized society.
From it came his paradox, the Jevons paradox, which is a description of a somewhat counterintuitive thing that happens in the world. You would think that as technological advancements continue, efficiency eventually would become so great that innovation is no longer needed. Surprisingly, the opposite is true a decent amount of the time.
The paradox claims that as systems become more efficient, the needs to sustain said systems become larger; therefore, a constant striving towards efficiency often does not lead to less consumption, but to more use, more demand, and more pressure on the very resources we thought we were saving.
A test for this concept is something most of us have in our homes and take advantage of. About 200 years ago, hot water was expensive. It was in short supply, but also time. No tanks were in your basement. You had to boil it on a stove, or you would stoke a fire under a container. So, it was only used for things that absolutely required it and not much else. Cooking occasionally, maybe laundry sometimes, and that was about it.
When home water heaters were invented, hot water in homes got dramatically cheaper. And the paradox’s claim started to test itself. People didn't just use the same amount of hot water without having to work as hard for it. There were a bunch of new uses for hot water. They started taking showers, a taken for granted action by us hundreds of years later.
Eventually, somebody invented the home dishwasher, which, as a normal household appliance, is a post-water heater invention. It is an idea that doesn't make sense unless you have affordable hot water in your home. When you make hot water cheaper, then demand for it goes up.
Suddenly people need the tanks to be way bigger. There's a bunch of uses that we didn't anticipate. So far, this just looks like pure Jevons. But here's the thing. Infinite hot water usage never became a problem. There was a ceiling that represented the amount that we needed, and hot water tanks eventually stopped getting bigger. There turned out to be a finite list of things that hot water is uniquely good for. And we found them. The paradox went on for a while, but then it settled down. This is a reminder that this paradox is not guaranteed. That nothing is guaranteed. The future is still technically uncertain; no one who bought the Gutenberg press could imagine newspapers. This is why we shouldn’t let technological advancements command our agency.
After 2030, this decade will undoubtedly be known as the decade of AI technologies. 2022 and beyond have drastically altered our lives in an unforeseeable direction. However, large companies try to convince us that if we pay them enough for giving the data to develop their technology, our quality of life will improve beyond our wildest dreams. Critics argue that the true plan is to improve products and personalize services, while masking a deeper business model built on surveillance and monetization.
And this is what the advertisers don’t want you to realize: AI’s future is not inevitable, limitless, or fully knowable, despite what the companies selling it suggest. AI may have limits too, but nobody can reliably say where they are yet. They may be physical, such as power generation and waste disposal. They may be economic, as companies discover that not every task benefits from an expensive system. They can also be legal/political, as communities affected resist data centers, or they may be cultural, as people simply decide they do not want automated systems mediating parts of their lives.
Will AI have a ceiling? Here’s the no bs answer: nobody knows. That fact should make us as the target base, more cautious, not more passive. When companies claim to know the future by telling you what you need, what they often mean is that they want the power to create it on their own terms. We do not have to accept that bargain. It is our duty as responsible consumers to act on that free will and wake up.
Shon Eric Hernandez is a Fulcrum fellow and MDC contributor. He can be reached at shondesil06@gmail.com
The debate over artificial intelligence has become divided between two powerful ideas: that we need a “moratorium” to slow AI until we can make it safe, and that regulating AI will slow the innovation we need for our economy and national security. Both concerns are legitimate. But “moratorium” and “slowing AI growth” are divisive terms: they reassure those concerned about AI's safety, economic and environmental consequences while alarming those who see AI competition and national security as urgent priorities. We need a different starting point—meaningful regulation that provides the safeguards necessary to protect people, communities and the environment while creating the conditions for AI to continue growing. The experiences of Europe and China challenge the assumption that regulation must slow AI.
China has combined increasingly detailed, technology-specific AI regulation with substantial government support for AI development. Recent studies provide evidence that this approach can promote innovation. One 2026 study of Chinese companies from 2017 through 2023 found that the creation of national AI innovation and development pilot zones significantly increased corporate digital transformation, in part through increased research and development, human capital and reduced transition costs. Another 2026 study, covering Chinese manufacturing companies from 2014 through 2024, found that the same policy significantly promoted the adoption of intelligent technologies.
Europe provides a different example. The European Union has adopted a risk-based AI regulatory system while AI investment continues to expand. AI accounted for 27% of European venture-capital investment in 2025, according to a European Commission report. More than 4,100 European AI startups in ten strategic industrial sectors had a combined enterprise value of €161 billion in 2025, according to a second European Commission report.
None of this proves that regulation automatically accelerates AI. Europe still faces significant challenges in financing and scaling AI companies, and regulation is only one factor affecting innovation. But these experiences undermine the assumption that regulation and AI growth are inherently incompatible.
The objective should be to regulate AI in ways that make it safer, fairer and more sustainable while allowing it to grow faster.
The scale of the challenge is already enormous. The International Energy Agency reports that electricity consumption by AI-focused data centers increased about 50% in 2025. Overall data-center electricity consumption is projected to nearly double, from about 485 terawatt-hours in 2025 to 950 TWh in 2030.
That growth makes regulation of AI's physical infrastructure increasingly important. AI requires enormous amounts of electricity, as well as land, water, transmission capacity and other resources. Regulation therefore needs to address environmental impacts and the costs imposed on communities.
But we should not respond by simply putting the brakes on AI.
The benefits of AI could be enormous. The challenge is to make sure those benefits are broadly shared while creating incentives for continued innovation. Regulation should address problems that markets alone may not adequately solve: the safety of increasingly capable AI, the distribution of its economic benefits, effects on workers and communities, and environmental impacts. At the same time, regulation should give developers clear rules, encourage competition and create public confidence in AI.
The current debate in Congress illustrates the difficulty.
Sen. Bernie Sanders and Rep. Alexandria Ocasio-Cortez have proposed the AI Data Center Moratorium Act, which would impose a moratorium on new data centers until Congress establishes safeguards addressing AI safety, workers, electricity costs, environmental impacts and community interests. The proposal reflects legitimate concerns about the speed and consequences of AI development.
But a moratorium based primarily on data-center construction is a blunt way to regulate a technology whose risks can vary enormously.
A huge data center can be running relatively ordinary AI. A much smaller facility could be developing an AI system capable of sophisticated autonomous action.
Infrastructure size is not the same thing as AI risk.
A better approach would classify AI according to the risks created by what the system can actually do.
At the basic level are ordinary AI systems such as today's widely used chatbots and productivity tools. They can be enormously useful but generally operate under substantial human direction.
A second category would be more capable frontier AI systems with substantially greater reasoning, coding, scientific or other capabilities and correspondingly greater potential risks.
The third category is autonomous frontier AI: highly capable systems that can independently plan and act, interact with external systems and pursue objectives with limited human direction. This is where the question of meaningful human control becomes especially important.
The regulatory requirements should rise with the risk.
Some applications may need to be prohibited altogether. Others may require extensive safety measures during both development and deployment. Some could be developed with relatively few restrictions but require rigorous testing and safeguards before deployment. Low-risk AI could remain primarily subject to ordinary safeguards.
This is broadly consistent with the logic behind Europe's AI Act, which uses four levels of risk rather than treating every AI application alike. It also provides regulatory sandboxes and testing environments intended to allow innovation while addressing safety requirements.
The highest-risk systems deserve particular attention.
Sen. Sanders and Rep. Greg Casar have proposed the Ban Artificial Superintelligence Act, which would permanently prohibit the development and deployment of what they call artificial superintelligence and temporarily pause advanced AI development until a federal regulator establishes safety rules. The proposal also calls for international agreements to prevent superintelligence from being developed elsewhere.
The concern about losing human control deserves serious attention. But intelligence by itself is not necessarily the danger. A machine that is vastly more capable than humans could be extraordinarily beneficial if humans retain effective control over what it can do.
The danger begins when increasingly capable machines are given the autonomy, authority and access to act beyond meaningful human control.
That principle should have a particularly clear application to lethal force. AI systems should not have independent authority to select human targets or to make and execute decisions to apply lethal force against people. Humans must retain meaningful judgment and effective control over any system capable of applying lethal force. The United States should also work with other nations toward international agreements establishing the same principle.
This does not mean that AI could never be used in military or other dangerous environments. AI can assist humans in detecting threats, analyzing information and defending against attacks. The critical distinction is between AI assistance and AI authority.
Getting regulation right will not be easy. Rules that are too weak will fail to protect people. Rules that are too broad or inflexible could prevent beneficial applications, reduce competition and push development elsewhere.
That is why the debate needs to move beyond the language of “moratoriums” and “slowing AI.” Those words frame the choice as a conflict between safety and innovation, when that should not be the choice. A moratorium may be an appropriate response to an immediate and specific danger, but making “slowing AI” the objective risks putting those concerned about safety on one side and those who see AI as essential to innovation and national security on the other. We need to focus instead on how to meaningfully regulate AI—creating the safeguards necessary to protect people, communities and the environment while providing the conditions for AI to continue growing.
The question is: “How do we regulate AI so that safety, fairness and environmental responsibility reinforce rather than undermine innovation?”
Europe and China offer different experiences, not definitive answers. Neither demonstrates that regulation automatically makes AI grow faster. But both challenge the assumption that regulation must slow it down.
The United States should learn from those experiences and build a regulatory system designed not to stop AI's growth, but to make that growth safer, broader and more sustainable. This approach has the potential to bring together those concerned about AI's safety, economic and environmental consequences with those focused on innovation and growth. Finding that common ground is essential to achieving any of these goals.
Michael A. Soderstrand is a retired professor of Electrical and Computer Engineering and currently lives in Columbus, Ohio.
PRINCETON JUNCTION, NJ — As generative artificial intelligence reshapes the digital landscape, a New Jersey student researcher is using the technology to combat media bias. Shaurya Jindal, an AI researcher and student at West Windsor-Plainsboro High School South, has launched Cogito News, an educational platform designed to build modern media literacy skills.
Cogito News was founded in response to growing concerns about partisan framing and selective sourcing in contemporary journalism. Recognizing that cognitive biases often influence how news is packaged and consumed, Jindal sought to build a tool that helps users critically evaluate the information they encounter daily. The platform's real-world impact and timely relevance were highlighted when Jindal was my guest on The Fulcrum Democracy Forum, an interview series dedicated to civic renewal and examining innovative approaches to strengthening democratic systems.
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The central innovation of Cogito News discussed across these civic platforms is its automated Bias Detector algorithm. The platform's mechanics are straightforward yet technologically sophisticated: users paste a URL or source text into the dashboard, and the platform routes it through two independent, open-source AI models. The system then analyzes subtle linguistic cues, rhetorical framing, and sourcing patterns to map the narrative inclination along a political spectrum of left, center, or right leanings.
Jindal's work on the platform aligns with his broader academic focus on artificial intelligence, dataset engineering, and machine learning models. His previous research, including comparative analyses of prominent model architectures like VGG-16, ResNet50, and YOLO, provided the foundational framework necessary to optimize the open-source linguistic models used for Cogito News. By deploying open-source models, the platform demonstrates how lightweight AI architecture can be repurposed for localized, high-impact educational utilities without requiring the massive compute infrastructure of enterprise frontier labs.
Beyond its technological components, Cogito News is primarily positioned as an educational utility. Jindal envisions using the tool in classrooms and with everyday readers to help them break down complex articles, spotlight systemic framing biases, and foster a healthier, more analytical approach to consumption in the digital age.
Hugo Balta is the executive editor of The Fulcrum and the publisher of the Latino News Network.
