Ken Rudin joins The Great Battlefield podcast to talk about his career as a journalist at NPR, hosting his podcast, The Political Junkie and why he's changed his nonpartisan orientation in the Trump era.
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Could AI help fund America's future? Explore how a sovereign wealth fund could reduce inequality, strengthen Social Security, and share AI-driven wealth.
In his recent encyclical Magnifica humanitas, Pope Leo XIV stated that humanity is at a critical turning point, facing a pivotal choice regarding Artificial Intelligence and modern digital technologies. Indeed, AI experts on both sides of the aisle have worried that accelerating advances in AI technology could cause major disruptions to our economy and workforce, including rising inequality and mass unemployment.
To address these looming challenges, the CEO of OpenAI, Sam Altman, has made a bold offer: his company will gift the US government a 5% stake. At the company’s current stock price and valuation of nearly $900 billion, a 5% holding would be worth roughly $42.6 billion. That’s a handsome sum, and Altman has also proposed that other AI companies should match his company’s offer.
If other AI-related companies, such as Google, Microsoft, Anthropic, Nvidia, and Meta/Facebook, also gift to the government 5% stakes, the total holdings would be worth around $800 billion. Under Altman’s plan, that endowment would then be used as seed money for a sovereign wealth fund (SWF).
What is a sovereign wealth fund? What are its pros and cons? In the last few weeks, why has everyone from Donald Trump to Bernie Sanders to Vice President J.D. Vance and California governor Gavin Newsom plugged their own version of an SWF?
Interest is growing because, in a time of mounting government debt and declining government funding for public goods and services, a well-designed sovereign wealth fund could do a lot to amass a huge pot of money that could then be used to pay for urgent needs such as replacement income for laid off tech workers, affordable housing, health care, basic income for the poor, Social Security retirement—all without using taxpayer dollars.
Does that sound too good to be true?
The money in a sovereign wealth fund is invested in a range of appreciating capital investments—whether stocks, bonds, real estate, energy, or technology companies—which are allowed to grow over a 5 to 15-year period. Those investments eventually turn into a much larger pool of money, which the government can then spend on various public goods and services.
Sovereign wealth funds are currently used by over 60 countries with over 100 SWFs (some countries, such as China, have more than one) that together manage over $10 trillion of assets. Norway, with only 2% of the US population, has the largest sovereign wealth fund in the world, with $2.1 trillion in assets, supercharged by oil revenue from state-owned energy companies, which is invested in stocks and other assets to grow the fund over time. Norway’s sovereign wealth fund has swelled its enormous investment portfolio to the point where it provides more money to Norway's public budget than its oil profits do.
China has two SWFs totaling about $3.5 trillion and is using its investment returns to try and take the global lead in AI technology and other industries. Saudi Arabia, Australia, Singapore, Abu Dhabi, Kuwait and Indonesia all have an SWF worth about a trillion dollars each. In the right hands—such as Norway’s—a sovereign wealth fund can really do a lot to generate a ton of wealth for spending on public goods and services without needing to raise taxes or incur more government debt.
In the wrong hands, such as Saudi Arabia, it has been used as a slush fund by unaccountable political leaders for their own amusement. Saudi leaders have done foolish things like starting a new professional golf league, LIV Golf, which attracted grotesquely overpaid celebrity golfers, quickly lost $6 billion, and is on the verge of going bankrupt.
It’s worth noting that sovereign wealth funds are not “foreign” to American shores. In fact, 20 US states already have their own sovereign wealth funds, including Alaska ($78 billion—greater than Alaska’s state GDP), Texas ($56 billion), New Mexico ($34 billion), Wyoming ($26 billion), and North Dakota ($8.5 billion). Most of these are seeded by oil revenues earned by each state, which are then invested so that the state can benefit from ongoing investment returns. Some states, like Oregon and Minnesota, seed their SWFs with revenues earned from the public’s use of state lands. Most of these SWFs are fairly small, a couple of billion dollars or less, and nearly all of them use their funds for a single purpose: to help fund public education.
The Altman plan is certainly innovative, since US companies don’t usually give an ownership stake to a government. Once the $800 billion in seed money is invested, it could yield a net gain of $1.4 trillion to $2.5 trillion, depending on how it is allocated and for how long. But it is designed to be a one-time “wasting trust”—the Treasury gets $800 billion dollars up front, and begins an orderly liquidation over 10 years. Once 10 years have passed, the fund is finished. The one booster shot of financial adrenaline would be over. The CEOs of AI companies can say, “Hey, we tried to alleviate the harms of our invention,” but the Band-Aid would be temporary. Given that, a 5% stake is actually insufficient, and the White House should insist on more.
Even better, Norway's and other countries’ sovereign wealth funds are self-replenishing “permanent funds,” which makes a big difference. The single-shot Altman plan wouldn’t come close to plugging the large gap in America’s looming financing needs. A better plan would be to establish a more permanent revolving fund by selling 10-year Treasury notes to the public on a month-to-month and year-to-year basis to raise seed money, which would then be invested for a decade to produce capital gains, with all Americans as beneficiaries.
With a well-designed Federal Permanent Revenue Fund, what might the federal government do with an extra three to four trillion dollars per year? It could plug the $2.5 trillion shortfall in Social Security, which by 2032 is going to result in America’s 52 million retirees taking a 22 percent haircut, a loss of about $8000 or more per year for many beneficiaries. Or the federal Department of Housing and Urban Development, which currently spends around $20 billion each year to build or preserve affordable housing, could receive a large injection of money to partner with states and ramp up housing production all across the country. Or it could be used to pay unemployment benefits to AI-displaced workers.
The possibilities—the needs—are endless. The returns to beneficiaries could take multiple forms, including housing, education, healthcare, climate mitigation, childcare, and other public services and goods. And by designing the fund to incorporate the best practices for anti-corruption management used by successful countries like Norway, Australia, and Singapore, it is possible to maintain the right institutional guardrails. Some might worry that the stock market could crash and everything will sink underwater. However, since 1928, the US stock market has risen on average about 10% per year, and the market is up roughly 3 out of every 4 years; over any five to 10 year period, investments in index funds tied to the S&P 500, the Nasdaq or Dow Jones have always risen, and often outperformed America’s top blue-chip companies such as General Electric, AIG, IBM, Ford, and General Motors.
The fact is, the greatest “gold rush” of investment gains has been occurring for the past 70 years in the US economy, and the vast majority of Americans have missed out. That’s why the top 10% of wealthiest Americans own 93% of all stock equities today. This is the exact unfairness in capitalism that Louis Kelso, the financial visionary of universal capitalism, was determined to address. Kelso was a corporate finance attorney who first crafted the blueprint with his basic formula of “form a regulated trust => raise seed money => invest seed money => let it grow over time => reap the future returns on behalf of the broader public.”
A successful sovereign wealth fund designed like Norway’s could produce greater ownership and wealth for all Americans from the future returns on the SWF's investments. These funds would allow everyday people to benefit from the wealth creation generated by America’s most valuable businesses, and by new technologies like AI. Once initiated, a fund will take several years for its investments to reach maturity and be fully loaded. So the sooner America gets going on its launch, the better.
Steven Hill was policy director for the Center for Humane Technology, co-founder of FairVote, and political reform director at New America. See more of his writing at his Substack newsletter DemocracySOS.
How the U.S. can rebuild and improve its federal data system by combining Census, citizen, AI, and nontraditional data for stronger public policy.
The business aphorism, “You can’t manage what you don’t measure,” applies to countries as well as corporations. The U.S. has relied since its founding on accurate Census, economic, and health and scientific data to set national policy. As an earlier Fulcrum article described, the Trump administration has put many data sources at risk, and advocates are working to save them. But they’re also asking: As we protect and rebuild the national data ecosystem, how do we make it better than it has been in the past?
I recently moderated a webinar on Meeting the Challenge of Measurement, co-sponsored by NAPA, CODE, and the Bridge Alliance, with four leaders in the field: Chris Jackson of the research firm SSRS, Beth Jarosz of the Association of Public Data Users, Francesca Perucci of Open Data Watch, and Dr. Stefaan Verhulst of the GovLab. I also interviewed former U.S. Comptroller Gene Ludwig, author of The Mismeasurement of America. Their insights, plus my organization’s recent work with USAFacts, provide three principles for moving forward.
First, the U.S. federal data system is essential and irreplaceable. There is no way to replace core federal data with some combination of research from academic, business, nonprofit, or other sources. The federal government plays two unique roles: setting standards for collecting data across the country in comparable ways and conducting very large-scale projects, such as the U.S. Census and climate, ocean, and Earth observation studies.
Second, federal statistics need improvement. An SSRS study found that about 70 percent of the public believe federal statistics are an important source of information, but just under half tend to trust those statistics. That low number may reflect increasing concern about data being politicized, but it may also show that people aren’t sure the numbers reflect their lived reality.
A growing number of experts now share that concern. Gene Ludwig has launched a nonprofit institute to develop better statistical indicators for unemployment, average wages, and inflation. In all those cases, the official statistics make life for low- and middle-income Americans look better than it is. Reanalyzing the government’s own data shows, for example, that the true unemployment rate may be closer to 25 percent than the official statistic of just over 4 percent. We need to take a fresh look at how we use the data we already collect.
Finally, we should not rely on federal data alone. While federal data collections may be the gold standard, they are often less timely, less localized, or even less relevant than other data. The ongoing decline in response rates to government surveys, along with other methodological issues, is forcing researchers to develop new methods. Approaches including community engagement, digital data analysis, and AI are fueling innovative new information sources.
One major development is the growth of citizen data, which Francesca Perucci describes as data created with meaningful community engagement, control, and some degree of ownership. In the U.S., for example, many communities have gathered local air and water quality data in the interest of environmental justice. Citizen data has become increasingly important worldwide, particularly in countries that lack the resources to develop a robust government statistical system. The UN Collaborative on Citizen Data, which Perucci co-chairs, has developed insights and guidelines that the U.S. could apply as well.
At the same time, there is growing interest in non-traditional data, which leverages the byproducts of society’s digitalization. Data from cell phones, drones, business transactions, and many other sources can be anonymized and statistically analyzed to generate new insights. Stefaan Verhulst, who has been studying this opportunity for years, described how the U.S. can use non-traditional data in a blog post prepared for our webinar. This data can provide new perspectives on economic opportunity, social connectedness, housing, employment, and many other social issues.
These new methods complement other kinds of data sources, including business data, independent survey data, and government administrative data, which continue to demonstrate their value. Combining federal and other kinds of data, as Beth Jarosz put it, can enable us to “triangulate toward the truth.” The result may be a broader, deeper, and more useful understanding of the U.S. and the world we live in.
You can watch the full video of the webinar “Meeting the Challenge of Measurement” on YouTube at:
https://www.youtube.com/watch?v=hz_TogRhigM&t=8s
Joel Gurin is president and founder of the Center for Open Data Enterprise. (CODE)
As nonprofits increasingly use AI to determine access to food, housing, and healthcare assistance, concerns over transparency, accountability, and algorithmic bias are growing. Explore how artificial intelligence is reshaping America's social safety net and why policymakers are being urged to act.
America's safety net has new administrators. They are not government agencies. They are nonprofits and are making decisions using algorithms.
Nationally, civil society organizations are using AI to determine who receives food assistance, emergency shelter, and medical referrals. In an era of federal and state budget cuts and struggles with the latest administrative databases that support necessary operations, nonprofits have gradually stepped into these roles. An unofficial AI welfare state is emerging, operating beyond the boundaries of official ones.
Government retrenchment over the past decade has pushed welfare functions toward civil society. Nonprofits are no longer just supplementing public services. In many communities, they are replacing them. AI accelerates this shift. Predictive tools that flag food insecurity, triage shelter demand or identify at-risk youth allow smaller organizations to scale their reach. This can be genuinely valuable. But scale introduced through automation carries its own systemic distortions. When machines allocate scarce social resources, the political judgments encoded in those models become invisible. Accountability disappears with them.
The accountability problem is structural. When a government agency determines welfare eligibility, legal safeguards apply due process rights, administrative review and legislative oversight. When a nonprofit uses a proprietary AI system to make equivalent decisions, none of those protections necessarily follow.
Algorithmic bias in social service allocation is not hypothetical. Research demonstrates that seemingly neutral models reproduce structural inequalities, systematically denying help to the communities most likely to need it. The vendors building these tools are not elected. Neither are the nonprofits deploying them. The individuals affected hold no formal right of appeal. Closing the accountability gap in algorithmic systems requires deliberate institutional design — it does not self-correct.
When nonprofits use AI, they usually transfer decision-making authority to their software vendors and proprietary models. These systems now indirectly shape who receives food assistance, job training, or mental health services. That is a welfare function. It carries welfare stakes. Algorithmic systems embedded in social institutions tend to entrench existing power asymmetries rather than correct them. The most affected citizens in communities rarely participate in designing or governing the tools deployed on their behalf.
Not all nonprofits have the same capacity. In metropolitan areas, large institutes have the budgets to acquire sophisticated AI platforms. Many of the smaller nonprofits serving rural & low-income populations in America simply cannot. The distribution of AI-assisted service delivery mirrors and monitors the geographic features of poverty itself. Economic mobility is already highly correlated with social capital and access to institutional resources. If an uneven AI infrastructure is laid on top of such a stratified social system, the result can be to exacerbate, rather than reduce, entrenched inequalities.
Federal AI policy has concentrated nearly exclusively on commercial and national security applications. There is hardly a thing that regulates the use of AI when it comes to nonprofit welfare delivery. But as long as policy interventions (e.g. anthropogenic policies) that call for human intervention in algorithms do not provide binding transparency, fairness and appeal standards, they remain structurally deficient. Nonprofits are behaving like quasi-governmental functions without the accountability of a quasi-governmental status. It is not a technicality, but a structural failure of democratic governance.
Congress and state legislatures need to do three things.
First, expand algorithmic accountability standards to all nonprofits implementing government-supported social services.
Second, require algorithmic impact assessments prior to deploying AI tools in welfare-adjacent programs.
Third, fund independent ongoing audits of all systems used by states to administer food, housing, and healthcare assistance.
America did not consciously choose to privatize its welfare state through software. It is happening by inaction, one algorithm at a time. The decision to govern this shift remains available. But the window is closing fast.
Feroz Ahmed is a Research Assistant at Lamar University, USA, where he conducts research on Management Information Systems (MIS), digital governance, artificial intelligence, and data-driven technologies. Alongside his academic work, he also serves as a 'System Security Analyst' with Amazon & Securitas Security Services USA Inc. in Seattle, Washington, where he is involved in security operations and technology risk monitoring.
AI isn't just for doctors. Consumer AI could improve patient outcomes, reduce medical errors, and reshape U.S. healthcare through smarter public policy.
When it comes to medicine, Washington, D.C., is not anti-AI. In a string of recent legislative announcements, the White House has made AI innovation a national priority. The U.S. Department of Health and Human Services (HHS) is exploring strategies to accelerate AI in clinical care. And the FDA has already authorized more than 1,500 AI-enabled medical devices.
But these efforts, similar to the approach taken by the private sector, have focused solely on advancing AI for clinicians, hospitals, and health systems.
What Congress and federal agencies have not embraced is the larger opportunity outside the walls of medicine: empowering patients with medical expertise through the same general-purpose AI tools they are already using today.
The Medical Potential Of Consumer AI
An estimated 133 million Americans currently use generative AI, and half of U.S. adults (67 million) are relying on these tools to answer health-related questions. Patients are asking large language models like ChatGPT, Claude, and Gemini about their symptoms, medications, lab results, chronic diseases, and whether they need to see a doctor.
A recent study in Nature Medicine explains why these patient-facing AI models deserve more attention from the federal government.
Researchers compared two specialized, physician-facing AI tools (OpenEvidence and UpToDate Expert AI) with three widely available, low-cost large language models (from OpenAI, Anthropic, and Google).
The result? General-purpose consumer AI models performed as well as or better than the tools built specifically for doctors and hospitals. That finding challenges the assumption that progress in medical AI depends on developing primarily specialized applications for clinical use. It does not. The tools needed to empower patients already exist as general-purpose large language models on their phones.
To accelerate that shift, government needs to help all Americans access these LLMs and apply the expertise they offer to maintain and improve their health.
The Opportunity To Improve America’s Health
If American medicine were achieving superb outcomes, a cautious and more institutional approach to GenAI might make sense. But with 400,000 people dying annually from misdiagnosis, 250,000 from preventable medical error, and at least half a million from poorly controlled chronic disease, the quickest path to saving lives and making healthcare more affordable is to help patients use the AI tools already accessible to them.
Although dozens of AI applications are possible, two opportunities would produce the greatest improvements in patient health.
First, conditions such as diabetes, hypertension, depression, and asthma affect 75% of Americans and are poorly managed. Less than half of patients with hypertension have their blood pressure under control, and an even smaller percentage of patients with diabetes have their blood sugar effectively controlled. Based on data from the CDC, successful control of these chronic conditions could reduce the number of heart attacks, strokes, and kidney failures by as much as 50%. Doing so would save hundreds of thousands of lives and reduce medical costs by hundreds of billions of dollars.
Patients could use generative AI to analyze daily clinical data and alert them when their blood pressure or blood glucose is not responding as expected, allowing for earlier medication changes. And the same tools could also reassure patients when everything is going according to plan and help avoid unnecessary visits.
In addition, these same large language models could guide patients at night, on weekends or during the long wait for a primary care or specialty appointment. A worried parent whose child spikes a fever at night should have a better option than guessing, searching social media, or deciding alone whether to go to the ER versus waiting for the pediatrician’s office to open the next day. That same expert guidance should be available to adults trying to decide whether an aging parent’s new symptom requires immediate urgent care.
The Role For The Federal Government
Assuming inexpensive LLMs perform as well as expensive medical AI tools, elected officials and federal agencies should expand access to them. In addition, they should create educational resources that help individuals and families use these tools reliably and safely.
This approach will be most valuable for the tens of millions of Americans who have difficulty accessing medical care, particularly those living in inner cities and rural communities.
To make access to medical expertise easier, Congress and governors should legislate funding to increase availability of these large language models in homes, libraries, community centers, senior centers, and public-health clinics. With access as the foundation for patient empowerment, NIH and HHS should then create plain-language educational tools that teach people how to enter medical information, write clearer prompts, ask follow-up questions, and recognize when a problem requires urgent medical attention.
The goal of patient empowerment would not be to replace clinicians. It would be to fill in the cracks between office visits and reduce the risks of medical errors and clinical complications before they arise.
If chronic diseases were well controlled, medical errors were rare, and clinical access was easily available at night and on weekends, caution and the incremental introduction of GenAI might make sense. But that is not the reality for tens of millions of Americans today.
With healthcare becoming harder to access and increasingly unaffordable, large language models like ChatGPT, Claude, and Gemini can help millions of Americans protect and improve their health. That should matter to elected officials and regulatory agencies. Encouraging the use of technology already available on people’s phones and in their homes would be an effective place to start.
Robert Pearl, the author of “ChatGPT, MD,” teaches at both the Stanford University School of Medicine and the Stanford Graduate School of Business. He is a former CEO of The Permanente Medical Group.
Journalist and Political Junkie Ken Rudin
