​The latest economic data tells a concerning story.

From a weakening labor market and rising healthcare costs to slowing growth and increased uncertainty, the warning signs are becoming harder to ignore. These trends are not accidental. They are the result of policy choices that have expanded the government’s role, distorted incentives, and increased complexity across key sectors of the economy.

In this episode of This Week’s Economy, we examine how these forces are playing out across jobs, Medicare, regulation, trade, and tax policy—and why they all point to the same conclusion: policy matters, and bad policy carries real costs.

The critical question is whether leaders will course-correct before these challenges deepen.

👉 Watch or listen to the full episode and explore more analysis with show notes at vanceginn.substack.com.

Originally published on Substack. 

The best policy debates are not really about policy. They are about whether families have the freedom to build a life. Last week—between spring break with my kids and fights over AI, healthcare, lockdowns, and government overreach—that truth was impossible to miss.

The Highlight of the Week

Last week was spring break with my kids, and honestly, it was the highlight of my week.

We played soccer, baseball, and tennis. We watched movies, laughed a lot, and slowed down just enough to remember what actually matters. Those moments go fast if you are not paying attention.

They also remind me why I do this work in the first place.

Policy is not abstract. It is not about another flashy hearing or a “solution” designed by the same people who caused the problem. It is about whether families have the freedom, stability, and opportunity to build a good life.

It is about whether parents can afford groceries. Whether workers can find opportunity. Whether patients can control their care. Whether innovators can build. And whether government knows its limits.

That thread ran through everything I worked on last week. As I wrote in Prosperity Through Pain, the purpose of the work is not the work itself—it is what that work allows: the laughter in the yard, the time with your kids, the life you are building together.

Innovation Needs Room, Not Fear

I spent part of last week writing about how AI could transform banking and the broader economy—expanding competition, lowering costs, and helping smaller institutions compete.

That future is possible.

But only if Washington and the states do not regulate it into the ground before they understand it.

• More than 1,500 AI-related bills have already been filed across state legislatures this year.
• Even in Texas, HB 149 showed how quickly fear can override evidence.

The takeaway: Markets adapt. Entrepreneurs solve problems. Bureaucrats slow both down.

A patchwork of vague rules will not make America safer. It will make America slower.

And in a global race, slower means losing.

Six Years After Lockdowns

This week marked six years since COVID reshaped American life.

I was in the room when those decisions were made. I opposed the lockdowns then, and the data since has only reinforced that view.

The Great Lockdown did not save America.

It devastated small businesses. It disrupted learning. It weakened liberty. It eroded trust.

And the worst part?

The same central-planning instincts that drove those decisions are still with us today.

The takeaway: Central planning fails in crises—and in everyday life.

This was never just about a virus. It was about power.

Healthcare Still Misses the Point

Healthcare debates last week made one thing clear: policymakers are still treating symptoms instead of causes.
The fight over Medicare Advantage and proposals like Tennessee’s SB 2040 to ban PBMs are perfect examples.

As I wrote in Stop Scapegoating Middlemen in Healthcare, targeting one piece of the system does not fix broken incentives. It just rearranges them.

• TennCare alone faces about $66 million in added costs from these kinds of policies.

That is not reform. That is expensive political theater.

The real solution is Empower Patients:

• Put individuals in control
• Expand no-limit HSAs
• Strengthen doctor-patient relationships
• Restore real price signals

​The takeaway: Healthcare improves when patients—not bureaucracies—are in charge.

Affordability Is Not a Mystery

Across healthcare, housing, energy, and taxes, families are feeling squeezed.

That is not random. It is policy.

Government spends too much, regulates too much, distorts too much—and then acts surprised when costs rise.

Whether it is:

• Tariffs raising prices
• Overspending fueling inflation
• Regulations restricting supply

…the result is the same.

As I explain in Correcting America’s Financial Future and across my writings:

You don’t get affordability through control.

You get it through competition, production, and freedom.

That same issue shows up in Texas water and the electric grid.

Texas does not mainly have a scarcity problem.

It has a control problem.

Why This Fight Is Personal

Everything I worked on last week—AI, healthcare, lockdowns, taxes, infrastructure—comes back to one truth:

People do better when they are free.

Spring break reminded me of that in the simplest way.

Playing ball in the yard. Watching movies. Laughing with my kids. Just being present. That is the life worth protecting.

Bad policy erodes that life slowly—through higher costs, fewer opportunities, and more dependence on systems that cannot love your family or build your future.

Only people can do that.

Government’s job is not to run our lives. It is to protect the freedom that makes a good life possible.

Three Takeaways for Policymakers

1. Stop regulating fear—start enabling innovation. Let markets evolve and address real harms with evidence, not speculation.

2. Decentralize power—people outperform planners. Centralized systems fail repeatedly. Push decisions closer to individuals.

3. Fix affordability at its source—government excess. Restrain spending, remove barriers, and let competition work.

The Bottom Line

Freedom works. Central planning does not.

That was true during COVID. It is true in healthcare, banking, energy, and the broader economy. And it will still be true long after today’s policy debates fade.

I am grateful for the work. But I am even more grateful for the reason behind it. Time with family.

A Direct Challenge

If you are a policymaker reading this, here is the question:

Will you keep expanding control—or will you trust people?

Because that choice determines whether families merely get by…
or truly prosper.
​
Stay engaged, stay principled, and keep letting people prosper.

That alone is progress.

For years, the conversation around artificial intelligence has been dominated by fear—fear about jobs, kids, misinformation, and control. And when policymakers operate from fear, they tend to rush into doing something—usually something heavy-handed.

This framework is different. It leans toward innovation, growth, and leadership.

The real question now is whether lawmakers across the country will follow that lead—or regulate this opportunity away before it fully arrives.

Because make no mistake: this is not just another tech trend.This is the early stage of the next economic revolution.

More Than a Buzzword

Let’s clear something up.

AI is not some mysterious force. It’s advanced computing—the continuation of tools we’ve used for decades in search, logistics, fraud detection, finance, and medicine. What’s changing now is the scale and speed.

That matters because it means AI will touch nearly every part of the economy.

We’ve seen this kind of moment before. The agricultural revolution transformed how we produced food. The Industrial Revolution reshaped labor and production. The digital revolution changed how we communicate and exchange information.

Each time, people feared what was coming. Each time, politicians felt pressure to step in and manage the transition. And each time, the real progress came not from top-down control, but from bottom-up experimentation.

Markets didn’t get everything right—but they adapted, learned, and improved far faster than any centralized plan ever could.

That’s still true today. Markets are far better at discovery than governments are at prediction.

A Better Direction from Washington

To its credit, the Trump framework reflects that reality.
​
It focuses on empowering parents, protecting children, supporting creators and intellectual property, defending free speech, and preparing workers for a more dynamic economy. It also makes clear that America should aim to lead—not slow down—the development of AI.

That’s a welcome shift.

It avoids the worst instinct in policymaking: assuming that because something is new, it must be tightly controlled.

And importantly, it does not follow the path of proposals we’ve seen elsewhere—from U.S. Senator Marsha Blackburn at the federal level to various state efforts like by Texas Senator Angela Paxton—that push toward app store mandates, platform controls, or restrictions on infrastructure like data centers.

Those ideas are built on a simple but flawed premise: that government knows better than parents, entrepreneurs, and consumers.

It doesn’t.

Where Things Go Wrong: The States

If there’s a real risk to getting AI policy wrong, it’s not coming from this framework. It’s coming from the states.

There have already been more than 1,500 AI-related bills filed across state legislatures this year. That’s not thoughtful policymaking. That’s a stampede driven by headlines and worst-case scenarios.
​
California, New York, and Colorado are leading the charge with some of the most aggressive proposals—vague “harm” standards, licensing regimes, audits, and broad oversight structures that sound reasonable in theory but would slow innovation in practice. Even “conservative” Texas has drifted in this direction. HB 149 (TRAIGA) ended up better than where it started, but it still reflects too much fear and too little evidence.

Here’s the problem: AI doesn’t stop at state lines.

Cloud systems, data flows, software deployment, and digital services operate across borders. A 50-state patchwork of rules doesn’t make the technology safer—it makes the system more fragmented, more expensive, and less competitive.
Who wins in that environment?

Not startups. Not small businesses. Not consumers.

The winners are the largest incumbents that can afford compliance costs—and foreign competitors operating under entirely different rules.

That’s not a free market. That’s regulation protecting the powerful.

Fear Has Always Been the Wrong Guide

Let’s be honest about what’s driving a lot of this.

Fear.

Fear that jobs will disappear. Fear that kids will be harmed. Fear that new tools will be misused.

Some of those concerns are real. But they are not new.

Every major innovation has brought disruption. Workers were displaced during industrialization. The internet created new risks alongside new opportunities. Social media has amplified underlying challenges that were already there.

But here’s what history shows clearly: trying to ban or overregulate innovation doesn’t solve those problems—it often makes them worse.

When government tries to shut things down, activity doesn’t disappear. It moves. It becomes harder to track, harder to manage, and often more concentrated in fewer hands.

Think prohibition. Think black markets. Think organized crime. You don’t eliminate risk. You shift it—and often amplify it.
Heavy-handed AI regulation would follow the same pattern:

• Driving innovation elsewhere
• Reducing transparency
• Concentrating power

That’s not protection. That’s unintended consequences.

What Lawmakers Should Do Now

There is a better path—and it’s simpler than many think.

1. Refuse the worst ideas. No digital ID systems tied to AI access. That’s a dangerous step toward centralized control that has nothing to do with innovation or safety.
2. Use the laws we already have. Fraud, exploitation, coercion, and abuse are already illegal. Enforce those laws instead of creating entirely new regulatory regimes.
3. Address the growing patchwork problem. AI is inherently interstate commerce. As James Madison wrote in Federalist No. 42, the Constitution empowers the federal government to prevent state actions that “embarrass the intercourse of the States.”

That’s exactly what a fragmented AI regulatory landscape would do.
​
A temporary federal pause or preemption of state AI regulations isn’t about expanding power—it’s about preserving a functioning national market.

The Stakes

China will push forward through central planning. Europe is already slowing itself with overregulation. America still has a different option. We can trust markets, empower people, and lead the next wave of innovation.

Or we can let fear drive policy—and fall behind.

The Trump AI framework is a strong start. But it will only matter if we resist the urge to overcorrect at every level of government.

Closing Thought

We live in what should be a free society.

That means accepting trial and error. It means trusting people to adapt. It means allowing innovation to move forward even when it’s imperfect.

America should not lose the AI revolution because policymakers were too afraid to let it happen.

Let parents parent.

Let entrepreneurs build.

Let workers adapt.

Let markets work.

Let People Prosper

If we get this right, America leads the next economic revolution.
​
If we get it wrong, we regulate ourselves into decline.

There’s a familiar pattern in health policy: politicians pick something voters are mad about, declare war on it, and promise quick relief. Right now, “ban the middlemen” is the fashionable version of that impulse. It sounds conservative. It sounds pro-consumer. And it looks like action.
​
But it’s mostly a cover story—because the real driver of high costs is the third-party system politicians helped build and still protect. Middlemen didn’t break a free market. They expanded inside a market distorted by government.

That’s why Tennessee’s SB 2040 matters far beyond Nashville. The bill would prohibit pharmacy benefit managers (PBMs) from owning or operating pharmacies, and it heads to Senate Finance on March 24. Supporters claim it targets conflicts of interest and will lower prices. The problem is that the bill doesn’t fix the incentives that drive high costs. It just rearranges the supply chain by statute—an expensive political shortcut that is likely to hit low-income patients first.

The Costs Don’t Disappear. They Move.

Start with the program that will feel this immediately: TennCare. It covers about 1.4 million Tennesseans and spends roughly $19.2 billion annually—serving about 20% of the state, covering about half of births, and insuring about half of Tennessee’s children.

When TennCare gets hit with higher costs, it isn’t abstract. It affects pregnant women, kids, seniors, and people with disabilities—those with the least flexibility to absorb higher costs or disruptions.

That’s why TennCare leaders have been blunt. In a recent legislative hearing, TennCare’s chief pharmacy officer warned SB 2040 would cause “a significant member impact on access to treatment” and “a significant fiscal impact to our program.” TennCare’s director reinforced that the agency has data showing “with reasonable certainty” there will be a fiscal impact.

The estimated impact is about $66 million—with roughly $24 million on taxpayers and $42 million effectively borne by patients. That’s the ugly reality: a bill marketed as “protecting patients” can function as a cost increase on the poor.

Access risk is real, too. CVS has warned SB 2040 could force closures of 134 Tennessee pharmacy locations, and additional reporting highlights the same threat and likely access impacts here. Maybe it’s posturing. Maybe it’s not. But lawmakers shouldn’t gamble with pharmacy access—especially in rural communities where “choice” can mean one pharmacy or none.

PBMs Are a Symptom of Third-Party Healthcare

Here’s the part the “ban them” crowd avoids: PBMs didn’t invade a free market. They grew inside a government-shaped system dominated by third-party payment and opaque pricing. When patients aren’t directly spending their own dollars, price signals weaken. When price signals weaken, complexity grows. When complexity grows, intermediaries expand to negotiate, manage claims, and administer formularies.

That’s why more regulation isn’t the solution. It’s the same institutional mistake conservatives criticized in the Affordable Care Act era: government stepping in to “fix” problems created by government distortions, without restoring market incentives. Changing the tool (banning an ownership model) doesn’t change the broken system that breeds complexity and middle layers.

This is also why the PBM debate rhymes with the price transparency debate. “Transparency” sounds conservative—sunlight and all that. But in a third-party system, the prices being displayed are often not real market prices. They’re administrative prices shaped by mandates, reimbursement formulas, cross-subsidies, and contracting games.

Forcing transparency on distorted prices can mislead patients, punish efficient providers, and give politicians another talking point without fixing the underlying incentive problem: patients still aren’t empowered buyers.

This Isn’t Just Tennessee—Texas and Other States Have the Same Temptation

Tennessee isn’t alone. Texas has seen similar political pressure to “do something” about healthcare costs with measures that sound market-friendly but sidestep the real reform: restoring patient control, competition, and direct price discipline. The temptation is bipartisan and constant—announce a crackdown, regulate the supply chain, and claim victory.

But the third-party system remains. Costs keep rising. Outcomes don’t magically improve. Real reform means empowering people, not rearranging intermediaries.

That’s the case I make in Empower Patients and Doctors to Heal America’s Healthcare System: focus on restoring the patient-doctor relationship, expanding competition, and reducing the government distortions that keep prices opaque and incentives warped.

Three Points for Lawmakers

1. Follow the TennCare warnings. If your own program leaders say costs rise and access falls, don’t pass the bill and hope for the best.
2. Stop banning business models. If there are bad practices, target conduct—not ownership structures—so you don’t shrink access and competition.
3. Lower barriers that block competition. Expand capacity at the point of care, especially by empowering pharmacists and reducing state restrictions that limit supply.

Call to Action

Before the March 24 hearing, Tennessee lawmakers should demand a straight answer: how does SB 2040 lower net drug prices and improve outcomes if TennCare expects higher costs and worse access?

If the evidence isn’t there, they should reject this political shortcut and pursue reforms that actually expand competition and empower patients.

If we want fewer middlemen, we need a system that needs them less. That doesn’t happen through bans or transparency theater. It happens through freedom: more entry, more competition, and more patient control.

Please subscribe today and check out my work at Ginn Economic Consulting at vanceginn.com for more resources.

Originally published at The Daily Economy. 

​Artificial intelligence has become the latest excuse for reviving one of the oldest bad ideas in economic policy: a universal basic income. Recent pieces in Newsweek, the LSE Business Review, and Fortune have all helped push the idea that AI may soon wipe out so many jobs that Washington will need to send everyone a check.

That makes for a catchy headline. It also makes for terrible economics.

The right question is not whether AI will disrupt work. Of course it will. The right question is this: after more than 100 local guaranteed-income experiments, what have we actually learned?

The answer is much less flattering to UBI than its promoters would like.

What 122 UBI-Style Pilots Show

A new AEI working paper by Kevin Corinth and Hannah Mayhew gives the best recent overview of the evidence. Per their study, there were 122 guaranteed basic income pilots across 33 states and the District of Columbia between 2017 and 2025. Those pilots allocated about $481.4 million in transfers to 40,921 recipients, with 61,664 total participants including control groups. The average recipient got about $11,765, the average pilot lasted 18.4 months, and the average monthly payment was $616.

That sounds like a mountain of evidence. It is not.

Of those 122 pilots, only 52 had published outcomes. Only 35 used randomized designs. Only 30 reported employment outcomes. So the case for UBI is not being built on some giant pile of clear, clean evidence. It is being built on a much smaller stack of studies, many of them weak, limited, or badly timed.

And here is the kicker. Among the 30 randomized pilots with published employment results, the average effect was a 0.8 percentage-point increase in employment. UBI fans will rush to wave that around. They should slow down.

AEI shows that the bigger and more credible studies tell a very different story. Among the four pilots with treatment groups of at least 500 participants, which together account for 55 percent of all treatment-group participants, the mean effect on employment was minus 3.2 percentage points. AEI also estimates a mean income elasticity of -0.18, which is consistent with standard labor-supply economics. 

In plain English, when people receive more unearned income, work tends to fall at the margin. Shocking, I know. Economics still works.

Credit: American Enterprise Institute

​Why the Evidence Is Weaker

Than the HypeThe AEI paper is useful not just for what it finds, but for how bluntly it describes the weaknesses in the evidence.

The average treatment group among those 30 studies was just 359 people, and the median was only 151. That is not exactly ironclad evidence for redesigning the American welfare state. Among the 26 pilots for which attrition could be measured, the average attrition rate was 37 percent. That is a giant warning sign. If enough people drop out, the reported results can become badly distorted.

The studies also varied widely in payment size, duration, sample composition, and even how outcomes were measured. The mean annualized payment was $7,177, equal to an average income boost of about 39.5 percent relative to baseline household income in the studies. Some pilots relied heavily on self-reported survey data. Some were conducted during or right after the COVID period — when labor markets, safety-net programs, and personal decisions were anything but normal.
​
AEI’s conclusion is appropriately cautious: these findings may not generalize to a permanent, universal, nationwide UBI under current or future conditions. That alone should cool off a lot of the AI-fueled policy hysteria.

​AI Will Displace Jobs. It Will Also Create Them

None of this means AI will be painless. Some jobs will shrink. Some tasks will disappear. Some workers will need to retrain, relocate, or rethink their careers. That is what happens when productivity rises and technology changes how goods and services are produced. It happened with mechanization, with computers, and with the internet. It will happen with AI.

But displacement is not the same thing as permanent mass unemployment. That leap is where the UBI argument falls apart. Economies are not fixed piles of jobs. They are dynamic systems of discovery, adaptation, and exchange. When costs fall and productivity rises, resources move. Businesses reorganize. Consumer demand changes. New occupations emerge. Old ones evolve. Some disappear. That churn is real, but so is the adaptation.

The answer to technological change is not to pay people for economic resignation. The answer is to make adaptation easier.

UBI Fails the Economics Test

There is a reason Ryan Bourne at Cato has argued that UBI is not the answer if AI comes for your job. It confuses a transition problem with a permanent income problem. Worse, it assumes that writing checks can substitute for the incentives, signals, and institutional conditions that actually create opportunity.

UBI also crashes into the budget constraint. As Max Gulker at The Daily Economy has noted, UBI is often sold through small pilots and vague moral language, but the national arithmetic is ugly. And as Robert Wright in another AIER piece points out, “universal” quickly means sending money to many people who are not poor while piling enormous costs onto taxpayers. (Bear in mind, the national debt is already rapidly approaching $40 trillion.) 

That is before getting to the public-choice problem. In theory, UBI supporters sometimes imagine replacing the welfare state with one simple cash transfer. In reality, government programs rarely disappear. Bureaucracies defend themselves. Interest groups protect carveouts. Politicians promise more, not less. So a UBI would likely be stacked on top of much of the current welfare state, not substituted for it. That is not reform. That is fiscal delusion with better branding.

A Better Answer: Remove Barriers to Work

If AI means more labor-market churn, then policy should focus on mobility, flexibility, and self-sufficiency. That means less occupational licensing, lower taxes, lighter regulation, fewer benefit cliffs, less wasteful spending, and more room for entrepreneurship and job creation. The government should stop making it harder for people to pivot.

It also means reforming welfare the right way. My proposal for empowerment accounts is not a UBI. It would be targeted to people already eligible for welfare, not universal. It would include a work requirement for work-capable adults, not detach income from effort. And it would consolidate fragmented programs into a more flexible account that families control directly, reducing bureaucracy and lowering spending over time as more recipients move toward self-sufficiency.

That puts it much closer to the classical liberal insight behind replacing bureaucratic control with direct support, while avoiding the fatal error of turning the entire country into a permanent transfer state. As Art Carden reminds us at The Daily Economy, there is a long intellectual history behind cash-based assistance. But today’s UBI politics are not really about shrinking the state. They are mostly about expanding it because elites fear AI.

Don’t Make Bad Policy Out of Fear

The UBI revival tells us less about AI than it does about politics. New technology arrives, uncertainty rises, and too many policymakers reach for the federal checkbook as if it were a magic wand. It is not.

After 122 local experiments, the case for UBI is still weak. The best evidence does not show a jobs renaissance. The larger studies show employment declines. The broader evidence base is riddled with small sample sizes, high attrition, and limited generalizability. That is a flimsy foundation for a permanent national entitlement.

AI will change work. It will not repeal economics. The best response is not fear-driven universal dependency. It is a freer economy with stronger incentives to work, save, invest, adapt, and prosper.