Business

Rural America is getting a bailout, but not from Trump—billionaires are riding to the rescue

Published

14 hours ago

January 14, 2026

Rural America is getting a bailout.

Billionaires are increasingly stepping in to plug gaps in services, education, and opportunity that many small towns say have been ignored for years. While Washington remains gridlocked over how to revive areas left behind by industrial and demographic change, a growing class of wealthy donors is quietly reshaping the economic future of the countryside with nine-figure checks and thousands of acres of land.

Minnesota billionaire Glen Taylor, who built Taylor Corp. into a printing empire and became his state’s wealthiest resident, is now redirecting a significant slice of his fortune back to the rural communities that raised him. The 84-year-old former dairy farm kid from outside Comfrey, Minnesota (pop. 376 as of 2024), is transferring farmland and securities worth roughly $100 million into the Taylor Family Farms Foundation, with a specific mandate to support rural areas in Minnesota and Iowa.

Rather than offering a one-time cash infusion, Taylor’s gift is structured to generate income for years, building on a 2023 transfer of about $173 million in farmland that already funds grants through regional nonprofit partners. Taylor said the move is rooted in his own upbringing in southern Minnesota, where he worked on farms and raised chickens, and in a desire to “make a positive impact on the lives of others in a region that I love so much,” Taylor said in a statement to the Observer.

Billionaire rural wave

Taylor is part of a broader pattern in which ultrawealthy donors are focusing explicitly on small-town and rural America rather than the big-city universities and museums that long dominated philanthropy. Investment banker Byron Trott, who grew up in Union, Missouri, has pledged $150 million to a network of universities to boost enrollment from rural students, a push that has already helped drive a 20% increase in applications.

Philanthropist MacKenzie Scott has similarly turned her attention to rural education, donating $36 million to North Carolina institutions such as Robeson Community College and Bladen Community College to bolster opportunities in some of the country’s poorest counties. Together, these gifts signal a recognition among billionaires that the country’s economic and political fault lines increasingly run between thriving metros and struggling rural regions—and that private money can move faster than federal policy.

Politics, power and dependence

The surge of billionaire attention comes as rural voters remain a core political base for Trump, whose “forgotten men and women” rhetoric helped power his return to the White House but has not translated into a sweeping federal revival plan for small-town America. In that vacuum, philanthropists like Taylor, Trott, and Scott are effectively writing their own rural policy agendas through foundations and grantmaking, deciding which towns get ambulances, which fire departments get radios, and which students get a shot at college.

Trump’s administration has announced a $12 billion bailout for farmers in the wake of a wipeout amid his tariff regime, particularly for soybeans. At one point in 2025, as Trump and Treasury Secretary Scott Bessent announced support for like-minded ally Javier Milei in Argentina, China cut its U.S. soybean purchases to zero and began buying them from Argentina instead. After a Trump-Xi summit, China resumed soybean purchases, and more recently Argentina has repaid its full $20 billion credit line. Kentucky soybean farmer Caleb Ragland told the Associated Press in early January that Trump’s aid for farmers was “a Band-Aid on a deep wound. We need competition and opportunities in the market to make our future brighter.”

For this story, Fortune journalists used generative AI as a research tool. An editor verified the accuracy of the information before publishing.

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Business

IPO boom times are back, with SpaceX and OpenAI on investors’ 2026 wish list. But be careful what you buy

Published

16 minutes ago

January 15, 2026

Jace Porter

In 1999, stock buyers had a cornucopia of new options as U.S. companies went public at a near-record clip. The crop included names like Nvidia and BlackRock that, for those who purchased them on the first day of trading, have delivered spectacular long-term returns.

Now the IPO market is heating up again. While 2026 will almost certainly not match the banner year of 1999, which saw 476 companies go public, investors should have far more choices than they did four years ago, when just 38 firms held an IPO. Those likely to debut this year include the giants SpaceX and OpenAI.

“We’re going to see some companies go public that are going to be defining the American technology and economic landscape for the next decade,” says Matt Kennedy, senior strategist at Renaissance Capital.

All of this is enticing for investors hoping to get in early on the next Microsoft or Google. But, as history shows, there is plenty to give pause to those looking to pounce on first-day share offerings.

More IPOs, more duds

Jay Ritter is a soft-spoken emeritus professor at the University of Florida who has acquired the nickname “Mr. IPO” for his exhaustive research on initial public offerings. His data shows that new offerings go on to beat the overall market in some years, but in other years the opposite is true—particularly in years that produce a bumper crop of IPOs.

While shares in Nvidia proved a winner, that wasn’t the case with the overall class of 1999 IPOs. That year, in fact, saw newly public companies deliver three-year returns of -48%. The number is especially sobering given that Ritter’s metric measures from the first-day closing price (which is almost always higher than the official offer price), and excludes nonconventional IPOs like reverse mergers.

For those tempted to dismiss this as ancient history—many members of the IPO class of 1999, after all, got clobbered by the dotcom crash—2021 provides another cautionary tale. That year saw a flood of 311 companies go public—the most in 20 years—but the three-year returns they collectively delivered came in at -49%. The reason for this is not particularly surprising.

“When every IPO is popping, that’s when you see deals thrown together in a hurry,” says Kennedy, noting that smaller, unprofitable companies that would ordinarily not make the cut can pull off an IPO in such a climate. He adds that investors face a further challenge during IPO bull markets because even strong companies are prone to listing at hard-to-justify valuations, increasing the odds of a future slump.

The upshot is that IPO boom times offer investors more opportunities, but also a lot more chances of a misstep. Meanwhile, companies that go public during lean years are more apt to be built to last.

19%

Average first-day return to IPOs, 1980-2025 (minimum offer price: $5/share)

$1.19 trillion

Aggregate first-day IPOs over that period
Source: Jay Ritter, U of Florida

Over the years, the path to going public has also shifted. According to Ritter, companies that debuted in the 1980s and 1990s were typically younger than today’s IPO entrants, but also more likely to be profitable. Surprisingly, though, Ritter says that profitability at the time of an IPO is not a big predictor of future success. He says that company sales are far better indicators, and firms that have $100 million or more in annual revenue are more likely to perform well over the long term than those that do not.

When to buy, what to expect

Any investor who has sought to purchase a newly listed stock has likely encountered a familiar frustration: Even if they seek to buy right when the stock lists, the price they see from their brokerage is higher than the official listing price.

This occurs because the banks that underwrite the stock offer the listing price to large clients, leaving retail investors to scramble for shares on the open market. Those who want a better price can do so by getting in even earlier—via a private sale or during a company’s pre-IPO “road show”—but that’s easier said than done.

According to Glen Anderson of Rainmaker Securities, which brokers private-share transactions, it’s possible to get hold of shares of firms like SpaceX or OpenAI, but it typically requires an investment of $250,000 or more.

But for the vast majority of investors who will acquire shares on the open market, timing can still play a role. There is no upside to seeking to purchase a stock right when it lists, says Kennedy of Renaissance, adding that it can even be a good idea to buy it at the end of the day or on the day after the IPO.

To get a true sense of a stock’s value typically requires waiting considerably longer for the dust to settle. Ritter makes the case that a newly public company’s first earnings report is not particularly helpful, noting that analysts and corporate executives are heavily invested in delivering results in line with expectations—meaning a firm will take any steps necessary to do so. He says a company’s true investment potential will become clearer after six months, which is when insiders are allowed to sell their shares—after which the share price will reflect the company’s fundamentals more than IPO hype.

All this said, the next Nvidia is likely out there among this year’s IPO crop, and for those who want to try to buy it on its debut day, the best approach is still old-fashioned research, says Anderson.

“You can press the buy button right at the opening for every new stock,” he says. “Or you can do the homework and see what a stock is really worth relative to its comps and valuation, and wait for the price you want. Otherwise, you are just rolling the dice.”

This article appears in the February/March 2026 issue of Fortune with the headline “IPO boom times are back—but be careful what you buy.”

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Business

Tech stocks took another beating as retail investors dump the Magnificent Seven

Published

46 minutes ago

January 15, 2026

Jace Porter

Tech stocks plunged yesterday after President Trump announced in a “proclamation” that he was imposing a new 25% tariff on imports of computer chips from foreign countries. Every single one of the Magnificent Seven tech stocks was down by the closing bell yesterday. Meta suffered the worst, down 2.47%. Oracle (not in the Mag 7 but closely related) was down 4.29%, perhaps because it is the hyperscaler most dependent on imported chips for its AI data center business.

The S&P 500 closed down 0.53%.

However, S&P futures this morning were up 0.36% prior to the opening bell. Traders may be buoyed by the fact that there is a rotation away from the Mag 7 going on among investors in S&P 500 stocks. The index was dragged down yesterday largely because the Mag 7 performed so poorly. But the notional “equal weight” S&P 500 actually rose 0.41%. It’s up 3.62% this year while the normal index is up only 1.18%.

The implication is that traders are selling down the Mag 7 but buying most of the other stocks.

Deutsche Bank reported that 318 of the S&P 500 stocks went up yesterday. “There was still a lot of resilience among equities more broadly, as most of the S&P’s constituents still advanced … We saw more of the rotation pattern at play since the start of the year, with the small-cap Russell 2000 (+0.70%) hitting a new record as it outperformed the S&P 500 for the ninth session in a row. Indeed, the Russell 2000 is now up +6.84% YTD, in contrast to a -1.49% decline for the Mag-7,” Jim Reid and his team told clients this morning.

As usual, retail investors led the way, according to JPMorgan. “This past week was exceptional for retail, sustaining the momentum from earlier this year. Retail investors bought $12.0B in cash equities—the largest weekly inflow since the post Liberation Day V-shape recovery,” Arun Jain and his team told clients.

Most of that was bought in the form of exchange-traded funds but $4.9 billion came in trades on single stocks that were not the Mag 7. Retail investors bought tech stocks that were not Mag 7 companies at 3.7 times the standard deviation above the average, Jain calculated.

Notably, the collapse of the Mag 7 is being driven in part by White House policy announcements. On that theme, Pimco chief investment officer Dan Ivascyn told the Financial Times that he was “diversifying” the asset manager’s portfolios away from U.S. equities precisely because the president’s economic policies are so volatile.

“It’s important to appreciate that this is an administration that’s quite unpredictable,” he said. “We’re diversifying … We do think we’re in a multiyear period of some diversification away from U.S. assets.”

ING’s Chris Turner said something similar in his note this morning. Referring to the wild swings in the price of oil, triggered by Trump’s on-again, off-again threats to bomb Iran, and the White House criminal investigation into U.S. Federal Reserve chairman Jerome Powell, he said, “Investors remain reluctant to chase new themes emerging from Washington on fears of policy reversal. That is probably the reason that the dollar and Treasuries have not sold off on the legal investigation into Fed Chair Powell. Ultimately, however, we think this attack on the Fed will add to the case for de-dollarisation.”

Here’s a snapshot of the markets ahead of the opening bell in New York this morning:

S&P 500 futures were up 0.36% this morning. The last session closed down 0.53%.
STOXX Europe 600 was up 0.37% in early trading.
The U.K.’s FTSE 100 was up o.5% in early trading.
Japan’s Nikkei 225 was down 0.42%.
China’s CSI 300 was up o.2%.
The South Korea KOSPI was up 1.58%.
India’s NIFTY 50 was down 0.26%.
Bitcoin was up at $96.7K.

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Business

AI will infiltrate the industrial workforce in 2026—let’s apply it to training the next generation, not replacing them

Published

1 hour ago

January 15, 2026

Jace Porter

A silent crisis is shaking the very foundations of modern society.

The industrial workforce responsible for building the global economy is at risk of crumbling. The people charged with keeping our power grids online, factories humming, utilities reliable, and supply chains moving uninterrupted are retiring at a fast clip. Sure, this may seem like the natural cycle of things as mass retirement opens the door to at least 3.8 million jobs. But it hides a deeply troubling reality: tacit knowledge, along with practical skills refined over decades of hands-on work, is at risk of leaving with them.

While technologies from artificial intelligence to robotics to computer vision are transforming industrial operations, we’re dangerously close as a society to losing the ability to diagnose a failing motor by sound, read analog engineering drawings, or understand the quirks of a 60-year-old machine that predates Disco.

This kind of expertise is rarely written down in one place and always valuable, especially when there’s a mechanical issue or system-level disruption. Meanwhile, generative AI is making information feel instantly available.

The tension here is real and consequential. The question facing junior industrial professionals across industries, from heavy manufacturing to utilities to supply chain: If software can answer questions in seconds, why spend years learning by doing (and, in some cases, failing)?

When it comes to industrial operations, the answer is actually quite simple. We can’t afford to lose earned knowledge or train a workforce that uses AI without understanding the system it supports from soup to nuts.

The opportunity with investing in AI is to preserve the knowledge needed to keep lights on, factories humming, and society moving, and apply it at scale. Success requires keeping pace with gen AI advancements while adapting to macro factors and global challenges that come in waves. This opens the door wider for AI working with humans (and vice versa) to build resilience into essential industries powering the world’s economy for decades to come.

AI’s Elevated Role: Not On Autopilot

Industry runs on machinery and management making the right calls. Consistently. Confidently. But it’s not that simple.

Across the industrial economy, it’s common for a small group of experienced workers to serve as keepers of an outsized amount of knowledge. They know which vibration or clanking noise spells trouble, which workaround keeps production going during a shortage, and which drawing accurately reflects the latest hardware installments in the field.

At the same time, many companies still operate using a patchwork of small group expertise, spreadsheets, and fragmented databases requiring manual collation. When one system goes down or an expert retires (or, frankly, is out sick), it’s nearly impossible to answer simple questions like: what parts do we have, which assets matter most, or where is money being wasted?

These aren’t small businesses or Mom and Pop shops. Manufacturing giants, automobile OEMs, fleet management companies, utilities, and defense contractors are among the collection of expertise-dependent organizations primed for AI support. Every organization is different but they encounter the same critical problem that AI can help solve: data is everywhere, it’s fragmented or siloed, and organizing it requires plumbing every system and file repository to combine relevant information. Humans can collate and organize data collections in weeks or months with a dedicated effort. Today’s AI, meanwhile, can organize data deluges in minutes or hours.

Trade Painstaking Decisions for Decision Intelligence

The other driving factor: Industrial work is full of tradeoffs. Factory managers, technicians, floor mechanics, and engineers are constantly faced with dilemmas: fix or replace, act now or wait, cut costs or reduce risk, maximize uptime or meet sustainability goals. These decisions affect millions of assets and must be made under regulatory scrutiny, often with incomplete information. AI helps people make better decisions, not turn on autopilot and zone out.

AI is good at pulling together signals from various sources and making sense of them in a way that humans understand immediately, such as maintenance history, sensor data, demand forecasts, market conditions, and environmental risks. When used well, AI can help teams plan, predict, and prioritize. AI backstops human judgment. With the available tech, neither human or machine should be left to their own devices.

This ability to support decision-making goes beyond convenience or cost efficiency. It’s a powerful industrial asset as power grids, utilities, and manufacturers face unprecedented demands from electrification, data center growth and expansion, and full-scale automation. AI can help spot problems earlier, justify investment choices, and safely extend the life of aging equipment. That is not automation for its own sake. It is about keeping essential systems reliable.

AI: A Workforce Equalizer for Trade and Technical Work

Younger workers (18-35) are often criticized for relying too much on technology, or expected to do so when a system falters or machinery requires maintenance. In reality, they want tools that help them do meaningful work safely and efficiently.

Younger workers are also among the first groups to fully embrace that AI advances insanely fast. The tech available today is good enough to accurately reflect seasoned experience, shorten learning curves, and close talent gaps with near-instant, but verified and context-rich data gleaned from real-world work.

Why that matters: AI can demolish the barrier to entry to industrial jobs without neutering the skills required to do the job. Younger pros benefit from AI’s ability to dramatically reduce time spent mining for information or wrestling with fragmented systems. AI actually renders jobs more technical and more rewarding. Both appealing to younger workforce members.

Industrial roles from field service manager to HVAC technician to factory shift worker keep the world running, yet they are often misrepresented as tech-agnostic or low-skill. In reality, they require deep expertise and a variety of skill sets. Across the industrial economy, AI is poised to accelerate skills training ten-fold and open the door for a new generation of industrial pros to step in—and here’s the important bit—without sacrificing quality, let alone imploding the entire system.

We’re seeing vocational programs at community college enrollment numbers tick up, increasing 16% in 2025 compared to last year. This is a signal that Gen Z is open-minded and ready to take on blue collar work in favor of desk jobs. It’s also evidence that AI is not only serving as an equalizer, but actively reshaping and advancing blue collar’s next generation.

Embrace AI as a Workforce Asset, Or Lose Everything

While industrial AI is just beginning to enter mainstream conversations thanks to, for example, $61B in data center contracts in 2025 alone and a buzzy race to collect GPUs for full-scale AI deployments in the physical world, the window to act is already closing. I estimate we have 1-2 years left to capture decades of industrial knowledge in AI applications and front-edge tech platforms supported by AI on the backend, or we lose it. Everything.

The industrial economy operates in the real world, with communities around the globe relying on it for jobs, electricity, and much more. People building AI to meet unprecedented demand need to ship practical tools that respect human experience, support better decisions, and make complex systems easier to understand—whether you’ve been on the job for four weeks or 40 years. Industrial operations are deeply technical, nuanced, and complex. AI alone can’t do the work at scale. Systems need industry-rich context and informed prompts from human counterparts to produce outcomes that solve problems and stand the test of time.

AI can (and should) be applied to industrial operations in an authoritative, but supporting role across sectors and specific use cases.

But industry must adopt innovation that preserves nuance, predictive maintenance inclinations, and incident-specific experience only possible from years of hands-on work. That’s how we add resilience to global operations. To do this in hours and days, not months, we need both AI and people. I’m optimistic that AI won’t hollow out the industrial workforce. In fact, incorporating AI at scale to support a younger workforce may be the only way to sustain it.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

This story was originally featured on Fortune.com

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