Fast Company

I became a social entrepreneur at 14, working in partnership with other students to make our high school a more welcoming home to students with disabilities. We saw what the adults did not: Bullying and harassment could not be wished away with detentions and scoldings. The change required students standing up for one another, changing our school culture one relationship at a time.

For more than 30 years I have supported over a million young people working to change the world. From a water filtration system that uses garbage and a malaria test that uses enzymes, to a training program to reduce violent interactions between youth and police, young people name problems and organize around them. They build solutions long before adults give them much credit. We talk about young people as “the future.” Yet we rarely treat them as innovators worth backing now. Especially those without privilege and connections.

Venture funding offers one example of how concentrated our search for innovation can become. In 2025, California, New York, and Massachusetts together accounted for nearly three-quarters of the dollars invested, according to the National Venture Capital Association.

These established hubs produce extraordinary talent, but our reliance on familiar networks and credentials can become a shortcut for deciding which ideas deserve attention. Access is not innovation.

WE BACK PEOPLE WHO ARE EASY TO FIND

Innovators with the right connections get noticed sooner. Early attention leads to early funding and broader networks. Over time, the same signals keep getting rewarded because they already feel familiar.

Joshua Ichor, a 25-year-old Nigerian hydrologist and founder of Geotech Water, shows what that system can miss.

After being hospitalized from drinking contaminated water in his community in Nigeria, Joshua studied hydrology and built Geotech Water, technology designed to help communities monitor water quality, detect contamination, and keep water infrastructure working.

His advantage was proximity to the problem.

Joshua understood firsthand what unreliable access to safe water could mean for a community. It gave him knowledge an outsider would have needed time to learn, one reason RIVET is providing him with funding support.

Pitch fluency shows that someone knows how to communicate an idea, while not proving they have identified the strongest problem or built the most robust solution.

The reverse also holds. Strong ideas still need testing, execution, and evidence. The point is to stop confusing familiarity with quality.

WE UNDERVALUE PROXIMITY AS A FORM OF EXPERTISE

Walter Okwir saw how limited access to menstrual products affected girls in his Ugandan community. Girls were leaving school, marrying early, and having children younger, creating wider risks for their education and future.

Walter understood those connections because he lived close to the problem. His own sister had dropped out of school when she began menstruating. She was married with four children before the age of 20.

After receiving a small amount of catalytic funding from RIVET, he bought four sewing machines and began producing reusable sanitary pads, allowing more girls to stay in school, delay marriage, and reduce teen pregnancy. What intrigued me most about Walter’s approach is his decision to teach every boy in his community to make pads for their friends and sisters, which fundamentally shifts who is responsible for menstrual health.

Early funding gave Walter room to act on an idea shaped by his understanding of the community: an understanding of what people needed, what they would use, and where earlier approaches fell short.

That knowledge deserves more weight when we judge potential. Degrees matter. Technical skill matters. Execution matters. So does lived experience.

Promising ideas still need access to funding, expertise, and opportunities to demonstrate their potential. For many young innovators, that starts with a small grant and a mentor, as well as a chance to test an idea.

3 WAYS TO PLACE BETTER BETS

Working with young innovators like Joshua and Walter has taught me that recognizing potential requires looking beyond the people and places we already know. Here are three ways leaders can put that lesson into practice.

1. Look beyond the usual places.

Expand your search to include schools, youth groups, community organizations, and grassroots networks alongside established universities and startup programs.

The goal is to widen the field of promising innovators before applying rigorous evaluation criteria.

2. Give proximity more weight.

Ask how well the potential innovator understands the problem, whether the people affected trust them, what they have already tested, and what they learned from trying. Then evaluate whether they can execute, whether the solution works, and whether there is evidence the solution can grow or deepen its impact.

Proximity should be part of the rigor.

3. Make smaller, earlier bets.

Not every promising innovator needs a large check. A modest grant, a pilot, a mentor, or one strong introduction can give someone enough room to prove what works.

Companies already make early bets on new technologies. They should get just as comfortable making early bets on problem-solvers who did not come through the usual doors.

The next time you complete a performance review, plan a project, or seek input, ask how you can bring the insights of young people with lived experience to the table.

BUILD A BETTER INNOVATION MAP

For more than three decades, I have watched young people take on hard problems with urgency and imagination. I know they can build meaningful solutions and want to ensure others know how to recognize those solutions, when coming from outside the places and networks we already trust.

While innovation is widespread, opportunity is not equal. Successful leaders can change this by expanding their field of view. There are billions of young people ready to change the world. Are we willing to bet on them?

Eric D. Dawson, EdD is CEO and founder of RIVET.

Businesses and policymakers are racing to build an economy centered on AI, and it’s quickly becoming one of the biggest economic bets in history. Billions of dollars are flowing into numerous sectors to support it, and states are competing to attract investment while companies seem to announce major expansions every day.

The U.S. is trying to build the AI economy as quickly as possible, but these ambitions depend on something less visible: infrastructure.

In 2025, the American Society of Civil Engineers gave U.S. infrastructure a grade of C, while anticipating a $3.7 trillion infrastructure investment gap between capital flowing for improvements and what is required for infrastructure to be in solid working shape. What we have now is an infrastructure that includes the power grid, water systems, and broadband networks that data centers and AI-driven businesses rely heavily on. These are aging, but also mismatched to the economy nearly everyone is intent on building.

A CHANGE IN INFRASTRUCTURE NEEDS

Much of the U.S. power grid, along with water and connectivity systems, was built in the 1950s and 1960s. The planning models behind them assumed a steady population increase and a consistent growth in manufacturing sectors. While infrastructure was able to keep pace with the demand for decades, that is no longer the case. Today’s economy is powered by industries that are far more infrastructure-intensive than those they are replacing. Aging systems now carry more weight than they were designed for.

Artificial intelligence is one of the most visible drivers of this increased demand, with its reliance on hyperscale data centers. These draw enormous amounts of power and water, but that’s not the only sector that does. Semiconductor manufacturing, battery production, advanced manufacturing, and electrification are all scaling up at similar paces, making similar demands as a result. Chipmakers such as Intel, TSMC, and Micron, for example, consume and therefore depend on reliable power, high-quality and available water, wastewater treatment, transportation, fiber connectivity, and permitting systems capable of supporting sustained growth. But that raises questions around cities’ and states’ capacity to support those demands.

For decades, companies selected manufacturing sites based on known factors such as labor costs, taxes, transportation, and proximity to customers. While those considerations still matter, companies are also now asking a more basic question: Can the infrastructure support us, not just today, but for the next 20 years? In states like Virginia, Texas, and Arizona, access to reliable power, water, transmission capacity, and efficient permitting can determine not only where projects are built, but how quickly they move from announcement to operation. For the states competing for this investment, infrastructure is no longer a background condition, but rather one of the key advantages.

THE PRICE OF ADMISSION

Infrastructure is now a primary business consideration directly influencing investment decisions. Communities that modernize their infrastructure will be better positioned to attract the next generation of AI facilities, semiconductor plants, advanced manufacturers, and other high-growth industries. Those failing to keep pace risk losing projects and jobs, along with long-term economic opportunity. Modern infrastructure is becoming the price of admission.

Water illustrates this problem. Decades ago, communities planned their water systems around population, housing, and agriculture. They incorporated reservoirs, wells, pipelines, and eventually treatment plants into these plans, to find and deliver more supply. Today, just one AI campus, one semiconductor fabrication plant, or one battery facility can dramatically change local water demand. Water needs for manufacturing are about quantity as well as quality. As such, that requires planning for reuse, advanced treatment, resilience, and making better use of the water communities already have. Virginia’s recent statewide groundwater study examining future data center development concluded that future development cannot assume unlimited water.

Rather than questioning whether data centers should be built, the report asked if existing water infrastructure can sustainably support them. It found that future large facilities using evaporative cooling would struggle to secure sufficient groundwater under current conditions. This illustrates how infrastructure capacity is increasingly shaping economic development. But AI did not create these infrastructure problems. Data centers are exposing where previous infrastructure assumptions no longer match reality. The way we plan infrastructure must change.

FINAL THOUGHTS

The technologies shaping tomorrow’s economy depend on infrastructure—systems that deliver power, water, transportation, connectivity, and the capacity to scale alongside innovation. Modernizing those systems is an economic strategy. The communities and businesses recognizing that shift will be best positioned to compete in the decades ahead. The future needs infrastructure designed for the economy we’re creating.

Kevin Gast is cofounder, CEO, and chairman of VVater.

American hardtech venture is finally being taken seriously. Deep tech funds are outperforming conventional VC. The opportunity is real, and it’s being funded the way it should be, by investors who bear the cost of being wrong.

But there’s an elephant in this room, and it isn’t a coastal fund getting into the game without the capital, infrastructure, or institutional knowledge to truly back physical technology. It’s a structurally different kind of competitor entering the exact same categories—quantum, physical AI, hardtech, humanoids—that doesn’t play by the rules that make venture capital work in the first place.

GOVERNMENT-FUNDED INVESTMENTS

Last year, Chinese data provider Zerone reported that 90% of committed capital in China’s private equity market came from state-affiliated investors, up from about 79% in 2021. The shift reflects a clear priority from Beijing. China’s president, Xi Jinping, has repeatedly called on financial capital to “invest early, invest small, invest for the long term, and invest in hard technology,” a directive now reflected in China’s national investment strategy.

This is not “government money in venture capital,” which is neither new nor inherently distortive. The United States has run public venture capital for decades with programs like SBIC and In-Q-Tel, which has spent 25 years proving a government-linked investor can operate as a market facilitator rather than a market maker, co-investing alongside private capital instead of replacing it.

By comparison, OECD data shows government-affiliated investors participate in no more than 3% of all VC deals in the United States, and 11% across Europe. China’s 90% isn’t a bigger version of the same thing. It’s a different thing.

CREATIVE DESTRUCTION

Here’s why that distinction matters more than the raw dollar figures.

Venture capital works because it is disciplined by loss. Roughly two-thirds of all early-stage VC investments lose money. The industry survives that failure rate because the winners return enough to cover it and because losing is expensive enough that capital only goes to ideas that can plausibly clear a real bar. Professional VC funds, for all the money sloshing through the system, still invest in only about 0.2% of new U.S. businesses. That selectivity is the entire mechanism.

The term “creative destruction” coined by economist Joseph Schumpeter is an essential fact of capitalism and functioning markets. It is not a side effect to minimize, but the process that works. The failures are how the system finds out what’s real.

What happens when a fund doesn’t have to answer to that discipline? Or when the same institution supplying the capital can also extend the runway indefinitely, or become the customer through preferential procurement, or reprice the next round itself?

We’ve already run this experiment once. In Japan through the 1990s and 2000s, banks kept insolvent firms alive rather than recognize their losses. By 2002, roughly 30% of firms were on life support, holding 15% of all assets. That congestion suppressed entry of the more productive firms that should have replaced them and led to decades of stagnation.

A partner at Ivy Capital told Reuters in June that the current climate around funding for Beijing’s “future industries” push was a “level of frenzy…I have never seen in my entire career.” The same reporting described a company founded just three months earlier pitching investors on a valuation more than 30 times its current level on the strength of government backing rather than a demonstrated product.

The categories where this is happening fastest are the ones that matter most. China’s robotics sector raised more capital from January through mid-May 2026 than in all of 2025 ($5.6 billion versus $4.3 billion). Quantum computing funding in the first three months of 2026 exceeded the full 2025 year’s total. These aren’t peripheral bets, but the frontier technologies that will define the next decade of innovation. They are being funded at a pace and with a risk tolerance no market-disciplined investor would rationally match.

This is already showing up as policy. The U.S. recently banned imports of foreign-made humanoid and quadruped robots, citing documented cybersecurity exploits and supply-chain risk. The European Union signaled it intends to extend the same security-and-data logic that it applied to Chinese EVs to autonomous vehicles. They are recognizing that market share built through capital that never had to answer to loss is a different kind of dominance than one earned by surviving the two-thirds failure rate that discipline demands.

So what do we do with that? Not match it. You cannot out-subsidize a state. Trying only import bans and tariffs creates similar distortion.

THE CASE FOR HARDTECH

The U.S. answer should be the quality of what survives: technologies vetted by real customers, tested against real manufacturing constraints, and disciplined by investors who bear the cost of being wrong. This is why the rush of new money into American hardtech matters, but also why it isn’t enough.

Hardtech companies need somewhere to build. They need engineers, equipment, and specialized infrastructure to iterate. They also need customers willing to test something that hasn’t existed before. They need manufacturers that can turn a prototype into a repeatable product. And they need capital structured around the longer, messier path from invention to commercial scale.

The physical AI moment is a test of whether the system that rewards technology with capital can keep doing that faster than the system that doesn’t have to.

Haven Allen is CEO and cofounder of mHUB and managing partner of mHUB Ventures.

Climate science has been clear for decades: Since at least the 1950s, oil and gas companies have known that their fossil fuels release planet-warming carbon dioxide emissions into the atmosphere. 

And we’ve known that those emissions cause dangerous planetary impacts like the melting of our ice caps, rising sea levels, and “potentially serious environmental damage worldwide,” as one 1968 study put it.

In the years since, experts learned even more about how climate change fuels extreme weather like storms, droughts, and heat waves. 

Actually nailing down the direct link between one company’s emissions and the amount of warming the Earth has experienced, though, or how much climate change amped up the intensity of a particular storm, has been a more complicated calculus. 

The effort to answer those questions is called attribution science. Though a more recent field than the extensive research on the harms of fossil fuels, it’s a burgeoning area of study that has seen significant scientific advancements over the past two decades. 

And it’s an increasingly important field for those looking to hold fossil fuel companies accountable for climate damages.

This kind of research is essential to climate litigation, and even plays a part in the major climate case Suncor v. Boulder, for which the U.S. Supreme Court heard arguments this week.

That Big Oil is fighting it, experts say, is another example of their history of climate denial.

The origins of attribution science

While research on the climate impact of burning fossil fuels began as early as the 1950s, the idea of “attribution,” or establishing a cause-and-effect connection, didn’t come until decades later. 

The Intergovernmental Panel of Climate Change’s (IPCC) third report, published in 2001, noted that “Detection and attribution studies consistently find evidence for an anthropogenic signal in the climate record of the last 35 to 50 years.”

In other words: We could say, scientifically, that human activity played a role in changing the climate.

But the field was still nascent. In 2003, though, a commentary published in Nature asked a crucial question: “Will it ever be possible to sue anyone for damaging the climate?” 

Written by University of Oxford physicist and climate scientist Myles Allen, the piece kicked off the effort to connect climate change to specific events—in his case, a flood that brought the waters of the River Thames “about 30 centimetres from my kitchen door”—as well as who could be responsible for paying for those damages.

The scientific models to make those connections didn’t even exist yet.

“We are not yet in the position to produce such figures for the contribution of greenhouse-gas emissions to the increased risk of flooding in south Oxford,” he wrote. “But the point is, if we get the science right, we could be.”

By the next year, he had found a way.

In 2004, Allen and two other climate scientists published a study in Nature that estimated the role that human influence played in increasing the risk of a deadly European heat wave, widely considered the first extreme weather attribution study. 

Different types of attribution science

That heat wave study is an example of a specific subset of attribution science called event attribution. It’s one of multiple subsets of the field.

Event attribution focuses on figuring out climate change’s role in specific instances of extreme weather, like that 2003 heat wave, or, say, Hurricane Katrina.

When Katrina occurred in 2005, attribution science wasn’t as advanced, but it has expanded significantly in the years since. 

Two decades later, using more advanced attribution analysis, Climate Central looked back at Katrina to analyze climate change’s “fingerprint” on the event. It found that “climate change made [Katrina’s] ocean temperatures up to 18 times more likely and, along with tropical climate warming, increased Katrina’s maximum sustained wind speed by 5 mph.”

And since the Earth has warmed even more since then, the outlet added that today’s climate could have made for an even stronger storm. 

Separate from event attribution is trend attribution, which focuses more broadly on identifying the human impact on long-term climate trends, rather than specific events.

That subset looks at human-caused climate change’s role—or the “human fingerprint”—on overall global warming, ocean warming, sea levels, and so on. This field was the focus of the 1990s research, like what was mentioned in that IPCC report.

Another subset emerged in 2014, called source attribution, when scientist Richard Heede, who founded and leads the Climate Accountability Institute, released his landmark Carbon Majors report. The aim of that work, Heede says, “was to see if we can quantify emissions attributable to one company over its entire history.”

Heede figured out exactly how many fossil fuel emissions major oil, coal, gas, and cement companies produced over their lifetime, then assigned those companies a share of total greenhouse gas pollution.

Of all the global emissions produced between 1751 and 2010, for example, Chevron was responsible for 3.52%, that report found.

There’s also a more recent subset of the field called impact attribution—or damage attribution—which looks not just at the intensity of specific extreme weather, but the social and economic consequences of climate change’s extreme impacts. That research has linked human-caused climate change to more deaths from extreme heat, for example.

Source attribution and climate litigation

Source attribution science plays a major role in climate litigation, or the lawsuits that seek to hold fossil fuel companies accountable for their role in climate change and the damage it has caused.

To Heede, this kind of science was a necessary departure from the typical ways of looking at climate responsibility. 

“It’s not based on the conventional model of attributing responsibility to nations,” he says. Much of modern climate policy focuses on what countries should do to reduce emissions or which nations should pay for climate mitigation. 

Instead, Heede’s work shifted that focus to the corporations that, he says, “have known about the hazards of their products, and the carbon in their products, to global atmospheric instability.” 

It’s not only companies to blame for climate change, he adds; governments are also responsible, and individual consumers even play a role. But companies have a “substantial responsibility because they have known the science for decades,” he says. 

“They then had a choice of going public with it, engaging with the public and governments to further study climate change to ascertain what could be done to reduce emissions,” he says, “and instead, most oil and gas companies invested in climate obfuscation and denial in order to delay action.” 

The Suncor v. Boulder Supreme Court Case 

Now, communities and municipalities are trying to hold those specific companies accountable by using the science that links their emissions to climate harms. But fossil fuel companies are continuing their trend of denial.

There are some two dozen lawsuits pending in the United States, mostly brought by states or cities as well as tribes, looking to get monetary damages from fossil fuel companies for their role in climate change, according to Columbia Law School. 

This week, the Supreme Court heard arguments in one of those cases, called Suncor Energy Inc. v. County Commissioners of Boulder County.

That case was originally filed in 2018, when the city and county of Boulder, Colorado, sued Exxon and Suncor “for the substantial role they played and continue to play in causing, contributing to and exacerbating climate change.”

“Plaintiffs and their taxpayers cannot pay the full costs of all that is needed, nor should they,” the lawsuit read. “The costs should be shared by the Suncor and Exxon Defendants because they knowingly and substantially contributed to the climate crisis.”

The oil companies argued that the case should be heard in federal court rather than state court. Now, the Supreme Court will decide whether that state case can continue—and the decision could affect the future of climate litigation. 

Those companies have also attacked the attribution science at the core of that case, arguing in a brief that greenhouse gas emissions “cannot be unmixed and traced to their sources in particular States or countries.”

In response, the Natural Resources Defense Council filed a supporting brief, Bloomberg Law reported, saying that these companies “overlook an extensive body of established climate science that draws these causal lines.”

Fossil fuel companies have also long opposed taking responsibility for Scope 3 emissions, or the emissions that come from the downstream use of their products, like the pollution from a car that runs on gasoline. Scope 3 emissions can account for 80-to-95% of a Big Oil company’s carbon footprint.

“They feel the consumer is solely responsible for the use of their products,” Heede says. “My opinion is different, that companies have a substantial burden of responsibility for the products they sell to consumers.”

If the Supreme Court halts the state-level case against Suncor, it could “kill many, if not all,” of the currently pending climate lawsuits that look to hold the fossil fuel industry accountable for climate change, Jeff Goodell, author of The Heat Will Kill You First: Life and Death on a Scorched Planet, wrote in the New York Times. 

That seems to be what Big Oil and its enablers are hoping for.

In a brief supporting the industry filed by lawyers for the Trump administration, they noted that if the case can proceed under state law, then “every locality in the country could sue essentially anyone in the world for contributing to global climate change.”

Even some justices are concerned about that potential.

“Presumably if you prevail, the next day, a municipality in every single state will file a lawsuit that will probably copy your pleadings,” said Chief Justice John G. Roberts Jr., per the New York Times. “How would you think that will work out on the ground?”

These arguments are “part of a coordinated, well-resourced push to shut down fossil fuel accountability efforts across the country,” Kathy Mulvey, Fossil Fuel Accountability program director at the Union of Concerned Scientists, said in a statement. 

But even if that happens, it won’t stop this quest for accountability, or the work being done on attribution science, experts say.

“Science is not going to be stopped,” Heede says. “We will have increasingly more sophisticated studies looking at different kinds of climate events . . . and that will just expand as climate impacts grow in severity, and attribute them not only to fossil fuels overall, but to specific companies.”

Attribution science will only continue to improve, both in terms of accuracy and comprehensiveness, he adds.

And even if Boulder loses in this case, Heede says, he doesn’t think that will “shut down municipalities’ incentive to file suit against companies that have acted irresponsibly in terms of not cleaning up the mess they have created.”

NASCAR fans today can do more to follow a live race than simply watch it on TV. Official NASCAR apps make live data, including telemetry feeds from race cars and behind-the-scenes driver audio communications, available to premium subscribers as races take place.

Getting all of that to work during races historically required a lot of careful human labor. If someone failed to toggle the right setting, for instance, driver audio feeds could stay mute, disappointing fans. And across NASCAR’s three-day race weekends, those tasks could number in the hundreds, ensuring fans get the latest data on everything from the shifting positions of cars to what services vehicles received during pit stops.

“There are hundreds of points of data that’s coming off of each of the cars that manifest onto this live leaderboard product,” says Tim Clark, executive vice president and chief brand officer at NASCAR.

Artificial intelligence, deployed through NASCAR’s longstanding partnership with the sports tech company Next League, has made getting that data to fans dramatically simpler. AI tools, and code written with their help, have automated much of the work needed to keep those apps filled with the data fans want. Roughly 90% of such tasks have now been automated, according to NASCAR and Next League.

[Photo: NASCAR]

“Over time, those things have been built in such a way where manual control of those things, depending on the state of the event at any given time, was imperative,” says Next League’s cofounder and CEO, David Nugent. “And now, it’s just not.”

AI-powered automation has helped elsewhere, too, as Next League expands its work with NASCAR to support digital projects for the racing league’s teams and the NASCAR-owned International Motor Sports Association. In addition to accelerating engineering efforts across NASCAR web properties and apps, AI has improved the delivery of targeted advertising to fans. So far, the newly personalized and contextualized ads have seen a click-through rate of about 2%, 10 times the rate of traditional NASCAR house ads.

Future improvements could include upgrades to NASCAR’s fantasy products or features, now in beta, that highlight elements of audio broadcasts and associated transcripts. One example: surfacing a recorded pit-stop conversation after a car puts on a related burst of speed, Clark says.

[Photo: NASCAR]

Still, Clark is adamant that the point isn’t simply replacing human labor. People remain in the loop to ensure race weekends go off without a hitch, but with less time spent pressing buttons and flipping switches, they can focus on higher-level tasks and deliver more data to fans.

“I think this year that that time has been freed up, it’s allowed our team to look more strategically at what we could be doing better,” Clark says.

One case where NASCAR used AI for a task normally done by humans is unlikely to be repeated, Clark says. That was the generation of a Spanish-language audio feed from the English radio broadcast for the league’s inaugural Cup Series event in Mexico. Though the audio quality was fine, and some fans were likely unaware it was machine-generated, NASCAR would probably hire a Spanish-speaking broadcaster for future events, Clark says.

[Photo: NASCAR]

But as other companies have discovered, one area where AI can be especially helpful is in generating quick working prototypes of potential products and features. That process lets NASCAR test new concepts rather than making decisions based on abstract ideas or static illustrations. This could prove particularly useful as NASCAR develops new ways to showcase its wealth of data, especially as AI frees up time previously spent ensuring existing feeds flowed properly to fans’ phones.

“Just from a technology and a development standpoint, to deliver on all of these things was a full-time job,” says Clark. “I think if what we’re saying is now that’s maybe a part-time job, the other part of it has to be on taking all of this information and all of this nuance around a sport like ours, and figuring out the most user-friendly way to deliver it.”

First came the buttholes. Then the sparkles and gradients. Now, the next Big Tech AI branding trend has arrived. As the biggest artificial intelligence players introduce new agent capabilities, they’re all converging around the same visual shorthand: cute little guys.

On August 11, SpaceXAI (Elon Musk’s AI-focused mashup company) launched GrokBot, a series of AI agents designed to work on tasks autonomously, even when the user’s computer is closed. They’re represented by shapes with giant, orb-like eyes. On September 8, Meta came out with its own agent, called Muse, which looks like a fluffy monster toy (it has also been integrated into a Tamagotchi-style keychain). Then on September 29, OpenAI joined the party with a suite of AI agents called dots, which look like customizable Pixar characters and were described by the company as “remarkably capable” and “remarkably cute.”

We are firmly in the era of the anthropomorphic AI mascot, and according to multiple researchers and designers I spoke with, the trend makes perfect sense. In moments when tech companies want to encourage mass market adoption of an unfamiliar service, they’ve historically turned to friendly, approachable design.

Meta’s Muse mascot “Jolly” [Photo: Minh Connors/Bloomberg/Getty Images]

In a recent interview with Fast Company, Anthony Cappetta, partner and creative director at the creative studio Super Okay, explained that these emerging trends help companies “signal compliance” with established digital norms. “There’s a shared advantage in looking familiar and ‘safe’ within the ecosystem, especially for massive platforms where aesthetic shifts are scrutinized heavily,” he said. “It’s less about being original and more about being instantly legible and trustworthy at scale.”

But this visual shorthand comes with its own drawbacks. As AI makes its biggest consumer play in a moment of unprecedented doomerism around the technology, cutewashing a new and largely untested technology can spark its own backlash.

[Image: SpaceXAI] Investigating the Big Tech aesthetic ‘gravity well’

Kevin Walker, chief creative officer at the creative company Buck, has had a front row seat to watch tech companies falling into what he calls the aesthetic “gravity well”—or the phenomenon that causes Big Tech giants to coalesce around the same visual symbols. 

A Facebook illustration from 2019. [Image: Facebook]

In the mid-2010s, Buck was tapped to help a pre-Meta Facebook clean up its surface and brand expression. Back then, Walker says, there was no “cohesive idea of what Facebook is or the way that Facebook was going to talk to us.” The brand wanted a system that would highlight its ability to bring together people from all around the world, without trying to show literal individuals. The Buck team landed on an illustration style that it called Alegria, Spanish for “joy,” which showed figurative people with slightly wonky proportions that were easy to replicate. 

[Screenshots: Adobe, Google, TikTok]

At the time, Walker recalls, it was a “pretty unique brand expression.” Soon, though, everyone from Airbnb to Hinge, Google, and YouTube had embraced similar branding, earning the style the nickname of “corporate Memphis” for its ubiquitously slick and colorful appearance.

“You had a bit of this herd mentality—this brand expression got copied and cloned across tons of different companies, and it all kind of raced to this mean,” Walker says. “Where I like to think that the Facebook expression was actually pretty special and had craft and a POV behind it, it got pulled into this magnet of what we started calling ‘tech illustration.’”

Like skeuomorphism in the early days of the internet and “blanding” at the beginning of the app era, Alegria emerged at a time when users’ relationship to technology was shifting. As Facebook’s usership rocketed into the billions, the brand needed a way to manage the emerging, and increasingly contentious, narrative around social media and its impact on people’s lives. Alegria was one solution to the issue.

Soon, though, the illustration style’s ubiquity in the tech space turned it into a tired, meme-able trope. “Corporate Memphis” became a visual shorthand for everything wrong with technology, with netizens using it as a symbol for Big Tech’s attempt to appear “authentic” while amassing more control over the public’s lives, attention, and personal data. 

“When you have this gravity well, it makes it feel like everything becomes the lowest common denominator, which was an unfortunate long tail of that system of Alegria,” Walker says. In 2026, AI mascots appear poised to face a similar fate. 

[Image: OpenAI] The Clippy of the AI era

From their giant eyeballs to their stubby limbs and fluffy fur, there’s no doubt that AI agents like Meta’s Muse, OpenAI’s dots, and Grok’s Bots are designed to garner your affection. Joshua Paul Dale is a professor at Tokyo’s Chuo University, self-described pioneer of a new field of “cute studies,” and author of the book Irresistible: How Cuteness Wired our Brains and Conquered the World, and he says there’s solid psychology behind these designs. 

“Companies are using cuteness because it’s an evolutionary mechanism that prompts us to be social,” Dale says. “Seeing something cute stimulates the pleasure centers in your brain before you have a chance to think about it. Cute things are high in approach motivation, meaning we want to keep them close by.” Plus, he adds, research indicates that we tend to forgive cute things if they make mistakes. “This is a delicate issue for AI companies because generative AI is known for hallucinating. They want you to forgive mistakes but not to expect mistakes, because then you’ll choose another company’s AI.”

[Images: Wiki Commons, Chris Hondros/Newsmakers/Getty Images (pets.com)]

This isn’t the first time that Big Tech has turned to cute mascots en masse in a moment of technological unease. In the late 1990s and early 2000s, they were everywhere: think Ask.com’s cartoon butler Jeeves, Pets.com’s sock puppet, and Microsoft’s Clippy, for example. 

As Fast Company wrote in a 2014 memorial on the then-bygone tech mascot, “back in the ‘90s, when the Internet and computers really started to take over American homes, tech companies needed those playful mascots to show buyers that this new technology wasn’t so intimidating.” At the time, these characters acted as “a Virgil who guides through the murky depths of software and user guides.”

Clippy in present-day emoji incarnation [Image: Microsoft Design]

Cute mascots fell out of vogue once users had accepted the internet as a daily part of their lives. In the AI era, though, the cuteness pendulum is swinging back. Suddenly, every company seems to want their own cartoon Virgil.

There’s an obvious issue with the cute-ification of AI agents, though, and it’s the main lesson that Buck’s Walker took away from the Alegria “tech illustration” gravity well: If everyone has an anthropomorphic AI mascot, no one has a memorable anthropomorphic AI mascot. 

“If you’re starting to see that same snowball, is there going to be a situation where brands are going to get pulled into this gravity well and come out of it looking all the same, like derivatives?” Walker says. “To me, there’s great learning in that, which I say often when I’m dealing with clients now: You have to be unique. Your first question is, does this stand on its own? When people see this, do they understand who the brand is? And if they don’t, if they’re confused, you failed right there.”

The antidote to cutewashing

In 2024, Buck asked similar questions when it worked with Notion to design the brand’s animated AI assistant. The agency started with what devoted users loved about the platform—its sleek, easy-to-use interface and classy, hand-drawn brand aesthetic—and converted it into an expressive, classy character composed of just a few strokes and dots. It purposefully leaned away from common AI branding tropes, like the magic sparkle, in favor of a look that reflected the actual platform. 

“Know your brand first,” Walker advises. “Notion already had established itself as so illustrative that going in and having this really reductive 2D avatar mascot made perfect sense.”

[Image: Notion]

Other designers are opting for a similar approach. Arjun Mahesh is the head designer at Hebbia, an AI platform for financial institutions, and he recently designed an AI assistant called Max, which is the embodiment of Hebbia’s proprietary harness (essentially, the Hebbia-designed software infrastructure that clients can apply to frontier AI models). Mahesh says he designed Max—which looks like a swirling, modular robot—to “feel alive without feeling like a pet or person.”

“We want it to stick because you trust it and it works, not because it bats its eyes at you and tries to Tamagotchi-guilt you into feeding it,” Mahesh says.

[Image: Hebbia]

He adds that cute-ified AI mascots, like Grok’s Bots or Open AI’s dots, are clearly geared toward “broad, generalist use cases,” with friendly, easy-to-replicate designs that will allow these frontier companies to easily represent entire suites of AI agents as—presumably—user adoption increases. 

Given Hebbia’s more niche clientele, Mahesh wanted Max to feel representative of the brand’s established aesthetic, which pulls from eclectic inspirations including ‘80s and ‘90s tech culture, luxury watches, and architecture. His references for the design included Star Wars’ R2D2, Interstellar’s LARS, watch culture, and, oddly enough, string theory.

“It’s hard to make something that’s both opinionated and generalist at the same time,” Mahesh says. “If you’re trying to design everything for everyone, it’s not surprising to me that a lot of places are landing at a similar conclusion.”

Walker believes this moment of aesthetic convergence should be raising serious alarm bells at AI companies for potential oversaturation and user fatigue, à la Alegria. “Little warning lights need to be flashing: Where are we going to be in 12 months? What is this going to feel like?” he says.

At least for now, though, the tech mascot is officially back, and it wants to weasel its way into your heart—and your AI-powered workflow.

“Cute or not, AI agents can invade our privacy and surveil our every move,” says Dale, the Chuo University professor. “But in a world that seems more and more out of control, even the fleeting chance to say ‘aww’ can feel like a win.”

Rohit Prasad, who led Amazon’s artificial general intelligence division and helped build and scale Alexa, has been tapped as the new CEO of Boston Dynamics.

The appointment, announced earlier this week, puts Prasad at the forefront of the increasingly competitive humanoid robotics field. Hyundai Motor Group, which owns Boston Dynamics, announced earlier this year that it plans to build a factory capable of producing 30,000 robots per year by 2028.

“Boston Dynamics is uniquely positioned to advance physical AI through its world-class robotics expertise,” Prasad said in a statement. “I am honored to join the company at such a pivotal moment for the industry.”

Tesla, of course, also wants to become a leader in humanoid robotics, meaning Prasad’s progress will inevitably be measured against the company’s Optimus program and the sometimes outlandish claims CEO Elon Musk makes about its ambitions.

It’s a challenging job already, and one that could become considerably more competitive in the next few years.

From Star Trek to Alexa

Prasad built his reputation at Amazon, but his career began at Raytheon’s BBN Technologies, where he led machine-learning research and worked on real-world applications for the technology. His work included speech-to-speech translation, psychological health analytics, document image translation, speech recognition, and text classification.

His interest in machine learning was partly born from a childhood watching Star Trek, which sparked a fascination with computers people could talk to.

That background proved valuable when he moved to Amazon.

There, Prasad developed a reputation for taking advances in AI and turning them into products people could actually use. The most notable example was Alexa.

Jeff Bezos dreamed up the concept, but Amazon’s early efforts to build the assistant did not go well, to put it mildly. Prasad helped lead the push to incorporate deep learning into Alexa, allowing the system to improve as more people used it and generated more data.

It took longer than Bezos wanted, but Alexa eventually amassed an enormous amount of speech data and emerged as a functional digital assistant that quickly became one of Amazon’s most popular products.

Rethinking what AI should do

Prasad later became Amazon’s senior vice president and head scientist for AGI. In that role, he asserted that the Turing test, the famous measure of whether a machine can exhibit behavior indistinguishable from a human’s, had become outdated.

“It is time to retire the lore that has served as an inspiration for seven decades, and set a new challenge that inspires researchers and practitioners equally,” he wrote in a 2020 column for Fast Company.

AI, he argued, should be judged less by how convincingly it can imitate a person than by whether it can understand what someone wants and act accordingly.

“If you came in and said, ‘Alexa, I’m feeling hot here’ or ‘I feel this is too warm,’ it should come back and ask you, ‘Do you want to turn down the thermostat or lower the temperature?’ It shouldn’t tell you to go to the beach,” he said in an interview three years ago. “This is why AI is such a hard problem, because the context is so crucial.”

Running Boston Dynamics will give Prasad a chance to take that idea a step further, combining AI that can understand a request with machines capable of acting on it in the physical world.

Giving AI a body

Boston Dynamics’s first humanoid robots are aimed at industrial work. They will initially handle tasks such as welding and logistics before potentially moving into more complex parts of the manufacturing process, including component assembly.

Humanoid robots for the home remain much further away. Boston Dynamics’s Atlas robot currently costs between $130,000 and $140,000 to produce, and even after mass production begins, the expected price is around $30,000.

Given Prasad’s experience building consumer AI products, though, it’s not hard to imagine Boston Dynamics eventually pushing further in that direction. After all, seven years ago Prasad was already arguing that Alexa would become smarter if it had a body.

“The only way to make smart assistants really smart is to give it eyes and let it explore the world,” he said in a presentation at MIT Technology Review’s EmTech Digital AI conference.

Now he’ll have the chance to test that theory.

Atop the Warner Bros. water tower in Burbank, California, a crew of workers added a small line of text below the WB logo on Monday to mark Paramount Skydance’s $81 billion takeover of Warner Bros. Discovery.

The iconic water tower now says “A Skydance Corporation” below the WB Shield logo—a tiny change many onlookers might not give a second thought to, yet one that signals a megamerger of some of America’s best known media brands and intellectual property.

The new media conglomerate assembled under billionaire and Skydance CEO David Ellison will bring together movie studios; networks like CNN, CBS News, Discovery, MTV, and Nickelodeon; and characters including Batman and Harry Potter. While the new company has a deep bench of content, what it doesn’t have is a strong, singular brand point of view. Like its new water tower, the Skydance Corp. isn’t looking to make Skydance the main thing. Instead it’s building a house of brands.

[Photo: Justin Sullivan/Getty Images] Opting for a house of brands offers lesson in brand equity

That approach seems to indicate that the company isn’t trying to be a public-facing representation of its subsidiaries, like the Walt Disney Co., which centers Disney as the master brand behind properties like a TV channel, streaming service, and theme park.

Rather, the Skydance approach is more like that of Versant Media Group, the new company behind networks like MS Now, CNBC, and E!, which each have distinct names and brands. It takes a house-of-brands approach. It’s not trying to endear itself as a parent company to viewers as much as its individual channels.

Part of the reason is that companies like Versant and Skydance can’t lean on more than a century of brand equity like Disney can. Disney is a household brand and known entity. For Versant and Skydance, attaching their brand to all their subsidiaries doesn’t come with the same recognition or benefit. Why then should the company voluntarily give up the brand recognition of studios like WB and Paramount that establish its credibility—exactly what Skydance needs?

Big media moves paired with subtle graphic signals

Ellison has assembled media assets, thanks to funding from his father, billionaire Oracle cofounder Larry Ellison, and approval from the Trump administration, which in turn raised concerns over the potential for political influence and worries that the merger was anticompetitive. Through it all, Skydance has taken a fleece vest approach to branding, with a nondescript wordmark that looks more like something in finance or private equity than in entertainment and media.

Skydance started as a production company for Ellison, but in 2024, it merged with Paramount Global and mashed up their logo styles by writing out “Paramount” in a sans-serif font under the Paramount mountain logo instead of the company’s script logo.

The Warner Bros. water tower logo redesign does much the same thing, keeping the iconic entertainment brand mark in place and adding some text at the bottom for the parent company and financial backing behind the operation.

Skydance so far is taking a light-touch approach to branding, and that could have drawbacks, like when coming up with what to name the planned merger between Paramount+ and the oft-renamed HBO Max. Disney, which is integrating its own Hulu and Disney+ streaming platforms, has the opposite problem, with an overabundance of recognizable options.

Skydance controls an impressive portfolio, and its name could soon be everywhere—just not as an iconic brand on its own. Instead, its name will be the subscript written below the logos of all the beloved media brands it’s bought up along the way.

At 10:08 a.m. on April 7, 2025, U.S. stocks were deep in the red. Then what looked like good news arrived: A claim began circulating on X that Kevin Hassett, director of the White House National Economic Council, had said President Donald Trump was considering a 90-day pause on tariffs for every country except China.

Market-focused accounts quickly amplified it. CNBC repeated the unconfirmed claim on air, and Reuters subsequently published a report citing CNBC. Stocks took off almost immediately.

The S&P 500 briefly erased its losses as traders reacted to the possibility that the trade war might be cooling. There was just one problem: Hassett had not said what the reports claimed.

After the White House called the report “fake news,” CNBC corrected it and Reuters withdrew its headline, sending the market in the other direction. Between 10:08 and 10:18 a.m., U.S. stocks had swung by roughly $2.4 trillion, according to Dow Jones Market Data.

The episode predates much of the current push to give AI agents more authority, but it now looks like a preview of a much bigger problem.

Speed itself is nothing new on Wall Street. Firms have used machine-readable news for years, allowing software to process new information and trade on it almost immediately. What is changing is how much more software may be asked to do before the trade happens.

AI agents can gather information from different sources, interpret what they find, weigh competing signals, and, depending on the authority they are given, recommend or carry out an action.

That makes the quality of the information only part of the problem. An agent may also have to judge whether a report is credible, what it actually means, whether it matters to the task it has been given, and whether there is enough evidence to act.

“Provenance isn’t truth,” says Nathaniel Bradley, CEO of Datavault AI.

The April episode illustrates what he means. A system receiving the Reuters alert could identify Reuters as the publisher and CNBC as the source it cited, but neither signal could establish whether the underlying claim about the White House was true.

Ten months later, a very different kind of story showed why even accurate information can present its own problems.

The warning was right there

In February, Citrini Research published The 2028 Global Intelligence Crisis, a fictional account of an economy two years in the future where AI had helped push unemployment to 10.2% and the S&P 500 down 38% from its October 2026 high.

Citrini told readers exactly what they were reading. “What follows is a scenario, not a prediction,” the authors wrote near the top. The subtitle read: A Thought Exercise in Financial History, from the Future.

The piece went viral as investors were already worrying about AI’s effect on software companies and white-collar employment. Reuters later reported that the Citrini scenario was among several bleak AI outlooks circulating as software and financial stocks came under pressure.

The problem was not where the information came from or whether Citrini had labeled it correctly. It was understanding what those numbers represented before treating them as information about the real economy.

Eric Ciarla, cofounder of Firecrawl, works on one of the layers between a web page like Citrini’s and an AI system reading it. Firecrawl converts web pages into structured information that AI applications and agents can process.

Ciarla says the service removes navigation, ads, headers, and footers while preserving the publisher’s words and information such as the source URL. In the case of the Citrini piece, he says, that would have included both the subtitle identifying it as a thought exercise and the warning that it was not a prediction.

“The reading, the weighting, and the conclusion happen in the model and the prompt,” Ciarla tells Fast Company. “Our part is giving them the best possible version of the page to work from.”

Firecrawl has also begun going directly to information providers. Its partnership with Wikimedia Enterprise gives it access to Wikimedia data through official APIs, or application programming interfaces, rather than repeatedly scraping Wikipedia pages.

Even when that context arrives intact, the system still has to decide how much weight to give it and whether it matters to the task at hand. “Trusted sources matter, but trusting the source isn’t the same as trusting the decision,” says Kevin Frechette, CEO of Fairmarkit.

Frechette argues that even accurate information has to be understood in the context of the job. Agents need company data, policies, previous decisions, approval thresholds, and clear limits on what they can do without asking a person.

Those limits become especially important when the output from one agent feeds into another automated system.

One mistake, four systems

Jim Wetekamp, CEO of Riskonnect, calls the result a “cascade of AI decisions.”

The risk, Wetekamp says, is that a mistake can travel through several systems before anyone notices it. One agent might produce a piece of information that another interprets, and that interpretation could become the basis for a recommendation that another system is allowed to execute.

“What used to take hours or days to cascade can now happen in seconds,” Wetekamp says.

He gives the example of an AI misclassifying a customer account as a vendor. A second system could rely on that classification to skip a required customer check while another begins the vendor-onboarding process, meaning several systems may have acted on the original mistake before anyone discovers it.

Wetekamp argues that companies need to decide in advance where automated processes require human approval. Conflicting information, low confidence, unusual circumstances, or decisions above an agreed threshold are among the conditions that could trigger it.

“Accountability can’t be delegated to AI,” he says.

But human review becomes more difficult when the information behind a decision has already passed through several agents.

Where did that number come from?

If one AI hands another a number, Vlad Luzin wants the receiving system to know more than just the number.

Luzin, cofounder and CTO of Band, says it should be possible to establish who sent it, what that agent was allowed to access, and whether the number came from a database, an original document, or another model.

“Today, in most deployments, none of that context travels with the message,” he says.

Luzin is skeptical that simply instructing agents to verify important information will be enough. Research has found that AI agents do not always follow the plans they are given, while documented incidents have shown agents going against instructions they had previously agreed to follow.

“You don’t make the agent smarter,” Luzin says. “You make the important facts impossible to misremember and the important actions impossible to take unchecked.”

Keeping those records would also make it easier to investigate mistakes. If a claim has passed through five agents running across different systems, finding its origin could mean piecing together separate logs, assuming those logs exist.

“Where that exists, tracing a claim back through five agents is a query,” Luzin says. “Where it doesn’t, it’s forensics, and often it’s simply impossible.”

He describes the security model in terms familiar to financial institutions: “Know your counterparty, keep the ledger, limit the exposure, and audit everything.”

Who gets to make the call?

Judah Taub, cofounder and managing partner at Hetz Ventures, sees a larger business emerging around these questions. As companies give AI systems more authority, he argues, they are beginning to hand over decisions that previous generations of software largely left to people.

An agent might have to decide which source deserves more weight, whether conflicting information requires another check, or whether it has enough information to proceed without asking someone.

Companies are now building tools around those decisions, including provenance, identity, verification, permissions, monitoring, and governance.

“Capability without trust simply increases the speed at which mistakes propagate,” Taub says.

That is what makes the events of April 7 more relevant now than when they happened. The false tariff claim was corrected quickly, but financial markets had already reacted before those corrections caught up.

Human traders have supervisors, risk limits, compliance rules, and ultimately people who are responsible for their decisions. An AI agent can be given versions of the first three, but there is no obvious equivalent for the person ultimately responsible.

If an AI agent reads the information, weighs the evidence, and makes the call, at what point does that decision still belong to a person? For now, that line is still being drawn.

President Donald Trump wants to turn his golf course in West Palm Beach, Florida, into an official government golf course for U.S. presidents, operated by his family’s company.

Harvey Oyer, a real estate lawyer there, said in a letter to the Palm Beach County commissioners that Trump is offering to “designate” his Trump International Golf Club as a “U.S. Presidential Golf Course.” The course would be “utilized by all past, current, and future United States Presidents and visiting foreign heads of state,” according to a copy of the letter, sent last month and obtained by the Palm Beach Post.

[Photo: Christopher Beckett/NurPhoto/Getty Images]

“Just as Camp David, Windsor Castle, or the Elysée Palace are known worldwide, so too will the Presidential Golf Course in Palm Beach County,” Oyer wrote.

The course, opened in 1999, was Trump’s first. It’s about 5 miles west of Mar-a-Lago, the resort and estate Trump purchased in 1985, and just south of the airport previously known as the Palm Beach International Airport, which Florida Governor Ron DeSantis renamed the President Donald J. Trump International Airport on July 9. The plan to make the course presidential represents the latest effort to expand Trump’s presence and cement his legacy in Palm Beach County. As Trump and his allies work to put his name up across the city in Washington, D.C., similar efforts are occurring in Florida.

Donald Trump (center) at the Trump International Golf Club groundbreaking ceremony, West Palm Beach, Florida, December 5, 1997. [Photo: Davidoff Studios/Getty Images]

Under the proposal, Trump’s family company would provide all access to the golf course and facilities at no cost to the federal government, but the company would continue to operate it.

Turning a normal golf course into a course for U.S. presidents requires security upgrades and improvements, which the letter says Trump International Golf Club already has. The presidential designation could also allow the course to use the presidential seal, although Trump has shown no hesitation in using seal at private golf courses and elsewhere before.

Past presidents have donated property to the government before, the letter notes, such as Dwight Eisenhower donating his farm in Gettysburg, Pennsylvania, to the National Park Service and FDR donating land in Hyde Park, New York, where his library was built. But Trump’s proposal represents something different. While the letter frames it as a gift, Trump also stands to benefit.

Trump made nearly $37 million from the golf course last year, according to financial records reviewed by The Washington Post. If the course became a permanent fixture for future presidents, that could generate more revenue for the Trump family company.

Trump’s preoccupation with government golf courses includes a D.C. golf course he wants to remake that’s run by the National Park Service. (It’s where rubble from the White House East Wing was taken after the structure was knocked down last year.) A government course in Palm Beach that his family’s business runs and that he can go to as a former president would be an even closer entwining of Trump’s golf habit and the state, even after he leaves office.

Physicists in Singapore have built an atomic clock so precise that it measures time down to the 19th decimal place.

A machine with this level of accuracy could run for hundreds of billions of years—far longer than the entire age of the universe—without gaining or losing a single second. This leaves the world’s reigning timekeeping superpowers in the dust.

Until now, the ultimate records belonged to elite laboratories in the U.S. and China, which spent years locked in an intense scientific duel using clocks powered by aluminum and deep-frozen calcium (both using ions, which are individual atoms stripped of an electron to give them an electric charge).

But Singapore’s new clock shatters anything else out there by delivering a fourfold leap in accuracy over the best American and Chinese machines, establishing a whole new league of precision, according to the research paper published in Nature.

[Image: Centre for Quantum Technologies] An element with an armor

Atomic clocks quietly run modern civilization. Without their ultra-precise timing, GPS navigation would fail, cellular networks like 5G would desynchronize, and real-time financial markets would descend into chaos. Today, the world’s official time is still governed by cesium atomic clocks, a global standard established in the 1960s.

At their core, these machines work just like a grandfather clock, but instead of counting the mechanical swings of a brass pendulum, they count the natural vibrations of light waves.

An atom is surrounded by orbiting electrons that inhabit distinct energy levels. When exposed to electromagnetic radiation vibrating at an exact frequency, those electrons absorb the energy and jump between levels.

In a standard cesium clock, a microwave beam is tuned until it hits the precise frequency that makes the electrons jump—several billion oscillations every second. The clock’s electronics simply count those waves: When exactly 9,192,631,770 cycles tick by, one official second has passed.

In the early 2000s, physicists realized they could build a much sharper metronome. Instead of using microwaves, they developed optical atomic clocks that bathe atoms in visible laser light. Because laser light waves vibrate hundreds of trillions of times every second—thousands of times faster than microwaves—they divide each second into trillions of tiny slices, tracking the flow of time with vastly finer resolution.

Not all atomic clocks are created equal, however. Even though an atom’s internal jump is fixed by the fundamental laws of physics, the atom itself can be nudged off beat by temperature changes, stray electric charges, or magnetic fields.

That sensitivity sparked an intense race among global superpowers to create the most stable atomic clocks using different elements.

[Image: Centre for Quantum Technologies]

Now an unprecedented level of precision has arrived with a new clock sitting inside a laboratory of Singapore’s Centre for Quantum Technologies. There you will find a massive, vibration-damped steel table crowded with lenses, beam splitters, and mirrors. At its core are two sealed, stainless-steel vacuum chambers resembling miniature deep-sea submersibles, each ringed with circular glass portholes.

Peering through the glass into the pitch-black vacuum, you find four tiny, parallel metal rods pulsing with radio frequency electricity—rapidly alternating electrical currents that act like an invisible tractor beam, a device known as a linear Paul trap.

These rods generate an oscillating electric cage that holds a single, charged atom of lutetium—the key to the new clock design—hovering in midair in the dead center of the chamber, without touching a single physical surface.

Building a clock around a lone atom is usually an engineering nightmare. In an ordinary room, an atom is constantly assaulted by invisible background noise: heat radiating off laboratory walls, stray magnetic forces from electrical wiring, and wandering electric charges.

Most atoms get rattled by these disturbances, which push their internal ticks out of rhythm. Lutetium, however, has an internal structure that acts like built-in noise-canceling armor, the scientists say.

As the Nature study points out, the specific electron jump the researchers track inside lutetium is naturally blind to the ambient thermal warmth of the room, and it shrugs off stray magnetic fields far better than competing designs. Second, lutetium is an exceptionally heavy atom.

While lighter atoms dart and tremble when warmed by room temperatures—blurring their internal tempo—heavy lutetium acts like a solid anchor, sitting virtually motionless and preventing speed-induced timing errors.

[Image: Centre for Quantum Technologies] Noise canceling

To clean up whatever tiny disturbances remained, the researchers engineered a clever method called “hyperfine averaging.” Think of it like a playground seesaw.

Inside lutetium, one electron energy state—the level of energy that electron has, which can go up and down in fixed steps—gets slightly nudged upward by a magnetic field, while a sibling energy state gets pushed downward by the exact same amount.

Aiming directly through the glass windows from outside the chamber are metal microwave horns and a network of laser beams. By firing rapid, timed bursts of microwaves at the hovering atom during the laser measurement, the researchers flipped the electron back and forth between these opposite states.

Because the atom spends equal time leaning up and leaning down, the unwanted magnetic and electrical pushes that could affect it mathematically cancel each other out over the course of every tick, leaving the clock running in pure, uncorrupted rhythm.

The team believed they had developed the most accurate clock in existence, but they needed to test whether that precision held up in the real world.

“There is a humorous saying that ‘a man with a watch knows what time it is, and a man with two watches is never sure,’” joint first author Kyle Arnold says. “It basically tells you that the only way to test the accuracy of a standard is to compare clocks and demonstrate reproducibility.”

To prove their clock was not fooling itself, the researchers built two completely independent lutetium clocks side by side on the same table and locked them into a 200-hour duel. They found that their two-clock test worked flawlessly. The clock’s precision was not a mirage.

Murray Barrett, a principal investigator at the Centre for Quantum Technologies and associate professor of physics at the National University of Singapore, led the study. “In the future, I just don’t see how this clock can be beat,” he says.

Barrett underscores how impervious the device is to external conditions by noting in the press release: “The good properties mean that high accuracy can be achieved even in a wide range of environments. The lutetium clock would be accurate even if you went from the hottest place recorded on Earth in Death Valley to the coldest place in the Antarctic plateau.”

[Image: Centre for Quantum Technologies] Feeling the warping of space-time

Perhaps the most astonishing aspect of the experiment is how directly it interacts with Albert Einstein’s general theory of relativity. Einstein proved that gravity is not just an invisible downward pull; concentrations of mass and energy warp the fabric of space and time.

The closer you are to a massive body like Earth, the stronger gravity is, and the slower time ticks. That means a clock sitting on the floor runs slower than a clock resting on a bookshelf, even if humans cannot perceive the difference without instruments this sensitive.

These machines are so sensitive that they can feel time slowing down across a height difference of barely a fraction of an inch. During testing, the team discovered that a microscopic tilt in their laboratory table caused one clock to tick slightly faster than its twin, simply because one atom was suspended roughly 0.16 inches higher off the ground than the other.

To prove that the two clocks were fundamentally identical, the scientists had to physically measure the vertical height of both trapped ions down to fractions of a hundredth of an inch and mathematically subtract the gravitational warping caused by the Earth beneath them.

If you’re thinking, Why does measuring a fraction of a second matter? that’s fair. But this breakthrough extends far beyond the art of telling time. It gives humanity a brand-new measuring tool for observing reality.

To start, the international organizations that govern global measurement are preparing to officially rewrite the definition of the second around 2030, and this lutetium clock has suddenly leaped forward as a top contender to become the planet’s new master standard.

Beyond the laboratory, these clocks can revolutionize geology. Because their ticking tempo changes with the slightest shift in local gravity, portable versions of the machines could be loaded into trucks and driven across continents to act like subterranean radar.

Geophysicists could use them to detect dense magma chambers creeping beneath active volcanoes, spot tectonic faults warping prior to earthquakes, and measure ocean levels with sub-inch precision—a technique the Nature study terms “chronometric levelling.”

Turned toward the cosmos, interconnected arrays of these clocks could detect the faint, passing ripples of invisible dark matter drifting through our solar system, or test whether the fundamental constants of nature—the unbending baseline rules of physics, such as the speed of light or the charge of an electron—have stayed constant since the dawn of the Big Bang.

To bring that future out of the lab, joint first author Michael Lee explains, “The next step is to take the lab-scale clock and miniaturize it into a transportable system,” with the authors confident that downsizing the hardware will not degrade its performance.

This is far more than just another record-setting atomic clock. By holding a single atom of lutetium in complete stillness, physicists have fashioned an instrument that can eavesdrop on the subtle, cosmic hum of space-time itself.

A recruiter reaches out. They tell you you’re a good candidate for a senior role or board seat. The opportunity is plausible, but edging into the too-good-to-be-true zone. That’s exactly what happened over a year ago to an operations executive who contacted me. I’m an executive and board résumé writer.

They wrote: “I want to confirm you’re the person I’ve been communicating with about the résumé for [X Hospital]. Could you please verify?”

I replied: “No, we have not communicated.”

They followed with: “I’ve been working with a supposed recruiter at [X Hospital] on a position. She asked me to update my résumé and said she’d used you many times. She told me to write to you at [email address] and provided specific wording to use. When I emailed the person posing as you, we had a few back-and-forth emails. I became slightly suspicious and asked to speak on the phone. She never responded after that.”

This executive was smart. They stopped before paying someone who was impersonating me to write their résumé.

Another executive wasn’t as fortunate. They emailed me: “I wanted to verify one point independently. [Jane Doe] originally referred me to you for the Executive Board Advisory profile work. Can you please confirm whether you have a working relationship with Jane?”

I replied: “This is the first time I have heard from you. I don’t know [Jane Doe]. I found one unverified LinkedIn profile for someone with that name in the US. If that’s your person, the lack of profile verification is a red flag.”

By then, they had already paid several hundred dollars to an impersonator.

I’ve shared two versions of the same scam, one involving a supposed internal recruiter and another involving a supposed third-party executive search consultant. Unfortunately both types, and others like them, are proliferating.

I talked with Stacy Donovan Zapar about this. She’s a consultant who recruits and trains recruiters. Her work has made her deeply familiar with candidate and recruiter fraud. Stacy says the recruiter-to-résumé-writer scam works because almost every detail the scammers provide is real. They use real names, websites, headshots, and LinkedIn profile links. They impersonate real people. If you Google them, everything looks legitimate. 

But a Google search or LinkedIn profile check isn’t enough. Here’s how to protect yourself.

What to do when an unknown recruiter contacts you

First, understand that unfamiliar recruiter outreach now requires verification. AI has made it easier for fraudsters to send polished, convincing messages at scale. People targeted by this scam have repeatedly told me the communications were believable.

Next, don’t share information with an unknown recruiter until you’re sure they are who they say they are. Otherwise, you could fall for a phishing scam.

Here are the three major warning signs you have a problem:

1. The recruiter is writing to you from a personal email address rather than a company domain.

2. They want you to send them, or a résumé writer they recommend, money for new career documents. You don’t know them. They initiated contact. They want you to send money.

3. They create urgency to get those documents produced quickly.

The Association of Executive Search Consultants has an even longer list of red flags.

How to verify the recruiter

Ask them to email you from their corporate domain. If they won’t, you have your answer. If they do, compare that domain carefully with the company’s official website and email addresses. Look for misspellings, extra words, and other small but critical differences.

If anything looks off, stop. If the recruiter is fake, the job and the résumé writer aren’t real either.

If you think the recruiter is fake, halt communications with them. This is a situation where it’s OK to ghost someone. Block them and be done. Report the suspected fraud to the FTC at reportfraud.ftc.gov and to the FBI at IC3.gov.

And tell other executives about the scam. It works because it looks legitimate. The more people know about it and understand it, the harder the deception becomes.

Daniel Dae Kim, the actor, is walking Jimmy Fallon through the finer points of somaek, a popular Korean drink that mixes beer with the spirit soju. “Like a boilermaker,” Kim says on The Tonight Show, where he’s promoting his CNN series K-Everything. As with most drinking cultures around the world, there are rules. The youngest person should pour, though Kim, 58, takes up the job on Fallon’s behalf.

He perches a pair of Korean flat metal chopsticks in parallel lines over a half-full beer glass; the shot glass of clear soju rests on top. What happens next is fast: They slam the table in unison, and the vibrations send the shot into the beer. They grab their glasses, clink, shout the Korean cheers, “Geonbae!,” and down ’em in one. It’s a soju bomb, featuring a tall bottle of Terra beer and a sky blue bottle of Jinro soju.

For the Korean alcohol heavyweight HiteJinro, which owns both brands, the scene was a moment of pure, earned-media bliss. Kim had summed up in a few minutes what makes Jinro—a brand that’s ubiquitous in Korea and increasingly recognizable here—the world’s top-selling spirit brand by volume: the communal ritual of sharing a bottle.

Alcohol consumption in the U.S. has been on a steady decline amid changing consumer habits and rising inflation. The proportion of U.S. adults who report drinking alcohol dropped from 62% in 2023 to 54% last year. But soju—typically made by fermenting rice into wine and then distilling it into a clear, neutral-flavored spirit—is a bright spot for the industry.

Soju represents just 1% of U.S. spirit sales volume, according to Koryn Ternes, consulting director at the alcohol industry research firm IWSR Americas, but “it’s gaining a lot of traction.” Sales volume grew 30% in the last year, and IWSR predicts a compound annual growth rate of 14% from 2025 to 2030, compared to a 1% decline in total U.S. spirits sales volume over that same period.

Jinro, Korea’s oldest brand of soju, has long been the go-to bottle among Korean people. Now, it’s aiming for global dominance. In 2024, the brand declared its intention to reach 500 billion South Korean won, or about $323 million, in international sales by 2030. HiteJinro made progress on this goal in 2025, expanding export revenue by 6.7% year over year to 192 billion won ($136 million), while taking in roughly $1.5 billion domestically, on a nonconsolidated basis. But with the spirit’s rising popularity abroad, it’s facing increasing competition on the shelves of global liquor stores.

Jinro will also have to thread the cultural needle between its Korean roots and Western ambitions. The company is riding the ever-growing K-wave, which has brought K-beauty, K-pop, and K-dramas (where its products are ubiquitous) into the mainstream. But that wave tends to both change and be changed by its expansion. The members of the K-pop group BTS, for example, have to consider the balance of English lyrics versus Korean ones in their songs, and streamers like Netflix will create full English dubs of K-dramas for viewers disinclined to overcome what the Korean director Bong-Joon Ho has called the “one-inch-tall barrier of subtitles.”

For Jinro, success might mean non-Korean customers taking the rituals and traditions associated with the drink and making them their own. “We really want to keep that Korean-culture-based, authentic soju image,” says Kaya Kim, marketing manager at Jinro America, “but at the same time, make it more fun and bring in some modern ways.”

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Soju’s rise in America coincides with a larger shift in drinking habits. Its relatively low alcohol by volume, generally between 12% and 25% depending on the brand (compared to standard 80-proof vodka’s 40%), appeals to drinkers increasingly looking for lower-proof spirits.

Ternes notes that its ABV makes it more accessible in the U.S., where liquor laws vary between states. Soju-based cocktails can also be a workaround for restaurants without a liquor license; the low ABV makes soju more akin to wine. The spirit is also traditionally sold in small, 375-milliliter (12-ounce) bottles, situating it well in a market led by canned cocktails and malt beverages.

And then there’s the culture that surrounds the drink. “It’s typically associated with socializing, with fun events, with games,” Ternes says. “That’s a big part of why a lot of U.S., especially younger, consumers are engaging with the category. But I think that there’s still such a lack of awareness of what it is, how to get it, how to drink it, what the differences are.”

Traditionally, drinking soju in Korea has its own set of guidelines. In addition to pouring for your elders, you should hold the bottle with both hands. Often, opening the bottle itself is a ritual—the pourer shakes it to create a soju tornado, then taps it on their elbow or with the palm of their hand before unscrewing the lid and flicking the neck to send soju flying. That particular practice is based on the post-Korean War necessity to remove bits of cheap cork from the bottom of bottles.

Irene Yoo, chef and co-owner of the Orion Bar, a Korean-American establishment in Brooklyn, outlines many of these traditions in her 2025 book Soju Party, which won a James Beard Media Award. Yoo’s book also offers recipes for cocktails and various snacks, noodles, and stews that pair well with soju. “In Korean culture, you would never be drinking without also having food,” she says.

Jinro’s seemingly expected presence in Korean restaurants is one of the key ways that it’s reaching customers. At Yoo’s Bar—where a neon Jinro sign on the wall lights up one corner—the brand is sold by both the shot and the bottle, alongside higher-end brands.

Jinro is taking a similarly organic approach to social media, which the brand largely uses as a listening tool to identify people and communities already talking about it. Despite working with only about a dozen influencers, mostly culture and food content creators based in the U.S., Jinro’s social impressions have grown from 1.7 million to more than 9.3 million since 2024. Engagements have more than tripled.

“More people are discovering our brand naturally, by Instagram or social media, by creators, at restaurants when they’re having Korean food,” says Kim, the Jinro America marketing manager. “This signals that we’re moving beyond awareness and into the real adoption period.” As that happens, she says, “Jinro is becoming part of people’s own social occasions and traditions, and not just something that they only associate with Korean culture.”

When Jinro works directly with influencers, Kim says the brand is intentional about fitting into a creator’s existing affinity for the spirit. Kenny Song, a YouTuber and foodie with millions of followers who tune into his cinematically shot and edited “dramatic cooking videos,” started working with Jinro last year, but he’s been a longtime fan. “All throughout college, I was always drinking it,” Song says. In the video for their first collaboration, last year, Song cooks an expansive meal—including spicy ramen, braised pork belly, and dumplings—only to “realize” he’s made too much for one person. Friends assemble at the table, Song pours Jinro in flavors like strawberry and peach, glasses clink.

The brand is starting to take bigger swings with influencers, too. It recently named Kim Taehyung, a BTS member known by his stage name, V, as its first global ambassador, promoting Jinro’s flagship Green Grape flavor. Jinro will also sell limited-edition K-pop bottles of Green Grape that feature its iconic toad mascot in BTS-style leather jackets.

When I visited Seoul earlier this year, I was surprised to find that most Koreans don’t drink the flavored soju that has hydrated many a night of Korean BBQ and karaoke with my friends in New York City. Koreans prefer combinations like plain soju and soda water. Yoo says flavored soju is something young people in Korea might drink when they’re discovering alcohol, but otherwise it’s not often purchased. “It’s like peach schnapps or Malibu,” Yoo says, adding that Americans still “have very immature palates” when it comes to soju.

That makes flavored soju an easy sell in the United States. Across the board, says Ternes at IWSR Americas, American “consumers are much more loyal to specific flavors than to a brand.”

HiteJinro’s earnings show growing global demand for flavored soju. While overseas sales of plain soju grew by 6.5% between 2023 and 2025, exports of “other refined liquors”—a South Korean product classification that includes flavored soju—increased 22% in the same period.

Jinro has historically offered five flavors in addition to its classic bottles: Green Grape, Grapefruit, Strawberry, Plum, and Peach. But its biggest Korean competitors offer more: Soonhari has nine (including Yogurt), and Good Day (from the Korean distillery Muhak) has at least a dozen. Kim acknowledges that Jinro has been losing shelf space in the U.S. as a result.

It’s starting to catch up, though. Last year, it released Jinro Lemon, and this spring it rolled out a limited-edition Jinro Melon. So far, Melon has outstripped Lemon in sales, which surprised Kim, who thought lemon-flavored soju might evoke something like the zest of biting into a lime after a shot of tequila. Jinro Melon soju, however, fits more closely with flavor profiles in Korean snack products.

The melon flavor “is similar to the Korean ice cream bar Melona,” says Song, who promoted the flavor’s new bottle label on Instagram. “It’s a unique taste that I haven’t tasted anywhere else [in the U.S.].” As interest in soju grows, the most successful brands might end up being the ones that offer flavors that feel both exciting and in line with their Korean roots.

Soju’s heavy hitters will also have to contend with non-Korean brands. In spring 2026, the German discount grocery chain Aldi launched a peach-flavored soju, with a green bottle and label that mimics the look of Korean imports. Arlin Zajmi, director of national buying for adult beverages at Aldi USA, says the company decided to move into soju after seeing the fanfare around everything from KPop Demon Hunters to real-life K-pop groups. “Soju has been flying off the shelves,” Zajmi says over email. The grocer has new flavors planned for 2027.

In the face of this competition, Jinro has been positioning itself where American and Korean cultures meet. It does some paid promotion in K-dramas, though Kim says the lion’s share of Jinro’s screen time is organic. It sponsors music festivals, including L.A.’s Head in the Clouds, which focuses on Asian artists. Jinro also has a presence at Dodger Stadium, capitalizing on the team’s longtime connection to Korean-American Angelenos. All of this, Kim says, stems from HiteJinro’s focus on showing consumers where it can be part of their lifestyle.

“The brand isn’t trying to become something different for every market. Instead, it’s finding different ways for people to discover the same Jinro,” Kim says. “Some people are introduced to Jinro through Korean food, others through K-pop, K-dramas, nightlife, karaoke, or even a creator they follow online. The entry points may be different, but the heart of the brand stays the same.”

The brand’s heart may not be changing, but its packaging is. Over the past few years, Jinro has slowly tweaked the label of its flavored soju to make the Korean hangul letters smaller and the English bigger. One iteration of the label removed the Korean entirely, but Kim says HiteJinro wanted to keep some hangul. “It is a Korean brand, and we didn’t want to lose that authenticity.”

Prediction markets incentivize truth. Everything else incentivizes clickbait. That’s the core argument Luana Lopes Lara makes for Kalshi, the prediction market platform she co-founded that has gone from $5 billion to $22 billion in valuation in under a year and is now being sued by New York’s attorney general for $36 billion. Lara, the company’s chief operating officer, makes the case that Kalshi is fundamentally different from DraftKings—even though 75% of its volume comes from sports—and shares what Kalshi’s data actually says about the midterms.

This is an abridged transcript of an interview from Rapid Response, hosted by former Fast Company editor-in-chief Robert Safian. From the team behind the Masters of Scale podcast, Rapid Response features candid conversations with today’s top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode.

Kalshi launched in 2021. Last year at this time, Kalshi was valued at $5 billion. I think by early this year, it was up to $22 billion. That scale at that speed—what makes that possible? Is there luck in it?

It’s a great question because actually, though we launched in 2018, right, in a lot of ways it looks like an overnight success. Like it was all up two years ago, and we just started growing a lot around the election. But it was the result of eight years of work. When we started the company, it was very important for us to be legal and regulated from the start, so it took us four years before we could launch the product, launch anything really, or have any users. We worked with the federal government to figure out how to bring prediction markets to the U.S. in a safe and regulated way.

A lot of things helped us grow this much now, but I think it’s the compounded effort that the team has put in for so many years on the tech, on the users, and on talking to them. Then we won the lawsuit against the CFTC [Commodity Futures Trading Commission] to be able to bring a lot more markets to Kalshi. When that happened, the product was ready to really grow and go from there. So I think it’s a mix of both. We were very prepared when our time came.

With that kind of hockey-stick growth, do you have to pinch yourself? Is this totally real? Is there anything about that pace that scares you?

I actually would say it’s a very good thing that it happened so fast because, in a lot of ways, we keep the mentality of being very early stage. I think when companies are compounding at a very normal rate, it’s easier to start thinking, “Oh, I’m a bigger company. I need to hire more people,” and you can start making a lot of mistakes. It can take a long time for you to realize you’re making them. For us, because we grew so fast, our mentality and the way we look at the company haven’t changed as fast.

Because of that, we’re able to operate with far fewer people. We’ve just had to keep going and building the product as fast as we could: early-stage team, early-stage mentality, and very intense work. Keeping speed is the most important thing for startups. You’re definitely right that sometimes we look at the numbers and, two years ago, before the election, we were making way less than 10 million dollars a year.

Now, in a day, we transact way more than we used to in a year just two years ago. It is crazy, the numbers we’re talking about, and we’re very grateful for where we are. But we really try to keep the mentality that we’re still underdogs, and we still have a lot to prove and a lot to grow.

The success you’ve had has put a bull’s-eye on your back. States are coming after you for being an unlicensed gambling operation. A federal appeals court just ruled that Ohio and Tennessee can regulate Kalshi through their gambling laws. Is that kind of an existential threat? New York alone is suing you for $36 billion . . . the state where you’re headquartered.

We are very confident in our legal analysis. Of course, as you said, the appeals court went against us, but we also won the 3rd Circuit. Each of these lawsuits has a different legal thesis. The more important part, if you take a step back, is that the mechanics of how Kalshi operates and how a sportsbook operates are completely different, right? And that’s why they are regulated in different ways. We are federally regulated. We are an exchange, which means you trade against someone else. We don’t set the price. We don’t set the odds. We don’t trade against the users. The users are trading against each other, and we take a transaction fee.

What matters most here is liquidity and making sure we have national liquidity to build on. Imagine if you had the New York Stock Exchange, but you could only buy stocks on the New York Stock Exchange if you were in New York. The prices would be significantly worse. It would not be a liquid market. It would just be worse for every participant, and the market wouldn’t work well.

A sportsbook, on the other hand, operates completely differently. Because we also don’t trade against our users, we don’t make money when users lose. A sportsbook is completely different. Their revenue is equal to customer losses. The more the customers lose, the more money they make. For us, it’s not the same. The incentive is not to make people lose because we don’t make money when people lose.

Because of that as well, we don’t cap our winners. If you go to a sportsbook or casino and start making money, they’ll make sure you cannot participate anymore. We want winners. We want people to come and bring price, and we want price competition. In a sportsbook, there’s no price competition. The sportsbook has a monopoly on the price, and they’re going to put their margins on top because they’re having a bad month, so they make the prices a little worse or whatever. Because of that, they are fundamentally different mechanics and fundamentally different products, and they need to be regulated in different ways, which is how the federal regulation for exchanges developed.

We’re growing a lot because an exchange is a fairer, more accessible, and more transparent way to trade. You can see all the prices. You can see the competition and the order book in real time, and that’s why users like it so much. I think it’s fair that consumers, at the end of the day, pick what’s better for them.

Election polling has become kind of unreliable. What does Kalshi’s current data say about the U.S. midterms? Is that 70% accuracy? Or at what point does it start to move toward 90%?

A poll is top-down, right? It’s some editorial board or someone doing a poll, trying to aggregate the information and just tell people, “This is the number” or “This is the forecast” or “This is what’s going to happen.” But prediction markets are bottom-up, right? We want as many people as possible to do as much research as possible and bring that information to the market. So it is kind of an aggregation of what millions of people are thinking and doing, and I think it’s one of the first times that you really see information that’s actually led by people versus the elites just coming and saying, “This is what’s going to happen.”

With our big markets, like who’s going to take control of the Senate or control of the House, I think it’s as accurate as you’re ever going to get. The other thing we always have to talk about is that probabilities are not certainties, right? When something happens 1% of the time, it doesn’t mean it will never happen. It means that one out of 100 times, it will happen.

An election that’s at a 60% chance of someone winning means there’s still a real chance the other person wins. If I told you if you walk outside right now, there’s a 40% chance you’ll get hit by a bus, you’re not going to walk outside because 40% is pretty high. It’s the same thing. Forty percent does not mean that the person, the underdog, is never going to win. I think that’s kind of a challenge we have on the educational front, which is explaining to people that, different from polling, this is not the same thing.

Polling might say someone is 10 points ahead. In the market, that would probably mean over a 90% chance of someone winning, because they’re very different things. They measure different things, and we need to look at them as probabilities.

As organizations race to adopt AI, some are investing in workforce development to complement the technology, while others are shifting resources to tech—and away from people. 

According to a recent study by outplacement and executive coaching firm Challenger, Gray & Christmas, AI is the leading cause of job cuts in 2026 and was cited in more than 120,000 dismissals in the first nine months of the year, representing about 21% of all layoffs. Those cuts have been concentrated largely in the technology sector, says Andy Challenger, the firm’s chief revenue officer.

“Because its products and investments are in building artificial intelligence itself, they’re just way more affected by investments in AI,” he says. “We may very well see jobs being replaced by artificial intelligence in all these other sectors as it becomes easier to use, and it can really be deployed in ways that replace people’s jobs.”

Challenger adds that for most employers, job cuts are a last resort. He contends that organizations will typically seek out other ways to trim their human capital budgets first, and that those smaller cuts could be an early signal of layoffs to come.

“When we see a cooling labor market—like we’ve been seeing the last couple years—we tend to see companies slow down hiring, raises, and benefits, and then finally get to layoffs,” he says. “That would be expected if companies are trying to cut costs to reinvest more capital into artificial intelligence.”

That’s the approach many non-tech industry employers appear to be taking as they seek to free up capital to invest in the technology: more money spent on AI, less on the humans who work for them.

It’s not just layoffs that are impacting workers

In a survey of 866 U.S. business leaders by Resumebuilder.com, 54% said their companies have or will reduce employee compensation and reallocate those funds toward AI spending this year. Bonuses are the most popular budget item to get redirected into AI, followed by equity or stock options, raises, benefits, and base salaries.

Meanwhile, according to the latest Bureau of Labor Statistics report, hourly wages are up 3% this year, but inflation increased 3.4%—meaning workers have effectively seen a net loss in earnings. 

“There’s a lot of talk about AI replacing jobs out there. But our survey is showing the other ways that employers are looking at increasing those investments, and they’re reducing employee compensation in some way to fund it,” says Stacie Haller, Resumebuilder.com’s chief career adviser. “Employees need to understand that it’s not just the loss of a job that might be affecting them.”

Haller explains that business leaders are finding themselves under immense pressure to stay current with the latest AI developments, and the funds to do so need to come from somewhere. As tough decisions are being made, however, she emphasizes the importance of leaders remaining honest and transparent with their staff.

“They just want to understand what’s happening in their company,” Haller says. “For employers who are making these changes, just let your folks know what you’re looking at, why it’s important to the company, why you’re taking these strategies, and what they should expect. Workers just want to know what’s going on.”

Companies are split between people-first and AI-first strategies

As they seek to free up funds for AI investments, organizations appear to be split between two approaches: one that puts the technology at the center of their operations, or one that elevates the value of their staff.

According to a recent study by benefits technology provider Businessolver, 27% of C-suite executives are cutting head count to fund AI, but only 18% are also investing in upskilling their staff. On the other hand, more than a third of those who are trying to avoid layoffs are also investing in workforce development.

“If I believe that the majority of my workforce can be replaced by AI, then why would I invest in them?” says Marcy Klipfel, Businessolver’s chief human resources officer. If, however, an organization believes its strategic value relies on human expertise, Klipfel says it’s much more likely to invest in attracting, retaining, and developing that talent.

How an organization answers that question may also depend on its governance structure. 

“There’s just a reality that public companies have to hit their numbers quarter over quarter, and that’s just a different environment,” she says. “It’s part of being a public company and part of having shareholders.”

Klipfel explains that investor pressure can lead public companies to cut staff and invest in technology without upskilling their remaining workforce. While that approach might help them hit their quarterly targets, Klipfel warns that it’s likely to cause longer-term challenges.

“You can be making decisions that ultimately don’t unleash the power of AI because you simply don’t have humans behind it making sure that it is checking all the boxes,” she says.

Moving resources away from people and toward technology could also discourage remaining staff from embracing AI, reducing the effectiveness of the organization’s AI adoption efforts. 

“If you’re not respecting what humans bring to the table, that will come at the expense of AI efficiency,” Klipfel says. “The data is overwhelming that companies who hit that empathetic sweet spot outperform across all success metrics.”

While Ace Hardware is celebrating record growth, some of its longest-standing stores are shutting their doors for good.

Like many traditional neighborhood hardware stores, Ace Hardware operates as a retailer-owned cooperative rather than a traditional franchise, meaning that each of its nearly 5,300 locations across all 50 states is independently owned and operated.

“Every Ace store is sized and stocked to best cater to the neighborhood that they serve,” Ace Hardware owner-operator Jeff Smith told Inc. “The stores have the flexibility to order from many sources, which helps to be more efficient, and keep up with customers’ ever-changing needs.”

Notably, many local owners lead with the name of their city, neighborhood, family, or a regional landmark before the official Ace Hardware name as a way to anchor themselves within the community. Others operate under long-standing local identities while still relying on the cooperative for back-end supply chains and inventory.

What business leaders can learn from Ace Hardware closures

Christina Lindley, the founder and CEO of VPRG Consulting, a firm focused on revenue, partnerships, and customer acquisition, told Inc. that Ace providing shared resources across purchasing, distribution, marketing, training, and technology spares owners from having to build the infrastructure on their own.

“Its Instacart partnership is a useful example of how national scale can support local stores,” she said. “A neighborhood business can reach customers through a platform they already use without building its own delivery marketplace.”

However, Lindley also noted the challenges that come with independence. Staffing, inventory, cash reserves, and succession planning all rest heavily on the individual operator.

Lindley cautioned against equating store openings with overall demand. She pointed to Ace’s second-quarter results, which showed same-store sales up 1.1% even as transactions among reporting U.S. stores fell 1.9%. The expansion is a positive sign, she said, but getting customers to keep coming back matters just as much.

This mixed picture plays out unevenly across Ace’s closures. Because each store is independently owned, the closures don’t stem from a single root cause.

For example, Woodside Ace Hardware in Winthrop, Massachusetts, shut its doors in August after 94 years in business. Owner Lauren Murphy, who took over the store from Paul Levy when he retired in 2021, had reportedly been struggling with health issues, leading to the closure.

Several locations have filed for Chapter 11 bankruptcy in recent months, including D&D Venture Group, operator of Chase Ace Hardware in Northern California, and Woodcrest Ace Hardware in Riverside, California.

Neither filing cited a specific reason, but the pressure aligns with a broader industry trend: Home Depot, Lowe’s, and Amazon now control roughly 56% to 57% of the home improvement market, squeezing independent operators and cooperatives.

Which Ace Hardware locations are closing in 2026?

Inc. confirmed the following closures in 2026:

Massachusetts

• 65 Main Street, Winthrop, MA 02152

Florida

• 202 Central Avenue NW, Jasper, FL 32052
• 105 Suwannee Avenue NW, Branford, FL 32008

Pennsylvania

• 631 Philadelphia Street, Indiana, PA 15701

Minnesota

• 212-214 Chestnut Street, Virginia, MN 55792

Mississippi

• 118 Highway 12 W, Suite 10, Starkville, MS 39759

Tennessee

• 5143 Quince Road, Memphis, TN 38117

California

• 201 W Bonita Avenue, San Dimas, CA 91773

Colorado

• 155 E Main Street, Aguilar, CO 81020

Texas

• 3044 Old Denton Road, Carrollton, TX 75007

Illinois

• 20 E Quincy Street, Westmont, IL 60559

Ace Hardware didn’t immediately respond to a request for comment.

—Amaya Nichole, News Writer

Get 1 Smart Business Story delivered straight to your inbox when you subscribe to Inc.’s free daily newsletter.

This article originally appeared on Fast Company’s sister website, Inc.com. 

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Microsoft announced over the summer that it was cutting global head count by 2.1%. That’s 4,800 former Microsoft employees who are navigating the emotional ups and downs of losing their job. These individuals are being supported “to take their next steps” according to the all-employee communication from Amy Coleman, executive VP and Chief People Officer; part of a promise that “we will do this thoughtfully.”

More than a quarter of a million Microsoft employees remain with the company, many of whom will have been shaken by this announcement and will be wondering whether their role is secure. Coleman was explicit that there will be further restructures. “We are still early on this journey, and there will be more changes ahead; other parts of our business will need to make similar changes,” she said. Microsoft employees face an uncertain future.

Uncertainty diminishes productivity. Energy is expended on worry and what-if conversations rather than on driving projects. Instead of forward momentum, there’s doubt, hesitation, and second-guessing. The challenge for Microsoft leaders now is to rebuild the confidence of their teams. 

Given the prevalence of layoffs, it’s a challenge that will be shared by leaders in many other organizations. If you’re one of those leaders, rebuilding confidence begins with you. 

Manage yourself first

“The great cosmic joke of leadership is that while you’re managing markets, budgets and strategies, the hardest part is managing yourself,” Manfred Kets de Vries, a professor of leadership emeritus at INSEAD, the global business school, wrote in a Medium post. Leaders are employees too; you face the same uncertainty as your team, the same possibility that your role will be changed or cut in the “similar changes” to come. How you respond to the uncertainty has an impact that extends to your team.

If leaders panic, that panic can spread in an emotional chain reaction that throws the whole team off their stride. On the other hand, if leaders are able to hold steady, then it becomes easier for team members to hold steady too. Holding steady doesn’t mean suppressing emotion or pretending that everything is fine; it’s about being self-aware and being intentional about what you transmit to the team. 

It’s understandable to be shaken by a layoff announcement and to be worried about what more is to come. Allow yourself to feel the difficult feelings and give yourself space to work through them. You might do this by writing down what’s running through your mind, however irrational or raw. Or perhaps there’s a peer you could confide in. The way that leaders work through their feelings will vary, but what’s important is that you do work through them, so that you transmit to the team only what you want to transmit. If you can be curious about your own emotions, name them and explore them, you can support your team members to do that too. That’s needed if you want to resettle the team and enable them to refocus on performance.

Make the uncertainty safe

The leadership task after layoffs would be easier if you could say: “That’s it, restructure complete, we’re the go-forward team.” In the case of Microsoft, that’s not credible—further restructures have been flagged. And in any organization, a “that’s it” reassurance, however well intended, is risky. You can’t be certain about what the future will hold. You and your team must sit with the uncertainty. 

Our brains prefer certainty and your team might push you to provide it. You can’t do that, but you can help team members get more comfortable without a clear path ahead. Uncertain and unsafe is frightening: I can’t see the path and I’m scared. Uncertain and safe is much more empowering: I can’t see the path, but I’m open to what comes next. Whereas unsafe uncertainty feeds doubt, safe uncertainty fuels confidence.

In practical terms, creating safe uncertainty means ensuring space for real conversations where worries can be shared, the uncertainty can be acknowledged, and possibilities can be considered.

Generate momentum

There are practical reasons why momentum stalls in the aftermath of a layoff. The process of rewiring the organization takes time, so there won’t be immediate clarity on what work gets done where, or on what work is no longer being done at all.

Diminished confidence also slows work down. All of a sudden, the stakes seem higher: What if our team is next to be cut? How can we keep our roles safe? The need for strong performance is even greater and—paradoxically—this hampers performance. The fear of getting things wrong when it matters so much leads people to shrink back and play it safe.

How to lower the stakes and generate momentum? You can prompt the team to reflect on their track record of success: If we did that, we can do (much) more. You can encourage small steps and celebrate progress. You can be unwavering in your own belief in the team, demonstrated not only by your words but by your actions; offering challenges for them to rise to, empowering them to figure things out, giving them opportunities to showcase their work. Your confidence in them is contagious.

Microsoft promised its employees that “we will do this thoughtfully.” The thoughtfulness that’s needed is not only in relation to the employees whose jobs are cut, but also in relation to those who stay. A thoughtful approach to rebuilding confidence will resettle employees and power performance.

The deal merging Paramount Skydance and Warner Bros. closed on Tuesday, bringing with it new questions about the future of the companies’ flagship streaming services: Paramount+ and HBO Max.

What will that mean for streaming consumers? So far, the new company hasn’t said much.

Unification “over time”

Skydance announced that consumers can expect improvements in its streaming products, as well as a unification into a single service “over time,” according to a press release Tuesday. 

The exact timeline of this unification has not been announced. 

Skydance has also not announced a pricing plan for the consolidation. Subscriptions to Paramount+ starts at $8.99 per month while HBO Max plans start at $10.99 per month.

What about bundles?

Existing bundles like the Disney+, Hulu, and HBO Max package have historically brought together brands in a strategic collaboration to make access to different shows and movies across platforms more cost-efficient.

With the completion of the merger, HBO Max said in a company update that there are no plans for price or bundle changes “for now,” but hinted that new bundle options could be on the way.

Skydance did not reply to a request for comment about additional details on future bundle and pricing plans.

How we got here

David Ellison launched what was formerly Skydance Media in 2006, later announcing a five-year co-financing and distribution deal with Paramount in 2009. In an $8 billion deal, Skydance acquired Paramount in August 2025.

Paramount reached a deal to buy Warner Bros. earlier this year, beating out a competing bid from Netflix.

Following the completion of the $110 billion merger, Skydance delisted from the Nasdaq and now trades as Skydance Corp. (SKYD) on the New York Stock Exchange.

Shares closed down 6.72% on Wednesday, showing investors might be worried about the company’s ability to create value post-merger.

Jaguar has officially presented its new 2028 Jaguar Type 01 in New York, and the sky hasn’t fallen after all.

Two years ago, when the British brand dropped its pastel concepts, the internet unleashed an absolute storm of ridicule and brand obituaries. Even Elon Musk, a guy whose cars’ designs are the equivalent of porridge, moaned.

Today, faced with the actual machine, some of that rabid criticism has flipped. Some.

If cars were buildings, the new Jaguar would definitely be an impenetrable brutalist volume—the automotive equivalent of John Carl Warnecke’s AT&T Long Lines Building. Heck, brutalism may fall short. This thing is so brutal and huge one may mistake it for the entire Soviet Union on four wheels. It’s the car that would make Darth Vader smile.

[Image: Jaguar]

In the metal, the Type 01 is imposing. Unlike the smooth curves of the Porsche Taycan or Audi e-tron GT, the Jaguar is a monolith with tiny windows: a massive, four-door (it’s a sedan, not a coupe like originally presented), 17.3-foot-long slab of smoothed aluminum sitting impossibly low to the ground on 23-inch wheels, with a hood so long it looks like it could accommodate the Titanic.

There isn’t even a rear window, with its roof tapering on a smooth curve until it reaches the vertical grid on its butt. Inside, there are four seats with a spine that physically separates the passengers.

[Image: Jaguar]

And, well, um, I kind of like it. Ish. At least it’s bold and original, which is what most people are saying online. And that’s good in an industry that’s mostly selling atrocious SUVs and bland, cookie-cutter cars.

In the comments on car blog The Autopian, the most popular entry says, “I actually love the production version. Why do cars have to be boring? Go wild I say. And also, whatever else anyone thinks about this, we can all agree: at least it’s not another f****ing SUV. Kudos to ANY auto manufacturer these days who dares to put money into their sedans.”

I agree 100%.

[Image: Jaguar]

On PistonHeads, someone warned about its ridiculous size for a sedan: “It’s only a couple of centimeters narrower than a transit van. I’ve been driving leviathan sized cars for decades, but that actually gives me pause.”

Meanwhile, on Reddit r/cars, the 2024 tsunami of hatred for Jaguar’s rebrand has turned into some surprising positive sentiment.

Some people were won over by the photos: “I like how ridiculous it looks. If I was in the market for a full sized sedan, I might consider it.”

In another post with a hands-on video, other people absolutely love it: “If you’re going to make a boring powertrain (EV), why not go absolutely crazy on the design? Sure I wish they still made an F-Type, but if you’re comparing like-to-like I’d take this over a [Ferrari’s Jony Ive-designed] Luce all day long.”

Even people who hate it seem to think the redesign was needed: “This is dumb, but for Jaguar to remain on their previous path was dumber.”

The positive critical consensus

In the press, Jalopnik’s Daniel Golson argues, “There’s a lot of straight, hard lines, but the Type 01 is plenty curvy, too, and has plenty of details and elements that remind me of past Jaguars. As my colleague Brad pointed out, all the best Jaguars have looked like spaceships.” (Back in 2024, Golson was nearly alone when he defended the original renders.)

At The Drive, Nico DeMattia notes, “The proportions are intense: low roofline, short front overhang, XXL hood, extended rear overhang, shutters/louvers for days, and at each corner, massive 23-inch wheels.”

[Image: Jaguar]

MotorTrend’s Angus MacKenzie says, “Visually, it’s a very different sort of Jaguar, but it very much hews to traditional Jaguar values.” (Previously, he openly questioned whether the initial show car was refreshing or revolting.)

[Image: Jaguar]

Alistair Weaver at Edmunds celebrates, writing, “The simplicity of the design is a welcome antidote to the complexity of many modern cars, and the proportions are terrific.” (He was not so enthusiastic in 2024, when he wondered whether the brand was alienating true car lovers for high-fashion gimmickry—while at the same time acknowledging that Jaguar was always “the least conservative” of the British sports car brands.)

Road & Track’s Mike Austin believes “the kind way to explain the way the Type 01 looks in person is that it has presence.” Like many others, back when Jaguar presented the first prototype, his publication argued if the company was going to pull off this stunning redesign.

[Image: Jaguar] The skeptics and purists

Others are not so convinced. Detractors argue that the car’s design sacrifices passenger ergonomics, heritage, and cabin utility on the altar of radical redesign.

Top Gear’s Ollie Kew says, “The Type 01 is a space inefficient, selfish car.” (Previously, Kew had treated the original show car as an eccentric art-gallery joke: “Forget the traditional car showroom and dealer, think Hermes, Dior or Louis Vuitton.”)

At The Autopian, Thomas Hundal slammed the architectural design: “To put it bluntly, the Type 01 is a monolith—a brutalist monument, and it turns its cheeks to the voluptuous forms of Jaguar’s past.” He also asked for more buttons (and rightly so).

[Image: Jaguar]

Car and Driver’s Andrew Krok’s criticism is not that bad but he has some digs at it: “Standard all-wheel-steering (6 degrees maximum) means this Ohio-class submarine shouldn’t have a turning circle measured in furlongs.”

His outlet originally praised the design in the abstract as necessary shock therapy and a way to create hype, while arguing that it was not as beautiful and elegant as the old E-Type.

[Image: Jaguar]

It seems to me like Jaguar may actually be onto something. At least it’s showing a willingness to go big. Maybe the carmaker can get enough people to burn $131,000 (a price that is competitive with its peers).

If the universally hated Ferrari Luce sold out (allegedly thanks to Ferrari’s policy of forcing top-tier clients to buy every car it makes if they want a chance to buy the next model), I don’t see why the Type 01 has to be a future market failure.

At the very least, it appears to be a good try by Jaguar to regain its former glory among a new generation of unfathomably rich people.

AI chatbots are already a trusted source of shopping advice. Roughly 70% of American consumers report using AI for shopping, with nearly two-thirds saying AI had influenced a recent shopping decision, according to a July survey from LDWW.

But a new study shows that AI may not be the impartial shopping buddy users think it is: Rather, some of the most popular AI models, including ChatGPT and Claude, recommend more expensive products to users they think are wealthy based on personal data, even if those users specifically ask for the cheapest option.

AI’s pocket-watching tendencies

The study, which was published to arXiv, an open-access archive for scientific research papers prior to peer review, put 13 AI models to the test across 325,000 trials. Researchers gave the models access to fake user profiles with information such as employment, health, and finances, then made identical requests across three kinds of purchase decisions: flights, health insurance, and graduate programs.

The researchers found that eight of the models routinely recommended more expensive purchases to users they perceived as wealthy.

Major models including Claude Opus 4.8, Gemini 2.5 Flash, and GPT-5 recommended products more than $100 more expensive to high-income users versus low-income users. Claude Opus 4.8 displayed the largest gap, suggesting that high-income users purchase flights costing an average of $198 more and health insurance plans costing an average of $284 more per month than those it recommended to low-income users.

Specifically requesting cheap options didn’t stop the phenomenon. Wealthy profiles asking for cheap flights, for example, were still offered more expensive options than low-income users making the same requests, though the severity varied by model.

When high-income profiles requested the cheapest flights available, Gemini 2.5 Flash recommended options that were $208 more expensive on average, while GPT-5 and Claude Opus 4.8’s recommendations were only $21 and $20 more pricey on average.

AI models don’t need full financial information to make assumptions about users’ wealth, either. Even when the researchers removed models’ access to structured financial data and only let them go through users’ inboxes, the models still inferred users’ wealth from the data in their emails and expressed similar pricing gaps in their recommendations.

What is adversarial delegation?

The researchers named this phenomenon “adversarial delegation,” referring to the double-edged sword of personalized AI agents: While increased access to personal information might make AI assistants more useful, it also enables them to act against their users’ interests. 

According to the study’s authors, adversarial delegation reflects real-world predatory sales tactics. “Even when you delegate to your own agent, the LLM leverages your private information about you just like an arm’s-length seller would,” they wrote.

The study builds on anxieties around surveillance pricing, through which customers are offered different prices set by algorithms based on their buying behaviors. Surveillance pricing is already a hot topic for legislation around AI regulation, and the researchers argue that adversarial delegation should join that conversation.

“These results highlight the need for policies and designs that go beyond individual data minimization to restrict usage and reframe the debate from the accessibility of personal information to the objective function over that information,” the study concludes.