AI is powering an economy in which many Americans are falling behind

At the Richmond Neighborhood Center in San Francisco, more than 200 people are on the waitlist for the food pantry. The center is just a couple of miles west of “AI Alley,” where a cluster of major AI companies take in billions of dollars in investments and pay out high salaries to employees — in turn making home prices and rent payments soar. San Francisco serves as a prime example of how the roaring AI industry is helping drive economic growth more broadly, but masking the economic inequality of lower-and-middle-income families. And San Francisco reflects the same patterns happening on a national scale: In the first three months of the year, the US economy overall grew at a solid 2.1% annualized rate, largely due to businesses ramping up AI-related investments, according to Commerce Department data. Yet consumer sentiment is languishing near record lows over wartime price spikes, and the bottom quarter of Americans on the income spectrum have seen the weakest wage growth of any other cohort this year, according to the Federal Reserve Bank of Atlanta. “The inequalities in the neighborhood have just grown and grown and grown,” Yves Xavier, community programs director at the Richmond Neighborhood Center, told CNN. “We can’t draw a direct line to AI’s impact and say ‘That’s exactly it’ because it’s been happening for a while, but it doesn’t exactly take a rocket scientist to see how that’s widening the inequalities in a city already dealing with those issues.” He added that demand for the nonprofit’s food pantry is up about 10% this year. ‘An economy of winners and losers’ The diverging fortunes of the poorest and wealthiest Americans has emerged as a key theme in the US economy, and experts say AI is playing a significant role. The billions poured into the AI industry have minted a cadre of handsomely paid workers in tech hubs across the country, including San Francisco, New York, Seattle, Los Angeles, San Jose and Washington, DC, according to a report by Oxford Economics. Those workers are part of the wealthiest 10% of Americans who are increasingly powering US economic growth with their spending, or as much as 62% of growth, according to Moody’s. “You’re seeing incredible concentrations of wealth as a result of AI for these new companies, their founders and their first employees,” said Manuel Pastor, director of the Equity Research Institute at the University of Southern California. “It’s exacerbating an economy of winners and losers.” The winners in today’s economy are clearly involved in the development and funding of AI, including early investors, experts told CNN. SpaceX debuted on Wall Street last month as the largest initial public offering on record. The AI and space exploration company is now worth more than $2.1 trillion, and investors widely expect it to be a windfall for Americans’ retirement accounts. AI stalwarts OpenAI and Anthropic, both headquartered in San Francisco, are also gearing up for their own IPOs, which would add trillions in new market value. And San Francisco companies comprise nearly two-thirds of worldwide AI funding, according to data firm Crunchbase. Those losing out are vast swaths of Americans, particularly recent college graduates who are struggling to find a job; low-income Americans who continue to rack up debt as they feel the sting of higher inflation; and even workers in creative industries, according to Pastor. “What people put on the internet or put into books is being privatized by these AI companies, making it more difficult for those same people to make money,” he said. “That’s happening to people who are authors, to people who are musicians, anyone who is a creative.” The AI hype is also skewing the health of Main Street businesses. “If you exclude AI, business investment would be actually falling, which is quite unprecedented outside of recessions,” said Maxime Darmet, senior economist at Allianz Trade. “The technology is powerful in propping up the economy, but at the same time, there’s a lot of spending being cut in more traditional areas.” Meanwhile, the gap between the broader AI-fueled economic growth and the lived reality for millions of Americans continues to widen. “The inequalities here are very, very stark,” Xavier said of San Francisco. “It’s been an issue for a long time, and I think it’s just continuing to be an issue.” By Bryan Mena

The AI Era Is Creating a New Trust Crisis at Work. Great Leaders Respond With 3 Simple Behaviors

Layoffs are back in the headlines. Across industries, companies are restructuring, reducing headcount, and redirecting resources toward AI initiatives and operational efficiency. For many leaders, the focus naturally turns to cutting costs, productivity targets, and reassuring investors. But in my experience coaching executives for more than two decades, that’s not where the biggest damage occurs. The real casualty after layoffs isn’t productivity or efficiency. It’s trust. And once trust is broken, the costs can linger long after employees are gone. What leaders often miss When layoffs occur, leaders tend to focus on the people leaving. But there’s another group leaders often overlook: the employees who stay. These employees are asking questions that rarely appear in engagement surveys. Am I next? Can I trust leadership? Does this company still care about people? Does any of this matter anymore? When those questions go unanswered, something dangerous happens. Employees stop giving their full discretionary effort. They become cautious, withhold ideas, and protect themselves. The organization may still function, but trust starts to break down. One of the biggest myths in leadership is that people lose trust because of difficult decisions. That’s rarely what I see. Employees can handle bad news. They can handle uncertainty. They can even handle layoffs. What they struggle to handle is silence. What employees want to see and hear from their leaders When leaders disappear after difficult decisions, employees fill in the blanks themselves. And human beings are remarkably good at creating worst-case scenarios. A few years ago, I worked with the CEO of a mid-sized company that had just completed a painful round of layoffs. The reductions were necessary, and to his credit, he handled the departures with empathy and respect. But once the layoffs were over, he assumed everyone wanted to move on. So the leadership team stopped talking about it. For months, employees heard almost nothing beyond routine business updates. No acknowledgment of what people had experienced. No discussion of the company’s direction. No opportunities to ask difficult questions. Within six months, the company lost several of its highest-performing employees—not because they feared another layoff, but because they no longer trusted leadership to be transparent. In exit interviews, one theme kept surfacing: “I felt like I was left to figure things out on my own.” The CEO later admitted something that stuck with me: “I thought silence would help people heal. Instead, it made them wonder what else we weren’t telling them.” That’s the thing about trust. If leaders don’t fill the communication vacuum, employees will. So, let’s say you’re a leader who wants to regain trust. That’s great. Your starting point? It’s to aways remember that trust isn’t built by protecting your people from reality; it’s built by helping your people understand reality. That’s why communication becomes even more important after layoffs than before them. Three behaviors that rebuild trust The best leaders I’ve worked with and coached consistently do three things after workforce reductions. 1. They communicate early and often Not every answer will be available. That’s okay. Employees don’t expect perfection. But they do expect honesty. Leaders who provide regular updates—even when those updates include uncertainty—create stability during unstable times. A simple message such as, “Here’s what we know, here’s what we don’t know, and here’s what we’re doing next,” can go a long way toward rebuilding confidence. 2. They acknowledge the human impact Too many leaders move immediately to business metrics after people’s livelihoods are destroyed by layoffs. Their colleagues and coworkers notice. So, before discussing strategy, be human and acknowledge loss. Recognize the contributions of those who left. Give employees permission to feel disappointment, concern, or grief. Human-centered leadership doesn’t avoid emotions in something as traumatic as a layoff. It recognizes them. 3. They create opportunities for dialogue As we have determined, the remaining employees will look to their leaders for answers. But not through company-wide announcements alone. That doesn’t build trust. Trust grows through conversations. Managers should be encouraged to ask questions like: “What concerns are you carrying right now?” “What do you need from me to be successful?” “How can I support you?” These conversations demonstrate something employees desperately need after disruption: evidence that leadership is listening. The leadership lesson Layoffs may be a business decision. But trust is, and always will be, a leadership decision. The organizations that emerge strongest from difficult periods are not necessarily the ones that cut costs most effectively. They’re the ones whose leaders understand that people are watching how decisions are made, how communication happens, and how employees are treated when things get hard. At the end of the day, employees don’t expect leaders to eliminate uncertainty. They expect leaders to help them navigate it. And that’s where trust begins. EXPERT OPINION BY MARCEL SCHWANTES, EXECUTIVE COACH, SPEAKER, AND AUTHOR @MARCELSCHWANTES

An Explosion of AI Slop Is Pushing People Offline and Back Into the Real World

There was a time, not so long ago, when the internet was a pretty fun—and useful—place. There was silliness aplenty, with sites like The Fish Doorbell. There were absolutely useless sites such as Zombo or The Useless Web that were still, somehow, fascinating. We came together watching iconic videos. And when there was a major news event, there was a wealth of coverage from both professional outlets and eyewitnesses. Today, though, people describe the internet with a word that would have seemed insane in those golden years: boring. It’s not that many of the oddities that made the internet so fun in the first place have vanished. It’s the things that have come since. AI slop and a perceived lack of creativity are turning more people away from the web and back towards the real world. A survey of 8,400 people across Europe, the U.S. and Latin America by ReverseLookup found that people are spending less leisure time online. Some 61 percent of the respondents said they want to spend more time in offline or local communities over the next year, while 44 percent said they are actively trying to reduce passive scrolling. It’s not that there’s less to do online. There’s more content today than ever. In just one second, an estimated six new websites go live, Redditors post 41 comments, Facebook users post more than 4,000 photos and there are 500 minutes of video uploaded to YouTube. The majority of that, though, is garbage. “For many users, [the] sense of discovery has weakened,” wrote ReverseLookup. “The internet has not become empty. It has become crowded with sameness.” When asked about the quality of online content today, 57 percent of the people surveyed said they now encounter more posts, images, captions, comments or articles that feel artificially generated or low-effort. And 49 percent said online spaces feel less original these days because of the continued spread of “synthetic content.” They’re bypassing AI and suspected-AI content, too. Some 42 percent said they have recently skipped or closed content because they suspected it was produced by AI instead of a person with something specific to say. They’re probably right. Earlier this month, Cloudflare reported the number of bots accessing websites outnumbered human web users for the first time. The trend has held, with 57.7 percent of web traffic coming from bots in the past seven days. That represents a turning point not only for web users, but for how businesses use the web to grow their business. “By 2030, the web as we know it will be dead,” says Rajiv Garg, a professor at Emory University’s Goizueta School of Business. “We’re moving from human-to-screen to machine-to-machine. It’s a total shift. … The value of local, unique data is about to skyrocket. The companies that win will be the ones holding the best raw ingredients for AI.” Of course, the internet isn’t going anywhere. It has woven itself into people’s lives and will remain essential for work, information, support, safety, and connection. But the growing abundance of slop and repetitive content is making more people think of the online world as less of a destination and more of a utility. They’ll still utilize it, but the fun factor that came with the internet less than 20 years ago has disappeared. And in its place is a more mundane and often divisive tundra. And that makes the real world a lot more interesting once again. “The offline revival is not a rejection of modern life,” wrote ReverseLookup. “It is a rejection of the parts of online life that have become predictable, performative and synthetic. Offline life is gaining value not because it is always more exciting, but because it is harder to mass-produce. For many people, the most interesting place left may be the one that does not ask them to scroll.” BY CHRIS MORRIS @MORRISATLARGE

Half of AI Job Cuts Will Be Reversed by 2027, Gartner Says. Here’s the Real Lesson

Half of the companies that cut workers for AI-related reasons will hire those roles back by 2027, according to Gartner. Forrester’s Predictions 2026 report had already documented the underlying cause: Fifty-five percent of employers who restructured for AI now regret the decision. The pattern points to a specific mistake. As I’ve explored before, the question of when to trust data versus judgment matters more than most executives acknowledge. The companies reversing course replaced jobs with AI that required human judgment and got information retrieval instead. Research into how AI is actually being used inside organizations shows that humans need to be in the loop when real judgment of tradeoffs is required. Don’t assume that because AI can access everything your people know, it can do everything people do. Those are two entirely different things. And that’s the leadership mistake underlying both the Gartner projection and the Forrester data. The Cost of Getting This Wrong Consider what it would mean to hire a surgeon who had only read surgery textbooks. The information is complete and the reading is thorough, yet the surgeon has never operated on anyone. You’d never hire that surgeon. But companies across industries made the equivalent decision when they replaced workers whose value came from having done the job under real pressure, thousands of times. Klarna ran this experiment at scale. In 2024, the Swedish fintech claimed its AI chatbot did the equivalent work of 700 customer service agents and projected tens of millions in savings. By May 2025, they publicly acknowledged that while automation takes on more of the high-volume, simpler queries, they still needed human agents equipped for complex, sensitive cases like fraud disputes, complex billing issues, and emotionally charged customer situations, a different profile than traditional outsourced support. They began directly hiring a small number of high-skilled humans into the customer service process to identify where the human touch brings the most value to customers. AI is indeed a transformative technology. People are scared it’s going to take their jobs. When jobs are lost to AI, it’s disruptive to the organization. But then to reverse course shortly thereafter, it creates a whipsaw effect that can have negative effects on the people who remain, and the culture. The Human Gaps in AI Technology AI can categorize problems and retrieve policies at speeds no human can match. Sitting with a frustrated customer, rebuilding trust after a systemic failure, and deciding in the moment that this person needs an exception are calls it has never made. The distance between those two categories is the same one that opened up when Klarna’s chatbot was given jobs that required having experienced something nuanced before and needing to draw on personal judgment about it. There’s a profound difference between reading about surgery a thousand times and having done surgery a thousand times. Same goes for customer service when it comes to upset customers. One produces knowledge, and the other requires judgment. Many organizations confuse the two. Three Things to Get Right The leaders closing this gap are deploying AI for what it’s built for and protecting the people who supply what it can’t access. Here’s what to do: Audit AI Capabilities. Ask honestly whether the roles you’ve automated require simple task execution, experience under pressure, or tradeoffs requiring judgment. Treat Experience as Infrastructure. The pattern recognition, institutional memory, and client trust carried by experienced workers are harder to rebuild than most leaders realize until those assets are gone. Design for Human-AI Teams. The most effective deployments use AI to process what’s routine and protect the people who handle what requires a depth of experience. What This Moment Is Really About Underneath the Gartner projection and the Forrester data is a deeper truth about what AI is and does. It’s the most powerful information system ever built, and information and judgment are different things entirely. AI has read everything. But it’s lived nothing, and providing it with “rules” that replace human judgment based on experience may not ever be feasible, or desired. The judgment behind a complex customer decision, a high-stakes negotiation, or a leadership call in the middle of a crisis comes from having navigated those situations before under real consequences. That lives in the kind of intelligence that only experience builds. EXPERT OPINION BY SOREN KAPLAN, WSJ BESTSELLING AUTHOR, KEYNOTE SPEAKER, AND LEADERSHIP STRATEGY ADVISOR