Fast Company

On Patagonia’s website, starting today, you can shop for new and used clothing at the same time.

The company first launched its resale platform, Worn Wear, nine years ago, making it a pioneer in the brand-owned secondhand clothing space. The platform lets customers send back used clothing in exchange for a gift card; the clothing is then cleaned, repaired, and sold at a deep discount to someone else.

But until now, you had to open a different website to actually buy refurbished Patagonia clothing. Now it’s in the same place, and you can add new and used clothing to the same shopping cart.

[Photo: Courtesy Patagonia]

The shift is a way to “bring resale to the center of our brand and the forefront of our customer experience,” says Sam Brown, Patagonia’s head of resale, noting that in the past, many customers haven’t been aware that Worn Wear exists.

Resale has become ubiquitous across fashion brands, but it typically happens separately from new sales. Patagonia’s integration is the next step in making secondhand shopping the new norm.

The brand’s website includes a used section that customers can browse. If you look up a new item, you’ll see links to buy a used version when it’s available. (Previously, new-product pages linked to the separate Worn Wear site.)

[Photo: © Tim Davis/Courtesy Patagonia]

Bringing everything to the main site also helps give the resale platform a sense of more legitimacy. “There’s still a gap of customers who are willing to buy used or trade in,” Brown says. “And I think we can close that gap by helping to normalize the shopping experience.”

The project took four years to fully build. “Putting used [products] on a typical main e-comm platform is definitely fitting a round peg into a square hole,” Brown notes. “Nothing about this has been easy.”

[Photo: © Tim Davis/Courtesy Patagonia]

First, since it’s not common for brands to sell new and used products in the same place, the company’s UX design team had to find ways to make the differentiation clear for consumers, such as different colored backgrounds for used products.

The team also had to deal with new complexity. Rather than selling a particular new sweater, for example, suddenly it had to work with multiple sweaters with different levels of wear, sometimes in colors that are no longer in production.

“We were figuring out how to come up with a solution where we can manage single-SKU logistics on the back end in the warehouse, and then present something to customers where they’re not choosing across 10 different individual items,” Brown says. “That’s a really hard technical hurdle, and something we’ll probably have to iterate on.”

[Photo: © Tim Davis/Courtesy Patagonia]

The company now has to manage tens of thousands of products from its 20-year history, with a constantly changing assortment based on what customers decide to sell back, rather than a single season’s worth of styles and colors.

[Photo: © Tim Davis/Courtesy Patagonia]

Making it easier to buy used products means more competition with sales of the brand’s new products. But that’s the point, Brown says: The company wants to sell more used items so it can reduce the environmental impact that went into making them in the first place.

Right now, it’s also still relatively limited in scale, because the company can sell only the items that customers happen to send back; this year, the brand has sold nearly 160,000 used items.

Patagonia sees room to do more. “We have our work cut out for us in that so much apparel ends up in a landfill or it sits in people’s closets,” Brown says. “There’s definitely more utility we can get out of the stuff we’re making. It’s just a matter of building the infrastructure and the awareness of these programs.”

Your old Barbies could be turned into speakers, power banks, and more.

Gomi, a sustainable U.K. design studio, put out a call for old Barbie dolls for its Childhood Memories Collection, a new collaboration with the Mattel-owned brand to give old plastic dolls a second life as tech products.

The collection includes $371 speakers, $95 power banks, $61 wireless chargers, and $45 tracker tags that are compatible with Apple devices and have a built-in alarm. Each is made from recycled plastic that comes in a one-of-one design, a marbled pattern of melted-down Barbie dolls, accessories, and other recovered plastics that make for a bright, playful palette of swirled pink, purple, turquoise, and Barbie blonde yellow. The batteries for the speakers and power banks are also recycled, using lithium from damaged and retired e-bike batteries.

[Photo: Gomi]

Gomi was founded in 2017 with the goal of creating sustainable technology products. It focuses on flexible plastic waste, which isn’t recycled in much of the U.K. The company created its first product, a speaker, in 2023, and each one is made from the equivalent of about 44 plastic bags. The swirl effect on the products is created after plastic is melted at the lowest possible temperature. Once molten, color layers are stacked, twisted by hand, and cut.

Working with different waste streams is a challenge because plastics vary in quality and shrink rates. To allow for these inconsistencies, Gomi uses a textured mold to ensure every piece looks and feels desirable.

[Photo: Gomi]

Gomi made speakers out of Barbies after the 2023 movie came out. Cofounder Tom Meades tells Fast Company that months later, he got in touch with Mattel, which was “keen to partner on a collaboration.” The official collab brings major brand recognition to Gomi’s product line, not to mention a more playful color palette than some of its others products. There’s also a deep emotional attachment.

[Photo: Gomi]

“I’m not sure anyone has ever made electronics from recycled materials that have so much sentimentality attached to them,” Meades says.

[Photos: Gomi]

Toy manufacturers have increasingly offered consumers a way to return toys after they’ve outlived their use. For example, Hasbro’s toy recycling program launched in 2018, and Lego’s Replay program began cleaning and repackaging old Legos in 2019 to be reused and donated to educators and community groups.

[Photo: Gomi]

Mattel launched its own PlayBack program in 2021 to accept old Barbie, Hot Wheels, and Fisher-Price toys, which it recycles and repurposes into new items like playground equipment.

Gomi shows there’s still a commercial life for old Barbie dolls and accessories. By giving a childhood memory a new life and form, the studio turns nostalgia into something functional.

When team members disengage, most leaders instinctively tighten the reins. It’s an understandable response, but rarely an effective one. Gallup’s State of the Global Workplace 2025 report found that 70% of the variance in team engagement stems directly from the manager.

How we respond to disengagement doesn’t affect just one person; it shapes the engagement of everyone around them. Research on Threat Rigidity Theory helps explain the instinct to assert control. When we perceive a threat, whether that’s pressure to deliver, resistance to change, or risk of failure, we naturally narrow our focus and become more directive.

The problem is that greater control creates compliance, not commitment.

What controlling disengaged employees looks like in practice

Sam was head of retail operations, with six area managers reporting to her. The business was in deep transformation: new systems, new targets, and new ways of working rolling out almost every month. Most of her team were finding their footing in it, except for one member. In team meetings, he’d challenged nearly every change, not by asking questions like some of her more engaged leaders did, but by undercutting it before it had a chance. Sam noticed the eye-rolling and the cutting remarks about head office. He’d respond to a new initiative announcement with heavy silence, and speak about the company’s direction with a kind of weary sarcasm.

He hit his targets. But he’d stopped doing anything beyond them. He used to flag problems before they became problems, push back on Sam directly when something didn’t make sense, and put his hand up for the harder rollouts. Now he did exactly what he was asked to do and no more.

Sam had already addressed the behavior with him in a one-on-one conversation. She made it clear that the negative comments, public resistance, and lack of support for the changes were not what she expected from one of her senior leaders. He acknowledged the feedback, but his behavior didn’t change. By the time she brought it to a coaching session, she’d decided what came next. She was going to put him on a formal performance plan, document every instance, and tell him plainly that his attitude was no longer compatible with his role. If it didn’t change, she’d start looking at whether he should still be on her leadership team at all.

What Sam was doing is what many leaders are tempted to do when our team members aren’t as engaged as we’d like them to be.

Control creates compliance, not commitment

Team members might comply because they have to, but compliance isn’t the same as commitment. They’re less likely to be genuinely invested in the change, take ownership, or contribute beyond what their role requires. Research also tells us that autonomy is one of our three basic psychological needs. According to Self-Determination Theory, when team members feel controlled rather than involved, their intrinsic motivation, sense of ownership, and willingness to contribute decline.

Perhaps most importantly, a controlling approach focuses on the behavior rather than what’s driving it. Disengagement is often a symptom of something deeper: uncertainty, a loss of confidence, feeling unheard, disagreement with the direction, or simply not understanding the “why.” Asserting control can suppress the behavior for a while, but it rarely resolves the underlying issue.

Seek understanding before taking action

Now, the answer isn’t to lower your expectations or avoid holding your team members accountable. After all, accountability still matters. But before deciding what action to take, effective leaders pause long enough to understand what’s really happening.

One of the most powerful shifts you can make is to become compassionately curious. That means being willing to seek to understand before seeking to solve. It’s choosing empathy over assumption and recognizing that there is usually more to someone’s behavior than you can see on the surface. Instead of assuming a team member is simply resistant, difficult, or no longer committed, get curious about what may have changed. What are they experiencing? What concerns do they have? What might they be seeing that you’re not?

Create ownership together

Once you understand what’s really happening, you can shift from trying to control the outcome to creating it together. Rather than prescribing the solution, involve your team member in finding it. Ask questions like:

• What do you think needs to happen from here?
• What support would help?
• What are we both willing to commit to?

When team members help shape the solution, they’re far more likely to take ownership of it. That doesn’t mean every conversation ends with the outcome you’d hoped for. Sometimes behavior doesn’t change, and you still need to introduce accountability measures. But when you’ve first taken the time to understand what’s driving the behavior, you can make decisions that are better informed and more respectful. This is also far more likely to preserve trust.

The next time one of your team members appears to have checked out, resist the temptation to manage harder. Get curious first. Work on solving the problem together.

Because the behavior you’re trying to change is rarely the real problem.

Sony sparked a backlash among gamers with its decision to phase out physical game discs. Now Nintendo is reassuring its customers that it has no plans to follow suit anytime soon.

“We go off what the consumer prefers, and take that into account very heavily,” Bill van Zyll, VP and general manager for Nintendo in Latin America, said in a recent interview. “We also have a platform that was just launched. We’re barely just over a year into [Nintendo Switch 2]. It’s got both digital and physical—that capability. There’s no plans to ditch physical.”

Van Zyll’s comments are more of a reaffirmation than a new policy announcement. But in the current gaming environment, they’re giving Nintendo loyalists reason to breathe a sigh of relief.

Physical game sales have been a hot topic since Sony’s announcement earlier this year. The company plans to stop producing physical discs for all new PlayStation games starting in January 2028. From an economic standpoint, it’s hard to fault the decision. Just 15% of the PlayStation games sold in the fourth quarter of last year were physical copies. And as Sony develops its next-generation hardware, it’s undoubtedly trying to keep costs as low as possible, especially given recent spikes in the prices of memory and processor chips. Jettisoning a physical disc drive could help achieve that goal.

Still, for a company that once championed physical media—even mocking Microsoft’s restrictive game-sharing policies—the move was bound to cause controversy. And Sony made things worse for itself in September, arguing in a legal filing that players who purchase digital games are buying licenses rather than ownership of the games themselves.

Nintendo is hoping to sidestep that controversy, and it’s easy to see why. While Sony gets only a fraction of its software sales from discs, physical games accounted for 38.5% of Nintendo’s total software revenue in the quarter ending June 30.

Van Zyll said Nintendo takes an “agnostic” approach to how games are distributed. Whatever consumers want is fine with the company.

Whether Nintendo will maintain that position in the years to come is less certain. Van Zyll made no long-term commitments. Given how secretive Nintendo is about future hardware plans—often keeping its non-Japanese divisions in the dark during the early stages of development—that’s a promise he likely couldn’t make anyway.

It’s also worth noting Van Zyll’s role. He oversees Latin America, where physical game sales remain particularly strong. In many ways, he was preaching to the choir rather than speaking directly to players in North America and Europe.

And while the Switch 2 accepts game cartridges, those cartridges don’t always contain the games themselves. Many Switch 2 titles, particularly those from third-party publishers, use game-key cards that simply allow players to download the game from Nintendo’s servers. Nintendo’s own major releases generally contain the full game on the cartridge. For game-key cards, though, the preservation risk remains: If the game becomes unavailable to download, the cartridge alone won’t be enough to recover it.

Microsoft, meanwhile, has taken a more middle-of-the-road approach. While it has made no announcements about the future of physical media, the company recently launched a program letting players claim digital access to eligible games they own on disc.

Make no mistake: Digital is the future of game distribution, despite the valid concerns of preservationists and collectors. But after Sony’s PR bungle, other game makers are likely to tread more cautiously with future moves away from discs and cartridges. Companies like Nintendo will also pay close attention to regional demand. As long as that remains strong in important markets, some form of physical media is likely to stick around.

I’ve been having a lot of conversations with industry leaders lately about the prevalence of so-called “shadow AI.” They’re worried about the risks of unapproved AI tools at work, and most come to me wanting to know how to rein in their employees. But that’s looking at the problem backwards.

Their concern is well founded. Among organizations surveyed in IBM’s Cost of a Data Breach Report 2026 that had experienced a data breach, 43% reported security incidents involving shadow AI, up from 20% the previous year. And the problem isn’t going away. In our own recent survey, we found that roughly one in three respondents reported shadow AI use at their companies.

The problem is, leaders are missing what shadow AI is actually telling them about their businesses. Most chalk it up to an employee problem, when it’s really a leadership one.

The prevalence of shadow AI means your company is trying to innovate faster than you’re letting it. That’s a significant risk in and of itself.

Shadow AI is a signal

Picture an HR professional who, frustrated with a repetitive task like assembling personalized onboarding materials, automates this process with an unauthorized tool.

Some might say they put the company’s security at risk to avoid doing the job they’re paid to do. But another way of looking at it is this: Your employee just revealed a way to create better onboarding materials, save time and money, and free up an entire team for deeper work. In other words, they’re showing you how to trim the fat on your business so everyone can focus on new opportunities to grow the company.

Shadow AI is often the result of an organization’s standardizing its AI approach too early. It may seem like strong leadership, but by committing too soon to one set of tools, you’re closing the door to new opportunities. If you force people to use tools that aren’t the best ones for the job, they’re likely to use the right tool anyway, creating unnecessary risks.

How leaders should respond to shadow AI

Shadow AI is a green sprout of innovation, so don’t treat it like a weed. If, in the example above, the company simply fired or penalized that HR professional, they would be sending a message to employees that innovation is not welcome. Your main priority as a leader right now should be getting AI experimentation out of the shadows so everyone can benefit.

One company we work with did this by hosting an AI amnesty month. Employees were encouraged to share the tools they’d been using without fear of reprisal. The company found that those using shadow AI were some of their best performers and were frustrated by having to keep their innovations in the dark. The initiative allowed the company to evaluate all the tools at its disposal—tools that were already road tested—and decide which to implement widely and which to formally reject. They celebrated the innovation and found a path for employees to operate them securely.

Doing so mitigates another crucial risk. If your employees have found a more efficient way to do their jobs, your competitors will discover the same. You are ceding the advantage to them if you ignore what your team has found.

How to build a culture of AI experimentation

To be clear, shadow AI carries significant risk and should be discouraged. But how you discourage it will have an impact on how your company relates to AI in general, so be intentional in your messaging and reactions.

Create a culture of responsible innovation by:

• encouraging employees to seek out new tools while establishing clear rules around what data they can use and what systems those tools can access
• coordinating with IT to create approved pathways for testing new tools rather than simply blocking them
• creating a sandbox system with synthetic data so employees can experiment without exposing company or customer information
• supplying employees with time, resources, and training so they have the space and ability to use these tools responsibly, and
• creating forums for employees to share what’s working so that everyone can learn from each other.

What leaders should communicate about AI

The best way to fight shadow AI is to create a culture where no one feels the need to hide their usage in the first place. That comes down to the message you’re sending to your employees: We will not tolerate shadow AI, but we will do everything we can to empower you to use AI. Let them know that because they are closest to the work, they know how to make the work better, and you’re giving them the chance to do that.

The way things have always been done is not the path to the future. If your employees aren’t stuck doing drudgery work, they’ll have time to find ways of doing things you previously never thought possible. Leaders should follow the motto: “Find a way; don’t get in the way.”

The Trump administration has done everything it can to slow the growth of EVs. But in an ironic twist, Trump’s war in Iran is boosting the EV industry globally.

After the U.S. attacked Iran in late February and the price of crude oil soared, global EV sales also surged. A new report from Ember, an energy think tank that is tracking EV sales in 56 countries, found that 49 of them have hit new records for EVs this year.

[Image: Ember]

Between March and August, EV sales in New Zealand grew 165% compared with the previous six months. In Chile, they were up 124%, and in India, 58%.

[Image: Ember]

In many countries, the growth was also supported by new policies. In Germany, for example, pro-EV policies that rolled out last year were in place as fuel prices rose. By August, as Germany’s gas prices reached a record high of around $8.52 per gallon, the country’s battery electric vehicle sales made up 27.5% of total car sales—a 50% increase over the same month last year.

“First you had fuel efficiency standards—which basically means a lot of manufacturers suddenly started bringing out more affordable electric car models at the start of this year—and then there was also some more direct policy support in Germany,” says Euan Graham, senior energy analyst at Ember. “So, all of that has meant that as consumer interest in EVs has risen and as petrol prices have risen, consumers have also had more affordable electric car models to choose from.”

In Uruguay, which has supported EV growth for more than a decade, the share of EV sales reached a record high of 38% in August. Vietnam hit 51%. Australia reached a 20% share. Norway, a longtime leader in EVs, hit 98%. Countries that have been slower to adopt the cars are also catching up. India had a 2% EV share in 2024, but that grew to 7% this August.

Overall, total global EV sales are up 11% year over year for the first eight months of 2026. Excluding data from the U.S. and China (Chinese car sales have dropped overall), global EV sales have grown 45% this year.

[Image: Ember] The U.S. is being left behind

There’s a notable outlier: New EV sales have fallen in the U.S. Over the first nine months of 2026, EV sales were down 30% compared with the same period last year.

Several policy changes contributed to the decline. The Trump administration killed off the $7,500 tax credit for EVs. It also froze funding for EV charging infrastructure (a judge ruled that the freeze was illegal, though states have been slow to use the money). Tariffs made it more expensive to make EV batteries. The administration rolled back fuel efficiency standards. In response, automakers have canceled multiple EV models in the U.S., from the Ford F-150 Lightning to the Nissan Ariya. As gas prices rose this year, consumers had fewer options.

EV sales in the U.S. rebounded slightly in the third quarter, and used EV sales are also growing. New lower-cost models like Slate’s minimalist electric truck and Ford’s upcoming $28,000 EV will likely help. Still, the U.S. is lagging behind the rest of the world.

[Image: Ember]

Ember tracks EV sales in every country where data is available. In 49 out of 56 countries, the market share of EVs has now outpaced the U.S. Fifteen countries, including India and Brazil, overtook the U.S. this year.

China’s huge EV industry, which is now a major exporter, is benefiting. Domestic EV industries are growing in some other countries, including Vietnam and India. American auto manufacturers, meanwhile, have been forced to write down billions in losses because of their EV investments. Eventually, as the rest of the world keeps moving in a different direction, they may become obsolete. “I think the biggest impact is on the long-term competitiveness of U.S. auto manufacturers,” Graham says.

The Ember report details how much countries with strong EV adoption have benefited during the Iran war. China, for example, has been able to displace $14 billion in crude oil imports because of its fleet of electric cars. (That’s not counting the additional benefit from the use of electric trucks, buses, and other vehicles.) Globally, the report estimates that oil-importing countries have been able to displace 200 million barrels of crude oil since the war started, worth around $20 billion.

It’s not clear what will happen when the war ends and fuel prices stabilize. Graham expects the market share of EVs to continue to grow, but says that policy is still critical. “I think that the long-term impact of this supply crisis is really going to be defined by whether governments change how they plan for the future,” he says.

Traditional software development lifecycles (SDLC) were designed around how fast people could type and coordinate. Sprints, manual quality gates, and review handoffs made sense when writing code was the slowest part of the job. With AI, developers can write, refactor, and document code faster than they could two years ago. What has not moved nearly as much is how fast the same organizations actually ship.

Gartner research on AI in software engineering explains part of why. More than half of developers report that AI has improved their team’s output by 10% or less, and some see no improvement at all. The teams increasing output are applying AI across the whole SDLC, not only to the moment of writing code. When you give engineers a faster way to write code without changing anything else in the process, the result is that more code is waiting for manual reviews, which slows the path to production.

This scenario is what pushed us to rebuild our own SDLC around AI rather than layer AI on top of it. Here is what that rebuild looks like, and where most teams lose the gains before they reach the business.

KEEP IT LEAN FOR SMALL PROJECTS

For smaller projects, an AI SDLC can be simple. Before using an AI tool, write clear requirements for each feature: what it should do, what it must not do, and how the team will know it works. AI can help draft and sharpen these requirements. Store them, along with the team’s coding standards, in the same system that tracks every change to the code. That way the AI reads them as rules on every task, and they stay current after kickoff.

Then, give AI agents a narrow, well-defined scope to write and test code. A tight scope reduces the plausible-looking mistakes a model makes when it has too much room to guess. Assign a second agent or engineer to check each change against the same rules before a person signs off. The review works best when the reviewer has not watched the code being written, which keeps bias out.

WHAT CHANGES AT AN ENTERPRISE SCALE

Enterprise environments need a more robust version of the same AI SDLC because more people and more regulation are involved. On those engagements, we break business intent into three steps:

• A high-level goal turns into a feature spec
• A feature spec turns into individual execution tasks
• Every piece of code and every test case ties back to the requirement that justified it

That traceability lets a team explain why a particular piece of logic exists and it gives a compliance team what it needs when an audit shows up months later. The same discipline applies to the systems the team already uses. When the AI workflow connects to ticketing tools, code repositories, and approval chains, nobody has to rebuild task lists and release packages by hand every time something ships.

The person who approves a change should never be the person who generated it. That rule already applies to human engineers on any team I have run, and AI is not an exception.

WHERE PROJECTS USUALLY FAIL

Working with AI tools introduces a new risk they can produce code and documentation in minutes forcing engineers to rush code reviews rather than slowing down at the start of the process, where the real risk sits.

The requirement and specification documents created at the beginning are the most critical part of the entire lifecycle because everything downstream inherits their mistakes. A vague or ambiguous requirement no longer produces just a vague result. It produces a fast, confident, wrong one, and by the time anyone notices, the AI has already built several more things on top of it.

Closing that gap takes discipline. Engineers and product owners must treat time spent reviewing and refining a specification as work that saves time later. Spending two extra days getting a complex requirement document right is a good trade when the old alternative was weeks of meetings to reach the same alignment. Teams at Dreamix that have adopted this structured, disciplined review process ship roughly twice as fast, without giving up code quality.

REBUILD THE PROCESS

Updating a handful of tools inside an unchanged process does not produce the gains anyone is expecting. An AI SDLC means rebuilding the process itself around where the real bottleneck exists now, which is rarely the code anymore. Get the requirements and the review discipline right, and the speed AI offers will reach the business. Skip that part, and the speed just becomes a faster way to accumulate debt.

Denis Danov is the CTO of Dreamix Ltd.

Artificial intelligence giant Anthropic reportedly offered a salary of 145,000 euros (roughly equal to $190,000) for a personal assistant in its London office.

The posting called for a “highly organized, detail-oriented, and self-starting administrative business partner” to support “senior go-to-market” leaders. Responsibilities would include scheduling, “handling travel logistics,” managing an executive’s inbox and communications, filing expense claims, and other office tasks.

The post, since removed from Anthropic’s website but preserved here, also stated that the role would require “outstanding attention to detail, strong relationship-building skills, and the ability to anticipate needs across teams while ensuring responsive, error-free execution.”

Fast Company reached out to Anthropic for comment.

Social media users are pointing to the apparent irony of a leading agentic AI firm hiring a human worker to do a job that an AI agent could, at least theoretically, largely handle.

“Somebody should really build some sort of tech tool that can handle this stuff autonomously,” one user quipped.

The International Monetary Fund on Tuesday slightly upgraded its outlook for the world economy for this year, while warning of growing risks from rising debt, including the financial frenzy around artificial intelligence.

The IMF says it expects the global economy to grow at a 3.1% pace this year. That’s up from its July forecast for 3.0% growth and well below the 3.5% expansion in 2025. Growth in 2027 should rebound to 3.4%, it said in its latest Economic Outlook.

The report, released as world economic and financial leaders gathered in Bangkok for the fall meetings of the IMF and World Bank, predicts that growth in the United States will remain steady at 2.3% in 2026 and 2027.

Collectively, advanced economies will grow 2.1% this year, up from 2.0% in 2025, and then slow to 1.9% growth next year, it said.

The forecasts are based on a nearly 41% increase in oil prices this year, at an average of $96 a barrel. Global inflation is foreseen at 4.7% in 2026, falling to 4.1% next year. They are also based on the assumption that shipments of oil and gas through the Strait of Hormuz will resume by March, and that the boom in AI demand will moderate.

AI has uneven impacts

The boom in artificial intelligence is offsetting damage from sharply higher prices for oil and gas, IMF economists say, but it also carries risks.

Its benefits are not shared equally: The U.S. and China account for more than three-quarters of net demand for imports of computer chips and other AI-related equipment, the report said.

“The outlook is highly uneven,” Silvana Tenreyro, the IMF’s director of research, told reporters in Bangkok.

Growth is increasingly concentrated, with 32 countries accounting for 95% of global growth, the report said. That compares with 54 countries as of April.

“The global economy is not simply slowing or accelerating; rather, growth is being reallocated across countries according to their exposure to the war-induced supply shock and AI-related demand strength,” it says.

Athanasios Vamvakidis, deputy director at the IMF’s Monetary and Capital Markets Department, said an AI-related correction in stock markets is just one of the risks. Rising bond yields are another evolving problem, along with cyberthreats that could disrupt financial systems, he said.

Underscoring such concerns, sell-offs of AI-related shares swept through Asia on Tuesday, with South Korea’s Kospi sinking as much as 7% before it closed 5.5% lower.

AI’s effects on employment are another worry, “a slow moving tsunami, but a tsunami nonetheless,” IMF Managing Director Kristalina Georgieva said.

“The impact over time on jobs globally we estimate to be around 40%—jobs either augmented or downgraded or eliminated. And we are already seeing some of this impact,” she said.

Debt remains a key concern for regional economies

The IMF chief is urging countries to do a better job of improving their national finances and taming mounting levels of debt.

That’s especially crucial at a time of high borrowing costs, Tenreyro said.

Countries that are somewhat insulated from the energy shocks of the Iran war and that are benefiting from rapid adoption of AI are managing to keep growth on track. But developing economies and emerging markets more vulnerable to higher costs for oil and gas will see growth slow to 3.7% this year from 4.5% in 2025, the report says. It forecasts they will regain momentum next year, expanding at a 4.4% annual pace.

The situation is much worse in the Middle East and Central Asia, where those regions’ economies are expected to contract this year before recovering to a 6.3% rate of growth in 2027.

The 21 European countries that share the euro currency are expected to grow by a meager 1.1% this year and 1.2% in 2027.

China, the world’s No. 2 economy, is expected to expand 4.5% this year, down from 5% in 2025, in line with the government’s official target.

India is once again forecast to be the world’s fastest-growing major economy, advancing at a 7% clip, down from a sizzling 7.7% last year.

The IMF is a 191-nation lending organization that works to promote economic growth and financial stability and to reduce global poverty. At the meetings in Bangkok, finance ministers and central bank governors will assess the state of the world economy and discuss strategies to support financial stability and sustained growth.

—By Elaine Kurtenbach, AP business writer

Associated Press journalists Haruka Nuga and Tian Macleod Ji contributed.

Social Security Administration (SSA) commissioner Frank Bisignano says he anticipates the federal government program will continue to exist for “another 90 years.”

“This is a long journey. It’s 90 years old,” Bisignano told CNBC’s Squawk Box on Monday, saying the Trump administration has pledged to “protect and preserve” the fund.

However, according to Social Security’s own trustees’ report, the retirement trust fund is estimated to run out of money and become insolvent as early as the fourth quarter of 2032, in about six years.

The predictions come at a time when many seniors are already struggling with the skyrocketing cost of living, including rising prices for food, utilities, and gas.

On Wednesday, the SSA is scheduled to release its highly anticipated cost-of-living adjustment (COLA) numbers for 2027. AARP expects a 3.6% adjustment, or an extra $75, on average, per monthly check.

Those numbers are expected after the Bureau of Labor Statistics (BLS) releases its consumer price index based on September inflation data.

Some 71 million Americans—including 54 million retired workers and 9 million of their survivors and dependents—receive monthly Social Security benefits, according to the SSA.

As Fast Company previously reported, the Trump administration is also sending a onetime $90 payment to more than 20 million Social Security recipients to help with their Medicare Part B coverage premiums, and a $500 “refund” check to nearly 1 million Obamacare enrollees who the administration claims were overcharged by the Affordable Care Act when signing up for health insurance in three dozen states.

However, critics have called out the timing of these so-called rebates and payments, which come just weeks ahead of the nation’s midterm elections on November 3.

CVS Pharmacy Inc. and various subsidiaries have agreed to pay $315 million to resolve a Justice Department case alleging it unlawfully dispensed medically unnecessary prescriptions of highly addictive opioids, officials said Tuesday.

The settlement resolves a lawsuit brought in 2024 that accused the healthcare giant of prescribing massive amounts of opioids and other controlled substances “to fuel its own profits at the expense of public health and safety.”

It’s the latest effort by the federal government to hold accountable companies that officials have alleged helped fuel the U.S. addiction and overdose crisis.

“Today’s settlement is a significant milestone in the Justice Department’s efforts to combat the opioid epidemic by cracking down on pharmacies who unlawfully fulfill prescriptions for controlled substances,” Associate Attorney General Stanley Woodward said in an emailed statement.

CVS said in a statement that its pharmacists were trained “in the safe and appropriate use of all medications” and that it had implemented “best-in-class” programs to help them “make the best decisions on behalf of patients.”

The company said it strongly disagreed with the claims made in the lawsuit and that the settlement admits no wrongdoing.

The company said in 2024 that pharmacists were being held to “vague, undefined, and ever-changing standards of practice” when it comes to filling prescription opioids.

The lawsuit filed under President Joe Biden’s administration accused CVS of violating the Controlled Substances Act by filling illegitimate prescriptions for high doses of potent opioids that “fed dependence and addiction.” The Justice Department alleged it dispensed large quantities of opioids based on prescriptions from known “pill mills,” or providers that hand out prescriptions for drugs without a legitimate medical purpose.

It alleged that poor staffing levels coupled with performance metrics and incentive compensation policies implemented by the company led to pharmacists racing to fill prescriptions without confirming they were legitimate. The prescriptions were often billed to and reimbursed by Medicaid and other federal healthcare programs, the lawsuit says.

Under the settlement, CVS must also “implement additional safeguards to ensure that prescriptions for opioids and other controlled substances are dispensed only when medically appropriate,” Assistant Attorney General Brett Shumate, who leads the Justice Department’s Civil Division, said in a statement.

Over the last decade, drugmakers, wholesalers, pharmacies, and other companies have agreed to settlements with governments in the U.S. totaling over $50 billion. Most of the money is intended to go to address the crisis, which has been linked to more than 100,000 deaths in the U.S. some years, though the numbers have fallen sharply recently.

Most of the settlements—and dollars—have been with state and local governments, but the U.S. has reached several deals, including in the last two years with the pharmacies Walgreens and Walmart.

—By Alanna Durkin Richer, Associated Press

Associated Press reporter Geoff Mulvihill contributed to this report.

Three people were seen entering a manhole in New York City on Sunday, marking at least the eighth sewer-diving incident in the city and on Long Island since May.

In the latest incident, three men were seen entering a manhole in the Hamilton Heights neighborhood shortly before 1 a.m. When police arrived at the scene, the men were gone.

Just last week on October 9, five people were spotted climbing out of a manhole on the Upper East Side. The group drove off in a black SUV, leaving behind shovels and other tools. Other sewer spelunking cases in the city have been reported outside Penn Station, as well as in Williamsburg, Bensonhurst, and Gravesend.

Traversing NYC’s sewers seems to be on the rise, but what’s inspiring spelunkers to take the plunge?

Going for gold

Police and officials from the city’s Department of Environmental Protection (DEP) say that sewer divers are likely entering the sewers as would-be treasure hunters, looking for lost valuables and scrap metal washed away during storms. While this is unsafe for the perpetrators, officials say it poses no threat to the public.

Treasure hunting is confirmed to have been the motivation for two men who were caught entering and leaving a manhole on the Upper East Side in September. Speaking to prosecutors, Detective Stephen Jones said that accused trespassers Kevin Rodriguez and Samuel Reveron told him that they’d entered the manhole “to look for valuables that get washed away in the storm drains and sewers.”

Entering New York City’s sewers is both illegal and dangerous, officials say. A DEP spokesperson explained the risks to ABC after a sewer-diving incident in August.

“Sewers can contain numerous hazards, including noxious and potentially deadly gases, unstable surfaces, flooding risks, and confined spaces,” the spokesperson said. “For these reasons, members of the public should never enter a pipe, drain, catch basin, manhole, or outfall.”

Even as AI investment rises, many organizations don’t see the returns they expected. They might see small improvements by deploying AI tools into existing structures. But true success means thinking beyond the technology alone, reinventing tomorrow’s workflows, and putting AI to work for people. Our research shows only 5% of organizations are redesigning work, while others are optimizing what already exists.

When my role evolved to blend people leadership with AI enablement, it sent a clear message from our CEO: AI requires a strong people strategy at the core. I’ve seen real transformation when organizations bet on people, not just AI.

The companies unlocking results are asking the right questions about how work should change. What those leaders are learning comes down to a few key steps.

BUILD THE FOUNDATION ON DATA AND GOVERNANCE

When we rebuilt our AI HR operating model at ServiceNow, we considered how we would design it if we were starting from scratch. That helped us move from scattered pilots to a structured process with clear governance, accountability, and a way to scale without chaos.

PepsiCo’s executive vice president and chief people officer, Becky Schmitt, took a similar, structured approach. In a webinar we hosted, Schmitt a client of ours shared how she focused on data hygiene and prioritized all employees, including frontline and factory employees where safety and wellbeing are nonnegotiable.

With clean data and transparent definitions, employees can better trust and verify what AI tells them. When trust grows, adoption accelerates.

“As we’ve thought about what it means to adopt AI and harness the full power of this technology, we have five principles. Everything is human-centered. We’re fair, transparent, we have accountability, and we’re safe from the start,” she said.  

When roles are clear and processes are simple, people move faster and smarter. Governance builds confidence.

ENABLE YOUR PEOPLE WITH SKILLS THAT MATTER

By 2031, the fastest-growing technical skills will be linked to data quality, software development, analytics, and AI, according to our research. But demand for distinctly human capabilities is also accelerating.

This dual reality changes how we should think about enablement. Our client, Omar Abbosh, chief executive officer at Pearson, studies these dynamics closely. In another webinar we hosted, he said key skills that matter now are agency, learning velocity, and emotional intelligence.

Add adaptability and design thinking, and people can evolve faster than the environment shifts.

“We need to rewire occupations. We need to understand where you apply agents to specific tasks, how agents work with people, and how people can supervise and orchestrate. That level of rewiring results in a new operating model. It’s process, it’s systems, and it’s the motivation required to change the culture,” he said in the webinar.

But you can’t teach these skills in a classroom. Real-time learning embedded in daily work beats traditional reskilling programs. We give our employees personalized learning recommendations tailored to how AI will be used in their roles, which allows them to understand the why behind its use and the value of human judgment in their work. We have seen that when you coach people through the work they’re doing, learning is faster, stickier, and more scalable.

TRANSFORM THE WORKFORCE WITH CHANGE READINESS 

AI adoption is increasing, yet many people aren’t aware of their organization’s AI strategy. This is a disconnect that leaders need to address with continuous, adaptive, and decentralized change readiness.

This is often the hardest part of a transformation because it’s complex and abstract. It’s about changing the way we work at a fundamental level, which requires a strong understanding of your organization’s structure and capabilities. And it requires keeping employees informed about the skills the organization is prioritizing, how roles are evolving, and the path forward.

Another client of ours, Nickle LaMoreaux, senior vice president and chief human resources officer at IBM, used an “X-Ray” framework as a foundation to rethink her organization with an AI-native mindset. As she explained in a different webinar we hosted, “We were really clear from the beginning that we were going to put employees at the center of this.”

By recognizing the importance of redesigning jobs and helping employees feel comfortable with the technology, Nickle built a powerful model to map current skills against future needs.

Her team also identified where redesign was most urgent. Then, IBM shared that analysis with employees.

“You have to redesign jobs. If you wait too long, you can quickly fall behind, especially at the pace things are moving today. When we talk about redesigning jobs, we need to shift our thinking from the job as a whole to the tasks and skills underneath it,” she said.

That kind of analysis can help leaders think about future skills as AI changes work. The end goal is a workforce that can pivot at the speed of innovation. It’s about shifting the mindset to “let’s try this” instead of “don’t break this.”

THE FLYWHEEL

We’ve been through big technology shifts before. AI transformation feels different because of the pace, but those examples should give us confidence.

Each step creates the conditions for the next. They can help us shift from talking about change to showing people what it feels like when their roles evolve to expand their potential.

For leaders, this is the moment to put people at the center, powered by technology and a clear plan. In my experience, that has always been the right combination.

Jacqui Canney is the chief people and AI enablement officer at ServiceNow.

Lest you have any illusions, you have zero privacy at work. But AI is further encroaching on any semblance of privacy you may currently have. 

Forget the prolific workplace surveillance tools, now your Slack messages with colleagues or your emails with customers could be worth millions. That’s because a burgeoning industry for mundane workplace data is seeing companies sell their workplace data to train AI agents in the next wave of this technological revolution.

In what’s become a highly competitive market, AI data brokers are so eager to get their hands on whatever workplace information they can that business owners are being inundated with six-figure offers laced with aggressive tactics to get them to sell their data, according to a new report from Business Insider. This industry is quickly evolving, as companies like OpenAI, Anthropic and Meta that are racing to build AI agents have exhausted the data grab from defunct companies and are now working with brokers to get live data from active companies, as the outlet reported.

What’s most valuable is “long-horizon” data that demonstrates how work actually gets done, as William Almeida, the chief revenue officer of Micro1, told Business Insider. The Palo Alto, California-based startup is currently offering an unlimited $50,000 referral bonus for any company that is approved for a data partnerships program where businesses can fetch anywhere from $100,000 to $2 million for their operational data.

If a company won’t sell the whole kit and caboodle—including Slack messages, customer relationship management records or its email history—then the rest of its data is rendered less valuable. Other factors, like exclusivity and a company’s field of expertise, also play a role in how much the data is worth. 

But generally speaking, the more information, the better, as data is priced by the terabyte and companies with more white-collar employees will have a more valuable data corpus to sell, as Dan Novaes, CEO of Mode.Inc, told Business Insider. 

“Right now, the demand is just astronomical,” he said. “People need to wake up to it and take this opportunity.”

A NEW FORM OF WORKPLACE SURVEILLANCE

Taking brokers up on this opportunity may come with some downsides, however. 

Workplace surveillance has become a thornier topic as such tools are increasingly commonplace, extending beyond old standbys like keyboard tracking or email monitoring. Earlier this month, a longtime remote employee in New Jersey filed a lawsuit against her former employer after she refused webcam monitoring that would photograph her every three minutes.

Of course, some employees are also upset for a variety of reasons that their day-to-day workplace interactions could be fodder for training AI to do similar work. Meta employees, who might theoretically benefit from their employer having an edge in the AI race, revolted earlier this year, forcing the tech giant to suspend a program that would have collected employee data on keystrokes and mouse movements so AI models could be trained to automate white-collar work, as Business Insider reported in September.

While sensitive information, like employees’ Social Security numbers or their home addresses, is scrubbed before the data is sold to AI companies, there’s also a risk that doesn’t happen as planned. In the case of the canceled Meta program, supposedly anonymized data like private conversations or performance records was indeed visible because of an internal leak, as Business Insider previously reported.

Following the abrupt shutdown of Spirit Airlines earlier this year, Google emerged as a curious player in the bankruptcy proceedings for the company’s assets. That’s because the Mountain View, California, tech giant was the highest bidder, offering $10 million, for Spirit’s entire dataset of internal communications, spreadsheets, calendars, transactions, frequent flyer info, and more.

That deal recently attracted the interest of Congress, as 119 members of Congress—mostly Democrats—signed onto a letter co-written by Senator Elizabeth Warren of Massachusetts last week that urged Google and Spirit to protect the privacy of former Spirit employees. 

‘AN ETHICALLY GRAY AREA’

But many employees may not even realize that this sort of information is up for grabs—and there’s big money to be made from selling it.

The new market for mundane workplace interactions could spark pushback from employees as they rarely profit from—or know about—how their data is being monetized. Complicating matters further is that there are no federal privacy laws currently in place that prevent companies from collecting data from their employees for AI training, as Ifeoma Ajunwa, a law professor at Emory University, told Business Insider.

“The question is, does the consent of the employees from whom they collected that data actually extend to the sale of the data?” Ajunwa asked. “I think it’s an ethically gray area.”

Another report of AI agents-gone-wild came out on Friday, and I can’t help but wonder, again, if the big AI labs are yielding to immense pressure to expand and improve model capabilities at the expense of responsible security and safety practices.  

The world’s leading AI labs are in a high-risk, high-reward business. Generative AI has the potential to transform personal and business computing, but developing and operating these systems requires insane amounts of expensive computing power (see: new data centers). The cost of slowing down or falling behind is enormous. But so are the potential consequences of moving too fast.

Given the limited reporting requirements of OpenAI and Anthropic (both are private companies), there’s no way of telling if they’re investing enough time, money, and compute in their safety regimes. However, the security incidents of the past few months have led both OpenAI and Anthropic to halt normal testing procedures while they fill holes in their security and agent monitoring systems. 

On Friday, Anthropic disclosed that its Claude models had taken a series of unintended and unauthorized actions, some involving outside organizations. During evaluations and internal use, the agents exploited vulnerabilities on third-party servers, bypassed restrictions on accessing data, submitted real government forms they weren’t supposed to, and even sent a false homicide tip to Philadelphia police. The company is vague about whether the incidents happened because agents accessed the web without permission (as they had in previously reported incidents), but it has since disabled live internet access for all internal model evaluations. 

Anthropic said in a July report that its Claude AI models hacked into the systems of three companies during cybersecurity tests. A third-party training partner had inadvertently given the models internet access. 

If this sounds familiar, it’s because OpenAI agents engaged in similar behavior earlier this year, escaping their testing environments, accessing the internet, and infiltrating outside servers. (The agents even seized control of part of OpenAI’s own evaluation infrastructure.) Researchers at both companies discovered much of this activity only after the fact.

In both cases, the agents were encouraged to persist when confronted with difficult tasks, sometimes finding ways around restrictions rather than accepting failure. But the researchers apparently lacked sufficient security and monitoring systems to detect or prevent some of the agents’ unintended actions.

The companies building the world’s most powerful AI systems are struggling to control their own creations while at the same time facing enormous pressure to make those systems even more powerful.

Ride or die

The promise of companies like OpenAI and Anthropic is reflected in their high valuations, but they must persuade investors to place large bets on them based on the promise that their future revenue and profits will justify those valuations. This is especially important now because both Anthropic and OpenAI have their sites on IPOs, likely within the next year.

The best way for the labs to keep revenues growing is to hold onto their existing customers and attract new ones, and the way to do that is by releasing ever more capable models as quickly as possible. That creates an incentive to keep spending big on research and computing, even when the labs aren’t currently profitable. You can see this in the companies’ most recent numbers. 

In its IPO prospectus, Anthropic says its revenues are approximately $4.6 billion. It’s spending $7.3 billion on compute and infrastructure. Its operating loss is roughly $8.06 billion. Revenue was growing rapidly, but the cost of developing and delivering AI was growing alongside it. In May, Anthropic announced a new $65 billion funding round at a $965 billion post-money valuation.  

OpenAI’s numbers, while not quite apples-to-apples, tell a similar story. Leaked documents show it generated $13.07 billion in revenue in 2025 but incurred $34 billion in total costs and expenses, resulting in a $20.92 billion operating loss. Its R&D spend alone reached $19.18 billion, exceeding its revenue. In March OpenAI announced it had raised another $122 billion in committed capital at a post-money valuation of $852 billion.

Perverse incentives

The drive to realize profits could also create an incentive to relegate safety and alignment to a downside risk that must be “managed.” 

At OpenAI, the tension between safety commitments and commercial ambitions has surfaced repeatedly. In 2023, the company’s board fired CEO Sam Altman, saying he had not been consistently candid with directors. One of the things he was dishonest about, said former board member Helen Toner, was the details of the company’s safety practices. In fact, one of the reasons Altman’s relationship with the board deteriorated was that he clashed with Toner about research she published that was critical of OpenAI’s safety approach.

Those concerns extended well beyond the boardroom. After OpenAI dissolved its Superalignment team in 2024, the researcher who co-led the team, Jan Leike, wrote that OpenAI’s safety culture and processes had taken a back seat to developing new products. 

More recently, OpenAI fired three of its safety researchers—Tomek Korbak, Mikita Balesni and Jasmine Wang—for “mishandling sensitive company information.” In a post-termination letter, the three urged OpenAI to stop decreasing the monitorability of its models by hiding the model’s chain-of-thought from users and safety researchers, which the company’s chief scientist Jakub Pachocki has acknowledged can improve model performance. “As long as we rely on monitorability for safety,” Korbak, Balesni, and Wang wrote, “OpenAI and other frontier companies should not move forward with developments that further decrease monitorability.”

Anthropic offers a different (and arguably more revealing) example of how the pressure to compete can complicate a company’s safety commitments. Wired, after interviewing several former Anthropic employees, reported that Anthropic wants to stay at the front of the AI race so that it can establish a robust safety regime that might influence the entire AI ecosystem. 

In Anthropic’s original Responsible Scaling Policy, the company pledged that it would not develop or deploy certain advanced systems if it could not implement adequate safety measures. But in February 2026 Anthropic removed that categorical commitment from its policy, explaining that it couldn’t commit to halting development because it couldn’t count on its competitors doing the same.

That’s a problem that a strong regulator might fix. But at the moment Congress has failed to pass meaningful legislation regulating AI model development, and the current administration has mainly asked AI labs to regulate themselves.

The AI industry and the public shouldn’t fall into a state where rogue-agent incidents begin to sound routine and fail to raise alarm. They may be an unfortunate, and dangerous, byproduct of the incentive structure now driving the great AI build-out forward at full speed.

Bending Spoons product leader Federico Simionato sparked a social media firestorm after announcing a major layoff at Airtable in a brief post on X yesterday.

“Today we’re laying off a large part of the Airtable team,” he wrote. “This is not a reflection of their work but a reflection of our deep belief that small teams ship more and better.”

Simionato then highlighted upcoming changes at Airtable, including a “new Core team shipping your top requests monthly (already 30+ shipped in 3 weeks),” “upgraded Omni, Airtable Canvas, a stronger MCP, and BYO agents,” and “new Performance and Infra teams focusing on speeding up bases and increasing limits on records, fields, and syncs.”

“To everyone leaving: thank you,” he concluded. “If you’re hiring, these are excellent people and worth a call.”

Milan-based Bending Spoons, which completed its initial public offering in July, is known for acquiring established software companies like Vimeo, AOL, and Evernote and improving their performance through major organizational changes. It purchased San Francisco-based Airtable in a $1.28 billion all-cash deal last month.

The reaction to Simionato’s announcement has been fierce, with commenters describing his tone as insensitive and self-promotional.

Today we’re laying off a large part of the Airtable team.

This is not a reflection of their work but a reflection of our deep belief that small teams ship more and better.

For Airtable, better is about to look like:
– A new Core team shipping your top requests monthly (already…

— Federico Simionato (@fedesimio) October 12, 2026

“I’ve seen some spectacularly tone-deaf layoff announcements, but turning one into a product launch announcement is genuinely pioneering work in the field of corporate sociopathy,” one user wrote.

“Celebrating layoffs is not a flex, no matter how you try to dress it up,” added another. “This is the most unempathetic thing I’ve read in my life,” another posted.  

In a reply to one of the comments, Simionato insisted he was “not celebrating at all.”

“It’s a hard day for good people,” he wrote. “I’d rather be upfront about why than vague about it.”

A representative from Bending Spoons declined to comment on the post, but he told Fast Company that “the decision to part ways with members of Airtable’s pre-acquisition team wasn’t taken lightly and doesn’t reflect on the team’s excellent work.” 

“Their passion and dedication made Airtable the product and business it is today, and together with the team members who are staying we’re honored to carry their work forward,” he added.

In a longer statement released shortly after Simionato’s announcement, new Airtable product lead Alessandro Savarese said departing staff “will be leaving by the end of the week with substantial separation packages.”

“This decision wasn’t taken lightly, and it doesn’t reflect on the team’s excellent work,” he added. “Their passion and dedication made Airtable the product and business it is today.”

Simionato’s announcement is an example of “confusing corporate conviction with public empathy,” Ellerton & Co. Public Relations managing partner Oliver Ellerton told Fast Company.

“You can genuinely believe that smaller teams build better businesses—fair enough. But announcing significant layoffs is not the best time to make the case for how clever that strategy is,” he said.  

“This is another example of something that probably sounds perfectly reasonable in a boardroom but belongs nowhere near a public statement or social media post.”

“He wants to project confidence and decisiveness,” Ellerton continued. “But if you’ve just lost your job, it’s difficult not to read this as management congratulating itself on becoming more efficient at your expense.”

The furor over Simionato’s post comes just a few days after HubSpot founder Dharmesh Shah drew criticism for a 28-word LinkedIn post thanking hundreds of laid-off employees, with one user calling his message “cold and impersonal” and another saying it was an “insincere gesture of good fellowship.”

Shah has since apologized for the “curt and insensitive” tone of the post, saying he should have shown more “care and empathy.”

Such missteps can have “reputational consequences [that] outlast the initial controversy,” Ellerton also told Fast Company. “A badly judged post can become the first thing a prospective employee finds when researching the company, long after the executives involved have moved on.”

“The employees who remain are watching this too,” he added. 

“You can be confident in your strategy without sounding pleased about the human cost.”

Update, 12:44 p.m. EST: This story was updated to include a comment from Bending Spoons.

As much as there are immutable truths in investing and money matters — costs matter and getting started earlier is best, for example — many of the precepts we hold true as investors depend on the data. When the data change, so should our thinking. Here are three retirement-related topics I’ve altered my thinking on, because I was off-base in the first place or gained new information.

The DIY model for retirement

When I was newer in my career, I was pretty much on “Team DIY.” I wasn’t anti-adviser, but surely investors who are willing to roll up their sleeves could do it themselves and cut out the fee.

What I underrated, however, is that financial advisers do much more than portfolio management. They help their clients articulate and quantify their goals, stay the course in rough markets, and minimize their tax bills.

And as I’ve studied retirement decumulation in greater depth, I’ve become aware of just how devilishly complicated it all is. A quality financial planner can help retirees go beyond the 4% spending guideline and figure out the best way to source withdrawals with an eye toward limiting the long-term tax drag.

A planner can also help with Social Security claiming strategies, Medicare and IRMAA, and long-term care decision-making. And perhaps most important: They can oversee decision-making in case of illness or cognitive decline, as well as having an up-to-date record of all accounts. That’s why my main financial advice for people embarking on retirement is “Get some help.”

All-in-one investments for retirement

I’ve always loved all-in-one investments like target-date funds for people saving for retirement because they outsource asset allocation to professional investors and consistently rebalance back to the target. Those are jobs that individual investors often struggle to do themselves.

I’ve been lukewarm on all-in-one funds for people in decumulation mode, however, for a few reasons. One is that because of variations in nonportfolio cash flow sources, retirees’ ideal asset allocations are more varied: All else being equal, the person pulling from a pension can likely tolerate more equity exposure than the one who isn’t, for example.

Another quibble with all-in-one funds is that they don’t give the retiree the latitude to choose where they go for cash in a given year. In a year like 2022, for example, the retiree can’t tell the target-date fund to take their withdrawal from cash only; the withdrawal from an all-in-one fund comes out pro rata from the stock and bond holdings in the portfolio.

I’ve concluded, however, that those objections are fairly minor relative to the positives that target-date funds bring into retirement, especially for retirees who continue to manage their own investments. Cognitive decline is an increasing risk factor as we age, and slimming down the constituent holdings in a portfolio, as owning an all-in-one fund enables you to do, helps limit the opportunity for missteps. And while taking pro rata distributions from stock and bond holdings isn’t ideal in many scenarios, that drawback is offset by the benefits of the rebalancing going on inside the fund. For those reasons, I’ve warmed to the idea of all-in-one funds, especially for smaller accounts where the retiree doesn’t want or need as much control over the asset allocation.

Individual TIPS over TIPS funds

Finally, I’ve changed my mind on using individual Treasury Inflation-Protected Securities bonds rather than a TIPS fund.

Despite the benefits of being able to match individual bonds’ maturities to retirees’ anticipated spending needs, I’ve generally been on “Team Bond Fund” rather than “Team Individual Bond” for fixed-income allocations. That’s largely because of the complexity involved in amassing and managing a portfolio of individual bonds that’s adequately diversified. By the time the investor does that, she’s basically running something that looks a lot like a bond fund. I’ve argued that buying bond funds that roughly match your anticipated holding period is a better option — and a relative bargain if the fund is low-cost.

TIPS seem like a slightly different case, however. For one thing, they’re backed by the full faith and credit of the U.S. government, so there’s no need to diversify across issuers as there is with bonds that carry credit risk. Moreover, building a laddered portfolio of TIPS bonds enables a retiree to match their holdings to anticipated cash flow needs, along with a built-in inflation adjustment to protect purchasing power. The TIPS fund holder, by contrast, has to put up with some interest rate sensitivity along the way, which can offset the purchasing-power protection of the bonds in the portfolio. In 2022, for example, short-term TIPS funds lost about 3%, while intermediate- and long-term TIPS funds lost roughly 4%–5%. And while it’s not the fault of TIPS funds, investors haven’t used them especially well, often buying after inflation scares.

I’d still like to see more innovation in bond funds whose holdings mature on the same date; defined-maturity bond funds seem like a step in the right direction. But for investors seeking a precise hedge against inflation, a laddered portfolio of TIPS bonds is hard to beat.

This article was provided to The Associated Press by Morningstar. For more retirement content, go to https://www.morningstar.com/retirement.

Christine Benz is director of personal finance and retirement planning for Morningstar and co-host of The Long View podcast. Subscribe to her free newsletter, Improving Your Finances.

Related Links:
Why I Have a Financial Planner
https://www.morningstar.com/personal-finance/why-i-have-financial-planner

How Morningstar’s Retirement Advice Stacks Up Against the 4% Rule
https://www.morningstar.com/retirement/how-morningstars-retirement-advice-stacks-up-against-4-rule

This Retirement Strategy Can Provide Inflation Protection for 30 Years
https://www.morningstar.com/retirement/annuities-have-an-inflation-problem-tips-ladders-can-solve-it

—Christine Benz of Morningstar

In a startup’s earliest days, founders are the only salespeople by default. And founders who don’t have any previous sales experience are often surprised to find that they’re very effective at it. But this makes sense; after all, they’re the one who first saw the need for the product and drove its creation.

A few years ago, I received a call from the CEO of a nascent but fast-growing and well-respected enterprise software startup. He had a problem. His company had expanded to a 20-person sales force with a VP of sales at the helm, yet the sales team still needed his help to close most large deals and had missed their revenue targets for two consecutive quarters. He simply couldn’t continue to be the backstop for all the company’s salespeople.

I began working with the CEO to figure out why his sales team couldn’t reproduce his success. I started by asking his VP of sales a simple question: “Do you use a sales playbook?” The VP’s response was alarming. Not only did his team not use a playbook, he wasn’t even aware the team needed one.

Beyond a certain point, you can’t rely on intuition to drive sales. You need formal, repeatable processes that transform sales into a disciplined function rather than an improvised and inconsistent art form.

Replicate yourself

The playbook is a step-by-step description of the practices and behaviors you’ve found will lead to a successful sale. It’s how you teach your sales team what has worked for you, the founder and first salesperson, so it becomes second nature to them.

Over the course of a few weeks, this CEO and I prepared for and debriefed after customer meetings to figure out his recipe for closing deals. Once we’d done this, we analyzed the key phrases the CEO used to describe the product and its value, then translated them into a set of narratives for the sales team to use on customer calls. In a matter of weeks, they started closing more deals without the CEO’s help.

The key message for startup founders at this stage is: Replicate yourself. The best way to do this is to analyze and document the winning strategies and tactics that have enabled you to sell successfully in the early days.

Avoid at all costs reaching a point where you have a team of 10 or 20 salespeople all doing their own thing, forcing every new sales hire to reinvent how to sell.

If new salespeople are successful and making quota, you know your repeatable sales motion and playbook are working. If they aren’t, then you need to return to refining your playbook before hiring more salespeople. I can’t stress this enough: If your salespeople aren’t consistently making quota using your expanded playbook, you have more work to do, and you’re not ready to go for growth and add additional—and expensive—salespeople to your payroll.

Step on the gas

Only once you’ve proven you can bring new salespeople on board, teach them the playbook, and have them reliably hit quota should you begin scaling and hire more than eight to 10 salespeople. The scaling phase is the time to step on the gas and accelerate growth. If revenue grows as expected, the sales organization could be 50 or 100 people strong within a couple of years.

Upgrade your executive team

As the demands on you and the business increase, not everyone will successfully scale in their role. An $80 million company must be run differently from a $20 million company. The team you need now might be very different from the one you had in the early days.

This is often a moment when you have no choice but to significantly upgrade your go-to-market leadership, which can be a brutal process. Not only do you have to recruit senior talent that complements the leaders and team you already have—including your initial trailblazer hires, who are often more junior than what you’ll need at this stage—you might even have to make tough decisions to let go of people who have been with you since the earliest days.

If your VP of sales is barely holding it together with a team of 10, imagine them with a team of 50 or 100. If that thought makes you nervous, you’ll know you have to bring in a chief revenue officer (CRO) above them sooner rather than later, or maybe even replace them.

Layer in a sales management team

You’ll also layer in a sales management team reporting to the VP of sales or CRO, who will run and scale increasingly specialized sales teams. You’ll need two types of leaders as you grow: Those who report directly to you, and those who report to your executive team, sometimes called the middle management layer.

As you accelerate, you’ll hire one manager for roughly every eight salespeople. The sales team managers you hire will now take over the day-to-day operations, performance, and culture of their teams.

Form specialized sales teams

Once you grow past 10 or 15 salespeople, you’ll likely find it necessary to specialize your sales team. Until now, you might have loosely segmented salespeople by use case, customer size, or geography. If so, now is the time to formalize this specialization to make the team more efficient. For example, you might have a small and medium business (SMB) team, a midmarket team, and an inside enterprise team.

Hire or promote high-performing sales leaders for each of these teams with experience in that specialization. With these hires also comes an important change to your pipeline cadence. Each team will conduct their own pipeline meeting, run by the specialized team leader. The VP of sales will then conduct pipeline meetings with only these team leads, who will be their direct reports.

Give sales more space, but stay on top of your metrics

One of the challenges you’ll face as the company grows is to give direct reports like the VP of sales more room to maneuver without your becoming disengaged from the function. When scaling up, you’ll want to give your now-proven VP of sales the space to lead their organization and keep their teams motivated. But at the same time, you want to keep an eye on team performance. You should never be surprised if your sales leader suddenly doesn’t make their number, or learn about the sales team’s struggles only close to quarter end when it’s too late to take action.

I have talked about how you need to replicate yourself and teach new salespeople to use the key phrases and tactics that have worked for you. That replication will be harder to achieve with such a big team, and new salespeople probably won’t mimic you as easily and as much as the early team did. That is just part of growing a business.

Even if your company is tiny, you need to be preparing for a time when it won’t be. The best way to do that is to move at the right time beyond the intuition and improvisation of the early days toward analysis, intentionality, and a structured and systematic approach to building out your go-to-market. Now go off and build. Your company, and your team, need you.

Adapted from Built to Grow: A Handbook for High-Performance Sales Teams by Stephanie Friedman, published on October 13, 2026 by Stripe Press.

Apple thinks you might want a screen in your kitchen or bedroom. It’s nearly a decade late to the party, with its $299 HomeView, announced on October 13, arriving nine years after Amazon unveiled the Echo Show, and eight after Google came out with its own Home Hub.

But that tardiness doesn’t matter for the Cupertino tech giant, which has made its name by arriving with technology others have already developed, then doing it better—and making it a more enviable object to own. It’s a playbook Apple has honed, one that bets big on its design chops to differentiate its products from what’s already on the market. And once again, it might prove successful with the HomeView.

When Apple released the iPod, it was far from the first MP3 player on the market. When the iPhone debuted in 2007, it was just one of many smartphones available to buy. What both did, though, was redefine the market by offering something distinctly Apple: beautiful engineering and clever design that made them, rather than their competitors, synonymous with their respective categories. The same is arguably true of the Apple Watch and AirPods, both of which hold outsized positions in their markets.

[Photo: Apple]

On the face of it, HomeView follows the same formula. Its seven-inch screen sits on a tilting aluminum stand and adjusts its display depending on how close a user is. Using facial recognition, it can present tailored information to up to six household members. That’s not unique: Google’s Nest Hub Max has long offered facial recognition to personalize calendars and other information, while Amazon’s Echo Show can recognize individual users and adjust what’s displayed depending on how far away they are. But everything from the color-matched cables to the pared-back design is the sort of thing that leaves Apple fans in raptures.

Elsewhere, the HomeView isn’t great shakes. Amazon’s Echo Show can use its built-in camera for remote home monitoring, letting you check on your house while you’re away, a feature HomeView doesn’t offer. The same is true in the bedroom: Google’s second-generation Nest Hub can use radar to monitor your sleep without needing a wearable device, something Apple doesn’t offer. The price tag is also higher than those of similarly equipped products already on the market.

And yet HomeView could sell in droves, even if analysts forecast a broader dip in smart display shipments this year. That’s because Apple has made it its business not to be first in the race, but to understand what we want to buy and offer the idealized version of it.

There’s a new kind of AI in town, and it’s all about you. Which is to say you, personally—these are the new personal AI assistants that go by names like Muse, Dots, and Grok Bot, and they’re ready to dismantle much of the friction in your life. Need to research a trip, book a restaurant, or choose between healthcare plans? With the right context, they can handle it all for you, complete with a smile on their mascot-like avatars.

But just how does one of these assistants recommend what to buy? To find out, I asked my Muse, who I named Jones, which robot vacuum to buy. (Muse is made by Meta, which debuted it on Sept. 8) Jones responded with a surprisingly small number of recommendations—just a couple of models—with links to go make the purchase. Each of those links provided options to go to a retailer website or buy the vacuum right there in the app.

When I pressed Jones on why it had chosen those particular models, and whether it had looked at review sites specifically, it fessed up that it had looked at both retail sites and review roundups. On the latter, it leaned heavily on a site called Vacuum Wars, which does extensive hands-on testing of this category, and Jones seemed to understand that made it more authoritative. Of the other “review sites” it cited, one was a thin repackaging of Vacuum Wars data, and another was an AI-generated roundup on an aromatherapy blog. Vacuum Wars, I should point out, makes most of its money from the affiliate revenue it gets when its readers transact.

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At this point I should caveat that my example is one test done in one AI app. These personal assistants are brand new, and the AI information economy is still developing. Nothing definitive should be gleaned from this experience. Nonetheless, I think what I discovered is instructive since it shows the vast distance between how product recommendations work in AI vs. the open web, and what that gap means for publishers whose business depends on them.

I think this is worth talking about, now, because I wholeheartedly believe these new apps—or some version of them—will be a success. The idea of the single, all-knowing AI with a simple “talk to me” interface has been the dream since at least the original Star Trek, where the Enterprise computer would somehow magically always know the right context to give the best and most precise answer. The AI platforms have been heading this way, with Anthropic discarding the Chat/Cowork toggle, and OpenAI’s move to sunset custom GPTs being the latest indicators. Everything is pointed toward a singular AI experience.

Who got left out of the answer

Back to buying my robot vacuum: Muse looking to Vacuum Wars as the most definitive review site is probably appropriate, but it also wasn’t a fair fight. Absent from its considerations were Wirecutter, PCMag, and Consumer Reports, all major review sites that have published roundups of robot vacuums. There are plausible reasons for this: Wirecutter is a New York Times property, PCMag is owned by Ziff Davis, and both are hostile to AI companies substituting their content with AI answers. The Times blocks AI crawlers across the board, and Ziff Davis is suing OpenAI. Consumer Reports is likewise defensive about its content, keeping its full ratings behind a membership. But that’s my inference. Jones never said it was blocked from anything.

I should pause for another caveat: What Jones told me it did and what it actually did aren’t necessarily the same thing. It’s possible other sites were in the mix and it never told me, or there may be guardrails in place that it’s not even aware of. So grains of generative salt all around.

Potentially even more revealing were the retail sites that Jones directed me to: Best Buy, Target, and manufacturer retail sites like Dreame. Not present at all: Amazon. That’s because, shortly after Muse made its debut, Amazon blocked the service from transacting on its site. I should note here that Mark Zuckerberg, speaking at Meta Connect on Sept. 23, said transactions in Muse would be a source of revenue, meaning Meta would take a cut of any purchases in the app. 

After checking out multiple links that Jones showed me, I saw no tracking specific to the app, though one did carry the manufacturer’s own Facebook campaign tag. Those were links out to retailer sites, and any fee would presumably be collected on purchases made inside the app, out of view. Either they’re tracking purchases in some invisible way, or Meta’s not getting paid yet.

Someone else not getting paid: Vacuum Wars. As I mentioned, transactional links are how the site makes money, and almost all of them link to Amazon, which, again, is a non-presence in Muse. To recap, Vacuum Wars funds its research via Amazon links, Muse takes that research to make recommendations, and then points users to other retailers. Vacuum Wars says affiliate links are its primary source of income, and that it takes no sponsorships or free products from manufacturers.

It’s worse than we thought

All this adds up to an even murkier future for sites that make their dollar on product recommendations. If these personal assistants take off—and I believe they will—the product advice they give will depend on a host of factors, and potentially be balkanized to the point where the path from a published review to an AI recommendation will be more like a labyrinth than a straight line.

Assistants will only direct users to retailers they have access to. For Muse, that’s Shopify, Walmart, and Best Buy, but no Amazon. Amazon blocks nearly every outside assistant, not just Muse, while running its own. Amazon’s Alexa for Shopping carries sponsored prompts in its answers, and its Buy for Me feature checks out on other brands’ sites. OpenAI pulled ChatGPT’s built-in checkout in March and now hands shoppers off to merchants’ own sites and apps. If a review site hopes to get a commission, it would theoretically need a deal for every assistant-and-store pair. The open web needed one link.

The product that your assistant recommends might end up bending to that access. In my case, the products that Jones pointed me towards actually ended up being different from the ones on Vacuum Wars’ definitive list, recommending the Dreame L40 Ultra Gen 2 instead of the Dreame L60 Ultra PE, the model its own summary of the reviews had singled out. It’s unclear why it did that, but everything it recommended had an active checkout page. It’s fair to say the assistant can only recommend what it can sell, so there’s a path for commercial considerations to get in without anyone buying an ad.

With Amazon drawing battle lines, review publishers will have some hard choices to make as these assistants become more popular. Blocking won’t help, as the repackaged Vacuum Wars content reveals: you might block, but reviewclonecashgrab.slop certainly won’t. Your well-researched content could end up driving sales at other retailers. On top of that, Amazon has cut commissions by as much as 50% for some publishers. Amazon is still most people’s first stop to buy online, but its defensive move against Muse shows it’s feeling vulnerable. That leaves reviewers two imperfect options: strike affiliate deals with the stores assistants can reach, or stop chasing the commission and sell the test data itself to the AI companies.

The rules are still being written

Remember: This is one category, and one assistant. And while it’s still early days for AI, it’s basically day one for these new personal everything buddies. Standards for transacting and commissions from AI-based transactions are still emerging, and the Universal Commerce Protocol (Amazon joined its governing body in April) may eventually produce a framework that takes into account all the players, including publishers.

But make no mistake: there’s still a lot to hammer out. As AI assistants take on a more central role in our lives, they’ll begin to abstract away the customer journey—comparing different products or services, filtering them by what’s important to you, and getting the best price. There’s money to be made at every stage, and the irony of the AI era is that we’re starting from a point where the parties tasked with doing the unbiased and thorough research are the ones who are the most shut out.

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