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For the first time in history, podcasts have overtaken talk radio as the most-listened-to medium for spoken-word audio in the United States. Podcasts, including video podcasts, eclipsed AM/FM talk radio (which notably doesnt include listening to music on the radio), with 40% of listening time, as opposed to 39% for radio, according to Edison Researchs Share of Ear survey. Researchers have tracked these statistics over the last decade. In 2015, AM/FM radio accounted for 75% of the time Americans spent listening to spoken-word audio. At the time, podcasts accounted for just 10%. Year over year, that gap has slowly closed, as podcasts boomed in popularity, increasingly keeping us company on daily commutes and during menial tasks. Over half of Americans, 55%an estimated 158 million peoplelisten to a podcast monthly, and 40%, or 115 million, listen every week. This year, the scales finally tipped. Although the difference is only 1 percentage point, this is the first time podcast listenership has surpassed radio. Whether the gap continues to widen remains to be seen. Watching podcasts has become a growing trend over the past year, perhaps shifting the balance in podcasts favor. YouTube said viewers watched 700 million hours of podcasts each month in 2025 on living room devices like TVs, up from 400 million the previous year. Streaming platforms like Netflix have inked deals with iHeartMedia and Barstool Sports to bring podcasts to their services. Daytime talk shows have also suffered blows, including the recent cancellations of both Kelly Clarksons and Sherri Shepherds TV talk shows. Apples audio-only app has taken a hit as well, falling from 15.7% of monthly podcast listeners preferred platform in 2022 to 11.3% in 2025. But audio-only isnt going anywhere, at least for now. According to Triton Digitals annual podcasting report, only 7% of audiences exclusively watch their favorite podcasts, while 13% exclusively listen. The remaining 80% alternate between the two. The meaning of the word podcast has vastly expanded and grown increasingly diffuse as our media habits shift, Joe Berkowitz recently wrote for Fast Company. As for the future of podcastingnot talk radio, not TV chat show, but instead a secret third thing.
Category:
E-Commerce
eBay is laying off about 800 employees, or 6% of its full-time workforce, saying the move is a push to align with its strategic priorities. It comes a week after the company announced it was acquiring second-hand clothing app Depop from rival Etsy for $1.2 billion. Depop is popular with millennials and Gen Z, and is part of eBay’s bid for younger consumers, who are gravitating to second-hand shopping online for sustainability and financial reasons. eBay Inc. (EBAY) was trading up 3.3% in midday trading at the time of this writing. This is eBay’s third round of layoffs since 2023. The online second-hand retailer cut 1,000 jobs in 2024 (9% of its workforce), after it cut 500 jobs in 2023 (or 4% of its workforce), per TechCrunch. We are taking steps to reinvest across our business and align our structure with our strategic priorities, which will affect certain roles across our workforce,” a spokesperson for eBay tells Fast Company. “We are grateful for the contributions of the employees impacted and are committed to supporting them with care and respect. The Silicon Valley-based online retailer has also been heavily investing in artificial intelligence. The eBay spokesperson said the cuts are not AI-related. eBay financials This latest round of layoffs comes just two weeks after eBay reported strong fourth-quarter earnings for 2025, with revenue coming in at $2.97 billion, beating estimates of $2.88 billion; and adjusted earnings per share (EPS) of $1.41, beating estimates of $1.35. “2025 was a milestone year for eBay, and our results reflect the strength of our strategy and the disciplined execution behind it,” eBay CEO Jamie Iannone said in that earnings release. “As we continue to harness AI to elevate thecustomer experience worldwide, eBay is in the strongest position it has been in years.” eBay has a current market capitalization of $40.2 billion.
Category:
E-Commerce
AI has not changed the importance of judgment in product leadership. What it has changed is the cost of getting it wrong. Early in my career, I learned a principle that still guides how I think about building products: The strongest decisions rarely start with perfect data. They start with conviction, a hypothesis shaped by experience, customer insight, and pattern recognition. What ultimately separates high-performing product organizations from average ones is how quickly and confidently instinct is validated. That validation is the true role of product analytics, and increasingly, it is where AI amplifies its value. Analytics tests whether what you believed would happen actually did, and to inform what you do next. When treating analytics as a decision engine rather than a reporting layer, it fundamentally changes how teams operate. ANALYTICS SPRAWL REDUCES CLARITY Across nearly every organization I have worked in, regardless of size or industry, one pattern shows up with remarkable consistency: analytics sprawl. Google Analytics, Amplitude, Mixpanel, Adobe Analytics, and Pendo are all excellent tools, adopted with good intent to solve real problems. However, when allor even severalcoexist within a single organization, they often create fragmentation that undermines decision-making. The issue is not the tools themselves, but the absence of a clear leadership decision to standardize. When analytics lives across multiple platforms, each with its own methodology and definitions, even basic questions become difficult to answer. AI magnifies that problem. Ask a simple question like, How many monthly unique visitors do we get? With data spread across multiple analytics platforms, there is no clean answer. You cannot aggregate the numbers. There is no deduplication. Slight differences in definitions erode trust. Teams stop discussing insights and start debating whose data is correct. That is not a tooling failure. It is a decision-making failure. INCONSISTENT DATA SCALES CONFUSION This challenge matters even more in an AI-driven world because AI depends on coherence. Models train on ambiguous metrics. If foundations are inconsistent, AI will scale confusion faster than any human ever could. Especially in organizations with multiple business units and products, analytics must start before dashboards, instrumentation plans, or AI ambitions. It starts with clarity. This comes from understanding what decisions must be made with confidence and what questions must be answered consistently across teams. Once that is established, everything else follows. Selecting the right product analytics platform is based on business requirements, not convenience. That platform may differ by context. In fact, I have yet to implement the same analytics tool twice. What stays the same is the discipline required to make analytics and AI effective at scale. Instinct may start the journey, but data must validate it. Tool sprawl is a leadership choice rather than a technical inevitability, and shared definitions matter far more than dashboards or models. Analytics and AI only matter when they improve decisions. When that foundation exists, AI becomes a true force multiplier, and organizations gain speed, trust, and the ability to scale. Insights surface faster, patterns emerge sooner, and teams spend far less time reconciling data and far more time acting on it. Leaders move from reacting to signals to shaping outcomes. Without that foundation, AI simply makes bad analytics louder. A SIMPLE CHALLENGE FOR LEADERS If you lead product, technology, or digital teams, here are three simple questions to consider: How many analytics tools does your organization use across your products? Do your teams share the same definitions for basic metrics? Can you answer a question once and trust the answer everywhere? If those answers vary, the issue is not analytics or AI. It is decision-making. If your AI strategy is ahead of your analytics foundations, you are scaling uncertainty, not intelligence. Darren Person is EVP and chief digital officer of Cengage Group.
Category:
E-Commerce
The average long-term U.S. mortgage rate slipped this week below 6% for the first time since late 2022, good news for home shoppers as the spring homebuying season gets rolling. The benchmark 30-year fixed rate mortgage rate fell to 5.98% from 6.01% last week, mortgage buyer Freddie Mac said Thursday. One year ago, the rate averaged 6.76%. The average rate has been hovering close to 6% this year. This latest dip, its third decline in a row, brings it closer to its lowest level since Sept. 8, 2022, when it was 5.89%. Mortgage rates are influenced by several factors, from the Federal Reserves interest rate policy decisions to bond market investors expectations for the economy and inflation. They generally follow the trajectory of the 10-year Treasury yield, which lenders use as a guide to pricing home loans. The 10-year Treasury yield was at 4.02% at midday Thursday, down from around 4.07% a week ago. Mortgage rates have been trending lower for months, helping drive a pickup in home sales the last four months of 2025, but not enough to lift the housing market out of its slump dating back to 2022, when mortgage rates began to climb from pandemic-era lows. Sales of previously occupied U.S. homes remained stuck last year at 30-year lows. And more buyer-friendly mortgage rates this year werent enough to lift home sales last month. They posted the biggest monthly drop in nearly four years and the slowest annualized sales pace in more than two years. Still, with the average rate on a 30-year mortgage now below 6% as the annual spring homebuying season begins, it could encourage prospective home shoppers who can afford to buy at current rates to shop for a home this spring. Assuming rates stay below 6%, buyers and sellers are going to start getting back into the market, said Lisa Sturtevant, chief economist at Bright MLS. March is when the spring homebuying season typically begins to ramp up and with rates at a three-and-a-half year low, it could be a barn burner of a spring homebuying season. Alex Veiga, AP business writer
Category:
E-Commerce
Welcome to AI Decoded, Fast Companys weekly newsletter that breaks down the most important news in the world of AI. You can sign up to receive this newsletter every week via email here. Anthropics stance on autonomous weapons may not survive the future Much of the AI world is watching closely as Anthropic tangles with the Pentagon over how the government can use the Claude models. Anthropic has a $200 million contract with the Pentagon, but the contract says the military cant use the AI companys models as the brains for autonomous weapons or for mass surveillance of Americans. Defense Secretary Pete Hegseth insists, after the fact, that the military should be able to use the Anthropic models for all lawful purposes. Hegseth summoned Anthropic CEO Dario Amodei to the Pentagon for a Tuesday morning meeting, in which he reportedly gave Anthropic until 5:01 p.m. Friday to comply with the Pentagons demand. If Anthropic fails to do so, Hegseth threatened to invoke the Defense Production Act to compel the AI company to supply its models with no guardrails. Hegseth also said the government would declare Anthropic models to be a supply chain risk, meaning that all government suppliers would be directed to avoid or discontinue use of Anthropic models. Amodei said in an interview after the Hegseth meeting that his company has no intention of complying with Hegseths demands. (Hes got a strong case: After all, government officials agreed to the terms.) Amodei explained that the military relies on human judgement to avoid violating peoples constitutional rights. If AI is making the decisions, there will be no human being to object. Amodei is right, and his companys willingness to stand up for its values is laudable. The trouble is, were rapidly heading for a future where autonomous systems become the norm in warfare. For years, the defense establishment talked about keeping the human in the loop in AI weapons systems. Often that human is a government lawyer who can make calls on rules-of-engagement issues on the battlefield. Today the Pentagon is talking more about fully autonomous weapons that can manage more of the kill chain, or the series of communications and decisions around the destruction of a target. Military leaders often say that whoever can use technology to shorten the kill-chain will win wars. Things like electronic warfare (cyberwar), hypersonic missiles, and drone swarms are making war faster and response times shorter. This may eventually preclude the opportunity for human review and decision-making. Increasingly, the U.S. military may be forced to take humans out of the loop in order to stay competitive with its adversaries. So the result of Anthropics standoff with the Pentagon may be that a safety-conscious AI lab is forced out, and a generally less scrupulous company like xAI is chosen as the alternative. Trump rips off Mark Kellys idea for powering new data centers In his State of the Union address, Donald Trump spent a few minutes on the subject of new data centers for AI, which has over the past few months become a hot button issue for voters. While the tech industry says it needs hundreds of new data centers to support all the AI it’s building, a growing number of voters now understands that the power grid improvements needed to power the data centers may increase their energy bills. I have negotiated the new Ratepayer Protection Pledge, Trump crowed. We’re telling the major tech companies that they have the obligation to provide for their own power needs. Politicos might recognize that message, as it closely echoes what Arizona Senator Mark Kelly, a Democrat, has been saying for months now. Kellys AI for America plan would create an industry-financed AI Horizon Fund to pay for energy-grid upgrades and workforce reskilling. According to Kellys plan, Congress could require data center developers to buy or lease enough land to contain both their facilities and the renewable energy infrastructure to power and cool them. The data center operators could also be required to pay to connect the renewable sources to the local grid, should the power they generate go unutilized. Trumps idea is more of a suggestion. As of now its non-binding, just words. And there was no mention of how the tech companies would generate their own power. Elon Musks xAI, for example, brought its own power to its massive Colossus data center in Memphis. Unfortunately, they were dirty methane-powered turbines, and the facility quickly became one of the areas biggest polluters. High numbers of young tech job seekers AI-cheated on skills tests Cheating on technical hiring assessments went through the roof in 2025, with fraud attempts more than doubling, according to new research from CodeSignal, which runs a developer-skills evaluation platform used in hiring software engineers. The research found that 35% of proctored assessments showed signs of cheating or fraud last year, up from just 16% in 2024. The biggest culprits? Plagiarism, having someone else take the test for you, and sneaking in AI tools that aren’t allowed. The jump was especially noticeable among entry-level candidates. Fraud rates for junior roles nearly tripled year over yeargoing from 15% to 40%making early-career hiring a particularly vulnerable spot in the recruiting pipeline. In a press release accompanying the report, CodeSignal CEO and cofounder Tigran Sloyan partly blamed the normalization of AI tools, noting that 80% of Gen Z reportedly uses AI in daily life, which has made the line between acceptable help and outright cheating much blurrier. Accessibility to AI also makes unauthorized assistance harder to detect and raises the stakes for maintaining fair and reliable skill evaluation, he noted. CodeSignal’s detection systemswhich combine AI analysis, human review, and digital monitoringidentified a few common patterns across flagged assessments. About 35% of candidates frequently looked off-screen, suggesting they were consulting outside resources during the test. Another 23% showed unusually linear typing patterns, where complex solutions just appeared with barely any pauses or debugging. And 15% had answers that looked a lot like known solutions or leaked content. (It’s worth noting that these numbers reflect attempts that were actually caught, not cases where someone successfully slipped through.) The data also surfaced some geographic and procedural gaps. Fraud attempt rates hit 48% in the Asia-Pacific region, compared to 27% in North America. Testing conditions made a big difference, too: Candidates in unproctored environments shoed score jumps more than four times larger than those being actively monitored, which pretty clearly shows that proctoring works as a deterrent. As for how CodeSignal catches all this: the company says it’s spent a decade building out its fraud-prevention infrastructure, which it’s now applied across millions of assessments. It uses a proprietary “Suspicion Score” and leak-resistant test design to flag things like plagiarism, proxy test-taking, unauthorized AI use, and identity fraud. More AI coverage from Fast Company: Harvard study shows AI stock trading rivals many picks made by fund managers He built a hit podcast about the Epstein files. Its entirely AI-generated What if the SaaSpocalypse is a myth? This AI note-taking startup thinks its building the steering wheel for chatbots Want exclusive reporting and trend analysis on technology, business innovation, future of work, and design? Sign up for Fast Company Premium.
Category:
E-Commerce
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