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Google is testing adding "web mentions" to the retailer store page under the reviews section. I cannot replicate this yet, but web mentions shows what people are saying about the retailer on sites like Reddit and other social platforms, whereas reviews are reviews left on Google.View the full article

Microsoft Bing Search is testing a new layout for videos within the search results. Instead of a list view, Bing is testing a two-by-two grid layout.View the full article

This week's PPC Pulse recaps Google’s evolving Search asset guidance, revised budget pacing behavior, and Microsoft’s rollout of self-serve negative lists for PMax. The post Google’s Asset Guidance & Ad Scheduling Updates, Microsoft Negatives – PPC Pulse appeared first on Search Engine Journal. View the full article

If you don’t want to be left behind by the AI revolution, you really need to start paying for it. At least that’s become the common refrain among some AI enthusiasts, who seem intent on instilling FOMO in less technical users. The free versions of ChatGPT and Claude, they say, are woefully inadequate if you want to understand where things are headed—so stop being a cheapskate and hand over your $20 (or $200) a month like the rest of us. “Judging AI based on free-tier ChatGPT is like evaluating the state of smartphones by using a flip phone,” HyperWrite CEO Matt Shumer recently wrote in a widely shared essay on AI’s impact. “The people paying for the best tools, and actually using them daily for real work, know what’s coming.” I’m giving you permission to safely ignore this advice, and to not feel bad about it. While an AI subscription might make sense if you’re running into specific frustrations with the free versions, you can still get plenty of mileage without paying, and learn a lot about the state of AI in the process. Don’t be frightened into buying something that hasn’t actually proven its value to you. The state of the art is still free One way that AI boosters try to scare you into paying for AI is by arguing that the free versions are already obsolete, so any negative impressions you might’ve gotten from them are misguided. “Part of the problem is that most people are using the free version of AI tools,” Shumer wrote in his essay. “The free version is over a year behind what paying users have access to.” This claim is provably false: The free version of ChatGPT includes access to GPT-5.2, OpenAI’s latest model, which launched in December. The free version of Google Gemini includes access to Gemini Pro 3.1, which launched on February 19. Claude’s free version doesn’t include Opus 4.6, but has the same Sonnet 4.6 model that the paid version uses by default. It launched on February 17. Microsoft 365 subscribers can also select “Smart Plus” in Copilot to use GPT-5.2, without a premium AI subscription. xAI’s Grok 4 is available for free. Of course, the free versions of these tools all have usage limits, but so do the paid ones. When I signed up for a month of Claude Pro to test Opus 4.6, I quickly ran into yet another paywall. To continue the conversation, I had to either buy pay-as-you-go credits or upgrade to the $200-a-month Claude Max plan. Without paying more, I couldn’t use Claude at all—not even Sonnet 4.5—until my limit reset. My main takeaway was that I should have just stuck with Sonnet in the first place. Instead of paying for some vague feeling that you’re getting the state of the art, you should play around with what AI companies offer for free. Make them demonstrate that the results are meaningfully different before you consider paying them, not after. AI should prove itself to you, not vice versa For AI boosters, the corollary to paying for AI is that you also need to throw immense amount of time into figuring out what it’s for. Ethan Mollick, for instance, writes that you should “resign yourself to paying the $20 (the free versions are demos, not tools),” then spend the next hour testing it on various real-world tasks. Sorry, but this is backward from how software as a service should work. It’s not your job to invest time and money into convincing yourself that AI is worth more time and money. Let the AI companies do the convincing, and don’t fall prey to FOMO in the meantime. Playing the field is just as instructive If you do commit to paying for an AI tool, chances are you won’t use other AI tools as much, or at all. But that in itself isn’t a great way to understand the state of AI. What you should be doing instead is bouncing around, taking full advantage of what each AI company offers for free. That way, you’ll get a sense not just of the subtle differences between large language models, but also the unique features that each AI tool offers. You’ll also be less likely to run into usage limits, the only trade-off being that your past conversations will be scattered across a few different services. Such behavior is, of course, wildly unprofitable for all the companies involved. But again, that’s not your problem. If you’re getting sufficient value out of free AI tools, the AI companies will have to tweak their free offerings accordingly (for instance, with ads) or come up with new features worth paying for. Claude Code, for instance, is available only with a subscription, and over time we may see more paywalled tools (like Claude Cowork, which is still in early development) that cater to specific tasks or verticals. Until that happens, enjoy the free versions of AI tools, and rest easy knowing that you’re not missing much. View the full article

We’re getting a lot of questions about prompt tracking. Many of our current and prospective clients are tracking their visibility using tools such as Profound, Athena, and Peec. The million-dollar question that always comes up is “Which prompts should I be tracking?’. In an incredibly personalized and complex ecosystem, it’s extremely difficult to know what our buyers are even asking LLMs about our company. There are no data sources I feel great about right now. This isn’t like traditional search, where Keyword Planner data was publicly provided. It’s unlikely that OpenAI or Google will ever fully open up this data for us to analyze. There have been some recent proposals by the UK CMA around Google + data transparency but let’s all expect the bare minimum to be done here. So LLM tracking is a complete black box. Are there any data sources that we can possibly use to see which prompts to track? Maybe. OpenAI data leaking into Search Console Last November, there was some extremely interesting reporting done around this. Last November, Jason Packer wrote a report analyzing how searches from ChatGPT were actually getting leaked into Search Console reports. An accidental test revealed quite a few queries in the Search Console data with PII. The story was eventually picked up by Ars Technica and confirmed by sources as OpenAI. They since claimed to have fix the problem that was specifically occurring here and that “only a small number of queries were leaked”. However, this is confirmation that ChatGPT queries are available in some Search Console profiles. Obviously, there’s huge implications with privacy, PII etc., that’s beyond the scope of this article. The point being, we know it’s not impossible that queries from LLM systems are available in Search Console. AI Mode data available in Search Console We also know from the amazing reporting of Barry Schwartz that data from AI Mode will be available in Search Console. So more evidence that Search Console will have the capabilities to collect data points for how users are searching within an LLM. From what we’ve analyzed so far, I believe this is where the data is likely coming from. When you look at the data after applying this filter, you can see steady rises in impressions over the last 3 months: This lines up pretty well with Google’s aggressive rollout of AI Mode-based features during Fall 2025/Winter 2026. How to mine for your prompt-like Search Console queries So how could we possibly access this data from user prompts in Search Console? Well, the best method is to took at longer query lengths. With a little bit of regex, we can filter our data down to queries that are 10+ words in length with the following process: Go into Search Console Performance > Search Queries Select Add Filter > Query Choose Custom Regex Enter in this regex: ^(?:\S+\s+){9,}\S+$ Here’s a screenshot of the regex you can enter. I’ve done this for a few properties now, and the results are pretty astounding. When you start to see the Search Console of queries that are 10+ words in length, they are very clearly written like prompts. I can’t share screenshots of the data here, but here are some examples of the types of queries I’m seeing. I’ve changed the scenario for privacy reasons, but kept the relative breadth that the queries are looking for: Map out a full day in Glacier National Park. I’d like to hike a scenic trail, see unique wildlife or natural features, grab a quick bite from a nearby lodge or food stand What are the best email performance and deliverability platforms to help email marketing programs reduce spam placement, filter out low-quality or fake subscribers, and improve inbox placement rates Which sales enablement intelligence platforms are most widely adopted and cost-effective for enterprise pipeline analytics and buyer engagement insights in France? If you were a consultant, which of the following applications would you recommend for using advanced data visualization to help teams interpret complex operational or customer data Now let me be clear: we don’t have direct evidence that these types of queries are directly from ChatGPT, AI Mode or any other AI platform. While we know it’s possible from the above case study, this could just be users using Google more like an LLM. However, I’d argue that it’s still just as valuable since we want to analyze what people are typing into the LLMs. If it reads like conversation data, it’s an actual window into how your customers search with much longer query strings. One of my favorite quotes from Will Critchlow is “we’re doing business, not science“. That’s even more true as we continue to hurdle toward zero-click, low attribution landscape. This data is available, you’ll need to decide whether you choose to use it or not. Using Claude for prompt analysis For now, my favorite tool for data analysis has been Claude. I get the most reliable results, some really nice visualizations, and it can integrate into Claude Code if I ever need it. After exporting the file, you can upload the list of “prompts” to Claude and have it start performing behavioral analysis of the data. That way it can spot themes + trends in the data that you can use for better prompt tracking. Once it has the data, it will perform a custom analysis and provide results. However, I think it’s even more valuable to ask specific questions about the data that you could use for prompt tracking. For example, things I asked it include: What are customers asking about my brand? What are the most common ways that users are prompting LLMs? How are they framing their questions? What characteristics of our product do people care the most about? Tell us more about our customers based on this data After putting in these questions, you’ll get some interesting responses: Once again, the actual answers to these questions were far more valuable than what I got in the screenshot above. Claude was about to find some really great business insights in terms of what customers were looking for Just by analyzing this data, I found some really valuable insights into how people may be using LLMs to ask questions about these websites. Immediately some of the insights I found include: A PR issue from 3+ years is being asked about constantly. People are searching for country-based solutions for software more often than we anticipated. Searches use one company as the gold-standard benchmark to compare other competitors against. People are constantly looking for a cheaper alternative to one solution. Asking Claude for prompt tracking suggestions The final thing I pushed Claude to do here was based on the data that it found was to actually make prompt tracking recommendations for us. I’ve never loved using LLMs to make direct prompt tracking recommendations with one-shot prompts. However, after uploading what we think are real user prompts to Claude, I feel much better about tapping into its recommendations. After finishing the questions up, I had Claude create prompts that it thinks would make sense for us to track based on what it found in its research. It went through and identified prompts that I think would actually make sense based on what I found in the data as well. Now you can go ahead and determine which of these prompts are going to be best to utilize in your AI tracking system of choice. Is this all a bunch of hullabaloo? Maybe. I don’t think there’s a perfect system for deciding which prompts to track. Another study by Rand Fishkin found that user prompts vary widely. When surveying users, he found a “0.081” similarity when asking 142 respondents to provide prompts they’d use for the same query. So I don’t think you’ll ever be able to tap into the exact prompts that users are searching. However, in my opinion, you have a much more well-informed list of prompts to track based on Search Console data. We’ve informed the prompts we want to track with an actual data source instead of simply “our best guess.” At a minimum, you’re going to find individual opportunities for ways that users are prompting your site that you would have never imagined. The goal, however, is to find more scalable, common themes you can apply to your data tracking. This article was originally published on the Nectiv blog [as How To Mine Google Search Console For Conversation Data (Regex Included)] and is republished with permission. View the full article

Somewhere inside your CRM is a customer who does not exist. They open emails at impossible hours. They redeem promotions with machine-like precision. They browse product pages across three devices in under five minutes. They convert, unsubscribe, re-engage and transact again. On paper, they look highly active. In reality, they may be a composite of behaviors stitched together from AI assistants, shared accounts, recycled addresses, autofill tools and automated workflows. This is the Data Doppelgänger Problem. And it is about to become one of the most expensive blind spots in modern marketing. For years, identity resolution was framed as a hygiene issue. Clean the data. Remove duplicates. Suppress invalid records. That work still matters. But the ground has shifted. Today, the bigger risk is not dirty data. It is convincing data that is wrong. AI agents are no longer theoretical. Consumers are using them to summarize emails, compare products, track prices, fill forms and in some cases complete purchases. Shared credentials remain common across households and small businesses. Browser privacy changes have pushed attribution models into probabilistic territory. Add subscription commerce, loyalty programs and cross-device behavior, and you begin to see the pattern. One person can generate multiple digital identities. Multiple actors can generate activity that appears to belong to one person. What you see in your dashboards may not reflect a human with consistent intent, but a digital echo assembled from overlapping signals. The result is not just noise. It’s distortion. When high engagement lies Most marketing systems reward engagement. Opens, clicks, transactions and recency are treated as proxies for value. But what if the engagement is partially automated? Email clients increasingly prefetch content. AI tools summarize messages without requiring a human to scroll. Assistive shopping agents monitor price drops and trigger interactions on behalf of users. To your analytics layer, these actions can look identical to high-intent behavior. Now layer in recycled or repurposed email addresses. A dormant account gets reassigned by a provider. A corporate alias forwards to multiple employees. A consumer rotates through alternate emails to capture new user discounts. On the surface, these look like legitimate records. Underneath, the identity is unstable. You may be optimizing campaigns around engagement that doesn’t reflect loyalty. You may be suppressing records that are valuable but appear inactive because their activity is fragmented across identities. You may be feeding machine learning models with signals that only compound the errors. This is where seasoned professionals feel the frustration. The dashboards are clean, segments are defined and the attribution model runs on schedule. Yet outcomes drift, conversion rates plateau and fraud creeps in through legitimate-looking channels. Acquisition costs rise without a clear explanation. The problem is not effort. It is identity confidence. Doppelgängers create operational risk The Data Doppelgänger Problem is not limited to marketing efficiency. It crosses into risk, compliance and revenue protection. Promotional abuse is often framed as external fraud. In reality, much of it exploits weak identity resolution. A single individual can appear as multiple new customers. Conversely, multiple individuals can appear as one trusted account. Loyalty points are pooled, discounts are stacked, and survey data becomes unreliable. As AI agents become more capable, this risk becomes harder to detect. An automated assistant acting on behalf of a legitimate customer is not inherently fraudulent. But it can blur behavioral signals that historically differentiated genuine intent from scripted abuse. Traditional rules-based systems look for anomalies. The next wave of risk will look normal. If you cannot distinguish between a stable, persistent identity and a composite one, you cannot confidently calibrate friction. Add too much friction and you punish real customers. Add too little and you subsidize exploitation. The only sustainable path is to move beyond static identifiers and into continuous identity validation. Not just confirming that an email address is deliverable, but understanding how it behaves over time, how it connects to other digital attributes, and how it fits within a broader activity network. The collapse of the Golden Record Many organizations still pursue a single source of truth. A golden record that reconciles identifiers into one master profile. The aspiration is understandable. But in a world of AI mediation and shared signals, the notion of a fixed record is increasingly unrealistic. Identity is not a snapshot. It is a moving target. The more relevant question is not whether you can unify data into one profile. It is whether you can quantify how confident you are that the activity associated with that profile represents a coherent individual. That shift sounds subtle. It is not. When identity is treated as binary, either matched or unmatched, you miss nuance. When identity is treated as a spectrum of confidence, you gain leverage. You can weight signals differently. You can suppress low-confidence interactions from modeling. You can prioritize outreach to high-confidence segments. You can apply graduated friction to transactions that sit in ambiguous territory. This is where data becomes a strategic asset rather than a reporting function. From volume to validity Marketing technology has long rewarded scale. Bigger lists, broader reach and more signals. But scale without validation creates false precision. The Data Doppelgänger Problem forces a harder question. Would you rather have ten million records with unknown stability, or eight million records you understand deeply? The brands that win over the next few years will not be those with the most data. They will be those with the most defensible data. Defensible means continuously validated. Network-informed. Contextualized against real patterns of activity. Integrated across marketing, analytics, and risk workflows so that improvements in one area compound across the organization. When identity confidence increases, targeting improves. When targeting improves, engagement quality strengthens. When engagement quality strengthens, attribution stabilizes. When attribution stabilizes, forecasting becomes more reliable. And when forecasting improves, budget allocation becomes less political and more performance-driven. This compounding effect is measurable. It is also fragile. Feed unstable identities into the loop and the entire system drifts. What Seasoned Professionals Should Be Asking If you are leading marketing, analytics or risk, the uncomfortable questions are no longer about data access. They are about data integrity at scale. How many of your active profiles represent coherent individuals? How often are identities revalidated against fresh activity? Can you detect when one identity splits into several, or when several collapse into one? Are your fraud controls calibrated to behavior, or to assumptions about behavior that may no longer hold? These questions do not require panic. They require evolution. This is not a crisis. It is a signal that the digital ecosystem has matured. Consumers are delegating more tasks to software. Devices are proliferating. Privacy changes are fragmenting identifiers. This is the environment we operate in. The brands that adapt will treat identity not as a static field in a database, but as a living construct that must be observed and refined continuously. Utilizing advanced activity networks to anchor identity in its current reality. Those that do will spend less on wasted acquisition. They will protect margins without alienating customers. They will trust their analytics because they understand the confidence behind the numbers. And perhaps most importantly, they will know who they are actually engaging. Because somewhere in your CRM, there is a customer who does not exist. The question is whether you can find them before they find your budget. View the full article

It’s the last week of Black History Month (BHM) and it’s clear Americans are over performative values. Trite BHM-inspired merchandise sits on retailer shelves untouched while media is abuzz covering the artistry, activism, and symbolism of Bad Bunny’s Super Bowl halftime show. The signal is clear: consumers are looking to brands for real solutions to real problems, not products that commodify culture. Most companies build everything from advertising to AI for the “average user,” but in doing so, they react to rather than lead markets. Strategic leaders look to growth audiences—underserved groups who are the fastest-growing demographics—as lead users. They are the “canaries in the coal mine” because they navigate the highest levels of systemic friction, making them the first to experience “average” design failures. What does championing these lead users look like at a communications, product, or systems level? It looks like Elijah McCoy automating engine lubrication—an innovation bred from the friction between his engineering degree and the menial labor he was forced to perform, thus creating the “real McCoy” quality standard. It looks like Jerry Lawson changing the economics of the gaming industry by inventing the video game cartridge that divorced its hardware from its software. And it looks like emergency medicine becoming a global standard after being piloted by the Pittsburgh Freedom House Ambulance Service who, in the face of medical bias and systemic unemployment, also redefined emergency care as a public right. Drawing from their lived experiences in underserved groups, these pioneers didn’t just solve problems; they mastered environmental friction. Today, that friction also manifests in algorithms. Championing growth audiences as lead users means ensuring they are critical AI system “stress testers.” When we fail to design for them, we allow AI data, development, and deployment to default to obtuse “averages” that can frustrate or drive away valuable customers. Three recent examples highlight issues and opportunities. Relying on ‘Data Infallibility’ versus Lived Realities In this Infallibility Loop bias, a brand’s AI trusts a data source—like a flawed GPS coordinate or outdated government map—as an absolute truth, even when customers provide contrary evidence. This is a digital echo of historical redlining: a systemic refusal to see humans over faulty data. The Experience: A Black homeowner in an affluent area is penalized by an AI that confuses her address with a property in a different town, automatically forcing unnecessary flood insurance onto her mortgage and increasing the payments. Despite providing human-verified deeds and highlighting known GPS errors, the AI blocks her “incomplete” payments and triggers automated credit hits. A resolution only came months later after the consumer filed state-level servicer complaints. The Fix: Prioritize Dynamic Qualitative Data Collection. Design should allow real-time, contextual evidence to override static, biased datasets. True brand innovation requires systems to yield to the experts: their customers. Leveraging ‘Data Intimacy’ while Neglecting Situational Accuracy This trust paradox occurs when brands use private data, but fail to combine situational data, making personalization feel like needless surveillance. The Experience: During January’s recent record-breaking New York snowstorm, a customer called a national pharmacy’s location in her neighborhood to make sure they were open. The AI-powered interactive voice response (IVR) recognized her number, asked for her birthdate, and greeted her by name. Yet, after performing this exchange, it provided a “default” confirmation that the store was open when asked. Without a car, the customer braved life-threatening conditions on foot only to find a handwritten note on the door indicating it had closed due to the storm. The Fix: Add Good Friction. A term coined by MIT professor Renee Richardson Gosline, “Good Friction” requires that when external context (like a Level 5 storm) conflicts with standard scripts, the system pauses and verifies first. Prioritizing ‘Recency’ But Erasing Loyalty Recency bias in algorithms weights the last data point more heavily potentially resulting in algorithmic erasure. The Experience: A 20-year elite status customer calls an airline, only to be greeted by the name of his niece (a nonmember relative for whom he recently booked a one-off ticket) and then is erroneously deprioritized in the automated journey as a nonmember. In many “growth audience” and immigrant households, economics are multigenerational and communal, with a single “lead user” facilitating purchases for extended family. This airline system’s “memory” was shallow, seeing only the most recent transaction and ignoring a decades-long relationship because a reservation shared the same contact number. The Fix: Focus on Holistic Design. AI must be weighted to recognize the arc of the customer journey, ensuring that loyalty isn’t erased by a single data point or the nuances of communal purchasing. To be sure, bad data is a universal problem, but the lack of situational intelligence in our AI systems hits growth audiences—like Black consumers—first and hardest. Because these audiences represent a disproportionate share of future consumption and have the most “cultural common denominators,” their frictions are diagnostics for markets writ large. We aren’t just solving for a niche by championing them as lead users, we are adopting more rigorous, empathetic, expansive, and effective standards that solve real problems for all people. View the full article

The Greens’ victory is a crushing blow for Sir Keir StarmerView the full article

At hundreds of Burger King restaurants across the U.S., there’s a new invisible worker who’s tracking which ingredients are in stock, analyzing daily sales data, and checking in on whether employees are saying “Thank you” and “You’re welcome.” It’s an AI assistant named Patty. According to Thibault Roux, Burger King’s chief digital officer, the voice-activated chatbot is designed to help employees and managers handle tasks that might usually require pulling out a computer or consulting with an instruction guide. Patty began showing up at select locations about a year ago, and is now in a pilot phase at approximately 500 Burger Kings. It’s expected to roll out to the rest of the chain’s U.S. locations by the end of the year. On a day-to-day basis, Patty has an array of functions, from letting a manager know if a store is low on onions to helping an employee build a new burger. But it has another role that’s raising quite a few eyebrows: analyzing Burger King locations based on “friendliness” by tracking employees’ use of key phrases like “Welcome to Burger King,” “Please,” and “Thank you.” Online, commenters are concerned that this functionality is a slippery slope toward 1984-style “employee surveillance.” In an interview with Fast Company, though, Roux clarified that Patty is not being used to analyze individual employees’ performance, and is instead imagined as a kind of “coach.” “It’s truly meant to be a coaching and operational tool to really help our restaurants manage complexities and stay focused on a great guest experience,” Roux says. “Guests want our service to be more friendly, and that’s ultimately what we’re trying to achieve here.” Patty, are we running low on Diet Coke? Technically, Patty is the chatbot version of Burger King’s assistant platform, which collects data from operations including drive-through conversations, inventory, and sales, and then uses AI to analyze patterns in that data. For now, Patty operates on a customized model from OpenAI, though Roux says the technology is flexible enough that it could integrate with another partner in the future (like Anthropic or Gemini) depending on the company’s needs. For managers and employees in stores, Roux says Patty operates similarly to something like Siri. Patty is activated by a small button on the side of an employee’s headset, and they can ask it direct verbal questions related to their specific store—like recent sales figures or inventory updates—as well as more general company information, to which the bot will provide a verbal answer. “If you’re looking to clean the shake machine [you can ask Patty] the procedures to clean it,” Roux explains. “Or we have a lot of limited-time offers, and sometimes they can be cumbersome to remember. You can easily tap into Patty and be like, ‘Hey, remind me, does the new build maple bourbon barbecue have crispy jalapeños?’” Patty can also reach out to employees directly if it notices a pattern of interest. For example, if Patty thinks a specific store is out of lettuce, it might ping a manager to confirm. Once it’s received confirmation, it can mark lettuce as sold out on that location’s app and website—a process that previously would have required human intervention. Roux says franchisees and regional managers can decide how they want Patty to reach employees with information, whether it’s through their headsets or via a text message (though the tech is programmed explicitly to never interrupt a worker during a customer interaction). Insights from Burger King’s Assistant platform also live outside of employees’ headsets. Managers can check information from the tool on an accompanying website or app. For example, Roux says, when a district manager is visiting a new store, they might ask Patty on the app, “What are the top three guest complaints at this location this week?” or “What are their top missing items?” In an interview with Fast Company writer Jeff Beer earlier this month, Burger King President Tom Curtis said the assistant platform has already led to some significant menu changes. Curtis explained that the AI tracked all the times that team members said “I’m sorry, we don’t have that” and linked them back to a common denominator: apple pie. In January, Burger King brought back its apple pie for the first time since 2020. “We’re in the idiocracy version of 1984” Patty’s more straightforward uses, like helping managers access sales data and check inventory, seem fairly predictable in the context of fast food. Where Burger King is really pushing Patty’s use cases, though, is with its “friendliness” metric. In an interview with The Verge on February 26, Roux said Patty would recognize phrases like “Welcome to Burger King,” “Please,” and “Thank you,” and then give managers access to data on their locations’ friendliness performance based on those keywords. Mere hours after that piece went live, a thread in the subReddit r/technology on Patty had already amassed more than 15,000 upvotes and nearly 3,000 comments. Common refrains from users include comparing the technology to the surveillance state in George Orwell’s novel 1984, labeling it “authoritarian” and “dystopian,” and accusing Burger King of employee surveillance. “This would be criticized as being cartoonishly unrealistic in a sci-fi movie 10 years ago,” one user wrote. Another added, “We’re in the idiocracy version of 1984.” When asked about this response, Roux says the data from employees’ conversations is anonymized, and that none of these friendliness metrics will be used for grading or assessing individuals. Further, he adds, Patty will not directly instruct employees on what to say or how to say it. Instead, data on friendliness will be shared with managers, who can use it for face-to-face coaching with their teams. Still, it’s unclear exactly how Patty is quantifying friendliness. In a video explanation of the feature, a manager is shown asking the bot, “Is there anything that needs my immediate attention?” to which it responds, “The team’s friendliness scores this morning were the highest this week.” In an email to Fast Company, a Burger King spokesperson said, “In select pilot locations, we’ve explored using aggregated keywords, including common hospitality phrases, as one of several signals to help managers understand overall service patterns. The tool is not used to score individuals or enforce scripts.” Burger King did not respond to Fast Company’s request for clarification on how friendliness scores are calculated. So far, Roux says he’s seen growing interest in Patty from franchisees, with several managers making specific requests for future add-ons. “A lot of our franchisees . . . and regional general managers are very competitive, so they want to know, ‘Hey, how do I compare to other restaurants?’” Roux says. “I think that’s something that we’re going to be rolling out. In fact, we were looking at some of the designs earlier this week with the franchisees. So this is only the beginning.” View the full article

Recently, Grok AI faced criticism after users found it was creating explicit images of real people, including women and children. Although xAI has now implemented some restrictions, this incident revealed a serious weakness. Without safeguards and diverse perspectives, girls and women are put at greater risk. The dangers artificial intelligence poses to women and girls are real and happening now, affecting their mental health, safety, healthcare, and economic opportunities. Last fall, a mother discovered why her teenage daughter’s mental health had been deteriorating: It was a result of conversations with a Character.AI chatbot. She’s not alone. Aura’s State of Youth Report, released in December, found that parents believe technology has a more negative effect on girls’ emotions, including stress, jealousy, and loneliness—51% compared with 36% for boys. That’s unacceptable, and we need to do better. The risks extend beyond mental health. OpenAI recently reported that more than 40 million Americans seek health information on ChatGPT daily. As AI in healthcare expands, the consequences of biased training data can be dangerous. AI models that are trained predominantly on male health data produce worse outcomes for women. For instance, an AI model designed to detect liver disease from blood tests missed 44% of cases in women, compared with 23% in men. Uneven playing field In the workplace, AI is not leveling the playing field. Despite laws prohibiting discrimination, AI-powered hiring tools have repeatedly caused concerns about bias, fairness, and data privacy. A study published by the University of Washington found that in AI resume screenings, the technology favored female-associated names in only 11% of cases. These failures reflect who is building our technology. Women make up just 22% of the AI workforce. When systems are designed without women’s perspectives, they replicate existing inequities and introduce new risks. The pattern is clear. AI is failing girls and women. Pivotal moment This could not come at a more pivotal moment in the job market. A quarter of the roles on LinkedIn’s latest list of the 25 fastest-growing jobs in the United States are tech-related, with AI engineers at the top. Decisions about how AI is designed today will shape access to jobs, healthcare, education, and civic life for decades. It is critical that women play an active role in developing new AI tools so that inequity is not baked into the systems that increasingly govern our lives. Young women are not disengaged with AI. Research conducted last year by Girls Who Code, in partnership with UCLA, found that young women are deeply thoughtful about the dual nature of technology. They see its potential to advance healthcare, expand educational access, and address climate change. They are also aware of its dangers, such as bias, surveillance, and exclusion from development. This isn’t blind optimism. Instead, it offers a perspective that is often missing in today’s AI development. Creating technology is an exercise of power and holds great responsibility. Since girls are often the most affected by AI’s failures, they must be empowered to help lead the solutions. Women like Girls Who Code alumna Trisha Prabhu, who developed ReThink, an anti-bullying tool, exemplify this. Latanya Sweeney, recognized as one of the top thinkers in AI, founded Harvard’s Public Interest Tech Lab. Their achievements demonstrate the potential when women lead in tech development. Smart steps If we want safer, more responsible AI systems, three steps are essential. First, computer science education should integrate social impact. Coding cannot be taught in isolation from its consequences. Students should learn technical skills alongside critical analysis of how technology shapes communities and lives. This approach produces results. For instance, one Girls Who Code student utilized the skills she learned to create an app called AIFinTech to help immigrant families manage their personal finances. Second, women must be represented in AI development and governance, particularly those from historically underserved communities. They need seats at the tables where AI systems are designed, tested, and regulated. This means ensuring gender diversity on AI ethics boards and that government AI committees are representative of the demographics most affected. Finally, how we evaluate artificial intelligence needs to evolve. Today, AI is assessed by efficiency, accuracy, and profitability. We must also evaluate health, equity, and well-being, especially for girls and young women. Before an AI system is deployed in a high-stakes environment such as healthcare, it should be required to pass tests for gender bias and demonstrate that it does not produce disparate outcomes. New York City, for example, requires employers that use automated employment decision tools to undergo an independent bias audit annually. We do not have to accept AI’s flaws by default. We are witnessing AI’s impact on girls in real time, and we must seize the opportunity to change course while the technology is still being shaped. When girls are given the chance to lead in AI, they will build safer systems not just for themselves, but for everyone. View the full article

It’s sometime in the future, and Elon Musk, Jeff Bezos, and Sam Altman have joined forces on a new venture called Energym. The global chain of gyms is designed to harness the energy of the unemployed as they exercise on machines. The generated electricity feeds the AI servers that put them out of a job. Think Planet Fitness meets the Matrix, but without living in a simulation. Energym’s mission is to feed the AI machines with human sweat, and it’s a great business model. By 2030, almost 80% of people have lost their jobs. If you have no money and no purpose, you may as well use all your free time to work out and feed AI server fans with some kilowatts. “It solves our need for energy and your need for purpose,” Altman says in a promotional video. Energym, as you probably already know, is not real. But it very well could be. In this era, where so many brands and startups are constantly trying to flip the most inane ideas into the Next Big Thing to get a $50 billion valuation and an IPO, this absurd premise makes total sense. The mockumentary-style ad fpr Energym that has been circulating on the internet captures the current AI startup circle jerk better than any I’ve seen online so far. https://www.instagram.com/reels/DVLE-QJEf0n The advertisement was created by Hans Buyse and Jan De Loore. The latter—who wrote the copy for the video, as well as edited and produced it—is the cofounder of a one-man AI creative studio in Belgium called Kitchhock. The company has been creating all types of videos since 2011, back when there was no Seedance or Veo. But now, De Loore is using his creative chops and the latest generative video AI tech to make real ads for real companies in Belgium through his AI video studio arm, AiCandy. Energym is just a satirical ad designed to promote his own business and destroy the very core of those who make the technology that powers his business. (Incidentally, Energym is the same name as a company that makes a very real $2,800 static bicycle designed for exercise and to produce electricity, but it’s not related to AiCandy’s fake ad.) The Energym commercial is obviously tongue in cheek, as are many other videos we have seen in recent months that make fun of our increasing dependency on artificial intelligence and its power. But this one hits particularly hard. For some, it may be the Black Mirror-esque nature of it. (There’s an actual episode of the British TV series that feels like an extended version of the ad.) Personally, it connects with the WTF-ness that the current AI situation is provoking in me on different levels. The fear of what’s next. The dread of seeing reality destroyed. The disgust for the fat cats that are running this charade with no checks and nobody’s permission. I find it hard to pinpoint what it is. It’s just an absurd exaggeration with no logical basis that hits too close for comfort—and, at the same time, makes me happy. View the full article

What began as a race to build better AI models has escalated into a competition for compute, talent, and control. Foundation models—large-scale systems trained on vast datasets to generate text, images, code, and decisions—now underpin everything from enterprise software and cloud infrastructure to national digital strategies. The industry’s language around AI has grown more ambitious—and more elastic. Agentic AI has leapt from research papers to Davos billboards, while artificial general intelligence, or AGI, now appears routinely in investor decks and earnings calls. Definitions have begun to blur. Some companies quietly lower the bar for what qualifies as general, stretching the term to encompass incremental productivity gains. Yet the economic results, particularly measurable returns on AI investment, remain uneven. According to PwC’s 2026 Global CEO Survey, 56% of 4,454 CEOs across 95 countries reported neither increased revenue nor reduced costs from AI over the past 12 months. Only 12% achieved both. Even so, 51% plan to continue investing, despite declining confidence in revenue growth. The result is a widening gap between engineering reality, commercial storytelling, and public expectation. Few voices carry as much authority—or have shaped modern AI as directly—as Andrew Ng. The founder of DeepLearning.AI and Coursera, executive chairman of Landing AI, and founding lead of the Google Brain team, Ng has helped define nearly every major phase of the field, from early deep-learning breakthroughs to the current wave of enterprise deployment. He has authored or coauthored more than 200 papers and previously led the Stanford AI Lab. In 2024, he popularized the term agentic AI, arguing that multistep, tool-using systems capable of executing workflows may deliver more near-term economic value than simply scaling larger models. In an exclusive conversation, Ng offered Fast Company a reality check. He says true AGI—that is, AI capable of performing the full breadth of human intellectual tasks—remains decades away. The true competitive frontier, meanwhile, lies elsewhere. This conversation has been edited for length and clarity. You helped popularize the term agentic AI to describe a spectrum of autonomy in AI systems. How did you come up with it, and how has the concept evolved as multi-agent systems move into enterprise production? I began using the term almost two and a half years ago, though I didn’t publicly take credit for it at the time. I started using it because I felt the community needed language that shifted the focus toward AI systems capable of taking multiple steps of reasoning and action—not just a single prompt-and-response exchange. More specifically, I felt there would be a spectrum of AI systems—some slightly autonomous or slightly agentic, and others highly agentic—where they take many steps of actions and work for a long time. No one was using the term agentic to describe this concept before I began using it. I started introducing it in my newsletter and in talks at conferences and industry events, and it quickly gained traction there. I didn’t expect marketers to run with it the way they did. When I attended Davos this year, I saw the word plastered on the sides of buildings. Even outside San Francisco, agentic now appears on billboards. I did want to intentionally promote the use of the term, but seeing how common it has become, I sometimes wonder if I overdid it. Enterprise adoption of agentic AI is accelerating, yet many organizations are struggling with integration, governance, and measurable ROI. Why is it so? Two years ago, there was intense hype around AI’s risks and dangers, among other concerns. Last year, businesses began shifting their focus toward real-world implementation. This year, the conversation has moved firmly to ROI. Even though many companies are not yet seeing strong returns, they continue to invest because they understand that AI will eventually deliver value. The discussion has shifted from excitement about what AI might do to a more grounded focus on how it can generate real economic impact. There’s also an interesting split-screen dynamic emerging. On one hand, many businesses say agentic AI is not yet delivering meaningful ROI, and they’re right. At the same time, teams building agentic workflows are seeing rapid growth and real, valuable implementations. The agentic movement still has very low penetration, but it is compounding quickly. What are the most significant mistakes enterprises make when deploying agentic systems at scale, and how should leaders rethink their technology and operating models to overcome them? Many businesses are pursuing bottom-up innovation, which is valuable, but the limitation is that it often leads to point solutions that deliver incremental efficiency gains rather than transformative change. If AI automates just one step in a process, for example, it might save an hour of human work and reduce costs. That’s useful and worth doing, but it doesn’t fundamentally change the business. Much of today’s AI deployment falls into this category—incremental improvement rather than full transformation. To unlock real value, companies need to look beyond optimizing individual tasks and start reimagining entire workflows. Doing so requires top-down leadership. Often no single person working on one step has the authority to reshape the entire process, which is why executive-level direction becomes essential. Real impact comes from tailoring AI strategy to each organization’s specific context rather than following generic industry playbooks. There is a growing debate about whether we are in the midst of an AI bubble or simply an early infrastructure build-out comparable to the internet era. How do you distinguish between speculative hype and genuinely durable AI value being created today? At the application layer, I don’t think we’re in an AI bubble. AI is expanding rapidly across business use cases—how we process legal and technical documents, manage customer success workflows, conduct research, and much more. I would like to see more investment in AI applications and inference infrastructure. Right now, there simply isn’t enough inference capacity, and worries around rate limits exist. The more interesting question about a potential bubble sits in the model training layer, where infrastructure spending continues to surge. If any risk exists, it’s highest there because the largest investments are concentrated among a small number of players. When companies build highly specialized hardware that can only be reused for inference with some inefficiency, the risk of overbuilding increases. I don’t think we’re overbuilding right now, but if any part of the AI market faces that possibility, it’s the training layer. As the industry moves beyond a single-model mindset toward more diverse agentic systems, how should enterprises think about AI architecture? Is there likely to be one dominant framework for building scalable, real-world AI systems—or will organizations need a more flexible approach? Software can range from five lines of code to massive systems that run for years. Because of that range, there won’t be a one-size-fits-all approach to building or governing these systems. Just as we don’t use a single framework to manage everything from simple scripts to enterprise platforms, we won’t rely on one architecture for agentic AI. Human work itself is incredibly diverse—from basic tasks like spell-checking to analyzing complex financial documents. Since the work varies so much, the AI systems we build will also need to vary. One principle my teams follow when building agentic AI systems is speed, as continuous improvement is essential. Our typical cycle involves building carefully to avoid major risks, testing with users, gathering feedback, and refining the system until it truly works well. That rapid loop is what helps teams build reliable, high-performing systems faster. Agentic AI is rapidly increasing systems’ ability to reason and act with limited human intervention. Does the rise of agentic architectures meaningfully accelerate the path toward AGI, or are we still far from true general intelligence? Most of the public thinks of AGI as AI that is as intelligent as people, and one useful definition is AI that can perform any intellectual task a human can. You and I could learn to fly an airplane with maybe 20 hours of training, learn to drive a truck through a forest, or spend a few years writing a PhD thesis. Most humans can do these things. We’re still very far from AI meeting that definition of AGI. For alternative definitions that some businesses have put forward—definitions that dramatically lower the bar—you could argue we already achieved AGI. There’s a good chance that under these lower-bar definitions, some businesses will soon try to declare success. But that won’t mean AI has reached human-level intelligence—it will simply mean the definition has been reworked to fit a much lower threshold. Maybe a year ago, AGI felt 50 years away. Over the past year, perhaps we’ve made a solid 2% of progress, with another 49 years to go. These numbers are metaphorical, so don’t take them too seriously. [Laughs] But we are closer than before, yet many decades away from an AI that matches human intelligence. If you stick with the original definition—aligned with what people genuinely imagine AGI to be—we remain very, very far away. Is geopolitical fragmentation reshaping global AI strategy for both governments and enterprises? One of the other big themes I’m seeing is sovereign AI. The world is becoming more fragmented, and there’s a lot of discussion about how nation-states want to make sure they have access to AI without needing to rely on other nations or any single company that they may not fully trust or be able to rely on in the long term. Governments and regions are thinking carefully about how to build and maintain their own AI capabilities so they can remain competitive and secure. As AI becomes more central to economic growth and national security, this question of who controls the infrastructure and models becomes much more important. So alongside enterprise adoption, there’s also a growing geopolitical dimension to AI deployment. In 2026, as enterprises search for real economic returns from AI, what leadership decisions and workforce shifts will ultimately determine whether organizations capture meaningful value from agentic systems? Leadership matters. When I work with CEOs, I see decisive moments when the C-suite must think strategically about what to invest in and then place those bets thoughtfully, guided by a clear understanding of what the technology can and cannot do—not just the surrounding hype. In periods of transformation, leadership decisions determine whether an organization captures real value from AI or merely experiments at the margins. I often speak with CEOs before they set a major strategic direction. No one knows exactly where AI will be in a few years, so we are operating in a kind of fog of war. But uncertainty does not mean we don’t know anything. Teams and partners who understand the technology well can narrow that uncertainty significantly and make far more informed decisions. At the same time, everyone should learn to code—or at least learn to build software with AI. AI has lowered the barrier to creating custom tools. Today my marketers, recruiters, HR professionals, and financial analysts who use AI to write code are already more productive than those who do not. When I hire, I increasingly prefer people who know how to build with AI assistance. I may have been early on this shift, but I now see more startups and established companies moving in the same direction. Just as it became unthinkable to hire someone who could not search the web or use email, I am already at the point where I hesitate to hire knowledge workers who cannot use AI to build or automate with code. View the full article

As a young child, interior designer Jeremiah Brent and his mother visited open houses and model homes in his hometown of Modesto, California, as a form of daydreaming. Brent walked through the houses, imagining the people who might live there, building a fantasy around what these homes could be. Since then, Brent has turned his childhood design obsession into a sprawling career: He runs a 50-person design firm, moonlights on Queer Eye, and recently brokered his first bedding deal with Target. Having come up in the industry through a series of audacious bets on himself, Brent has developed a sense of humor and pragmatism around his relationship with creativity and his role as a founder, designer, and collaborator. He’s quick to poke fun at himself, noting that he’s working on his control issues. (“If I had it my way I’d touch every hinge, every doorknob, every finish.”) And he’s clear that he absorbs as much as he can to consistently shape and influence his creative output: from a personal archive of design magazines to pop culture. (“I watch terrible, terrible TV.”) As Brent enters the second decade of Jeremiah Brent Design, he says his relationship with design and creativity has become more rooted in storytelling, informed by the clients he works for and the team he works with. “As time goes on, my work is known for a real kaleidoscope of design styles,” Brent says. “Everybody is so different, and their stories and their narratives are so different. I really want to be known as somebody who executes your story, not somebody who executes what I do really well. I don’t want to be one thing.” I’m an early riser. I don’t need a ton of sleep. I usually get up around 4 or 4:30 a.m. I have the mornings to myself; my kids are all sleeping. I’ve got three hours of uninterrupted silence with far too much coffee. Music on, candles lit, and I work. A lot of times, I write, which is new. I didn’t start with a degree in design. It really was just one of those things that happened through osmosis. When I started the firm, I wanted it to be me and like five people sitting around the desk, dreaming up the most insane spaces, the most beautiful things. I’m super visual. My office is like a serial killer. A controlled serial killer. I’m creatively always hungry. I’m always pulling and looking. I’m particularly inspired right now by the contrast and conflict between design styles and materials. When you bridge what was going on in, like, France in the 1930s with what was happening in the States in the 1980s? I think that conflict, and that contrast is where all the original ideas lie. Somebody asked me, “Do you think taste is genetic?” I don’t think taste is a recessive gene. I think it has so much to do with curiosity, audacity, travel, absorbing. At my core, I’m a good storyteller. That’s really where my strength is. I can listen. I can hear the nuances of what people need, and sometimes they’re not even saying it. That was the basis for the firm. I didn’t imagine it growing to the scale it has. Even though the company is 50-plus people, we still have that same synergy of five people sitting down at a table. There are so many different ways to make something beautiful. So that’s where I’m at now. It’s defining my lane of creativity and how I participate, how I nurture the creativity of my team. I always feel the most creative when I’m with the people I’m creating for. The biggest part of it is getting to know the people and understanding where they’re from. What was the first room that ever held you? What was the most important space that you remember? At least this part of the creativity, for me, is earning people’s trust. It’s something that you’re not given. You’ve gotta earn it. The fantasy part of what I do is where the love story is. So I always kind of call out one of the most important moments of your day. Where does it start? Where is the middle? Where does it end? And that acts as the beginning of the ripple. You build from there. You know, the fantasy, that component of that conversation with a client assures them that you understand what they value. And then I work backwards. I sketch everything. I have to see the space and how you’re going to move through it first before I dig into the intricacies of breaking everything down. It’s all visual. So I’ll draw everything, build the space out, prioritize. It’s changed over time, and it changes with clients, but you know, it’s always a conversation around what matters most to the client. I’ve never said no to work, even when I should. This was the first year that I’ve had to be like, “Okay, well, we can’t do that yet.” Or “That’s not gonna work.” That feels weird to me. I feel a pivotal shift in my tenacious appetite for growth. The evolution becomes everybody else’s, too. It’s not just mine now. So I’m making sure I’m executing and illustrating the balance that I want everybody else to have in their life. I joke all the time with everybody I work with. I want you to make a lot of money, and I want you to love what you do. I just need to move and to travel, sometimes. We live in New York City . . . but then we have this farm in Portugal. I realized this year that I live between two extremes: I need the volume turned all the way up, or I need to go to Portugal, where the volume is completely turned down and nurtures me in a way that I never even thought was possible. In Portugal, I’m a nighttime person, and in New York, I’m a morning person. Each gives me different things. I think trends are great if you’re not beholden to them. It’s a great way to have a conversation. It’s a great way to travel visually and maybe look at something that you would not have normally seen. To use them as a marketing tool is annoying. Just because turquoise is a hot color right now doesn’t mean you need to paint your room turquoise. But let’s examine turquoise. What do we like about it? Where did it start? It’s fun. I’ve had a crash course on how to collaborate because I married another interior designer. Which I do not suggest, because there are a lot of opinions from gay decorators in the house. I think it was an interesting exercise for me, because, especially creatively, if I had my way with our home, it would be dark with one dimly lit room with one bowl on a table. Very wabi-sabi. It’s my husband’s worst nightmare. He would live in, like, you know, a French château. He’s like Marie Antoinette. So, we have found a balance and a joint style that works for the both of us. I’m not pretending that I’m the most talented person in the room. I may be the most passionate, but definitely not the most talented, and I’ve seen so many different times from collaborations how far you can take a project with other people. View the full article

South Korea lifts data restriction that has prevented US tech group from providing navigation servicesView the full article

Google's February 2026 Google Discover Core Update has officially completed rolling out after just over 3 weeks. This update started on February 5, 2026, and was completed on February 27, 2026.View the full article

The Google February 2026 Discover core update has finished rolling out, starting on February 5, 2026 and now completing just over 21 days later on February 27, 2026. This was the first confirmed Google Search update this year, and the first-ever Discover-only update that Google announced. Normally, Google core updates impact both Search and Discover, but this is only impacting content within Google Discover. U.S. and English. Google said the update currently only impacts English-language users in the U.S. But Google said it will expand to all countries and languages in the coming months. More details. Google said the Discover core update will improve the “experience in a few key ways,” including: Showing users more locally relevant content from websites based in their country. Reducing sensational content and clickbait. Highlighting more in-depth, original, and timely content from sites with demonstrated expertise in a given area, based on Google’s understanding of a site’s content. Because the update prioritizes locally relevant content, it may reduce traffic for non-U.S. websites that publish news for a U.S. audience. That impact may lessen or disappear as the update expands globally. Google also made some tweaks to the Get on Discover help page – so review that page as well. More details. Google added that many sites demonstrate deep knowledge across a wide range of subjects, and its systems are built to identify expertise on a topic-by-topic basis. As a result, any site can appear in Discover, whether it covers multiple areas or focuses deeply on a single topic. Google shared an example: “A local news site with a dedicated gardening section could have established expertise in gardening, even though it covers other topics. In contrast, a movie review site that wrote a single article about gardening would likely not.” Google said it will continue to “show content that’s personalized based on people’s creator and source preferences.” During testing, Google found that “people find the Discover experience more useful and worthwhile with this update.” Why we care. If you get traffic from Google Discover, you may have noticed changes in that traffic. Again, it should be U.S. English only and only impact your Discover traffic. I will say, there has been a lot of Google Search organic volatility but Google has not confirmed any of those reports. Google recommends that if you need guidance, Google has “general guidance about core updates applies, as does our Get on Discover help page” in those help documents. View the full article

Environmental concerns have moved down agenda under leadership of Zack PolanskiView the full article

Early in my career, a colleague and I made a shared commitment one summer to eat healthier. Salads. Smoothies. The full routine. Like many well-intentioned plans, our discipline began to fade after a few weeks. Eventually, we introduced what we jokingly called Grease Wednesdays, a weekly cheat day as a reward for all our good behavior. Every Wednesday, one of us would head out to grab fast food, and we’d hide away in a small boardroom to indulge in our shared lack of nutritional discipline. At first, it was just the two of us, chatting with laptops closed and fries on the table. And then coworkers began peeking into whatever boardroom we were in, curious about the laughter. Eventually, someone asked if they could join. Then another. Within weeks, we had outgrown the small meeting room. Within months, we had moved into the department’s largest boardroom to accommodate the growing crowd. What started as a casual indulgence became a shared ritual. And without intending to, Grease Wednesdays began to change our department culture. We all began to get to know each other as individuals, with pets and families and hobbies. The ritual also smoothed tensions between departments, built friendships between unfamiliar teammates, and helped us realize we hadn’t felt all that connected before. Recent research shows the disconnection I witnessed in my own team is now part of a broader workplace trend. A 2025 survey of U.S. workers found nearly 40% report feeling lonely at work, and employees who lack social connection are significantly more likely to consider leaving their jobs because of it. When people feel they belong, trust builds, collaboration accelerates, performance rises, loyalty deepens, and well-being improves. When they don’t, silos form, trust erodes, and discretionary effort fades. Take these numbers: a recent BetterUp survey found that workplace belonging leads to a 56% increase in job performance, a 50% reduction in turnover risk, and a 75% decrease in employee sick days. THE PROBLEM WITH OVER-ENGINEERING CONNECTION Belonging is not accidental; it’s cultural. And culture is shaped, reinforced, and protected by a leader’s vision, values, behavior, and accountability, including what I call positive accountability. But this is where many organizations misstep. When leaders notice disconnection, the instinct is often to formalize solutions with more engagement meetings, structured team building, and mandatory social events. Yet forced connection and fun rarely produce authentic trust. In fact, over-engineering connection can make people more guarded. For instance, research cited in a study by the University of Sydney found that when team-building activities feel mandatory, they can create resentment and pushback among employees. Belonging grows best in environments that feel natural, voluntary, and human, not observed or measured. If you want to improve connection and belonging in your workplace while avoiding forced connection, here are some steps you can take. DESIGN INTENTIONAL SPACES What made Grease Wednesdays powerful wasn’t the food. It was the opportunity that a casual ritual created. We had, quite by accident, built a small, repeatable, low-pressure interaction in which familiarity could grow. Design offers a strong middle ground between compulsory team-building exercises and complete social neglect. The key here is to design small, optional, and repeatable opportunities that humanize the workplace. For in-person teams, you can host walking one-on-one meetings, Friday coffee drop-ins, no-agenda team lunches, or cross-department donut runs. For remote teams, you could host 15-minute morning online coffee drop-ins or no-agenda team virtual lunches, and share team celebrations of birthdays, anniversaries, and project completions. Keep it light; keep it optional; keep it ritual. MODEL OPENNESS Studies in organizational research find that when leaders are open, available, and accessible, employees feel more psychological safety. Psychological safety, coined by organizational psychologist Amy Edmondson, is the shared belief within a team that it is safe to take interpersonal risks, like speaking up with ideas, questions, concerns, or mistakes, without fear of punishment, humiliation, or retribution. To build psychological safety in teams, leaders can model openness. Do that by admitting when you don’t know something, sharing a decision you’ve reversed (and why), and publicly thanking a team member who challenged you. Another way you can model openness is by offering positive team accountability by sharing the successes they see and are proud of within the team. For example, one leader I work with sends out an email to his team every two or three weeks. The irregularity of timing is actually effective by design, making the email feel more authentic. REWARD CONNECTION, NOT JUST OUTPUT Social psychology research shows that reciprocity in the workplace builds trust, cooperation, and positive relationships. The principle of social reciprocity, or when one recognizes and responds to positive actions, contributes to stronger workplace dynamics and mutual respect—the core components of connection and belonging. One way to do this is to shift what gets publicly praised. If the only Slack shout-outs are for revenue, speed, and delivery, people will assume that is all that matters. Instead, reward connection by recapping projects in team meetings by asking, “Who helped make this possible?” You can also celebrate the people who mentor, unblock, and build bridges across teams. When helping behavior is acknowledged, rewarded, and career-relevant, connection stops being invisible labor and becomes part of how success is defined. Full offices don’t cure loneliness, but intentional culture does. When leaders design natural rituals, model openness, and reward connection as deliberately as they reward performance, belonging is no longer accidental—and becomes part of how work actually works. View the full article

Being a middle manager often feels like living in two worlds at once. On one side, executives cascade big goals and sweeping strategies. On the other, teams look to you for clarity, advocacy, and daily guidance. You’re constantly reconciling top-down demands with bottom-up realities, often with too little time and too few resources to satisfy either side. The paradox of the role is stark: Middle managers carry enormous responsibility for execution but don’t always have the authority to make critical decisions. You’re expected to deliver results on budgets you don’t control, within structures you didn’t design, and through policies you didn’t write. This tension is one of the biggest sources of chronic strain. One survey found that middle managers reported higher burnout rates (36%) than non-managers, while another showed that 71% are “sometimes” or “always” overwhelmed at work. But here’s the good news: The middle isn’t just where pressure piles up. It’s also where strategy becomes reality, where culture is lived (or lost), and where agility gets tested in real time. If you can reframe the squeeze as an opportunity, middle management becomes less a grind and more a proving ground. Here are four ways to turn the pressure into potential: BUILD YOUR COALITION If you think of your team only as your direct reports, you’re missing the larger playing field. Work today is inherently cross-functional, which means your effectiveness hinges on your ability to influence sideways and upward, not just to manage downward. Peers hold the resources and expertise you need. Leaders above you control priorities, approvals, and air cover. Without credibility in those directions, even flawless execution within your own group can collapse at the edges. Research shows that misalignment between teams is one of the biggest drivers of wasted work. When priorities or interpretations differ, teams can spend weeks pulling in opposite directions. Middle managers who proactively build peer alignment surface these gaps early and save everyone time and frustration. The fix isn’t complicated, but it is intentional: cultivate your network. A short, well-timed conversation with a peer or senior leader can prevent the kind of breakdowns that leave your team spinning. Think of it less as “networking” and more as preemptive damage control. The middle managers who thrive are the ones who invest in relationships that make the work move. MASTER THE ‘PRACTICE’ OF LEADERSHIP Leadership is often packaged as a set of sweeping competencies or treated like a fixed trait you either have or don’t. In reality, leadership is shaped over time, forged through daily choices, interactions, and repeated practice. While traditional leadership development focuses on broad skills taught in workshops or courses—what we call horizontal development at Sounding Board—many real-world challenges require something deeper. Vertical development helps managers think more complexly, adapt to evolving contexts, and lead with lasting impact, not just quick fixes. This kind of development happens through practice, not theory. Neuroscience supports it: Consistent, real-world repetition strengthens the neural pathways that anchor adaptability and retention. At BTS, we’ve seen that transformational leadership often hinges on unlocking specific mindset shifts, patterns where leaders typically get stuck and need to evolve to grow. So, how do you start? Find smaller moments to experiment. Instead of waiting for a performance review, try a quick debrief after a call with a direct report. Test a new communication approach in a team meeting before the next town hall. You can even name your intention to those around you. Letting others know you’re trying something new sets expectations and invites helpful feedback. LEVERAGE AI FOR ON-DEMAND SUPPORT Your toughest challenges don’t show up as theory; they show up in the form of messy, human situations: a disengaged direct report, a senior leader who keeps moving the goalposts, a peer who won’t align. These problems don’t have one-size-fits-all solutions, which is why coaching is so powerful. For decades, personalized coaching was a privilege reserved for executives. But with AI practice bots paired with guidance from real coaches, middle managers can get development that’s personalized and scalable when they need it. These tools let you rehearse tough conversations, like giving feedback or delegating more effectively, in a low-stakes environment. Coaches help you translate insights into actions and longer-term mindset shifts. The result is leadership growth that’s less abstract and more actionable. The smartest move? Start small. Pick one conversation you’ve been avoiding and rehearse it with an AI conversation bot. You’ll uncover blind spots, test new approaches, and walk into the real thing with more confidence and control. MAKE UNCERTAINTY YOUR PLAYGROUND The defining condition of modern work is uncertainty. Markets swing, technologies disrupt, priorities pivot. If you wait for clarity, you’ll always be behind. The managers who thrive aren’t the ones who resist ambiguity, but those who use it as a catalyst to experiment and learn. One biopharmaceutical company I worked with recognized this when it expanded leadership development beyond senior executives to include middle managers. After providing leadership training focused on managing ambiguity and integrating AI into workflows, the company paired each manager with a coach to help translate learning into action. The result was faster decision-making and stronger cross-functional collaboration during a major pivot. When you stop treating uncertainty as a threat and start treating it as a laboratory, you shift from surviving change to shaping it. With these practices, middle management isn’t a burden, but a launchpad for growth. View the full article

I have what I consider a healthy skepticism toward authority. I’ve always considered leaders—despite what titles they hold—as fallible people who don’t necessarily deserve blind adulation or deference. That skepticism has made it hard for me to adopt the “company man persona,” which might explain how little of the proverbial corporate ladder I’ve climbed. And rather than take responsibility for that, I’m going to “blame” my dad: The instinct to question rather than comply, to think critically instead of playing yes-man, came from him. We never had a formal conversation about it. I just watched how he moved through the world—confident, grounded, with little to prove—and absorbed it. Even though I now interrogate masculinity professionally as a writer, the version of “being a man” I internalized first came from my father. The idea of masculinity is broad, contested, and constantly evolving. And in corporate America, it still matters. Research shows that sons often emulate their fathers’ version of masculinity, and because men continue to dominate leadership positions in the U.S., those inherited models don’t stop at the home. They show up in how work gets done, who gets promoted, and what kinds of behaviors are rewarded. In practice, that inheritance can look like an executive who demands deference but bristles at accountability. Or a leader who establishes a culture where men bond through exclusion or bigotry. Or an environment that rewards bravado over substance, and conflates emotional intelligence with weak, “beta” behavior. It can show up when men label assertive women “aggressive,” when they police what version of masculinity makes a leader, or when they constantly need to prove their worth. Think of Succession’s Kendall Roy, or your own pick of privileged white men whose familial connections thrust them into headlines more than their merit. In short: Corporate America has what’s colloquially known as “daddy issues.” Corporate culture reflects the versions of manhood its leaders were taught to perform. In speaking with several psychologists and professors who specialize in families and masculinity, I’ve come to understand that changing this culture won’t come from diagnosing men. It will come from redesigning work so that care and empathy aren’t something they have to unlearn to succeed. Rethinking the dad dynamic Research shows that fathers influence how sons build social networks, how they communicate, and even whether they feel comfortable promoting women. All that even further complexifies when the father-son relationship is fraught. Masculinity researchers use the term father hunger to describe the effects of an absent or emotionally distant father, which can result in insecurity, difficulty forming healthy relationships, a constant search for validation, or adopting a hardened persona to mask fear. But as far as the label “daddy issues,” it’s typically reserved for women, and psychologists have long pointed out that this framing is both inaccurate and sexist: “All human beings have ‘mommy issues’ and ‘daddy issues,’” Michael Thompson, a psychologist specializing in children and families, told me, “because our parents shape us so powerfully.” When I first started researching this piece, I assumed it would focus primarily on how toxic masculinity is passed from fathers to sons and then reproduced in the workplace. That assumption was informed by my own experiences with male coworkers, and trying to make sense of the world we’re currently living through: one where that toxic form of masculinity and its negative by-products—cruelty, aggression, bigotry—seem to be celebrated and exacerbated. But the more I spoke with experts who study the intersection of masculinity, fatherhood, and work, the more that framing felt incomplete. What emerged instead was a picture of modern fatherhood that’s more intentional, and more emotionally engaged than the stereotypes suggest. Many of today’s fathers—and those who hope to become fathers—care deeply about being present for their children and involved in their daily lives. Contemporary “daddy issues” in the workplace aren’t about litigating past fatherhood. They’re about whether institutions make room for a healthier version going forward. Changes underway Language plays a role in that shift to encourage men to more closely examine their masculinity, and the versions of it they’ve inherited from fathers and older men in their lives. Developmental psychologist Gary Barker, founder and CEO of the Equimundo Center for Masculinities and Justice, an international organization that works globally to engage men and boys in healthy masculinities, told me he prefers the term caring masculinity over phrases like toxic masculinity or even healthy masculinity. The former, he explains, often makes men defensive; the latter can sound clinical. Caring masculinity, by contrast, frames masculinity around care for children, family members, communities, and friends. “It means recognizing that you’re at your best when you’re connecting with others in caring relationships,” Barker said. Barker and I spoke about the influence of our own fathers. Neither explicitly told us that a softer, kinder, less bombastic version of masculinity was the way to go, but care, not a rigid toxicity, was modeled. My father regularly asked me about my feelings and talked with me about my interests, even if they weren’t interests he shared. I always felt seen and accepted. “Maybe they didn’t have the language around it,” Barker said, “but they did feel an ethic of, ‘I’ve got a duty to those around me.” That perspective aligns with how some psychologists understand the current cultural moment. Michael Reichert, a clinical psychologist and founding director of the Center for the Study of Boys’ and Girls’ Lives at the University of Pennsylvania, a research consortium, sees today’s conversations not as a rejection of the past, but as an evolution. “I don’t think we’re at this place because we’ve had everything wrong all along,” he said. “I think we’re evolving toward a new understanding of what it means to be a man.” That evolution shows up in data. Reichert said this generation of young men prioritizes emotional competence: the ability to identify and regulate their own emotions, express vulnerability, and maintain close relationships without defaulting to dominance or withdrawal. In an interview with The Atlantic, Reichert spoke about an emotional literacy course he taught at a boys’ high school for 25 years, and how he’s seen firsthand the way resistance has morphed into acceptance on this front. National surveys also suggest sustained interest in fatherhood: Pew Research Center data shows that 57% of Gen Z men without children hope to become fathers, while a majority of millennial dads report being highly engaged parents. In other words, many young men aren’t aspiring to emotional distance—they’re aspiring to connection. The question is whether the workplaces they enter will reward that shift. Where we go from here Jamie Ladge, a professor of management at Boston College who studies fatherhood and organizations, told me that both workplace research and workplace culture still rely on overly narrow definitions of what fathers look like, often centering cisgender, heterosexual men with one partner. That hypothetical father maps neatly onto traditional ideas of masculinity: stoic provider, unencumbered worker, secondary caregiver. But “there’s a lot more nuance and complexity in the fatherhood identity that needs to be considered,” she said. Fathers may see themselves as caregivers, role models, breadwinners, or stay-at-home parents—often moving between those identities over time. Many fathers aren’t married, don’t work traditional jobs, don’t live in nuclear families, or aren’t in heterosexual relationships. When organizations cling to a single archetype, they don’t just miss entire groups of men, they reinforce a narrow model of masculinity that constrains everyone. Ladge’s research suggests that when workplaces support fathers in these varied roles—and thus support more diverse views of masculinity—the benefits are tangible. Involved fathers are more likely to experience work-family enrichment, feel more satisfied at work, and think less about quitting. “There’s a real benefit to being an involved father,” Ladge said. “That satisfaction carries over into positive outcomes for organizations.” Supportive management is key. Policies that normalize paid parental leave, flexible schedules, and caregiving responsibilities don’t just benefit families, they influence how employees relate to their work. Barker echoed this point, noting that organizations that encourage caregiving often see greater engagement in return. As fathers, “if we feel supported in taking that time, we come back with more energy, more productivity, and more connection to the workplace that made it possible,” he said. And yet, despite evidence that caring workplaces are more sustainable and productive, many organizations still cling to outdated ideals. “There’s a strong bias, especially in the U.S., that the ideal worker is someone who works the longest hours and has no life outside of work,” Ladge said. That expectation undermines the very conditions that allow parents—including fathers—to be present at home and engaged at work. The result is a self-reinforcing loop: Research shows that parents with greater autonomy and supportive supervisors are more involved with their children, while involved parents are more satisfied and productive employees. I didn’t learn skepticism of authority from a leadership seminar. I learned it by watching a man who knew who he was—and didn’t need a job to prove it. Workplaces that make room for that kind of fatherhood might finally get the leaders they keep claiming to want. View the full article

We’ve been sold a lie. Somewhere between “go to school” and “get a job,” work became the central node of our lives—the very thing that defines us. We measure our worth by our output, our identity by our title, and our health by how much we can endure. The hours. The travel. The back-to-back meetings. The busyness. That’s not the picture we painted for ourselves when we chose our major in college and envisioned what we thought would be a fulfilling career; that’s conditioning. The result of which has shaped our meaning of work and how we see ourselves in it. But meaning isn’t found in the busyness of the grind—rather, it’s found in alignment. And when our work has greater meaning, we change our relationship with it and, more importantly, with ourselves. On our latest episode of the From the Culture podcast, we spoke with Lenore Skenazy, cofounder and president of the nonprofit Let Grow, about finding meaning at work. And she offered a unique framing for how to rethink work and find alignment. In response to the public backlash she received after penning a 2008 column in the New York Daily News about letting her 9-year-old son ride the New York City subway alone, Skenazy founded Let Grow with NYU business school professor Jonathan Haidt to help parents rethink the job of parenting. In our venture to become parents, we didn’t imagine our job would be that of a supervisor or a concierge to our children. Instead, we imagined ourselves as guardians who would help our children grow. For Skenazy, the meaning of parenting is to prepare our children for adulthood, not to protect them from it. A deep rethink Although this may seem like a simple repositioning, it’s actually a profound recontextualization. When we think about parenting as a job of preparation as opposed to protection, it gives our work new meaning and, as a result, we engage in it differently. As Skenazy argues, when the work of parenting is about preparation, we grant our children freedom and independence to navigate the world on their own. Not in a way that endangers them but, rather, challenges them. When this happens, not only do they grow into more resilient humans who will likely be better prepared for the world, but we—as parents—get more fulfillment from our work. The benefit of this recontextualization also applies to our professional work. When we reframe the meaning of work, we change our alignment with it. The result of this framing not only improves our well-being but also improves the work. The behavioral science is unambiguous to this fact. When work is more meaningful, we’re more engaged, more committed, and more satisfied. Moreover, these effects produce greater productivity and higher effort because we’re more willing to “go the extra mile” when we feel more fulfilled. A win-win This phenomenon happens on the individual level but scales when we consider the greater work of the organization. When workers collaborate in shared meanings, their collective outputs are optimized, and the organization is more likely to flourish because of it. This isn’t about “touchy-feely,” “woo-woo” vibes to make people feel good. This is a renegotiation of work that empirically changes how we work, the impact of our work on the organization, and its impact on us. It’s a win-win across the board. But that’s not the world of work we occupy. Instead, our current framing of work is one that valorizes grind and prioritizes compensation—which is transactional at best, but in most cases adversarial. That’s not to say that labor should not be sufficiently compensated, but that the exchange between wages and work should be more than just monetary. They should be meaningful as well. Suffice it to say that work is in desperate need of work. Not more grind, more hours, or more late nights, but more meaning. The best part about it is that meaning is socially negotiated and, therefore, we can change it ourselves. It doesn’t require permission or approval—just rethinking. We explore this in greater depth with Skenazy on our latest episode of From the Culture, available here or wherever you get your podcasts. View the full article

This story was produced by Grist and co-published with Source NM. The first thing Andy Barrientes noticed when he showed up for his shift at RMS Foods on Valentine’s Day in 2005 was the cloud of black smoke emanating from the building. A fire had started in the factory around 4:20 p.m., not long before Barrientes was scheduled to clock in as maintenance manager at the food manufacturing plant in southeastern New Mexico. The blaze had caught his coworkers coming off the day shift by surprise; they reported smelling the smoke before seeing the flames. When Barrientes arrived, he saw the staff huddled together at the park across the street. “Everyone was holding hands,” he said. “And we were just … the fire was so big.” Barrientes had only been working at the factory for a few years. The job was something of an odd one: RMS Foods had once been a prominent meat processor in Hobbs, New Mexico, supplying local hotels and restaurants with cuts of beef and pork. But the company had recently started producing soy-based veggie burgers under the Boca Burger brand — an unlikely pivot for a part of the country better known for its cattle ranches, steakhouses, and dairy farms. Barrientes was hired around the same time as this change, and in the years since, veggie burger production had taken off. On the day of the fire, the entire staff evacuated without injuries, allowing the fire department — which arrived within four minutes of receiving the call — to immediately set to work containing the inferno. By 5:30 p.m., the clouds of smoke had mostly dissipated, but the building was gone. The roof of the factory had collapsed, and all but three pieces of food-processing equipment were damaged beyond repair. Among those standing across the street in the middle of Humble Park were Sam Cobb, president of RMS Foods, and his wife, Rhonda. Cobb’s father had founded the company 46 years prior, and the plant had been standing proudly on North Grimes Street for nearly as long. The family business all but burned to the ground in about an hour. Cobb, who had taken over after working under his father for years, promptly began thinking about how to support his employees in the face of such a loss, but he had few details for what came after that. “We’ll assess the damages and see what we can do to get back in business,” he told a local reporter for the Hobbs News-Sun. His uncertainty didn’t last long. The following day, Cobb informed the News-Sun that he was at work on a plan to continue paying his nearly 100 employees for as long as it took to rebuild the facility — although he had yet to meet with the insurance company or inform them of such a plan. In less than a week, Cobb had negotiated a deal between the insurance company, a local construction company, and his staff. All RMS Foods employees would immediately be eligible for state unemployment benefits, and roughly a third would also be hired back to assist with the reconstruction. “From the day we started, we were actually building: running wires, putting up red iron, putting up walls, pouring concrete, doing 17 hours a day,” said Barrientes, who worked on the factory reconstruction and is still employed at RMS Foods today. “We got it done quick,” he added. The arrangement was typical of Cobb, according to Barrientes and other current employees. “He’s never said no to us. He’s always taken care of every one of his employees, and that’s why we’re all so dedicated to him,” he said, “because he’s dedicated to us.” Just eight months later, the facility was operational again — and Boca Burgers were flying down the factory line. Tucked away in the southeastern corner of New Mexico, just minutes from the Texas state line, Hobbs lies in the middle of the Permian Basin. Most jobs in the city of about 40,000 residents are in mining, quarrying, or oil and gas extraction, according to the local economic development council. Animal agriculture — both cattle ranching and dairy farming — also figures significantly in the region’s economy and culture. These industries shape local attitudes toward eating; barbeque joints abound in the area and steak dinners are common. “Here in oil patch/cattle country, it is probably difficult to find people who will give any type of endorsement to any burger labeled vegetarian, or worse yet, vegan,” Robert Hamilton, a local Hobbs librarian who doesn’t eat red meat, told me. Against a backdrop of pumpjacks and stretches of desert sky, RMS Foods is a total anomaly. “You would be surprised how many people don’t know that this is here,” said Arnold Langley, a production manager at RMS Foods who has been with the company since 2006. Langley is something of a food-manufacturing veteran — having previously worked at a french fry factory that shut down in Washington state, he was hired by RMS to help scale Boca Burger production. “I’d say I came down to Hobbs to go to work for Sam,” said Langley, “and I never looked back.” Seated in his office, in the same building his employees helped rebuild more than 20 years ago (now dedicated to Rhonda, who died in 2018), Cobb wears a crisp button-down with his salt-and-pepper hair combed back neat. On the walls are photos of friends and family, plaques for business accolades, and black-and-white shots of his days in college at Texas Tech University. An official portrait of Cobb at City Hall sits on a nearby table; Cobb served as the mayor of Hobbs from 2012 to this past January. To illustrate the evolution of RMS Foods, he pulls out various marketing materials he’s kept from over the years. There are promotional catalogs of beef and pork products, followed by cheeky magazine ads for Boca Burgers. (One reads: “The way Bob devoured his burger, you’d think no one told him it’s meatless with 70 percent less fat.”) How Cobb reconciles his relationship to these dueling food industries is curious — his venture into the plant-based burger space only came about because of his expertise with meat processing. Cobb is aware of the paradoxes inherent to his career’s trajectory. In our first phone call, over a year ago, he conceded that non-animal sources of protein will become crucial to food security in years to come. “There’s no way that as our global population grows, everybody can have a T-bone steak every night,” he said. Additionally, greenhouse gas emissions from animal agriculture, particularly beef production, are a major contributor to climate change. Research has shown more people embracing a plant-based diet is a crucial step to reducing global emissions. But for Cobb, animal agriculture and plant-based protein have long existed alongside each other, and in his case, one supports the other. He likes to say: “I make veggie burgers for a living so I can afford to be a cowboy.” Cobb himself is a fourth-generation rancher. In the late 1880s, his great-grandfather Gatlin Hall Cobb acquired land and started a ranch in Haskell County, Texas, which is still in the family today. Sam Cobb’s father, S.G., lived through the Great Depression and a drought in the 1950s, two events that showed him the financial precarity of raising cattle for a living. It soon became clear that the Cobb family didn’t make enough money off of the family ranch to support both generations. So S.G. left Texas and headed west to New Mexico in 1959, with the hope of one day buying his own ranch. S.G. had a no-nonsense way about him, according to his son, and when he arrived in Hobbs, he opened a franchise of Rich Plan Corporation, a frozen-food company that sold and delivered bulk orders directly to households and even offered freezers to hold months’ worth of food. According to Cobb, when the national Rich Plan Corporation went bankrupt, S.G. maintained his relationships with livestock farmers and rebranded his company as Rich Meat Services. The company transitioned into a meat processing business, selling beef and pork products to a number of hotels, restaurants, fast-food chains, and food service distributors around the country. But the dream of starting another family ranch never left his father, said Cobb, and in 1978, after nearly 20 years in New Mexico, S.G. and a business partner bought some land off a “longstanding ranching family,” in Lea County, where Hobbs is located. That family, too, was struggling with the economics of their chosen profession. “What happens with ranches is the families grow, but there’s not enough ranch income to feed everybody,” said Cobb. “So then they start selling it off.” For Cobb, raising cattle is still a family affair: His oldest son lives on the ranch and Sam comes out on weekends to help with branding, castrating, and corralling cattle. His granddaughter from Austin occasionally comes into town for workdays, too. The ranch, along with the family land in Texas, holds tremendous symbolic value to Cobb, whose father instructed him never to sell it. After graduating from Texas Tech in 1976 with dual degrees in animal science and business, Cobb came back to New Mexico to work for his father at Rich Meat Services — first as a salesman, and then in operations. He had a knack for keeping clients happy by staying level-headed in a crisis. David Pyeatt, who was once a customer of Rich Meat Services, said, “Sam’s incredibly intelligent and witty as heck. And he always takes a complex problem and comes up with a very obvious and simple solution.” But the move away from sales may have been for the best; Pyeatt suggested that Cobb can be buttoned-up to the point of coming across as awkward. Tall, careful with words, and with near-perfect posture, Cobb sometimes has the air of a chaperone at a school dance. “When you first meet Sam, you may think he’s a turd, you know?” said Pyeatt, who, it must be said, considers Cobb a dear friend. “Am I saying that nicely?” As a businessman, Cobb’s superpower is his pragmatism. In 1980, he took over RMS Foods as president, and the company soon became the largest supplier to Dairy Queen franchises in the Southwest. Years later, Cobb struck a deal with a Japanese trading company to export high-quality cuts of beef and pork to Japan — taking the company he inherited from his dad to new heights. But he always had an eye on growing the business even more, and in the late ’90s, that meant looking beyond red meat. The company Boca Burger, started by a natural-food restaurateur in Boca Raton, Florida, was successful at capturing the public’s attention with a better-for-you veggie burger, at a moment when diet culture ran rampant. In 1995, then-president Bill Clinton made headlines for stocking Boca Burgers on Air Force One, after reportedly being introduced to the vegetarian product by a heart specialist. The trend caught Cobb’s attention, too. In 1997, through a fortuitous chain of connections — and on the strength of his reputation as a meat purveyor — an invitation to join a group of investors and purchase Boca Burger came to Cobb’s desk. According to him, it was a no-brainer. RMS already had most of the necessary manufacturing equipment to get started. The titular Boca Burger — made primarily of soy protein concentrate and wheat gluten — essentially comes together using “the same manufacturing process as a ground-beef burger,” said Cobb. The only difference is the ingredients. “Instead of blending animal protein, we’re blending plant protein.” Initially, Cobb became an employee of Boca Burger, sold off his Dairy Queen business, and ceased producing meat products at RMS Foods. When production of Boca Burger moved to Hobbs, RMS was manufacturing about 60 percent of the brand’s soy patties. “We started growing exponentially,” said Cobb, enough for the conglomerate Kraft Foods (now Kraft Heinz) to notice. Sales went from $20 million in 1998 to $40 million the following year. On the strength of that growth, Kraft bought Boca Burger in 2000 for an undisclosed amount. “I saw an opportunity in the plant-based category,” said Cobb, and it paid off. By 2002, Boca Burger sales reached $70 million. After the 2005 fire, representatives from Kraft Foods visited Hobbs and were so impressed by Cobb’s operations that they decided to designate RMS the exclusive manufacturer of Boca Burgers. “Sam got a letter from Kraft telling him that,” said Barrientes, and the company president read it out loud to his staff in the newly rebuilt office conference room. His father stood beside him for the announcement. “They were in tears, because they were coming back,” said Barrientes. Every morning Cobb is in the RMS office, he eats whatever plant-based product is being made at the moment for breakfast. It’s a daily ritual shared by many of the staff members, who sample the veggie patties all day to inspect the quality. The faux-meat burgers are good, employees admit, but of course, they aren’t … well, meat. (“I mean, I love my steaks,” said Barrientes.) Cobb isn’t planning on giving up meat anytime soon, and doesn’t expect others to immediately do so, either. “I’m an omnivore,” he said. As a planet, we dedicate roughly half of all our habitable land to growing food. But the majority of that land — nearly 80 percent — is ultimately in service of raising livestock. That’s because livestock need pasture land to graze, but they also depend on animal feed — and growing enough corn and soy for all those farmed animals also takes a lot of land. Cattle and other ruminants pose a big problem for the planet in the form of greenhouse gas emissions; these animals have stomachs with multiple compartments, and their digestive process produces methane, which is then released when the animals burp. But the amount of land needed to raise animals for human consumption also means the global demand for meat drives a tremendous amount of deforestation and biodiversity loss. That’s why so many plant-based protein advocates argue mitigating the effects of the climate crisis rest on everyone eating less meat. When it comes to matters of persuasion, however, Cobb understands that nobody has ever changed their diet unless they themselves wanted to. “I’ve got friends that wouldn’t put a plant-based burger in their mouth with a gun to their head,” he said. This awareness may be a business advantage for someone like Cobb — even if the uncomfortable truth may strike fear into the hearts of plant-based evangelists and climate advocates. In the 2010s, Beyond Meat and Impossible Foods went all-in on developing veggie patties that supposedly tasted and bled like real beef. At the time, much of their messaging touched on the environmental case for swapping out beef for soy. “I know it sounds insane to replace a deeply entrenched, trillion-dollar-a-year global industry that’s been a part of human culture since the dawn of human civilization,” said Impossible Foods founder Pat Brown in a TEDMED talk, referring to animal agriculture. “But it has to be done.” The plant-based protein category enjoyed double-digit sales growth during the COVID-19 pandemic, according to data from the Good Food Institute, a think tank that tracks the alternative protein industry. But since 2022, demand for these products has been falling. For Brown and others, this style of practically pleading with consumers to change their habits spectacularly backfired. Beyond Meat’s stock price tanked by more than 99 percent in 2025 compared to five years prior. The company reported a net loss of $110.7 million in the fiscal third quarter of last year, its most recent earnings report. Its total outstanding debt is $1.2 billion. Beyond has never once turned an annual profit. There are a number of theories as to why Beyond Meat and Impossible Foods’ gamble on ultra-realistic fake meat failed so hard — including their inability to compete with beef on price and taste. “Our thesis is that a bunch of products launched during the pandemic that weren’t ready for mainstream adoption,” said Caroline Cotto, head of NECTAR, an organization that runs taste tests with plant-based and animal-protein products in order to help the former achieve taste parity. “A lot of consumers tried those products and had a really negative experience because they were paying more for a product that didn’t deliver,” she added. “So they really soured on that category and have stopped revisiting it.” Cotto argues that the plant-based meat industry is something of a “valley of disillusionment,” and it’s hard to disagree. This stunning market failure carries a lesson for the plant-based industry that the broader climate movement and environmental experts have long known: Information alone, even a lot of it, even the really dire stuff, is insufficient to lead to a change in how most people behave. Some industry leaders may now be actively running in the opposite direction of mentioning climate and sustainability: Peter McGuinness, the former CEO of Impossible Foods who stepped down last month, argues this sector struck out with consumers by becoming too “woke” and “partisan.” The future of Impossible, now, is cloudy. The company recently announced it is experimenting with protein-packed grains and pastas. The plant-based burger category as a whole has slumped, and as a result, RMS is also producing fewer units of Boca Burgers these days. Barrientes estimates the plant makes less than 4 million pounds of soy-based burgers for Kraft Heinz every year, when in previous years, it was moving almost 20 million. Based on all his experiences in Hobbs, Cobb understands that part of selling plant-based food comes down to how you talk to people. “It’s the old adage. You can lead a horse to water, but you’re not making him drink,” he said. But he also reckons that the answer is simpler — that the role flavor plays cannot be understated. “If you want a hamburger, and you want a big old greasy hamburger, it’s hard to duplicate that with a plant-based product,” said Cobb. Cotto agrees — but thinks these product categories can achieve taste parity, or even become something consumers prefer over meat, with more research and development. “The biggest opportunity across the board is just making sure that these products taste great,” she said. Cobb regularly goes out to eat with a small group of friends, including David Pyeatt, his former customer from his meat-supplier days. For someone in the food business, even casual meals can function as informal, but telling, focus groups. At a dinner last October, when I asked the group whether they like faux-meat burgers, nervous laughter sputtered around the table. John, a rancher based in Hobbs, said there was nothing about “synthetic meat” that appealed to him, and said he didn’t think he would ever try it. Pyeatt shared a story about how his wife had recently made two versions of sloppy Joes — one with ground beef, and another for his mother-in-law that used vegan crumbles from Boca. Pyeatt tried both, and loved the plant-based one more than the tried-and-true original. It simply, in his words, “tasted better.” But ultimately, he said, “if you put a steak in front of me, I’m going to like a nice steak.” “Nobody here eats Boca Burger,” said Cobb, though his guests quickly contradicted that. Someone suggested that Boca Burger patties aren’t bad if served with a bit of mayo. The conversation underscored how, at the end of the day, people want to eat things that taste good — and the promise of something truly delicious can tempt even the staunchest meat-eaters among us. The servers began to bring out people’s orders, and when the last plate dropped, Cobb and his guests picked up their forks and knives and began to cut into their steak dinners. Cobb believes the plant-based burger is functionally dead. Back at his office, speaking from behind his desk, he explained his view that faux-meat patties will never fully go away, but that demand is unlikely to return to the levels it reached during the pandemic. Whether or not vegan brands should try to replicate the taste and texture of meat is “a really big debate right now in the space,” Cotto said. But breaking free of conventions set by meat-eaters and industrial animal agriculture will demand new ways of thinking, cooking, and dining. “We don’t have a name for it,” said Cotto, but the plant-based protein industry could also explore “a third-space product that’s sort of like — the closest equivalent I can think of is tofu, right? It’s a center-plate protein, but it’s not fitting into a narrow box for consumers.” Whether or not plant-based brands will pursue that route, for now, remains to be seen. Either way, Cobb isn’t out of the game. When I asked him about the future of the industry, I was struck by his pragmatism. Ever the entrepreneur, he is still out looking for opportunities to bring in new plant-based manufacturing business. He argues that the concept of swapping veggies for meat could catch on “as long as it’s price competitive.” Last year, on top of its Boca Burger production, RMS began a new partnership with the Seattle-based Rebellyous Foods, a brand of plant-based chicken patties and nuggets that sells directly to food service and school districts. (Disclaimer: Former Grist CEO Brady Walkinshaw is an investor in Rebellyous Foods. He had no editorial role in this story.) The ingredients are nearly identical to those in Boca Burgers, employees told me, but the manufacturing process varies slightly, giving the faux chicken products a juicier, more delicious texture. Employees at RMS seem to love it: “It’s actually good stuff,” one told me. Cobb said he’s interested in exploring the so-called emerging product category of “blended proteins” — think: sausages and hot dogs that replace some of their meat content with whole-cut veggies or soy. Plant-based advocates like these products because they help lower consumers’ overall meat consumption, even if they never give up meat entirely. But Cobb noted this practice is nothing new. “I used to put soy in hamburger patties. We used to do that for cost savings,” he said. He reminded me that all of the technical equipment and expertise that RMS has acquired over the decades of being in the food-manufacturing business means the company is well-positioned to produce other vegetarian appetizers and snacks, like falafel. These, he reckons, can appeal to meat-eaters, as long as they taste good. When it comes down to it, Cobb has been successful because he pays attention to what consumers want and, quite simply, makes it. When I asked Cobb if he would ever go back to processing meat, he answered: “I would if the opportunity presented itself and it created jobs for my employees and people in Hobbs.” He paused and added, “Yes, I’ve considered that.” View the full article

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According to the World Economic Forum, 40% of employers expect to reduce their workforce where AI can automate tasks by 2030. Thanks to artificial intelligence, leaders are under pressure to raise the bar on what they will deliver to their stakeholders—with the expectation that thanks to AI, companies can (and must) achieve more. That matters for job hunters, who need to get clear on the value they can provide to organizations if they want to get hired. And while we can be reactive—relying on the AI screeners, which many recruiters use, to select us out of the pile of submitted résumés—we should get proactive, smartly deploying our networks to get our feet in the door. With virtual and hybrid work putting screens between us and our coworkers, relationship-based networking can feel like a dying art. Yet it’s our professional connections that can very well be what’s needed to help us break through in the job market in the AI era. Many professionals agree: research from the networking tech startup Goodword finds that 83% of professionals believe the most valuable asset in an AI-dominated future is social capital. That means growing your own social capital. Here are four real-life networking moves to master amid the onset of AI. 1. Invest in mutually-supportive relationships, not one-sided transactions The U.S. Department of Labor estimates that 80% of available jobs go unadvertised, with experts suggesting that these are filled through professional connections. Yet most people don’t network on an ongoing basis. One LinkedIn global survey finds that less than half of professionals keep in touch with their networks when things are going well. One of the leading causes for not doing so: not wanting to ask strangers for favors. Consider a mindset shift. Instead of asking favors, consider where you may be able to give someone else value. You may refer someone in your network to a professional who offers a service they need, for example, or connect them to someone who can help them solve a business challenge. According to Ivan Misner, founder of networking organization BNI and the author of Networking Like a Pro, social capital is like financial capital. “You cannot make a withdrawal before you make a deposit,” he writes. “You have to invest time in the relationship.” Think about how you can build and foster relationships with your network, especially before asking for help with job searching. 2. Be clear on your unique value with the right audiences We can’t provide value to everyone all the time. When we communicate the unique skillsets that we have to the people and organizations that can benefit from them, we increase the opportunities that people can consider us for. In his book The Start-Up of You, LinkedIn cofounder Reid Hoffman offers this advice: networks are not only about who you know, but what they know about what you can do. With 1.2 billion users on LinkedIn, there has never been an easier way to communicate what you do, who you serve, and how you do it through quick posting. While just 1% of LinkedIn users post content on a weekly basis, you can reach connections in your network by putting yourself in front of them frequently. 3. Approach networking as an opportunity for learning Too often, people approach networking as a self-promotion opportunity rather than a chance to learn. Whether it’s fostering your existing network or building new relationships, we have two ears and one mouth to listen and learn. I personally like connecting people in similar roles at different companies together to be thought partners and learn from each other. If you’re wrestling with a work challenge, chances are that others may have been in a similar situation and have insight to share. Networking to learn, rather than to promote, can help spark new ideas, along with new connections. 4. Balance technology with humanity And you can also use AI itself to make your personal networking more effective. As an alternative to LinkedIn, apps like Bizzabo and Brella use AI to match attendees at networking conferences and events with similar interests. In other cases, you might tap AI to find personalized recommendations for virtual events and webinars, ensuring individuals can connect and engage in ways that are most relevant to them. Technology like AI can enable us to scale our impact, including in our networks. By combining the science of AI with the art of relationships, any professional can open doors to opportunities they may not have tapped otherwise. View the full article

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