Curiosity is one of the most consequential forces in human history. Every scientific breakthrough, technological leap, and cultural advance begins not with knowledge, but the desire to know. At its core, curiosity drives us to close the gap between what we know and what we want to know, a cognitive itch triggered by uncertainty and resolved through learning and the pursuit of meaning.

Curiosity as an evolutionary advantage

Early humans who explored their environments, experimented with tools, and learned from novel stimuli were more likely to secure resources, avoid threats, and pass on their genes. As a result, curiosity became embedded in our biology, reinforced by neural reward systems that make learning intrinsically pleasurable.

In line, neuroscientific research shows that curiosity activates the brain’s dopaminergic pathways, the same circuits involved in motivation and reward, which explains the positive correlation between curiosity and impulsivity. When we encounter a gap in our knowledge, we experience a mild form of cognitive discomfort. Resolving that gap produces satisfaction, reinforcing future exploration. In that sense, curiosity is a built-in, biologically coded feedback loop for learning.

But evolution also imposed constraints. Curiosity, like any adaptive trait, is beneficial only within limits. For example, there are many scenarios in which too much exploration could prove fatal. A hunter-gatherer wandering too far from their tribe risked encountering predators or hostile groups. In such contexts, restraint was adaptive. Curiosity had to be expressed with caution.

This tension between exploration and exploitation remains with us today. We are wired both to seek novelty and to prefer predictability. The familiar is efficient but boring; the unknown is exciting but costly.

History offers similar patterns. During periods of intellectual repression, such as the Inquisition, curiosity was actively punished, making individual inquiry dangerous. By contrast, the Enlightenment celebrated curiosity as a virtue, unleashing scientific and philosophical progress. The same underlying human drive manifested differently depending on cultural conditions.

Curiosity, then, is universal, but its expression is highly variable.

The paradox of AI: A triumph that threatens its own foundation

Fast forward to the present, and we are witnessing one of humanity’s greatest achievements: artificial intelligence. The convergence of mathematics, computer science, and data has enabled machines to simulate aspects of human cognition. We have, in effect, built systems that can approximate, emulate, and even surpass our thinking.

Even if AI stopped evolving tomorrow (which seems unlikely), its implications are already profound. AI can augment human capability, accelerate problem-solving, and democratize access to knowledge. It functions as a cognitive copilot, allowing individuals to perform tasks that once required entire teams.

But every technological advance also carries unintended consequences. And in the case of AI, the risks are not just economic and ethical, but psychological, too.

Specifically, AI threatens to erode curiosity.

The erosion of curiosity in the age of artificial certainty

Curiosity depends on uncertainty. It requires a gap between what we know and what we want to know. AI, by design, collapses that gap.

When answers are instantly available, prepackaged, and delivered with confidence, the motivation to explore diminishes. Why struggle with a problem when a machine can solve it in seconds? Why engage in deep learning when surface-level understanding is sufficient to get by?

This is what I have described elsewhere as “artificial certainty.” AI does not just provide answers; it creates the illusion that we understand them. The output is coherent, fluent, and persuasive. But coherence is not comprehension. The result is a shift from active to passive cognition. We consume knowledge rather than generate it. We outsource thinking rather than exercise it.

A useful analogy is physical fitness. Imagine a world where machines do all the lifting for you. Your muscles would atrophy. The same applies to the mind. Curiosity is a mental muscle, and like any muscle, it weakens with disuse. In this sense, AI is the equivalent of a microwave for ideas. It delivers fast, convenient results, but often at the expense of depth and craftsmanship. We move from “slow thinking,” which is effortful and reflective, to “fast consumption,” which is effortless but shallow.

There is also a linguistic irony worth noting. “Deep learning,” once a human aspiration, is now primarily associated with machines. Meanwhile, human learning risks becoming increasingly superficial, if not dormant. To be sure, such concerns may ultimately prove overstated, as they often have in the past. Socrates, after all, warned that writing would erode memory, fearing that reliance on external tools would weaken internal capacities. Yet history also suggests that overcorrection is safer than complacency.

There are, in fact, good reasons to be vigilant: When effort is removed from the learning process, engagement tends to decline; when answers are readily available, the incentive to question diminishes; and when cognition is outsourced too readily, the underlying skills can atrophy. The point is not to resist technological progress, but to ensure that convenience does not quietly displace the very mental habits that made such progress possible in the first place.

What the science says about cultivating curiosity

If curiosity is both essential and at risk, the obvious question is: Can it be developed?

The answer is yes, but not in the simplistic way often suggested. Curiosity is influenced by both stable traits and situational factors. While some individuals are naturally more curious than others, environments and habits play a critical role.

Before diving into what to do, it is worth pausing on a more basic question: How curious are you, really?

The first step is a proper self-assessment. Not the flattering version you might hold of yourself, but a more objective view grounded in data and external feedback.

Curiosity is closely linked to well-established personality traits, particularly openness to experience, one of the Big Five, which captures intellectual curiosity, imagination, and a preference for novelty. Science-based assessments can provide a reliable baseline here. So can 360-degree feedback, which often reveals a gap between how curious we think we are and how we are experienced by others. Even informal input from colleagues, friends, or mentors can be illuminating, especially when it highlights whether you ask thoughtful questions, challenge assumptions, or genuinely engage with new ideas.

Equally important is specificity. Curiosity is not a uniform trait. People are rarely equally curious about everything. Reflect on where your curiosity naturally shows up and where it does not. You may be deeply inquisitive about ideas but indifferent to people, or fascinated by technology but incurious about history, culture, or opposing viewpoints.

Mapping these patterns matters, because developing curiosity is not about becoming universally interested in everything. It is about understanding your blind spots and deliberately expanding into areas where your instinct is to disengage.

First, intrinsic motivation matters. Studies grounded in self-determination theory show that curiosity flourishes when individuals feel autonomous, competent, and connected to others. In practical terms, this means people are more curious when they pursue topics that genuinely interest them, rather than those imposed externally. The implication for organizations is clear: forced learning rarely produces genuine curiosity.

Second, exposure to novelty is key. Curiosity thrives on diversity of input. Interacting with people from different backgrounds, disciplines, and perspectives increases the likelihood of encountering information gaps. This is why interdisciplinary environments are often more innovative. They create friction between ideas.

Third, habits of reflection enhance curiosity. Research on learning and memory suggests that active engagement, such as writing, teaching, or debating, deepens understanding and sustains curiosity. Passive consumption, by contrast, leads to the illusion of knowledge without real insight.

Fourth, time allocation matters. Curiosity requires cognitive space. In environments dominated by urgency and efficiency, there is little room for exploration. Scheduling time for reading, thinking, and unstructured inquiry is not a luxury; it is a necessity.

Fifth, tolerance for uncertainty is crucial. Individuals with a high need for cognitive closure prefer quick answers and are less likely to engage in open-ended exploration. Developing comfort with ambiguity, through practices such as Socratic questioning or deliberate exposure to complex problems, can enhance curiosity.

Finally, there is evidence that curiosity can be trained through small behavioral interventions. For example, prompting individuals to generate questions before receiving answers increases engagement and retention. Similarly, framing tasks as puzzles or challenges can activate curiosity-driven motivation.

These findings align with the broader argument that curiosity is not a fixed trait but a dynamic capability shaped by both internal and external factors.

The role of leaders in modeling curiosity

While individual strategies matter, curiosity is ultimately a social phenomenon. It is shaped, amplified, or suppressed by cultural norms.

From early childhood, curiosity is not simply an individual trait but a product of developmental context. Parents, teachers, and early environmental experiences play a decisive role in shaping how, and whether, curiosity endures into adulthood. Research in developmental psychology shows that children whose caregivers respond contingently to their questions, encourage exploration, and tolerate uncertainty tend to develop higher levels of intrinsic curiosity.

Conversely, environments that emphasize compliance, correct answers, and performance over inquiry can suppress exploratory behavior over time. Educational studies also find that classroom climates prioritizing rote learning and standardized outcomes often erode students’ natural inquisitiveness, even when baseline curiosity is high.

Longitudinal evidence suggests that these early patterns persist, shaping adult tendencies toward intellectual risk-taking, openness, and lifelong learning. In short, curiosity is cultivated or constrained early, but its trajectory can be reinforced or reversed later, especially through social and organizational contexts.

This is where leadership becomes critical.

Leaders set the tone for what is valued. If they prioritize certainty, speed, and efficiency above all else, curiosity will decline. Employees will learn to avoid questions, minimize exploration, and focus on immediate outputs.

Conversely, leaders who model curiosity create environments where inquiry is rewarded. This does not mean celebrating randomness or distraction. It means demonstrating intellectual humility, asking better questions, and showing a willingness to challenge assumptions.

One of the most powerful signals a leader can send is admitting what they do not know. This reduces the perceived cost of ignorance and encourages others to engage in learning. It also counteracts overconfidence, which is one of the main barriers to curiosity.

Leaders can also design systems that embed curiosity into workflows. This includes allocating time for experimentation, encouraging cross-functional collaboration, and measuring not just outcomes but learning processes.

Importantly, curiosity must be linked to performance. It is not about asking more questions for their own sake, but about asking better questions that lead to better decisions.

In the age of AI, this becomes even more important. As machines take over routine cognitive tasks, the human advantage shifts to areas that require judgment, interpretation, and creativity. 

These are all downstream of curiosity. But judgment without experience is meaningless. AI can simulate answers, but it cannot substitute for the depth that comes from actually engaging with the world.

There is a difference between consuming a microwaved meal and cooking one from scratch, sourcing ingredients, understanding how they interact, and adjusting along the way. The former is efficient and convenient; the latter builds intuition, tacit knowledge, and real expertise. In the same way, relying on AI-generated outputs without cultivating firsthand learning experiences produces a thin version of competence, what might be called artificial understanding.

Curiosity, when acted upon, pushes us into those richer experiences that give judgment its substance and make our thinking genuinely our own.

Curiosity as a strategic imperative

The rise of AI has not just expanded access to information; it has quietly eroded the premium once attached to possessing it. When virtually all answers are instant, abundant, and convincingly packaged, the differentiator is no longer what you know, but how you engage with what can be known.

In that sense, the economics of expertise are shifting. Knowledge, at least in its most accessible forms, is becoming commoditized, while the capacity to interrogate, refine, and build on that knowledge is becoming scarcer and more valuable. This is where curiosity earns its value, not as a soft or “nice to have” trait, but as the underlying mechanism that sustains learning over time.

Without curiosity, the risk is not ignorance, but something more insidious: the illusion of understanding. AI can generate coherent explanations, summarize complexity, and produce plausible insights at scale. But unless these outputs are met with questioning, skepticism, and a desire to go beyond what is given, they are unlikely to translate into genuine insight or better decisions.

The danger, then, is not that machines will think for us, but that we will gradually outsource the very effort required to think well, confusing fluency with depth and access with mastery.

This places a different kind of demand on individuals and organizations. The task is no longer simply to adopt AI tools or increase their usage, but to integrate them in ways that augment rather than atrophy human judgment.

At the individual level, this implies a degree of intentionality that is often underestimated: cultivating habits that prioritize inquiry over convenience, depth over speed, and exploration over closure.

At the organizational level, it requires more than rhetoric about innovation. It calls for environments where questioning is not penalized by the pressures of efficiency, and where time spent exploring is not automatically seen as time wasted.

And at the leadership level, it demands a visible commitment to curiosity as a norm, expressed less through slogans and more through behavior: the questions senior exeuctives ask, the uncertainty they tolerate, and the assumptions they are willing to revisit, will all shape the organization’s level of curiosity and appetite for learning.

There is an obvious irony here. The more capable our machines become at producing answers, the more valuable it becomes to remain interested in the questions. This is not a nostalgic defense of human uniqueness, but a pragmatic recognition of where advantage now lies.

In a world where everyone has access to the same tools, and AI becomes as ubiquitous as smartphones, Wi-Fi, or electricity, the differentiating factor shifts to how those tools are used, and that, in turn, depends on the quality of human curiosity brought to bear on them.

Seen in this light, curiosity becomes a strategic necessity, one that shapes not only how individuals learn, but how organizations adapt and compete in an environment where knowing is easy, but understanding remains hard, and is quietly becoming a niche pursuit.

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As 7.4 million Americans sit unemployed, the path to employment has completely changed. Amid fake listings, AI filtering of candidates and widening talent pools, job seekers believe that they’re competing against a hiring ecosystem that penalizes honesty and rewards perception.

The result? A hiring environment where the signals employers have traditionally relied on to evaluate candidates have become deeply unreliable. Now, both sides are operating with diminishing trust in each other. 

What’s Driving the Deception?

Hiring today is not facing a character problem, but a structural one. When candidates believe that presenting themselves accurately will cost them a job offer, the rational response is to become the person they think the employer is looking for. But when this approach becomes standard, those who still choose to tell the truth take on an “honesty tax,” the systemic disadvantage honest candidates face when exaggeration becomes the market norm.

GCheck’s Trust in Hiring Report revealed that 93% of job seekers have lied or embellished their experience during the hiring process, while 60% do not believe they would have been hired had they presented their qualifications more accurately. This is beyond a confession—it’s a market signal. 

Part of what drives this dynamic is opacity on the employer side. When candidates do not know what will be verified, they assume the answer is minimal, and they calibrate their self-presentation accordingly. In fact, GCheck found that although 88% of job seekers believe misrepresentation puts businesses at risk, 53% assumed employers wouldn’t verify their claims and only about a quarter (26%) report ever being caught lying or exaggerating.

Verification that is invisible to candidates is not a deterrent. It is permission. And thanks to artificial intelligence, candidates can disguise their true skills and identity almost instantaneously. 

AI Accelerates Dishonesty in Hiring

LinkedIn’s 2025 Work Change Report estimates that 70% of the skills used in most jobs will change by 2030, driven largely by AI. When job seekers navigate a market where the definition of “qualified” is constantly shifting, the pressure to appear more capable than they are significantly intensifies. AI has not created that pressure, but it has handed candidates sophisticated tools to act on it at every stage of the hiring process. 

Employer concerns have moved beyond job seekers’ using AI to compile resumes or assist with writing. Now, the degree to which AI has migrated into live interviews and assessments is worrisome. 

GCheck found that 61% of candidates have used AI to rehearse interview answers until they sounded more impressive than authentic, and 25% reported deploying an AI avatar in place of their own face during a virtual interview. ​

The result is a hiring process where trust is eroding on both sides. On one hand, candidates feel pressure to optimize and automate their performance in a highly mediated, virtual environment; on the other, employers struggle to assess who is genuinely behind the screen. When interviews are increasingly remote, scripted and technology driven, the lines between preparation and performance become blurred. This highlights how broken and transactional the modern hiring process has become. 

There’s also an emerging phenomenon of systematic embellishment, distortion or fabrication of professional qualifications across resumes, interviews, and references as a deliberate competitive strategy driven by market pressure and weak verification expectations. It’s been dubbed “careerfishing,” and it’s no longer the behavior of a fringe group. 

What Employers Must Do to Rebuild Trust

Rebuilding trust in hiring is not only a technology problem, but also a standards and transparency issue. Employers who treat verification as a confidential back-end process get exactly what opacity produces: candidates who assume they can game the system, largely because they can. Three leadership-level shifts matter most here:

• Make verification standards visible. Communicate what will be checked before a candidate applies. Transparency disrupts embellishment at its source, not after the offer. The FTC’s guidance on employment background checks under the FCRA already mandates disclosure at specific stages. Moving that clarity upstream changes candidate behavior earlier in the process in measurable ways. For example, candidates who know credentials or work samples will be actually verified are less likely to exaggerate or rely on AI-generated materials they cannot defend later. 
• Make screening decisions reviewable by a person. Candidates who know a human will review findings, not only an algorithm, engage with the process more honestly.
• Make verification proportionate to actual risk. Applying the same screening depth to every role signals to candidates that the process is performative. Calibrating scope to genuine role risk makes verification more credible, more defensible, and more likely to deter the embellishment it is meant to catch.

In recent years, hiring integrity has evolved from a checkbox exercise into a strategic priority. When AI-driven careerfishing corrupts the foundational data a company uses to build its workforce, the damage surfaces in performance gaps. The goal is not to catch more people lying. The goal is to build a hiring environment where honesty carries a genuine advantage rather than a competitive penalty. 

When employers operate transparently and verify consistently, they stop performing diligence and start practicing it. That distinction is what separates organizations that attract trustworthy people from those that inadvertently select for the most convincing ones.

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Last weekend, when Puck announced that the sustainable fashion startup Everlane had been acquired by the Chinese ultra fast fashion retailer Shein, it sent shockwaves throughout the fashion world. Michael Preysman, who founded Everlane in 2011, was just as shocked.

“I found out the same time as everyone else,” he said in a LinkedIn post a week ago. “I’m not involved with the company anymore, and like many, am still digesting the news.”

Well, Preysman is done digesting. And it seems that he’s ready to do something about it. Preysman just announced stillradical.com, a new venture that we know little about other than the bare bones website it launched with.

The website lays out the new vision with brevity:

“I started Everlane in 2011. Last week, the current management team sold it to Shein. So we’re starting over. Same principles, but a new take. And this time: no venture capital, no private equity.”

The site says you can learn more by signing up for a waitlist. (Preysman did not respond to a request for comment.)

Preysman launched Everlane when he was in his mid-20s, after starting his career in finance. His vision was to sell high quality products directly to customers online, without the markup of middlemen like department stores. This helped kickstart the direct-to-consumer movement that dominated the 2010s, producing brands like Away, Warby Parker, Allbirds, and Glossier. To fuel its growth, Everlane took an undisclosed amount of venture capital.

A few years in, Preysman turned his attention to the human and environmental impact of the fashion industry. Everlane promised to eradicate virgin plastic from its supply chain, and showed customers inside the factories they used, to highlight how it was paying attention to the working conditions of laborers.

All of this was good for business. By 2016, Everlane was valued at $250 million, although it was unclear whether it had ever become profitable. In recent years, its growth slowed. It went through two rounds of layoffs, once during the pandemic and then again in 2023.

L. Catterton—the venture capital wing of the luxury conglomerate LVMH—bought a majority stake in Everlane in 2020. Shortly after, Preysman left the company to launch a new supplements brand called Magna in 2024.

For many, Everlane’s acquisition by Shein was a disappointing final chapter for a company that stood for optimism and ethics. Clearly, Preysman felt the same way about it.

This new business suggests that he hasn’t given up on the idea of sustainable fashion. However, he’s realized that venture capital is not the right tool for launching an apparel business. It will be fascinating to see what lessons Preysman has taken from the rise and fall of Everlane, and how we plans to build his new company differently.

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Unlike some of his industry peers, OpenAI CEO Sam Altman has been surprisingly skeptical of the notion that AI is displacing workers. In an interview a few months ago, he argued that AI was a convenient scapegoat for some companies, echoing what some economists and experts have expressed about the narrative that AI is driving layoffs across corporate America. 

“I don’t know what the exact percentage is, but there’s some AI washing where people are blaming AI for layoffs that they would otherwise do. And then there’s some real displacement by AI of different kinds of jobs,” Altman said at the time. 

In an interview this week, however, Altman made a bolder statement, suggesting there was little evidence AI would do extensive damage to white-collar jobs, despite predictions to the contrary. 

“I’m delighted to ⁠be wrong about this,” he said on Tuesday during a virtual appearance at the Commonwealth Bank of Australia conference, according to a Reuters report. “I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than ​has actually happened.” 

“My intuitions were just off,” he added. “People are like, ‘oh, you could have saved the world a lot of fear mongering and a lot of doom and gloom.’ But at the time I was like, ‘I see this is a real risk. We should probably ​talk about it.’”

Part of the reason for this realization, Altman claims, is that he underestimated the human element that so many jobs require. He had tried using AI to field emails and Slack chats, but increasingly found himself responding to those messages himself—which apparently led him to believe the impact on jobs will be different than he had originally anticipated. 

“I don’t think we’re going to have the kind ​of jobs apocalypse that some of the companies in our space advocate or talk about,” he said. 

While companies have repeatedly cited AI and automation when conducting layoffs, the labor market does not yet reflect a mass reduction in jobs across the workforce. On top of that, even as tech leaders remain bullish about the promise of AI, there are signs that all their spending may not yield the results they are expecting. 

In another recent interview, an Uber executive cast doubt on the idea that the company’s AI investments had meaningfully boosted productivity, despite blowing through its 2026 AI budget in just a few months. On the Rapid Response podcast, Uber president Andrew Macdonald claimed the growing use of Claude Code tokens had not necessarily resulted in better features for consumers. 

“That link is not there yet, right? I think maybe implicitly there is more that is getting shipped, but it’s very hard to draw a line between one of those stats and, ‘Okay, now we’re actually producing 25% more useful consumer features,’” he said. 

Still, that awareness may not help preserve jobs, especially as companies demand greater productivity from their workforce. 

Whether or not AI can replace workers, tech employers continue making cuts to headcount to offset their sweeping AI investments. Some workers are already feeling the effects of widespread AI adoption, from Amazon warehouse workers to people who hold administrative jobs—and despite the concerns about white-collar employees, researchers have found there could be significant downstream effects for workers without college degrees. 

For all his talk, even Altman has noted that there’s a chance the fallout from AI could be worse than it seems right now—and that it could eventually come for his job, too.

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Most enterprise generative AI investments have yet to deliver the value companies envisioned, and every day, more leaders are recognizing that people lie at the heart of the struggle. In this year’s AI & Data Leadership Executive Benchmark Survey, 93% of executives leading AI and data efforts identified human issues around culture and change management as the primary obstacle to adoption. McKinsey Global Managing Partner Bob Sternfels put it plainly on HBR’s IdeaCast: “Half if not more of the secret sauce” in getting value from AI, he said, “is organizational change, as opposed to technology implementation.”

As such, many leading companies have launched initiatives over the past several months to drive AI adoption across their workforces. These efforts run the gamut from carrot-to-stick approaches, with some rolling out hackathon programs and prizes for innovative uses. Others use weekly logins and token consumption as proxies for performance.

A PERFORMATIVE APPROACH

Leaders are right to focus on the people side of adoption. They need to be deliberate, however, about what they’re encouraging. I’ve learned something in my three decades helping some of the world’s largest companies through culture transformation. Employees prioritize what leaders model, incentivize, and reward. And initiatives built around shallow metrics can do more harm than good.

It’s understandable why many leaders today celebrate deliverables simply because they were made with AI, or reward employees for integrating it into workflows. Facing underwhelming internal adoption metrics, many have come to see any increase in AI usage as a win. At my firm, however, we call this “trophy-style” AI adoption—which is to say, a performative approach focused more on usage than results. It’s focused on participation trophies over proof of impact. Leaders need to be wary of this trap.

Because as anyone following the “workslop” problem or the emerging research on cognitive atrophy will know, not all AI use cases are created equal. Trophy-style adoption creates a dangerous illusion of progress, where activity masquerades as impact. In other words, we’re rewarding output over outcomes. A culture built around shallow adoption risks more than struggling to achieve ROI; in some cases, it might leave employees less equipped to meet business needs than prior to AI.

IMPACTFUL ADOPTION

Impactful AI adoption will look different based on the company, a person’s role, and many other factors. For some, it means deepening the quality of the same work product. For others, it means increasing output without sacrificing quality. And for still others, it means getting the same work done in less time, repurposing time and energy toward new questions and tasks. All the best adoption initiatives, however, will be reverse-engineered from the larger business strategy. They will be built around metrics that connect to it.

The process of designing an adoption initiative should start with clarity and specificity around big-picture questions. What does value look like for our organization? How can different roles change to better deliver it? Leaders cannot lose sight of these framing questions as they determine what gets modeled and encouraged.

Wise ones will drive for real business impacts that come from the usage. And when they showcase strong use cases, they will not just reward speed or deep integration. Instead, they will keep the focus on the larger picture, taking great care to explain the meaningful organizational outcomes driven by the use case. In Gagen MacDonald’s latest white paper, we dive into what it takes to do this well, and what organizations can do to bridge the separate realities that exist between leaders and employees around AI.

Because while the employees who create the most impact with AI will certainly use it frequently, it’s a mistake to think of usage as synonymous with impact.

And given how much companies have spent and plan to keep spending on this technology, it’s not a mistake many leaders can afford to make.

Maril MacDonald is founder and CEO of Gagen MacDonald.

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At Google, AI is reshaping employees’ titles and how they work. Last month, Google Cloud’s senior director and chief evangelist Richard Seroter told Fast Company that software engineers have turned into product engineers, or architects, as they move away from manual coding to directing teams of AI agents. It seems that AI also changed how Google’s CEO, Sundar Pichai, works, too. 

“I just think the CEO job is not that complicated,” Pichai said when asked how close AI is to replacing him as a CEO during a recent interview with The Verge. “There are aspects of it where I think [AI] is going to be very, very helpful in terms of decision-making.”

The CEO added that AI can “make more rational choices over time.” He also said that “there are very, very few decisions which are really consequential, and most decisions aren’t.” Instead, Pichai said, making the decision and keeping the company moving forward is most important.

“Done correctly, these tools are going to allow us to operate at the next level in everything we are doing,” Pichai added. “It’s not like you won’t do what you were doing before. You will start from a higher foundation.”

Pichai likened AI agents to the advent of other innovations in the workplace, like spreadsheets. “I have to think back to, ‘how did people do all this financial analysis before’?” he asked. “I’m sure it changed over a period of three to four years fundamentally, and we got used to it.”

With AI, some companies are restructuring their organizations completely. 

Block CEO Jack Dorsey said he wants 6,000 direct reports, effectively eliminating middle managers. Meta announced plans to create an AI engineering team with 50 engineers that reported to a single manager. Pichai didn’t confirm or deny that similar extreme restructuring would take place at Google. 

“Leaders and people are incredibly important,” Pichai said. “And it depends. Some companies have a much narrower suite of products, and so different structures may work. When you’re running something at the scale of Google Cloud, it’s important that there is a CEO in charge.”

Still, as Google uses AI “more effectively,” roles at the company have changed. Pichai said that developers at Google went from merely using AI tools to assisting AI agents with coding, while some engineers direct teams of AI agents. Last month, the CEO announced that 75% of the company’s code was AI-generated.

During the interview, Pichai also chimed in on the current trend of commencement speakers being booed for drumming up AI. Eric Schmidt, the former CEO of Google, was booed at the University of Arizona during his commencement address when he spoke about the rise of AI.

“AI is the most profound technology humanity’s going to deal with,” Pichai said in the interview. “It’s happening at a very fast pace. I don’t think humans have evolved to process this much change, and the rate of change particularly over the last few years is incredibly high.”

While Pichai acknowledged people’s concerns about how AI is changing the job market and the economy, he added that AGI—or artificial general intelligence, a hypothetical AI that matches or exceeds human cognitive capabilities—is on the horizon, “coming sooner rather than later,” and that it is “important that we as a society understand it and are preparing as much as possible.”

If that’s any indication of how Pichai will talk about AI during his commencement address at Stanford University in June, we’ll have to wait and see how his words go over with those grads.

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The current AI boom reminds me of the dot-com era, which I watched unfold from venture capital in the late 1990s and early 2000s. Lots of hype. Eye-watering investments. Genuine transformative potential. Most conversations about AI today focus on the obvious value, productivity, and efficiency gains. That’s real, but it’s the shallow end. The deeper potential is something else entirely: ending the linear take-make-waste economy and with it, our reliance on fossil fuels.

For half a century, the global economy has run on a simple, destructive model. Extract finite resources from the Earth. Manufacture mostly disposable products. Throw away. Repeat.

Petroleum into packaging and apparel. Oil in cars. Critical minerals in the backbone of nearly every modern technology. The list is long, but the pattern is the same. We treat finite resources as if they were infinite, when we all know they are not.

COVID and the recent conflict around the Strait of Hormuz have made clear how fragile these supply chains really are, and why our dependence on finite resources concentrated in a handful of geographies is no longer a defensible strategy. The linear model strands value and creates strategic dependence.

THE ALTERNATIVE

Circularity is not a new concept. It refers to an economic model where materials already in circulation are infinitely regenerated, reducing the need for extraction and putting to work what’s already above ground, much of it currently bound for landfill.

Circularity creates resource efficiency, strengthens supply chains and opens up new material sources. Instead of depending on a small number of extraction hubs, reserves diversify dramatically. Countries and industries gain genuine control over the materials they need. And the economics of reusing what’s already in circulation, rather than sending it to landfill, are increasingly hard to argue against.

According to a new report from Circle Economy and Deloitte, our lack of circularity is costing the world €25.4 trillion a year, equivalent to nearly 31% of global GDP. Circularity is far more than a sustainability measure. It’s an economic imperative, and right now the cost of ignoring it shows up in resource inefficiency, premature product disposal, underutilized assets, and mounting sovereign and supply chain risk.

AI is what gets us closer to making circularity the default economic model of the future, not the exception.

Biotechnology, the practice of engineering biology to design new industrial processes, has long been used to solve global challenges. Insulin. Vaccines. Biofuels. Biomaterials. But its potential for circularity has been constrained by the sheer complexity of biological systems and the time it takes to discover and validate new solutions.

AI’s strength is finding patterns in vast, complex biological datasets that sit beyond human cognitive capacity. It dramatically narrows the search space and shortens the time to discovery and validation. For circularity, that opens the door to rapidly advancing fields like protein design and the discovery of new enzymes capable of regenerating end-of-life materials (plastic packaging, apparel, and critical minerals in e-waste) into virgin-identical inputs.

AI applied to biotechnology is the mechanism that can make circularity viable at global scale, and in doing so, end modern society’s reliance on fossil fuels and the linear economy.

THE NEXT 50 YEARS

The world order of the last 50 years will not apply to the next 50. The raw materials that power everyday life will become more valuable, not less, and the economies that control them will hold enormous strategic power. Circularity breaks that dependency. And AI, the same technology being hyped today for productivity gains, is what makes it possible at the speed and scale the world actually needs.

AI is not without risk. It has to be designed responsibly, built ethically, and powered by clean energy. Otherwise, it simply adds to the problem it could solve. But if we get that right, the dot-com era will look modest by comparison. This is the technology that could finally close the loop, and with it, end our reliance on fossil fuels.

Paul Riley is founder and CEO at Samsara Eco.

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Given its profound implications for the workforce, the environment, and humankind as a whole, it’s no surprise that almost everyone has an opinion on artificial intelligence these days, including the pope.

Less than a year after his election, Pope Leo XIV just released his first encyclical, a pastoral letter aimed to offer guidance.

Titled Magnifica Humanitas: On Safeguarding the Human Person in the Time of Artificial Intelligence, the 42,300-word letter offers a glimpse into the pope’s stance on AI, highlighting various concerns over the dangers of technology as well as a need for safeguards to be put in place.

“Calling for prudence, rigorous evaluation and even, at times, a slower pace in adopting AI does not mean opposing progress,” Pope Leo wrote. “Instead, it is an exercise of responsible care for the human family.”

How have people reacted to the letter?

While the letter aims to bring attention to AI and its dangers, it is also having an additional, unexpected response—bringing a fresh cultural relevance to an otherwise antiquated institution: the Catholic Church.

“Pope Leo really makes me empathize with my grandma hanging a framed photo of John Paul II in her kitchen,” a user said on X in a post with over 7,000 likes.

Another responded: “I’ve been Catholic grandma-maxxing ever since the guy was elected. I put one up too. lmao.”

Others are taking the chance to inject humor into the conversation.

A user said on X: “Now I’ll have to baptize myself Catholic so that every time they tell me to use Chat GPT, I can say that ‘my religion forbids it.’”

“[Pope Leo XIV] released a statement that says it’s absolutely critical for the survival of mankind that I have a summer situationship,” another added.

Still, some users did not agree with the pope’s stance, including Blake Scholl, founder and CEO of Boom Supersonic.

“Bad take from the Pope,” he said in an X post. “Tech revolutions tend to eliminate some jobs while creating others. If we cling onto jobs, we’d still be plowing fields by hand out of fear of disruption.”

Reactions to the encyclical highlight not just a conversation around technology, but also a quiet shift toward embracing the Catholic Church. After suffering declines in attendance in the wake of the institution’s abuse scandals, local archdioceses are once again welcoming record-high numbers of new converts.

The Holy See has also had other notable internet moments of late, like a recent video of the pope doing the 6-7 hand gesture while chatting with a group of young kids.

Users are, in fact, noticing the slow shift. “Me when young: The Catholic Church is an archaic and out-of-touch relic that will fade away in the modern world,” a user said on X.

That same user added: “Me now: The Catholic Church may be our last salvation, pun intended. Welcome to the resistance.”

The widely circulated encyclical is not Leo’s first time bridging a discussion between the Vatican and emerging technologies. In the past, he has expressed concern over the effects of AI on human development and offered personal advice on using it.

“Use it in such a way that if it disappeared tomorrow, you would still know how to think,” he told a high school student in Honolulu.

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Ferrari’s first-ever all-electric vehicle, the Luce, was designed to look “entirely new,” the company says, and so far the reaction to the new EV has been polarized.

Shares of the Italian luxury automaker fell in premarket trading Tuesday after Ferrari unveiled the car named after the Italian word for “light” on the anniversary of its first Rome Grand Prix victory.

The Luce, expected to sell for about $640,000, was designed by former Apple chief design officer Jony Ive and Marc Newson through their design collective LoveFrom.

Online, people expressed their disappointment. The Luce doesn’t look like a Ferarri, some complained, suggesting it has the sleek, compact, and rounded forms of an iPad rather than the automaker’s traditional sports car look with angular, aerodynamic forms.

On Reddit, users called the Luce “awful,” and compared it to Waymo. Car & Driver commenters didn’t think it has Ferrari DNA. X users called it a “beautiful ugly car” and asked if it was available on Temu or designed by Hot Wheels.

In a statement, Ive explained that the car’s electric drive train allows for a “radically new architecture,” adding that it encouraged a spaciousness that most sports cars cannot afford.

“The design approach was driven by a determination to explore, exploit, and celebrate the unprecedented opportunities afforded by a new power source,” Ive said. “While inspired by this extraordinary opportunity, we were mindful of the challenges of moving beyond the visceral engagement and soulful engine noise of the past.”

The EV has more than 60 new patents, and it could be the closest thing we get to an “Apple car” after Apple dropped its EV project in 2024. Its shell-like form is reminiscent of a computer mouse, as if the whole top is a multi-touch trackpad you can click.

The exterior design is built on the concept of a glass house with a light and airy interior. The interior lighting effects, meanwhile, are dynamic and futuristic: Front and rear lights are transparent and slowly recede after the car is switched off. After docking the key, Ferrari yellow glows from the key across the interface.

While much of the Luce looks and feels like a computer, the physical controls give the interior an elevated analog interface—with its knobs, switches, and buttons offering a satisfying, tactile experience that replaces the sterile sensation of an iPhone touchscreen.

“We really wanted every part, every component, to be designed as an individual product. So it’s like dozens and dozens of cameras and watches,” Ive told Fast Company earlier this year when Ferrari previewed the EV’s steering wheel, center console, seat, and dashboard components.

The negative initial response to the Ferrari Luce is par for the course when it comes to automakers rethinking what a car and car brand should look like as they adapt for EVs. But that’s exactly what Ferrari was going for by tapping outside designers to bring a new perspective and design language to the project.

The Luce “lights the way towards the future,” the company said in a statement. “Not merely the ‘electric Ferrari’ but an entirely new Ferrari.”

Priced as high as it is, Ferrari doesn’t really need to convince the wider public to like its new car in order to actually sell it. There’s only a limited number of customers who can afford an EV that costs more than the average U.S. home. By designing something bold and new, Ferrari hopes to cater to buyers who can afford a car that doesn’t look like anything else out there.

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Being critical of AI is far from a fringe position in the United States. Recent polling shows that half of U.S. adults feel more concerned than excited about the increased use of AI in daily life. And among Gen Z specifically, excitement and hope around artificial intelligence are falling while anger over the tech increases, with 42% of Gen Zers saying AI makes them anxious.

But those increasingly common AI-critical sentiments are reportedly raising flags with the federal government. More than a thousand pages of unpublished reports acquired by Wired show a worrying trend across America: Federal intelligence agencies and domestic law enforcement are targeting “anti-technology extremists.”

Counterterrorism under The President

Earlier this month, President Donald The President and counterterrorism czar Sebastian Gorka shared the federal government’s current counterterrorism strategy. In it, Gorka laid out what he claims are the biggest terrorist threats to the U.S., naming “violent left-wing extremists, including anarchists and anti-fascists” as one of the “three major types of terror groups” facing America.

The President’s foreword to the strategy concluded with his message to domestic terrorists: “We will find you and we will kill you.”

According to the unpublished reports detailed by Wired, anti-tech extremism is subject to the same surveillance and potential criminalization laid out in that strategy. One such report, sourced from the New York Intelligence and Counterterrorism Bureau, introduces the term “anti-tech violent extremist” in the context of widespread AI adoption.

“The chaotic atmosphere that may result from emergent AI technology in the next five years may fuel large-scale protests that devolve into civil unrest and anti-tech violent extremist activity, especially in large urban areas such as New York City,” the report reads.

Some of the acquired documents come from fusion centers, which serve as links between federal intelligence agencies and state and local law enforcement departments. These fusion centers are reportedly on the lookout for threats to data centers, an especially controversial aspect of the AI boom.

Though 7 in 10 Americans oppose the local construction of data centers, The President has gone so far as issuing an executive order to fast-track their development.

A report from one western Pennsylvania fusion center claimed that “adversarial actors, including state-sponsored entities, criminal groups, and extremists, such as homegrown violent extremists or environmental extremists, may target U.S. data centers.”

It continued: “These actors could also exploit the strategic importance of data centers to the U.S. economy, using them for activities like cryptocurrency mining or leveraging third-party entities, such as front companies, to gain access to U.S. data and infrastructure.”

A dangerously broad category

Though the documents purport to be targeting anti-tech “extremism,” there’s a fine line between extremism and peaceful protest—and some reports suggest that intelligence agencies could conflate the two.

For example, a report from the Northern Virginia Regional Intelligence Center claimed that extremists are engaging in preoperational planning to target data centers based on observed behaviors. But in its breakdown of suspicious activity reporting (SAR) indicators, the flagged behaviors could just as easily be carried out by peaceful protestors, including “expressed/implied threat,” “observation/surveillance,” “photography,” “testing/probing of security,” and “attempted intrusion.”

Additionally, fusion centers are reportedly keeping tabs on tech-critical protests and civic activities. That includes reporting on local budget meetings and school board meetings, along with protests like the “Tesla Takedown” movement, which critiques Elon Musk’s outsize influence on the U.S. government.

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Jensen Huang has some pointed words for leaders who blame company layoffs on artificial intelligence.

“I think the narrative that connects AI to job loss, for many of the CEOs that are doing it, is just too lazy,” the Nvidia cofounder and CEO said in an interview with Channel NewsAsia. “AI has just arrived. How is it possible they’re already losing jobs? How is it possible that AI became productive and useful only six months ago, and they were somehow laying people off two years ago because of AI?

“It doesn’t make any sense,” Huang added. “It was just a way for them to sound smart, and I really hate that.”

While Huang didn’t name-drop any specific CEOs or companies, AI-linked layoffs have permeated several industries in recent weeks and months. Standard Chartered CEO Bill Winters received backlash when he announced the company would cut 7,000 jobs over the next four years to replace “lower-value human capital” with tech. Just last week, Meta laid off 10% of its workforce to offset heavy spend on AI initiatives. One report from outplacement and executive coaching firm Challenger, Gray & Christmas found that AI drove 25% of job cuts in March. 

Still, Huang is not convinced by leaders who blame layoffs entirely on the advent of AI.

“I think we’re scaring people, and that’s irresponsible,” he said. “I think we should tell a balanced story, a balanced narrative about the potential of this technology.”

Huang isn’t the only one pushing back on the existing narrative. Labor experts have said for months that AI has been used as a scapegoat to justify layoffs that may really be happening for more pedestrian reasons, such as poor profit margins. An in-depth analysis published last October by the Brookings Institution and Yale University’s Budget Lab found that the proportion of jobs that are at high risk of being replaced by AI had remained fairly steady since ChatGPT’s launch in 2022.

Either way, corporations are excitedly adopting AI tools to automate tasks and maximize profits. According to a new survey by consulting firm Mercer, 99% of CEOs are prepared for AI-driven layoffs in the short term. Most affected by AI’s impact on the workforce are young workers who are just starting their careers. And a recent report by consulting firm Oliver Wyman found that CEOs who would reduce junior roles in the next year or two doubled to 43% from 17% last year. 

In the interview, Huang returned to a point he made in past talks and interviews: that people are not going to lose their jobs to AI, but rather to those who know how to better use AI. “I would say to the people who are worried about losing their jobs to AI, to learn AI,” Huang said.

“It’s more likely that AI will elevate your job,” he said. “Try to engage [in] it. Don’t be afraid of it. Of course, the industry has to be really thoughtful about building AI in a safe way and a guardrail way and make sure that it’s deployed in a proper way.”

As Huang sees it, AI might change the way people work, but it shouldn’t be used as an excuse behind every round of layoffs. And while the rise of AI is creating anxiety among workers, he believes the people who will thrive are those that learn to work with it instead of fearing it. “Everybody has to be part of this,” Huang concluded.

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We’ve written a lot about how AI is coming for your job. Now AI is coming for your music, flooding streaming platforms with “AI music slop.” But instead of curbing it, Spotify’s CEO Alex Norström is doubling down and embracing AI-generated music—claiming it offers artists protection from piracy, and music-lovers more freedom to listen to and create more of the kind of music they want.

Last week, Spotify and Universal Music Group (UMG) announced landmark licensing agreements, paving the way for Spotify to launch a new tool for premium subscribers. The tool enables them to create AI-generated song covers and remixes of their favorite songs from participating artists and songwriters on the platform.

The deal has prompted two very different reactions. On Wall Street, the move sent shares of the stock up 16% last week, per the Financial Times. (On Tuesday, shares of Spotify Technology SA (NYSE: SPOT) were up nearly 2% in midday trading at the time of this writing.)

However, the move also prompted swift backlash from recording artists and music fans alike.

“I quit Spotify after many many years because of their attempts to integrate AI into music,” one Reddit user responded. “At this rate, Spotify won’t just be a streaming platform anymore,” another quipped.

Norström has defended the move as a “rewarding outcome for artists and songwriters” that will compensate musicians, arguing Spotify is offering a “controlled” alternative for musicians to make money, instead of having their work ripped off. Critics, however, are wary.

“I think if you are going to have AI music, it’s clearly better that you have AI music that is rooted in consent,” composer Ed Newton-Rex, who campaigns to protect creators’ copyrights, told The Guardian. “[However] the big question will be whether fans can share remixes they make for other people to listen to. If they can, I think you get into dangerous territory. These AI remixes will flood Spotify and drown out other songs, which will in turn put pressure on more musicians to sign up to the AI remix feature.”

The bottom line, Newton-Rex said, is that this could end up making it even harder for musicians, who now would have to compete with AI-generated work.

And that fear is already being realized. Three different AI-generated songs have already broken into the top of Spotify’s “Viral 50” charts. Heard that new band The Velvet Sundown on Spotify? Sorry to tell you, but that’s AI-generated.

According to a 2025 study from Deezer, a global streaming app, some 50,000 AI-generated songs are uploaded daily to its platform, with 97% of respondents unable to discern whether or not the tracks were fully AI-generated.

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Drivers for ride-hailing apps such as Uber and Lyft in Massachusetts became the first in the nation Tuesday to certify a union, marking a milestone in the growing effort to organize gig-economy workers amid ongoing concerns over pay, expenses, and working conditions.

The victory could provide a model for similar campaigns gaining traction in states including California and Illinois, where labor organizers are increasingly targeting app-based industries as drivers also grapple with the rapid expansion of self-driving technology.

Fully driverless commercial rides without a human operator are not currently permitted in Massachusetts.

The certification became possible after the state’s voters approved a 2024 ballot measure creating a first-in-the-nation framework allowing ride-hailing drivers to unionize and bargain collectively while remaining independent contractors. Organizers say the union could ultimately represent nearly 70,000 drivers statewide.

As drivers waved signs and chanted, with the gold dome of the Massachusetts State House providing a backdrop, labor leaders described the victory as the largest private-sector organizing win since Ford autoworkers unionized in 1941.

Jean Fredo, who has driven for Uber for more than seven years, said he hopes the union will bring better pay, stronger protections against sudden deactivations, and more stability for drivers.

“With the union, it will not feel like we’re working for nothing,” he said in French through a translator. “Now the money will not only stay in the billionaire’s pockets. The money will actually come to the workers who work very hard.”

Fredo said when he started driving for Uber, he appreciated the flexibility and the ability to make his own schedule while still being present for his family. But over time, he said, he found himself working longer hours while earning less as gas and maintenance costs climbed.

Drivers can also lose access to the apps with little warning or recourse, he said.

“I live with stress—always scared to lose my app,” Fredo said. “This is not a way to live.”

Fredo said he immediately joined the organizing effort when he heard about it and later helped sign up hundreds of other drivers at airports and gathering spots around the Boston area.

At one point during the rally, Fredo pumped his fists over his head while showing a photo of his family to the crowd.

“This is my family,” he said. “I’m fighting for a better life for them—just like everyone else is fighting for their families. My dream is to save and send my kids to college, and I believe we will get there.”

A labor fight shadowed by automation fears

Supporters say rising vehicle costs, fluctuating pay, and opaque app algorithms have fueled frustration among drivers who often work long hours while paying for gas, insurance, maintenance, and vehicle wear-and-tear themselves. Uber and Lyft have argued that drivers value the flexibility of app-based work and have opposed efforts that could reclassify workers or alter the industry’s business model.

The organizing effort has unfolded alongside the rapid expansion of autonomous vehicle technology. In Massachusetts, autonomous vehicles can be tested on public roads, but current regulations still require a licensed human operator inside the vehicle. Fully driverless commercial operations without a human in the car are not permitted statewide.

Waymo has expanded driverless taxi operations in cities including San Francisco, Los Angeles, and Phoenix. The rollout has drawn scrutiny over traffic disruptions, safety investigations, and incidents involving stalled or malfunctioning vehicles, while also heightening anxiety among some ride-hailing drivers about the future of their jobs.

Julie Blust of the App Drivers Union said drivers across the country regularly communicate with one another about changing conditions in the industry, including the expansion of autonomous vehicles in California.

“We now know what’s happening there,” she said. “Drivers are seeing pay go down, and there are real concerns about safety and job security as automatic vehicles expand.”

Organizers increasingly see unionization as a way for drivers to collectively respond to the growth of autonomous vehicle companies, she said.

“Drivers now have an official organization and can speak with one voice about what’s happening in this industry,” Blust said. “We cannot let billions of dollars leave Massachusetts and go to Silicon Valley. That money feeds people’s families, that money pays the rent. That money goes into small businesses.”

Uber and Lyft “engaging in good faith”

The bargaining process is also unfolding as Massachusetts regulators consider broad new ride-hailing regulations proposed this spring involving safety standards, driver oversight, and proposals involving electric vehicle fleets. Days before the union certification, Uber warned in a blog post that some of the proposals could raise costs and reduce flexibility for drivers, while supporters said the changes are intended to strengthen safety and accountability.

In an emailed statement Tuesday, Uber said it would work with the union and state regulators as the bargaining process moves forward.

“As we enter this next phase, we will work closely with the ADU, our broader driver community, and the Department of Labor Relations,” the company said. “Together, we will ensure that driver flexibility and hard-won benefits remain the foundation of our progress.”

Lyft also said it planned to engage with the new bargaining process.

“As this new process moves forward, we’re committed to engaging in good faith,” the company said in a statement. “Lyft does well when drivers do well, and we’ll stay focused on helping drivers succeed while keeping ride-share affordable and dependable for everyone who counts on it.”

—By Leah Willingham, Associated Press

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Each year, June marks Pride Month for the LGBTQ+ community and our allies. This is a time for both celebration and acknowledgment of the progress we have yet to make. In recent years, there has been significant attention on the role that businesses play during Pride Month. As the CEO of one of the largest LGBTQ+ organizations in the U.S., I’ve worked with numerous companies—across all industries, sizes, and locations—looking to support our community in meaningful ways. Here are a few insights to guide how you show up for the community during Pride, and beyond:

KNOW THE DATA

Nearly 1 in 10 adults in the U.S. identify as part of the LGBTQ+ community. For Gen Z adults, that number climbs to more than one in five. As societal progress allows more and more people to openly identify as LGBTQ+, the average company’s consumer base, audiences, investors, and employees reflect that growth, too. It’s estimated that the LGBTQ+ community in the U.S. had roughly $1.4 trillion in buying power in 2021 (a figure that has surely gone up since then)—and that estimate grows to $3.9 trillion worldwide.

While the news cycle and current climate around LGBTQ+ policies may change from moment to moment, one thing remains constant: LGBTQ+ people are here to stay. Supporting the LGBTQ+ community is a long-term investment that signals how in tune you are with your audience. Whether you have been a decades-long supporter, or are considering showing up for the first time this Pride Month, your options for being a meaningful ally are limitless.

SUPPORT LGBTQ+ PEOPLE WHO WORK AT YOUR COMPANY

Pride Month is a great opportunity for business leaders to consider how they’re supporting their LGBTQ+ employees through their policies and practices. Employers should consider whether the benefits offered are inclusive of LGBTQ+ staff needs. For example, do you offer adoption and family planning support benefits? Do employer-sponsored health insurance plans cover gender-affirming care for transgender and nonbinary staff? Do you maintain an employee resource group for the LGBTQ+ community?

Benefits and resources aside, business leaders can take minor but meaningful actions to shift the day-to-day language used at their company, that may make LGBTQ+ staff feel seen. Use gender inclusive language where possible. Create opportunities to share and respect people’s pronouns.

Hiring LGBTQ+ employees can help your company incorporate diversity of thought and experience to better understand LGBTQ+ consumers. Hiring LGBTQ+ leaders can also provide possibility models for your LGBTQ+ staff who never thought it was possible to advance in their industry.

LOOK TO YOUR PEERS FOR GUIDANCE

Many companies have been supporting the LGBTQ+ community for decades; there are role models to learn from. If you’re looking for opportunities to show up this June, remember that you are not alone. Many companies have been in your exact spot, and it may be helpful to reach out to your counterparts at peer institutions to learn more about what they do during Pride Month.

As an example, The Trevor Project has partnered with MAC Cosmetics for three years to help ensure their stores’ employees and customers find welcoming, safe environments where they can show up as their full selves. Through this partnership, our experts have supported their makeup counter staff in learning and using inclusive language, and sharing powerful online stories to highlight the role that beauty and makeup can play in affirming someone’s gender identity or expression.

LEARNING IS ALSO TAKING ACTION

In nearly every state and community across the country, LGBTQ+ organizations, community centers, resources, and advocates are actively working to make life better for LGBTQ+ residents. Take some time to research what groups exist wherever you call home, and reach out to connect with them directly. The Equality Federation is a great place to start your search, as it operates a national network of more than 50 state-based LGBTQ+ organizations. To learn more about how best to support the LGBTQ+ people in your life, talk to them. Ask who and where they turn to for support, and what would be meaningful for them this Pride Month.

There is no single “right” way to be an ally during Pride Month. Allyship is a verb, so as long as you are showing up authentically, taking tangible action, and working to support the LGBTQ+ community at your company, you’re doing it right.

Jaymes Black is CEO of The Trevor Project.

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We are living through a golden age of faking it: the AI stunt that earns a news cycle and dissolves the moment you press on it, the activation that is shared for five minutes and forgotten, and the commercial that’s more about the celebrity starring in it than the brand. Merriam-Webster named slop the word of 2025. It’s the equivalent of an artificial sweetener; surface-level buzz at best, no substance beneath.

So, for the sake of timeline cleansing and inspiration, let’s talk about one of my favorite topics, dogs, the things they do, and dog shows.

Every year, several million people watch dogs trot around a ring in televised dog shows. Viewers pick favorites, develop strong opinions about terriers, and text their family 11:00 p.m. about an old English sheepdog named Graeme like something important is at stake. This year was Westminster’s 150th anniversary. I went and what I saw wasn’t really about dogs. It was about what happens when you give people something genuinely worth showing up for.

PARTICIPATION IS WHAT MATTERS

Human beings are not wired for passive consumption. We are wired for shared experience. Attention is like a rental that depreciates in value the moment the ad spend runs out whereas participation compounds.

At the Crufts dog show, the British counterpart to Westminster, presenter Claudia Winkleman could barely get through an interview without stopping mid-sentence to stare adoringly at a golden retriever. The clips of this love gaze spread everywhere. People who had never heard of Crufts shared the clips. Nobody engineered that. They just gave her a microphone, put her near some dogs, and trusted that if the love was real, people would find their way to it.

When you give someone a genuine role rather than a passive seat, the thing stops being something they watched and becomes something they were part of. That’s a different relationship entirely. That’s brand love, and it doesn’t show up in a post-campaign report. It shows up three years later when someone is still telling the story.

START WITH WHAT PEOPLE CARE ABOUT

At Johannes Leonardo, we like to work with brands that understand this and aren’t asking how to get people’s attention. They’re asking something harder: Is there something real here that people would make their own?

Westminster has been asking this question since 1877. The answer has always been the same: Start with something people genuinely care about, give it a stage, and get out of the way. Don’t engineer fake participation. Don’t optimize for attention.

The foundation must be true first, and if it is, people will show up. They’ll shout ‘GO GRAEME’ at a dog they’ve never met. They’ll text their people and make it theirs.

The dog show organizers understand something brand marketers should too. Every hollow moment, every manufactured feeling erodes trust and people’s appetite to seek out brands that are genuine. We can’t afford to keep spending that trust down because when everyone is faking it, the moment a consumer catches you doing the same is the exact moment they lose interest and walk away.

FINAL THOUGHTS

This year, 600,000 people watched Westminster on TV and 50,000 attended in person. Millions more joined in through group chats, their TikTok For You pages. The key to success? A group of people who really like dogs started the group that remains committed to its initial mission. And 150 years later, it’s still pulling people in because they feel like part of the experience.

People haven’t changed. They still want to show up. They still want to belong to something. The only question any marketer or creative person should be asking, the only one that has ever really mattered, is whether you give them something worth showing up for.

Helen Andrews is the CEO of Johannes Leonardo.

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Apple’s Tim Cook isn’t the only well-known tech CEO stepping away from the chief executive role this year. Now, the founder and CEO of Dropbox (Nasdaq: DBX), Drew Houston, has announced he is making a similar move at the company he is synonymous with. Here’s what you need to know about Houston’s departure from the chief executive role and how investors are reacting to the news.

What happened?

Today, Houston announced he will be retiring from the chief executive role at the cloud storage provider. Houston has been with Dropbox in the role since he founded the company in 2007.

It’s hard to understate how revolutionary a cloud storage solution like Dropbox was 19 years ago, when Houston brought online cloud storage to the masses. Before Dropbox, the standard way most users transferred files between their devices was by using external hard drives or USB sticks, or emailing files to themselves.

Dropbox proved there was an easier way to transfer files: using the burgeoning cloud. The technology was simple and reliable to use: Add a file to your Dropbox, and it was available nearly instantly on all your devices. That ease quickly made Dropbox a Silicon Valley darling and a popular platform with everyday internet users.

Of course, since then, online storage and file sharing have become integrated into nearly every major internet platform, with Dropbox now facing stiff competition from the likes of Google Drive, Apple iCloud, and Microsoft OneDrive.

Still, the progenitor of the online storage and file-sharing space has a large user base even today. The company says that Dropbox has 700 million global registered users, of which more than 18 million are paid users. For its most recent quarter ending March 31, Dropbox reported $629.5 million in total revenue.

Why is Dropbox’s founder stepping aside?

It should be noted that Houston is not departing his CEO role yet. Though the Dropbox founder announced his departure from his chief executive role today, it will not occur until later. Houston did not give a date when the departure would actually happen. And once it does, he will stay on at Dropbox as executive chairman.

What has happened today is that Houston is no longer Dropbox’s sole CEO. Now Houston’s title is co-CEO, and he shares that role with the company’s other newly appointed co-CEO, Ashraf Alkarmi. Alkarmi’s previous role was product chief; he has been with the company since 2024.

After a transition period in which the two co-CEOs lead the company together, Houston will become executive chairman, leaving Alkarmi as the company’s sole CEO.

In an email to employees that Dropbox later published on its website, Houston did not say why he was changing his role, but alluded to his interest in artificial intelligence.

“My focus right now is making sure Dropbox is in the strongest possible shape,” Houston wrote. “But knowing me, it won’t be long before I’m getting credit card alerts for my Cursor token spend.”

The Dropbox founder told CNBC that he is interested in the entrepreneurial AI space because “there’s never been a more exciting period to be building things.”

Dropbox stock drops on Houston’s upcoming departure

After news of Houston’s departure was announced, shares in Dropbox fell in early morning trading. Later, DBX shares were trading down about 2.3% to $26.80. That puts Dropbox stock firmly in the red, year to date. Since 2026 began, Dropbox’s shares have declined about 2.95%.

Over the past 12 months, DBX shares have been down about 5.6%. They are also well below their all-time high of over $43 per share in June of 2018, months after the company went public.

Dropbox, like most software-as-a-service companies, has been facing increased pressure and apprehension from investors in the age of AI, which many expect to eat into the business models of legacy SaaS companies. In the first quarter of 2026, Dropbox reported $629.5 million in total revenue, up just 0.8% from the previous year. 

However, despite Dropbox’s most recent lackluster quarter and stock price, Houston will be stepping aside at a company that made history on the stock market. Dropbox was the first company from the Y Combinator startup accelerator to go public, which it did in 2018.

At the time, Dropbox was valued at around $12 billion. Today, the company is valued at around $6.2 billion.

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The promise of frontier AI has always sounded like a utility: abundant intelligence, available on demand, as easy to access as electricity, water, or cloud computing. The metaphor is powerful, and for good reason. Utilities scale because they abstract complexity away. You don’t need an engineer from the power company sitting in your office every time you turn on the lights. 

And yet, the most sophisticated AI companies in the world are increasingly doing something very different: They are sending people. 

OpenAI recently announced the OpenAI Deployment Company, explicitly designed to embed forward deployed engineers (FDE) inside organizations working on complex problems in demanding environments. These engineers, according to OpenAI, will work with business leaders, operators, and frontline teams to identify where AI can make the biggest impact, redesign workflows, and turn those gains into durable systems. Anthropic is hiring FDEs for its applied AI team, people who embed directly with strategic customers to drive enterprise adoption and ship real-world applications. And Google is doing the same. Is that a coincidence? 

That is revealing. Because if intelligence were already a true utility, this would not be necessary. You would not need to send your own engineers to every customer to make the faucet work. 

The paradox of AI as a utility 

This is the paradox at the heart of the current enterprise AI model: The industry speaks the language of scale, abundance, and platforms, but its delivery model increasingly resembles high-end consulting. 

That does not mean the work is unimportant. Quite the opposite. Forward deployed engineers are often solving the real problem: taking frontier models out of the demo environment and making them function inside messy, regulated, fragmented organizations. They deal with permissions, legacy systems, compliance, data quality, workflows, operational constraints, and all the things that make companies different from benchmarks. 

But that is precisely the point. The need for these people is not merely a commercial innovation. It is a symptom. It tells us that the product, as currently packaged, is not yet enough. 

In the previous articles in this series, I argued that large language models were never built to run a company, that enterprise AI must move from tools to systems, and that the systems that finally work will not look like chatbots or copilots, but like intelligence embedded into the organization itself. The FDE phenomenon confirms that argument from the vendor side. If the AI lab has to send engineers to reconstruct context, redesign workflows, and make the system operate under real constraints, then the missing layer is not imagined. It is sitting there, being supplied manually. 

The preplatform pattern 

Every major technology industry goes through an artisanal phase before it becomes industrial. 

Before enterprise software became packaged, implementation was bespoke. Before cloud platforms became mature, companies needed armies of specialists to configure infrastructure. Before the web stabilized around browsers, standards, hosting providers, content management systems, analytics, and design conventions, building a website required far more custom work than it later would. 

Forward deployed engineering belongs to that same historical pattern. Palantir popularized the model years ago. Its own description of the forward deployed software engineer role is based on engineers working directly inside customer environments to make software function in operational reality. 

That model made sense for Palantir because its customers often had extremely complex, high-stakes, highly specific environments. But when OpenAI and Anthropic begin to converge on similar patterns, the signal is different: the frontier AI industry is discovering that models alone do not cross the enterprise gap. 

That does not make FDEs a failure. It makes them a transitional form. They are what appears before a category has found its true platform layer. 

SAP does not send SAP employees to every customer 

This is where the comparison with mature enterprise software becomes useful. 

SAP does not scale by sending SAP employees into every customer. It has a vast partner ecosystem. Salesforce does not implement every customer itself. It has AppExchange, now evolving into AgentExchange, and a large ecosystem of partners, independent software vendors, and systems integrators. The platform company creates the substrate; the ecosystem industrializes delivery. 

That distinction matters. When the vendor itself has to supply the scarce human expertise required to make the product work, the category is still immature. When partners, integrators, templates, standards, and repeatable architectures take over, the category begins to scale. 

This is why the current FDE wave should be read carefully. It is not proof that frontier AI has become a platform. It is proof that it has not yet become one. 

A true platform reduces the need for bespoke intervention. A preplatform product depends on it. 

The business model trap 

There is another problem, and it is more subtle: Once forward deployed engineering becomes a source of revenue, prestige, customer lock-in, and strategic proximity, it becomes harder for the vendor to eliminate it. The very people solving the product’s incompleteness can become part of the business model that depends on that incompleteness. 

This is classic innovator’s dilemma territory. Clayton Christensen’s argument was that successful companies often struggle not because they fail to see the future, but because their existing business models make the future unattractive or cannibalistic. In this case, the dilemma is simple: If a frontier AI company builds the layer that makes deployments repeatable, modular, and partner-scalable, it may undermine the bespoke, high-touch model that currently brings it close to the largest customers. 

That is why the real platform may not come from inside the companies training the models. It may come from another layer. 

The missing layer is not another model 

The temptation, as always, is to assume that the answer is a better model. A larger model. A more agentic model. A model with longer context, more tools, more memory, more reasoning traces, and more autonomy. 

But the FDE model suggests something else. If engineers are being sent into customers to map workflows, understand constraints, connect systems, structure context, govern access, and turn AI outputs into operational outcomes, then the missing piece is not simply intelligence. It is architecture. 

More specifically, it is the layer that turns company reality into something AI systems can operate within: 

• persistent context, 
• process structure, 
• permission models, 
• constraint management, 
• feedback loops, 
• workflow state, 
• business semantics, and 
• outcome tracking. 

Today, that layer is often reconstructed manually by expert engineers on each deployment. Tomorrow, it will have to become infrastructure. 

That is the real opportunity. 

Why this is really BPR with agents 

This also connects directly to the return of business process reengineering (BPR). 

In 1990, Michael Hammer’s famous Harvard Business Review article, “Reengineering work: Don’t automate, obliterate,” argued that companies should not use technology merely to speed up outdated processes. They should redesign the processes themselves. The idea was right, but in many cases the technology of the time was not yet capable of supporting the ambition. 

AI changes that, but it also makes the problem more demanding. If companies merely insert AI into existing workflows, they get faster versions of obsolete processes. If vendors merely send engineers to customize each deployment, they get artisanal transformation that does not scale. 

The real breakthrough comes when the redesign itself becomes systematized: when business processes are not just automated, but represented, governed, adapted, and optimized continuously. 

That is the point at which enterprise AI stops being a consulting engagement and starts becoming a platform. 

The FDE is the clue 

This is why the forward deployed engineer is so interesting. The FDE is not the future of enterprise AI. The FDE is the clue that the future has not fully arrived. 

The role exists because current systems still require humans to bridge the gap between general AI capability and specific organizational reality. Someone has to translate the company into the machine. Someone has to interpret constraints. Someone has to determine which workflows matter. Someone has to connect data, process, action, and outcome. 

But history suggests that once a repeatable layer appears, the artisan becomes less central. Web consultants did not disappear after the web matured. But “build me a website” stopped being a mysterious custom engineering problem for most organizations. Enterprise resource planning (ERP) consultants did not disappear after SAP matured. But the ecosystem became standardized enough that the vendor did not need to personally deploy the product everywhere. Cloud architects did not disappear after Amazon Web Services (AWS) became a platform. But infrastructure became programmable, repeatable, and scalable. 

The same thing will happen here: Forward deployed engineers will not vanish. But if enterprise AI becomes a real platform category, they will become exceptional rather than foundational. 

The real test of a platform 

The test is simple: Can the system work without sending the lab? Can it understand the company without a bespoke mapping exercise every time? Can it operate under constraints without manual reconstruction? Can it adapt to workflows without a team of engineers sitting inside the customer? Can partners build on it? Can customers configure it? Can it scale beyond the handful of enterprises that can afford white-glove deployment? 

Until the answer is yes, we should be honest about what is being sold. It is not AI on tap. It is AI on tap, with plumbers included. 

And that is fine, for now. Every category has its artisanal phase. The mistake is confusing that phase with the destination. 

What comes next 

The next stage of enterprise AI will not be defined by who has the most impressive model or the largest deployment team. It will be defined by who builds the layer that makes those deployment teams less necessary. 

That layer will not merely answer questions. It will represent the company. It will encode processes, constraints, permissions, memory, and outcomes in ways that AI systems can actually use. It will allow models to operate inside the business rather than hover above it. It will turn bespoke deployment into repeatable architecture. 

When that happens, the current FDE boom will look obvious in retrospect—not as the final form of enterprise AI, but as the bridge between demos and platforms. 

And when the real platform layer appears, the industry will change very quickly. Because utilities do not scale by sending engineers to every sink. They scale when the plumbing is already there. 

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Conversations about youth mental health and addiction are often treated as separate issues—different experts, different headlines, different policy conversations. But for today’s young people, these challenges are deeply intertwined. The same forces driving rising levels of anxiety, stress, and social comparison are also shaping their risk for substance use. 

In The Anxious Generation, social psychologist Jonathan Haidt argues that the rapid shift to phone-based childhood fundamentally changed the developmental environment for young people. Attention, reward, identity formation, and peer validation have been rewired by digital platforms engineered to maximize engagement. The result has been a sharp rise in anxiety, depression, loneliness and emotional fragility among adolescents and young adults.

THE PROBLEM WITH NICOTINE

These are the same behavioral systems modern nicotine products are built to exploit.

Nicotine has always targeted the brain’s reward pathways. But a new generation of “smart” nicotine products increasingly mirror the mechanics of social platforms themselves: frictionless engagement, instant reinforcement, personalization, and compulsive use loops. Paired with high nicotine content, these products are not only appealing to young people but can create a dangerous path toward addiction. 

For decades, public health framed addiction primarily as a knowledge problem: If young people understood the risks, they would avoid harmful substances. But today’s landscape is much more complex. Most youth and young adults already know the dangers of nicotine and addiction. What they’re battling instead is a new landscape of nicotine products that have been billed as “safer,” paired with a social environment that amplifies mental health strain while simultaneously normalizing use. Young people are navigating unprecedented levels of stress inside digital and commercial ecosystems explicitly engineered to keep them engaged, consuming and coming back.

Research from Truth Initiative shows that youth nicotine use is strongly intertwined with stress, anxiety, and broader mental health challenges. Additionally, a survey we published found that 67% of young adult nicotine users plan to quit nicotine in 2026, with most citing physical and mental health as their top motivations. Increasingly, young people are recognizing that nicotine isn’t relieving stress in the long-term—it’s becoming part of the stress cycle itself.

That’s why calling attention to the connection between stress and nicotine use is an important component in helping young people. Outsmart Nicotine, the latest campaign from Truth Initiative’s EX Program, showcases how nicotine use can become a cycle tied to everyday stressors like school, work, social life, and family pressures. While nicotine may create a temporary sense of relief, that relief is fleeting. Cravings and withdrawal quickly follow, reinforcing a cycle that can deepen dependence while worsening anxiety, depression, and stress. Our goal is for this latest effort to help young people, particularly those who may not have considered quitting yet, recognize this pattern and connect them with free, evidence-based resources to quit nicotine for good.

Prevention and cessation can no longer focus on only the product; it must also confront the environment shaping the behavior. Addressing youth addiction without also addressing broader youth mental health doesn’t paint the full picture. The next generation of prevention must recognize that emotional well-being, attention ecosystems, and substance use are not parallel issues but interlocking ones. Addiction today does not happen in isolation. It unfolds inside an economy built to capture attention, shape behavior, and monetize engagement at almost any cost.

Kathy Crosby is CEO and president of Truth Initiative.

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Gabriel Landeskog wears the small sensors in the insoles of his skates for practices and games. He wears them in his sneakers when he’s training and, maybe most handy of all, while taking his dog for a walk.

Those spins around the block and ice record all of his biomechanical measurements. The numbers provided a blueprint in helping the Colorado Avalanche captain resume his career after a three-year gap caused by a complicated knee injury. Now, they keep him at his gritty, goal-scoring best.

The collected data ranges from movement patterns to his asymmetry and whether he’s favoring his surgically repaired right knee. It calculates in-game/in-practice workloads, stride characteristics, and the mechanics of how his feet interact with various surfaces—ground or ice.

The details paint a picture to inform Landeskog when he’s reaching maximum capacity and needs a break. That way, the technology prevents him from reaching overexertion levels in training that might set him back for days, possibly weeks.

“This detects any red flags before I even feel them,” said Landeskog, whose team trails Vegas 3-0 in a Western Conference Final in which he has two of the Avalanche’s six goals. “It’s been super important for me, and a huge help.”

The assist goes to Plantiga, an AI-driven movement platform that helps athletes stay on top of their game and prevent injuries. The company’s cutting-edge technology is being utilized by players and teams in the NBA, NFL, WNBA and MLB, along with colleges, elite sprinters, weekend warriors and, of course, NHL players such as Landeskog.

“What we’re trying to detect is the smoke before the fire,” explained Matthew Jordan, the vice president of performance science at Plantiga as well as an associate professor, faculty of kinesiology/sport medicine center, at the University of Calgary. “Imagine you’re at the point where your knee is just at the cusp of the next day it’s going to be like, ‘My knee’s killing me. I can hardly walk.’ We can see in the data before you reach that tipping point.”

Landeskog’s return from knee injury

Landeskog’s knee issues began after a skate blade cut his right knee during the 2020 playoffs in the Edmonton pandemic bubble. He worked his way through it and helped the Avalanche to a Stanley Cup title in 2022 by beating Tampa Bay.

That Cup clincher, though, was his last game for a while. After missing a full season, Landeskog underwent cartilage replacement surgery on May 10, 2023.

Introduction to Plantiga

In the spring of 2024, Landeskog was introduced to Plantiga, the Vancouver-based human analytics company founded by Quin Sandler and his late father, Norman McKay. They wanted to create a way to monitor the movement of athletes with wearable in-shoe technology.

Landeskog reached out to the Plantiga team through the strength and conditioning coach Marcin Goszczynski. The 33-year-old Landeskog met with Jordan at a game when the Avalanche were playing in Calgary.

“We discussed his injury and his frustration with the process,” Jordan recounted. “You have to remember at this point the tunnel was dark and long—there was no light . . . we were miles from the end of the tunnel.”

Jordan connected Landeskog with a Canadian ski racer who went through a similar injury.

“It was a relief for Gabe to know that another athlete out there had been able to conquer this injury,” Jordan said. “He has among the best mindsets, and he is 100% resilient and gritty to the core.”

By utilizing Norman, a movement intelligence layer named after Sandler’s father, potential changes in Landeskog’s biomechanics were flagged before they could escalate.

“We’re trying to put really good data [together] that him and his trainer will use,” Sandler said. “There’s this fine Goldilocks zone that we help him stay in, and honestly he’s been killing it.”

Staying on top of the data

Landeskog returned last season for Game 3 of the playoffs against Dallas, which was his first NHL contest in 1,032 days. His comeback continued this season, when he had 14 goals and 21 assists over 60 regular-season games.

Throughout the season, Jordan tunes in to watch Landeskog’s strides on the ice. He sometimes notes instances he wants to examine further simply because the numbers may be outside the Swedish forward’s normal range.

“Essentially, put out the ‘smoke’ before it turns into a ‘fire,'” Jordan explained. “In an athlete’s world, a fire can mean a new injury, a reinjury to the tissue, a loss of performance, or a setback in rehab.”

This application is similar to the Oura Ring, which constantly collects health and wellness metrics. Plantiga, though, tracks human movement through a laboratory-grade inertial measurement unit sensor that captures 400 data points per second.

Translation: An athlete’s movement can be captured with 20 to 30 times more granularity than with a smart phone or watch.

“A supercharged human movement measuring device,” Jordan said.

One way to get a baseline for Landeskog’s gait and biomechanics was through walking. For that, an assist goes to his dogs, the late Zoey and now Mila, who were eager participants on those data-collecting excursions.

“We can see subtle things in your walk patterns well before it manifests as something very clinical or significant,” Jordan said.

Taking out the guesswork

What the data did for Landeskog was take the guesswork out of his training program.

“He’d get on the ice and be like, ‘Oh, I feel good today.’ Jordan said. “It’s like, ‘I think I’m just going to go hard. I feel like my knee feels really good. Oh [no], I went too far. My knee’s flared up. I’ve got to take a week off.’ With all these setbacks he couldn’t catch any progression.”

Now, when the numbers indicate Landeskog should rest, he pays attention. He’s a finalist for the Bill Masterton Memorial Trophy, which is awarded to the NHL player who exemplifies perseverance, sportsmanship, and dedication.

“I’m humbled and honored by it, but I think for me, the ultimate prize I’ve already won,” Landeskog said. “That’s to continue working and getting to play hockey.”

AP NHL: https://apnews.com/hub/stanley-cup and https://apnews.com/hub/nhl

—Pat Graham and Stephen Whyno, AP Sports Writers

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BTS has turned sold-out stadium tours, albums, and merch drops into global events. Now, the group is taking on another challenge: reinventing the Oreo cookie.

Oreo is teaming up with the Korean supergroup on a limited-edition cookie that blends Korean street food inspiration with fan-focused collectible packaging and custom cookie designs.

The new Limited Edition Oreo & BTS Cookies feature a creme flavor inspired by the hotteok, the sweet Korean brown sugar pancake sold at street markets. The cookies also introduce Oreo’s first-ever purple wafers, a nod to BTS’s globally devoted fanbase, ARMY.

A collaboration rooted in nostalgia

According to Matt Foley, VP of Oreo, the collaboration stands out because BTS had a genuine connection to the brand before discussions even began.

“We know the best Oreo collaborations are when we really cocreate with a partner and there’s a real genuine connection between the two,” Foley tells Fast Company. “What made this one so special was when we reached out, we found out quickly that there was a real emotional connection there with the members of BTS. They actually grew up eating Oreo in Korea, so this is a full circle element around this whole partnership, and it made it feel really authentic right from the start.”

Foley says BTS played an unusually hands-on role throughout the process, contributing to the flavor inspiration, embossments, and packaging design.

“This was a really collaborative process for us,” Foley says. “BTS got deeply involved right from the beginning, because they wanted to make sure the product genuinely reflected the relationship with them that they have with their fans.”

The BTS collaboration is the latest in a growing line of music-focused Oreo releases that have increasingly turned the cookie brand into a pop culture player.

Previous collaborations have included a split-cookie release with Post Malone featuring salted caramel and shortbread-flavored creme, a horchata-inspired cookie tied to Selena Gomez, and a vibrant pink-and-green Chromatica-themed collaboration with Lady Gaga.

Oreo also previously partnered with Blackpink on a highly sought-after Asia-exclusive release featuring strawberry and dark chocolate creme combinations.

Why Korean culture mattered to the campaign

Oreo’s collaboration with BTS arrives as Korean culture continues to influence global trends across entertainment, beauty, fashion, and food. Foley says the brand wanted to approach the partnership authentically rather than simply capitalize on the popularity of Korean culture.

“Korean culture is absolutely influencing mainstream culture right now, and brands have to recognize that in a real way, not just chase it opportunistically,” Foley says. “So, for us, BTS was that bridge . . . and felt like a really natural fit.”

The cookies are designed to be collectible. The release includes 13 unique embossments on top of the cookies, including members names, BTS iconography, and hidden messages intended for fans.

That 13 number is a nod to the group’s 13th anniversary.

Built for collectors and superfans

Foley says the team designed the collaboration with collectors and superfans in mind.

“We always ask ourselves . . . when we start to design the packaging, the embossments, the cookie, and the whole experience of how we’re going to roll it out: Would this lead to someone wanting to go out and collect it, bring it home, hold on to it, maybe even put it in on in a case at home?” Foley says.

The packaging draws inspiration from Korean street markets, using vibrant visuals intended to mirror the atmosphere and energy associated with those spaces.

Oreo is building an interactive fan campaign around the release. Beginning June 8, fans can submit digital “love letters” to BTS through a QR code on the packaging or the campaign website as part of what the company describes as a global fan movement.

More fan experience than ad campaign

Foley says the company approached the rollout more like a fan experience than a traditional advertising campaign.

“We . . . definitely didn’t treat this anything like a traditional campaign, and we thought of it more as building a fan experience around Oreo and BTS,” Foley says. “We knew the ARMY is one of the most engaged communities in the world, so being passive and just sending messages out was never going to cut it.”

The Oreo & BTS collaboration will roll out across more than 80 countries, making it one of Oreo’s largest global entertainment partnerships to date.

Fans can preorder the Limited Edition Oreo & BTS Cookies beginning June 1 through Oreo’s website, with retail availability beginning June 8 while supplies last.

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On Monday, Pope Leo XIV made history by becoming the first pope to personally present an encyclical, a letter of great importance in which a pope explains his views on a major moral or social challenge facing the world, to his followers.

The leader of the Catholic Church didn’t do so on his own, however. He had help in unveiling the encyclical, “Magnifica humanitas: On safeguarding the human person in the time of artificial intelligence.” 

The Anthropic cofounder and self-proclaimed atheist Christopher Olah was also present.

An unlikely speaker

The Vatican doesn’t normally invite outsiders to speak, let alone those in the tech industry. But Leo, who has issued numerous warnings on AI before, clearly had an urgent point to make due to “the gravity of the moment,” he said.

In the pope’s view, that moment is one where a few powerful people control the fate of the world. Leo didn’t name names, but it seemed clear he was speaking about—and to—the billionaire executives in charge of the largest tech companies.

“When such power is concentrated in the hands of a few,” he warned in the letter, “it tends to become opaque and evade public oversight, increasing the risk of distorted forms of development that give rise to new dependencies, exclusions, manipulations and inequalities.”

He continued, explaining the importance of “freeing technology from monopolistic control and opening it to discussion and debate” by “making it human-friendly and restoring it to the plurality of human cultures and ways of life.”

Interestingly, Leo and Olah weren’t at odds over the point, or any of the views presented in their speeches. In fact, the tech founder and religious leader appeared deeply aligned.

Where does Christopher Olah fit in?

Out of the gate, Olah presented himself as a different kind of tech founder: one who is cautious, even worried, about AI. 

He began his speech by calling himself a person who got into AI work because he “has a desire to help things go well for humanity.” 

Still, he warned that even those who feel similarly can easily get caught up in the technology’s capabilities.

“Every frontier AI lab, including Anthropic, operates inside a set of incentives and constraints that can sometimes conflict with doing the right thing,” he told the audience.

He even asserted that he shares Leo’s view that AI is dangerous when controlled by a powerful few.

“AI development is concentrated in a handful of wealthy nations. How can we ​ensure the gains of AI are shared globally?” he asked.

AI could ‘displace human labor’

Interestingly, the tech founder is known for his groundbreaking research on AI “interpretability,” or how to make machines more human friendly. He previously led interpretability research at OpenAI.

So his stance that humans must be able to remain in control of the technology is not much different from Leo’s.

Both Olah and Leo also spoke to something that has been on the minds of many in recent years: AI’s impacts on workers.

While we’ve heard those in the tech sector make wildly appealing promises about AI’s ability to create jobs, Olah didn’t stick to the script.

“There is a real possibility that AI will displace human labor at a very large scale. If that happens, supporting those who have been displaced will be a moral imperative of historic proportions,” he said.

A call for collaboration

Perhaps the greatest takeaway from both Leo and Olah, however, was their joint call for continued collaboration.

Olah pressed the point that while those who are skilled in science and math may be the ones creating powerful technology, that doesn’t mean they are the people best equipped to make choices about how it is used.

He said conversations among tech leaders and religious and moral leaders are crucial in keeping AI in check.

“It is through dialogue and mutual effort, through the push and pull, that humanity will achieve great things,” Olah said. “That is what I see in Magnifica humanitas, and it is why I am grateful to his holiness and to the church for taking up this work of discernment,” he added.

Leo echoed the call, noting that “the church wishes, with humility and frankness, to be part of conversations on artificial intelligence.”

He said the church may not have “technical answers” and doesn’t wish to “displace those with expertise.”

Instead, he offered the idea that the church has something else to give: “a wisdom concerning the human that our present time desperately needs.”

That wisdom, according to both Leo and Olah alike, may be hard to find when talking to the powerful few who are at the helm of AI.

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You spent hours researching the company, rehearsed your answers, and asked smart questions. You walked out of the interview feeling like you nailed it.

Then you sent a thank-you email. Something like: “It was great to meet you. I’m very excited about the opportunity and look forward to next steps.”

And just like that, you missed a huge opportunity to close the deal.

Too many executives treat the follow-up as a courtesy to signal interest and show you know the social script. Here’s the uncomfortable truth: at the senior level, everyone who makes it to the final round is prepared, credentialed, and poised. The interview itself is often not enough to separate the winner from the runner-up. Your follow-up can make the difference and be your most powerful persuasion tool.

Set Yourself Up During the Interview

The power of the follow-up depends on information-gathering during the interview. Bring a detective’s mindset, where curiosity is key. Ask great questions – those that both help you understand what they need and give you an opening to share how you can help them.

And, as the interview is wrapping up, use these two questions to surface any issues with your candidacy:

1. Just so I’m clear about what you’re looking for, I’m curious as to how I compare with the other candidates.
2. How do you feel about moving my candidacy forward in the process?

These questions make some candidates uncomfortable. But there’s a well-worn sales principle that applies here: the sale doesn’t begin until you find out what their objections are. If you don’t know their doubts, you have no shot at addressing them.

Hiring managers respect executives who ask for and can handle candid feedback. If they share a concern, you’ve just been handed the most valuable information. In fact, many of my clients have turned a “no” into a “yes” based on this feedback.

Write an ‘Impact Email,’ not a Thank You Note, to Gain Advantage

A well-crafted Impact Email demonstrates the ability to synthesize a complex conversation, identify what matters most, and communicate it clearly. These are core executive skills. Second, it tells them you’ll bring that same rigor and follow-through to the role itself.

Address their objections directly. If they express any hesitation about your experience, motivation, or fit, respond in a positive way, without reinforcing the objection. If they wondered whether your experience translates to their industry, explain why it does. If they questioned whether you’d be satisfied with the role’s scope, reinforce your motivation.

Reconnect your experience to their specific problems. Show them how a particular challenge maps directly onto something you’ve solved. Bring up things you forgot to bring up, or that you didn’t emphasize enough. Show them you heard them – use words like “You…” and “Your…”

Tell a short version of your best story. If you shared a strong example in the interview that landed well, you may want to reinforce it, as this repetition helps your message to stick.

Reinforce your enthusiasm for them – the organization, team, or mission. If something they said genuinely moved or interested you, say so. Show them you “get” them. Authentic enthusiasm addresses one of the most common concerns about executive hires: whether they will be engaged or out the door in a year.

Case Study

My client Ben had a great series of interviews for a Chief Commercial Officer role. He was feeling good about his chances. In the final round, he met with the CEO, Sarah. Ben thought the conversation was going well, so he was surprised by Sarah’s answer to the “How do you feel about moving my candidacy forward?” question. Sarah said, “Frankly, I’m not going to move you forward. The CCO needs a strong analytic background to steer the business development team, and I don’t feel yours is strong enough.” Caught off-guard, all Ben could say was “I’ll address this in my follow-up.”

In his Impact Email, Ben emphasized his analytic skills and the data-driven decision-making that led to many career successes. He also did something else: he proposed analyzing a dataset they would send him and delivering recommendations. A week later, the CEO responded with a spreadsheet filled with sales data. Ben delivered the analysis and then got on the CEO’s calendar to discuss the results.

In this next meeting, they had another very positive conversation. At the end of it, Ben asked the same question, “How do you feel about moving my candidacy forward?” This time, the CEO said she felt great about it. Ben then asked: “How do I compare with the other candidates?” Sarah said, “You’re one of the top candidates, but I have another candidate who’s done this exact same job before, so I’m leaning towards her.”

So, Ben wrote another Impact Email. In it, he couldn’t say he had held the CCO role before. What he did instead was emphasize a specific competitive advantage he possessed, which more than compensated for this.

He got the offer.

The Bottom Line

In a good interview, you learn a lot: what’s keeping them up at night, what they’ve tried that hasn’t worked, gaps they’re hoping to fill, skepticism about your candidacy, and where they’re genuinely excited about what you could bring. You’ve gathered a detailed brief on what they need. And then you say, “Thanks for your time”? No. Influence their decision-making with an Impact Email.

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Resilience is a favorite buzzword for many entrepreneurs. You’ll see it throughout pitch decks, founder stories, and LinkedIn posts. The idea is, if you’re able to endure enough, you’ll successfully come out the other side.

Some 83% of founders experience high stress, struggling with imposter syndrome and rapidly losing confidence in the idea they were certain would work out. In this context, the resilience story is a motivating one. But the idea is also highly misleading.

Among the 90% of startups that fail, most founders are likely resilient right up until the end. They push through setbacks and persist with their idea, despite plenty of evidence suggesting that it won’t work.

Take Theranos, which likely started with good intentions, forging on despite its product failing to live up to its claims. Ultimately, this resulted in the founder and her deputy being sent to prison. Or MySpace, once the leader in social media, which continued to focus on aggressive monetization over user experience, even as Facebook stole market share.

The unfortunate truth is that one of the key factors of being an entrepreneur is knowing what challenges aren’t worth enduring, and when a change is needed instead.

It’s adaptability that makes the difference

As a founder, one of my most formative experiences was getting robbed in Los Angeles.

I had a flight booked to leave the United States and head back to Europe. Yet, without my ID or immigration documents, I had to stay an additional six months while waiting for replacements.

I spent the days afterward telling myself not to worry and that everything would be okay, until it dawned on me that wishful thinking wouldn’t keep a roof over my head. I had to accept the reality of the situation and find a way to deal with it before my problems became a whole lot worse. So I took a job as a director’s assistant on a film set, and spent the next few months there.

That ordeal taught me an incredibly valuable lesson: When things go wrong (and they inevitably will), what gets you through isn’t showing strength, but finding a way through it. Resilience helps you to cope with adversity, but dealing with it requires adaptability. You have to be willing to do the difficult but necessary thing.

It’s a distinction that has seen many startups turn a bad idea into billions of dollars. Instagram started as a mobile check-in app, and the founders of YouTube originally conceived it as a video dating platform. Shopify began as a snowboarding store, and Slack was an internal tool for a gaming company.

Would blind resilience have achieved the same result? Chances are, without change, those big names would have ended in early failure.

Avoiding necessary change isn’t showing resilience

I went to my first day on set expecting to be introduced to cutting-edge, high-tech ways of working. After all, it was Hollywood, where they throw billions around. Instead, I found myself underwhelmed by how outdated and inefficient the entire preproduction process was.

People were still managing script breakdowns, schedules, and budgets on Word and Excel (and occasionally even on paper). Reviews and approvals could take months, if not years. And simple mistakes would add days to shoots, increasing production costs.

Any attempt to point out these inefficiencies resulted in some variation of how it’s “the way the industry has always worked.” It’s a mentality that many industries have struggled to shift, and while they’re often confused, resistance to change isn’t resilience. It’s stubbornness that often holds businesses back and threatens their long-term survival.

True resilience isn’t sticking to the status quo, but keeping the show going when a pandemic shuts down in-person production, or staying afloat when war grinds your logistic line to a halt. Adaptability, on the other hand, is not only about surviving those hardships, but being ready and willing to change no matter what the world throws at you.

Change catches up to everyone eventually

Resilience will only get you so far, especially during times of change. Ask Blockbuster, which turned down the opportunity to acquire Netflix—now a $400 billion company—for $50 million, putting its faith in its own model despite brick and mortar’s clear struggles. Or Yahoo, which passed on acquiring Google for $1 million after underestimating the growth potential of search.

We’re now in a similar spot with artificial intelligence. While it’s still in its infancy, it’s already setting those who invest in it apart:45% of organizations say they’re achieving a great deal of value from AI, and an additional 45% say they’re achieving moderate value. Less than 1% say the investment isn’t paying off at all.

For a relatively small investment, companies big and small can drastically improve their efficiency. I’ve seen it with my startup, Filmustage. By leveraging AI, production teams have already saved over 3.5 million hours of manual work. That brings costs down dramatically, with studios having saved over $119 million.

Founders pay a price if they insist on doing things “the way the industry always has.” Resilience may delay the inevitable, but without adaptability, budgets eventually bleed dry and businesses cannot survive.

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