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Hello and welcome to Modern CEO! I’m Stephanie Mehta, CEO and chief content officer of Mansueto Ventures. Each week this newsletter explores inclusive approaches to leadership drawn from conversations with executives and entrepreneurs, and from the pages of Inc. and Fast Company. If you received this newsletter from a friend, you can sign up to get it yourself every Monday morning. In recent weeks, Ive been recommending and forwarding to friends and associates three smart stories that crossed my desk. Each ostensibly offers insightful or timely snapshots of modern American business. But upon deeper reflection, these very different pieces also shine a light on the state of middle-class U.S. workers and consumers, whose struggles may only intensify. Together, these storiesalong with a number of economic indicators like stubborn inflation and slipping consumer sentimentoffer CEOs and business leaders a warning about the risks of capitalism that works for the few and not the many. Mind the gap Daniel Currells guest essay in The New York Times shows how Walt Disney World Resort has evolved from an accessible all-American vacation to a luxury experience targeting high-net-worth households. Wealthy visitors can pay for premium passes that let them bypass lines; one tech executive quoted in the article experienced 16 attractions in seven hours. Meanwhile, Scarlett Cressel, a bus driver who could not afford to pay for special ride reservations and other perks, managed nine attractions over 14 hours. Adding to her frustration, a mobility scooter she rented to help her navigate the park broke down. Its a powerful metaphor for the middle class quite literally being left behind. Disney is hardly alone in pursuing rich customers. Currell, a management consultant, says hes worked with dozens of companies that are abandoning the mass market. Many of our biggest private institutions are now focused on selling the privileged a markedly better experience, leaving everyone else to either give upor fight to keep up, he writes. Roger Lowensteins Wall Street Journal essay, Howand WhyU.S. Capitalism Is Unlike Any Other, helps us understand how we got here. The work is a sweeping review of the forces that shaped an economic system (bolstered by legislation that protects the sanctity of contracts and created public schools to educate workers) that focused on opportunity, individualism, and risk-taking. Those values led to the innovation and entrepreneurship that have long made America the envy of the world. And yet: Inequality 2.0 is alive and well, he writes. American capitalism remains fiercely competitive, remarkably productive, resilient in the face of a thousand doomsayersand the author of a persistent wealth gap. Also in the Journal, Theo Francis offers an unsettling assessment of the disconnect between markets and the health of the middle class. He explains how the recent strong earnings seasonbuoyed by job cuts and higher pricesmay actually be hurting consumers, on whom the U.S. economy relies to keep spending. The gains enjoyed by companies and their investors arent softening the unease consumers and employees feeland might be obscuring signals that ordinary Americans are putting their anxiety into action, Francis writes. And anxiety is likely to only increase with the deployment of generative artificial intelligence (gen AI) solutions that are already replacing entry-level work. Corporate support for the middle class However, companies have an opportunity to strengthen rather than hollow out the middle class. They can invest in workforce development to train employees for jobs of the future and, like a previous generation of capitalists, champion policies that support this cohort and help them increase their spending power. Lowensteins article reminds us that the abolition of debtors’ prisons and the creation of forgiving bankruptcy laws essentially helped codify opportunity for Americans. If companies dont move to address inequality by supporting compassionate and commonsense policies that can uplift Americans, they may find themselves dealing with more extreme correctives. Lowenstein writes that the response to the robber barons of the Gilded Age was antitrust prosecutions, reformist legislation, the Great War, and the Great Depression. He quips: Cures for inequality are sometimes worse than the affliction. Is your company addressing income disparities? Readers, do you feel companies have a role to play in addressing income disparities, and if so, what can business leaders do? Send your examples to me at stephaniemehta@mansueto.com. I’ll feature some of the most compelling in a future newsletter. Read more: Capitalism 2.0 Capitalism needs a rebrand to win over Gen Z Darren Walker on how to save capitalism from itself Is the middle class okay?
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E-Commerce
Were entering an era of computing that feels less and less human-centered. Paradoxically, tech companies remain fixated on mining every detail of our personal data. The familiar, task-specific interfaces we once used are being pushed aside, replaced by generative AI and LLM-driven chatbots that upend how we interact with software. Instead of opening a dedicated app for writing, research, coding, or even emotional support, were funneled into a single chatbot window. OpenAI promises to, Let AI do the work for youdesigned to handle any task, while Anthropic touts Claude as a fantasy-fulfillment engine with the tagline “If you can dream it, Claude can help you do it.” The pitch is clear: These tools are promoted as a one-stop shop for everything. The shrinking interface Search engines have trained us to expect answers from a single field. Now chatbots take this a step further: the text box has swallowed other applications, even as its output often requires endless refinement and fact-checking through a text box. And text isnt the endgame. Voice assistants like Alexa, Siri, and Google Assistant primed us to learn hands-free interaction, which is eventually coming to replace that text box. Tech companies are chasing a future where speech replaces typing, and the interface nearly disappears. The real prize isnt usability for us, but the value gained from capturing what we say and how we say it, by, and for, them. This change marks a sharp departure from the last 45 years of interface design. Apple, inspired by Xerox PARC, championed user-centered design: graphical icons, so-called “what-you-see-is-what-you-get” (WYSIWYG) editors, intuitive metaphors that empowered people to create and communicate. For decades, this approach made computing accessible. But with the rise of big data, priorities shifted, as people generated ever-larger archives of digital traces (emails, documents, photos, browsing histories), and were persuaded to store these in clouds for easy retrieval. Tech companies soon realized that, taken together, this data formed a vast global knowledge corpus that could be mined and monetized. The rise of surveillance-driven business models pushed firms toward increasingly quantitative forms of user profiling. The focus shifted from designing tools to help us get work done to extracting patterns that served corporate goals. We ceased to be seen as people with needs and instead became raw material for metrics, models, and market dominance. As big data informed the models for AI and LLMs, this shift accelerated. These systems now operate on top of what we do, but without the capacity to understand why we do it. Stripped of the context discovered through qualitative research, quantitative analysis can easily misinterpret intent. Chatbots produce inconsistent answers depending on the prompt, and we must constantly refine queries just to get something useful. For those who mistake these tools as truth machines, the risks are profound: even fatal, as in documented cases where chatbots coached people to commit self-harm. This lack of contextual, qualitative research isnt new. Appleoften held up as the gold standard for user-centered designinitially resisted user research. Early on, Steve Jobs insisted that people dont know what they want until you give it to them. Pieces of this bias eventually became imbued in Apples culture, where it has persisted in various ways over the years, spreading from Apple through the rest of Silicon Valley and beyond as former employees changed jobs and/or founded new companies. Media and business school case studies perpetuated this myth and as a result, there is a growing tech industry, a culture of quantitative-data-first product design, reinforced by a bias that big data mining is the only measurement for understanding people. Its not. This quantitative big data collection movement comes with another syphoning of our free labor and time: the survey. Were also being used to nudge companies algorithms via the endless surveys sent to us after every engagement, with nagging businesses demanding our feedback on the agents, algorithms, services, and products that theyve already tracked us engaging withall to collect even more data about us. Its exhausting. Scaling at our expense Companies justify this approach as a way to scale an interface (for them). But scaling often means flattening differences among users and does not work well for cultural differences in a global context. The all-in-one chatbot promises universality yet introduces new frictions: translation errors when models are trained on mismatched languages, hallucinations from incomplete training sets, and endless cycles of prompt refinement. Instead of simplifying, these systems demand more labor from users in the form of prompt refinement. The effect is recursive. We feed chatbots queries, they pass these queries to LLMs that pattern match words to generate answersright or wrongthat then circulate back into search engines, which are themselves increasingly infused with LLM output (making verification a nightmare). Meanwhile, our conversations with chatbots are now being mined to train future models. The user interfaces on our devices have become less like tools and more like receptacles for collection. Where we once used a tool to get our work done, we now train tools to do the work so that we can, in turn, finish ours. This dynamic exploits our energy and labor, propping up systems that may one day replace us (if they haven’t already). Todays design trajectory aims to erase the interface altogether, replacing it with conversation under surveillancemechanized eavesdropping dressed up as dialogue. Behind the scenes, algorithms sift and stitch together fragments of training data (not always accurate, not always complete) to generate songs, code, images, or advice pirated from humanity’s corpus built over lifetimes. Sometimes that LLM advice filtered through a chatbot extends into domains as risky as psychological counseling or nuclear operations, putting us in harms way and potentially at great risk. At scale, this is terrifying. It isnt fair to say that user centered design is goneyet. Its still here, but the target users have changed. We used to be the users centered by companies; now the LLMs are their focus. And like it or not, our role now is to enable that success.
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E-Commerce
There is a troubling trend spreading across some of todays most recognizable tech companies. Spotify, Shopify, Dropbox, and others are cutting training programs and significantly reducing entry-level hiring. At first glance, the decision might seem sensible: reduce costs, restructure teams, and prepare for a future driven by AI. In reality, it is a short-term move that will cause long-term damage. The numbers tell the story. The unemployment rate for recent college graduates has climbed to 5.8%, higher than the national average and the worst in a decade outside the pandemic. More than 40% of graduates are underemployed, working in roles that do not require a college degree. Entry-level postings in the United States have fallen by more than 40% since mid-2022. Even graduates in traditionally safe majors such as computer science or engineering are struggling to find jobs that match their training. Young professionals are not just looking for a paycheck. They want a chance to learn, to join a program, and to work with a team that believes in their potential. When companies dismantle these programs, they are not simply shrinking headcount. They are cutting off the future of their own talent pipeline. AI is advancing rapidly, but despite the hype, it cannot replace human intuition, creativity, and judgment. These are the qualities that truly differentiate companies. What we need now are people who can work alongside AI: analysts who know how to use machine learning to make smarter decisions, design better products, and deliver more personalized customer experiences. That kind of talent is not built overnight. It is developed through years of deliberate investment. The path forward is clear. We need a new model for workforce development, one that embraces what I call Human Digital Resourceshumans and AI working side by side, each playing to their strengths. Humans bring judgment, creativity, and empathy. AI brings speed, scale, and pattern recognition. Companies that design roles and training with this collaboration in mind will outperform those that treat AI as a replacement strategy. The Danger of Short-Term Thinking The labor market for new graduates is already challenging. Opportunities are shrinking. Unemployment among recent grads is high, and even industries once seen as secure are slowing their hiring. Cutting analyst or associate programs may satisfy investors for a quarter, but it leaves companies with a weakened leadership bench for years to come. These programs have always been launchpads for promising talent. They build business acumen, sharpen technical skills, and embed cultural knowledge that cannot be hired overnight. One financial services firm I know kept its analyst program during a period of layoffs. Today, it has a deep bench of AI-literate, future-ready leaders. Competitors that cut their programs are now scrambling to fill those same roles, paying more to hire externally and facing longer onboarding timelines. The downside is more than a temporary talent gap. It is the slow erosion of institutional knowledge, innovation capacity, and cultural continuity. These are elements that cannot be replaced by technology or by hiring quickly from the outside. Human Plus AI Is the Real Advantage AI is changing the game, but it is not replacing the players. It can process vast amounts of data in seconds, yet it cannot create culture, uphold values, or build trust within teams. Think of it as an open-book test. Without skilled humans who know how to use the book, it is useless. Even AIs own development proves the point. ChatGPT was trained by thousands of human reviewers, most of them college-educated, who provided judgment, context, and feedback to improve the tool. AI is powerful, but it still depends on human expertise to evolve. Forward-looking companies are already building Human Digital Resources. They are hiring and training employees to integrate AI into their daily work. At one firm, Gen Z analysts were given early access to AI tools. Within months, they became the companys internal AI experts, streamlining processes, improving efficiency, and teaching senior staff how to use the technology effectively. A New Shape for Organizations For most of the 20th century, companies were shaped like pyramids, with a large base of junior employees feeding into progressively smaller layers of management. More recently, some consultants have suggested a diamond model, with a narrower base, a wider middle, and a slim top. The future will likely be something else entirely: a fluid blend of consulting-style apprenticeships, the flexibility of the gig economy, and AI-enabled work. Professionals will contribute to multiple projects across different companies, with AI acting as a force multiplier. This model rewards adaptable, multiskilled people who can work across ecosystems. Gen Z is well positioned to thrive in this environment. They are digital natives who grew up with technology and are already experimenting with AI tools. Instead of sidelining them, companies should tap into their curiosity and comfort with technology, reduce administrative work, and give them meaningful problems to solve earlier in their careers. One leading financial institution recently offered an example of getting this right. It has invested in both culture and technology, treating AI as a tool for empowerment rather than a threat. This approach strengthens talent pipelines while others risk letting theirs dry up. AI Changes Work, It Does Not Remove It History offers perspective. The industrial revolution created new kinds of skilled labor. The calculator replaced manual math, yet we still teach math, so people know how to use calculators effectively. The internet reshaped communications without eliminating the need for communicators. AI will follow the same pattern. It will redefine roles, raise the bar for human skills, and amplify those who are trained to use it well. Technology may become the baseline, but people will continue to create value in ways that are unique and hard to replicate. A competitor can match your tools, but they cannot match your culture, your innovation, your trust, or your ability to execute through people. Companies that continue hiring and training early-career professionals will be better positioned for this transition. They will have AI-literate teams with deep knowledge of the business. Those that do not will face leadership gaps they cannot fill quickly or cheaply. Competitive advantage Learning compounds over time and so does neglect. I have seen companies that invested in analyst programs years ago now benefiting from leaders who came up through the ranks. Those early investments paid off in loyalty, expertise, and competitive advantage. Cutting these programs today is like removing the foundation from a building to save on maintenance. It might hold for a while, but eventually it will collapse The future of work is not less human. It is more human. It is more mentorship, more learning, and more collaboration between people and machines. Companies that embrace Human Digital Resources now will lead the next decade. Those that do not will be left wishing they had thought furher ahead
Category:
E-Commerce
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