Informed i’s Weekly Business Insights

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When Federal Reserve Chair Jerome Powell stepped up to the podium on Jan. 28, the room hushed in anticipation. The reporters attending the Federal Open Market Committee press conference in Washington hung on Powell’s every market-moving word. Would he hint at adjustments to the overnight lending rate? Would he discuss the effect of tariffs on prices? Would he address questions about his successor?

Online, another group was monitoring Powell’s performance no less closely, but for a very different reason. On Discord and X, dozens of traders had been discussing their bets on which words would come out of Powell’s mouth. Many were sure he’d say “good afternoon.” (He usually does.) Others wagered on “shutdown,” “layoff,” “yield curve” or “egg,” among the 44 different terms offered on the prediction market platform Kalshi.

From his parents’ house in Orlando, Austin Minton, a 23-year-old livestreamer with shaggy blond hair, shared his thesis that Powell wouldn’t say “Trump,” pointing out that the Fed chair hadn’t done so in recent remarks. Minton had also wagered that Powell wouldn’t say “projection,” since the recent government shutdown meant there was less economic data available, but that he would say “renovation,” in reference to the reconstruction of the Federal Reserve building, which critics including the president were saying raised questions about Powell’s integrity. Before the speech even began, bettors had traded more than $2.8 million on Kalshi’s Powell market.

“Good afternoon,” Powell said. Minton pumped his fists. “Yes!” he exclaimed. “Easy three bucks.”

Powell began reading his prepared remarks. His words echoed an earlier FOMC release, but he added new material, mentioning a “shutdown” (market odds had been at 67%), “softening” (59%) and “layoffs” (76%). Moving on to the Q&A with reporters, he hit “restrictive” (62%), “Beige Book” (the Fed’s biquarterly report, 30%) and “tariff inflation” (13%). When Powell fielded a question about the Justice Department investigation of the Fed building project, Minton braced himself, hoping not to hear the president’s name. Powell dodged the question, prompting Minton to rejoice and praise him: “That’s the GOAT!”

As the presser went on, the percentage odds on the remaining words declined on Kalshi, as they typically do on a “mention market” like this. By the last question, it seemed a safe bet Powell wouldn’t utter “pandemic” (now at 17%), “projection” (12%), “uncertainty” (8%), “trade war” (3%) or “renovation” (2%).

Then, however, he did something astonishing. Responding to the final questioner, he went on a mention spree. “The Summary of Economic Projections … we hadn’t had a pandemic in 100 years … a trade war of this scope … there’s great uncertainty at different points.” It was the prediction market equivalent of a last-second pass leading to a 60-yard zigzag run through defenders, capped by a headlong dive across the goal line. Minton, who’d bet that Powell wouldn’t say “projection,” was devastated. “Oh my God, at the end? Last question? C’mon, Powell!”

The press conference ended. The traders on Discord debriefed on their wins and losses. But something was off. On Kalshi, the odds on “renovation” were suddenly climbing. (Kalshi doesn’t resolve markets and pay out winnings until some time after an event finishes.) Minton was confused. “Did he say ‘renovation’? This is crazy.” He pulled up a clip and saw that one of Powell’s final answers included a rare slip of the tongue: “When it comes to technological developments that raise potential output, some kind of technological renovation”—Powell then corrected himself—“you know, revolution like happened in the ’90s here, and like may be happening now with AI … .” The tape didn’t lie: Powell had said “renovation.” Minton speculated that it had been a Freudian slip fueled by Powell’s determination not to talk about the investigation.

The Discord chats went berserk. One trader, Foster McCoy, who was on a voice channel with a dozen others, heard grown men screaming. “I lost my mind,” McCoy later recalled. “Like, what are the odds it hits as a buzzer beater?” “IVESTIGATE POWELL [sic]” wrote one Discord user, jokingly implying that Powell had made insider trades. “Might be the most insane market I’ve ever traded,” tweeted a respected 21-year-old trader who goes by Esoteric Catboy and who said he’d made more than $5,000 on the press conference.

Mention markets are the high-octane, fast-twitch speed competitions of the prediction market world. But they’re just one corner of it. Users of Kalshi and its primary rival, Polymarket, can bet on events major and minor, from politics to sports to culture to the weather. Recent markets on Kalshi have included whether certain words would be used during a Palantir Technologies Inc. earnings call, whether Elon Musk would win his court case against OpenAI and whether the highest temperature in Seattle on Feb. 4 would be within a certain range. Polymarket users have bet on whether the US would strike Iran on a particular date, whether a given Trump cabinet member would be the first to leave office and whether Jesus Christ would return before 2027.

Prediction markets were once a fringe obsession of economists and election wonks, but in the past year or so, they’ve gone from obscure to everywhere. The industry is booming thanks to a combination of marketing, distrust in traditional sources of information and a newly friendly regulatory environment. Late last year, Kalshi Inc. and Polymarket, both headquartered in New York City, raised funds at valuations of $11 billion and $8 billion, respectively. In the weeks leading up to the Super Bowl, Kalshi users were betting more than $2 billion a week, while Polymarket users were just shy of that, according to user-compiled data on Dune Analytics.

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Over the past few years, remote work has fundamentally reshaped one of the oldest assumptions about modern life: that where you live must be tied to where you work.  Increasingly, professionals are discovering that their careers are no longer anchored to a single city—or even a single country.

That shift is already visible in migration patterns. Recent reporting on where Americans are increasingly relocating abroad currently highlights how housing affordability, lifestyle flexibility and global mobility are reshaping where people choose to live.

The digital nomad economy is growing just as quickly. Workforce consultancy MBO Partners estimates that roughly 18.5 million Americans in 2025 identified as digital nomads, a figure that has grown more than 150% since 2019 as remote work transforms professional mobility.

A September 2025 Gallup analysis found that roughly one in four U.S. employees now work remotely at least part of the time, a shift that continues to expand the number of professionals able to consider living abroad—or build location-independent careers as entrepreneurs, creators and digital nomads.

The growing population of location-independent workers is beginning to influence how countries think about residency, immigration policy and digital nomad visa programs.

For remote workers, founders, freelancers and creators whose careers can travel with them, geography is becoming increasingly flexible.

Across Europe, Asia and parts of the Americas, a growing number of countries and regional communities are experimenting with an unusual strategy to attract new residents: offering financial incentives to move there.

In some cases, the incentives include direct cash grants. In others, they take the form of tax breaks, housing subsidies or startup support aimed at entrepreneurs, remote professionals and digital nomads willing to relocate.

For people already considering a move abroad—or exploring digital nomad visas and other residency pathways—these programs are becoming part of a broader conversation about global mobility. Relocation incentive programs are simply the next chapter in that story.

Eight countries currently offering relocation incentives to attract new residents are:  Italy, Switzerland (Albinen), Japan, Spain, Greece, Ireland, Croatia and Chile (Patagonia Startup Programs).

Relocation incentives can be appealing, but they rarely represent a simple financial windfall. Many programs require commitments such as purchasing property, starting businesses or remaining in the area for several years. Some programs also include age limits or income requirements designed to attract younger residents and entrepreneurs who can contribute to local economic growth. In many cases, the incentives are structured as multi-year grants or subsidies rather than lump-sum payments, ensuring that new residents remain in the community long term.

Still, these initiatives reveal a broader shift in global migration patterns. As populations age and urban centers continue to grow, smaller communities are increasingly competing to attract new residents—and the rise of remote work is making that competition possible.

For remote workers, entrepreneurs and creators whose careers are no longer tied to a single location, relocation incentives may simply be one more factor in a much larger decision about where to live.  Many professionals exploring these programs are also considering countries that offer strong infrastructure for remote work, vibrant international communities and emerging digital nomad visa pathways.  As global mobility continues to expand, the question for many professionals is no longer whether they can live abroad—but which place might offer the most compelling version of daily life.

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“Anyone wanna host a get together in SF and pull this up on a 100 inch TV?”   The author of that post on X was referring to an online intelligence dashboard following the US-Israel strikes against Iran in real time. Built by two people from the venture capital firm Andreessen Horowitz, it combines open-source data like satellite imagery and ship tracking with a chat function, news feeds, and links to prediction markets, where people can bet on things like who Iran’s next “supreme leader” will be. 

According to the author he has reviewed over a dozen other dashboards like this in the last week. Many were apparently “vibe-coded” in a couple of days with the help of AI tools, including one that got the attention of a founder of the intelligence giant Palantir, the platform through which the US military is accessing AI models like Claude during the war. Some were built before the conflict in Iran, but nearly all of them are being advertised by their creators as a way to beat the slow and ineffective media by getting straight to the truth of what’s happening on the ground. “Just learned more in 30 seconds watching this map than reading or watching any major news network,” one commenter wrote on LinkedIn, responding to a visualization of Iran’s airspace being shut down before the strikes.

Much of the spotlight on AI and the Iran conflict has rightfully been on the role that models like Claude might be playing in helping the US military make decisions about where to strike. But these intelligence dashboards and the ecosystem surrounding them reflect a new role that AI is playing in wartime: mediating information, often for the worse.

There’s a confluence of factors at play. AI coding tools mean people don’t need much technical skill to assemble open-source intelligence anymore, and chatbots can offer fast, if dubious, analysis of it. The rise in fake content leaves observers of the war wanting the sort of raw, accurate analysis normally accessible only to intelligence agencies. Demand for these dashboards is also driven by real-time prediction markets that promise financial rewards to anyone sufficiently informed. And the fact that the US military is using Anthropic’s Claude in the conflict (despite its designation as a supply chain risk) has signaled to observers that AI is the intelligence tool the pros use. Together, these trends are creating a new kind of AI-enabled wartime circus that can distort the flow of information as much as it clarifies it.

But an abundance of information, which AI is undeniably good at assembling, does not come with the accuracy or context required for real understanding. Intelligence agencies do this in-house; good journalism does the same work for the rest of us. It is, by the way, hard to overstate the connection this all has with betting markets.

AI has also long made it cheaper and easier to spread fake content, and that problem is on full display during the Iran conflict.  The result is an ocean of AI-enabled content—dashboards, betting markets, photos both real and fake—that makes this war harder, not easier, to comprehend.

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Do you think of Walmart as a media or technology company? If not, maybe you should. More than half of Walmart’s operating-income growth now comes from its newer growth platforms: online retail media, membership services, and marketplace operations.1 For a business founded on everyday low prices and physical stores, this development reflects something bigger: Walmart has been on a more than decade-long journey to deliberately build new engines of growth on top of its core, and those engines are now materially reshaping performance.  But it is not alone.

What sets them apart is not luck or timing. It is how they commit to growth, how they develop growth engines, and how they accelerate with technology.  

How leaders get ahead and stay ahead.  The authors identified three common characteristics that leaders embody to drive sustained, profitable business growth and outperform the competition.

1. Consistent commitment to funding business growth.  To fund growth, you have to fuel it. While every CEO says growth matters, outperformers translate growth ambitions into concrete, sustained commitments. They articulate a clear growth strategy, commit resources to it, and move decisively.  This level of commitment is evident in how growth outperformers invested through downturns. They continued to fund R&D, launch products, and build capabilities while peers slowed spending—typically investing multiples more than competitors. Leaders do not treat growth as a one-time plan but as an ongoing process, regularly refreshing portfolios and entering new categories.
2. A diversified portfolio of business growth engines.  Outperformers build a portfolio of growth engines over time rather than relying on one or two bets. They strengthen the core business, expand into close adjacencies, and test new sources of growth, allocating talent and capital to each with clear accountability.  Crucially, this portfolio is actively managed and well executed. Leaders track performance closely, double down on growth engines that are working, and stop or pivot initiatives that are not delivering. Many turn to adjacencies that build on existing assets, such as omnichannel models or platform-based businesses.
3. Technology as an accelerator to value.  Business growth leaders know how to harness technology not just to drive growth but to accelerate it. They are successful because they don’t simply develop use cases but systematically integrate data, digital tools, and AI into strategy, operations, and decision-making.  That focus is evident today, with many top growers investing in AI to redesign end-to-end workflows, improve speed and precision, and unlock new business opportunities. A deliberate mix of organic investment and targeted M&A helps build capabilities to accelerate pace.

Leaders at companies with aspirations to outperform should consider these questions:  Are our growth aspirations and commitments bold enough to allow us to grow faster and more profitably than the market?  Do our resource allocations match our growth priorities?  How many independent growth engines do we actually have today—and how many rely entirely on the core?  Which adjacencies genuinely build on our strengths, and which are distractions dressed up as growth?  Where can AI and agentic AI help us build up our competitive advantages?  Which strategically critical capabilities should we build organically, and which would benefit from being developed through thoughtful partnerships or acquisitions?

What distinguishes business growth leaders is not better foresight but greater conviction. They invest when uncertainty is highest, build capabilities rather than chase headlines, and treat growth as something to be engineered rather than hoped for. In a world where only a small minority truly outperforms, those choices increasingly determine who pulls ahead and who falls behind.

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Over the past two decades brands learned to optimize their keyword strategies so that they would appear at the top of search engine results. They now face a new challenge: optimizing for AI. Many consumers already use LLMs to research products or compare prices. A July 2025 survey of 750 U.S. consumers, conducted by the management consulting firm Kearney, found that 60% of shoppers expect to use agentic AI to make purchases within the next 12 months. Every major AI company is developing agents in anticipation of mainstream adoption. To cite one example, OpenAI is collaborating with payment processors like Stripe and PayPal and retailers like Walmart and the shopping platform Shopify to facilitate purchasing within ChatGPT. It is laying the groundwork for an automated and complete customer journey. That means companies will soon be managing their brands in an era when agentic AI, built on top of LLMs, works on behalf of customers, completing transactions without human assistance.

Most brands are unprepared for this shift. Executives will have to ask themselves critical questions, such as: How do we adapt our communications strategy when our primary audience may not be human? What happens to brand relationships in a world mediated by AI agents? How can we prepare for a future in which both sides of the customer relationship are increasingly managed by AI? This issue won’t be solved with a simple technical fix. Companies must fundamentally rethink how brands, customers, and AI interact.

The Three Types of AI Agent Interactions.  Most consumers aren’t delegating the act of purchasing to AI yet. But they are increasingly using LLMs like ChatGPT the same way they use Google: for prepurchase research. They’re asking about product features, comparing options, and reading AI-synthesized reviews before making their own buying decisions. 

As AI agents become more prevalent, the traditional relationship between brands and consumers is giving way to a new set of interaction modes—some mediated by AI and others driven entirely by it. In addition to direct, human-to-human engagement, three emerging types of interaction are beginning to coexist in the marketplace.  In the first type of relationship, brand agents engage directly with human customers. Unlike traditional AI chatbots that simply answer questions, these agents help consumers explore products, make decisions, and access services in new ways.  In the second type, consumer agents act on behalf of individuals across multiple brands.  In the third type, full AI intermediation, AI agents interact autonomously on both sides of the transaction without direct human involvement.

Brands must evaluate which aspects of traditional customer relationships to preserve and which ones to evolve. To guide this shift, brand managers should focus on three critical stages of agentic adoption. First, determine if you need to deploy an AI agent at all. Second, if you do, you must persuade consumers to use your brand’s agent instead of their own. And third, for consumers who prefer their own AI agents, you must ensure that these autonomous intermediaries choose your brand.

The rise of AI agents is fundamentally redrawing the contract between companies and consumers. Connections that once formed the foundation of brand relationships are being reshaped, often mediated, and sometimes entirely managed, by AI. To succeed, companies must operate effectively across the full spectrum of encounters—from fully human exchanges, to interactions with brand agents and consumer agents, to fully autonomous AI intermediation.

Consumer use of agents will vary based on a customer’s relationship with the brand and the nature of the product or service. Even within a single brand, a consumer might prefer mixed modes, such as delegating routine tasks to consumer agents, consulting brand agents for detailed inquiries, and concluding important transactions with human associates. Consider which options work best for your customers, and use them at the right times to help your brand succeed.

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In professional service firms, quiet excellence once defined leadership. A partner earned influence through expertise, loyalty, and discretion. But in an era of high transparency, where every meeting can be replayed, every comment rated, and every decision scrutinized online, competence alone no longer sustains trust. Visibility has become the new test of leadership.

Across the Big Four and other large consulting partnerships, performance is now measured not only by what leaders achieve but also by how clearly their contributions can be seen and understood. Each LinkedIn post, client review, or internal dashboard becomes a signal of credibility. The leaders who rise are those who make their impact legible, showing how their work serves clients, colleagues, and the company’s mission. As one senior executive put it, “If people can’t see your value, they assume it’s not there.”

The shift isn’t about ego or self-promotion. It’s about legitimacy. Transparency has changed how organizations grant authority. In a system built on peer recognition and client trust, the ability to project competence visibly — through clear communication, consistent behavior, and aligned digital presence — now anchors personal reputation. Those who master visible legitimacy become the reference points that others follow. 

To help leaders learn how to do this well, the authors interviewed 19 senior partners and executives from global audit and consulting firms. Their experiences reveal how leaders can transform visibility from a superficial display into a governance asset — a means of reinforcing trust and accountability. From their insights emerges a model with three interdependent levers: internal recognition, external reputation, and digital trust. Together, these components form what we call the Visibility-Legitimacy Model: a practical framework for leading credibly in the age of transparency.  In this new environment, the challenge for leaders is not to attract attention but to deserve it — by behaving consistently and with integrity.

Individual leaders should take these three actions to effectively increase their company’s and their own visibility:  make your contribution legible, serve visibly beyond your role, and cultivate a coherent presence. 

Leadership in professional service firms has always relied on competence and trust. What has changed is how that trust is earned. In an age of transparency, leaders can no longer rely on quiet dedication. For leaders, visibility must be managed as carefully as performance. For organizations, it must be governed with the same discipline as financial control. Visibility is not a communication exercise but a system of accountability.

Executive committees and boards should also take actions aimed at developing leaders and managing organizational visibility:  eEmbed visible integrity into talent evaluation systems; reward stewardship, not self-promotion; and treat digital presence as an early signal, not a performance metric.

The leaders who make transparency an act of service, not self-display, will thrive. They understand that in the economy of trust, what is seen shapes what is believed, and what is believed defines who leads.

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When Microsoft CEO Satya Nadella told employees in October that he was giving up running the tech company’s commercial businesses, he said that he was doing so to increase his focus on Microsoft’s technology work—and very specifically on AI. Nadella explained that Microsoft’s continued success would depend on equipping customers with new artificial intelligence capabilities to make it “the partner of choice for AI transformation.” 

With that act, the 58-year-old Microsoft chief, whose 12 years in the corner office are an eternity by Fortune 500 standards, was telegraphing that mastery of AI was nonnegotiable. During Nadella’s extremely successful run, shares have risen 11-fold and Microsoft has joined the very tiny club of companies with valuations above $3 trillion. But he won’t remain relevant or effective if he doesn’t stay on top of AI and how it’s changing his industry—and neither, for that matter, will his peers in any industry. 

This new reality is taking shape as several of the most high-profile Silicon Valley CEOs are extending their tenures into their second decades. They include 53-year-old Sundar Pichai (10 years at Google, six heading its more recently formed parent, Alphabet), and Apple’s 65-year-old Tim Cook (14 years as CEO). It’s becoming clearer that AI will play a major role in how much longer these CEOs remain at the top. 

But elsewhere in tech, and across the Fortune 500, such long tenures will likely become increasingly rare—at least during the first waves of the AI boom. Indeed, the numbers are already beginning to shrink. The average global CEO tenure has declined to 7.2 years, below the highs of 8.4 years recorded in 2021 and 2023, according to leadership advisory firm Russell Reynolds Associates. (Tech CEO tenures are roughly in line with the cross-industry average.) And that figure will likely continue to drift downward for a few years. The firm surmises that that’s because boards are closely monitoring CEO effectiveness and whether they respond to change with precision and adaptability, considerations AI is bringing to the fore. And those boards are quicker to act if performance lags.

In addition to generating more churn, wider adoption of AI may also shake up the demographics of the CEO pool. Industry observers expect the next wave of CEOs to skew younger, as boards seek leaders who are fluent in AI. And CEOs may also need youth—or at least youthfulness—to help stave off burnout as AI generates a faster rate of change inside their companies. 

It’s certainly not coincidental that longevity and AI success have gone hand in hand in Big Tech. Nadella, who comes from a product background, is blazing a trail and showing other longtime CEOs how to acknowledge and approach the rise of AI: Microsoft’s early investment in OpenAI, and its integration of ChatGPT with its Azure Cloud business, are hallmarks of his tenure.

Pichai, meanwhile, has turned Google from a laggard in generative AI to a major threat to ChatGPT, by OpenAI CEO Sam Altman’s own admission. Pichai has committed the company to an “AI-first” strategy, placing machine learning at the center of Google’s products, research, and infrastructure and making sure AI is never an afterthought. 

As for Apple, many critics say that under Cook, it has fallen behind in the AI race. Several senior leaders left the company in the fourth quarter of 2025, and there is considerable speculation that Cook may be preparing to step aside. 

Though elder statesmen by the relatively youthful standards of tech, Nadella and Pichai have figured out how to navigate a technology that is changing how business operates. It’s not about pure AI skills a computer scientist might possess, but rather about AI savvy and understanding how AI can help them, and the companies they lead, to compete.

Still, the urgency of CEOs needing AI-oriented sensibilities is hardly limited to tech companies. Indeed, every industry stands to be transformed by AI.

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According to the author, a few months ago he was talking with one of his clients—an executive who had just lived through his company’s second restructuring in two years. He said something that stuck with the author: “It’s not the change that exhausts people. It’s the unpredictability.” That line could easily apply to Tesla.

Over the past couple of years, Tesla has cut prices aggressively, announced large layoffs, reshuffled priorities, doubled down on AI and robotics, and continued to hinge much of its identity on Elon Musk’s public persona.  One quarter, it’s a growth story. The next, it’s a cautionary one.   From the outside, it looks chaotic. From the inside, it’s a high-wire act between ambition and operational reality. Here are five leadership realities that show up in that tension. 

1. Vision can carry you far but not forever.  Tesla’s early momentum was powered by boldness. Electric vehicles weren’t just transportation. They were a mission. That kind of vision attracts talent. It attracts capital, and it forgives early mistakes. Eventually, however, markets mature. Competitors improve, and customers compare pricing, reliability, and service, not just aspirations.   At some point, how you execute has to carry the weight that your vision once did. 
2. Price cuts send signals beyond sales.  Tesla’s aggressive price reductions weren’t just tactical moves to increase demand. They also signaled pressure. When you lower prices repeatedly, you may gain volume, but you may also compress margins and reset customer expectations.   As a leader, you may tend to focus on the immediate metric — units sold, quarterly revenue, and so on. However, your pricing decisions echo longer than one earnings cycle. 
3. Founder-driven brands are powerful and fragile.  There’s no separating Tesla from Elon Musk. That has been a strength and a liability. A charismatic founder can accelerate momentum in ways committees never could. However, when the brand and the individual become inseparable, volatility increases.  When you’re the public face of your company, when public perception of you swings, so does perception of the company. That’s leverage. It’s also exposure.
4. Layoffs change more than payroll.  Tesla has gone through significant workforce reductions. On paper, that improves efficiency, but inside an organization, it shifts something else: psychological safety. When employees see rapid cuts, they recalibrate how secure they feel, how long they expect to stay, and how openly they speak.  Productivity doesn’t always drop immediately, but culture changes quietly. Cost discipline matters, and so does stability. 
5. Betting on the future can distract from the present.  Tesla’s emphasis on AI, robotics, and autonomous driving keeps it positioned as a technology company, not just an automaker. That narrative matters to investors. Customers buying vehicles, however, still care about the quality of the build, delivery timelines, and service experiences. The future is compelling. The present is what pays the bills.  When you are a strong leader, you find a way to manage both without letting one undermine the other. 

Tesla may continue to defy expectations. It has done so before, but the broader lesson for you to remember is this: Momentum is not the same as durability. You can generate excitement, you can generate headlines, and you can even generate short-term growth. The harder work is building something steady enough to withstand swings in perception, pricing, and personality.  Ambition moves markets, but consistency builds companies.

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It’s not a secret that 9 out of 10 startups fail because founders don’t realize they already have an advantage. Many assume it has to be something “big”: connections in Silicon Valley, a million-dollar seed fund or a groundbreaking patent.  In reality, a competitive edge hides in seemingly ordinary factors — industry experience, professional connections, niche skills and even past failures.

Mature founders show a 30% success rate compared to 18% for first-timers because they’ve already gained a strong reputation with investors, learned common pitfalls and built a network that speeds up hiring and collaborations.

Even if you’re new to entrepreneurship, you can uncover your unfair advantage by focusing on what you already have.

1. Zoom in on your background.  Around 42% of startups fail because there’s no market need. On the other hand, founders who have personally experienced the problem their product solves naturally validate demand. Investors increasingly look for “founder/market fit,” and your journey is the data no money can buy. Personal experience gives you key advantages, from a deep understanding of pain points to an authentic presence on the market.  Start by listing the problems that frustrate you daily. For each one, ask yourself: “Could I work on this for ten years?” Then identify the moment you first encountered the problem, as it often forms the foundation of your story.
2. View limited resources as an advantage.  Nearly 25–30% of bootstrapped startups become profitable early, compared to 5–10% of VC-funded ones. Bootstrapped companies are also more resilient in crises, with 35% fewer layoffs during downturns.  Constraints spark creativity. When you don’t have a budget for advertising, you’re forced to invent new ways to market; when you don’t have a team, you gain a deeper understanding of every process. To turn your limitations into an advantage, start by identifying your current constraints.  For each one, ask yourself: “What creative solution does this make possible?” Make sure to focus on unit economics rather than general growth.
3. Develop hard-to-copy skills.  Cross-disciplinary companies are 30% more likely to launch successful innovations. A T-shaped profile, highly sought by executives, combines deep expertise in one area with a broad understanding in others: it’s a programmer who’s also a designer or an engineer who’s also a marketer. Your unfair advantage often lies at the intersection of skills in the same way.  Map your own T-profile: identify your area of expertise and the skills that form the base. Next, pinpoint two to three related areas you could quickly learn and focus on problems that sit at the intersection of these skills.
4. Analyze competitors and market.  Around 44% of companies have no competitor tracking system, while e-commerce startups show an 80% failure rate when unable to differentiate.  Instead of fighting for market share, consider creating a new space. Build a strategy canvas outlining key industry factors and levels of offering, then locate yourself and three to five competitors. Look for opportunities to stand out in a radically different way.
5. Use customer feedback.  Companies using a Voice of Customer (VoC) strategy generate 10x more annual revenue. 35% of actionable product ideas come from customer feature requests, yet 40% of companies don’t collect feedback. Your customers are essentially R&D volunteers, but only if you quickly and systematically turn insights into features.  To leverage this advantage, implement customer surveys at key touchpoints, create a team channel for sharing feedback patterns, and dedicate 10% of product development time to feedback-driven features.