Your Expertise is Your Liability in AI Era

Mar 10, 2025

Beware of Success Stories!

It's natural to look at successful companies or leaders and assume their strategies hold the magic formula for success. But that's exactly where survivorship bias sneaks in and trips us up.

Survivorship Bias Simplified: It's the logical misstep of focusing solely on winners, forgetting all those who tried the same playbook but didn't make it. We hear endlessly about triumphs—yet the countless failures fade quietly into the background, skewing our understanding of what truly leads to success.

In the AI era, where change is lightning-fast, relying on this bias is especially dangerous. It tricks our brains into believing incomplete narratives, leading to flawed decisions.

Planes That Returned

**“Illustration of WWII plane damage analysis”**

Think back to WWII: analysts studied bullet holes on returning planes, noticing wings and tails took the most hits. Their initial instinct was logical: reinforce these damaged areas.

But mathematician Abraham Wald saw the flaw immediately: the planes hit in critical spots like engines or cockpits never returned. Survivorship bias had hidden these vital clues. Wald recommended reinforcing areas where surviving planes weren't hit—spots that, when damaged, proved fatal.

This counterintuitive thinking saved countless lives and became a powerful lesson in survivorship bias. It highlights how easy it is to misinterpret reality by only seeing the survivors. The real insight comes from asking, "What about the ones that never made it back?"

When we focus exclusively on visible winners, we ignore the invisible evidence buried in failures. This leads us to overly optimistic conclusions, thinking that winners succeeded solely due to brilliant strategies or talent, ignoring luck and hidden factors.

Consider investing: If we only measure performance based on active funds without counting those that failed and closed, results appear overly positive—dangerously misleading.

In business, copying only successful companies can fool us into believing specific practices guarantee success. But we remain blind to numerous companies with identical strategies that quietly vanished. It's a recipe for disaster.

Always remember: success stories are powerful, but they're only half the story. True wisdom comes from seeing both survivors and those who didn't make it.

History Loves to Repeat Itself (Especially When It Comes to Biases!)

Survivorship bias isn’t a new trick our minds play; it’s an age-old pitfall we've fallen into repeatedly during major technological shifts.

Flashback to the Industrial Revolution: Skilled artisans believed their craftsmanship would survive factory automation. Yet, history remembers the industrialists who adapted, not the countless skilled blacksmiths and weavers who vanished, silently forgotten. We marvel at innovators like Henry Ford, overlooking the hundreds of carmakers who didn't make it, despite similar ideas and efforts.

Dot-Com Déjà Vu: In the late '90s, investors and entrepreneurs worshipped internet startups. Amazon and eBay became legends, fueling the belief that rapid growth and losing money initially was brilliant strategy. But when the bubble burst, we learned painfully that most companies (think Pets.com or Webvan) following the same "brilliant" playbook crashed and burned.

We can't just look at Amazon's survivors and assume their path guarantees success. That's how hype blinds us, as investors painfully discovered when $5 trillion vanished almost overnight.

Computer Revolution Misses: IBM and Apple thrived in the personal computer boom, but giants like Digital Equipment Corp. (DEC) fumbled. DEC’s leader, Ken Olsen, famously scoffed at the idea of home computers in 1977. He wasn’t clueless—he was an expert stuck in the past paradigm. Fast-forward, and now even IBM and Apple risk becoming laggards in AI innovation. Expertise can become obsolete quickly if we fail to see beyond our past victories.

Kodak’s Fatal Mistake: Kodak practically defined photography, yet despite inventing digital cameras in 1975, they clung to their profitable film business. Kodak thought their success in film guaranteed dominance in any photographic future. They misread their core business as selling film rather than preserving memories. By doubling down on printing rather than embracing digital sharing (think Instagram’s missed opportunity), Kodak spiraled into bankruptcy by 2012.

Blockbuster’s Netflix Moment: Blockbuster dominated video rentals, confidently dismissing Netflix’s early DVD-by-mail model as trivial. Executives laughed off Netflix's idea, believing their brick-and-mortar success guaranteed future relevance. But Netflix thrived, innovating into streaming, while Blockbuster’s late-fee-driven stores turned from assets into burdens. Blockbuster, the giant survivor, became another forgotten victim of its own past success.

What These Stories Teach Us:

Every technological revolution leaves a graveyard of companies clinging to yesterday’s wins. Survivorship bias convinces leaders, “We've thrived before; we know best.” It blinds them to threats, ignoring those who tried similar strategies and failed.

Today, with AI reshaping industries overnight, the pattern repeats—but faster. Leaders must recognize the trap: past victories don’t ensure future survival. The ground shifts beneath us rapidly, and being an incumbent isn’t immunity—it's vulnerability, unless you're actively challenging assumptions and adapting aggressively.

Remember Kodak and Blockbuster. Don’t become tomorrow’s cautionary tale.

AI is Reshaping Sectors and Exposing Biases

Survivorship bias might be leading some experts astray in each. The hidden risks of following “business as usual” advice in a world that’s anything but usual.

Finance

Finance and technology have always danced closely—from telegraphs to trading algorithms—but AI? That's a whole new rhythm, accelerating everything from trading floors to credit scoring. Picture this: By the 2010s, algorithms already drove 60–80% of U.S. and EU equity trading. The charismatic floor trader shouting orders? He's now an endangered species.

It’s tempting to rely on past wins—“Hey, this strategy’s always worked!”—but markets evolve. The 2008 financial crisis shattered assumptions about safety built on survivorship-biased models. AI might feel infallible with its data-crunching prowess, but relying solely on historical success overlooks hidden risks. Remember, yesterday’s winning formula could tank tomorrow. The real edge? Blending human intuition with AI’s speed and precision, staying adaptable, and continuously learning. After all, markets aren't just irrational—they’re relentlessly innovative.

Healthcare

Healthcare stands at the crossroads of tradition and innovation. Sure, senior surgeons might think, "I've done fine without AI," but that's the classic trap of survivorship bias—missing out on game-changing insights from millions of patient records AI can scan in seconds. It's not about replacing seasoned doctors; it's about amplifying their expertise.

Yet caution is essential. An AI symptom checker might shine in clinical trials but stumble with real, diverse patient populations. Imagine an AI dermatology tool that nails lighter skin diagnoses but fails darker ones. Ignoring this diversity perpetuates unseen biases.

Healthcare's future winners will be experts humble enough to harness AI without surrendering critical judgment. AI isn’t here to steal the doctor’s seat—it’s the world's most brilliant assistant, ready to elevate care, if we're wise enough to guide it properly.

Tech & SaaS

Tech thrives on disruption, and AI is its boldest disruptor yet—think electricity meets the internet on steroids. Giants like Google felt invincible until ChatGPT jolted their complacency. "We’ve always dominated search" suddenly sounded dangerously like Nokia insisting, "People love physical keyboards."

Enterprise software providers (Oracle, SAP, legacy IBM) risk similar fate if they cling solely to past victories. Today's clients expect AI-driven insights, not just data warehouses. The "we've always done it this way" mantra isn't just outdated—it's risky.

For startups, survivorship bias whispers dangerous myths: "Uber succeeded, so my 'Uber-for-X' must too!" or "OpenAI hit the jackpot; my AI venture is next." Reality check: Many startups fizzle precisely because they mistake exceptional success for a replicable blueprint.

Even media companies aren't immune. Sure, AI might not replace Pulitzer-winning journalism yet, but ignoring AI-assisted content creation could spell irrelevance. Flooding platforms with generic AI-generated noise won’t help either. Balance authenticity with automation—use AI for grunt work, freeing humans for high-value creativity.

Future-proofing your strategy means constantly questioning assumptions: Is your way truly the best, or just comfortable because it survived yesterday's battles? Invite diverse perspectives. Remember, today's uncomfortable questions prevent tomorrow's uncomfortable truths.

Why Do We Keep Falling for It?

Survivorship bias is sneaky—and there's a reason our brains keep tripping into this particular trap.

Humans naturally hunt for patterns and crave coherent narratives. Randomness, chaos, or plain luck feels unsatisfying. It’s why we love those inspiring success stories: our minds quickly spin a tale of smart choices and brilliant moves, ignoring the unsettling truth that some wins might simply be good fortune.

Psychologists highlight two culprits here: the just-world hypothesis (believing people get what they deserve) and hindsight bias (thinking outcomes were obvious after they happened). Spot a successful entrepreneur, and your brain jumps in: "She must've cracked the code—let's decode her success!" It's reassuring to believe skill always triumphs, rather than admit she might've just won a lottery of timing or circumstance.

Consider Apple’s legendary rise: today it seems inevitable, a journey from garage startup to trillion-dollar titan. Yet, rewind to 1997, and Apple was one decision away from bankruptcy—Michael Dell famously joked they should shut down and refund shareholders. Had Apple folded then, we'd call their now-celebrated decisions reckless blunders rather than visionary moves.

We're social learners, too—wired to mimic success. Mentorship and advice typically flow from winners, rarely from those who've failed, no matter how insightful their lessons might be. Conferences spotlight successful CEOs, not the entrepreneurs who've hit rock bottom—even though the latter could offer invaluable wisdom on pitfalls to avoid.

Ego plays a role here as well. Admitting luck’s role in our success bruises our pride. It's far more comforting to believe we're exceptional—masters of our fate—than acknowledge that others with equal talent simply didn't catch the same breaks. This mindset breeds overconfidence and resistance to new approaches, locking us into what worked yesterday rather than exploring what might work better tomorrow.

The antidote? Awareness is just the beginning. Challenge yourself to seek out the hidden narratives—the silent evidence of failures you never hear about. Ask boldly, “What’s missing here? Who tried this strategy and failed miserably?” Use techniques like pre-mortems (imagine your plan crashes and burns—why did it fail?) or scenario planning to broaden your view beyond rosy success stories.

Organizations that embrace failures as openly as successes build resilience and true learning. In the AI era, experimentation is relentless. Cultivating a culture that learns from flops as readily as from wins isn't just smart—it's essential for long-term growth.

How can we put these insights into practice?

• Don’t Call Yourself a “Generalist” Because It’s Trendy in AI
If you’ve spent most of your career immersed in one or two domains—say, as a software developer or a CTO—reading a few “connecting the dots” articles on Sales, Marketing, and GTM doesn’t make you a generalist. Mastery takes more than casual exposure.

The real value of a generalist isn’t knowing a little bit about everything—it’s having the depth to integrate across disciplines and execute with conviction.

• Ask, "What's Missing Here?"
When exploring a success story or advice, always challenge yourself: "Who didn't make it, and why?" If you're admiring an AI startup that's thriving, dig into a few that flopped. What pitfalls did they hit? This broader perspective ensures you aren't just dazzled by winners. Bring this up in meetings: "Are we only spotlighting successes? What's happening with the silent majority who didn't make headlines?"

• Do a Pre-mortem (Imagine the Crash)
Before launching a big idea, pretend you've fast-forwarded to failure. Have your team list all the possible reasons it could have bombed. It’s like finding bullet holes before the plane takes off—catching vulnerabilities you'd otherwise miss. You'll often uncover optimistic assumptions and create stronger contingency plans.

• Diversify Your Inputs
In the fast-paced AI landscape, fresh perspectives often matter more than legacy experience. Don't just rely on seasoned experts; actively seek insights from newer voices—young analysts, data scientists, even outsiders. Encourage "red teams" or contrarian thinkers to challenge prevailing assumptions, poking holes to keep groupthink at bay.

• Revisit and Update Your Benchmarks
Use current data to set realistic expectations, not outdated success metrics. If 90% of startups in your niche fail, don't assume you'll automatically land in the lucky 10%. AI resets what’s possible—yesterday’s impressive 5% efficiency gain could look weak next to AI-driven 50% improvements today. Constantly recalibrate your benchmarks.

• Embrace a Learning (and Unlearning) Culture
Companies thriving long-term don't just learn—they actively unlearn outdated practices. Set aside budget and time for experiments, even when your current methods seem solid. Think of Kodak’s cautionary tale—clinging to film when digital was the future. Institutionalize experimentation, especially with emerging tech like AI.

• Think from First Principles
When unsure, strip things back to basics. Ask, "What's the real problem we’re solving, or the core customer need?" Blockbuster failed by thinking they ran stores rather than delivering convenient entertainment. First principles thinking lets you pivot faster and smarter, freeing you from clinging blindly to tradition.

• Keep Humans in the Loop (Initially)
AI can inherit human biases hidden in training data. For crucial decisions, pair AI insights with human oversight initially. As confidence builds, gradually reduce oversight. For now, think of humans and AI as co-pilots, each covering the other's blind spots.

• Celebrate and Analyze Failures
Encourage openly discussing failures—whether a botched product or AI misstep—as valuable learning experiences. Host "failure fairs," document lessons learned, and normalize calculated risk-taking. When failure is transparent, survivorship bias loses its grip, paving the way for genuine innovation.

• Practice Strategic Agility with Scenario Planning
The AI-driven future is uncertain, so avoid linear thinking like, "We've grown 20% annually, next year must be the same." Instead, map out multiple scenarios—best, moderate, and worst-case—to anticipate disruptions. Prepare yourself for curveballs like regulations or groundbreaking innovations.

• Maintain Healthy Skepticism (Especially with Experts)
In the hype-filled AI era, question bold claims. If an AI evangelist says, "AI will eliminate X profession within two years," probe deeper. If a seasoned expert dismisses AI capabilities entirely, ask why. Skepticism keeps you grounded in evidence, helps spot blind spots, and protects against nasty surprises. Think like an aircraft engineer—learning from both the planes that made it back and those that didn’t—to build a resilient, foolproof strategy.

Thriving Beyond the Survivors

Survivorship bias teaches us a humbling truth: Reality is broader than the stories of success we celebrate.

In a time of rapid AI-driven change, this humility is more important than ever. This Age will produce its share of spectacular winners – new expert figures, dominant companies, miraculous breakthroughs. We will hear a lot about them.

But lurking behind every AI success will be many attempts that failed or pitfalls avoided. To navigate this era, we must avoid being seduced by the shine of survivors alone.

Expertise remains valuable, but only if it evolves. The best experts – whether doctors, CEOs, or engineers – are those who treat their past success not as a certificate of infallibility, but as one data point in a changing landscape.

They combine the wisdom of experience with the fresh eyes of a beginner when confronting new challenges. AI will challenge experts by automating some tasks, but it will also augment experts who embrace it.

Those who fall victim to survivorship bias may either ignore AI (to their peril) or overhype it without caution (to their peril as well). The sweet spot is a balanced view: pragmatic optimism.

From psychology we learned our minds filter out failures, so we must put them back into our analyses.

If you’re in a field being transformed by AI, don’t listen to the loudest voice (be it a famous guru or a flashy new tool). Do your homework: find out what the dissenting opinions are, and why.

If you’re an expert, consider mentoring or collaborating with someone versed in AI – exchange knowledge. If you’re new and growing in your career, respect expertise but also question conventional wisdom – often innovation comes from asking why things are done the “accepted” way.

Overcoming survivorship bias leads to more resilient strategies and richer understanding. It lets us appreciate that success is often as much about avoiding certain traps as it is about executing good practices.

The only constant is change, having this broader lens will help ensure that we don’t ride the wave of one technology, only to be drowned by the next.

We can become true survivors in the long run – not by chance, but by design, learning from both the victories and the defeats that came before.

Pura Vida,
Praveen