Just another In Hindsight experiment here at Hindsight Investor. The idea of turning 60K to 6M sounds like a pipe dream. In Hindsight, it's easier that you think. If you had put $10,000 each into AAPL (Jan 2007), NFLX (Apr 2010), NVDA (Nov 2016), LLY (Apr 1995), MA (Aug 2006), and BKNG (May 2007) and simply held you’d be sitting near $6,000,000 today.
But it also hides the truth. None of these were “easy buys” in real time. Each entry date came with uncomfortable uncertainty, credible bear cases, and stretches of sickening volatility.
For each name, you’ll see what would have made a rational investor pull the trigger, and what could have reasonably pushed them away. Finally, we’ll distill repeatable patterns a practical screening checklist for spotting the next cohort of potential 100× outliers before hindsight turns them into “obvious” picks.
We’re not simulating returns here. You already have the math for each $10k → $1M arc; we focus on decision context at entry.
Hindsight bias is real. The same charts that look linear and inevitable today were punctuated by 30–70% drawdowns, product flops, regulatory scares, and leadership uncertainty.
Chart of LLY - Despite it being a great company now, it would not have been an easy journey holding through the years.
Your job then: decide with partial information whether a company’s product-market fit, leadership quality, competitive moat, and runway justified sizing an idea to $10,000 and holding through pain.
Apple (AAPL) – 18 years
Netflix (NFLX) – 15 years
Nvidia (NVDA) – 9 years
Eli Lilly (LLY) – 30 years
Mastercard (MA) – 19 years
Booking Holdings (BKNG) – 18 years
Average? ~18 years of conviction.
That’s almost two decades of market cycles, crashes, and doubt — and every time your stock doubled, it tested your patience to sell early.
Thesis then: A premium PC/iPod company is attempting to reinvent the phone.
Leadership & culture (2007): Steve Jobs had rebuilt Apple’s product discipline (iMac → iPod → iTunes) and a reputation for taste + integration. Execution quality was perceived as elite, but Apple remained a fraction of today’s scale.
SAN FRANCISCO, CA - The introduction of Apple IPhone -- Macworld on January 9, 2007 in San Francisco, California. (Photo by David Paul Morris/Getty Images)
Core revenue pillars: Mac + iPod.
New bet: iPhone (unveiled Jan 9, 2007). Not just “a better phone,” but an integrated mobile computer tied to an ecosystem (hardware + software + services) and, soon, a developer platform (App Store launched 2008).
Competitive field: Nokia, BlackBerry, Motorola, Microsoft/Windows Mobile, Palm. All had distribution, carriers, and entrenched enterprise share. Apple had none of that.
Macro backdrop: Late-cycle expansion; the 2008–09 financial crisis was less than a year away. Any premium consumer device faced cyclical risk.
Jobs could repeat the iPod/iTunes flywheel with a bigger total addressable market (TAM).
Touch UI + full web + app platform signalled a category shift from phones to pocket computers.
Hardware-software integration and design advantage were real moats.
Carriers controlled distribution; enterprise loved BlackBerry; Nokia owned scale.
Apple was unproven in phones; execution risk high.
A consumer recession could kneecap adoption.
In hindsight, buying Apple in 2007 wasn’t about spotting a shiny new gadget it required believing that the iPhone would fundamentally change human behavior.
Back then, phones were utilitarian tools for calls and texts; few imagined they’d evolve into personal hubs for communication, entertainment, and commerce. To invest in Apple meant betting that mobile devices would become an indispensable extension of daily life, and that Apple’s ecosystem not Nokia’s or BlackBerry’s would define that shift.
It also meant enduring the 2008–09 financial crisis, when consumer spending collapsed and even believers questioned whether the iPhone was just hype. In hindsight, that conviction, not the iPod or iTunes success — was the real differentiator between those who held through uncertainty and those who didn’t.
Thesis then: A U.S. DVD-by-mail company is pivoting to streaming with limited exclusives, thin margins, and no global presence (yet).
Leadership & culture (2010): Reed Hastings was respected for long-term thinking (culture deck, willingness to self-disrupt). Team had shown operational excellence in subscriptions, logistics, and consumer UX.
DVDs were still meaningful.
Streaming existed (since 2007) but catalog was mostly licensed, not original.
No major originals until 2013 (House of Cards).
International expansion beginning (Canada 2010; LatAm/Europe followed).
Competitive field: Cable bundles; early Hulu; tech giants circling (Amazon), studios wary of licensing power to a disruptor.
Macro backdrop: Post-crisis recovery; consumer belt-tightening ironically helped lower-cost entertainment. Broadband penetration rising; smartphones/tablets emerging.
On-demand streaming fits consumer behavior; DVDs would sunset.
Scale → better licensing terms → flywheel; international expansion multiplies TAM.
Culture/tech edge in personalisation and product UX.
No moat without exclusives; studios could claw back rights.
Unit economics under pressure as content costs rise.
Execution missteps (e.g., 2011 Qwikster) could crater subs and trust.
In hindsight, investing in Netflix around 2010 meant trusting a company mid-pivot with no clear proof it would work. Margins were razor-thin, streaming was untested, and the DVD-by-mail business that built its brand was already fading.
Yet, like Apple, Netflix had visionary leadership under Reed Hastings, who was willing to sacrifice short-term profit for long-term relevance. The shift toward on-demand viewing was underway, but far from guaranteed. Bandwidth was limited, studios controlled content, and few foresaw just how rapidly consumer habits would change.
Back then, betting on Netflix wasn’t obvious; it was a hard but wise choice, made by those who believed streaming would redefine entertainment consumption globally.
Thesis then: A gaming-GPU leader is becoming the compute engine for AI, data centers, and accelerated workloads.
Leadership & culture (2016): Jensen Huang had spent a decade pre-building the AI stack—CUDA, developer tooling, and GPU architectures—before Wall Street cared. This was a founder-led, engineering-centric culture.
Gaming was the cash machine.
Data-center GPUs (training/inference) were inflecting; early hyperscaler demand.
Software ecosystem (CUDA/cuDNN) created switching costs for developers.
Competitive field: AMD in GPUs; Intel in CPUs; custom silicon threats (TPUs) on horizon. But Nvidia owned mindshare + tooling for ML researchers.
Macro backdrop: 2016 marked broader AI credibility (ImageNet progress, AlphaGo). “Cloud first” architectures scaling; crypto mining (2017) would later add noise.
Multiple S-curves: gaming, data center AI, autonomous vehicles, visualisation.
Platform moat via software + community, not just chips.
Gross margin expansion with mix shift to data center.
Stock had already ripped in 2016—fear of buying the top.
Cyclicality (PC, crypto) could whipsaw revenues.
Big tech might insource chips; open-source toolchains could erode CUDA lock-in.
In hindsight, investing in Nvidia in 2016 meant recognizing that its GPUs were no longer just for gaming. They had quietly become the computational engine behind deep learning and AI. Around that time, breakthroughs in neural networks (like image recognition and AlphaGo’s victory over the human Go champion) were driving a surge in demand for high-performance parallel computing. Nvidia’s CUDA software ecosystem and early investment in data-center GPUs positioned it perfectly to capture that wave.
Without the AI boom, Nvidia’s growth would likely have been steady but not explosive; gaming alone could not have produced the same 100× trajectory.
The 2016–2025 run was powered almost entirely by AI adoption across industries from cloud computing and self-driving cars to generative AI models all of which required Nvidia’s hardware and software stack. In hindsight, the real bet in 2016 wasn’t on semiconductors; it was on AI becoming the next industrial revolution.
Thesis then: A blue-chip pharma with blockbuster exposure (Prozac) and a credible pipeline aiming to replace expiring cash cows.
Leadership & culture (1995): Experienced big-pharma management; R&D-driven organization; disciplined capital allocation; long history in diabetes and psychiatry.
Prozac dominated headlines and revenue—patent cliff looming in early 2000s.
Late-stage pipeline included Zyprexa (antipsychotic), Gemzar (oncology), Humalog (rapid-acting insulin)—a credible lineup.
Competitive field: Peers with their own blockbusters; generics threatening post-patent; payer pressure on pricing.
Macro backdrop: Booming U.S. economy (mid-90s), aging demographics; healthcare politics occasionally flaring but no structural collapse in pricing yet.
Diversified therapeutic focus; strong balance sheet; pipeline capable of backfilling patents.
Defensive characteristics with secular tailwinds (aging, chronic disease).
R&D risk: expensive trials, binary outcomes.
Patent cliffs could cause multi-year stagnation.
Litigation/side-effect headlines (e.g., antidepressant controversies) could dent sentiment.
Back then, LLY was best known for Prozac, the world’s leading antidepressant, along with a solid diabetes business and a promising pipeline. It wasn’t a speculative biotech it was a well-run blue chip but its future still depended on developing new blockbusters to replace aging patents.
From 1995 onward, Lilly cycled through major product eras: Zyprexa (antipsychotic), Cymbalta (antidepressant/pain), Alimta (oncology), Trulicity (diabetes), and most recently Mounjaro, its breakthrough obesity and diabetes drug. Each wave of innovation kept the company compounding steadily, not explosively.
Holding from 1995 to today means sitting through 30 years of slow, disciplined compounding, regulatory cycles, and periods when the stock went nowhere. Adjusted for inflation, that $10,000 was roughly $20,000–$25,000 in today’s dollars, so it was no small bet.
The only realistic way most investors would have held that long is if Lilly was tucked inside a “coffee-can portfolio” — a buy-and-forget basket of quality companies meant to age quietly. In hindsight, LLY was one of those rare names that rewarded extreme patience: not a moonshot, but a steady compounder that turned discipline itself into the biggest alpha.
Thesis then: A freshly public network poised to toll the migration from cash to electronic payments globally.
Leadership & culture (2006): Post-IPO management focused on governance cleanup (from bank consortium roots), innovation (contactless), and international growth.
Core: four-party card network—fees on transaction value and volume.
Growth vectors: debit penetration, contactless, e-commerce, cross-border, emerging markets.
Competitive field: Visa (larger), American Express/Discover (distinct models), regional schemes, cash. Regulatory overhang (interchange debates) was non-trivial.
Macro backdrop: Late-cycle credit boom → Global Financial Crisis (2008–09) on deck. Consumer leverage high; travel would seize up during recessions.
Massive, long runway: digitization of everyday spend.
Network effects + brand trust; high incremental margins; asset-light scalability.
Secular tailwinds (e-commerce, globalization) independent of any single merchant.
Recession shock → volume declines; travel-related revenue cyclical.
Regulation (e.g., debit fee caps) could compress profitability.
Visa’s scale advantage.
In hindsight, investing in Mastercard after its 2006 IPO meant betting on the rise of digital payments long before it became obvious. You had Visa, Mastercard, and Amex — all credible players — but Mastercard offered a clean, asset-light model with global reach and huge runway as the world shifted from cash to cards.
It looked “obvious” only in retrospect. Back then, payments were still seen as cyclical and sensitive to recessions; the 2008 crisis tested that belief hard. Holding for 19 years meant trusting that every economic cycle would push more transactions online and that Mastercard’s network would quietly toll every one of them.
Thesis then: A dot-com survivor quietly morphing into the global leader in online hotel booking (via Booking.com, ActiveHotels, Agoda).
Leadership & culture (2007): Post-crash discipline under Jeff Boyd; acquisition savvy; performance-marketing excellence; Europe-first hotel focus.
Shift from “Name Your Price” airline gimmick to agency-model hotels (pay at property, wide inventory, great UX).
International first: win independents in Europe; expand assortment density → demand flywheel.
Competitive field: Expedia dominant in U.S.; traditional agencies; meta-search rising. Later: Airbnb (alt-accommodations). But hotel fragmentation favored aggregators with superior supply density.
Macro backdrop: Peak-cycle travel demand pre-GFC; then a violent recession that crushed travel volumes and sentiment. Mobile wave was ahead (opportunity + new UX standard).
Hotels = better take rates vs flights; Europe underpenetrated online.
Marketplace/network effects: more hotels → better choice → more demand → more hotels.
Data-driven customer acquisition and conversion.
Deep cyclicality in travel; exposure to discretionary spend.
Competitive bids for the same keywords/channels; risk of CAC inflation.
Platform risk: Google meta-search moves; later, Airbnb.
Holding it all the way to today was far from easy. You would’ve had to endure the 2008 financial crisis, which crushed travel demand, and later the COVID-19 pandemic, which froze it completely. Along the way, Booking faced fierce competition from Expedia, Google’s tightening grip on search traffic, and the rise of Airbnb redefining lodging. Each of those moments gave investors plenty of reasons to sell.
Yet, in hindsight, the COVID-19 era was also the setup for the next boom. While it felt catastrophic at the time, the pandemic created an immense wave of pent-up travel demand. People weren’t just ready to travel again they were starved for it! So when borders reopened, Booking’s scale, brand, and global hotel network positioned it to capture the rebound almost effortlessly. The challenge wasn’t seeing that travel would recover it was having the patience to stay invested through the darkest stretch when that recovery felt anything but certain.
Apple, Netflix, Nvidia were founder-driven with a history of self-disruption.
Lilly, Mastercard, Booking displayed process excellence: disciplined capital allocation, operating leverage, and talent density.
Investor takeaway: If you can’t underwrite the people, you can’t underwrite the pivot. Study track records of strategic shifts, not just quarterly beats.
Mobile computing, streaming, AI, payments digitization, aging + chronic disease, online travel were not fads. They were decade-long S-curves.
Investor takeaway: Size positions where the macro tailwind is structural, not cyclical. Your hold period becomes your edge.
Ecosystems & switching costs: Apple’s OS/services, Nvidia’s CUDA stack.
Network effects & density: Mastercard’s merchant/cardholder mesh; Booking’s hotel inventory + reviews.
IP & pipelines: Lilly’s R&D engine.
Data & personalization: Netflix’s product and content flywheel.
Investor takeaway: Look for flywheels where each incremental customer improves the product for the next. That’s compounding inside the business before it hits your brokerage account.
Netflix burned cash to secure originals and global scale.
Nvidia invested years ahead in software for GPUs.
Booking funneled profits into performance marketing and supply expansion.
Investor takeaway: Short-term pain, long-term power—if management is disciplined and the market structure rewards scale.
Buying after Apple’s iPhone unveil felt like chasing.
Buying after Nvidia’s 2016 surge felt like chasing.
Buying before Netflix originals felt speculative.
Buying before Mastercard’s first recession as a public company felt risky.
Buying after Priceline’s dot-com collapse felt foolish.
Buying Lilly in 1995 looked “plain vanilla,” easy to underweight.
Investor takeaway: Expect discomfort. If a thesis is both consensus and comfortable, you’re probably late or underpaid.
Every 2×, 5×, or 10× milestone along the way wasn’t just a financial checkpoint — it was a psychological ambush. Each time the portfolio multiplied, a rational investor faced the same internal voice: “Take the profit before it disappears.”
The paradox is that it’s often easier to hold a losing stock than a winning one; losses trigger hope, while gains trigger fear — fear of giving back what’s already “earned.”
Across these six cases, the true test wasn’t spotting Apple, Netflix, or Nvidia early; it was resisting the deeply human impulse to sell too soon. Every surge brought new reasons to exit — stretched valuations, macro scares, pundits calling the top. Yet, the difference between a 10× and a 100× outcome was doing nothing when doing something felt safe. That’s what stops even rational investors: not ignorance, but the inability to stay still while success unfolds slowly, uncomfortably, and against our instinct to cash out early.