A Premortem Deep Dive
Unpacking the (Un)foreseen Failures of Our Top Holdings
A "premortem" is a foresight exercise where, instead of waiting for an investment to fail to conduct a "postmortem," we proactively imagine its failure in advance. We fast-forward to a future point - in our case, May 2030 - and assume the worst has happened: our investments have significantly underperformed.
This isn't about predicting the future with certainty. Instead, this mental exercise is a tool for proactive risk management. By intentionally imagining how a successful company in our portfolio could fail, we challenge our assumptions, uncover hidden vulnerabilities, and identify potential blind spots that might otherwise go unnoticed. It forces us to consider a wider range of scenarios, allowing for better preparedness and potentially informing decisions to diversify or even adjust our investment theses.
Join us as we dissect the potential pitfalls, overlooked threats, and strategic missteps that could lead to the significant underperformance of some of the world's most powerful tech titans, including Amazon, Alphabet, Visa, and ASML.
Premortem Case: Amazon (AMZN)
Amazon's decline by 2030 stemmed from a perfect storm of eroding core strengths, overwhelming regulatory and legal pressures, intense competition, and severe internal human capital strain.
E-commerce Fragmentation: Its "everything store" model was chipped away by highly specialized online retailers and resurgent direct-to-consumer (DTC) brands, offering curated experiences and competitive pricing. Traditional retailers also recaptured share with strong omnichannel strategies. This led to unsustainable retail margins.
AWS Deceleration: While still large, AWS's growth sharply decelerated due to intense competition from Azure, Google Cloud, and specialized AI infrastructure providers. Amazon's massive AI investments failed to yield anticipated returns, hampered by costly GPU constraints and competitors' more seamless AI integrations.
Global & New Venture Failures: Ambitious global expansion and diversification into areas like healthcare and autonomous vehicles proved to be costly failures, resulting in significant write-downs and a drain on resources. International markets became increasingly hostile due to stricter regulations, geopolitical tensions impacting supply chains, and persistent cash flow issues.
Antitrust Actions: A global regulatory onslaught, particularly in the EU and US, forced Amazon to unbundle Prime services, restrict private label promotion, and limit customer data usage for advertising. This led to massive compliance costs and substantial fines, severely hindering its data-driven competitive advantage.
Unionization & Labor Costs: Widespread unionization across its fulfillment network resulted in substantial increases in payroll costs, rigid work rules, and reduced operational flexibility, particularly impacting its already lower-margin retail segments.
By 2030, Amazon's once-powerful flywheel had stalled. Its "everything store" vision fragmented, margins all across the board crumbled under aggressive competition, and AWS's formidable growth decelerated. Costly failures in new ventures and a hostile global regulatory environment became significant drains. Critically, unprecedented antitrust actions forced fundamental changes to its business model, while widespread unionization and escalating labor costs crippled its operational efficiency. These combined forces fundamentally altered Amazon's growth trajectory and profitability, leading to significant underperformance for investors.
Premortem Case: Alphabet (GOOGL)
Alphabet's decline by 2030 was primarily driven by a tectonic shift in how users access information (bypassing traditional search), intensified regulatory pressures that broke down its walled gardens, and the continued struggle of its "Other Bets" to achieve meaningful profitability.
Search & Advertising Disruption: AI Disintermediation of Search: The most significant blow came from the rapid maturation and widespread adoption of highly capable AI agents and conversational models (e.g., advanced versions of Perplexity, ChatGPT, or new entrants). Users increasingly obtained direct, synthesized answers and completed complex tasks (like trip planning or research) directly within these AI interfaces, without needing to click through to traditional search results or websites. This drastically reduced ad impressions and click-through rates, gutting Alphabet's core search advertising revenue, which once constituted over 50% of its total.
Privacy-Led Ad Decline: Stricter global privacy regulations (e.g., post-cookie advertising environments) significantly hampered Google's ability to collect and leverage user data for highly targeted advertising. This forced a shift towards less lucrative contextual advertising and first-party data strategies, impacting advertising effectiveness and overall revenue.
Antitrust Breakup/Restrictions: Years of global antitrust lawsuits, particularly from the US DOJ and EU, culminated in significant structural and behavioral remedies. This included forced divestitures of parts of its ad tech business, limitations on default search engine agreements (e.g., with Apple), and mandated interoperability for Android and Chrome that leveled the playing field for competitors. These actions directly chipped away at Google's network effects and revenue streams.
Cloud (Google Cloud Platform - GCP) Underperformance: Despite continued investment, GCP struggled to significantly close the gap with AWS and Microsoft Azure. While growing, it remained a distant third, unable to capture enough enterprise market share to meaningfully offset the decline in core advertising. Intense price competition and the perceived "vendor lock-in" risk continued to be headwinds, despite Google's AI capabilities.
"Other Bets" as Persistent Drains: Alphabet's ambitious "Other Bets" (e.g., Waymo, Verily) continued to consume substantial capital without reaching widespread commercialization or profitability. Regulatory hurdles, slower-than-anticipated technological breakthroughs (e.g., Level 5 autonomous driving remained elusive for broad deployment), and market adoption challenges meant these ventures remained long-term money pits rather than significant revenue drivers, diluting overall company profitability.
By 2030, Alphabet was no longer the undisputed gateway to the internet. Its core search and advertising engine was fundamentally altered by new AI paradigms and aggressive regulation, while its supplementary ventures failed to pick up the slack.
Premortem Case: Visa (V)
The primary driver of Visa's decline was the unstoppable rise of Account-to-Account (A2A) payments and Central Bank Digital Currencies (CBDCs), effectively bypassing Visa's traditional card network.
A2A Ubiquity: Government-backed A2A systems (like India's UPI and Brazil's Pix) not only solidified their domestic dominance but also inspired similar, free, real-time payment systems to proliferate globally, often becoming the default. In developed markets, open banking has matured significantly, making direct bank payments seamless and secure, spurred by merchant demand for lower fees and improved user experience.
CBDC Adoption: Several major economies, including the Eurozone and China, successfully rolled out retail CBDCs, offering instant, cost-free digital cash that directly competed with card payments. Cross-border CBDC initiatives also began to reduce reliance on traditional payment rails for international transfers.
Big Tech & Fintech Disintermediation: Major tech companies and fintechs increasingly nudged users towards direct bank links or proprietary A2A solutions within their platforms, further embedding payments outside of Visa's network. The blocked Plaid acquisition continued to haunt Visa, leaving it without a strong foothold in the critical US open banking evolution.
Niche Crypto Growth: The Bitcoin Lightning Network carved out a growing, albeit niche, role for low-cost, instant cross-border micropayments and remittances, demonstrating another viable rail outside the traditional financial system.
Regulatory Squeeze: Governments globally prioritized lower payment costs and fostered competition, leading to significant pressure on interchange fees and consistently scrutinizing any of Visa's attempts to acquire or integrate with emerging A2A players, limiting its defensive strategies.
By 2030, Visa's role transformed from a primary payment rail to a secondary one, primarily used for specific cross-border scenarios or by legacy systems. Its core revenue streams were significantly compressed as the world moved to more direct, cheaper, and often government-controlled payment methods.
Premortem Case: Uber (UBER)
Uber's decline by 2030 was primarily driven by a relentless onslaught of adverse regulatory decisions, an inability to secure a sustainable long-term cost advantage, and intensifying competition that ultimately prevented it from becoming the dominant demand aggregator for ride-hailing services.
Mandatory Employee Classification: The most significant blow came from a global trend of governments (starting with key states/countries) mandating that gig drivers be classified as employees. This led to massive increases in labor costs (minimum wage, benefits, overtime, payroll taxes), erasing the gig-economy cost advantage and pushing unit economics deep into the red.
Operating Restrictions: Stricter licensing rules, fleet size limitations, and even outright bans in congested urban centers proliferated, severely limiting Uber's operational scale and market access in key, lucrative areas.
Failure to Aggregate Autonomous Demand: Uber's long-term vision hinged on a future dominated by autonomous vehicles (AVs), believing its platform would become the largest demand aggregator for these driverless services. However, this strategy failed. While AVs did begin to scale in select urban areas, the market fragmented. Specialized AV companies (e.g., Waymo, Tesla, Cruise) successfully launched their robotaxi services in geofenced zones, often securing exclusive partnerships with local authorities or directly integrating with municipal transit. These AV players became their own demand aggregators, offering significantly lower fares and superior reliability within their operating zones, cutting Uber out of the most profitable future of ride-hailing.
Local Champions Thrive: Well-funded local and regional ride-hailing apps (e.g., Bolt, Grab, Didi, and countless smaller players), often with strong government ties or culturally tailored services, solidified their dominance in their respective markets. This made it difficult for Uber to gain or maintain market share without aggressive, unprofitable subsidies, particularly when faced with local preference.
Delivery Wars Intensify: Uber Eats faced relentless competition from DoorDash, local restaurant direct-delivery services, and even grocery chains launching their own delivery networks. This led to further fee compression, increased marketing spend, and margin erosion in the food delivery segment.
Successful Unionization: Where employee classification didn't happen, driver unionization efforts gained significant traction, leading to collective bargaining agreements that significantly increased driver pay, benefits, and introduced more rigid work rules, directly impacting Uber's bottom line and operational flexibility.
By 2030, Uber remained a recognizable brand, but it was a shadow of its former self. Its core business model, reliant on independent contractors and aggressive growth at all costs, had been fundamentally broken by regulatory pressures. Moreover, its grand ambition to be the central platform for the autonomous future failed, as specialized AV companies became their own aggregators.
Premortem Case: ASML (ASML)
ASML's decline by 2030 was primarily a result of competitors successfully eroding its EUV monopoly, significant delays and cost overruns with its next-generation technology, a materialization of customer concentration risk, and a detrimental talent drain, all exacerbated by persistent geopolitical headwinds.
EUV Monopoly Eroded by Competitors: While ASML once held a near-monopoly, by 2030, competitors (e.g., Canon, Nikon, and even emerging state-backed players) achieved significant breakthroughs, developing viable, albeit potentially lower-spec or throughput, EUV systems at more competitive price points. This created a fragmented market, pushing ASML to defend its high-end niche while competitors captured a growing share of the mainstream EUV demand with "good enough" alternatives, impacting ASML's sales volume and pricing power.
High-NA EUV Technical Roadblocks & Cost Overruns: ASML's highly anticipated High-NA EUV systems faced persistent and significant technical hurdles. Issues related to complex optics, mask patterning, and precise overlay at sub-nanometer levels proved far more challenging than anticipated, leading to repeated delays in achieving high-volume manufacturing (HVM) readiness. These delays resulted in substantial R&D cost overruns and pushed back the timeline for widespread customer adoption, directly impacting ASML's most lucrative future revenue stream.
Talent Drain: ASML struggled to attract and retain top-tier engineers, physicists, and software talent. The extremely long development cycles for its cutting-edge systems, coupled with attractive opportunities in faster-moving fields like AI and quantum computing, led to a critical exodus of key personnel. This diminished ASML's ability to innovate, debug its complex systems, and accelerate its product roadmap, further benefiting competitors.
Customer Concentration Risk Materialized: With geopolitical actions limiting market access (e.g., to China), ASML became even more dependent on a few major customers (TSMC, Samsung, Intel). Any cyclical downturn in capital expenditure from just one or two of these giants, or a strategic decision by them to diversify their lithography suppliers, had an outsized and immediate negative impact on ASML's order book and financial performance.
Geopolitical Decimation of Market Access: The US-China tech war intensified, leading to near-total prohibitions on ASML selling any advanced lithography tools (including DUV, not just EUV) to a once-major market like China. This severely constrained ASML's addressable market and forced it to recalibrate its revenue projections downward, contributing to investor uncertainty.
By 2030, ASML's once-uncontested position as the sole provider of indispensable EUV technology was broken. Technical setbacks for its most advanced tools, a critical loss of talent, and an over-reliance on a shrinking pool of major customers, all compounded by an unforgiving geopolitical landscape, severely hampered its growth and profitability, leading to significant underperformance for investors.
Thanks for reading!
Stiliyan Loukanov, Feather Fund
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