Markets and the Myth of First Mover Advantage

“It ain’t what you don’t know that gets you into trouble, it’s what you know for sure that just ain’t so.” ~Mark Twain

An often-referenced truism in competitive analysis is “first mover advantage.” Analysts often suggest this company advantage is virtually insurmountable, thinking the race to scale matters most in determining winners and losers. Amazon is the primary example used as supporting evidence, inspiring copycats who seek to pursue what Netflix founder Reid Hoffman coined as a “blitzscaling” strategy.

What this analysis misses is that scale is a necessary but not sufficient requirement for long-term success. This is especially true in the fast-paced technology industry. New inventions and innovations result in the trailblazer experiencing fast growth that is the envy of the rest of the world. It’s a matter of when, not if, competitors attempt to launch a rival product.  What matters most in determining long-term success is understanding product switching costs.

High switching costs do not immunize a market leader from disruption. Rather, they give the incumbent an opportunity to respond before losing relevance or going out of business. Backward compatibility needs helped Microsoft Office fend off a nascent Google Docs and made it difficult for ARM processors to take share from Intel in personal computers. Apple’s ecosystem of iCloud, FaceTime, and iMessage made it challenging to switch to Android smartphones.  NVIDIA’s CUDA programming language has made it hard for rival AMD to make significant inroads in AI chips. SAP’s ERP software became so embedded in enterprise workflows that most customers viewed replacing it akin to performing open heart surgery.  Outside of technology, switching costs have allowed established banks to thwart newer, tech-focused rivals (i.e. neobanks).

Viewing stock market machinations through this lens tells a different story than narratives spun by pundits who mostly seem obsessed with forecasting asset bubbles and their imminent bursting. The recent bumpy technology stock performance actually started about a year ago when the market experienced sharp declines caused by fears that Chinese new entrant DeepSeek presented low-cost competition. This caused doubts about returns on AI investment and drew into question the accepted belief in the inevitability of OpenAI’s world dominance, prevalent since the launch of ChatGPT in late 2022. Of course, markets climbed “the wall of worry” throughout much of 2025, rebounding to post the second consecutive year of 20+% gains led by technology stocks.

Signs of increasing AI competition woke the market from its bout of Amnesia. Last November, Alphabet launched its latest Gen AI model, Gemini 3, which surpassed ChatGPT on several performance benchmarks. Coincidentally, that launch came on the heels of OpenAI founder Sam Altman’s infamous podcast appearance where he appeared easily irritated by basic questions about OpenAI’s future profitability. Perplexity’s recent product announcements for its offering, Claude, have fueled concerns that incumbent enterprise software companies are not safe from competition either, threatening high profit margins and growth.

Company earnings and guidance shed additional light on the state of AI competition. Alphabet, Amazon, Microsoft, and Meta forecast spending $650 billion on capital equipment spending to approximately $650 billion this year, spooking the markets. Reliance on OpenAI, once seen as a major benefit, is now a key risk. Microsoft shares declined sharply after Microsoft management noted that about half of its backlog was from OpenAI.

It’s too early to know whether OpenAI or Microsoft will join the likes of IBM, AOL, Yahoo, Friendster, and Nokia as first movers to become long-term losers. Recent market moves highlight markets reassessing this potential risk as investors realize it is too difficult to forecast the implications of AI innovation at this early stage. Focusing on understanding product switching costs is what will matter most in determining long-term profitability and ultimately stock performance.