Thomson Reuters' stock plummeted 20% due to a new AI competitor, despite the company reporting 7% revenue growth and aggressively adding its own AI features. This shows that in the current market, strong present-day performance is irrelevant when investors fear future disruption from nimbler AI startups.
The primary threat from AI disruptors isn't immediate customer churn. Instead, incumbents get "maimed"—they keep their existing customer base but lose new deals and expansion revenue to AI-native tools, causing growth to stagnate over time.
The "SaaS-pocalypse" isn't about AI replacing software overnight. Instead, AI's disruptive potential erases the decades-long growth certainty that justified high SaaS valuations. Investors are punishing this newfound unpredictability of future cash flows, regardless of current performance.
The downturn in software stocks isn't tied to current earnings. Instead, investors are repricing the entire sector, removing the premium they once paid for its perceived safety and stable, long-term contracts, which are now threatened by AI disruption.
Initially viewed as a growth driver, Generative AI is now seen by investors as a major disruption risk. This sentiment shift is driven by the visible, massive investments in AI infrastructure without corresponding revenue growth appearing in established enterprise sectors, causing a focus on potential downside instead of upside.
Unlike mobile or internet shifts that created openings for startups, AI is an "accelerating technology." Large companies can integrate it quickly, closing the competitive window for new entrants much faster than in previous platform shifts. The moat is no longer product execution but customer insight.
A company can beat earnings and still see its stock fall if its actions (e.g., high CapEx) contradict the prevailing market narrative (e.g., the AI bubble is popping). Price is driven by future expectations, not just present-day results.
The recent software stock wipeout wasn't driven by bubble fears, but by a growing conviction that AI can disintermediate traditional SaaS products. A single Anthropic legal plugin triggered a massive sell-off, showing tangible AI applications are now seen as direct threats to established companies, not just hype.
Unlike previous tech waves, AI's core requirements—massive datasets, capital for compute, and vast distribution—are already controlled by today's largest tech companies. This gives incumbents a powerful advantage, making AI a technology that could sustain their dominance rather than disrupt them.
Sierra CEO Bret Taylor argues that transitioning from per-seat software licensing to value-based AI agents is a business model disruption, not just a technological one. Public companies struggle to navigate this shift as it creates a 'trough of despair' in quarterly earnings, threatening their core revenue before the new model matures.
Despite massive spending and partnerships, Microsoft, Amazon, Apple, and Meta have failed to launch a defining, consumer-facing AI product. This surprising lack of execution challenges the assumption that incumbents would easily dominate the AI space, leaving the door open for native AI startups.