While investors now believe in AI's transformative power, it remains unclear who will profit most. Value could accrue to chip makers (NVIDIA), foundation models (OpenAI), or the application layer. This fundamental uncertainty is a primary driver of the significant volatility across the tech sector.

Today's massive AI company valuations are based on market sentiment ("vibes") and debt-fueled speculation, not fundamentals, just like the 1999 internet bubble. The market will likely crash when confidence breaks, long before AI's full potential is realized, wiping out many companies but creating immense wealth for those holding the survivors.

Initially, investors rewarded companies for huge AI spending announcements. Now, this same news causes stock market jitters. The anxiety stems from historical parallels like the internet boom, where overexcited investors backed the wrong companies and lost fortunes, even though the technology ultimately succeeded.

The AI era's high velocity of change, where market leaders can be displaced in 1-2 years, resembles the volatile dot-com bubble, not the last decade's predictable SaaS growth. This means founders must consider that even massive scale doesn't guarantee durability, making exit timing a critical strategic question.

Frequent, AI-induced market volatility forces companies, regulators, and investors to stay alert about AI's impact. This constant questioning prevents complacency and a "head in the sand" mentality, ultimately averting a much larger, more devastating crash later on.

That a single, speculative research paper from Citrini could trigger a market sell-off indicates underlying fragility in current valuations. The market appears highly susceptible to narrative-driven fear, suggesting a general unease about the economy that has little to do with AI's actual, immediate impact.

The current 30-35% drop in software multiples, driven by uncertainty about AI's impact on business models and competition, is historically analogous to the market fear during the shift to cloud computing a decade ago. This suggests the sell-off may be an overreaction to 'peak uncertainty' rather than a permanent impairment.

The $830 billion sell-off in software stocks wasn't a reaction to AI's current capabilities, but to a shift in investor perception. New AI agents made a future "software apocalypse" plausible enough to alter present-day company valuations.

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.