Unlike past tech shifts where imagining the future was the challenge, AI's potential is widely accepted. The primary difficulty for investors is no longer forecasting the technology's success, but determining what that widely-anticipated future is worth today. The problem has shifted from one of imagination to one of financial discipline and valuation.

The same uncertainty AI injects into equity valuations also affects credit. While a four-year bond for a major software company seems safe, a 30-year bond is far riskier, as the company could be disrupted. This dynamic could lead to structurally steeper credit curves in the future.

For the first time, the high-multiple software industry faces a potential existential threat from AI. Even the possibility of disruption is enough to compress valuations, causing massive dispersion where indices look calm but underlying sectors are experiencing extreme rotation.

The "SaaSpocalypse" isn't about current revenues but a collapse in investor confidence. AI introduces profound uncertainty about future cash flows, causing the market to heavily discount what was once seen as bond-like predictability. SaaS firms must now actively prove they are beneficiaries of AI to regain their premium valuations.

In the early 1980s, stock prices were low because investors foresaw the coming IT revolution but were unsure which companies would win or lose. This created broad uncertainty, depressing incumbent valuations—a historical parallel to the current market trying to price the impact of AI.

SaaS business models derive value from long-term customer relationships. AI's disruptive potential makes the 10-year outlook for any software company extremely uncertain. This means the entire SaaS category is currently mispriced, though it's unclear if companies are over or undervalued.

Investors no longer just discount future cash flows; they question their very existence due to AI risk. This fundamental shift to an "if" mindset creates demand for a massive margin of safety, leading to drastically lower P/E multiples and higher discount rates.

The market cannot reconcile two mutually exclusive scenarios: 1) If AGI is real, the long-term value of most existing companies is near zero. 2) If AGI is not real, the massive valuations of AI leaders are unjustified. This unresolved conflict creates a fundamental pricing problem and massive systemic risk.

While AI investment has exploded, US productivity has barely risen. Valuations are priced as if a societal transformation is complete, yet 95% of GenAI pilots fail to positively impact company P&Ls. This gap between market expectation and real-world economic benefit creates systemic risk.