While AI's impact on business is significant, the ultimate catalyst for market euphoria will be its application in healthcare. When AI-driven drug discovery makes 'living forever' a tangible possibility, it will unlock an unprecedented level of investor optimism.
Traditional metrics like GDP fail to capture the value of intangibles from the digital economy. Profit margins, which reflect real-world productivity gains from technology, provide a more accurate and immediate measure of its true economic impact.
As AI gets exponentially smarter, it will solve major problems in power, chip efficiency, and labor, driving down costs across the economy. This extreme efficiency creates a powerful deflationary force, which is a greater long-term macroeconomic risk than the current AI investment bubble popping.
In today's hyper-financialized economy, central banks no longer need to actually buy assets to stop a crisis. The mere announcement of their willingness to act, like the Fed's 2020 corporate bond facility, is enough to restore market confidence as traders front-run the intervention.
Unlike the dot-com bubble's finite need for fiber optic cables, the demand for AI is infinite because it's about solving an endless stream of problems. This suggests the current infrastructure spending cycle is fundamentally different and more sustainable than previous tech booms.
Pundits predicting a recession based on dwindling consumer savings are missing the bigger picture: a $178 trillion household net worth. This massive wealth cushion, 6x the size of the US economy, allows for sustained spending even with low income growth, explaining why recent recession calls have failed.
Michael Saylor’s adoption of Bitcoin for MicroStrategy's treasury wasn't just about inflation; it was a strategic pivot because AI and big tech were rendering his business model obsolete. Bitcoin, as a scarce asset, becomes an attractive safe haven for companies facing inevitable creative destruction from AI.
Today's AI is largely text-based (LLMs). The next phase involves Visual Language Models (VLMs) that interpret and interact with the physical world for robotics and surgery. This transition requires an exponential, 50-1000x increase in compute power, underwriting the long-term AI infrastructure build-out.