The hurdles enterprises face with AI—such as shifting funding models from CAPEX to OPEX and integrating third-party vendors—are not unique. These are the same obstacles companies overcame during the transitions to personal computers and cloud computing, proving the tech adoption lifecycle is a historical constant.

The rush to implement AI for operational savings is creating a bubble. While the technology is transformative long-term, companies are discovering that AI-generated work requires significant human oversight to catch costly errors. The true value will emerge once the initial hype settles.

BlackRock's COO argues that while AI provides individual productivity boosts, we haven't started the "first inning" of enterprise implementation. The real work involves complex organizational design and business process re-engineering, a phase that most companies have not yet reached, meaning hype outpaces integration.

The massive capital pouring into AI infrastructure far outpaces real-world adoption by enterprises and society. This "human metabolism" gap suggests hyped use cases are years away, setting the stage for a significant market correction after the current wave of IPOs, similar to the dot-com bust.

Even if AI progress stopped today, it would take 10-20 years for the economy to fully absorb and implement current capabilities. This growing gap between what's technologically possible and what's adopted in the market creates a massive, long-term opportunity for innovators.

Historical technology cycles suggest that the AI sector will almost certainly face a 'trough of disillusionment.' This occurs when massive capital expenditure fails to produce satisfactory short-term returns or adoption rates, leading to a market correction. The expert would be 'shocked' if this cycle avoided it.

Applying economist Carlotta Perez's technology cycle theory, the current AI landscape is still in the early 'invention' stage, not the 'deployment' stage. This suggests today's LLMs are not the final form, and significant innovation is needed before predictable, scalable business models emerge.

Just as companies scrambled for a "web strategy" and then a "mobile app," they now chase an "AI strategy." History shows this frenzy will subside, and AI will become an integrated tool. The fundamental job remains: build valuable products customers will pay for.

AI is currently a challenging business because it's in a heavy infrastructure investment cycle, similar to the early days of the web or cloud. Significant value creation typically occurs years after this initial investment phase, and the market isn't there yet.