Corporate America has decided AI is a mandatory strategic bet, shifting from ROI-based adoption to “willing it into existence.” This top-down mandate ensures a 1-2 year boom in AI spending, creating a period of presumed success before a potential retrenchment.

Companies feel immense pressure to integrate AI to stay competitive, leading to massive spending. However, this rush means they lack the infrastructure to measure ROI, creating a paradox of anxious investment without clear proof of value.

Large enterprises navigate a critical paradox with new technology like AI. Moving too slowly cedes the market and leads to irrelevance. However, moving too quickly without clear direction or a focus on feasibility results in wasting millions of dollars on failed initiatives.

The same organizational slowness that hinders enterprise AI adoption may paradoxically benefit society. This inertia acts as a natural brake on the rate of AI-driven disruption, giving the broader economy and workforce more time to adapt to transitional chaos.

The era of 'token maxing,' where enterprises used AI models without cost constraints, is ending. Companies like Microsoft are now scrutinizing the ROI of their AI spend, leading to budget cuts and a potential deceleration in the hyper-growth seen by model providers.

The default assumption is that slowing innovation is inherently bad. With a technology as potent as AI, a deliberate slowdown is a feature, providing critical time to understand the systems, manage disruptions, and build governance structures before irreversible consequences occur. A true halt is not the alternative.

The constant leapfrogging between AI labs and shifting architectural paradigms makes enterprise teams hesitant. They fear backing the wrong technology and getting locked into a strategy that will soon be deprecated, leading to inaction.

The frenetic pace of AI innovation creates decision paralysis for large corporations who hesitate to invest in technology that will soon be outdated. A more predictable development cycle could provide the stability needed for enterprises to commit, actually boosting economic adoption and integration.

While AI labs release powerful models at an astonishing pace, large organizations are notoriously slow to adopt new technologies. This bureaucratic 'human friction' might be an unintentional benefit, providing society with the necessary time to grapple with the profound changes AI will bring.