The AI race has been a prisoner's dilemma where companies spend massively, fearing competitors will pull ahead. As the cost of next-gen systems like Blackwell and Rubin becomes astronomical, the sheer economics will force a shift. Decision-making will be dominated by ROI calculations rather than the existential dread of slowing down.

For the past 18 months, AI excitement has created a rising tide that boosted fortunes for all major tech companies. This is changing. In the next year, their strategic bets, investments, and results will diverge dramatically, revealing clear winners and losers as "the tide goes out for some people."

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.

Many AI implementation projects are being paused or canceled due to a lack of immediate ROI. This reflects Amara's Law: we overestimate technology in the short term and underestimate it long term. Leaders must treat AI as a long-term strategic investment, not a short-term magic bullet.

The initial enterprise AI wave of scattered, small-scale proofs-of-concept is over. Companies are now consolidating efforts around a few high-conviction use cases and deploying them at massive scale across tens of thousands of employees, moving from exploration to production.

C-suites are more motivated to adopt AI for revenue-generating "front office" activities (like investment analysis) than for cost-saving "back office" automation. The direct, tangible impact on making more money overcomes the organizational inertia that often stalls efficiency-focused technology deployments.

The current AI hype masks a significant future risk: customers will churn if they don't see ROI beyond simple tasks like summarizing emails. For channel partners, ensuring deep user adoption of tools like Copilot is not just a value-add, but a critical defense against future revenue loss.

The dominant market driver will transition from macro risks like tariffs and policy uncertainty to micro, asset-specific stories. The key focus will be on company-level analysis of AI capital expenditure plans and their impact on earnings.

Instead of abstract productivity metrics, define your AI goal in terms of concrete headcount avoidance. Sensei's objective is to achieve the output of a 700-person company with half the staff by using AI to bridge the gap. This makes the ROI tangible and aligns AI investment with scalable, capital-efficient growth.