The AI industry is generating extraordinary revenue, but the key question for investors is not who builds the most powerful model, but who captures the most durable value as the technology matures.
Until recently, leading AI companies benefited from advantages in computing power, training data, and research talent. Today, many of the most advanced models perform similarly, while the cost of using AI continues to decline. As a result, competitive advantages based solely on model performance may become harder to maintain.
For investors, this raises a structural question: if the models themselves become commoditized, which parts of the AI stack retain pricing power?
The answer, increasingly, lies above and below the model layer.
Companies that help businesses adopt AI, integrate it into existing workflows, manage the underlying infrastructure, or embed it into everyday applications may be better positioned to benefit as AI usage expands.
What This Means for Investment Strategy
The convergence of AI models does not make the investment opportunity any less compelling. Instead, it changes where value may accrue.
Investors should consider where AI is being used, who can meet the computing needs that support it, and which companies can help large organizations adopt it smoothly. Additionally, open-source models are making the model layer less unique, which could put pressure on pricing but also accelerate adoption and benefit companies that help put AI to work.
Positioning for the Next Phase of AI Value Creation
The early phase of AI investment was defined by capability differentiation. The next phase will be defined by execution and integration.
The investment implication is clear: the companies best positioned to capture AI's next wave of value are those with the distribution reach, infrastructure scale, and organizational capability to ensure that AI moves from experimentation to institutional deployment. As AI becomes more widely available, the ability to translate technology into real-world business outcomes may matter more than incremental differences in model performance.
The structural opportunities in this next phase are significant. Identifying them requires moving beyond model benchmarks and engaging with the harder, more durable questions of adoption, integration, and enterprise execution.
For more information on related investments and insights, please listen to our William Blair Thinking podcast, Monthly Macro: China’s Open-Weight Challenge and the Future of AI Economics, recorded on August 4, 2026, featuring William Blair macro analyst Richard de Chazal, and equity research co-group head of technology, media, and communications, Arjun Bhatia.