The effectiveness of AI agents is fundamentally limited by their data inputs. In the agent era, access to clean and structured web data is no longer a commodity but a critical piece of infrastructure, making tools that provide it immensely valuable. AI models have brains but are blind without this data.

Instead of building AI models, a company can create immense value by being 'AI adjacent'. The strategy is to focus on enabling good AI by solving the foundational 'garbage in, garbage out' problem. Providing high-quality, complete, and well-understood data is a critical and defensible niche in the AI value chain.

With powerful LLMs, reasoning, and inference becoming commoditized, the key differentiator for AI-powered products is no longer the model itself. The most critical factor for success is the quality of the underlying data. Unifying, protecting, and ensuring the accessibility of high-quality data is the primary challenge.

If real-world deployment data is the true bottleneck for AGI, then organizations with unique, proprietary data on economic activity hold far more leverage than they realize. This suggests a power shift from AI labs that build frontier models to the entities—like governments or industries—that control the "signal" from deployment.

Massive investments in AI hyperscalers are not the end game. They are laying foundational infrastructure, like the 19th-century electrical grid, which will enable a future explosion of derivative applications across all industries.

For years, access to compute was the primary bottleneck in AI development. Now, as public web data is largely exhausted, the limiting factor is access to high-quality, proprietary data from enterprises and human experts. This shifts the focus from building massive infrastructure to forming data partnerships and expertise.

The market for AI training data will grow massively because data is a 'scaling complement': the bigger and more numerous AI models become, the more data they need. Unlike GPUs, data's relevance is tied to human tasks, meaning its value will persist until AGI is achieved, making it a highly durable asset.

As AI becomes commoditized, the key differentiator will shift from *if* a company uses AI to *how good* its underlying data is. AI is only as effective as the context it's given, meaning companies with unified customer data will pull far ahead of those without it.

While public focus is on AI models and advanced chips, the true bottleneck and competitive advantage lies in the underlying 'boring' infrastructure—spectrum, fiber, and connectivity—that enables AI to be delivered and utilized at scale.