In its compute allocation meetings, Anthropic sets a non-negotiable floor for model development compute. This ensures they stay at the AI frontier, reflecting a belief that the long-term returns on intelligence outweigh short-term revenue opportunities.

Anthropic's core strategy is that possessing the most powerful AI model provides a dual advantage. It not only serves high-end use cases but also acts as an internal tool to accelerate AI research, enabling the company to produce smaller, cheaper models more quickly than competitors.

Anthropic's intense focus on AI for coding wasn't just a market strategy. The core belief, held since 2021, was that creating the best coding models would accelerate their internal researchers' work, creating a powerful flywheel that improves their foundational models faster than competitors.

A year ago, AI labs relied entirely on venture capital to cover massive losses. Now, companies like Anthropic have turned profitable, using revenue from high-margin inference services to fund their own R&D and compute expansion, marking a major shift in their business model.

Anthropic's resource allocation is guided by one principle: expecting rapid, transformative AI progress. This leads them to concentrate bets on areas with the highest leverage in such a future: software engineering to accelerate their own development, and AI safety, which becomes paramount as models become more powerful and autonomous.

Dario Amodei reveals a peculiar dynamic: profitability at a frontier AI lab is not a sign of mature business strategy. Instead, it's often the result of underestimating future demand when making massive, long-term compute purchases. Overestimating demand, conversely, leads to financial losses but more available research capacity.

The mission to achieve AGI often conflicts with the commercial need to build a product. This creates a critical tension for founders: Should limited, expensive GPU resources be allocated to long-term research or to powering the revenue-generating product that funds that research?

As compute becomes the primary bottleneck, AI labs will shift from broad access to a strategic allocation model. They will measure the "Return on Invested Tokens" (ROIT) to ensure their most scarce resource is given to the small subset of researchers who drive the majority of progress.

The two leading AI labs are acquiring compute at a faster rate than the rest of the world. Their share of new compute is projected to rise from 30% this year to over 50% by 2028, dramatically accelerating the concentration of AI power and capabilities.