Early AI training involved simple preference tasks. Now, training frontier models requires PhDs and top professionals to perform complex, hours-long tasks like building entire websites or explaining nuanced cancer topics. The demand is for deep, specialized expertise, not just generalist labor.

LLMs have hit a wall by scraping nearly all available public data. The next phase of AI development and competitive differentiation will come from training models on high-quality, proprietary data generated by human experts. This creates a booming "data as a service" industry for companies like Micro One that recruit and manage these experts.

The era of advancing AI simply by scaling pre-training is ending due to data limits. The field is re-entering a research-heavy phase focused on novel, more efficient training paradigms beyond just adding more compute to existing recipes. The bottleneck is shifting from resources back to ideas.

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.

The next leap in biotech moves beyond applying AI to existing data. CZI pioneers a model where 'frontier biology' and 'frontier AI' are developed in tandem. Experiments are now designed specifically to generate novel data that will ground and improve future AI models, creating a virtuous feedback loop.

The winning strategy in the AI data market has evolved beyond simply finding smart people. Leading companies differentiate with research teams that anticipate the future data requirements of models, innovating on data types for reasoning and STEM before being asked.

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 future of valuable AI lies not in models trained on the abundant public internet, but in those built on scarce, proprietary data. For fields like robotics and biology, this data doesn't exist to be scraped; it must be actively created, making the data generation process itself the key competitive moat.

The value in AI services has shifted from labeling simple data to generating complex, workflow-specific data for agentic AI. This requires research DNA and real-world enterprise deployment—a model Turing calls a "research accelerator," not a data labeling company.