The primary bottleneck for advancing AI is high-quality, tacit data—skills and local insights that are hard to digitize. Individuals can retain economic value by guarding this information and using it to train personalized AI tools that work for them, not their employers.
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As AI commoditizes execution and intellectual labor, the only remaining scarce human skill will be judgment: the wisdom to know what to build, why, and for whom. This shifts economic value from effort and hard work to discernment and taste.
Since LLMs are commodities, sustainable competitive advantage in AI comes from leveraging proprietary data and unique business processes that competitors cannot replicate. Companies must focus on building AI that understands their specific "secret sauce."
AI models lack access to the rich, contextual signals from physical, real-world interactions. Humans will remain essential because their job is to participate in this world, gather unique context from experiences like customer conversations, and feed it into AI systems, which cannot glean it on their own.
While compute and capital are often cited as AI bottlenecks, the most significant limiting factor is the lack of human talent. There is a fundamental shortage of AI practitioners and data scientists, a gap that current university output and immigration policies are failing to fill, making expertise the most constrained resource.
AI is commoditizing knowledge by making vast amounts of data accessible. Therefore, the leaders who thrive will not be those with the most data, but those with the most judgment. The key differentiator will be the uniquely human ability to apply wisdom, context, and insight to AI-generated outputs to make effective decisions.
AI's value is overestimated because experts view complex jobs as simple, solvable tasks. The real bottleneck is the unproductive effort required to build a custom training pipeline for every company-specific micro-task. Human workers are valuable precisely because they avoid this “schleppy training loop” by learning on the job, a capability current AI lacks.
The internet leveled the playing field by making information accessible. AI will do the same for intelligence, making expertise a commodity. The new human differentiator will be the creativity and ability to define and solve novel, previously un-articulable problems.
AI tools like LLMs thrive on large, structured datasets. In manufacturing, critical information is often unstructured 'tribal knowledge' in workers' heads. Dirac’s strategy is to first build a software layer that captures and organizes this human expertise, creating the necessary context for AI to then analyze and add value.
The ultimate value of AI will be its ability to act as a long-term corporate memory. By feeding it historical data—ICPs, past experiments, key decisions, and customer feedback—companies can create a queryable "brain" that dramatically accelerates onboarding and institutional knowledge transfer.
The most valuable AI systems are built by people with deep knowledge in a specific field (like pest control or law), not by engineers. This expertise is crucial for identifying the right problems and, more importantly, for creating effective evaluations to ensure the agent performs correctly.
