AI adoption is not limited to tech and white-collar work; it has become a universal business consideration. For example, a lumber mill in Vermont is using AI to sort planks, a task for which they struggled to hire skilled labor. This shows AI is being deployed as a practical solution to specific, localized labor shortages in legacy industries.

Bringing manufacturing back to the US won't mean a return of old assembly line jobs. The real opportunity is to leapfrog to automated factories that produce sophisticated, tech-infused products. This creates a new class of higher-skill, higher-pay "blue collar plus" jobs focused on building and maintaining these advanced manufacturing systems.

To find the leading edge of US reshoring, look beyond traditional industrial firms. Major technology companies like the "Mag7" are now aggressively hiring top-tier physical AI, robotics, and manufacturing talent. This signals a fundamental shift in where the most significant capital and innovation in US manufacturing are being directed.

Silicon Valley is biased towards open-ended knowledge work like software engineering. However, a larger, often ignored opportunity for AI lies in automating the repeatable, deterministic business processes that power most of the non-tech economy, from customer support to operations.

Companies like OpenAI and Anthropic are spending billions creating simulated enterprise apps (RL gyms) where human experts train AI models on complex tasks. This has created a new, rapidly growing "AI trainer" job category, but its ultimate purpose is to automate those same expert roles.

Knowledge work will shift from performing repetitive tasks to teaching AI agents how to do them. Workers will identify agent mistakes and turn them into reinforcement learning (RL) environments, creating a high-leverage, fixed-cost asset similar to software.

The fear of AI taking jobs is misplaced. With declining populations and aging workforces, essential industries like farming and trucking face severe labor shortages. AI-driven autonomy isn't a threat but a timely solution, filling critical gaps that humans are increasingly unwilling or unable to fill.

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.

Unlike debates around AI replacing white-collar jobs, physical AI is being actively pulled into industries like mining and farming. These sectors face severe labor shortages due to aging workforces and the dangerous or remote nature of the work, making automation a critical necessity rather than a threat to employment.