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.

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 internet's value stems from an economy of unique human creations. AI-generated content, or "slop," replaces this with low-quality, soulless output, breaking the internet's economic engine. This trend now appears in VC pitches, with founders presenting AI-generated ideas they don't truly understand.

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.

By replacing junior roles, AI eliminates the primary training ground for the next generation of experts. This creates a paradox: the very models that need expert data to improve are simultaneously destroying the mechanism that produces those experts, creating a future data bottleneck.

AI models have absorbed the internet's general knowledge, so the new bottleneck is correcting complex, domain-specific reasoning. This creates a market for specialists (e.g., physicists, accountants) to provide 'post-training' human feedback on subtle errors.

Internal surveys highlight a critical paradox in AI adoption: while over 80% of Stack Overflow's developer community uses or plans to use AI, only 29% trust its output. This significant "trust gap" explains persistent user skepticism and creates a market opportunity for verified, human-curated data.

A critical weakness of current AI models is their inefficient learning process. They require exponentially more experience—sometimes 100,000 times more data than a human encounters in a lifetime—to acquire their skills. This highlights a key difference from human cognition and a major hurdle for developing more advanced, human-like AI.