The industry has already exhausted the public web data used to train foundational AI models, a point underscored by the phrase "we've already run out of data." The next leap in AI capability and business value will come from harnessing the vast, proprietary data currently locked behind corporate firewalls.

Digital platforms can algorithmically change rules, prices, and recommendations on a per-user, per-session basis, a practice called "twiddling." This leverages surveillance data to maximize extraction, such as raising prices on payday or offering lower wages to workers with high credit card debt, which was previously too labor-intensive for businesses to implement.

The most immediate danger of AI is its potential for governmental abuse. Concerns focus on embedding political ideology into models and porting social media's censorship apparatus to AI, enabling unprecedented surveillance and social control.

Public internet data has been largely exhausted for training AI models. The real competitive advantage and source for next-generation, specialized AI will be the vast, untapped reservoirs of proprietary data locked inside corporations, like R&D data from pharmaceutical or semiconductor companies.

The power of AI algorithms extends beyond content recommendation. By subtly shaping search results, feeds, and available information, a small group of tech elites can construct a bespoke version of reality for each user, guiding their perceptions and conclusions invisibly.

Data has become a primary means of production alongside capital and labor. Following historical parallels with agricultural co-ops and labor unions, communities will likely form "data unions" to pool their data, enabling collective bargaining with large corporations and restoring individual power.

Tech companies often use government and military contracts as a proving ground to refine complex technologies. This gives military personnel early access to tools, like Palantir a decade ago, long before they become mainstream in the corporate world.

Claire Smith envisions a new biotech business model focused on aggregating vast, unstructured health data (genomic, clinical notes) to sell high-value insights to pharma. This "Palantir-style" approach turns data into a scalable product for target identification or patient stratification, avoiding the traditional drug development path.

The next frontier of data isn't just accessing existing databases, but creating new ones with AI. Companies are analyzing unstructured sources in creative ways—like using computer vision on satellite images to count cars in parking lots as a proxy for employee headcounts—to answer business questions that were previously impossible to solve.