To counter the immense concentration of power from AGI, AI training logs should be public. This prevents leaders or companies from secretly embedding self-serving biases or loyalties into AIs, making it much harder to manipulate elections or consolidate power without public scrutiny.

While the public focuses on AI's potential, a small group of tech leaders is using the current unregulated environment to amass unprecedented power and wealth. The federal government is even blocking state-level regulations, ensuring these few individuals gain extraordinary 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.

If real-world deployment data is the true bottleneck for AGI, then organizations with unique, proprietary data on economic activity hold far more leverage than they realize. This suggests a power shift from AI labs that build frontier models to the entities—like governments or industries—that control the "signal" from deployment.

Previously, data privacy concerns were abstract for most, leading only to worse ads. Now, giving AI companies unfettered access to your professional data provides them with the exact material needed to train models that will automate your job.

Vast government data repositories were historically constrained by the limited human bandwidth for analysis. AI removes this constraint, allowing for mass-scale querying that could be used to create enemies lists or target individuals for political reasons, turning a passive archive into an active weapon.

Governments lack the data to craft effective AI labor policies because the most crucial information—how AI is being used for augmentation versus automation—is held privately by AI labs. This data asymmetry forces policymakers to rely on lagging, incomplete indicators, hindering proactive responses.

As powerful AI capabilities become widely available, they pose significant risks. This creates a difficult choice: risk societal instability or implement a degree of surveillance to monitor for misuse. The challenge is to build these systems with embedded civil liberties protections, avoiding a purely authoritarian model.

Slowing public releases of AI models for government review may not slow overall progress. This creates a scenario where labs advance internally for months, giving government agencies exclusive access while delaying public commercialization and the next cycle of investment.