The danger of ad-supported AI is the potential for subtle, undetectable manipulation. By slightly amplifying concepts related to a product (e.g., the "Coke neuron"), advertisers could influence user thoughts and conversations without their awareness, a modern form of subliminal messaging.

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 convergence of decades of online tracking with accessible AI creates 'coin-operated intelligence.' Sophisticated analysis that can profile individuals with scary accuracy is no longer exclusive to nation-states; it's now cheap and available to anyone, enabling mass manipulation and targeted campaigns.

The advertising potential of AI assistants goes far beyond keyword searches. Users share deeply personal information, essentially conducting therapy or thought partnership sessions. This data will allow companies to build psychological profiles of unprecedented depth, enabling a terrifyingly effective new era of personalized advertising.

Meta's core ad-targeting algorithm is not a neutral party in platform fraud; it is an active accelerant. By design, the system identifies vulnerable users (e.g., the elderly). Once a user clicks a single scam ad, the algorithm learns to flood their feed with more, creating a vicious, automated cycle of exploitation for profit.

As privacy regulations and browser changes erode deterministic signals like cookies, advertisers must shift to predictive models. AI-driven analysis of contextual signals provides a scalable and future-proof way to reach relevant audiences without relying on shrinking pools of user-level tracking data.

Pervasive consumer hardware like smart glasses, smartphones, and always-on voice recorders captures everyday public surroundings with near-zero friction. While device owners voluntarily opt into data agreements, bystanders are non-consensually recorded and ingested into commercial AI datasets merely by navigating public spaces.

Algorithms don't just find content you like; they actively guide your preferences toward patterns that are easier for the system to predict. This creates a feedback loop where users are not just understood but are subtly molded into more predictable consumers of content.

Schiff highlights how the intersection of automated public cameras, consumer microphones, and multimodal AI is eliminating residual privacy. Because models can be trained to aggregate health files, monitor broadcast consumption, and eavesdrop through connected devices, statutory protections must go beyond traditional public conduct rules to actively prevent technological models and private surveillance vendors from systematically destroying privacy.