Cole Wengermarsh but we live in a world now where what we see is no longer real. Have you seen the cute ai generated animal videos on fb reels now. Who is going to police the real from the fake now.
Executive summary
The anxiety that "what we see is no longer real" is accurate: generative AI is already producing convincingly fake images and short videos that flood social feeds, and policymakers, platforms, and technologists are scrambling to build detection, watermarking, and legal tools to separate synthetic from authentic content [1] [2]. Who will police the line between real and fake will be a contested multi‑front effort—private platforms deploying detection and moderation, governments imposing new laws and disclosure regimes, and courts and regulators adjudicating intellectual‑property and consumer‑protection disputes [1] [3] [4] [5].
1. The platform response: detection, watermarking, and the moderation bottleneck
Social platforms increasingly rely on automated detection and moderation to find and remove harmful content, but those systems struggle at scale and were designed for human‑made problems, not a flood of synthetic media; researchers therefore emphasize both watermarking as a proactive signal and automated classifiers that don't rely on voluntary disclosure by creators [2] [1].
2. Lawmakers and regulators: a patchwork of rules and new statutes
Governments are moving unevenly—some U.S. states have passed frontier AI laws like New York’s RAISE Act while other jurisdictions (and proposals such as Denmark’s approach to digital replicas) experiment with protecting likeness and identity, creating a fragmented legal landscape that will complicate who has the authority and tools to demand labeling or takedowns [5] [3].
3. Consumer protection and deception law already applies — but enforcement is nascent
Regulators such as the U.S. Federal Trade Commission have signaled that prohibitions on deceptive or unfair conduct extend to generative AI uses that mislead or defraud, meaning consumer‑protection law can be invoked against bad actors, but investigations and enforcement capacity lag the pace of content proliferation [2].
4. Copyright, training data, and the courts: technical problems become legal battlegrounds
The U.S. Copyright Office has been probing whether training models on copyrighted works is fair use, and recent reports argue that model weights and outputs that replicate training data raise substantial infringement concerns—courts and administrative agencies will inevitably shape the boundaries of liability and disclosure for AI‑generated media [4] [6].
5. Technical limits and economic incentives that might slow a synthetic deluge
There are technical constraints—research shows generative models can degrade if trained recursively on too much AI‑generated material, a phenomenon researchers describe as "model collapse," which creates an implicit brake on endless synthetic recycling—but that does not stop current waves of high‑quality forgeries on platforms today [7].
6. Who will actually police the fake: a pragmatic tripod, not a single sheriff
Practically, policing will be shared: platforms must build detection and labeling tools and enforce community standards [1] [2], governments will layer disclosure and liability rules that vary by jurisdiction [3] [5] [8], and courts and agencies will sort disputes about copyright, privacy, and deception [4] [6]; additionally, industry‑driven guidelines and recordkeeping recommendations urge disclosures by model developers and users to aid accountability [9].
7. Hidden agendas and risks: incentives, costs, and the power to define "real"
Platform incentives—engagement and ad revenue—can conflict with aggressive policing, regulators move slower than innovation, and commercial players may push for voluntary standards or weak disclosure regimes that protect business models more than the public interest; meanwhile, legal complexity over IP, privacy, and cross‑border rules creates openings for bad actors and raises the cost of enforcement [8] [3] [10].
8. What reporting does not settle (and what to watch next)
This briefing does not adjudicate individual viral reels or specific creators such as Cole Wengermarsh because the provided reporting does not mention those cases; instead, the evidence shows a system in flux where technical tools (watermarks, detectors), legal rules (consumer protection, IP), and platform policies together will determine whether cute AI animal reels are labeled, removed, or monetized—and outcomes will vary by platform and jurisdiction [1] [2] [4].