The development of advanced surveillance in China required training models to distinguish between real humans and synthetic media. This technological push inadvertently propelled deepfake and face detection advancements globally, which were then repurposed for consumer applications like AI-generated face filters.

While commendable, an AI company's refusal to sell models for controversial uses like mass surveillance is a temporary solution. Technology diffusion is so rapid that within 12-18 months, open-source models will match today's frontier capabilities. A government seeking these tools can simply wait and use a widely available open-source alternative, making individual corporate 'red lines' ultimately ineffective.

Large, centralized AI models are vulnerable to 'distillation attacks,' where a smaller model can be trained cheaply by querying the larger one. This technical reality, combined with the moral hypocrisy of creators restricting copying after scraping the internet, strongly suggests a future dominated by decentralized, open-source models.

The proliferation of deepfakes is a positive development because it democratizes media manipulation, which was previously exclusive to well-resourced entities. This widespread availability of synthetic media will force the public to become more skeptical of video evidence and less likely to form opinions based on short, decontextualized clips.

The ease of finding AI "undressing" apps (85 sites found in an hour) reveals a critical vulnerability. Because open-source models can be trained for this purpose, technical filters from major labs like OpenAI are insufficient. The core issue is uncontrolled distribution, making it a societal awareness challenge.

AI can now replicate software functionality without copying source code, a "clean room" approach. This threatens not only proprietary software but also undermines the licensing structures of open-source projects, which rely on attribution and shared terms that can be bypassed by functional replication.

The rapid advancement of AI-generated video will soon make it impossible to distinguish real footage from deepfakes. This will cause a societal shift, eroding the concept of 'video proof' which has been a cornerstone of trust for the past century.

Unlike traditional software where a bug can be patched with high certainty, fixing a vulnerability in an AI system is unreliable. The underlying problem often persists because the AI's neural network—its 'brain'—remains susceptible to being tricked in novel ways.

Cryptographically signing media doesn't solve deepfakes because the vulnerability shifts to the user. Attackers use phishing tactics with nearly identical public keys or domains (a "Sybil problem") to trick human perception. The core issue is human error, not a lack of a technical solution.