Editorial
Research Report
The AI Transformation 100: Concrete Ideas for Fixing How We Work
Note from the author: To be clear about the stakes, we are now seeing the emergence of AI-generated child abuse material and non-consensual deepfakes that bypass traditional safety nets.
The notification arrives before the page even loads. A user in Texas taps into Pornhub and is immediately met with a state-mandated choice: upload a government ID, provide biometric confirmation or exit. Within days of enforcement under Texas HB 1181, the platform made a radical decision: it disabled access in the state entirely rather than assume the liability for storing sensitive identity data at scale.
This created a ripple effect felt across multiple jurisdictions. According to Pornhub officials, traffic to the site dropped 80% in Louisiana in January of 2023 when the state passed a similar law.
But the demand did not simply vanish. Instead, now, the same user can open an AI image generator, enter a prompt and receive customized, explicit content in seconds. No verification. No audit. No friction. This contrast is by design, and it's operational. To sway voters? To claim dominance? To cater to one crowd while depending on blindness of the other?
Table of Contents
- The Thesis of Symbolic Regulation
- The Substitution Effect and Political Signaling
- The AI Scaling Paradox
- The Economic Shield and the Drive for Dominance
- The Government's Demand for Unregulated Immunity
- Enforcement Feasibility: Centralized vs. Distributed
- The Human Impact and Identity Risks
- Conclusion: A System of Optimized Control
The Thesis of Symbolic Regulation
The current landscape suggests a blunt reality: state-level porn regulation produces measurable, visible disruption that satisfies political incentives, while unregulated generative AI expands the same content ecosystem exponentially because it serves economic and strategic interests. The system is simply reallocated, not attempting to reduce explicit content in any holistic sense; rather, it passes control toward domains that are harder to regulate and more valuable to scale.
The legislative pattern is consistent across states and dates.
Louisiana’s age verification law took effect in January 2023, followed by Utah and Virginia later that year. Texas enforcement escalated into 2024 through a cycle of litigation and compliance pressure. Missouri passed similar age verification legislation in 2025. In every instance, the sequence remained the same: compliance mandates led to platform resistance, which led to immediate traffic declines and an inevitable migration to alternative, predominately generative AI enabled channels.
The Electronic Frontier Foundation (EFF) documented that these laws require users to submit “highly sensitive personal information, including government-issued ID or biometric data.” This creates centralized data repositories with significant breach risks like digital "honeypots" for hackers.
However, these requirements did not eliminate demand; they altered the pathway. Virtual private network (VPN) usage surged in affected regions, a behavioral shift noted by Top10VPN as users circumvented geographic restrictions rather than abandoning access. VPN demand increased 282% in Missouri on November 29, 2025, the day before online age verification checks were implemented. In March of 2024, in the wake of Texas’s decision to implement age verification online, demand for VPN serves increased almost four-fold, jumping 275%.
The data shows a swap, a substitution; not reduction.
Related Article: Grok's Spicy Mode and Why Your Freedom Does Not Include Her Face
The Substitution Effect and Political Signaling
This substitution effect is noteworthy because it exposes the true function and intent of the law. If the outcome were genuinely harm reduction, policymakers would measure success through decreased consumption across all channels. Instead, the observable metric becomes platform-specific disruption. Centralized platforms lose traffic; the thing causing the disruption is silenced, while decentralized or unregulated systems absorb it.
This aligns with what political scientists describe as symbolic policy design. These are interventions that generate visible action to signal alignment with voter identity without actually resolving the underlying behavior.
In their 2024 article and 2022 statement to the January 6th Select Committee, Robert Lieberman and Suzanne Mettler focus on four historic threats to democracy:
- Political polarization
- Conflict over who belongs in the political community
- High and rising economic inequality
- Executive aggrandizement.
In their 2024 work, both argued that policies often operate as signals of alignment with voter identity rather than instruments of structural change. Age verification laws follow this model precisely: they produce a measurable event and a platform restriction that can be communicated to constituents, regardless of the downstream substitution.
The AI Scaling Paradox
Simultaneously, generative AI systems have scaled rapidly with limited enforceable constraints. The Stanford Institute for Human-Centered AI reported in the 2026 AI Index that generative models capable of producing explicit imagery are widely accessible. Many are open-source variants that can be downloaded and modified without centralized oversight.
Investigations by the National Law Review, which noted that AIM Intelligence's red team breached Anthropic's Claude Opus 4.6 in just 30 minutes, demonstrated that safety guardrails in commercial systems can be bypassed within minutes. These breaches expose major security gaps as autonomous AI capabilities rapidly advance. Through prompt engineering or model fine-tuning, users can produce explicit or restricted content with ease.
The speed differential is the most telling metric: content generation occurs in seconds, while regulated access to traditional platforms requires a multi-step identity verification process that can take minutes or result in an outright denial. These insights are not intended to promote traditional platforms, but rather to explore the duplicity of intent.
The Economic Shield and the Drive for Dominance
The economic layer of this digital landscape clarifies exactly why the current imbalance between regulation and innovation is allowed to persist.
In 2025, global corporate investment in AI more than doubled, with private investment growing at a rapid 127.5% to account for 60% of the total market. Generative AI remained the primary driver of this surge, growing by more than 200% and capturing nearly half of all private AI funding. During this same period, the number of newly funded AI companies rose by 71%, while billion-dollar funding events nearly doubled. These investment flows reflect a massive, locked-in trajectory. Industry leaders like Microsoft have committed billions to AI infrastructure, while Google continues to expand its generative model ecosystem across both consumer and enterprise products.
Federal policy has reinforced this momentum at every turn. In 2024, policy initiatives from the House Office of Science and Technology Policy (OSTP) positioned AI development as a top-tier national priority tied directly to economic competitiveness and national security. Consequently, any regulation that would meaningfully constrain generative systems would, by extension, interfere with that core priority. This reality leads to a critical question: is the state truly concerned with mitigating digital harm, or is it simply attempting to drive investment toward the very systems that create it?
National policy further amplifies this dynamic by framing safety as a hurdle rather than a goal. In July 2025, the White House released its AI Action Plan, which explicitly declares that securing a lead in the AI sector will usher in a golden age of economic competitiveness and national security. This plan outlines more than ninety federal actions aimed at exporting American AI packages, accelerating data center construction, removing regulatory barriers and ensuring that government contracts are awarded exclusively to models deemed objective and free from ideological bias.
The document treats AI as the cornerstone of American dominance, tying innovation directly to geopolitical power. Within such a narrative, there is very little room left for rigorous safety considerations.
Related Article: Digital Innocence Lost: How AI and Deepfakes Are Fueling the Next Generation of Child Exploitation
The Government's Demand for Unregulated Immunity
The practical consequences of this stance became clear months later when the General Services Administration (GSA) proposed contract guidelines requiring vendors to grant the government an irrevocable, royalty-free license to use their AI systems for any lawful purpose. Crucially, these guidelines would bar vendors from refusing outputs based on discretionary safety policies.
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This proposal followed a high-profile dispute in which Anthropic saw its $200 million defense contract canceled after the company refused to loosen internal safeguards against mass surveillance and fully autonomous weapons. As the EFF has noted, these draft rules would effectively require contractors to license their systems to the government for all purposes while disabling the very guardrails designed to prevent dangerous or unethical responses to official requests.
These combined policies reveal that the federal government is not merely uninterested in restraining generative AI; it is actively demanding unfettered access to its outputs and penalizing developers who attempt to impose ethical constraints. The structural reality is that national AI policy treats safety as secondary to strategic dominance. By prioritizing the removal of guardrails to ensure speed and access, the state effectively deepens the risk that generative engines will produce harmful content at scale. The result is a system that demands accountability from the individual through invasive verification while granting the engine itself total, unregulated immunity.
Enforcement Feasibility: Centralized vs. Distributed
The policy asymmetry becomes even more pronounced when examining the underlying mechanics of state coercion.
Traditional platforms, such as Pornhub, function as legacy institutions of the web; they are anchored by identifiable server farms, institutional banking connections and a clear legal presence that remains entirely susceptible to the state’s coercive power. Because these platforms exist as monolithic, traceable points of failure, state and federal governments can weaponize compliance requirements and execute enforcement actions with surgical precision.
In this environment, the state utilizes specific regulatory choke points, leveraging a platform’s need for banking stability and domain legitimacy to force adherence to age-verification mandates. Here, the gate is a visible, stationary target that the state can easily lock by threatening the platform's very ability to process a single transaction or host a single page.
In stark contrast, generative AI functions as a distributed infrastructure that fundamentally defies traditional geographic and corporate boundaries. These models are not tethered to a single corporate entity or a specific server; instead, they are hosted across diverse, redundant cloud environments, replicated through global open-source repositories and increasingly optimized for deployment on standard consumer-grade hardware. The US Government Accountability Office (GAO) highlights this structural shift in 2024, reporting that these systems introduce profound challenges in attribution, accountability and enforcement due to their decentralized nature and rapid, permissionless iteration cycles.
This technical reality suggests that a regulatory framework targeting specific outputs becomes effectively obsolete when production is no longer centralized. While a state can leverage massive fines against a corporation for failing to verify a user’s identity, it possesses no meaningful mechanism to penalize millions of individual nodes running open-source model weights in private, offline environments.
The result is a total regulatory collapse: the law successfully exerts control over the identifiable, centralized past while remaining functionally powerless against the anonymous, distributed future. This creates a vacuum where the engine of production accelerates in total silence, far beyond the reach of the state’s visible gates, rendering the entire concept of a "state-mandated choice" a digital fiction.
The Human Impact and Identity Risks
The human impact of this technological shift is defined by two parallel and contradictory risks.
On one hand, state-mandated age-verification systems require full identity disclosure, which increases user exposure to data breaches and creates centralized targets for malicious actors. On the other hand, the rapid rise of generative AI facilitates the creation of non-consensual explicit imagery, such as deepfakes, leaving victims with limited legal paths for defense. Once these outputs are categorized as "derived" or synthetic, the damage is often permanent, and there is virtually no recourse for those affected.
As the GAO has reported, current legal frameworks struggle to keep pace with AI-generated harms because liability structures remain unclear. This has created a profound irony in our digital policy: one system demands a verified identity while the other provides the tools to actively dismantle identity integrity. Both of these developments are measurable and documented, yet they are allowed to persist simultaneously, placing the burden of risk entirely on the individual.
The conflict between Anthropic and the Department of War (formerly the Department of Defense) reveals the moral vacuum of this regime. Anthropic made clear that its AI would not be used for mass domestic surveillance or autonomous weapons. In response, the Pentagon ordered unrestricted use and, when the company refused, canceled the $200 million contract and ordered other contractors to cease using Anthropic tools.
The Electronic Frontier Foundation observes that this dispute reveals a fundamental truth: privacy and safety are being negotiated in secret between tech giants and government agencies that both have poor records on civil liberties. The CEO of Anthropic, Dario Amodei, has argued that only Congress can provide meaningful restrictions against government surveillance and that the current legal framework has not caught up with AI’s capabilities. Yet Congress remains inert, leaving vendors to act as de facto guardians of privacy and making safety contingent upon corporate decisions.
The structural reveal is that the state simultaneously demands that AI companies remove safeguards for national security purposes while offering no statutory protections for citizens, thereby shifting the burden of ethical restraint onto private firms and eroding democratic oversight.
Related Article: Anthropic CEO Accuses OpenAI of 'Safety Theater' in Pentagon AI Deal
Conclusion: A System of Optimized Control
This systemic pattern persists not because of policy ambiguity or a genuine commitment to public safety, but due to a calculated alignment of commercial and political incentives.
State and federal governments accrue immense ideological capital through highly visible enforcement actions tied to performative moralism, establishing a self-sustaining political paradigm that rewards optics over outcomes. If the reduction of harm were the authentic objective, policymakers would mandate enforceable safety protocols across all digital vectors and accept the inevitable fiscal fallout and material loss of corporate tax revenue and investments.
Instead, technology companies continue to capitalize on systemic advantages through the rapid, unchecked deployment of generative models. Protective legal frameworks are enacted to shield these interests, while a hegemonic narrative of "innovative dominance" is established to justify the status quo. All of this occurs while state and federal policies actively discourage the restrictive regulation of generative AI, an omission that becomes particularly glaring when one considers that these policies were implemented immediately after massive public and private investments in these very systems were aggressively promoted.
The result is a split digital design that aggressively regulates what is easily monitored while deliberately accelerating what is not. Access to legacy, centralized content is becoming increasingly sluggish, invasive and punitive for the end user, whereas the automated generation of new, synthetic content is becoming faster, cheaper and entirely devoid of accountability. Ultimately, this regime is not designed to eliminate explicit or harmful content. Rather, it is calibrating the distribution of power: optimizing who maintains control over the data, which markets are permitted to dominate the global economy and which vulnerable populations are forced to absorb the societal consequences.
The state has constructed a paradox where the gate is tightening precisely where it is most visible to the electorate, while the engine of production expands in the shadows where it remains unburdened by the law.
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