Should ICANN Limit AI-Generated TLD Proposals

As generative artificial intelligence transforms creative and operational processes across industries, it has begun to reshape the contours of internet governance in unexpected ways. One such area of emerging friction is the rise of AI-generated top-level domain (TLD) proposals—suggestions for new domain suffixes like .music, .store, or .chat—formulated not by human entities with strategic intent but by algorithms trained on massive datasets of linguistic patterns, branding trends, and semantic associations. As ICANN prepares for the next round of gTLD expansion, the question arises whether it should intervene to limit or regulate these AI-generated TLD proposals, which could otherwise overwhelm the application process, dilute naming coherence, and create profound challenges for trademark protection, abuse mitigation, and namespace sustainability.

The original intent behind new gTLDs was to foster competition, innovation, and user choice in a namespace that had grown overly reliant on legacy TLDs like .com, .org, and .net. The 2012 expansion round opened the floodgates to over a thousand new extensions, from the commercially targeted (.app, .shop) to the geographically designated (.berlin, .nyc) and the whimsically abstract (.ninja, .xyz). While many of these TLDs were submitted by human applicants with specific community, branding, or commercial objectives, the next generation of proposals may include entries crafted entirely by machine—trained to mine linguistic niches, market gaps, and brand ambiguities at a scale and speed that humans cannot match.

Generative AI can now scan the entire corpus of registered domain names, linguistic corpora, industry glossaries, and social media trends to invent plausible and marketable TLD strings. Tools like GPT-4, Claude, and open-source models fine-tuned on internet naming data can propose thousands of syntactically valid, phonetically appealing, and semantically rich TLD candidates in mere seconds. Moreover, AI systems can simulate entire TLD business models, generate sample usage cases, and craft application narratives with minimal human supervision. For domain portfolio investors, shell corporations, or application mills, this presents a cost-effective and powerful strategy to flood the application pipeline with speculative proposals, hoping to corner linguistic real estate in the DNS before others catch on.

This raises serious concerns about the integrity and manageability of the TLD expansion process. ICANN already struggles with capacity constraints, evaluation delays, and stakeholder contention during each new round. A surge of AI-generated proposals could bog down evaluators with low-quality, duplicative, or purely speculative strings that lack genuine public interest or operational merit. Worse, AI-generated TLDs might unintentionally mimic existing brands, cultural terms, or sensitive geopolitical identifiers, not out of malice but due to algorithmic convergence. Such overlaps could trigger costly legal disputes, objections under the ICANN string contention resolution process, or accusations of regulatory negligence.

Furthermore, the proliferation of AI-proposed TLDs risks fragmenting the namespace in ways that reduce user trust and comprehension. One of the criticisms following the 2012 expansion was that the abundance of new suffixes confused users and made the web feel more opaque. If AI is allowed to produce endless variations—.shoppie, .cartify, .zonestore, .buymart—without meaningful usage differentiation, the TLD space may devolve into a cacophony of indistinguishable options, benefiting only speculators and defensive registrants. Brands would be forced to register across dozens or hundreds of semantically similar TLDs simply to protect their trademarks, fueling an arms race of unnecessary domain consumption.

There is also a concern about abuse. AI-generated TLDs could be optimized for phishing, brand impersonation, or keyword hijacking. Malicious actors could use AI to produce TLDs that resemble legitimate terms but with subtle misspellings or homophones, evading detection while tricking users. If accepted into the root zone, such TLDs could become permanent infrastructure for systemic abuse, especially if delegated to registries with lax enforcement policies. ICANN’s Safeguards on New gTLDs, though well intentioned, were not designed to contend with adversarial AI deployments that can outmaneuver content moderation and policy thresholds through sheer scale and semantic ambiguity.

Some might argue that rejecting AI-generated proposals is an overreaction. After all, AI can assist in improving quality, not just generating volume. It can help identify underserved communities, optimize naming strategies, and democratize access to TLD innovation for applicants who lack marketing consultants or branding firms. A blanket ban on AI involvement could stifle these benefits and arbitrarily limit creativity. But the issue is not AI-assisted creativity per se—it is the mass automation of speculative applications that exploit process inefficiencies, overwhelm governance structures, and reduce the namespace to a machine-generated abstraction rather than a space reflecting human communication, commerce, and culture.

ICANN is not without tools to address this dilemma, but decisive action is required. It could impose stricter thresholds for community engagement, public interest demonstration, or operational readiness in the application process—requirements that cannot be credibly fulfilled by an AI system operating in isolation. It might introduce rate limits or application volume caps per entity, discouraging mass submissions from algorithmically-driven shell structures. Alternatively, ICANN could mandate disclosures regarding the origin of each proposal, distinguishing between human-authored and machine-generated content, and subjecting the latter to heightened scrutiny.

There is also a need for public dialogue within ICANN’s multistakeholder model about the future role of automation in DNS governance. The same AI tools that generate naming proposals could, theoretically, be used to streamline registry operations, detect abusive registrations, or improve linguistic inclusivity in the namespace. But that future must be shaped deliberately, with safeguards, consensus, and human oversight—not left to the invisible hand of algorithmic entropy.

If ICANN fails to act, it risks repeating the pitfalls of its prior expansions, only at a much larger scale. The DNS root is not infinite in its governance capacity, even if technically capable of accommodating more strings. Opening the next application window without addressing the influx of AI-generated proposals could undermine confidence in the DNS, harm brand holders, confuse end users, and erode the legitimacy of ICANN’s stewardship. Just as AI is transforming the creative industries, law, and finance, it is now poised to reshape the semantic structure of the internet itself. The question is not whether AI should participate in this process, but how—and under what rules. Limiting AI-generated TLD proposals may be less about exclusion and more about ensuring that the internet’s most fundamental naming system remains intelligible, equitable, and governed by human intention rather than synthetic saturation.

As generative artificial intelligence transforms creative and operational processes across industries, it has begun to reshape the contours of internet governance in unexpected ways. One such area of emerging friction is the rise of AI-generated top-level domain (TLD) proposals—suggestions for new domain suffixes like .music, .store, or .chat—formulated not by human entities with strategic intent…