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What a shame!
Although I started writing this article six months ago, it’s now only a few days until the August 12 deadline for ICANN’s application submissions for new gTLDs. But better late than never, as the old saying goes. So here we go!
Fourteen years after the 2012 new gTLD program, ICANN opened the application window for the 2026 Round on April 30, 2026. Although ICANN’s application evaluation fee increased 22.7% from $185K per string in 2012 to $227K in 2026, indications are that this new window will get many more applications than the 1,930 applications filed in the 2012 round.
Technology, also, has evolved tremendously since the 2012 round of gTLD applications. Back then, Artificial Intelligence (AI) was not even on the radar, in contrast to the broad consumer and business product it is today. In three years to 2026 AI achieved an adoption rate of 53% (faster than the adoption rate of the PC or the internet), and AI adoption led to 26% and 50% gains in productivity in software development and marketing output, respectively.
AI can also help at almost every stage of preparing new gTLD applications, from the planning phase to evaluating responses to the application questions. Perhaps the first step in the application process where AI can help is preparing the business plan for a company applying for gTLDs. Although ICANN does not require applicants to submit a business plan, it provides data and information to help an applicant provide better responses to ICANN’s questions.
I settled with Anthropic’s Claude to prepare a business plan and financial model for a hypothetical Cayman Islands company (AIgTLDs) which will apply for two gTLDs. One gTLD is .BHANGRA for Bhangra music community, and the other is .LATTE, as in the coffee drink. As I asked requested, Claude provided the business plan and financial model as Microsoft Word, and Excel files, respectively. The financial model was comprehensive, and included projections of domain name sales, case flow, revenue, and other metrices under various scenarios, along with formulas.
The next step in the application process in which AI can help is the analysis of the Applicant Guidebook (AGB), which provides guidelines for applicants and the rules and regulations of the application. I asked Claude to analyze the AGB from the perspective of the number of questions, application types, and supporting documents (attachments) and financial information required. I got an informative 6-page document that helped me tame the 439-page AGB. My Claude chat revealed that the AGB had 225 application questions organized into 22 Question Sets.
Furthermore, 141 baseline questions apply to all applying entities, while the 84 questions are conditional upon the type of application (Community, Geographic Name, etc.). Claude also revealed that 54 (24%) of the 225 questions allow or require an attachment, with Question Set 7 (for Community gTLD applications) requiring the greatest number of attachments (17). Claude also provided a summary of the financial requirements for the four Profiles (Standard, Registry Operator, Top 25, and Government) of new gTLD applicants.
I then asked Claude to assign the drafting of responses to various to a human team, ChatGPT, and itself. I also asked Claude to indicate which of them will be the primary respondent, and which will be the secondary respondent/reviewer, and the rationale for the assignments. Claude provided me the information in a color-coded table in a Microsoft Word file.
The next step was to draft responses to the application questions, including drafting Registry policies. I thus asked Claude to prepare draft responses for the .BHANGRA and .LATTE applications. Given that my hypothetical. BHANGRA application is a Community Application, I asked Claude to prepare draft responses for 9 Question Sets (QS), starting QS 5 and 7 (Community gTLDs), QS 10-12, QS 14 (Financial Profile), and QS 18-20. On the other hand, the .LATTE is a generic TLD application, and as such, I asked Claude to draft responses to all the above 9 QSes for which draft responses to questions were provided, except for QS 7. The draft responses for .BHANGRA and .LATTE were provided in Word files which I could edit before submitting them.
The success or failure of a gTLD application also depends very strongly on the financial sustainability of the Registry that will operate it. I thus asked Claude to draft a financial profile template of my hypothetical company using data and information from the business plan and financial model it developed earlier. Claude faithfully provided me with an Excel workbook with, as required by ICANN, the Most Likely and Worst Case scenarios for its financial projections, as well as risk assessment framework and domain registration projections.
AI can also help evaluate the responses to the AGB questions before they are submitted on the TAMS. Obviously, it does not make sense to have an AI model evaluate the quality of responses it drafted. For this reason, I asked Gemini to evaluate the .BHANGRA application and the .LATTE application based on responses provided by against the requirements of the AGB give each a Pass/Fail grade. In the same vein, I asked Gemini to evaluate the financial profiles of both applications and provide each a Pass/Fail grade.
Gemini gave the .BHANGRA application and .LATTE application an overall grade of “Pass.” The .BHANGRA application passed because of its technical compliance, its strong definition of the community it will serve, and its commitment to safeguards. On the other hand, the .LATTE application “Pass” grade was based on its strong niche, technical compliance, and founder (myself) experience. However, the Pass grades were conditional upon the completion of other questions which required corporate and financial documentation.
The AI models provide a great start to completing the applications, but they are not a magic bullet. For this reason, the draft responses provided must be edited, and the information provide verified before the responses are submitted to ICANN. Despite this, the AI models can certainly considerably reduce the time, effort and cost of preparing responses to the AGB questions.
All this raises an important question: how can AI help in future rounds of gTLD applications? Given what already can be done with AI, applications almost all gTLD applications in the rounds after this 2026 round will be drafted by AI. Secondly, it is safe to assume that ICANN will leverage AI to evaluate applications in future rounds, meaning a drastic reduction in the cost of, and time taken to conduct application evaluations. This, in turn, should reduce the application and evaluation fees, the length of the application round and the duration between successive rounds, and lead to a Golden Age of inclusiveness in the domain names industry.
So it’s a shame this article is coming a bit too late. Nevertheless, the effort that went into it would have been worth it if only one applicant finds it useful, or if it starts an important discussion on the role of Artificial Intelligence (AI) in ICANN’s new gTLD program, and in the domain names industry at large.
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Katim,
It is good to hear from you. I appreciate the work you have put into this piece.
You are right that AI can help applicants draft better applications and navigate the technical requirements of the 2026 gTLD round. But the real issue is not the application; it is the institution that evaluates it. You tested AI on community TLDs, but the problem is not the application. The problem is the governance
So I say the shame is not the AI. The shame is that community applicants might be seduced by the AI bell, thinking it will level the playing field. It is not. It cannot be.
AI cannot prevent ICANN staff from drafting endorsements for your competitor. It cannot stop the Board from ignoring an IRP ruling. It cannot overturn the waiver that blocks you from ever having your day in court.
You suggest that AI can help evaluate applications and assign a “Pass” or “Fail” grade. But in the real world, the grade does not matter.
In my case, we co-authored the rules in the 2012 New gTLD Applicant Guidebook. We followed every requirement. We had the endorsements from the African Union Commission and the UN Economic Commission for Africa. We had the technical capacity, the financials - which scored higher than the competitor’s- and a six-year global campaign. We won the IRP. The panel found that ICANN’s Board had acted inconsistently with its own Articles of Incorporation and Bylaws. We still lost the string.
Even if AI helps a community applicant win the string, even if ICANN gives it to them for free, what exactly is the point? How does wrapping an identity in a TLD build a community? Does a .civilrights TLD build the civil rights movement? Does a .metoo TLD build the #MeToo movement? Communities are built by content, by programs, by action, not by a domain name. After being in this industry for two decades, I am trying to grapple with this.
As Mohd Hashim notes in his recent CircleID analysis, DUMs track size, not value. A community TLD might look successful on paper while adding nothing to the community it claims to serve.
The problem is not the application. The problem is the process. And AI cannot fix a broken process.
I hope this helps.
Many thanks, my dear Sister Sophia, for your sobering comments, which are largely valid. There is no question that the digital divide in the Domain Names industry is huge, and seems to get wider all the time. I probably should have prefaced or ended my article with a note about the challenges many in developing countries, Africa especially, continue to face in developing vibrant domain names companies, and participating effectively in the industry. There certainly is a lot of work that remains and needs to be done. How much work? Well, we'll soon find out when data on the new gTLD applications is revealed. Cheers!
Thank you for your thoughtful response, Katim, and for acknowledging the digital divide.
But I want to be clear about something. ICANN is not an industry. It was never set up to be one. It was established to coordinate unique identifiers, the address book of the internet. That is a technical coordination function, not a commercial branding engine.
The moment ICANN started treating gTLDs as branding opportunities, it stepped outside its mandate. And that is the problem. Branding does not need a gTLD to be effective. A domain ending does not build a brand. Content, trust, and reputation do.
That is why I referenced Mohd Hashim’s DUM analysis in my earlier comment. The point is not that there are too few registries. It is that the commercial expansion of the namespace has created an industry that serves itself, not the public.
The question is not how to make that industry more inclusive. The question is whether it should exist at all?