In the current landscape of high-stakes immigration, a dangerous trend has emerged: the over-reliance on AI to draft National Interest Waiver (NIW) petitions. While AI is an impressive tool for basic tasks, using it to navigate the complexities of Matter of Dhanasar is proving to be a high-risk gamble. As an NIW practitioner, I am increasingly seeing the fallout of the AI NIW petition. Here is why relying on an algorithm to argue your national importance is often a recipe for a denial.
Who Is Directing the Case?
To be clear, AI is not without its uses. In terms of sheer efficiency, such as formatting, summarizing long technical papers, or cleaning up prose, it is an impressive asset. The speed it can articulate complex sentences is much better than a human.
However, while AI is efficient at the writing, it is not smart enough to be the one developing the ideas. A successful NIW petition requires a strategist who understands the legal landscape. Every written document, from the petition letter and personal statement to the recommendation letters, is an opportunity to insert specific legal arguments. This requires a high degree of planning and the use of precise phrasing that USCIS adjudicators are trained to scan for. AI lacks the human sense required to appeal to a human reader or to create the concrete, tangible narratives that connect technical research to the national interest of the United States.
The Prong One Confusion: Substantial Merit vs. National Importance
One of the most frequent mistakes AI makes is merging Substantial Merit and National Importance. While they both sit under the first prong of the Dhanasar test, they have distinct legal meanings. Substantial Merit focuses on the recognition of the field’s value to the United States. It establishes that the endeavor itself is worth doing and has a beneficial societal impact.
National Importance focuses on the broader impact of your specific work. It asks whether your particular proposed endeavor has significant potential to affect the country as a whole, such as through the economy, national security, or public health. AI models often produce a generic importance argument that fails to satisfy both requirements individually. A researcher may have work with incredible merit that simply lacks national importance as defined by USCIS. AI is not sophisticated enough to separate these two burdens of proof by itself, resulting in a petition that feels vague to an actual human officer.
The Teaching Trap: A Lesson in Legal Nuance
AI frequently conflates general field importance with your individual contribution. I recently reviewed a petition for a client who had been denied after working with a firm that relied too heavily on AI automation. The firm’s selling point was using the latest AI to prepare NIW petitions. The AI had emphasized the client’s role as a university teacher. Any experienced practitioner knows that the precedent case specifically states that teaching, while having substantial merit, does not satisfy the National Importance prong because it is a localized benefit. Because that firm relied on AI logic rather than legal expertise, they highlighted a fact that was legally harmful to the Dhanasar 3-prong test, leading straight to a denial.
Meaningless Fluency vs. Convincing Writing
AI is incredibly good at constructing grammatically perfect sentences, but it is not capable of convincing writing. I have reviewed many first drafts created by applicants using AI that essentially have no meaning. They are filled with sophisticated-sounding sentences that fail to convey a clear, logical argument. Adjudicators get frustrated if documents are not concise. A wall of meaningless fluency often signals to an officer that the petition lacks substance. Even if there is a valid argument made, USCIS adjudicators are going to be less enthusiastic to accept it if they must sift through longwinded text.
The Confidentiality and Training Gap
There is also an ethical reason why AI fails at this level. Serious immigration firms operate under strict confidentiality principles. Legitimate companies respect client confidentiality and do not feed actual documents to AI if it can be reviewed by third parties for large language model learning. Consequently, AI models are mostly trained on a graveyard of lower-quality, DIY, or failed petitions. AI is not learning from elite practitioners; it is learning from low quality examples.
A Tool Not a Substitute
AI is a tool, not yet a substitute for an experienced attorney. It can only create something as good as the prompt it is given. If the person prompting the AI lacks an accurate grasp of the legal arguments and human adjudicator logic required, the output will be inherently flawed. If you are betting your future in the United States on a petition, ensure it is built on professional judgment and a deep understanding of USCIS precedents, not just an algorithm telling you what it thinks you want to hear.
Contact us for an NIW evaluation to see if you qualify for this green card category.
Thath Kim II
US Attorney
Licensed in Oregon
11F 1108, 17, Seocho-daero 77-gil, Seocho-gu, Seoul, Republic of Korea