Context & Motivation
This idea builds on the concern that AI can widen inequality [A]. LevelField addresses agent speed, while Leaf Window explores open reasoning in decisions. This post tackles volume and persuasion.
Persuasive arguments have always shaped human history, but making one took considerable effort. You had to understand who you were trying to persuade, think clearly about your premise, and explain why it mattered to you in the context of your lived experience. The effort was necessarily in the prose. It was in the argument itself.
Think of a statement of purpose for admission to your dream university, a cover letter for your dream job, or a grant application for a brilliant startup you want to build. Making a strong, persuasive case took enough effort to match the significance of the reward at stake.
AI has disrupted two things in this social structure, where the written word has long helped protect what matters. First, words became cheap. Second, information became abundant. The effort required to produce (what would have been previously considered) a 80th-percentile persuasive argument fell so far that it no longer matched the high bar for the reward at the end.
You might argue that it's easy to tell a heartfelt argument from one written by AI, so why worry about the bottom 80 percent of applications? Doesn't the world care more about the top 20 percent anyway, since those are the ones most likely to win? That misses one important factor through which AI is increasing inequality: volume.
Before AI, the high barrier to writing a persuasive argument made it relatively easy for adjudicators to distinguish heartfelt applications from superficial ones, even with a quick read. The prose, research, grammar, storytelling, premise and flow all offered clues about the person behind the argument.
With AI, information became cheap, words became even cheaper, and distinguishing AI-written text from a human argument became considerably harder (especially with stronger models). At the same time, the volume of applications people had to review grew considerably. One job platform reportedly received 11,000 applications a minute [B], while an air quality regulator reportedly received around 20,000 AI-written emails [C]. After one freelance platform introduced a one-click writing tool, written proposals stopped being a reliable test of who would do the job well [D].
Target Audience
Hiring teams, grant funders and public agencies that need to identify genuine need and merit amid a flood of compelling submissions. Also, people who write plainly or in a second language and want their cases judged on their merits, regardless of which AI model they use (where AI writing is allowed) or their natural writing style (where prose is not a review criteria).
Customer Problems
A city housing fund opens applications for 500 grants. Thousands arrive within a week, most written fluently. Reviewers struggle to distinguish a family in crisis from a polished template. Some applicants submit multiple times. A parent who writes honestly scores below them all and is filtered out by an AI agent in the first stage. The challenge is choosing fairly when AI makes submissions plentiful and arguments persuasive.
HYPOTHESIS
- Flood: AI allows one human or campaign to send a large number of submissions.
- Tool gap: Humans with better AI tools can write more persuasive applications than those with weaker tools. However, humans with stronger writing skills can still write better than AI.
- AI writing: A sincere, well-written argument can still stand out, but AI is closing the gap quickly. The volume of submissions can bury sincere ones when human reviewers have limited time and resources.
- AI screeners: AI screens every application first, and only a handful reach a human [E].
Solutions
- Scarce applications: Each institution, whether a company, university or funder, accepts only a set number of applications per person within a given period. Over time, the bodies that govern each field agree on a shared limit across institutions. A lightweight identity check ensures that each application is counted against the right person. Within that limit, applicants can mark a few applications as favourites so they are reviewed first. Unlike the paid versions offered by some job platforms today, favourites are free for everyone. Scarcity becomes a signal of genuine interest and reduces the review burden.
- Criteria, not writing: Each institution tells applicants upfront whether good writing earns points and whether AI assistance is allowed. Once an application is clear enough to understand, it is scored only against the institution's stated criteria. Where AI is prohibited, applications that use it are not reviewed, although detection tools can be unreliable, especially against stronger models.As AI writing becomes harder to distinguish from human writing, allowing AI may be the better option, provided the same argument receives the same score whether it was written by a person, a stronger model or a weaker one. This keeps applicants out of a race to access better models. To test this, reviewers are sometimes given three versions of an application: one written by a person, one by a stronger model and one by a weaker model. All three should receive the same score.
- People decide: With fewer applications to review, humans can read them more carefully. AI can sort applications but never reject one on its own, and screening systems are tested for favouring AI-written submissions. Together, Solutions 1 and 2 also help prevent sincere arguments from being buried.
Outcome
- Applying becomes a considered choice again, and interest regains its meaning.
- Ideas compete on substance, regardless of the tool or language used to express them.
- Decisions that shape lives return to people talking to people.
- Fair review becomes something institutions measure rather than assume.
