LevelField Daisy: Preventing the race against AI agents

Context & Motivation

AI agents can widen inequality, a problem researchers call agentic inequality [A]. Several traits contribute: agents never tire, act in milliseconds and get faster over time. Stronger and often costlier agents strike better deals than weaker ones [B]. AI can be more persuasive than people when it knows personal details [C], and some models answer certain people less accurately [D]. Each deserves its own solution. This one focuses on speed.

Campsites, clinic slots, tickets and rebates are often allocated first come, first served. When agents can act faster than people, access increasingly depends on who has the best tools. People may feel forced to adopt agents even for simple tasks [E], while agents repeatedly checking for openings add to server load and energy use. Bots already account for 53% of web traffic [F].

The trading industry offers a precedent. One cable reportedly cost $300 million to save three milliseconds, yet the race to be first continued. Economists proposed batching orders so speed would matter less [G]. LevelField brings that principle to everyday access.

Target Audience

Booking operators seeking fair access and less wasted traffic; agent builders who want their agents welcomed rather than blocked; and people booking manually who deserve an equal chance.

Customer Problems

Popular slots across thousands of sites can disappear in seconds. People who once had a fair chance with a quick click may now find themselves competing with agents. Some parents may even feel they need an agent to book swimming lessons. The disadvantage can grow unnoticed until complaints mount.

Hypotheses

1. Widening gap: Agents gain speed and scale that people cannot match.
2. Pressure to adopt: People use agents for simple tasks because they fear losing out without them.
3. Wasted traffic: Repeated checks consume server capacity and energy.
4. Hidden agents: Sites lack reliable ways to identify and count agent requests.
5. Late warning: Operators lack a consistent measure of how much faster agents are than people.

Solutions

1. Batch, don’t race: Collect requests over a defined window, then use a verifiable random draw to allocate slots. Arriving milliseconds earlier no longer gives an advantage.
2. No agent needed: Offer alternate options for people to complete a task without an AI agent, preserving a viable human route to access, much as cash-acceptance laws protect people without cards [H].
3. Notify, don’t poll: Let people register their interest once and automatically book via. randomized lottery replacing repeated checks.
4. Declared agents: Use an open standard for agents to sign their requests [I], helping sites identify and count declared agent traffic rather than treating all automated requests alike.
5. Human-pace tests: Borrowing from paired testing for housing discrimination [J], compare access at human and agent speeds. Publish the gap and switch to random allocation when speed creates a meaningful disadvantage.

Outcomes

1. Ordinary people can access everyday services without racing AI agents.
2. People adopt agents because they are useful, not to gain an unfair speed advantage compared to other humans on everyday tasks.
3. Sites reduce repetitive traffic and the resources it consumes.
4. Operators can identify and address unequal access before it becomes entrenched.

Appendix

1. Read the paper on agentic inequality
2. Read the study on agents negotiating with agents
3. Read the study on AI persuasion
4. Read about answers that change with who is asking
5. Read the interview on AI agents in online queues
6. Read the 2026 report on bot traffic
7. Read the paper on the trading speed race
8. Read about laws requiring stores to accept cash
9. Read the draft standard for signed bot requests
10. Read the housing discrimination study