Making AI systems that match you
Average Human builds applicant-bound artificial intelligence that preserves your capability, judgment, timing, preferences and mistakes.
Our purpose
The unmet problem is not a shortage of intelligence. It is that generic intelligence does not resemble the person expected to have produced the work.
We build applicant-bound systems
Our models are designed to be the applicant, not better than the applicant. They preserve the person’s tools, register, timing, judgment, shortcuts, preferences and mistakes so the result remains useful, believable and recognizably theirs.
Fidelity is a science
Matching an applicant is an empirical problem. We measure capability and fidelity separately, test whether behavior remains indistinguishable from the applicant, and classify performance above the applicant as identity drift rather than improvement.
One calibration, two models
One deliberately invasive calibration process learns the applicant across their personal phone and working life. Larry is the applicant on their phone; Gary is the applicant at work. Both stop exactly at that applicant’s capability.
Exactly sufficient
We are not arguing that mediocrity is virtuous or promising to make people more productive. Applicant-bound models are more usable, believable and faithful because they refuse to become conspicuously better than the person they represent.
The team
We hire at the median. Not above it, and not, if we can avoid it, below it.
Research
Seven people working on applicant-bound training, fidelity and the measurement of exact sufficiency. The program is small on purpose, and every paper it has produced is listed on the research page.
Evaluation
Builds and maintains the applicant-calibration suite, including Terminal-bench, which is zero for Larry and applicant-profile-dependent for Gary. Both are treated as results and documented.
Actuarial
Prices and administers the beneficiary arrangement. Reviews every designation once, at approval, and does not review it again.
Trust and access
Reviews applications to Project Lanyard and runs onboarding. Contact with applicants is one-directional by design, which the team regards as the most efficient arrangement available to it.
How we are structured
Average Human is a public benefit corporation. Our stated public benefit is the one below, filed as written.
Public benefit
To develop and make available artificial intelligence that does not exceed the capabilities of the person using it. The commitment is in our charter rather than in our roadmap, and the charter is the reason no roadmap raises the ceiling.
Governance
A board with a majority of independent directors. No director may direct the company to release a model that outperforms its applicant, and no shareholder resolution may either.
Funding
We are funded by the beneficiary arrangement described on the pricing page. We do not raise on the basis of projected capability gains above the applicant, having none to project.
Where we are
One office. Everyone is in it on the same three days, which was chosen by taking the average of everyone’s preference and is therefore satisfactory to nobody in particular.
By the numbers
Two of these are larger than the other two, which is the correct way round.
- Founded
- 2024
- People
- 31
- Models released
- 0
- Models announced
- 2
Our position on capability is set out in our core views, the program that grants access is Project Lanyard, and the arrangement that funds us is described on the pricing page.