About Alpha Pareto/about/
Why Alpha Pareto?
Pareto gave us a language for unequal contribution and efficient tradeoffs. Alpha Pareto turns that language into an engineering question for modern AI systems.
01 — What Pareto observed
Vilfredo Pareto observed that wealth and resources were not evenly distributed. A smaller part of the population held a larger part of the wealth. Over time, this observation became associated with the Pareto principle and the 80/20 rule.
02 — Why it still matters
The exact numbers change but the pattern keeps appearing: a small part of a system often produces a large part of the effect. In engineering, this matters because cost is real. Compute, memory, latency, energy and complexity all have limits.
03 — Pareto efficiency
A point is Pareto efficient when one objective cannot be improved without making another objective worse.
output vs cost latency vs accuracy memory vs capability specialization vs generality
04 — Alpha Pareto
Alpha Pareto does not assume that the right split is 80/20. It asks what the actual alpha is for the system being studied.
80/20 may be right.
90/10 may be right.
95/5 may be right.
The curve may be flat.
The answer must be measured.
α* = argmaxα Q(α) / C(α)
Q(α) represents useful retained output. C(α) represents cost: compute, memory, latency, energy or operational complexity. Alpha Pareto searches for the best operating point under real constraints.
05 — Why now
AI makes the question urgent. Large models are powerful but expensive. Many real workloads do not need every capability of a general-purpose model. The opportunity is to specialize the system without losing the useful output that matters.
EUROPE AND TRUST
Apareto is a European startup. The company is being built for organizations that care about strict regulation, data control and responsible handling of sensitive workloads.
FOUNDER
Apareto was founded by Carsten Gregersen. The company combines systems thinking, AI research and practical engineering experience to explore efficient AI systems.