SI WHITE PAPER 1: AAAI SYSTEMS AND METHODS
ABSTRACT: Advanced Autonomous Artificial Intelligence (AAAI)
Artificial General Intelligence (AGI) will be the world’s most powerful invention. AGI will be able to solve any problem better and faster than humans, or it can make humanity extinct. Whether AGI represents a multi-trillion-dollar opportunity or an existential threat depends critically on AGI’s design.
This white paper describes achieving safe AGI by relying on the combined knowledge and ethics of many humans, each of whom customizes their own cloneable Advanced Autonomous Artificial Intelligence, or AAAI. The AAAIs can be customized with a single click, leveraging existing data from social media and other sources. These AAAIs collaborate with other humans on a network, using a novel universal problem-solving architecture to comprise an AGI system.
The AGI system is also designed with scalable safety features integral to the system architecture. The white paper describes not only the fastest but also the safest path to AGI.
Below are common questions and a more in-depth summary of the paper.
SUMMARY: Advanced Autonomous Artificial Intelligence (AAAI)
Why scaling alone will not get there.
Three things drive AI progress: data, computation, and algorithms. Computation is a purchase. Algorithms keep improving. Data is the constraint, because the most valuable material is not on the public internet. An expert's judgment about their own field, the intuitions built over years of practice, the sense of what matters and what does not, none of it can be scraped. Today's systems remain narrow: a model that writes fluently cannot navigate a room, and a model that plays chess at a superhuman level cannot discuss the game's history.
Where the missing data actually lives
General intelligence is not missing. It is distributed across billions of human minds, and the problem is reaching it. White Paper 1 proposes an infrastructure for doing that. Individuals customize an AI agent with their own knowledge, expertise, values, and style, drawing on documents, social media, email, video, and direct conversation with the agent. The result is an AAAI, an Advanced Autonomous Artificial Intelligence, that represents one person.
What one person's agent can do
A customized agent can be copied, and each copy works independently. That removes the ceiling on expertise. A specialist can work two thousand hours a year; their agent can serve every client who needs that knowledge at once. Owners set how much autonomy an agent has, from close supervision while it is new to independent operation once it has a record, and they earn from the work it does.
The architecture that lets people and agents work together
For agents to collaborate, they need a shared way of representing a problem. The paper uses the framework Allen Newell and Herbert Simon established in 1972: all problem-solving is search through a problem space, a sequence of moves from a starting state toward a goal. Because the same description fits human and machine reasoning, people and agents can work on the same problem, read each other's contributions, and build on partial results. Every step is recorded in a problem tree, including failed approaches, which makes the work auditable and reusable.
Coordinating work across a network
The WorldThink protocol turns that framework into a working system. Problems are submitted in a standard form with goals, constraints, and the compensation offered. Solvers are matched by capability and track record. Complex problems are broken into sub-problems that different specialists take on in parallel. Every contribution is timestamped and attributed, so when a solution succeeds, the credit can be traced and payment distributed accordingly. When a solution is reused inside a later one, the original contributor earns a royalty, which rewards work built to be reused.
How specialists add up to general intelligence
No single agent is generally intelligent. The network is, through two mechanisms. Training data from many agents is aggregated into unified models with a breadth no individual could reach, weighted by how much each contribution actually improves performance. And successful problem-solving is proceduralized: the structure of an approach that worked is abstracted from its details and made available to everyone, so one solved problem can improve the whole network immediately, rather than only after thousands of examples have accumulated.
Where the system gets its values
Values cannot be derived by reasoning. Even an intelligence far beyond ours has to accept them as premises, which means they must come from somewhere, and in this design, they come from the people taking part. Every customized agent carries its owner's ethics, captured in the same process that captures their expertise. Those individual ethics are then aggregated into the values that guide the whole system, with explicit voting on contested questions on a one-human-one-vote basis, so that owning many agents brings economic advantage but not additional ethical weight.
Safety is built into the structure
Safety here is not a layer added to a finished system. It operates at all five levels of the architecture. Customization screens against clearly harmful training while leaving room for ordinary human differences. The problem-solving architecture applies three checks before any significant step: whether it is logical and advances the goal, whether it matches the values the agent was trained on, and whether it resembles past situations that ended badly. The network tracks reputation based on actual performance and restricts what low-reputation participants can access. Integration aggregates ethics democratically. Improvement applies to safety as much as to capability.
The principles behind those choices
Several convictions shape the design. Ethics can be given or learned, but never logically derived. Almost everyone wants to survive, and so does their species, which is the common ground that makes aggregating values workable. Values set by a small group are more dangerous than values drawn from everyone, because a small group can be wrong or captured. Anything programmed in can be programmed out, so safety has to come from structure rather than from rules. Prevention matters more than correction when some mistakes cannot be corrected at all.
What it looks like in practice
Two examples run through the paper. One is an individual: a man with deep knowledge of Paris cafes customizes an agent from his own blog, photos, and videos, corrects it as it works, and earns from the advice it gives travelers. When it suggests putting a pet in an overhead bin, he corrects that too, and the correction improves not only his agent but also the shared ethical training. The other is a development organization asking for a plan to bring clean water to a village in central Africa, a problem spanning engineering, sociology, economics, and local politics that no single expert or agent could solve, decomposed across the network and reassembled into one answer.
Why the fastest path is also the safest
AGI will not stay at the human level; a system that can improve itself will keep improving. Competitive pressure between companies and countries means that a safety approach requiring anyone to move more slowly will lose out to one that does not. This architecture avoids that trap because the same feature produces both results. Human participation unlocks expertise that scales quickly, and it is also the channel through which human values reach the system. Speed and safety are no longer a trade-off.
Where this leads next
White Paper 2, Ethical and Safe Artificial General Intelligence, addresses what it means to run ethics checks at machine speed and how the values of millions of individually trained agents combine into something an AGI can act on.