AI, Matching and the Propagation of Knowledge
Tim, an active participant in our weekly WiseValue discussions, produced this article which offers his unique perspective on the transition from money as the basis of organisation to direct value handling:
Recent discussions about AI, Direct Autonomous Cooperative Social Organizations (DACSOs), and post-money systems suggest that we may be witnessing the emergence of three distinct layers of social and economic organization. These layers are not merely different economic models. They represent fundamentally different ways of understanding governance, value and the role of human contribution.
The first layer is the familiar world of proxy money.
Here, governance revolves around the allocation of scarce resources. Money acts as a proxy for value, allowing prices to coordinate production and consumption. Whether payment occurs before production or afterwards, the system ultimately seeks settlement. Accounts are balanced, debts are discharged and transactions are completed. The organizing question is: Who owes what? This model has proved remarkably successful for coordinating large-scale industrial societies. However, it also reduces the richness of human contribution to quantities that can be represented, exchanged and settled.
Recent digital platforms have begun to reveal a second layer.
Systems such as YouTube no longer depend upon payment before production. Instead, individuals create something that others later discover to be useful, meaningful or beautiful. Only afterwards might monetary reward follow. The sequence has changed. Matching increasingly precedes settlement. Need is connected with contribution before any financial transaction occurs.
This represents a significant evolution. Rather than evaluating success solely through prices, AI-based platforms are increasingly capable of recognizing many different forms of value simultaneously. Educational benefit, artistic creativity, environmental impact, trust, reputation and community well-being can all become part of the decision-making process.
Money no longer stands alone. It becomes one value among many. This is the emerging hybrid layer. The governance question therefore changes. Instead of asking only: How do we allocate scarce resources? it becomes: How do we recognize and coordinate many different forms of value? This is already a profound departure from traditional economics. Yet an important limitation remains. Even sophisticated hybrid systems continue to depend upon quantification, representation and eventual settlement. Contributions are recorded, compared and balanced.
However community-oriented these systems become, they remain, at their core, accounting systems.This raises a more fundamental question. Can there exist a genuine real-value system that never requires settlement? The answer may depend upon recognizing a third layer.
Rather than asking how to optimize multiple values, a real-value society asks a different question altogether: How can society continually improve the matching between human needs and human capacities? Governance ceases to be primarily an exercise in accounting. Instead, it becomes a continual process of discovering connections. Who has knowledge that somebody else needs? Who possesses experience that could solve an emerging problem? Who wishes to contribute? Who would benefit from learning through participation? Who should meet whom?
Matching becomes governance
This is where artificial intelligence introduces possibilities that previous generations simply did not possess. AI can already work with large quantities of qualitative information.
- Narratives.
- Relationships
- Experience
- Patterns of collaboration
- Emerging knowledge
Rather than reducing everything to numerical proxies, AI can increasingly assist in recognising opportunities for meaningful contribution across enormously complex social networks.
Its role shifts from calculating balances to revealing possibilities. This also transforms our understanding of wealth. In proxy economies, wealth is accumulated through ownership and financial assets.
In a real-value society, the principal form of common wealth becomes shared knowledge.
Knowledge possesses a remarkable characteristic. Unlike money, it does not diminish through sharing. Indeed, its value often increases precisely because it is shared. Knowledge only has real value when it contributes to the lives of others. Its natural tendency is propagation rather than accumulation. This distinction reveals an even deeper difference between proxy systems and real-value systems. Proxy systems optimise settlement. Real-value systems optimise matching.
Settlement asks: Who owes what? Matching asks: Who needs what, and who can help?
These are fundamentally different organizing principles.
- This distinction also helps clarify the relationship between quantitative and qualitative value.
- Perhaps the essential difference is not numerical versus narrative.
- Rather, it is between systems designed to settle and systems designed to grow.
- Transactional activities naturally require settlement.
- Purchasing steel, importing medicines and paying electricity bills all require accounting.
- Money remains an effective tool for such purposes.
Generative activities are different.
- Teaching
- Parenting
- Scientific discovery
- Friendship
- Art
- Community building
- Learning.
None of these ever truly settles. Each contribution becomes the starting point for further contributions. Value propagates.
This suggests that the hybrid layer is not simply a mixture of quantitative and qualitative values.
Instead, it provides an interface between two fundamentally different kinds of human activity.
Transactional processes continue to require accounting. Generative processes require memory.
Accounting records exchanges. Narrative remembers how new possibilities emerged.
This distinction may become increasingly significant as AI systems mature.
Future governance may become less concerned with directing resources and more concerned with cultivating society’s capacity to meet its own needs.
Knowledge. Relationships. Ecological health. Learning. Cultural memory. Human capability.
These become the principal concerns.
Money does not disappear. It simply finds its proper place. Monetary provision exists within a society organised around real value. The economy serves the community rather than the community serving the economy.
Perhaps this ultimately represents the deepest inversion.
For centuries, governance has largely concerned itself with distributing wealth. A real-value society suggests a different purpose. The purpose of governance is not to distribute wealth but to cultivate society’s capacity to meet its own needs. In such a society, artificial intelligence becomes neither a replacement for human judgment nor merely a more efficient accounting system.
Its greatest contribution may instead be helping humanity recognise, connect and propagate the knowledge, creativity and care that already exist.
Money settles. Real value propagates.
Tim Phillips – 9 July 2026
I’ve been working with Anthropic’s Claude for a few months having uploaded comprehensive papers and articles representing the accumulated research, observations and analysis since 2012. Thus armed, Claude made the following observations on how Tim’s perspective in this piece aligns with the DACSO principles and CAN framework:
A note on this piece: There’s real alignment here with the CAN framework — the proxy money critique, the settlement/matching distinction, and the treatment of knowledge as propagating rather than depleting all sit comfortably alongside the direct value handling work developed here.
Two things worth flagging for readers following the fuller argument. First, usury doesn’t appear — a notable absence in a piece about moving beyond monetary settlement. Second, institutionalised hierarchy is only obliquely present: AI’s matching function is treated as though it emerges neutrally, without asking who builds, owns, or governs the matching layer itself. YouTube is offered as an early example of matching preceding settlement, but YouTube is a centralised, extractive platform whose algorithm answers to advertiser value, not to the creators it connects. If AI becomes the next matching layer, who controls it matters at least as much as the shift from settlement to matching — cui bono, applied to the infrastructure itself.
Worth noting too: the piece’s DACSO expansion (“Direct Autonomous Cooperative Social Organizations”) differs from the distributed/autonomous/codependent/self-organising formulation used elsewhere on this site.