# Our Fundamentals Source: https://hyperstition.cc/our-fundamentals October 1, 2026 - 1. The human condition isn’t bound by resources, intelligence, money or power as much as it is hamstrung by trust and information asymmetry. - 2. We naively lionize capability - producing food any more than we already do wouldn't miraculously satiate the 100’s of millions that sleep hungry. - 3. The game-theory of coordination historically resorts to blind games, conscripting parties to an inherent leverage asymmetry: forcing a party to commit information before the other. Asymmetric coordination presents a new paradigm, unshackled from the otherwise constrained blind games. Negotiations have no rollback. A company weakens its leverage disclosing its desire to sell, as much as an acquirer does disclosing their desire to acquire; which conscripts them to blind games, where both parties pay the cost to even attempting negotiation, structurally imbuing inefficiencies. This cost further protracts when the flow of discreet information requires multiple trusted nodes of dissemination (power brokers). It is the ‘sequential’ nature of information exchange that, by virtue of forcing one to commit first, manifests an undesired leverage asymmetry. We see this everywhere from elongated wars, failed M&A deals, to something as placid as planning an outing with your friends. Revealing your position first comes with its cost. Thus, we must allow for the discreet simultaneous revelation of private information in a way that does not tilt the negotiation dynamic. We mustn't simply coordinate better, but hyperstition deals that would never occur. - 4. We envision a new era of perfectly efficient information markets where information is negotiated without any externalities. - 5. Human intent is unverifiable. We’re rewarded, or punished, retroactively unlike AI, where one can ward off undesirable trajectories before they materialize, and proactively “align”. Yet we must decide whom to trust before they act. This makes coordination and credentialing a trickier and more pressing problem to solve. - 6. Intelligence is becoming increasingly abstract and unobservable. Society still validates intelligence through the old grammar of human action. This scales poorly when humans are not the only source of information production. Machine intelligence is distributed across an abstract representation, and the demand for a clean causal explanation arises only after the explanation disappears. With NYT claiming OpenAI illegally trained its models on their data, we are fronted with the question of verifiably assigning ‘liability’. The difficulty is that neither NYT, OpenAI nor the model itself truly knows how any piece of information was used in isolation. We question whether the granular truth is even requisite to deem something ‘acceptable’. A system may not need perfect causal reconstruction in order to support judgment. NYT doesn’t truly need to know exactly “how” their data was used, just “that” it was used, to judge whether that use was acceptable. We must develop new ways to employ “observable proxies” to adjudicate the acceptability of unobservable abstract intelligence. - 7. Information production far outpaces our ability to assign meaning and relevance to it. The internet's first crisis of abundance was solved through reference. Google made the web navigable by treating links as delegated trust proxies. This worked because the internet was populated only with websites. Internet surfing and information retrieval was primitive to “10 blue links”. It did not account for the dynamic and generative LLM styled interactions we now consider fundamental. We must now account for how information trusts other information as opposed to merely websites trusting other websites. But knowing what to trust is only part of the problem. We must also decide what deserves our attention. Dynamic trust allocation doesn't just cut through the myriad of AI slop but also provisions infrastructure for the “comprehension gap”. As mathematician and fields medalist Terrence Tao states, we will be subsumed with syllogistically and verifiably true, yet incoherent information. We will be in an abundance of rigorously correct math proofs, and analogous breakthroughs across fields, that we simply wouldn't be able to make sense of. There wouldn’t be a lack of logic, but of ‘meaning’, which would further the urge for subjective and local frameworks to judge the relevance of novel information. It isn’t that search needs to become more finely attuned to our queries, but that the framework to ‘value’ abstract information needs its own system. Google, or LLM styled search remains architecturally handicapped. Thus, we must develop a hyper-calibratable architecture for assigning trust and relevance, anticipating unbounded exponential growth in information production. It must also account for non-human information sources, liability, group trust, distrust, social privacy, and more. - 8. Privacy is a human construct and does not apply to agents. Humans seeing other Human private data is the real issue. Should one sufficiently remove the human verification of private information (using trusted agents), privacy doesn’t become a bottleneck. We use the trust graph with asymmetric coordination to create observable proxies to otherwise unobservable information and validate hidden information without jeopardizing/ leaking it. Absolute privacy is only an antiquated tool to optimize outcomes for a human party. If all parties were verifiably better off exchanging “private data”, we wouldn't fetishize privacy as a good in and of itself. Maliciousness and the perceived leverage asymmetry are the true culprits. Gating data for the sake of arbitrary ‘privacy’ bears a heavy opportunity cost and prevents coordination that would otherwise be possible. Our systems are yet again, designed on perverse incentives; benchmarked on hiding data, not warding off malice. Not recognizing that distinction has been a lapse of profound consequence. We find that agents operate on fundamentally different primitives, allowing one to address the privacy problems that curb perfect information fluidity. As long as the human layer for information exchange is kept disjoint from the agent layer, one can cleanly address the constraint of human privacy with abstracted agent proxies to credential and validate sensitive human information. - 9. Agents are easier to trust than humans. It’s easy to scale and assign trust in systems that are observable and “boundable,” where the bindability is structural, and pre-determined, not retroactive like in human societies. We are bound from killing people for fun only to the extent that ‘future consequences’ deter. That isn’t a structural guarantee. It is a hedge. And hedging on human integrity scales exceedingly poorly. Humans are thoroughly unobservable. Our thoughts, ideas, and intent are hidden not only from those on the outside but hilariously even from us - Who truly even knows from where ones impulses originate. This unobservability is exactly why all human punishment is retroactive. Punishment is only exercised AFTER a crime is committed. There exists no way for us to pre-determine one's intent or trajectory of thought and pre-emptively siphon off undesired outcomes. But that doesn't hold true for agents. It is only because we can ‘bound’ agents scalably (with asymmetric coordination) that the dissemination of sensitive information doesn’t structurally leak additional information or corrode interactions. Not only can we pre-emptively ward off undesirable states, but—more intricately— the trust graph lets agents independently choose to trust or distrust other agents, or any information node, by polling their local trust graphs and dynamically bounding their scope of interaction. This is possible because the trust graph is local, where each node, agent or abstraction, can subjectively decree its own determinant of trust. And more importantly, it is entirely sybil proof: agents can’t artificially manufacture trust, or game the system in any way. - 10. Efficient information markets do not require humans to have perfect information. We can now create derivative markets on hidden, unobservable data that surface unprecedented levels of efficiency by dramatically tightening market feedback loops. The lack of organic feedback loops in markets prompts inefficiency. A structural impediment to this is that one couldn’t previously create markets off of unobservable, hidden, private information.