In my last two articles I’ve largely argued that, with the emergence of quantum computing and its intersection with generative AI’s sprint towards AGI, that digital trust is failing at the layer where accountability is supposed to live.
Both pieces arrived at the same conclusion: a chain is the wrong model and we don’t have a replacement.
That’s not entirely true. We don’t have a replacement fully articulated. But we have the pieces of one and they are scattered across fields that don’t talk to one another currently. What follows is an attempt to assemble those pieces into something coherent. Not because I think I’ve solved the problem, but because I think the solution is starting to take shape, and if we wait to name it, then I think we will keep investing in the wrong solutions.
The Reframe
The proposition is this: trust, in a world of autonomous systems and probabilistic computing, can no longer be linear. Accountability can’t go through a series of links in a chain and depend on each link to vouch for the other. It has to emerge from patterns we learn to recognize as opposed to signatures we verify. Almost like a constellation.
A chain locates trust in a system. A signature is verified, a certificate is checked, a commit gets audited, and if each check holds, then the chain holds. The trust exists in the artifact, independent of the observer. We inherited this model from history: notarization, custody-of-evidence, institutional verification. Cryptography just gave a stronger math for each of these links.
A constellation, on the other hand, locates trust on the relationship between the system and the observer. Cassiopeia is real in the sense that it is a useful navigational tool, but it is not native to the stars themselves. It’s a structure the observer brings and is only clear when you know what you’re looking for. Constellations do not depend on a single star to be verified. Even if one star is dimmer than you expect, or you misidentify one, the constellation still holds because it was always a pattern not a proof.
I think this is the move we have to make. We have to stop asking whether a single artifact is trustworthy and start asking whether a system’s behavior forms a pattern consistent with what it claims to be.
The Philosophical Vernacular
This is a significant paradigm shift I don’t want to gloss over.
Under a chain model, trust is a property of the system. Under a constellation model, trust is a capacity in the observer. It is something we cultivate, practice, and sometimes are bad at. For example, two people looking at the same night sky will see different constellations based on what they’ve been trained to see. The same will be true of digital systems. Trust will become perceptual, unevenly distributed, and require genuine investment to develop.
This sounds like a regression, and in some ways it is. We are letting go of the fantasy that trust can be certified. And that’s okay because that fantasy was always fragile. Every form of digital governance has always depended on the institutions we chose to believe in and that was never as rooted in mathematics as we pretended. The chain model was only going to work as long as the systems it governed were small enough that verification was possible in principle. We are past that point.
We are also gaining something in return. A constellation model is honest about what trust has always been: a social, contextual achievement that depends on observers who know how to look. It treats trust literacy as something we teach as opposed to something we assume. It accepts that trust does not have binary answers and is never going to.
Compatibilism, which I talked about in my determinism piece, sits comfortably here. We can finally stop arguing about whether a system is deterministic or autonomous and start asking whether its behavior, observed across many different vantage points, forms a coherent pattern we can recognize and reason about. Whether that coherence is derived with math or something else doesn’t matter as much anymore, because the pattern is the pattern.
The Technical Vernacular
None of this is useful if it can’t be built.
Trust, in a constellation model, is a statistical property of a behavioral distribution. It is inferred from many different observations, and becomes actionable when that distribution shows coherence across time, context, and the observer. The words here are important.
The statistical property isn’t a flag on an artifact, it is a characterization of how the artifact behaves.
In the behavioral distribution we are not a single action, but observing the shape of many actions and asking if that shape is consistent with who the system claims supposed to be, its purpose, and its constraints.
And many different observations are overlapping, partially redundant vantage points whose agreement falls on the evidence itself. One observer can be fooled, but a hundred who agree on a pattern can’t without all one hundred being compromised. This is a much harder attack surface than forging a digital signature.
Coherence is the mathematical analog of what we used to call integrity. Simply put, a system whose behavior forms a recognizable pattern for a week and suddenly diverges is not trustworthy.
Notice the framing doesn’t require interpretation. We don’t need to understand why a system behaves the way it does, we just need to characterize its behavior well enough to recognize when it strays from its path. Kind of like an immune system, or how humans have always trusted one another. We trust people whose behavior patterns we recognize, and lose that trust when that behavior changes.
Actual Implementation
We don’t have the infrastructure to build this constellation yet. It would require fingerprinting at scale, distributed witness networks, models trained specifically on coherence, and a principled way of combining observations to produce high-confidence signals.
I don’t even think we have the legal and regulatory vocabulary for this yet.
More importantly, the compute power for this would be incredible. But it’s inline with the confluence of quantum computing and the race to AGI.
Thankfully, we have the foundations to build this right now. We have telemetry, we just need to reframe it for behavioral characterization. We can start doing that today.
We are seeing decentralization in identity work, trust registries, attention networks, and beyond. But this work is a slow burn and has too much deference to the chain these efforts are meant to replace.
Foundational, we also already have the pattern literacy developing in the processes and tools that we are building to protect against autonomous threats. We need to study it, formalize it, and teach it as vigorously as we can.
The only thing I’m most skeptical about is the change in culture required for this new mode of thinking and operating. The chain model is ingrained deeply in our DNA, so moving to a model where accountability emerges from patterns instead of from authority is going to force resistance from every organization whose business depends on it being the authority. And this is not something we can solve with technology.
The Pattern We Haven’t Named
I want to end where I started, by admitting that I don’t fully know what I’m describing. The constellation is a metaphor and metaphors are scaffolding not buildings. The actual architecture will look much different from what any of us can imagine in advance.
But I think the shape is right. Trust has always been a pattern we recognized more than a property we verified and the chain model was an attempt to formalize that recognition. It’s worked well for a long time, but our systems are quickly outgrowing it and no amount of better math or audits will close the gap.
What comes next is harder. We will be asked to give up the promise of certainty and replace it with the discipline of perception. We will actually have to treat trust as a relationship instead of a credential.
This is the right direction though, and I think the honest version of this moment is to say it out loud, even if it’s been said before.
The stars don’t form constellations on their own, after all. We have to learn to see them.
Signal Intent 