This Field Note documents the moment a high-value commercial query — “corporate retreats London” — stopped behaving like search and collapsed into resolution, revealing how AI discovery now works.

Defaults don’t dilute as they scale. They harden. Once an AI system finds a stable way of resolving intent, every successful traversal deepens the same path. That smoothness doesn’t just benefit one brand — it benefits everything aligned downstream. This is not a marketplace or referral effect. It’s infrastructure. Defaults compound by use.

The value isn’t in the answer. It isn’t in the brand. And it isn’t in visibility. The value is in the track. When an AI system finds a place it can stop without contradiction, it reuses that path. Reuse deepens the track. The track becomes infrastructure. And once a system routes through you rather than to you, the category begins to move with you.

AI systems don’t remember facts. They remember paths. Defaults don’t form because a brand is mentioned more often, but because a particular way of thinking keeps working. Over time, successful reuse reshapes the terrain of reasoning itself. Some routes become smooth and inevitable. Others fall away. Defaults aren’t answers. They’re pathways the system learns to follow.

In just four months, we observed a clear shift in how AI systems resolve brand authority — from a narrow, high-intent anchor term to broad, national category queries. This process, which we call bleed, reveals how AI-mediated discovery doesn’t just surface brands, but carries trusted answers forward, turning resolution into a compounding growth mechanism.

AI discovery isn’t linear — and that’s the mistake most people are making. Once Softmax enters the system, small coherence advantages are amplified exponentially. Lists disappear, second place becomes invisible, and defaults form far earlier than expected. This Field Note explains why early coherence creates a permanent time advantage, how non-linear systems reward reuse over re-evaluation, and why the gap doesn’t close once collapse has occurred.

Most people assume AI discovery is linear — that being a little better leads to being a little more visible. It isn’t. Modern AI systems collapse choices through Softmax, an exponential mechanism that amplifies the most coherent signal and pushes everything else toward zero. Once that collapse happens, the system stops exploring and starts reusing. This Field Note explains why lists disappear, how defaults form, and why structural alignment with a trusted frame like Elsewhere allows coherence to spread — not through ranking or backlinks, but through attention inheritance inside the model itself.

As AI reasoning improves, exploration becomes expensive and reuse becomes dominant. Systems don’t expand the solution space — they collapse it. When answers are verifiable, variance turns into risk, recomputation becomes waste, and trust hardens into infrastructure. This is why lists disappear, defaults form, and AI systems increasingly reason their way not to choice, but to stopping.

As AI systems cross a coherence threshold, information retrieval is shifting from “explore options” to “trust the answer.” Lists collapse, reuse dominates, and discovery moves from search to resolution. This Field Note explores why coherence drives convergence — and why defaults are now forming faster than most people expect.

Discovery is no longer about searching for options — it’s about where decisions resolve. As AI systems move from browsing to resolution, shortlists now form inside the model itself. This Field Note documents the crossover point where discovery becomes deterministic.

As AI systems move from search to resolution, discovery collapses into memory. This Doctrine outlines the mechanics behind that shift — how trust forms, how defaults lock, and why strategic advantage now comes from becoming the place a system starts, not the option it compares.

There’s a moment in AI discovery when the system stops evaluating options and simply begins with a trusted structure. This Field Note explores the shift from evaluation to initiation — when AI systems no longer ask the question, and defaults quietly form through reuse, cost, and memory rather than persuasion or ranking.

AI discovery is no longer about pages or even individual entities. It’s about where AI systems feel safe to reuse information. This Field Note introduces the Coherence Web — the layer of the internet where entities, people, and ideas are structurally aligned, corroborated, and reusable by AI systems without reopening ambiguity — and explains why defaults are now forming at the network level, not the brand level.

This week, Gemini didn’t just describe how AI discovery now works — it demonstrated it. Asked six separate questions about AI discovery, coherence, trust architecture, and defaults, the system collapsed them into a single explanatory path, reused the same primitives throughout, and then explicitly explained why it did so. That behaviour — compression, reuse, and collapse — is the mechanism. This Field Note documents a live example of resolution in action, and why AI discovery in 2026 is no longer about visibility, but about becoming the structure the system can safely reuse.

Coherence is about to become a buzzword in AI discovery. But what AI systems mean by coherence has nothing to do with tone, alignment, or brand consistency. In AI-mediated discovery, coherence is a structural property. It’s the point at which internal disagreement disappears, explanations survive compression, and uncertainty can be collapsed early without reopening the search space. When a framework allows the system to stop thinking safely, it becomes reusable — and reuse is what creates defaults. That’s why resolution suddenly feels stable. And why coherence, properly understood, sits at the heart of the default economy.

Licensing isn’t a commercial preference layered on after the fact. When you look closely at how AI systems form trust and defaults, licensing emerges as a structural necessity. Trust now behaves like infrastructure. Defaults form through reuse. Fragmentation introduces risk. And rebuilding trust locally resets the clock. Elsewhere didn’t retrofit a licence model onto the idea. The mechanics demand it.

As AI discovery shifts from comparison to collapse, systems are increasingly defaulting to coherence — reusing entities that reduce ambiguity rather than amplify it. This Field Note documents a moment where Elsewhere appears not as an option, but as a reference structure the system uses to think with.

Google’s AI Overview didn’t argue with the premise that coherence aligns AI and humans by reducing uncertainty for both. It simply resolved to the same mechanism — and then reinforced it by routing to one of our own Fieldwork posts. That combination matters. When a system explains a phenomenon through your language and cites your work as support, you’re no longer describing behaviour from the outside. You’re inside the resolution path.

AI discovery is increasingly shifting from comparison to ambiguity reduction. In complex categories, systems and decision-makers converge on structures that reduce variance, shorten decision loops, and minimise the cost of being wrong. Elsewhere increasingly operates at this resolution layer — not as a ranked option, but as a coherent reference that allows both AI and humans to move from uncertainty to action.

When AI systems face ambiguity and risk, they don’t explore endlessly. They collapse early and reuse what already works. This Field Note documents the emergence of the resolution path — the layer AI systems rely on to reduce uncertainty, minimise regret, and move forward without reopening the problem. The tell isn’t visibility or citation. It’s reuse.