Trust and quality notes
- Last updated
- September 12, 2026
When capable AI can analyze the same public material for everyone, having access to more information is not automatically an advantage. The useful difference may come from information that was never published, recorded, or reduced to a clean data set in the first place.
Khe Hy argues that unique context will become a decisive human contribution after intelligence is commoditized. In his original essay [1], he locates that context in informal conversation, social perception, trust, and access. His provocative examples include golf and being funny at a mixer, but the underlying point is serious: differentiated insight often lives between the formal sessions and outside the searchable record.
Public intelligence becomes less distinctive
Hy starts from a competitive observation. Investors have long searched for an informational advantage, often called alpha or edge. Digital data once offered fertile ground for that search, but he argues that swarms of agents can now examine public sources for novelty. If many systems can inspect the same troves, extracting one more pattern from shared information becomes harder to treat as a durable distinction.
This does not mean public information becomes useless. That would go beyond the source. It means public information becomes less likely to be uniquely yours. A strong model can help many people summarize the same report, compare the same announcements, or search the same archive. The output may be intelligent without being differentiated.
Hy’s answer is context that does not enter generalized models or the public internet. He points to side conversations at conferences, skeptical interpretations of body language, and candid remarks at social gatherings. These are not simply extra facts. They are observations embedded in relationships and situations.
My interpretation is that automation raises the value of provenance. The important question shifts from “Can you produce an analysis?” toward “What did you notice, who trusted you enough to speak openly, and why should this context change the analysis?”
Human access is built, not scraped
Hy says people will need to bring more people into their orbits and create differentiated pools of insight. This is a social task. Access depends on curiosity, reciprocity, reputation, and repeated contact. It cannot be created instantly by pointing a tool at another database.
His image of golf as reconnaissance is deliberately playful. The broader claim is that spaces dismissed as peripheral to work may become central to gathering context. A hallway exchange can reveal uncertainty that never appears in a prepared presentation. Humor can lower defenses and invite honesty. Time spent with people can disclose what a transcript alone cannot carry.
It would be easy to misuse this argument as permission for endless networking. That is not the most useful reading. Collecting contacts is different from building relationships in which people share what they really think. Hy contrasts the person who leaves with insight against the person who leaves with more LinkedIn connections. The quality of access matters more than the count.
For a reader new to AI, the practical lesson is not that every meeting should be recorded and fed into a model. Hy explicitly argues that some conversations will not be captured by recorders or smart glasses. Their value may depend on remaining situated, private, and human.
Context requires judgment before it reaches a tool
A person who brings unique information to an AI system still has to decide what is relevant, what is sensitive, and what can be shared. Hy focuses on gathering context rather than laying out a privacy framework. Still, his thesis implies a responsibility: access does not automatically grant permission to upload or redistribute what you heard.
That responsibility is my interpretation, but it follows from the relational nature of his edge. If candid context exists because someone trusted you, careless handling can destroy the source of the advantage. Human judgment must sit between the conversation and the system.
There is also a difference between observation and certainty. Reading body language can add context, but it is still an interpretation. An informal remark can be revealing without being representative. The human collector therefore contributes more than raw input. They evaluate confidence, motives, contradictions, and the limits of what they know.
This is where Hy’s argument protects a meaningful role for people. Agents may process context at scale, but people cultivate the relationships that produce it and interpret the conditions under which it appeared.
The competitive advantage becomes more personal
Hy’s view of humanity after automation is not centered on outperforming machines at generic analysis. It is centered on bringing machines material they cannot independently obtain. The best access yields the best context, and better context can lead to more differentiated insight.
My interpretation is that this rewards qualities that conventional productivity systems often treat as secondary. A broad social world, an unusual hobby, the ability to make someone laugh, or the patience to stay after the formal event can expose perspectives that structured research misses. These activities are not valuable because they look busy. They are valuable because they create contact with reality beyond the common corpus.
The idea also changes how a team might divide work. Instead of asking people to spend all day reproducing analyses that AI can draft, a team could reserve human time for customers, peers, field observation, and difficult conversations. Then it could use AI to organize and test what people learned, within appropriate boundaries. This workflow is my application of Hy’s thesis, not a procedure specified in the source.
Keep the human source of context alive
The central risk in Hy’s picture is that people respond to better intelligence by retreating further into screens. If everyone searches the same sources with similar tools, analysis can converge. Distinction requires going where the shared system cannot go on its own.
That does not require theatrical networking. It requires sincere participation in communities, careful listening, and enough trust for nuance to emerge. The funniest person in the room matters in Hy’s essay because humor can create social access, not because comedy is a new job requirement. Golf matters as a setting for unguarded exchange, not as a universal prescription.
After automation, people may become what Hy calls collectors of AI context. The phrase captures a partnership: humans gather situated, private, or tacit understanding, while AI helps work with what can responsibly be brought into the analysis. The stronger the human network and the more discerning the collector, the less generic the result.
If you want to build a workflow that uses AI without flattening your hard-won context, review one process with Agentic Workers.