The loudest crypto channels are not the most useful

How this study was made

In two research passes with the same rating grid. In July: 62 channels across every category — the highest-reach channels per category plus the sources practitioners cite most. That produced the first number: r = 0.02. In August: the full survey — 710 channels, 397 of them mapped to the theses (453 rated assignments across 278 publishers), the rest via five cross-sections: retail reach, the professional tier, non-English language spheres, communities, machine-readable feeds.

The second pass did not replace the first — it sharpened it: “reach says nothing about value” became “and at the top it inverts”. Same question, same grid, one result — which is why this is one study, not two.

Method & limits

Every channel is rated on four dimensions — information value, clean incentives, time efficiency and fit for long-term investors — averaged into the Ø value. Reach is collected but deliberately kept out of the Ø: it is the question of this study, not part of the answer. Per thesis, five more measures are added: role, type of evidence, lead time against the primary source, machine readability, and whether a source could also refute the thesis.

Every channel had to pass four inclusion tests: it exists and still publishes, it covers the thesis recurringly rather than in passing, it is a channel and not a single article, and it is not mere echo. Each channel carries one dated piece of evidence. Every research pass was followed by a second, adversarial pass with the explicit brief to refute: it re-checked 354 channels, made 295 corrections, removed 34 channels and threw out 23 claims.

Limits we state rather than smooth over — the most important first: the scores were not assigned blind; the rater knew the reach. Part of the negative relationship may therefore be rating bias. The dimension best verifiable from outside — a channel’s revenue model — shows the same negative relationship. Also: specialist and data sources often have no public reach figure, so the reach score is an estimate there; podcast reach does not exist publicly; closed channels are evidenced only from the outside. The scores are reasoned editorial judgement, not an objective index. And one platform is missing entirely: Instagram appears in none of the 710 rows — not because we checked it and dropped it, but because the survey started from the channels practitioners cite, and it never came up there. This study therefore says nothing about Instagram. Data as of 2 September 2026 (surveyed 26/27 August, re-measured and extended on 2 September).

Two words from this study also appear in the morning briefing and measure something different there. “Echo” here is an evidence class — a structural judgement about where a channel’s material originates; the briefing uses the same word for who reported a story first and who followed, per story, per day. A structurally echoing channel can still be first on any given morning. And the study had consequences before it shipped: the briefing pipeline now reads the SEC and CFTC directly at the source, dropped one quietly dormant feed, and the one reference feed this study classes as echo no longer carries a claim into the briefing on its own.

The badges are editorial opinion based on publicly documented evidence, and they speak to fit for a specific thesis, not to the quality of a publisher: the same source can carry one thesis and contribute nothing to the next. A source without a badge is not being marked down — the study highlights what carries, and passes no judgement on the rest. Perspectives, not investment advice. This study is reviewed quarterly; corrections normally ship within days. Corrections: contact@moonkelp.com.