The same problem killing generic content is killing generic outreach

AI lowered the cost of producing halfway decent content to near zero. The result: total content volume exploded while average quality dropped. The signal-to-noise ratio collapsed. What rises to the top now is specific, insider, credible. Content that couldn’t have been written by a model with no lived context.

The same thing happened to outbound at the same time, for the same reason. AI made it easy to generate personalised-looking sequences at scale. Every SDR now sends messages that reference a recent LinkedIn post, name a competitor, and include a relevant pain point. The personalisation signals that used to differentiate a sequence now appear in every inbox. Recipients stopped reading them the way they stopped clicking banner ads.

The answer to both problems is specificity that AI can’t replicate.

In content, that means proprietary observation. Patterns you’ve seen across multiple implementations that nobody else has access to. A writer who has watched three different ABM programmes fail at the same stage has something to say that a model trained on public information can’t reproduce.

In outbound, that means signal precision. Using combinations of signals that indicate active buying motion on a specific account, not generic firmographic fit. A message triggered by a job posting for a RevOps hire, a pricing page visit, and a champion departure in the same 30-day window is specific in a way that no AI-generated sequence can fake without access to the underlying data.

The mistake is responding to noise by increasing volume. More content, more sequences, more touches. That compounds the problem rather than solving it.

The correct response is raising the specificity threshold. Publish the observation nobody else has access to. Send the sequence only when the signal combination is precise enough to justify it.

The teams that do this produce less and convert more. The teams that don’t get buried in the noise they helped create.