What is economic email filtering
The new category: filtering on whether a sender will pay a small cost, not on what their message says.

The basics that hold across nearly every attack class. Three layers, ten field notes, one starting point.
Most email security problems sit in one of three layers. Authentication on the sender side proves a domain is allowed to send. Content filtering at the receiving provider catches the obvious cases (mass blacklist domains, classic phishing kits, malware payloads). And a known-or-pay gate at the inbox door handles the senders the content filter cannot reliably classify: cold outreach, AI-generated pitches, polished first emails from unknown domains.
The first two layers are well documented. SPF, DKIM, and DMARC are standardized and supported in every major mail provider. Gmail and Outlook ship effective bulk-spam filtering by default. The third layer is newer and is the layer Rythm sits in. The field notes below cover both halves: how the existing layers work, where they break, and what fills the gap.
Read in order if email security is new to you. Skim by topic if you are evaluating a specific control. The reading list is curated to leave a knowledge worker with a working mental model of how an inbox is defended in 2026.
The new category: filtering on whether a sender will pay a small cost, not on what their message says.
A plain-English definition and the two-decade history of the idea.
The difference between encryption, custody, and what a provider sees.
The three sender-authentication standards and what they each do.
How mailbox providers score sending domains and IPs, and where the score lags reality.
A short explainer on the three things people mean when they say "the email did not arrive".
The difference between deletion, the spam folder, and a waiting room you control.
Two shapes that answer different questions about who reaches your inbox.
On the difference between probabilistic and deterministic systems, and why it matters in your inbox.
A founder note on why Rythm does not use ML to classify inbound mail.
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