Is AI Slop the New Spam?

Spam didn't disappear because people stopped sending it. It disappeared because the inbox got a filter.

Something new is filling the space. AI-generated content is arriving in inboxes, shared documents, vendor proposals, and internal memos at a volume the enterprise hasn't reckoned with. The mechanism is different this time. Spam came from strangers. What's arriving now comes from colleagues, counterparts, and vendors. It looks professional. It often runs two or three times longer than it needs to. And the person who sent it, in most cases, has not read it.

We call the consequence the decoding burden: the cognitive and time cost that transfers from the sender to the recipient every time an AI-generated communication goes out without review. The decoding burden doesn't feel like a cost to the sender. It is entirely a cost to the recipient.

The editor left the building

AI makes it possible to generate confident-sounding text on topics you don't understand well enough to write about yourself. An account manager can produce a technical summary of a product they've used twice. A coordinator can draft a strategic memo about a market they read one article about. The barrier to producing text has dropped to near zero. The barrier to producing accurate, well-calibrated, appropriately-scoped text has not.

AI output also reads as plausible at a glance. The register is professional. The sentences are complete. The document has the shape of a document. Unless you know the territory well enough to catch what's off or missing, it passes a quick scan. It only breaks down when someone who actually knows the subject reads it carefully. That person is usually the recipient.

And the edit step has been outsourced along with the writing step. When someone writes, they edit by necessity: deciding what to include, what to cut, what needs more specificity. When someone uses AI to generate content, those choices belong to the model. Sending the output without review means the sender has outsourced not just the writing but the judgment that should accompany it. The document arrives. The judgment didn't.

These three conditions compound. The output looks authoritative. The sender didn't review it. The recipient gets a multi-page document that requires either prior knowledge to interpret or significant time to decode. Neither cost was visible to the sender.

The cost doesn't disappear

The sender's experience is efficiency. A document that would have taken two hours to write took twenty minutes to generate and send. By the sender's metric, AI worked.

The recipient's experience is the opposite. They receive a five-page brief containing two paragraphs of useful information, surrounded by paragraphs that don't follow and conclusions that don't connect to the premises. Or they receive an eight-sentence email where three sentences would have done it, padded with context they didn't ask for. They spend time they don't have decoding something they didn't request.

The time didn't disappear. It transferred. Twenty minutes saved at the sending end appeared as thirty minutes of overhead at the receiving end. The decoding burden is this transfer: the cost the sender shed lands on the recipient.

At the scale of a team, the math becomes uncomfortable. Each recipient absorbs their portion individually. No line item. No flag. No system to surface it. The cost is real, distributed, and completely untracked.

People have always sent bad emails. What's changed is the rate. A person writing badly is constrained by how fast they can write. A person generating badly is constrained by nothing. The decoding burden doesn't scale with the number of bad writers in the organization. It scales with the throughput of the tools they're using.

The invisible ledger

Spam was visible because it came from outside. The inbox filter worked because the signal was strong: unknown sender, no prior relationship, suspicious domain. Something in the message declared itself as noise.

The decoding burden is invisible because it comes from inside. The communication is from a person you work with, in a format you recognize, on a topic that's legitimate. Nothing triggers a filter. There is no filter. Nothing about the signal looks like noise.

The productivity story makes this structural. AI adoption inside most enterprises is measured at the generation end: time saved drafting emails, documents produced per employee, summaries generated across functions. These metrics capture the sender's experience. They don't capture what happens downstream. The person who saved twenty minutes generating a vendor brief doesn't appear in the same dashboard as the three colleagues who each spent twenty minutes making sense of it. The org's AI productivity number went up. The aggregate cost went up too. Only one of those is being tracked.

No filter is coming

Spam became a manageable problem because it was legible to a system. It had detectable properties: unfamiliar senders, characteristic vocabulary, structural patterns that distinguished it from wanted communication. A filter could learn those properties and catch spam before the inbox saw it.

The decoding burden doesn't have those properties. AI-generated content sent without review isn't distinguishable from good content by an automated system. A knowledgeable reader can tell. But that reader is the one absorbing the cost, not the one positioned to prevent it at the source.

What would prevent the cost is a norm: review it before you send it. Not as a suggestion. As an expectation with accountability behind it. That norm doesn't exist in most enterprises. The technology moved faster than the culture around it, and the expectation that AI-assisted communication requires human judgment hasn't solidified into something most teams enforce.

Where it gets fixed

The organizations starting to address the decoding burden aren't waiting for a technical solution. They're setting expectations. Some have added a review step to AI-assisted communication workflows: before any AI-generated document or email goes out, the sender confirms they've read it. Not necessarily edited it. Read it.

A smaller number have started measuring the receiving end alongside the generating end. Tracking how often recipients follow up for clarification. How often documents get re-explained in the meeting they were supposed to prepare for. These are rough proxies, but they surface what the productivity dashboard misses.

The norm itself isn't complicated. Getting an organization to treat “review before you send” as a non-negotiable rather than a preference is where the work is. Especially when the time savings are immediate and the cost lands on someone else.

Until that norm exists, the decoding burden goes where it always goes: to the recipient. Not because the sender chose to impose a cost. Not because of policy. The cost transfers automatically the moment the message is sent.

Spam got filtered. The filter worked because the signal was legible and the cost was external. The decoding burden has neither property. It will keep arriving until the organizations receiving it decide it costs enough to stop.

Trackmind helps enterprises design AI workflows and the operational discipline to run them. Learn about our AI and ML practice.