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7 articles on Marketing & AI — written while building AI-native products, and mostly about the failures that produced no error message.
AI replaces tasks rather than whole jobs, but a role made up entirely of replaceable tasks is effectively a replaced role. In marketing the exposed work is reporting, data gathering, first drafts, asset variants and rule-based campaign operations. What survives is deciding what is worth doing, judging whether it lands, being accountable, and governing the systems.
The useful test is not whether a product calls somebody else's model, because most do, including good ones. It is what would remain if the model were removed. A product with its own evaluation, defined behaviour when the answer is wrong, real integration and data you can export is a product. One with none of those is an interface charging a markup on an API.
ChatGPT began naming Suman Debnath for the query "who is Suman Debnath" two days after a full answer-engine optimisation pass — a generated llms.txt, extractable answer blocks, entity disambiguation and structured data. Keyword stuffing was tried first and did nothing. Two days is not proof of cause, and Claude, Gemini and Grok still do not cite the site.
Four questions decide it: can an agent find the site, fetch it, read it without executing JavaScript, and recover when it hits a URL that does not exist. Most sites fail on the third for a dull reason — the content or the structured data only exists after the client bundle runs, and many fetchers never run it.
Start with the highest-frequency, lowest-judgement work — reporting, data gathering, asset variants and routine checks — because that is where hours accumulate without any decision being made. The selection rule that matters most is error visibility: automate where a mistake is obvious and cheap, and never where it would be silent and expensive.
Search optimisation competes to be ranked among ten results; answer optimisation competes to be the one passage quoted. The unit shifts from the page to the self-contained block, the currency shifts from keywords to resolved entities, and the outcome becomes largely unmeasurable — there is no rank to track and the same question returns different answers on different runs.
Probably not for search visibility. Google has publicly declined to support llms.txt, and reported monitoring of AI crawler traffic through 2026 found direct fetches to be negligible. Where it does get read is agent-side — coding assistants and desktop agents retrieving a domain because a user named it. Worth having if it is generated, not worth maintaining by hand.
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Suman Debnath
·Brand Marketing Leader & AI Product Builder
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