Ivana Flynn details how to optimise content for AI chatbots like Google’s Gemini and OpenAI’s ChatGPT.
For years, SEO professionals have obsessed over crawl budgets. We cleaned up duplicate URLs, removed redirect chains, fixed internal links and tried to make sure Google spent its resources on the pages that actually mattered.
Now we have another budget to think about. The token budget.
When an AI system reads your content, it does not see a beautifully designed page, admire your hero image or appreciate the 300 word introduction your writer added to make the article feel substantial. It processes text in smaller units called tokens.
Every useful fact consumes tokens. Unfortunately, so does every empty sentence, repeated answer, vague claim and paragraph that says absolutely nothing.
This creates a new optimisation question for affiliates. How much useful information are you communicating with the tokens you consume?
What is a token?
A token is a small unit of text processed by an AI model. It can represent a short word, part of a longer word, a number, punctuation or part of a URL.
A commonly used approximation is that one token represents around four English characters or roughly three-quarters of a word. The exact number changes according to the language, vocabulary, formatting and tokenisation system.
The technical definition matters less than the practical consequence.
AI systems have processing limits. A user question, conversation history, system instructions, retrieved pages, search results and the final answer may all compete for space within the available context.
Your article is not entering an empty room. It is arriving at a very crowded meeting.
If the useful answer is hidden under generic introductions, keyword variations and repeated explanations, the AI system may never use the strongest part of the page. The relevant passage can be shortened, overlooked or excluded while a clearer competitor becomes the source.
Token waste is the new crawl waste
The comparison with crawl budget is not perfect, but it is useful. In traditional SEO, we waste crawling resources through duplicate URLs, infinite filters, unnecessary archives, broken links and redirect chains. Search engines spend time processing pages that add no real value.
In AI search, we create similar waste inside the content itself. We repeat the same answer in the introduction, main section, conclusion and FAQ. We create hundreds of location pages with almost identical wording. We place the actual answer after five paragraphs explaining why the topic is important. We fill commercial pages with claims such as fast, safe, trusted and excellent without providing any evidence.
The page may be crawlable, indexable and technically perfect. It can still be expensive to understand.
This matters enormously in SEO for affiliate marketers. Our pages are already competing in markets where many sites cover the same operators, bonuses, payment methods and games. If ten pages provide similar information, the page with the clearest and most verifiable answer has an obvious advantage when an AI system needs to extract a passage.
The old competition was often page against page. The new competition can be paragraph against paragraph.
Fewer words are not automatically better
This is where people will misunderstand token optimisation.
The goal is not to publish the shortest article. It is not an excuse to create thin content, remove evidence or reduce every answer to one sentence.
A short but incomplete answer is not efficient. It is simply incomplete. The objective is to communicate the same amount of accurate and useful information with less waste.
If a 500 word explanation contains necessary conditions, examples, evidence and regional restrictions, it may be far more valuable than a vague 150 word answer. If a 2,000 word review repeats the same promotional claims under eight headings, it is not comprehensive. It is inflated.
Good token optimisation removes repetition and preserves meaning.
This distinction is particularly important in gambling content. An answer about withdrawals, licensing, bonuses or player eligibility often requires qualifications. The payment method may be available only in certain countries. The advertised bonus may exclude some games. Withdrawal times may depend on verification. A licence in one jurisdiction does not automatically authorise an operator in another.
Removing those details to save tokens would make the answer less trustworthy. Token efficiency is about information density, not extreme brevity.
Put the bottom line first
One of the simplest improvements is to use the BLUF method (bottom line up front). Give the reader the answer first. Then explain it.
Many SEO articles do the opposite. They begin with the history of online gambling, discuss the growth of mobile technology, describe how consumer expectations have changed and eventually answer whether a payment method is available.
By that point, the reader has lost patience and the AI system may have found a better passage elsewhere.
If the question is whether a casino accepts Revolut – begin with the answer. State whether it is directly supported, available through a bank card or not listed as an official method. Then explain the limitations, verification requirements and alternatives.
This structure works for humans because it respects their time. It works for retrieval systems because the relevant passage makes sense immediately.
A strong section should provide the answer, the important qualification, the supporting evidence and a practical explanation in that order.
Every section may need to survive alone
AI search may retrieve a passage rather than process your full article as one continuous story. This means an important section should be understandable even when it is separated from the rest of the page.
A heading such as ‘what you need to know’ communicates nothing outside its original context. Whereas, ‘how long do casino withdrawals take by bank transfer?’, identifies the exact question.
The opening sentence should then provide the direct answer and name the relevant entity. Pronouns that feel perfectly natural in a complete article can become ambiguous when a paragraph appears alone.
Writing “it offers fast withdrawals” is weak because the statement does not identify the operator, define fast or explain the conditions.
Writing that a named operator’s bank transfer withdrawal time depends on internal approval, completed verification and the receiving bank provides far more usable information.
However, please do not react by repeating the brand in every sentence. That produces unnatural copy and wastes the very tokens we are trying to use efficiently.
AI search needs evidence, not adjectives
Affiliates have spent years writing that platforms are safe, reliable, popular and fast. These words are cheap, however, evidence is valuable.
If you claim that an operator is licensed, name the regulator and provide the licence number where it can be verified. If you discuss withdrawal limits, state the payment method, currency, amount and date checked. If you call customer support responsive, explain when it was tested and how long the answer took.
Evidence requires more tokens than an unsupported claim, but those tokens are doing real work.
AI friendly content should not merely be easy to process. It should be credible enough to use.
This is where original affiliate research can become extremely powerful. Testing registrations, documenting payment journeys, recording support response times and checking terms creates information that generic AI content cannot easily reproduce.
Your strongest defence against becoming invisible in AI search is not producing more words. It is producing facts worth citing.
Tables can save everyone time
There are occasions where a table communicates more than several paragraphs ever could.
Payment limits, withdrawal times, bonus conditions, licence details and regional availability are obvious examples. A clear comparison can reduce repetition while showing relationships between facts.
But tables should not become a dumping ground for keywords. Each column needs a purpose, every figure needs context and changing information needs a visible review date.
A table with outdated data is not efficient, it is wrong.
Traditional SEO is not dead
Every new development in search brings the same dramatic announcement that traditional SEO is finished.
It is not! AI systems still need to discover, access and interpret information. Crawlable HTML, logical site architecture, internal links, canonical tags, structured data, reliable performance and consistent entity information remain essential.
A page that cannot be accessed cannot contribute to an answer.
The difference is that technical accessibility is no longer enough. Once the system reaches the content, it must be able to identify the useful information without spending unnecessary resources on noise.
Traditional SEO optimises the route from crawler to page. AI search optimisation must also consider the route from page to passage and from passage to generated answer. We need both.
Stop measuring content by length
The affiliate industry has an uncomfortable relationship with word count. We often commission 2,000 words because a competitor published 1,800. We expand simple topics until they meet an arbitrary target. Then we add ten FAQs that repeat what the article already said.
This may create a longer page, but it does not create more knowledge.
Instead of asking whether the writer reached the required word count, ask whether each section adds new information. Ask whether the page answers the main question immediately. Ask whether claims are supported. Ask whether the FAQ introduces genuine additional questions or simply repeats the headings above it.
My favourite editing test is very simple. If removing a sentence does not reduce accuracy, clarity, evidence or usefulness, why is the sentence there?
That question can be painful, especially after paying for the content. Ask it anyway.
The token economy changes the rules of competition
Websites are no longer competing only for ten blue links or the first organic position.
Definitions, facts, comparisons, explanations and evidence blocks may compete independently for inclusion in an AI generated answer. A site may have a powerful domain and still lose the citation to a smaller publisher that provides a clearer, more specific passage.
This is not permission to abandon brand voice or turn every article into a technical manual. Humans still need to enjoy the content. Personality, experience and opinion remain valuable, particularly when everyone else is publishing the same machine generated summaries.
The challenge is to combine personality with precision.
The token economy rewards content that communicates more value with less waste. It does not reward empty minimalism and it should not punish necessary detail.
The strongest approach is simple. Be concise enough to process efficiently, detailed enough to answer completely and credible enough to trust.
Ivana Flynn is an SEO consultant working predominantly within the igaming industry.
