Should you block AI training? A decision framework by business type

The question "should we block AI bots?" usually arrives as an instruction from someone who read a headline. The honest answer is that it depends on what your content is for, and that "AI bots" covers several different things with different consequences. The AI crawlers and robots.txt guide explains the mechanics: which user agents exist, which are for search and which for training, and how robots.txt groups work. This guide is about the decision itself.

Separate the two questions

There are two different decisions, and mixing them up causes most of the damage:

1. Should AI systems be able to find and cite my pages when people ask questions? This is about AI search: agents such as OAI-SearchBot, PerplexityBot, Claude-SearchBot and the user-triggered fetchers, plus Googlebot and Bingbot for the AI features inside their search engines.
2. Should my content be used to train future AI models? This is about training crawlers and tokens such as GPTBot, ClaudeBot, Google-Extended, Applebot-Extended and CCBot.

Blocking training has no effect on search rankings. Blocking AI search agents removes you from places where potential customers ask questions. A copied "block all AI" list often does both, which is rarely what a business wants.

What blocking training can and cannot do

It can	It cannot
Stop documented, compliant training crawlers from collecting new content	Remove content already collected or already in trained models
Signal your position, which may matter in licensing conversations	Stop copies of your content on other sites, archives or datasets from being used
Reduce crawl load from those agents	Stop non-compliant or undeclared scrapers
Be reversed at any time	Guarantee legal protection; that depends on law and contracts

For stronger enforcement, such as blocking scrapers that ignore robots.txt, you need server, firewall or CDN rules, often based on published IP ranges and behaviour. That is a security decision with its own costs, including the risk of blocking legitimate crawlers by mistake.

The trade-offs by business type

Publishers and content businesses

For businesses whose product is the content itself (news, specialist publishing, research, courses), training use is a real commercial question. Content used to train a model that then answers readers' questions directly can reduce visits and subscriptions. Many publishers block training crawlers while negotiating or holding out for licensing, and keep search agents allowed so they remain citable. Some go further for paywalled content. This is a legitimate strategy, but it should be decided at business level, with legal input, not by whoever edits robots.txt.

E-commerce

Shops mostly want to be known: their products, prices, policies and brand. Being represented accurately in AI models and assistants is usually an advantage, because people increasingly ask assistants what to buy. Blocking training crawlers rarely protects anything of value for a shop, and blocking search agents is usually harmful. The exception is distinctive editorial content, such as extensive buying guides that are a competitive asset, where some shops choose to block training while staying open to search.

SaaS and B2B

Software companies generally benefit when models know their product accurately: what it does, how it integrates, how it is priced. Documentation that models have learned from helps users get correct answers about your product. Most SaaS businesses allow both training and search, and focus on keeping documentation accurate and public. Proprietary material belongs behind authentication anyway.

Local services

A plumber, dentist or accountant has little to lose from training use and a lot to gain from being correctly described. Allow both, and spend the effort on accurate listings and clear service pages instead; see entity SEO.

Creative work

Photographers, illustrators, writers and musicians whose work is the product have the strongest reasons to object to training use, and many choose to block training crawlers on principle as well as for commercial reasons. They should be aware of the limits in the table above, and that images and text reposted elsewhere are outside their robots.txt.

A decision checklist

1. Is our content itself the product we sell? If yes, discuss training use at business level and consider blocking training crawlers.
2. Do we want to be cited and recommended in AI answers? If yes, keep AI search agents and user-triggered fetchers allowed.
3. Do we have content we license or plan to license? Get legal advice; robots.txt is only a signal.
4. Is crawl load a real problem? Check logs before blaming AI crawlers; see log analysis.
5. Who owns this decision, and when will we review it? Vendors and agreements change; revisit at least yearly.

Implementing the decision cleanly

• Name each crawler in its own User-agent group; do not rely on a catch-all group to cover AI agents, because a crawler only obeys the most specific group that names it.
• Keep search engine crawlers (Googlebot, Bingbot) in their own groups with the access you intend.
• Remember that some tokens, such as Google-Extended, are not separate crawlers: Googlebot still fetches the pages, and the token only controls use for model training.
• Check the result with the AI bot checker, which shows what your robots.txt allows for each named crawler.
• Check CDN and firewall settings; some products block AI crawlers by default, overriding what robots.txt says.
• Document the decision, the reasons and the date in your own records, so the next person to touch robots.txt knows why it says what it says.

Partial approaches

The choice is not all or nothing. robots.txt rules can apply to paths, so a business can treat different sections differently:

• Block training on premium or original sections (in-depth guides, original photography, research reports) while allowing it on product pages, documentation and the about page, which you want models to know accurately.
• Allow everything on marketing pages and protect member areas with authentication, which no compliant or non-compliant crawler should be able to pass.
• Treat different vendors differently, for example allowing a vendor you have a licensing agreement with while blocking others. This requires the discipline to keep the rules current as agreements change.

Whatever the split, write rules that are easy to read and test. A robots.txt file that nobody on the team can explain is a liability; the next person to edit it may undo the policy without knowing it existed.

What about llms.txt?

llms.txt is a proposed file that summarises a site for language models. It is about guiding use, not blocking it, and no major vendor has documented relying on it for training or search. It neither protects content nor guarantees visibility; see llms.txt.

A worked example

A hypothetical specialist travel publisher earns revenue from subscriptions to in-depth destination guides, and a free blog that attracts readers. After discussion, the owners decide to block training crawlers sitewide and keep search agents allowed, so the free blog remains citable in AI answers and links bring readers who may subscribe. Paywalled guides were already behind authentication. They check the CDN, find its default bot setting was blocking all AI agents including search ones, and correct it to match the robots.txt policy. A year later they review the decision against referral data and licensing offers received.

The cost of getting it wrong

Most mistakes in this area are not dramatic; they are invisible. A business blocks every AI agent, nothing breaks, and over the next year it is quietly absent from the answers its customers read. Or a publisher allows everything by default without ever deciding, and later finds its archive in training data it would have preferred to license. Both are avoidable by making the decision deliberately, writing it down with the reasons, and checking once a year whether the reasons still hold. Vendors add new crawlers, change what existing ones do, and sign licensing deals; a policy written in one year can quietly mean something different two years later.