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  • AI Companies Are Destroying Physical Books. Here’s Why Your Business Should Care.

    AI Companies Are Destroying Physical Books. Here’s Why Your Business Should Care.

    Imagine spending years writing a book.

    Then imagine an AI company buying a second-hand copy, slicing off its spine, scanning every page and sending the remains for recycling. The words survive—but now as data inside a private system built to generate commercial products.

    This is not a dystopian thought experiment. Court records show that Anthropic, the company behind Claude, bought and destructively scanned millions of print books while building an internal digital library and training its AI models.

    The easy reaction is outrage: A technology company destroyed books to build a machine that writes.

    But for creators, ecommerce teams and business owners, the more useful question is this:

    If AI companies can treat physical knowledge as a resource to acquire, process and discard, how should you expect them to treat your website, product descriptions, customer conversations and creative work?

    That is the part every business should be thinking about.

    What actually happened?

    According to documents disclosed in the US copyright case Bartz v. Anthropic, Anthropic created a large internal collection of books from two very different sources.

    First, it downloaded more than seven million books from pirate websites. Second, it legally bought millions of printed books and converted them into digital files.

    The physical process was destructive by design. Books had their bindings or spines removed so that loose pages could pass through high-speed scanners. Court filings described industrial cutting equipment, production scanners and recycling of the paper after digitisation.

    In a June 2025 ruling, US District Judge William Alsup treated those two routes differently:

    • Converting legally purchased print books into internal digital replacements was held to be fair use in this case.
    • Acquiring and retaining pirated copies for a general-purpose library was not excused as fair use.
    • Training on the works was also held to be transformative on the record before the court.

    That distinction matters. “The court said AI companies can steal books” is not an accurate summary. The ruling separated lawful purchase and format conversion from the acquisition of pirated material.

    Anthropic later agreed to a $1.5 billion settlement concerning pirated books, without admitting wrongdoing. The settlement did not erase the court’s earlier fair-use ruling on training and the destructive scanning of lawfully purchased copies.

    Were rare books really destroyed?

    This is where the viral version of the story often runs ahead of the evidence.

    It is confirmed that Anthropic destructively scanned millions of purchased books. It is also true that booksellers in several countries have reported strange bulk orders containing obscure, old and out-of-print titles. Some sellers suspect those orders are connected to AI training and that the books may be pulped after scanning.

    However, there is not yet public proof that AI companies are systematically targeting and destroying rare or antiquarian books across the industry.

    Anthropic told The Guardian that its acquisition programmes do not buy and destroy rare or antiquarian books. The identities and intentions of buyers behind many of the unusual bulk orders remain unclear.

    So the responsible conclusion is:

    Mass destructive scanning is documented. The broader destruction of genuinely rare books is a serious concern, but it has not been established at the same level of certainty.

    That nuance does not make the story unimportant. It makes the real story more credible.

    Why would an AI company want physical books?

    Because the open web is no longer enough.

    Modern AI models need enormous quantities of high-quality language. Books are especially valuable because they contain edited, structured, long-form thinking—something the internet does not always provide.

    Physical books also offer three advantages.

    1. They contain material that may not exist online

    Many older, specialist and out-of-print works were never turned into commercial ebooks. Their pages hold information that is effectively invisible to internet-scale data collection.

    2. Older books contain less AI-generated material

    As AI-generated text spreads across the web, training future systems on indiscriminate online data risks feeding models content produced by other models. Pre-generative-AI books are attractive because their human origin is easier to establish.

    3. Buying a physical copy can create a cleaner legal position

    The Anthropic ruling shows why acquisition method matters. Buying a copy, destroying it and keeping one internal digital replacement presented a stronger fair-use argument than downloading an unauthorised digital copy.

    In other words, this was not simply a knowledge project. It was also a data-sourcing and legal-risk strategy.

    The uncomfortable business lesson: your content is an input

    Most businesses still think about AI tools as products they consume.

    You pay for a chatbot, connect an API or add an AI assistant to your workflow. It feels like a normal software relationship: the vendor provides the tool, and you use it.

    But AI platforms are also built around inputs. They need language, images, behaviour, feedback and context. Your business may be a customer on one side of that system and a source of valuable data on the other.

    That does not mean every AI provider trains on every prompt or secretly takes every file. Policies, contracts and product settings differ. Enterprise and API offerings often include stronger data controls than free consumer tools.

    The point is simpler: never assume your content is protected merely because you created it or because it sits inside a tool you pay for. Protection comes from clear terms, technical controls and deliberate choices.

    What this means for ecommerce and marketing teams

    For an ecommerce business, “content” is not just blog posts.

    It includes product descriptions, photography, customer reviews, campaign concepts, brand voice, internal merchandising rules, conversion experiments, support tickets and pricing logic. Individually, these assets may look ordinary. Together, they describe how your company competes.

    If teams paste that material into AI tools without checking the terms, they may expose far more than a few paragraphs of copy.

    Consider four common situations:

    A marketer uploads next quarter’s campaign plan

    The document may contain unreleased offers, audience insights, budgets and positioning. The risk is not only copyright. It is confidentiality.

    A product team feeds an entire catalogue into a writing tool

    Generated descriptions may save time, but the input also reveals assortment strategy, attributes and product data. Who can retain it, and for how long?

    Customer service uses public AI tools to rewrite tickets

    Those tickets may contain names, addresses, order details or health information. Now the issue includes privacy and GDPR—not just content ownership.

    A creator builds a brand on a third-party model

    If the model, price, policy or output quality changes, the creator’s workflow can break overnight. Dependence becomes a platform risk.

    Five practical actions businesses should take now

    You do not need to stop using AI. You need to stop using it casually.

    1. Classify information before it enters an AI tool

    Create three simple categories: public, internal and restricted. Public material may be acceptable in approved tools. Internal content needs controls. Restricted data—such as personal information, credentials, contracts and unreleased financials—should not enter an unapproved system.

    2. Read the terms that matter

    Check whether the provider may retain inputs, use them to improve models, allow human review or share them with subprocessors. Confirm whether training is disabled by default, optional or unavailable for your plan.

    Do not let “enterprise-grade” function as a substitute for reading the contract.

    3. Keep an original source of truth

    Store product copy, research, images, prompts and campaign assets in systems you control. AI output should enter your workflow; your workflow should not live entirely inside one AI platform.

    4. Preserve human provenance

    Keep drafts, timestamps, licences and approval records for important creative assets. This helps demonstrate where work came from, what a human contributed and which material you had permission to use.

    5. Avoid single-model dependence

    Build processes around tasks and standards rather than one vendor’s interface. Where practical, keep prompts portable, retain exports and test a backup provider. The goal is not to switch tools every week. It is to maintain leverage.

    Legal does not automatically mean ethical—or wise

    The court’s decision addressed specific copyright questions under US law. It did not settle every ethical question raised by destroying physical books, nor did it create a universal rule for every AI model, dataset or country.

    A purchased mass-market paperback is not the same thing as a fragile edition with annotations, a distinctive binding or historical provenance. A digital text can preserve words while losing the object’s physical evidence.

    The environmental picture is complicated too. Recycling the paper is better than sending it to landfill, but buying, transporting, cutting and scanning millions of books still consumes material and energy. A company can follow a legally defensible process without proving it chose the most responsible one.

    For businesses, that distinction is essential. Compliance asks, “Are we allowed to do this?” Trust asks, “Will customers, creators and partners believe this is fair?”

    The strongest brands need an answer to both.

    The real story is not about paper

    Physical books make this issue visible because we understand what is being lost. We can picture the blade cutting through the binding. We can see the pages becoming data.

    Digital extraction is easier to ignore. A website can be scraped without an empty shelf. A creator’s style can be absorbed without a damaged cover. A customer conversation can become a data point without anyone hearing the paper shredder.

    That is why this story matters.

    AI is not magic floating above the economy. It is infrastructure built from human work: books, art, code, conversations, decisions and data. Businesses benefiting from these systems should ask where those inputs came from—and apply the same scrutiny to where their own information goes.

    Use AI. Experiment with it. Build with it.

    But do not confuse convenience with control.

    Frequently asked questions

    Are AI companies really destroying physical books?

    Yes, in at least one well-documented case. Court records confirm that Anthropic bought and destructively scanned millions of physical books, removing bindings or spines and recycling the remains after digitisation.

    Are AI companies destroying rare books?

    There are credible reports of unusual purchases involving obscure, old and out-of-print books, and booksellers suspect AI-related buyers. However, systematic destruction of genuinely rare or antiquarian books has not been conclusively established. Anthropic denies buying and destroying rare or antiquarian books through its acquisition programmes.

    Was Anthropic’s scanning ruled legal?

    In June 2025, a US federal judge held that converting lawfully purchased print books into internal digital replacements was fair use in the specific case. The same ruling did not excuse Anthropic’s acquisition and retention of pirated library copies.

    Can an AI company train on my business content?

    It depends on how the content is obtained, the provider’s terms, your product tier, applicable law and the settings or contract governing your account. Businesses should verify these conditions rather than assume all AI tools handle data in the same way.

    Should businesses stop using generative AI?

    No. Businesses should use approved tools, classify sensitive information, understand provider terms, preserve source files and avoid depending entirely on a single model or platform.

    Sources and further reading

  • Best AI Tools for Ecommerce Product Listings: A Practical Benchmark

    Best AI Tools for Ecommerce Product Listings: A Practical Benchmark

    Generic AI writing advice is everywhere. Ecommerce operators, however, have a more demanding problem.

    You do not need one clever paragraph. You may need 500 accurate product descriptions, hundreds of distinct SEO titles, marketplace-ready bullet points, consistent brand language, and a workflow that does not invent materials, features, or certifications.

    So which AI tool is actually best for ecommerce product listings?

    For this benchmark, we compare three widely used options:

    • ChatGPT with a structured custom prompt
    • Jasper
    • Copy.ai

    The goal is not to declare one universal winner. It is to find which tool fits each part of a real listing workflow—from a single product page to bulk catalogue production.

    What an Ecommerce Listing Tool Must Get Right

    A polished description is only one part of a useful listing. A serious evaluation should cover:

    1. Factual accuracy: Does the output stay within the supplied product data?
    2. SEO quality: Does it use the target keyword naturally in the title, description, and supporting copy?
    3. Conversion clarity: Are the main benefits easy to scan and understand?
    4. Brand consistency: Can it reliably follow a defined tone of voice?
    5. Channel adaptation: Can one source record become a Shopify description, Amazon bullets, a Google Shopping title, and social copy?
    6. Bulk workflow: Can a team process dozens or hundreds of SKUs without repetitive manual work?
    7. Editing effort: How much human correction is needed before publishing?
    8. Cost at scale: Does the workflow remain economical when catalogue volume increases?

    The best tool is therefore not necessarily the one that writes the most impressive first draft. It is the one that produces the highest proportion of publishable listings with the least risk and rework.

    The Test Product

    To keep the comparison fair, each tool should receive exactly the same source data.

    Product: Insulated stainless-steel water bottle
    Capacity: 750 ml
    Material: 18/8 stainless steel
    Insulation: Double-wall vacuum insulation
    Claim supplied by brand: Keeps drinks cold for up to 24 hours and hot for up to 12 hours
    Features: Leak-resistant lid, wide mouth, BPA-free lid components
    Colours: Black, blue, and sand
    Target customer: Commuters, gym users, and hikers
    Primary keyword: insulated stainless steel water bottle
    Brand voice: Practical, confident, and low-hype

    The tools should then be asked to produce:

    • An SEO product title
    • A 120–160 word product description
    • Five benefit-led bullet points
    • A meta title and meta description
    • A shorter marketplace version

    This test can be repeated across several categories—fashion, beauty, electronics, and homeware—to expose category-specific weaknesses.

    1. ChatGPT: Best for Flexible, Controlled Workflows

    ChatGPT becomes much more useful for ecommerce when it receives a structured prompt instead of a vague instruction such as “write a product description.”

    Its main advantage is flexibility. You can define the exact output schema, restrict it to approved facts, supply examples of your brand voice, and request multiple channel formats in one response.

    Where ChatGPT performs well

    • Producing several listing formats from one product record
    • Following detailed formatting and tone instructions
    • Rewriting copy for different audiences or markets
    • Generating structured output that can feed an automation
    • Iterating quickly when a draft needs a different angle

    Where it needs control

    • It may add plausible but unsupported claims if the prompt is loose
    • Output consistency can drift across a large batch
    • Bulk processing requires a spreadsheet, API, or custom workflow
    • Human review remains essential for regulated or high-risk categories

    Best use case

    ChatGPT is strongest for teams that want control and are willing to build a repeatable prompt or automation around it. It is particularly attractive when product data already exists in a PIM, ERP, spreadsheet, or ecommerce platform.

    2. Jasper: Best for Brand-Governed Marketing Teams

    Jasper is positioned around marketing content and brand consistency. That makes it relevant for ecommerce teams managing multiple writers, campaigns, or product categories.

    Its value is less about producing one description and more about giving a marketing team a controlled environment for reusable brand context and content workflows.

    Where Jasper performs well

    • Maintaining a defined brand voice across content
    • Supporting non-technical marketing teams
    • Reusing campaign and company context
    • Creating product copy alongside ads, emails, and landing pages

    Where it needs control

    • The additional platform cost needs to be justified by team usage
    • Output still requires verification against source product data
    • A polished interface does not automatically solve catalogue integration
    • Teams should test whether its workflow matches their actual SKU volume

    Best use case

    Jasper makes the most sense for established marketing teams that value governance, shared brand context, and a guided interface more than maximum workflow flexibility.

    3. Copy.ai: Best for Repeatable Go-to-Market Workflows

    Copy.ai has expanded beyond basic copy generation into repeatable workflows. For ecommerce, that can be useful when listing creation is connected to broader go-to-market tasks.

    For example, one product launch could require a product description, marketplace bullets, retailer outreach copy, an email, and social posts. A workflow-led system can reduce the number of disconnected steps.

    Where Copy.ai performs well

    • Turning repeatable content tasks into workflows
    • Producing multiple go-to-market assets from shared inputs
    • Helping teams standardise common generation processes
    • Supporting use cases beyond the product detail page

    Where it needs control

    • Workflow setup requires clear inputs and quality rules
    • Ecommerce-specific integrations should be checked for your stack
    • Generated claims still need validation
    • The benefit is smaller if you only need occasional descriptions

    Best use case

    Copy.ai is worth considering when product listing creation sits inside a larger, repeatable launch or sales-content process.

    Quick Comparison

    RequirementChatGPTJasperCopy.ai
    Prompt flexibilityExcellentGoodGood
    Brand governanceGood with setupStrongGood
    Structured outputsStrongModerateStrong in workflows
    Bulk automationStrong with API or custom toolingDepends on workflowWorkflow-oriented
    Ease for non-technical teamsGoodStrongGood
    Best fitCustom ecommerce operationsBrand-led marketing teamsRepeatable go-to-market processes

    These ratings describe workflow fit, not guaranteed performance. Plans and features change, so test each option against your own catalogue before committing.

    Before and After: What Better AI Input Changes

    The biggest performance difference often comes from the input, not the tool.

    Weak prompt

    Write a catchy description for this water bottle.

    Likely result: generic claims, exaggerated language, missing SEO structure, and possible invented features.

    Ecommerce-ready prompt

    Using only the approved product facts below, create an SEO title, a 120–160 word description, five benefit-led bullets, a meta title under 60 characters, and a meta description under 155 characters. Use the primary keyword naturally. Write in a practical, confident, low-hype tone. Do not invent claims, certifications, materials, dimensions, or guarantees. If required information is missing, flag it instead of guessing.

    That instruction makes the output easier to review, compare, and automate.

    A Reusable Product Listing Prompt

    Copy and adapt this template:

    You are an ecommerce product-content specialist.

Create:
1. SEO product title
2. Product description of [WORD COUNT]
3. Five benefit-led bullet points
4. Meta title under 60 characters
5. Meta description under 155 characters
6. [CHANNEL]-specific short version

Approved product data:
[PASTE STRUCTURED PRODUCT DATA]

Primary keyword:
[KEYWORD]

Secondary keywords:
[KEYWORDS]

Audience:
[CUSTOMER]

Brand voice:
[VOICE RULES]

Rules:
- Use only the supplied facts.
- Do not invent performance claims, certifications, ingredients, materials, compatibility, or guarantees.
- Prioritise customer benefits while preserving technical accuracy.
- Avoid repetition, filler, and unsupported superlatives.
- Flag missing information rather than guessing.
- Return the result using the requested headings.

    For bulk production, keep product inputs in fixed fields such as SKU, product type, material, dimensions, features, approved claims, audience, keyword, and prohibited terms. Consistent data makes consistent copy possible.

    How to Measure Real Conversion Impact

    A before-and-after example can show that the copy is clearer, but it cannot prove a conversion lift. To measure commercial impact, run a controlled test.

    Track:

    • Product-page conversion rate
    • Add-to-cart rate
    • Organic impressions and clicks
    • Click-through rate from category or search pages
    • Return rate
    • Customer questions caused by unclear information
    • Time spent creating and approving each listing
    • Percentage of drafts published without major edits

    Test a meaningful group of comparable products, keep pricing and promotions stable where possible, and run the experiment long enough to reduce normal sales variation.

    The most useful result may not be “AI increased conversion by X%.” It may be that the team cut production time while maintaining conversion rate and improving catalogue coverage. That is still a significant operational gain.

    The Verdict

    Choose ChatGPT if you want the most flexible option for custom prompts, structured output, and integration into an ecommerce automation.

    Choose Jasper if your priority is brand governance and a shared environment for a marketing team.

    Choose Copy.ai if listing creation is part of a broader, repeatable go-to-market workflow.

    For most technically capable ecommerce teams, a structured ChatGPT workflow is the best starting point because it can be adapted to existing product data and publishing systems. For larger marketing organisations, the governance and workflow experience of a dedicated platform may justify the extra cost.

    The winning setup is not the tool that produces the flashiest paragraph. It is the one that turns reliable product data into accurate, channel-ready content—with measurable savings and no unsupported claims.