AI can write them. Whether it should depends on what you plan to do afterward.
Every app needs terms and conditions, so that part is settled. The live question for most founders is whether they can skip the legal bill by asking a model to produce them. AI is genuinely helpful for parts of this work. It researches quickly, it explains what clauses are for, and it will hand you a complete-looking document in about a minute.
What it will not do is prepare terms you can actually rely on. And the gap between the draft and something reliable is usually wider than founders expect.
| AI does this well | This still needs an attorney |
|---|---|
| Explaining what a clause is for, in plain language | Deciding which clauses your specific product needs |
| Producing a first draft with the standard sections | Making the terms enforceable against your users |
| Spotting obvious gaps and internal inconsistencies | Governing law and dispute resolution that fit your business |
| Summarizing another company’s terms for comparison | App store, subscription, and minors requirements |
| Drafting the plain-language summary users actually read | Anything where being wrong is expensive |
Give it credit where it earns it. Used as a research and drafting assistant rather than as counsel, AI saves real time.
It is good at explaining what a limitation of liability clause does, why an indemnity exists, or what the difference is between terms of service and terms and conditions.
It is also good at issue spotting in a rough way. Describe your app and it will usually flag that you need something about user-generated content, or payments, or account termination. It will catch undefined terms, missing dates, and sections that contradict each other. That is real value, and it is work you would otherwise be paying someone to do.
And it writes a decent plain-language summary. If you want a readable version of your terms for your users, sitting alongside the full agreement, AI produces that well.
The pattern is that AI is useful wherever the task is explanation or structure. It struggles wherever the task is judgment.
The failures are consistent enough to predict.
AI drafts you a terms and conditions document. It does not build the thing that makes the document binding.
Whether your terms are enforceable depends largely on how your users agreed to them. Clickwrap, where a user affirmatively checks a box or clicks a button before proceeding, generally holds up. Browsewrap, where you assume that using the app implies agreement, often does not. That is a product decision about where the checkbox goes, what the sign-up screen says, and whether you have a record of who accepted which version.
AI does not know how your onboarding flow works, and it will not tell you that your beautifully drafted arbitration clause is unenforceable because nobody ever agreed to it. You can have a perfect document and no contract.
AI drafts may default to aggressive protection because that is what the training data looks like. Broad liability limitations, sweeping IP assignments, expansive licences over user content, and unilateral rights to change anything at any time.
Terms that overreach do not simply get enforced less. Depending on the jurisdiction, a clause can be narrowed or struck out entirely, which can leave you worse off than a modest clause that survives. Consumer protection rules are the usual limit, and they vary by state.
AI could pick a governing law, insert a mandatory arbitration clause, and add a class action waiver without asking where you are incorporated, where your users are, or whether those clauses are enforceable in the states that matter to you.
Enforceability varies. Some states restrict pre-dispute arbitration in consumer agreements, and some require specific opt-out language for a waiver to hold. A dispute resolution clause that fails can pull you into a forum you never wanted, which is precisely the outcome the clause existed to prevent.
Generic drafts miss the things that make an app an app. The recurring omissions:
AI drafts run the risk of merging the terms and the privacy policy into one document, or scattering privacy commitments through the contractual sections. Those documents do different legal jobs.
Your terms are a contract that binds users; your privacy policy is a disclosure that informs them. Blending them creates problems that surface later, particularly when you update one half and inadvertently reopen the other. We covered why in our post on combining terms and conditions with a privacy policy.
The intuition behind using AI is that a draft is most of the work and review is a quick pass. For terms and conditions, that is frequently backwards.
When a lawyer reviews an AI draft, the problems tend not to be typos and phrasing. They are structural: clauses that do not match what your product actually does, protection you thought you had that is not enforceable, missing requirements specific to your distribution channel, and a dispute resolution section built for a business that is not yours.
Fixing that is not editing. It is rebuilding the document while working around someone else’s structure, which can take longer than drafting cleanly from a template a firm already trusts.
So the saving is smaller than it looks, and sometimes it is negative. AI-generated terms and conditions save you real time on understanding what you need. They save considerably less on getting to something you can rely on.
Where the saving is real: you arrive at the conversation knowing what you want, having thought about your product, with specific questions rather than a blank page. That genuinely shortens the process. Just do not confuse it with having the work done.
You find out at the worst moment, because nobody tests terms and conditions until something has gone wrong.
The realistic consequences: a liability limitation that does not protect you in the dispute it was written for. An arbitration clause that fails, putting you in court. No enforceable basis to remove a user who is causing problems. An IP or license clause too weak to support what you are doing with user content, or so broad that enterprise customers refuse to sign. Removal from an app store for a compliance gap. And in a funding round or acquisition, a diligence finding that has to be fixed under time pressure.
Investors and acquirers read terms of service, and they read them carefully. A license clause that overreaches or an unenforceable assent mechanism becomes your problem at exactly the point you have least leverage.
Keep it, but put it in the right place:
Do not rely on AI-generated terms without review if you take payments, serve business customers who will read your terms during procurement, allow user-generated content, operate in a regulated field, have users outside the United States, or are raising money.
Before launch, and before your first paying customer.
The reason to do it early is that terms are difficult to change once people have accepted them. Amending an agreement in a way that affects existing users brings its own set of problems, and the version someone accepted in month two is the version that governs your dispute with them in year two.
An attorney does the parts AI cannot: deciding which protections your specific product needs, making sure the terms are actually binding, choosing dispute resolution that works where your users are, and covering the app store, subscription, and eligibility requirements that apply to how you distribute. That is preparation rather than drafting, and it is where the value sits.
It can produce a document with the standard sections in a matter of minutes. What it cannot reliably do is decide which protections your product needs, make the terms binding on your users, or account for the requirements specific to your distribution channel and jurisdiction. Treat the output as a starting point, not as an agreement.
The fact that AI drafted them does not make them unenforceable. But enforceability depends mostly on how your users agreed, and AI drafts the document without building the assent mechanism. A well-written document that nobody affirmatively accepted is a weak contract regardless of who wrote it.
Less than founders expect. The savings are real on research and on arriving at the conversation prepared. They are small on getting to terms you can rely on, because the problems in an AI draft tend to be structural rather than cosmetic, and structural problems mean a rewrite rather than an edit.
Yes, and it is a reasonable way to work, as long as you present it accurately. An AI draft is useful as a statement of what you think you need. Handing it over as a nearly finished document that needs a quick look tends to produce a slower and more frustrating process than starting fresh.
The core is similar, but apps carry requirements a website does not, including app store obligations, in-app purchase and subscription rules, and automatic renewal disclosures where subscriptions are involved. Generic drafts miss these regularly because they are written for websites and adapted afterward.
AI has made the first ninety percent of a terms and conditions document nearly free. That is a real change and it is worth using.
What has not changed is the last ten percent, which is where enforceability, jurisdiction, and the requirements specific to your product all live. That part still needs someone who will look at your actual business and take responsibility for the answer. And because those problems are structural, the fix is usually a rewrite rather than a review, which is why AI turns out to be a smaller saving here than it first appears.
The Social Media Law Firm prepares and reviews terms and conditions for apps, websites, and startups. If you have an AI draft, bring it to us and we will tell you honestly whether it needs edits or a rebuild.
Author
Ethan Wall, Esq.
Founding Attorney, The Social Media Law Firm
Nationally Recognized Social Media Lawyer
Legal Disclaimer: This content is for informational purposes only and does not constitute legal advice.
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