Did AI-written marketing get anyone users?
Not on its own, going by a thread of founders comparing notes. In May 2026 a founder asked r/SaaS how many users ChatGPT and Claude marketing had brought them, and said it had brought them none. The thread had about 96 comments when checked on 24 October 2026.
The founder's product targets involuntary and voluntary churn for subscription businesses that take payments through Stripe, Paddle, Razorpay, Cashfree and RevenueCat. In replies, the founder listed what they were already doing: LinkedIn and X posts, DMs, Instagram, Indie Hackers, Show HN, developer articles and personalised email. Search and AI-assistant visibility brought traffic, they said, but no paying users. When they asked Claude for leads, it named their competitors, even after they described their ideal customer.
The replies split three ways:
- Use it to execute, not to plan. The top-voted reply when we checked said AI "mostly repeats generic advice everyone already hears", and listed faster writing, research on pain points and personalisation as the uses that work.
- Talk to people yourself. Several commenters said their users came from talking to people directly, often in communities, with AI helping around the edges.
- A few small wins. One commenter said about 10 visitors a day arrive from ChatGPT, Claude and Gemini citations and one or two convert. Another said a batch of Claude-written outreach emails all opened the same way, and the rewritten batch did better.
Five comments had been removed by the subreddit's bot as low-effort AI content when we checked, a sign that this community filters out AI-written replies.
Why does ChatGPT give everyone the same marketing advice?
Because a general question gets the most likely general answer. Google's own guidance on generative AI content, last updated 1 October 2026, puts it plainly: generative models don't retrieve facts, they predict a likely sequence of words based on their training data. Ask "how do I market my SaaS?" and the likeliest words are LinkedIn, X, SEO, Reddit and communities. That list fits every company, so it tells no company what to do first, which is what the founder above ran into.
The fix is the context only you have. Anthropic's prompting guide for Claude says: "Think of Claude as a brilliant but new employee who lacks context on your norms and workflows." A new employee asked to get users, with no other context, would also suggest LinkedIn. Ask instead with a channel you've already picked, the exact customer, and ten sentences your buyers wrote about the problem, and the answer has to work with those.
Why does it name your competitors when you ask for leads?
Most likely because a request for leads gets matched to the names that appear most often next to your problem, and those are the companies that sell the fix. Several commenters in the thread gave that explanation, and it fits Google's description of models predicting likely words from training data. The founder in the thread saw this even after describing their ideal customer: it returned competitors that use the same keywords.
There are two separate lessons in that. The first is about your own visibility: buyers who ask a chatbot for options get the same competitor names. Our explainer on why ChatGPT recommends your competitor and not you covers the audit for that. The second is about lead finding. A lead is a person with the problem, ideally someone who said so recently. A chat answer built from what has been written about your category gives you companies in your category, which is the opposite list.
Does AI-written content still bring traffic?
Some, but mostly-AI articles do worse than their share of the web suggests. Graphite, an SEO agency, measured both sides in its own studies:
| Graphite finding | Figure | Study date |
|---|---|---|
| Share of new English articles that are primarily AI-generated | About 50%, flat since early 2025 | 15 May 2026 |
| Share of articles ranking in Google Search written by humans | 86% (14% AI-generated) | 14 Oct 2025 (Google results collected June 2025) |
| Share of articles ranking first in Google that are AI-generated | 7% | 14 Oct 2025 |
| Share of articles cited by ChatGPT and Perplexity written by humans | 82% | 14 Oct 2025 |
The May 2026 study sampled 55,400 URLs from Common Crawl and averaged three AI detectors. The October 2025 study says it did not test AI-assisted content with heavy human editing, and that it believes this may work. So primarily AI-generated articles are half of what gets published and a much smaller share of what gets found.
Google's guidance says generative AI can be useful for research and for adding structure to original content. Its spam policies list using generative AI to produce many pages without adding value for users as an example of scaled content abuse. Its October 2026 guidance adds that it is critical to fact-check and review AI-generated content before publishing. Our post on whether Google penalises AI-written posts covers that update in detail. LinkedIn's User Agreement says something similar about its own AI features: generated content might be inaccurate or unsuitable, so review and edit it before sharing.
And traffic is a different thing from users. The founder in the thread already had traffic. What they said they still needed was a way to reach the right customers.
What gets users instead?
Reaching a person who has the problem now, with a message about their situation. Paul Graham's Do Things That Don't Scale (July 2013) called recruiting users manually the most common unscalable thing founders have to do at the start. That is still the job. AI makes each step faster, but someone or something has to find the person and send the message.
| Signal | Where it shows up | What AI does well here | What you do |
|---|---|---|---|
| Someone posts the problem | LinkedIn, X, Reddit, forums | Sort posts by fit and urgency | Reply with something useful |
| Someone asks for a tool | Recommendation threads | Summarise what they need | Answer honestly, competitors included |
| Someone complains about a competitor | Reviews, replies, threads | Pull out the exact complaint | Ask what they would want instead |
| A company hires for the work you automate | Job ads | Read the ad for priorities | Write to the hiring manager |
| A new person takes over the role | Role changes on LinkedIn | Draft a short, relevant first note | Send it in their first weeks |
One commenter in the thread put the gap as timing: AI can suggest who to contact, but it can't tell you who just hit a trigger. Our list of 15 free buying signals a small team can act on covers where to watch for each.
Chat window or agent: what changes?
A chat window helps with one message at a time; an agent runs the whole loop from saved settings. For getting users, the difference is who does the finding, sending and remembering:
| Step | With a chat window | With an outreach agent |
|---|---|---|
| Who to contact | You find people, then paste them in | Searches for people from the signals and customer description you set |
| Context | You paste it into each conversation | Saved once and used on every lead |
| Writing | Drafts from what you paste | Drafts each LinkedIn or X message from the person's profile and signal |
| Sending and follow-up | You copy, send and set reminders | Sends within the daily goals you set and sends the follow-ups |
| When to stop | You remember | Stops when the person books, says yes, says no or asks to stop |
Siela's AI SDR is one example of the second column. It looks for signals inferred from what you sell, such as people asking how to solve the problem, comparing tools, posting a pain point or hiring for the work you automate. It scores each lead hot, warm or cold with a one-line reason and writes each LinkedIn or X message from the person's profile and that signal, checked by a second AI reviewer. It stops following up when someone books, says yes, says no or asks to stop. The replies, and what you learn from them, stay with you.
Prompts that do help
Prompts that give the model your data and one narrow job. These four come from the uses commenters in the thread reported, written as prompts you can copy:
- Pattern finder. "Here are the job titles, company sizes and sign-up notes of our 20 best customers. List the three traits most of them share that a stranger could spot on a LinkedIn profile or in a public post. Say which customers don't fit each trait."
- One-channel stress test. "We sell [product] to [buyer] at [price]. We've chosen [channel] for the next six weeks. List the five most likely reasons it fails for us specifically, and what we would see in week two if each is happening."
- Message from a signal. "This person posted: [paste post]. Their role: [role]. Write a first message under 60 words that refers to the post, asks one question about their situation and doesn't mention our product."
- Reply sorting. "Here are 30 replies to our outreach. Group them by what the person objected to or asked for, quote their exact words in each group, and count the groups."
Each one is small and checkable in a minute, which is where the founder in the thread ended up: AI was weak at planning and good at helping with execution. For posts, our guide to getting ChatGPT or Claude to write LinkedIn posts in your own voice shows how to feed it samples and rules.
A test for this week: pick 20 people who showed one signal from the table, draft each first message with prompt 3, edit and send them yourself, and count replies after seven days. That tells you more than another content calendar.
Frequently asked questions
Can ChatGPT find leads for my business?
Not reliably by asking it to. In a May 2026 r/SaaS thread, a founder reported that asking Claude for leads returned the names of their competitors, even after describing the ideal customer. It is far more useful when you give it data you collected, such as posts, profiles or replies, and ask it to sort, summarise or draft from them.
Why does AI marketing advice feel so generic?
Because a general question gets the most likely general answer. Google's guidance notes that generative models predict a likely sequence of words from their training data. Without your buyer, price, channel and real customer quotes in the prompt, the likeliest answer is the standard list of channels that fits every company and helps none in particular.
Is it worth publishing AI-written blog posts to get users?
Only with real editing and something original in each post. SEO agency Graphite's October 2025 study found 86% of articles ranking in Google were human-written, though it did not test heavily edited AI drafts. Google's spam policies target many AI pages made without adding value. Even when posts rank, traffic needs a clear path to signing up before it becomes users.
What should a founder use ChatGPT or Claude for in marketing?
Narrow jobs you can check quickly: finding patterns in your best customers, sorting replies and call notes, drafting a first message from a specific post, and stress-testing one channel you've already chosen. Leave the choice of buyer and channel to yourself, and keep sending and following up in a system that tracks who you contacted and when.
How long should I test outreach before deciding it doesn't work?
Long enough to send to a meaningful number of well-chosen people and wait for replies. A simple first test is 20 people who each showed one clear signal, a first message written for each, one follow-up, and a count of replies after seven days. If nobody replies, check who you picked and what you offered before you blame the channel.
Sources
- r/SaaS: How many users did you get from using chatgpt and claude for marketing? I got no one. (about 96 comments when checked) (19 May 2026, checked 24 Oct 2026)
- Google Search Central: Google Search's guidance on using generative AI content on your website (last updated 1 Oct 2026, checked 24 Oct 2026)
- Anthropic: Prompting best practices (Claude Platform Docs) (checked 24 Oct 2026)
- Graphite: AI Now Writes as Many Online Articles as Humans (15 May 2026)
- Graphite: How Does AI-Generated Content Perform in Search and Answer Engines? (14 Oct 2025)
- Google Search Central: Spam policies for Google web search (checked 24 Oct 2026)
- LinkedIn User Agreement (effective 3 Nov 2025) (checked 24 Oct 2026)
- Paul Graham: Do Things That Don't Scale (July 2013)