Cold Start and PMF for AI Products

Building AI products in 2026, the start is harder than at any time. The reasons are very real:

• Homogenization. "ChatGPT wrapper"-style apps are everywhere, and users can't tell you apart from them.
• Expectations raised. Big companies' free products are already very strong, and your paid version needs to be obviously better.
• Demos are easy, payment is hard. AI demos are all stunning, but whether users are willing to shell out money is another matter.

This piece covers how AI products start from 0, and how to avoid the pits that can be fatal.

One: First recognize: AI product PMF is harder to judge

Traditional SaaS PMF signals are relatively clear (retention, payment). AI products have additional interference:

• Many curious users. AI is novel, signups are inflated, but that doesn't represent real need.
• Free addiction. The free tier is used happily, but conversion to paid just won't budge.
• Big company crush risk. Once you make some progress, a big company update makes it free.

So when judging AI product PMF, don't look at signups/DAU, look at the "can't do without you" metrics:

• Whether users incorporate your product into a fixed workflow (high-frequency use).
• Whether they are willing to pay (real money is more honest than verbally "liking").
• Whether they actively recommend (NPS).

An AI product with a million signups and zero payments has no PMF.

Two: Before PMF, things you absolutely shouldn't do

During cold start, resources are scarce; doing one thing wrong kills you:

1. Don't burn money acquiring customers. Users pulled in by ads before PMF won't stay, and the money is wasted. Validate with organic traffic/community first.
2. Don't pile on features. Add RAG today, add an Agent tomorrow, each a half-finished product. Focus on one core capability and take it to the extreme.
3. Don't fight on general capability. Doing a "general AI assistant" head-on with big companies means certain defeat. Be vertical, be narrow.
4. Don't price too early / don't price at all. Free for too long breeds freeloader users, pricing too early blocks validation. Use "free trial + early-bird payment" to test the waters.
5. Don't pursue perfection before releasing. AI product v1 is always rough; getting it to real users early beats polishing behind closed doors.

Three: Three true directions for differentiation

In the red sea of AI applications, products that can establish a foothold usually occupy at least one of the following:

Direction 1: Vertical depth (most recommended). Don't build "an AI assistant for everyone", build "a dedicated tool for a specific profession/industry". Example: contract AI for lawyers, medical record AI for doctors, lesson prep AI for teachers, troubleshooting AI for ops.

• Vertical = you understand the industry know-how, and big companies' general products can't match your specialization.
• Users' willingness to pay for "understands me" is far higher than for general tools.
• Community/word-of-mouth is easier to build in vertical domains.

This is the most realistic differentiation for indie developers/small teams—find a vertical domain you understand, and go deep.

Direction 2: Data flywheel. The more the product is used, the more data, the more accurate the model/retrieval, the better the product—forming a positive loop.

• Example: a customer service Agent understands a company's knowledge base better the more it's used; a writing assistant understands a user's style better the more it's used.
• Once the flywheel spins up, latecomers are hard-pressed to catch up (their products don't have your data accumulation).
• Premise: your product can continuously generate valuable proprietary data.

Direction 3: Workflow closed loop. Not just "generation", but connecting the entire chain of generation → editing → collaboration → publishing/execution.

• Big companies do "generation capability", you do "complete workflow"—users pay for the peace of mind of the process, not just for generation.
• Example: not just generating code, but generate → review → test → submit PR as a one-stop flow.
• Once a workflow is embedded in user habits, switching costs are extremely high.

General AI capability belongs to big companies; vertical/data/workflow is yours. Pick one direction in cold start and dig in.

Four: Specific actions for cold start

With the above understanding, here are the concrete steps to land:

1. Pick a vertical + pain point. Not "do AI writing", but "do an SEO article assistant for indie bloggers". The narrower the better.

2. Find 10 real users to talk to deeply. Don't rush to write code; first confirm this pain point really exists and someone is willing to pay.

3. Build a minimum viable version. One core capability, rough is fine. AI makes MVP cost extremely low (one weekend).

4. Small-scale validation + iteration. Give the MVP to those 10 users, and see which ones really use it daily and which pay. Bad cases are gold mines.

5. Organic growth first. SEO, community, word-of-mouth. AI products are naturally suited for content marketing (the process of building is itself content).

6. Payment validation. Even very early, try to collect money—payment is the most honest PMF signal.

Five: "Content as growth" for AI products

AI products have a natural advantage: the process of building the product is itself good content.

• Write about the pits you step in, the decisions you make, the technical solutions—attracts like-minded users (developers/peers).
• Your articles/videos carry the cred of "I'm building an AI product", more effective than hard ads.
• Content accumulates SEO, long-term customer acquisition.

Many AI products' early users come from founders sharing the build process. Don't build in a vacuum; share as you build—this is the cheapest customer acquisition for cold start.

Six: The mindset to avoid being crushed by big companies

The biggest psychological burden of AI entrepreneurs: "What if Google/OpenAI releases a free version?"

Reality:

• Big companies do general, you do vertical/workflow—they're hard-pressed to customize for every niche scenario.
• Even if they do, your vertical depth + data + user relationships are still a barrier.
• Rather than worrying, do what they're unwilling to do / can't do well (grunt work, narrow scenarios, strong domain knowledge).

Remember: what big companies crush is "wrapper general", not "deep vertical". The latter is precisely big companies' blind spot.

Summary

AI product cold start is harder than traditional, but the opportunities are also concrete:

• PMF looks at payment/workflow embedding, not signup count.
• Before PMF, don't burn money, don't pile on features, don't fight on general capability.
• Three differentiation directions: vertical depth (first choice for indie developers), data flywheel, workflow closed loop.
• Cold start actions: pick vertical → talk deeply with users → small MVP → organic growth → payment validation.
• Content as growth: share as you build, the cheapest customer acquisition for AI products.

Remember one thing: in the AI era, the winners are not those with the "strongest technology", but those who "understand a specific scenario best". Big companies occupy the general; the depth of vertical scenarios is enough to support countless indie developers and small teams—find the one you understand, that others won't dig into, and dive in. That's where your PMF lies.