Summary of Key Points
Generative AI has made product development extremely inexpensive and fast, challenging the traditional approach of creating a Minimum Viable Product (MVP). In the past, an MVP was the lowest-cost route to test the market, but now AI can turn an idea into a polished demo within days. However, a demo alone is no longer sufficient to prove the existence of a market need. The core challenge for startups has shifted from “can we build the product” to “should this product exist in reality?” To validate the value of a product, five steps of “reality proof” are required: confirming the problem’s authenticity, user willingness to change their behavior, integration into workflows, user dependence on the product, and scalability. These steps cannot be accelerated by AI, making patience and careful validation of reality the key competitive advantage in the AI era.
Detailed Explanation
1. AI Makes Product Development Faster, but Demos Are No Longer a Pass
In the past, saying “we have built a product” carried significant weight, indicating months of effort and investment from the team. Today, with AI tools, someone familiar with them can create an interface, code, copywriting, and even test cases in just a few days, turning an idea into a seemingly complete product.
The Catch: Many mistakenly believe that creating a demo proves market demand, but a beautiful demo does not guarantee that customers will use the product, nor does a good SaaS solution ensure that users will change their habits. While AI has increased the speed of product development, it has not improved the ability to assess whether a product is worth making.
2. The Shift in Startup Challenges
The primary question for startups has changed from “Can we build it?” to “Should this exist?”
For example, if you want to create an AI tool for automating weekly reports, AI can quickly generate a demo. But do users really need it? Maybe the company already has a standard template, and employees prefer to spend 10 minutes writing the report rather than learning a new tool, or the boss may find the AI-generated content impersonal and unsuitable. These are real-world issues that AI cannot solve; it can only help you develop the product, not determine its relevance.
3. The Five Steps of Reality Proof
AI can compress development time, but the following five validation steps must be verified in the real world:
- Reality Proof: Is the problem truly worth solving? It’s not about whether there is a problem, but whether it is significant enough to address. For instance, long printing queues in an office might be a problem, but if users are accustomed to them or if the queue time is short, it may not be worth fixing.
- Behavior Proof: Will users actually use the product? Don’t rely on users saying they will; look at whether they pay for it, use it regularly, and change their habits. If users say the AI tool is useful but don’t use it, it indicates a false need.
- Workflow Proof: Does the product fit into existing work processes? A demo may work well in a lab, but in the real world, it must be integrated with existing systems, permissions must be obtained, and responsibilities must be clarified. Many AI tools are praised in meetings but fail to be implemented due to practical barriers.
- Retention Proof: Are users dependent on the product? It’s not about having users; it’s about whether they will continue using it after the free trial ends or if they will seek your help when the service stops. True dependence means users cannot function without the product.
- Scale Proof: Can the business be scaled? It’s not about replicating code, but about replicating the business model. If adding each new customer requires significant adjustments to processes, the company is not scalable.
4. The New Risk of “False Success”
Failed projects in the past were obvious: products were never built, or funds were exhausted. Today, failed projects may appear successful with attractive interfaces and complete features, but they fail to make a real impact on the market (e.g., users only use the product temporarily or it’s not integrated into workflows). This type of “false success” is more wasteful because you might think you’re making progress when you’re just working on something useless.
Solution: Continuously ask yourself, “What have I proven?” Have you confirmed the existence of a problem or that users are willing to pay for the product?
5. The New Lean Thinking
The essence of lean entrepreneurship is to reduce waste. In the past, the waste was unused code; now, it’s creating products that no one wants. In the AI era, the goal is not to develop products faster but to more quickly determine whether they should be made. For example, you could use manual processes or WeChat groups to simulate business workflows first; if no one uses them, there’s no need to invest in an AI product.
Core Principle: AI can help you test ideas quickly, but the direction of those tests must be correct. Verify reality before developing the product.
In Conclusion
AI has made product creation cheaper, but real market needs, user behavior, and organizational processes are the most valuable aspects that require careful validation. In the future, being faster is less important than being accurate—identifying the true value in the real world is crucial for successful startups.