This afternoon, a post-2000s AI entrepreneur visited Mankun Law Firm()for a discussion; his company currently consists solely of himself.
We initially intended to discuss the overseas expansion of AI products, but our conversation ranged from search traffic to product selection, from one-person companies to AI Agents(), and from cross-border payment collection to legal service products. After more than two hours, my strongest impression is that while it is indeed far easier to build a product with AI today than in the past, entrepreneurship has not thereby become simpler.
Over the past two years, this individual has developed numerous AI products and now focuses primarily on highly niche overseas AI product demands. He first assesses whether a specific niche demand has sufficient search volume and a high enough average transaction value before deciding whether to proceed. Once the demand is confirmed, much of the coding is handled by AI, and generic modules such as payment and user systems have long been standardized into templates. A new product can have a relatively complete website built within seven to fourteen days; smaller features can have a demo produced within a few days.
This has led me to reconsider what it truly means to be proficient in Vibe Coding()。
The fact that AI can perform tasks does not mean you know how to execute them
Vibe Coding is generally understood as a process where humans describe ideas in natural language, allowing AI to complete the majority of coding, debugging, and iteration.
Recently, I have also been using similar methods to revamp the Mankun Law Firm website and our firm’s workflows. Previously, having a product idea required engaging product managers, designers, and front-end and back-end developers, followed by waiting for scheduling; now, I can directly explain business scenarios to AI, have it produce an initial version, and then continuously refine it along actual usage paths.
Since the Spring Festival, my perception of AI has undergone a noticeable shift. Previously, it was used more for chatting, information retrieval, and assistance; now, it can truly deliver results. Accordingly, my requirements have changed: AI must not merely perform tasks that appear impressive but lack economic value. It must either help us save real costs, improve client experience, or generate new revenue opportunities.
This realization is easily exhilarating. Business personnel who were previously blocked by technical barriers suddenly possess the ability to build products themselves. However, after today’s discussion, I see more clearly that proficiency in Vibe Coding is merely an entry ticket.
This individual shared that, in order to identify his current product direction, he made numerous prior attempts. Some products had excessively high customer acquisition costs; others had affordable traffic, but users were unwilling to pay. The seemingly clear product selection methodology he now employs was gradually formed only after a series of actual failures.
He analyzes where similar websites obtain their traffic and observes whether users are willing to proceed to the payment stage. Low traffic is not necessarily a disadvantage; if demand is growing and users demonstrate a strong willingness to pay, such conditions may be more suitable for entry by small teams. Conversely, in a seemingly bustling sector, if all participants are competing for the same user base and advertising costs continue to rise, moving faster may result in incurring losses at an accelerated pace.
This point resonated deeply with me. Vibe Coding addresses “how to build a product,” but it does not automatically answer “what should be built.” If the underlying demand is misidentified, AI will only help you more efficiently create a product that no one needs.
I have had similar insights while recently developing the Mankun Law Firm website. Getting the pages to function merely indicates that the first step has been completed. Issues such as what questions clients search for, which articles are indexed by search engines, how Chinese and English pages correspond, where old links redirect, and whether the consultation entry points operate smoothly cannot be resolved simply by creating an aesthetically pleasing interface. These issues stem from actual business operations and must be validated within real user journeys.
The Technical Queue Is Disappearing
In the past, the most common bottleneck for professionals with product ideas was the lack of a technical team. One had to first document requirements that were not yet fully clarified, and then strive to persuade programmers to understand them. Each revision to the requirements increased costs. Many ideas never reached users because the team’s resources were exhausted through internal friction.
AI has significantly shortened this distance.
Today, entrepreneurs can first use AI to build a minimum viable product (MVP), allow users to engage with it in practice, and then decide whether to continue investing. In our discussions on scenarios combining AI and legal services, the traditional approach often involved preliminary discussions on organizational structure, financing, and development budgets. A more pragmatic approach now is to develop the one or two core features first, review a demo within seven days, and obtain initial feedback within twenty-one days.
I appreciate this pace. The professional services industry has traditionally tended to conceptualize products as heavy undertakings, as if initiation were impossible without a complete system. In reality, many needs originate from small starting points: a client is uncertain about how to proceed at a specific moment, and existing professional services are too costly to cover their needs. By effectively addressing this specific segment first, the product gains the potential for further growth.
Of course, speed comes with its costs. The fact that AI-generated code is executable does not mean it is secure, stable, or maintainable. When customer data, funds, payments, access controls, and professional judgments are involved, testing, review, and the definition of liability boundaries cannot be omitted. While Vibe Coding reduces development costs, it does not absolve entrepreneurs of operational consequences.
Our most extensive discussions were not about code, but about growth.
A remark by this friend left a deep impression on me: Unless one aims to build a world-changing product, the ultimate differentiation of most products lies in their users. Whether you can acquire users and whether they are willing to pay determines whether it constitutes a viable business.
This statement sounds simple, yet it is easily overlooked by product developers. After a feature is developed, one must still address search engine optimization, content strategy, distribution channels, payment collection, refunds, risk control, and customer service. This is particularly relevant for overseas AI products, where payment channels may suddenly tighten due to business type, content risks, or abnormal traffic patterns; having website traffic does not guarantee successful payment collection. The final step in a technical demonstration is often merely the first step in real-world operations.
This is also why I have consistently prioritized content and search engine optimization (SEO). In the early stages of my legal internet projects, I experienced large-scale paid user acquisition. Once traffic bidding enters a vicious cycle, customer acquisition costs rise continuously, eventually exceeding the customer lifetime value, which undermines the business model. Since founding Mankun Law Firm, we have largely avoided paid traffic acquisition, focusing instead on producing professional content continuously. It does not matter if an article receives no views on the day of publication; six months or two years later, potential clients may still find us through specific legal inquiries.
In the AI era, such content assets have become even more critical. Users increasingly consult AI first, and AI then seeks out materials it deems credible. If professional firms do not continuously publish their case experiences, business judgments, and proprietary insights, even superior services may disappear from new search entry points.
Therefore, when developing products through Vibe Coding, one cannot focus solely on development. Code, content, search, payment, compliance, and delivery must form a complete, integrated pathway. If any segment is missing, the product may remain stuck in a state of "appearing to be completed" without being truly functional or market-ready.
With AI augmentation, what lawyers need is product intuition.
This exchange also revealed another possibility for small-team entrepreneurship in the AI era.
An individual with industry expertise, combined with someone proficient in AI product development and growth, along with necessary professional services, can now accomplish tasks that previously required a team of over ten people. Teams can form temporarily around specific needs to rapidly develop demos, acquire users, and test willingness to pay. Upon successful validation, a more stable company can be established; if validation fails, the project is terminated and the team reconstituted. This approach is better suited to the current pace of technological change than building a large organization before identifying a strategic direction.
However, small teams do not imply an absence of division of labor, nor do they mean delegating everything to AI. It remains essential to clarify who defines requirements, who is responsible for product management, who reviews professional conclusions, who handles client data, and who bears ultimate liability. While AI can write code, execute workflows, and generate materials, it cannot handle equity arrangements, intellectual property matters, data compliance, payment disputes, or client commitments on behalf of the team.
I increasingly believe that the most competitive small teams in the future may not necessarily have the largest number of full-time employees, but will certainly excel at combining several capabilities: some members understand business scenarios, others understand product and growth, and others safeguard professional and compliance boundaries. Although AI reduces collaboration costs, human responsibilities must be defined with greater clarity.
I am frequently asked what lawyers should learn in the AI era. Many people’s immediate reaction is to learn prompt engineering, programming, or specific new tools. While these skills are useful, they evolve too rapidly to constitute a long-term competitive advantage.
A more critical capability is the ability to comprehend a business process: understanding why clients engage, the source of payments, how materials flow, which steps are most time-consuming, which are most prone to error, and which judgments must be made by lawyers. Only after gaining this understanding should one determine how to integrate AI into the workflow.
This reflects the experience I have accumulated while developing our official website, building an AI-enabled law firm, and researching legal technology products. Do not pursue a grandiose system from the outset. Instead, identify a genuine problem and develop it into a usable minimum viable product.
The greatest value of Vibe Coding lies in returning product development capabilities to those closest to the requirements. Lawyers, accountants, consultants, and operations managers, who previously had to hand off requirements to technical teams, can now create initial versions themselves. This will recreate opportunities for product innovation across numerous niche industries.
However, what ultimately determines whether a person can effectively leverage Vibe Coding is the depth of their understanding of the business.
The insight this friend provided me today was not about how impressive it is to "build a website in seven days," but rather that he has already integrated requirements, data, development, growth, and cash flow into a single framework for consideration. AI enables him, as an individual, to possess the execution capacity that previously required a small team; and more than a year of trial and error has taught him where to direct this execution capacity.
I am increasingly convinced that many successful products of the future will not emerge from the conference rooms of large corporations. They may instead originate from a customer consultation, a failed process, or a long-neglected niche need, and be rapidly developed by someone who truly understands the scenario, using AI.
As code becomes increasingly inexpensive, product judgment will become increasingly valuable.
