Summary of Key Points
The central argument of this article is that the current AI industry is in a period of "chaos," but precisely this chaos presents the greatest opportunity for ordinary individuals to transition into the field of AI. The reason for this is that industry standards have not yet been established, and the barrier to creating a demo (demonstration version) of an AI application is extremely low; even someone with a month of training can create one. However, companies lack the ability to assess the quality of AI talents and projects, and there is a dire need for AI professionals at the application level. The article also notes that the AI learning framework has evolved to version 3.0, moving from simply using tools to practicing on real projects to adopting a job-oriented knowledge structure. It recommends three key roles that are suitable for ordinary individuals to pursue in this transition and provides a model for companies to evaluate the competitiveness of AI candidates.
Breakdown and Interpretation
1. The AI Learning Framework Has Reached Version 3.0 – Stop Worrying with Old Thinking
Many people worry about whether it's too late to start learning AI or whether the rapid changes in the field mean there's no need to learn, but the issue lies in their failure to keep up with the evolution of the learning framework:
- Version 1.0: Focusing on learning how to use tools (such as writing prompts, building robots with Coze, or calling model APIs) is now outdated.
- Version 2.0: Moving on to working on specific projects (e.g., AI customer service, digital avatars), which requires not only tool proficiency but also an understanding of project design, data processing, and impact evaluation.
- Version 3.0: Developing a comprehensive knowledge framework tailored to the target role (for example, an AI product manager needs to understand Agent design, while a full-stack engineer should be proficient in both AI coding and deployment).
Those who are anxious often get stuck at Version 1.0, feeling that they cannot keep up. Those who think there's no need to learn lack a clear framework and thus miss the essential points. In fact, by shifting to Version 3.0, the concept of being "too late" disappears.
2. Opportunities in the Chaos: Low Barriers for Demos, but Companies Struggle to Differentiate Quality
The current chaos in the AI industry is evident in the following:
- Easy Creation of Demos: With basic knowledge of APIs and AI coding tools (such as CodeX), it's possible to create a knowledge base or an Agent demo within a month, which can look just as good as those created by experienced professionals.
- Companies' Difficulty in Differentiation: Many companies have only a superficial understanding of AI. For instance, a pharmaceutical company that wants to develop an AI knowledge base might receive quotes ranging from 100,000 to 3 million yuan. Even if they choose a well-packaged team, the demo may only be of mediocre quality and still fail to solve fundamental issues (such as model inconsistencies or irrelevant responses) because the team lacks the ability to develop production-grade applications.
This information asymmetry presents an opportunity for ordinary individuals: you don't need to be an expert; as long as you can create a decent demo, you can enter a company and gain experience through real-world projects.
3. Three Golden Roles for Transition
The article recommends three roles that are in high demand in the AI industry, suitable for people with different backgrounds:
- AI Product Manager (Agent Product Engineer): Suitable for former product managers or business professionals. This role requires understanding the core capabilities of Agents (such as Skills, Tools, and Context) and the ability to transform business expertise into reusable AI solutions. You don't need to be an expert in algorithms but should have a thorough understanding of the entire Agent system.
- AI Full-Stack Engineer: Suitable for those with programming skills. In the future, the boundaries between front-end and back-end development will blur, so you'll need to know how to code, integrate models, and develop Agents (e.g., creating a Mini WorkBuddy). This role offers the widest range of career opportunities.
- FDE (Financial Data Engineer): Suitable for project managers, industry experts, and those with strong communication skills. The key task is to transform complex business data into actionable AI solutions. For example, a project manager with ten years of experience in the B2B sector can transition to FDE by leveraging AI tools to help companies implement AI projects.
4. A Strategy for Transition
The correct approach for ordinary individuals to enter the AI industry is not to wait until they have mastered all the knowledge before applying:
- Meet the Basic Requirements at the Application Level: Learn how to use mainstream AI tools (such as Coze, Dify) and complete 1–2 real projects (even if they are just demos).
- Get Involved in Real Projects Quickly: Once you're in a company, gain experience with actual business operations, data, and production environments—these skills are hard to acquire online.
- Presentation Matters: Your resume should highlight your project achievements and knowledge framework, but what truly determines your success is your ability to learn and deliver results.
5. What Companies Look For: Four Dimensions of Competitiveness
Companies use the following four dimensions to evaluate AI candidates:
- Competence: Your existing industry experience (e.g., a doctor's understanding of the consultation process or an HR professional's knowledge of recruitment).
- AI Tools: Proficiency in using mainstream tools like CodeX and Coze.
- Project Achievements: Evidence of your work, such as self-developed knowledge bases or Agents.
- Knowledge Communication: The ability to clearly explain the logic behind your projects and your knowledge framework, demonstrating that you can think beyond just using tools.
By meeting these criteria in all four areas, you can stand out during this period of chaos.
Conclusion
The chaotic phase in the AI industry will not last forever. Now is the perfect time for ordinary individuals to make the transition. Don't worry about being late or wait for things to stabilize; seize the opportunity to get involved in real projects and gradually build your knowledge framework. After all, it is within this chaos that the greatest opportunities lie.