When “High AI Salaries” Become an Expensive Ticket to Enter the Industry: Who’s Paying the Price? And Who’s Really Getting Nothing in Return?
If you’ve been browsing short videos or social media recently, you’ve probably been bombarded with ads like these: “Switch to AI from scratch and earn a 250,000-yuan annual salary in half a year,” or “Say goodbye to the 996 work schedule and embrace big models to become a trendsetter of the era.”
As a financial journalist, I’ve seen too many similar “get-rich-quick” scams. But this time, when the most cutting-edge technology concept of artificial intelligence is packaged as a shortcut to a lucrative career, what I see is not the widespread benefits of the technology, but a carefully crafted scheme designed to exploit people’s anxiety.
This article exposes the truth behind the hype through the real experiences of four young people with different backgrounds: Zhang Shuai, Wang Zhihan, Chen Shu, and Lin Bing. Let me break down the logic, the pitfalls, and the harsh realities behind these AI training programs in plain language.
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The Art of Creating Illusions: Why Has “AI” Become the Perfect Cover for Deception?
Core Logic: Taking advantage of information asymmetry to turn uncertainty into certainty.
For recent graduates or those struggling in the workplace, the biggest pain point is not a lack of money, but not knowing where to go next.
1. Capitalizing on the “hot trend” narrative:
Over the past decade, the mobile internet has created countless stories of programmers becoming wealthy. Now, AI has become the new “hot trend.” Training institutions are clever: they don’t talk about complex algorithm principles; they only focus on the outcomes—high salaries at big companies, a massive talent gap, and the fear of falling behind if you don’t learn. This narrative taps into people’s FOMO (Fear Of Missing Out).
- *Plain language explanation:* It’s like during a stock market boom, the people selling shovels make the most money. The AI industry is indeed growing, but what these institutions sell is not the “shovels” (the core technology), but “tickets” (a way to alleviate anxiety).
2. Blurring the lines between “employment” and “training”:
The small print in the contract that says “no employment guarantee” is a legal safeguard, but in sales pitches, it’s exaggerated as “guaranteed job placement” or “job offers.”
- *Example:* Zhang Shuai thought he’d buy an “AI engineer” title for 29,800 yuan, but what he got was a bunch of coding exercises and resume coaching techniques.
- *Reality:* Real AI positions (like algorithm engineers or big model developers) require a master’s degree and a solid foundation in mathematics/computer science. Training courses focus on the “application layer” or even the “operational layer,” yet they promise the salaries of the “core layer.”
3. Precisely targeting pain points:
- For graduates (like Zhang Shuai and Chen Shu): The fear of becoming unemployed right after graduation.
- For newbies in the workplace (like Wang Zhihan): The feeling of low income and lack of career prospects.
- For midlife crisis individuals (like Lin Bing): The fear of being laid off or left behind by the times.
No matter who you are, if you feel insecure, AI training might seem like a solution, even if it’s actually worthless.
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The Truth About the Courses: What You Learn Is Not “Magic,” but Hard, Repetitive Work
Core Logic: Simplifying complex systems engineering into low-barrier, repetitive tasks.
Many people think learning AI means creating intelligent systems by writing a few lines of code, like in movies. In reality, training courses often focus on mundane, repetitive tasks.
1. The reality of being an “AI trainer”:
- *Example:* After starting work, Wang Zhihan had to process 200 English images daily, asking AI to translate them and then manually correcting the mistakes.
- *Plain language explanation:* This job doesn’t require deep understanding of machine learning; it just requires English skills, logical thinking, and the ability to sit still for long periods. It’s essentially data cleaning, a low-level task in the AI industry that’s easily replaceable as AI improves its own error correction capabilities.
2. Watered-down versions of “big model development”:
- *Example:* Chen Shu’s course included assignments like writing song lyrics, which were more for show than substance.
- *Plain language explanation:* Real AI development requires a strong foundation in mathematics (linear algebra, probability theory) and engineering skills. Training courses simplify complex concepts and use trivial projects to fill the curriculum. What you learn might be how to use APIs, not how to build models.
3. Hidden fees and project tricks:
- *Example:* Lin Bing found that the most valuable “project experience” was sold separately for an extra 10,000 yuan, and a half-year project was compressed into a week’s worth of instruction.
- *Plain language explanation:* This is a form of double-dipping. Without real project experience, resumes are worthless to HR. Institutions deliberately break down valuable components and sell them separately, and the short duration means you can’t produce meaningful work.
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The Harsh Reality of the Trial Period: Resume “Fragility” Lasts Only 3 Months
Core Logic: The information asymmetry leads to a “cat-and-mouse game” that will eventually expose the truth.
The main selling point of these institutions isn’t teaching you skills, but how to deceive HR.
1. Resume manipulation:
- *Institutions teach you to misrepresent tasks (e.g., calling API calls as “developing large model applications” or data labeling as “model optimization”).*
- *HR may not understand the details during interviews or may hire you to meet recruitment targets.*
2. The trial period exposes the truth:
- *Example:* Chen Shu noticed that many people failed during the trial period and moved on to the next company’s trial period.*
- *Plain language explanation:* In the real world, you’ll face real work scenarios, complex code, and high demands. Those who rely on memorized scripts and fake projects will quickly be exposed. The consequences include getting fired, damaging your reputation in the industry, and falling into a cycle of constantly switching jobs without building real skills.
3. The “employment rate” is misleading:
- *The claimed employment rates refer to receiving job offers, not long-term stability. Many “high-paying” offers are actually outsourced positions or short-term projects, or they’re just used to meet institutional targets.*
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Who’s Really Paying the Price? An Economic Analysis of Anxiety
Core Logic: Young people are buying not skills, but a sense of security and identity.
Why do they continue to invest, even though they know it might be a scam? Due to high sunk costs and social pressure:
1. A “gamble” for ordinary families:
- *Example:* Zhang Shuai’s parents, ordinary workers, sold their savings to pay for his training; Chen Shu hid it from his parents and rented an apartment in Beijing with his startup funds.*
- *Analysis:* For these families, 20,000–30,000 yuan is a significant investment, seen as a chance for a better life. The gamble blinds them to rational judgment.*
2. The illusion of social mobility:
- *Example:* Wang Zhihan, feeling inferior because he couldn’t afford a fancy meal, quit his job to learn AI in hopes of entering the “big company” circle.*
- *Analysis:* The AI industry is associated with elite status and high salaries, offering a perceived rise in social status. For those from lower-income backgrounds, it’s a chance for upward mobility. They’re buying an identity rather than actual skills.*
3. A final attempt for middle-aged individuals:
- *Example:* 38-year-old Lin Bing, laid off, turned to AI training in desperation.*
- *Analysis:* For those in their 30s, AI is a last hope. They fear being marginalized and are willing to pay dearly for a sense of progress, even if it’s just a placebo.*
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Advice for Ordinary People: How to Avoid the AI Training Scams
If you or someone you know is considering AI training, take a moment to ask yourself these questions:
1. What’s my foundation?
- If you have a background in computer science, mathematics, or physics, self-study, open-source communities, and practical projects are more effective.*
- If you’re a non-technical liberal arts graduate, direct learning in “big model development” is a dead end. Consider “AI application layers” (e.g., Prompt Engineering, AI product management assistant) or combining AI with your field (e.g., AI + law, AI + healthcare), but be cautious of quick-fix solutions.*
2. What do I really want to do?
- Do you want to be an algorithm engineer (creating AI)? The barriers are high, and training courses are ineffective.*
- Do you want to be an AI trainer/data标注er (feeding data to AI)? The barriers are lower, but the salary potential is limited and the job is susceptible to automation.*
- Do you want to be an AI product manager/application developer (using AI)? This requires business understanding and product thinking, which training courses can’t provide.*
3. *Can I afford this cost?*
- If you’re borrowing money or depleting your savings, don’t enroll in such courses.*
- Real learning is free: Bilibili, GitHub, official documentation, open-source communities. Paid courses offer value through community support and guidance, but you can achieve the same results by learning with friends.*
4. Beware of job guarantee promises:
- Any institution that promises guaranteed employment or a minimum salary is likely breaking the law.*
- Real employment depends on your skills and market demand, not on their internal connections.*
Conclusion
AI is a fascinating era, but it’s not a miracle worker or a money-making machine.
For most people, the right approach to AI is to use it to improve efficiency or find ways to leverage it in your field. The stories of Zhang Shuai, Wang Zhihan, Chen Shu, and Lin Bing reflect the widespread anxiety in our society.
Remember: Those who chase the latest trends may fly high, but they may also fall hard. Instead of gambling on a trend, focus on developing practical skills that can solve real problems. AI is a tool; the real value lies in the people who use it. Don’t let an expensive “pie” steal your time and money.