Summary of Key Points
The 2027 autumn recruitment season has seen a significant increase in the presence of AI-related positions—large companies are offering over 70% of their jobs to AI specialists (with ByteDance and Alibaba reaching 80%). However, the opportunities have not increased proportionally with the rise in job numbers; instead, the entry barriers have become higher. Vertical AI internship experience has become the most valuable asset for getting a foot in the door. The gap between those who can “use AI tools” and those who truly “understand the logic behind AI implementation” is becoming increasingly evident during interviews. Students from non-technical backgrounds can still try, but they need to plan ahead. Moreover, the specialization of AI roles (such as large model algorithms, embodied intelligence, and AI application layers) makes pursuing popular fields without proper preparation highly risky.
Detailed Analysis
1. A Surprising Increase in AI Positions, But More Opportunities Is an Illusion—Barriers Are Rising
Large companies use the proportion of AI positions to signal to graduates that the future lies in AI, but in reality, the difficulty of getting into these companies has increased.
- Data Highlights: According to PulseMai, the number of AI-related positions in campus recruitment from January to May 2026 increased by 47.3%, and their penetration rate rose from 26% to 37.5%. However, the total number of positions did not increase significantly; rather, non-AI roles were reduced.
- Real-Life Examples: Frida, a student majoring in Chinese language, submitted forty to fifty resumes but only got one interview with a large company. Xiao Bin, a master of software engineering, noted that this year’s demand for AI positions is more refined, and many of last year’s roles no longer exist.
- Reasons Behind the Increase: Large companies are looking for professionals who can actually get work done—not just for formality. For example, algorithm positions require knowledge of how models are implemented, and application roles need to present real-world business cases, naturally raising the entry standards.
2. Internships Are Crucial—Vertical AI Experience Is More Valuable Than a Top University Background
In the past, a prestigious university degree might have been enough to get into a large company, but now relevant AI internships are the key factor in selection.
- Success Stories from Non-Top Universities: Frida, from Henan Normal University (a non-top university), got an interview with Pinduoduo due to her internship in AI-generated advertising content. However, some mid-tier companies still prioritize academic qualifications.
- Cross-Field Success: Xiao Lu, a master of psychology, stayed at a large company through an internship with Tencent’s AI products. Her classmates from 985 universities struggled to get interviews without similar experience.
- The Need for Relevant Experience: Positions in embodied intelligence required specific internships; without them, even highly qualified candidates struggled.
3. The Gap Between “Using AI” and “Understanding AI” Is Significant—Interviews Reveal the Difference
Many think knowing how to use tools like ChatGPT or Midjourney means understanding AI, but detailed questions expose this misconception.
- Differences:
- Using AI: Proficiency in using tools (e.g., generating images with AI).
- Understanding AI: Being able to explain the rationale behind choices and how to optimize failures.
- Interview Examples: Frida was asked about work processes and optimization strategies during her Pinduoduo interview; Da Ze failed an interview on a document retrieval system because she lacked practical experience.
- Certificates Are Ineffective: Frida emphasized that real projects, videos, and results are more valuable than certificates.
4. Opportunities Exist for Non-Technical Backgrounds—Just Get Started Early
AI is not exclusive to technical roles; non-technical students can also benefit from AI, but they need to prepare early.
- Non-Tech Requirements: Marketing roles may require AI for graphic design and data analysis, while operations roles might use AI for content creation. Xiao Lu’s skills in using AI for reporting and data analysis made her more competitive.
- How to Get Started:
- Start with peripheral roles or small projects (e.g., building a campus Q&A system with AI).
- Accumulate relevant internships.
- Adjust your mindset: Failure in an interview doesn’t mean you’re not talented; it might just mean the role isn’t right for you. Focus on finding a suitable direction.
5. AI Roles Are Becoming More Specialized—Don’t Chase Trends Blindly
AI roles are now more specialized, such as large model algorithms, embodied intelligence, and AI infrastructure. Chasing popular fields without expertise can be risky.
- Challenges of Specialization: Da Ze initially applied to multiple areas but realized she lacked the necessary C++ skills for AI infrastructure roles and focused on agents and applications instead.
- The Rapid Pace of Change: Popular roles may become obsolete soon. It’s better to choose a direction where you have expertise.
Final Conclusion
This year’s AI recruitment is not about shortcuts; it’s about starting early and building a solid foundation. Accumulate relevant experience, distinguish between using and understanding AI, and choose a specialized direction. For those without a technical background, focusing on using AI for efficiency or accumulating practical experience can still be beneficial. Large companies need problem-solving professionals, not just those who talk about AI.