On October 22, 2025, Meta CEO Mark Zuckerberg approved a plan to lay off approximately 600 employees in the company’s artificial intelligence division. This marks Meta’s largest-scale layoff in the AI field this year, primarily targeting the core R&D unit known as the "Superintelligence Lab." Amid this layoff turmoil, the experience of Tian Yuandong, former Research Director of the FAIR team, has attracted widespread attention.

Tian stated that the layoff did not come as a surprise; instead, it accelerated his existing career plans. "I already had job offers before being laid off. I told my superiors: 'Well, I'm not very happy and might start looking around.' They were aware of this, so I wasn't particularly shocked by the layoff." He revealed that many companies, including major tech giants and startups, have contacted him over the past few days, offering various opportunities such as senior positions and co-founding roles.

Guangzhou Technology Headhunting Company observed that top AI talents like Tian are extremely sought-after in the market, with major companies actively reaching out through headhunting channels. "The high-end AI talents released by Meta’s layoff have immediately become the focus of competition among major tech firms," noted a consultant from Shenzhen Internet Giant Headhunting Company. "We have received urgent commissions from multiple companies hoping to connect with these laid-off AI experts."

AI Industry Trend: Talent Structure Transformation Amid Accelerated Automation

Tian believes this large-scale layoff reflects a deep-seated trend in the AI industry. "I think this is an industry-wide trend. I won’t comment on Meta’s specific situation as I’m not at liberty to disclose much, but the industry is moving in this direction." He pointed out that the level of automation in AI technology is constantly improving, which will profoundly reshape the talent demand structure of the industry.

"AI itself is the most automated field. For example, many people label data today, but tomorrow more powerful models may reduce the need for manual data labeling, and the day after tomorrow, even fewer people will be required as models become stronger," Tian explained. With the improvement of automated tools, many traditional AI tasks such as data labeling and model parameter tuning will gradually be replaced by automation.

Shenzhen AI Headhunting Company analyzed that this trend will lead to polarization in talent demand within the AI industry. "On one hand, demand for executive-level technical personnel will decrease; on the other hand, high-end talents with innovative capabilities and in-depth research skills will become even scarcer," said an expert from the company. "This explains why top research talents like Tian Yuandong are so sought-after."

Tian further pointed out that the talent demand in the AI industry will undergo fundamental changes in the future. "Overall, the number of people directly engaged in AI may decrease, but more and more people will explore using AI to build tools and other applications. That’s roughly how the process will unfold." He believes that basic researchers and application innovators will become the core demand in the future.

Open Source vs. Closed Source: Divergent Paths of Model Development

When discussing the development prospects of open-source and closed-source models, Tian stated that despite fierce competition, the open-source model will continue to exist. "I think there will still be open-source initiatives in Silicon Valley. For example, some companies I know, like Reflection AI, are working on open-source models with many requirements and ideas."

He believes that the key to future model development lies not in the opposition between open source and closed source, but in the specific use cases of the models. "Whether open-source or closed-source, once a model is launched, it can be used as a chat tool, search tool, or productivity tool—large companies may focus on these areas. However, there are many other directions: for instance, models can be used for scientific research, assisting scientists, or serving vertical sectors, which small companies can pursue."

Guangzhou Internet Technology Headhunting Company noted that this divergence will create opportunities for enterprises of different sizes. "Large companies may focus on closed-source development of general-purpose large models, while small and medium-sized enterprises can innovate in vertical sectors through open-source models," said a consultant from the company. "This means the demand for AI talents will become more diversified, with different types of enterprises requiring AI experts from different backgrounds."

Reflections on the LLM Path: Challenges of Data Efficiency and Learning Capability

When asked if large language models (LLMs) are the correct development path, Tian expressed a cautious attitude. "I think LLM is an interesting path, but I’m not sure if it’s the right one." He pointed out that the biggest challenge facing current LLMs is low data efficiency.

"The biggest problem is the need for massive amounts of data. With sufficient data, the quality of trained models will certainly be good, but they are definitely not as efficient as humans—that’s a major issue," Tian explained. Humans can learn quickly through limited samples, while current LLMs require massive amounts of data to achieve similar results. This efficiency gap is a fundamental challenge.

"Throughout human history, there have been many outstanding scientists with unique ideas. They didn’t read as many books or have access to as much data back then, yet they were able to discover new theorems, proofs, findings, or inventions." He believes that future AI breakthroughs may require entirely new learning algorithms, not just increasing data volume and model scale.

Hangzhou AI Headhunting Company observed that the recognition of LLM limitations is changing enterprises’ talent demand. "More and more companies are looking for talents with in-depth research in basic AI theories and learning algorithms, rather than just engineers familiar with existing LLM technologies," said an expert from the company. "This reflects the industry’s rethinking of AI development directions."

The Value of Reinforcement Learning: Active Learning and Problem-Solving Capabilities

When discussing the development prospects of reinforcement learning (RL), Tian expressed a positive view. "Essentially, RL is a search process. Its advantage is that for the difficult problems you present, it searches for solutions, and the quality of data and information obtained during this search is superior to fed data."

He believes that the core advantage of reinforcement learning lies in its active learning capability. "So I think the greatest benefit of reinforcement learning is that it enables active learning, which has a very positive impact on data distribution—that’s its core strength." This active learning capability allows models to solve more complex problems, especially in reasoning and decision-making.

Guangzhou AI Headhunting Company observed that the demand for reinforcement learning experts is on the rise. "As AI applications evolve from simple pattern recognition to complex decision-making systems, reinforcement learning talents are becoming increasingly important," said a consultant from the company. "We are seeing more and more companies recruiting reinforcement learning experts, especially in fields like autonomous driving, robotics, and game AI."

Computational Power and Scaling Law: A Future Prioritizing Efficiency

Tian expressed concern about the current trend of over-reliance on computational power in AI development. "This aligns with some of my previous assertions. I’ve been interviewed before saying that Scaling Law represents a pessimistic future." He believes that the development path relying solely on increasing computational power and data volume is unsustainable.

"If we told people in the past that increasing exponential samples or computational power would lead to linear improvements in performance, I think previous machine learning scientists would have considered this trivial." He pointed out that the real challenge lies in improving learning efficiency, not simply increasing resource input.

"I think at some point, people will realize that computational power isn’t everything—we may need a deeper understanding of models. And this change will gradually happen; that’s my view." Tian believes that future AI breakthroughs may come from a deeper understanding of learning mechanisms, rather than mere scale expansion.

AGI Headhunting Company analyzed that this emphasis on efficiency will affect the talent demand structure in AI. "Talents who can design efficient algorithms and improve model learning efficiency will become more important," said an expert from the company. "This means AI research will focus more on theoretical innovation and algorithm optimization, not just engineering implementation."

The AI Talent War: Avoid Chasing Short-Term Scarcity

When discussing the current fierce competition for AI talents, Tian offered unique insights. "It all depends on each person’s positioning. First, I want to correct a misconception: people shouldn’t fixate on who is currently scarce, because the definition of scarcity may change in a couple of years."

He advised AI practitioners to focus on their interests and long-term development rather than blindly chasing market trends. "So I think people should reflect on what they truly want to do, not just what companies prefer. That’s probably more important."

Tian explained that the AI industry changes very rapidly, and market demand may undergo fundamental changes in a short period. "But now the cycle has become very fast. By the time you decide to learn a popular technology, people around the world are already learning it. What you think of, others have also thought of." Therefore, he suggested that practitioners should choose their development direction based on their own interests and strengths, rather than merely chasing short-term market demand.

A consultant from AI Startup Headhunting Company stated that Tian’s views reflect the maturity of the AI talent market. "More and more high-end AI talents are focusing on long-term development and personal interests, not just salaries and positions," the consultant said. "This means startups need to offer more attractive visions and development space when recruiting talents."

Balancing Research and Engineering: The Rise of Full-Stack AI Talents

Looking back on his ten-year career at FAIR, Tian believes he has room for improvement in engineering capabilities. "My only regret is that I should have done more engineering work at FAIR—it might have been better." He stated that in the current stage of AI development, the combination of engineering and research capabilities is becoming increasingly important.

"When I first joined FAIR, I did more engineering work in the early years. For projects like Go, I handled most of the engineering myself. At that time, I was even criticized: 'This person is supposed to be a research scientist, but he’s always writing code.'" Tian recalled that later he shifted to more research work, but now he realizes the importance of engineering capabilities.

"But now you’ll find that in this era, people with strong engineering capabilities are actually more popular. Interestingly, those with strong research capabilities are also in demand, but the best are those who excel in both engineering and research." He believes that future AI talents need to possess both research and engineering capabilities, being able to transform theoretical innovations into practical applications.

Shenzhen Internet Technology Headhunting Company observed that the demand for such full-stack AI talents is on the rise. "More and more companies are looking for AI experts who understand both theoretical research and practical implementation," said a consultant from the company. "Especially in startups, this full-stack capability is particularly important because resources are limited, and one person needs to take on multiple roles."

Collaborative Evolution of AI and Humans

Regarding the future development of AI, Tian holds a cautiously optimistic attitude. "I think AI can automate a large number of tasks and enhance many people’s capabilities. I even feel that with large language models, I’m far more capable than before. So I believe there’s enormous potential to explore."

He believes that even if the development pace of AI technology slows down, its impact on various industries will still be profound. "So I deeply feel that a new era has arrived. Even if the progress of large language models isn’t fast enough, there will be plenty of opportunities in the next two to three years, or even three to five years."

Regarding his future plans, Tian stated that he is considering multiple possibilities. "As I mentioned earlier, I haven’t made a decision yet—I’m still in discussions. It’s been less than a week since the layoff, so I need more time to think." He hopes to find an opportunity that allows him to continue cutting-edge research while applying his findings to practical applications.

"When asked whether I want to focus on applications or continue my research, my answer is that ideally, I want to combine both. We can find a way to empower my scientific research while achieving many other things." Tian stated that he hopes to strike a balance between research and application to create greater value.

A consultant from ByteDance Headhunting Company noted that top AI talents like Tian have many options. "Major tech companies are actively contacting these AI experts laid off by Meta, offering very attractive terms," the consultant said. "The final decision may depend on personal career plans and interests, not just salaries and positions."

The Next Decade of AI Development

While Meta’s large-scale layoff has caused a stir in the industry, it also reflects that the AI industry is entering a new development stage. From Tian Yuandong’s in-depth sharing, we can see that the AI industry is undergoing a transformation—from scale expansion to efficiency improvement, from general-purpose models to vertical applications, and from single-capability to comprehensive competence.

During this transformation, the talent demand structure in the AI industry is also undergoing profound changes. Demand for executive-level technical personnel may decrease, while high-end talents with innovative capabilities, in-depth research skills, and engineering implementation abilities will become even scarcer. At the same time, interdisciplinary talents who can apply AI technology to specific industries and scenarios will become market hotspots.

For AI practitioners, Tian’s advice is: don’t blindly chase short-term market trends; instead, choose your development direction based on your own interests and strengths. In this rapidly changing industry, only by maintaining learning and adaptability, and continuously improving your comprehensive competence, can you stand firm in the fierce competition.

For enterprises, attracting and retaining high-end AI talents will become the key to competition. In addition to offering competitive salaries and benefits, it is more important to provide talents with a good research environment, development space, and innovation freedom. Only in this way can enterprises stand out in the fierce AI talent war.

The development of the AI industry is still in its early stages, with unlimited possibilities for the future. As Tian Yuandong said: "I deeply feel that a new era has arrived. Even if the progress of large language models isn’t fast enough, there will be plenty of opportunities in the next two to three years, or even three to five years." In this new era, AI will continue to profoundly change our way of life and work, creating more opportunities and challenges.