Andrew Ng, a visiting professor of computer science at Stanford University and the former head of Baidu AI and Google Brain, has just shared his annual review of the artificial intelligence (AI) landscape for 2025 on the social media platform X.

Moreover, he encourages everyone to utilize the holiday period for learning and software development. This endeavor not only refines existing skills and fosters new knowledge but also propels career advancement within the tech industry.

Andrew Ng presents three key recommendations:

• Enroll in AI courses
• Practice building AI systems
• (Optional) Engage with research papers

He provides several real-world examples to underscore the significance of these recommendations. For instance, developers either rediscovered standard RAG document chunking strategies, replicated existing agent AI evaluation techniques, or ended up writing disorganized LLM context management code.

"Had they taken some relevant courses, they would have had a better grasp of existing building blocks. While they can certainly reconstruct these blocks from scratch and potentially devise superior solutions, they could have spared themselves weeks of unnecessary labor," Andrew Ng remarked.

The hallmark of AI industrialization in 2025 is that core technologies have attained reliable and robust industrial-grade capabilities. The most notable breakthrough is the transition of reasoning models from a prompt-dependent technique to an intrinsic foundational capability of large language models (LLMs).

Initially, researchers unlocked reasoning potential through instruction models like "Think step by step." By 2025, new-generation models such as OpenAI's o1 and DeepSeek's R1 have internalized complex reasoning processes as standard operations. This evolution is primarily driven by fine-tuning based on reinforcement learning (RL), where models are trained to "think" before "answering" by rewarding correct output generation.

The resulting performance leap is remarkable. For instance, OpenAI's o1-preview outperformed the previous non-reasoning GPT-4o by 43% on competition-level math problems (AIME 2024) and by 22% on PhD-level scientific questions (GPQA Diamond). In programming capabilities, it even surpassed 38% of human competitive programmers on the Codeforces platform, compared to GPT-4o's 11%.

The enhancement in reasoning capabilities directly fuels the maturation of advanced AI application forms—agents and robots. When models learn to leverage external tools like calculators and search engines, their problem-solving abilities soar to new heights. For example, the tool-augmented OpenAI o4-mini achieved over a 3% accuracy improvement in a multimodal understanding test spanning 100 domains.

Reasoning models are becoming the cornerstone of autonomous evolution systems and scientific research. For instance, the AlphaEvolve project harnessed Google's Gemini model to repeatedly generate, evaluate, and modify code, ultimately crafting more efficient algorithms for real-world problems.

In 2025, salaries for top AI talents have soared to unprecedented levels. The defining moment of this trend was Meta's large-scale talent acquisition to establish its Meta Superintelligence Labs.

According to media reports such as The Wall Street Journal, Meta offered four-year compensation packages worth hundreds of millions of dollars to lure top researchers from companies like OpenAI, Google, and Anthropic. These packages included substantial cash bonuses and additional payments to offset the equity they relinquished by leaving their original employers.

Meta CEO Mark Zuckerberg even personally intervened and successfully recruited Jason Wei and Hyung Won Chung, core members of OpenAI's reasoning model research team. This fierce competition rapidly escalated talent prices across the industry.

In response to the talent poaching, companies like OpenAI were compelled to take countermeasures, such as accelerating equity grants and offering retention bonuses of up to $1.5 million. Andrew Ng, reflecting on this phenomenon, noted that for companies planning to invest hundreds of billions of dollars in data center construction, allocating a fraction to secure top talents is a sound business decision.

The industrial age hinges on massive infrastructure. In 2025, the explosive growth in AI computational power demand resulted in capital expenditures exceeding $300 billion across the entire AI industry in just one year. Consulting firm McKinsey predicts that by 2030, total investments to meet AI training and inference computational power demands could reach $5.2 trillion.

OpenAI launched the "Stargate" project in collaboration with Oracle, SoftBank, and others, planning to invest $500 billion and ultimately construct data center capacities of up to 20 gigawatts (GW) globally. Meanwhile, Meta is planning a $27 billion, 5 GW hyper-scale data center in Louisiana, USA.

The energy demands of these facilities are comparable to those of mid-sized cities, making their construction not just commercial ventures but strategic investments impacting regional economic development and the global energy landscape. This infrastructure competition signals that AI competition has shifted from soft power aspects like algorithms and models to hard power aspects like capital, energy, and land.

References:

https://www.deeplearning.ai/the-batch/issue-333/

https://x.com/AndrewYNg