Andrew Ng on AI: Powerful, Limited, and Transforming the Future
Andrew Ng, a leading figure in artificial intelligence, is widely regarded as one of the field’s most influential voices. Over a career spanning research, education, and investment, Ng has helped shape the modern AI landscape. He co-founded Google Brain, which later became part of DeepMind, Google’s flagship AI division, and served as chief scientist at Baidu, China’s tech giant. Beyond his corporate roles, Ng has built a significant public profile as an educator and thought leader, with over 2.3 million followers on LinkedIn and a reputation for making complex AI concepts accessible to a broad audience.
Despite the surge of investment in generative AI, Ng maintains a clear-eyed view of the technology’s capabilities. He emphasizes that while AI has achieved remarkable feats, it remains fundamentally limited. The development of artificial general intelligence (AGI)—AI capable of performing all human tasks at a comparable level—is still a distant goal. Modern AI training is highly complex and labor-intensive, and current approaches, including massive neural networks and extensive data preprocessing, are unlikely to produce AGI in the near future.
Ng has also been a prominent advocate for AI education. In addition to teaching computer science at Stanford University, he founded Coursera, one of the world’s largest online learning platforms, and oversees DeepLearning.AI, a platform dedicated to AI-focused learning. He argues that advances in coding tools and AI-assisted programming have made learning to code more accessible and essential than ever. Rather than discouraging people from coding, Ng believes that broader engagement with coding and AI will increase productivity, unlock creativity, and prepare the workforce for a future where AI is ubiquitous.
While recognizing AI’s promise, Ng is equally aware of its risks. The technology has raised concerns about mental health impacts, disinformation, and societal disruption. He stresses that these challenges should be addressed with thoughtful regulation focused on transparency rather than overly restrictive measures that could stifle innovation. Laws such as California’s SB 53 and New York’s RAISE Act exemplify this approach, aiming to increase accountability among AI developers while allowing innovation to continue.
Ng’s insight into the industry is strengthened by his connections with the next generation of AI leaders, including founders and executives at OpenAI and Anthropic. From this vantage point, he observes both the excitement and the speculative nature of current AI investments. He notes that early model training—the initial stages of AI development—is resource-intensive and carries significant financial risk, suggesting that parts of the AI sector may resemble a bubble. Nevertheless, demand for AI inference—the stage when trained models are deployed for real-world use—continues to grow rapidly, driving infrastructure expansion and the development of new data centers.
Looking ahead, Ng sees significant potential in emerging areas of AI. Voice-based systems, long envisioned in science fiction, are poised for broader adoption, transforming how humans interact with machines. He also highlights the growth of “agentic AI,” systems capable of autonomously performing tasks and making decisions, which he predicts will have rapidly increasing commercial value. While public hype around these technologies may fluctuate, Ng believes their practical applications and societal impact will continue to expand.
Ultimately, Ng views AI as a powerful, transformative technology that offers immense benefits while presenting real challenges. He emphasizes the need for a balanced perspective—one that celebrates AI’s achievements, acknowledges its limitations, and encourages responsible use and regulation. For Ng, the future of AI lies not in replacing humans but in amplifying human capabilities, expanding opportunities for education and innovation, and reshaping how society approaches work, creativity, and problem-solving.