AI in 2025 – A global perspective, with Kai-Fu Lee
Jan 2, 2025
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Kai-Fu Lee, a leading AI researcher and entrepreneur from Taiwan, explores the future of AI in 2025, emphasizing the contrasting trajectories of the Chinese and U.S. markets. He highlights how Chinese companies are crafting unique generative AI solutions without predefined SaaS layers, leading to specialized integrations. Lee predicts a shift towards 'AI-first' applications across industries, with significant reductions in operational costs and an increased focus on inference over training. The discussion also touches on the evolving role of productivity tools and innovative search technologies.
The Chinese market's adoption of generative AI is characterized by incremental development and a focus on creating domain-specific solutions, unlike the swift western reception exemplified by ChatGPT.
As we approach 2025, a shift towards AI-first applications is redefining user interaction and productivity in creative processes, dramatically altering how tasks like document creation and complex inquiries are approached.
Deep dives
Generative AI's Global Impact
Generative AI is making significant waves in Asia, particularly in China, although the scale is not yet comparable to the U.S. The emergence of numerous competitive models has made the technology more accessible, albeit with a slower adoption rate than witnessed with ChatGPT. Kai-Fu Lee highlights that while the research community is buzzing about generative AI's potential, the experience of users in China differs due to local dynamics. The ability of Chinese companies to create affordable, effective AI models is gradually fostering innovation and applications in the region.
AI-First Tools Reshaping Workflows
The conversation around AI tools emphasizes a shift toward AI-first applications, where AI takes the lead in creating content instead of users manually inputting commands. For instance, tools like PopAI allow users to prompt the AI continuously to refine outputs, showcasing the growing dependence on AI for generating various forms of written content. This paradigm shift stresses the importance of interaction with the system to achieve more nuanced results, reflecting a transformation in how individuals approach tasks such as document creation and research. The advancements in AI-writing capabilities enhance productivity while changing the nature of human involvement.
The Future of Search Technology
A new breed of search engine is emerging that aims to provide concise, accurate answers to complex questions, moving beyond traditional search paradigms. An example is Beagle, which aggregates multiple sources to deliver a singular answer, enhancing the efficiency of inquiry. Traditional search tools like Google, which often yield fragmented results, may become less appealing for intricate queries as users seek a more direct approach. This evolution signals a potential redefinition of how information retrieval will function in the future.
The Path to 2025 and Beyond
Looking ahead to 2025, expectations center on the maturity of multimodal applications and the integration of genuine AI-native tools into everyday use. Current practices in AI face challenges, particularly around scaling laws, which could impact the overall advancement of generative technologies. While there may be a wave of new applications, excitement exists about the emergence of agents and further developments in the robotics field. Balancing high user expectations with the realities of technology development will be crucial as industries navigate this rapidly evolving landscape.
Kai-Fu Lee joins me to discuss AI in 2025. Kai-Fu is a storied AI researcher, investor, inventor and entrepreneur based in Taiwan. As one of the leading AI experts based in Asia, I wanted to get his take on this particular market.
Key insights:
Kai-Fu noted that unlike the singular “ChatGPT moment” that stunned Western audiences, the Chinese market encountered generative AI in a more “incremental and distributed” fashion.
A particularly fascinating shift is how Chinese enterprises are adopting generative AI. Without the entrenched SaaS layers common in the US, Chinese companies are “rolling their own” solutions. This deep integration might be tougher and messier, but it encourages thorough, domain-specific implementations.
We reflected on a structural shift in how we think about productivity software. With AI “conceptualizing” the document and the user providing strategic nudges, it’s akin to reversing the traditional creative process.
We’re moving from a training-centric world to an inference-centric one. Models need to be cheaper, faster and less resource-intensive to run, not just to train. For instance, his team at ZeroOne.ai managed to train a top-tier model on “just” 2,000 H100 GPUs and bring inference costs down to 10 cents per million tokens—a fraction of GPT-4’s early costs.
In 2025, Kai-Fu predicts, we’ll see fewer “demos” and more “AI-first” applications deploying text, image and video generation tools into real-world workflows.