AI in 2025 – A global perspective, with Kai-Fu Lee

01/02/25 • 50 min

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.

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Exponential View

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.

Connect with us:

Exponential View

Previous Episode

AI in 2025 – The great normalisation, with Nathan Benaich

Nathan Benaich, Founder and General Partner of Air Street Capital, joins me to discuss AI in 2025. From runaway consumer adoption to evolving enterprise moats, from still-elusive AI-driven drug breakthroughs to the renewed vigour in robotics, several core themes stood out.

1. Frontier models & AI at scale

In 2024, we witnessed the astonishing growth of frontier models and their deployment on a massive scale. OpenAI’s GPT-4 and GPT-4 o1, Anthropic’s Claude and Google’s Gemini have all demonstrated that being “at the frontier” is increasingly the price of admission.

2. Consumers, voice and infinite worlds

On the consumer side, we have reason to believe 2025 will be the year of AI-enabled workflows that feel truly natural. Voice, multimodality and integration into daily routines—like transcribing my morning thoughts during a commute—are becoming routine.

3. Accelerating science & drug discovery

While AI accelerates lab automation and data analysis—improving reproducibility and speeding up processes—the promised “AI-designed blockbuster drug” is still in the pipeline. Clinical timelines and regulatory hurdles do not compress easily.

4. Geopolitics, funding and the sovereign question

As training costs skyrocket and models require unimaginable scale, questions mount... Who funds these massive compute requirements? Will nation-states view these labs as strategic assets, akin to telecoms or chipmakers?

5. From explosive capability gains to refined utility

We’ve grown numb to what was once astonishing—perfect speech synthesis, infinite text generation, zero-shot coding. The capabilities of models now surpass human levels in many benchmarks. The next major shifts may be subtler, or simply less obviously spectacular.

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Next Episode

The future of Human-AI coexistence, according to Kevin Kelly (co-founder of Wired, futurist, author)

Kevin Kelly is a co-founder of Wired Magazine and a renowned author and futurist. Decades ago, Kevin predicted much of today's technological and cultural landscape. In this discussion, he presents his new bold vision for what’s coming next: The Handoff to Bots.

In this episode, you’ll hear: