Kelvin Chan: From Math to Google AI, Nano Banana, How It’s Built & Where It’s Headed – E657

Jan 1

“I hope that AI becomes a partner to people rather than something that replaces or eliminates humans. I believe that in ten years AI will be more reliable, allowing us to trust it with many tasks. If robots become common, that is a good thing because they save time on labor like washing dishes. Today, language models still hallucinate, so we double-check their work. In the future, I hope we can rely on AI without constant verification, coexisting with it and becoming far more productive together.” - Kelvin Chan, AI researcher at Google

“One year ago, I did not expect image editing or image generation to become this good. There is always something new in this field, which is why I stay excited working in AI at Google. We do not know where the limit is, and that uncertainty drives me every day. Ironically, I have no artistic sense at all, yet I work on images. When I take photos for friends, they usually retake them because I cannot frame good shots. That became a motivation for me to work on image editing and generation, because now I can take a random photo and ask AI to adjust the angle or make it more artistic. It is genuinely useful, and it saves me from my friends’ sarcasm.” - Kelvin Chan, AI researcher at Google

“Google encourages us to use the AI tools we build because using them is the fastest way to understand what people need and what can be improved. When we build the tools and then use them ourselves, we learn how to refine them and create better models for the public. This feedback loop makes the work more effective and is what makes this an exciting moment to be working at the frontier of AI.” - Kelvin Chan, AI researcher at Google

Kelvin Chan, an AI researcher at Google, joins Jeremy Au to unpack his unconventional path from mathematics in Hong Kong to applied AI research across Singapore and the United States. They explore how AI research differs from traditional academic work, why iteration and results often matter more than theory, and how scale has transformed research culture from small experiments to highly collaborative, compute-heavy systems. The conversation covers the rapid evolution of image and video models including Google’s Nano Banana model, the push toward world modeling and embodied AI, and how AI tools are reshaping daily productivity for engineers. Kelvin also reflects on choosing AI in 2018 before it was mainstream, and why he believes the long-term future lies in AI as a trusted partner that augments human work rather than replaces it.

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