Trend Tuesday: Move Fast & Cure Things: China’s Breakthrough Innovation Strategy 🧬

Art by @basilonmypizza: https://lnkd.in/eF8FkWzN - https://basilhefti.ch/

Trend: Incremental, rapid iteration combined with AI-driven discovery is becoming a dominant innovation strategy globally—especially visible in the biotech sector.

"In recent months, China’s progress in artificial intelligence has stunned the world. A quieter yet equally significant shift is underway in biotech," says The Economist. 🤖🔬

A prime example: Akeso, a biotech company, developed a cancer drug outperforming Keytruda (one of the most lucrative medications ever), by nearly doubling progression-free survival times for lung cancer patients. This means patients live longer, but it is no cure: the cancer eventually progresses. An important improvement nevertheless, pointing in a hopeful direction. 📈

What's driving this? Three components can be identified:

🎯 Strategic Focus: Nearly two decades ago, China targeted biotech as a priority. Regulatory reforms cut approval times for human trials dramatically, from 500 to 100 days. This boosted career attractiveness and innovation readiness.

🔄 Iterative Innovation: innovative biotech companies rarely start from zero. Instead, they rapidly iterate, tweaking existing drugs to enhance safety, efficacy, and delivery. This approach mirrors the key strength of AI, where algorithms rapidly combine overlooked concepts, discovering novel materials, proteins, or solutions humans previously missed.

🕒 Speed: "We can do things two or even three times faster than anywhere else," notes Michelle Xia, Akeso’s founder. Rapid feedback loops (short cycles of "try-learn-correct") enable superior results.

🗣️ “People who want, find solutions. People who don’t, find reasons.” When working with data and AI, this mantra is a constant companion in all my projects. To facilitate creating value from data, we need to set the right conditions and win hearts. ❤️📊

The cancer drug story provides a universally applicable blueprint for innovation. Whether batteries, energy, or Large Language Models (LLMs), the same recipe applies:

🧠 Attract Talent: Draw experts into strategically important fields.

⏭️ Foster Rapid Iteration: Embrace swift feedback loops.

🏗️ Build on Foundations: Stand on the shoulders of giants—don't start from scratch.

We've all experienced the power of GenAI and large language models. And we see the massive innovation happening in biotech. Now, imagine the possibilities when these strategies converge.

I think it's a necessity for us to learn from these patterns and apply them in our cases.

What’s your perspective? Is Switzerland ready to emulate this rapid innovation?

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