I recently had the chance to listen to a conversation with Liang Ning, a respected expert in product thinking and business innovation.
Liang recently published book “Genuine Needs” (self-translated book name, 真需求, October 2024). Through her framework of “value-consensus-model,” she analyzes successful businesses from the ancient Silk Road to modern companies like SHEIN and Luckin Coffee. The book demonstrates how understanding “genuine needs” is the foundation of successful commerce. This perspective on identifying authentic human needs informs much of her thinking about product design, as I discovered while listening to her recent talk.
Adventures at Burning Man
The talk started with her story about Burning Man - this temporary city that exists for just eight days a year with basically no rules. Liang mentioned that both Chinese and Americans can program, but sometimes Chinese tech people don’t know how to “play”, hence she decided to attend the Burning Man after a SaaS event. The first thing to do there is picking a festival nickname, which is all about reinventing oneself.
Why WeChat’s “Blue Packet” Feels Disappointing
Liang had an interesting take on WeChat’s Blue Packet feature (for digital gifting). It’s designed as a shopping platform where the gift sender pays for a gift and the receiver can choose to accept it by providing their address, which the team believes preserves the receiver’s privacy. She called it “chicken ribs” (雞肋, meaning of little value). Traditional gift-giving is about showing the sender’s care, but the Blue Packet requires the gift receiver to manually input their address, and it even displays the price of the gift, which kind of kills the emotional vibe.
She put it simply: “Gifts are about emotional value, not efficiency.”
This explains why WeChat’s Red Packet worked so well - it created a fun group activity while cleverly getting people to use WeChat Pay (users get money from Red Packet, then they can spend it via WeChat Pay). The Blue Packet just didn’t create the same magic for gifting.
With 1.3 billion users, WeChat isn’t just an app anymore - it’s basically a digital living space. As Liang said, “‘Attention is all you need’ also applies to WeChat.” It should dig deeper into what people really care about.
How AI Is Changing Our Work
The part I found most useful was Liang’s framework for understanding AI’s impact on jobs. She distinguished between:
- Complex problems: Clear right/wrong answers (like making something cheaper; e.g. Elon Musk’s approach to Starlink)
- Difficult problems: No clear answers (like innovation; e.g. Musk’s Neuralink)
She described a career ladder that made a lot of sense:
- Simple projects
- Complex tasks
- Multiple tasks
- Starting new tasks (the hard part)
- Understanding emotions and connecting with people
This helped me understand why some jobs are more at risk from AI than others. Product managers or programmers who just handle routine tasks might be replaced, but those dealing with ambiguous challenges and building relationships will still be needed.
I liked her distinction between knowledge and experience:
- Knowledge: Information that’s been written down and can be widely applied
- Experience: Specific situations that often haven’t been put into words
Large Language Models are typically good at the first type of scenario because knowledges are everywhere online. However, they face challenges in the second type, particularly in scenarios that are niche and not well documented. For instance, they may struggle to understand how a child reacts to a mother’s cue to “come and have dinner.”
Technology Through History
Liang put AI in historical context:
- Industrial Revolution: Made things more efficient
- Internet Era: Made information more accessible
- AI Age: Probably moving from efficiency to better experiences
“What do people really want?” she asked. “Not more efficient work, but better life experiences.”
She shared a real story about a celebrity who created an AI agent based on a historical monk. The person became so emotionally attached, and one day she called the AI over voice and broke down crying. It shows how much we desire connection, even with digital beings.
Liang also suggested that we could observe passengers on trains, where we can watch them for hours. She noticed children chatting with LLM for hours - AI is so patient, unlike parents. This shows how AI can assist people in ways that humans are not willing to do.
Finding Opportunities in AI
For people looking for opportunities, Liang quoted Nietzsche: “there are no facts, only interpretations.” Different viewpoints reveal different opportunities.
She suggested focusing on:
- Industry leaders who shape markets
- Potential disruptors who keep pushing forward
- Neglected areas where AI could solve real problems
Unlike a few years ago when markets were crowded, today’s AI landscape is still pretty open. The best opportunities might be in basic technologies, cutting-edge applications, or ignored areas where AI could address practical problems.
What Makes a Disruptor
What makes someone a true disruptor? According to Liang, it’s not just improving things but fundamentally changing how an industry works.
She mentioned examples like Zhou Hongyi with 360 (offering free security software when others charged) and Lei Jun’s approach to Xiaomi cars (changing not just the product but how it’s marketed). Some wealthy people buy great minds’ time by, e.g. paying 200k CNY - Lei letting people buy his time by selling the first batch of his Xiaomi Su 7 car for 200k CNY, which basically equals “taking a photo with him + dinner time + a free car”.
Disruptors often have one thing in common: they’ve experienced the satisfaction of “flipping the table” early in life and keep looking for that feeling.
Personal Growth with AI
What stuck with me most was Liang’s take on learning in the AI age. Traditional education was about acquiring knowledge to use, but AI has made much of this knowledge easily available.
“Now, we learn to become better humans,” she suggested. “Learning is no longer about becoming tools ourselves, but discovering who we are and what we care about.”
This really hit me as I think about my own studies and career. In a world where AI can pour out knowledge on demand, our unique perspectives and experiences become our most valuable assets.
Final Thoughts
After listening to Liang Ning, I’m thinking about how technology changes not just what we can do, but who we are - what’s the niche that we humans can fit into as AI becomes smarter?
Great products won’t just make things more efficient; they’ll make our experiences and connections better.