Welcome to Deep Dive Panda! We've spent the week decoding the most important conversations from over 14 hours of Chinese tech, business, and AI podcasts.
A big shift is happening everywhere, from AI to city planning. Perfect, top-down plans are out. The new winning move? Getting your hands dirty. It’s about understanding how people and systems actually work, chaos and all.
The Unifying Theme
It's easy to fall in love with a spreadsheet. It promises a clean world where you can model users and predict growth in a straight line. For a long time, tech tried to force that clean model onto the real, messy world. That's over. Now, the big thinkers in China are all talking about why that approach failed and why embracing the chaos is so powerful.
This isn't just one trend. It's the same lesson popping up everywhere.
Look at local reviews. Gaode Maps tried to build the perfect, data-driven restaurant list. A spreadsheet approach. People hated it. Why? Because its rival, Dianping, gets it. Finding a good restaurant is a messy human drama of fake reviews, passionate arguments, and weirdly real photos. That chaos is the whole point. People trust it because it's human and flawed.
You see the same thing happening in AI. The dream of a perfect, god-like AI is being shelved for something more practical. OpenAI isn't just building a super-brain. They're making an "Agent Builder" that forces AI to follow a script. They're basically admitting that a perfect, all-knowing AI isn't reliable enough for real business problems. Even in drug discovery, AI researchers are finding that pure data isn't enough. They have to go "back to physics"—back to how things actually work.
Why It Matters
So what's the big lesson here for founders and investors? Look at Xigua Video. Bytedance backed them, and they had a huge, loyal audience of middle-aged guys in smaller cities. But the execs were embarrassed by their own users. They chased a "cooler" image that looked good in a pitch deck. In the end, they lost their real fans and never won over the cool kids. They picked the spreadsheet over their people, and it killed them.
This means your "unsexy" market could be your best defense. You can't just buy a real community. Bytedance threw billions at creators, but they couldn't fake the sense of belonging that kept people on a rival platform like Bilibili. For investors, a team that truly gets its messy, real-world niche is worth more than a huge budget chasing some "ideal" market.
The old question was, "How big is the market?" The new question is, "Does this team truly get its tribe?" The future belongs to companies built for reality, not for a slide deck.
Key Patterns & Strategic Takeaways
Clean Algorithm vs. Messy Marketplace. Gaode Maps' "we know best" algorithm felt like a black box nobody trusted. Dianping's chaotic, user-driven mess felt more real and reliable. The lesson: don't clean up the human parts of your platform. That's where the value is.
AI's Pragmatic Turn. AI is getting real. The goal is shifting from building a perfect super-intelligence to making practical tools that work. It's not about AGI anymore. It's about getting paid.
Ashamed of Your Own Users. The Xigua Video story is a huge warning. They had a massive user base but chased a "cooler" one they didn't have. Don't fall into that trap. Love the users you have, not the ones you wish you had. Their "unsexy" reality is your gold mine.
Your Identity is Your Moat. The best defense is a strong sense of who you are. A rural village that saw itself as "guardians of the pass" couldn't be bought out. You can't copy a real sense of purpose, no matter how much money you have.
When Data Fails, Go Back to Basics. If you hit a wall, go back to the source. AI for drug discovery is turning from big data back to physics. The real world is always a better source of truth than a report or a model.
Pivotal Quotes
"An empty brain, paired with even the strongest search engine, will only ever be a clumsy information porter."
"[If Solana were a normal company], its 'stock' wouldn't be $200, it might be $600. Because equity investors are willing to pay a premium for growth, which crypto investors currently don't."
This week's Deep Dive Panda covers 14 specific podcast episodes, including:
What's Next | Tech First — From AlphaFold to RNA target prediction, how is AI reshaping the future of drug discovery? | S9E34
Original: What's Next｜科技早知道 — 从 AlphaFold 到 RNA 靶点预测，AI 如何重塑新药研发的未来? | S9E34
Zhixing Bistro — E208 Dialogue with Yang Suqiu: In an era where knowledge is readily available, why do we still need libraries?
Original: 知行小酒馆 — E208 对话杨素秋：在知识触手可得的时代，我们为什么还需要图书馆？
That's how business works — This is the City 15 | Rediscovering Quanzhou
Original: 商业就是这样 — 城市就是这样15 | 重新发现泉州
That's how business works — Vol.229: Explain the Merger and Acquisition Issue Once and For All
Original: 商业就是这样 — Vol.229 一次性说清并购这件事
Original: Web3 101 — E65｜从打出「中国溢价」到豪赌DAT，夏焱资本朱俊伟聊金融老兵如何跳进Web3
Original: 贝望录 — 196. 海外商业观察丨望向南半球，中国品牌出海在澳洲市场也有“不容忽视的潜力”
Tech Stew — After declining the AutoNavi Street Sweeping List press conference, we talked about its feat.
Original: 科技乱炖 — 婉拒了高德扫街榜发布会后，我们聊了聊它 feat.津津有味
Watching the Rise and Fall of Great Buildings — 144. Female hero Duan Sihe and her hometown Mapingguan: "We once thought that electricity and roads were just dreams"
Original: 起朱楼宴宾客 — 144.女侠段四合和她的家乡马坪关：“我们曾以为有电有路只是梦”
Talk about words — #267: “Think of it as a storybook”
Original: 字谈字畅 — #267：「把它当做一本故事书」
Flipping Through Books — 246. Review of the mid-length video war between Xigua and Bilibili: Is 40 million DAU an asset or a burden?
Original: 乱翻书 — 246.西瓜与B站的中视频战争复盘：4000万DAU是资产还是包袱？
Original: 晚点聊 — 137: Agent 是机会，造 Agent 的工具也是|从OpenAI开发者日聊起
Original: AI可可AI生活 — [人人能懂] 从“覆盖度”、根号法则到AI评审团
AI Cocoa AI Life — [Everyone can understand] From binary rewards and punishments, dynamic auditing to thinking building blocks
Original: AI可可AI生活 — [人人能懂] 从二元奖惩、动态审计到思维积木
AI Cocoa AI Life — [Everyone Can Understand] Insight into the Earth, Efficient Learning, Perfect Deception
Original: AI可可AI生活 — [人人能懂] 洞察地球、高效学习、完美欺骗
Here are the key lessons & mental models from each episode:
1. What's Next | Tech First - From AlphaFold to RNA target prediction, how is AI reshaping the future of drug discovery? | S9E34
Original: What's Next｜科技早知道 — 从 AlphaFold 到 RNA 靶点预测，AI 如何重塑新药研发的未来? | S9E34
Managers, Not Workers: For years, drugs have targeted proteins—the "workers" of our cells. The new idea is to target RNA instead. Think of RNA as the "managers" giving the orders. If you can get to the manager, you can tackle diseases we once thought were undruggable.
AI's Family Tree Problem: AlphaFold's success wasn't magic. It worked by comparing a protein's family tree across thousands of species to guess its shape. But for newer targets like RNA, there's not much family history to look at, so the AI gets stuck. It can't figure things out with just one example.
Physics is the New Big Data: Today's AI is hitting a wall because it's running out of data. The answer isn't just bigger models. It's about going back to basics and building AI that understands the real-world physics of molecules. Then it won't need mountains of data to make a prediction.
Can't Find Data? Make It: If you don't have the data you need, you can just create it. One lab forced bacteria to evolve in a specific way, which generated the perfect data for their AI model. It's a clever way to turn a data problem into a bio-engineering puzzle.
"To truly solve this problem, we must return to physics and figure out the real physical interactions. Once you've figured that out, you don't need so much data... just like Newton's laws."
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