Guest: Zhu Ning, professor of finance at the Shanghai Advanced Institute of Finance, Shanghai Jiao Tong University; role at the time
Host: Qu Kai
Originally published: June 13, 2026 | Duration: 70 minutes 46 seconds
Edited: October 18, 2026 | Official episode
Does world-changing technology make every associated price reasonable? Does recognizing a trend mean knowing when to enter or leave? Discussing AI bubbles, Qu Kai and Zhu Ning repeatedly return to a simpler question: judgment can be wrong, so what would the consequences be?
This is not a timetable for escaping a peak. Zhu stresses the difficulty of proving a bubble beforehand and separates risk characteristics from forecasting a collapse. Product and startup readers can use the distinction among outcomes, ability, and environment while keeping inspectable failure conditions outside an exciting narrative.
1. Rising outcomes can amplify confidence, extrapolation, and herding
Listen: 01:27–03:59. Zhu identifies overconfidence, linear extrapolation, and herding. After prices rise, people remember having predicted them and credit their own ability. This encourages larger subsequent commitments.
Extrapolation extends the latest direction into the future: yesterday and today rose, so tomorrow appears likely to follow. It ignores changing conditions and other historical paths. Herding supplies another shortcut: friends' gains become evidence that one should participate.
The mechanisms reinforce each other. Others display success, strengthening the appearance of continuity; a fortunate result increases confidence in personal judgment. Technical expertise does not necessarily confer immunity and may supply more persuasive explanations for confidence.
Editorial inference: Product growth can produce a similar loop. Monthly revenue may rise because of pricing, seasonality, or one large customer rather than a feature. Hiring against the latest growth rate combines questionable attribution with extrapolation. Write alternative explanations before planning replication.
2. Recognizing technological value does not establish a reasonable price
Listen: 04:14–09:57 and 46:25–53:48. Zhu separates important technology from an asset deserving its valuation. Accepting the first does not settle the second. Revenue, corporate profit, and customers making money through a service are further distinct questions.
The conversation's statements that model companies make money include no financial records or consistent distinction between revenue and profit. They cannot become factual conclusions here. The useful question concerns the value chain: upstream receipts do not automatically establish downstream economics, and one growing stage does not validate the whole system.
Capital and narratives are not entirely empty. They attract talent, build infrastructure, and accelerate research, potentially improving technology. Zhu compares them to watering and fertilizing a tree: growth can accelerate without becoming unlimited. Narratives mobilize resources without removing technical constraints or replacing customer outcomes.
Editorial inference: Both dismissing all attention and treating attention as complete proof oversimplify matters. Record how funding becomes an artifact, how the artifact is used, and whether use covers continuing costs. Each connection needs evidence beyond a financing or traffic chart.
3. Four conditions invite examination rather than trigger a formula
Listen: 25:36–30:41 and 54:57–58:58. Zhu lists four necessary but insufficient conditions: a new concept, product, or technology; abundant liquidity; government support; and inexperienced investors. He emphasizes that their combination does not guarantee a collapse, particularly in the short term.
The qualification matters. This is the guest's classification from research and observation, not a universal theorem validated by this article. Calling AI consistent with these characteristics is a June 2026 judgment rather than a verified statement about today's markets.
He also discusses price slopes, funding support, skeptics becoming optimistic, and widespread public recommendations. These may prompt questions about crowding, but the episode supplies no thresholds, backtests, or false-positive rates. Public predictions can themselves change behavior. They should not become mechanical signals.
Asked how much decline constitutes a bubble, he personally suggests roughly forty to fifty percent while explicitly rejecting a scientific definition. That preserves uncertainty; it does not classify every decline of a given size as the same mechanism. Unchecked historical anecdotes should not become a peak-timing textbook either.
4. Networks and AI can expand information while deepening blind spots
Listen: 10:46–19:55 and 31:15–37:37. Zhu contrasts broad market trends with personal contribution, asking what actually produced a gain. People in the same era achieve different results, but one success cannot assign precise percentages to luck and ability.
His suggestion that people entering an industry during a trough may progress further includes selection effects: stronger interest, stricter entry, and later recovery. It does not establish that graduating into a weak economy generally causes success. The episode does not attach original research establishing that conclusion.
Networks shape what people hear and when. The same story can reach people at different entry costs. Being close to an industry does not guarantee accuracy. Repeated exposure to the same account can create an illusion of independent consensus.
AI adds another risk: easier retrieval and summaries do not provide institutional data, processes, or risk controls. Zhu's “double illusion” concerns both model errors and the user's belief that AI eliminates capability gaps. His views about future institutional and index-product dominance are forecasts rather than allocation instructions here.
Editorial inference: AI-assisted research should explicitly retain unverified facts, opposing material, and conditions supporting conclusions. More text repeating one source may increase certainty without increasing independent evidence.
5. Knowing a bias still requires arrangements that change action
Listen: 20:25–24:45 and 59:39–01:09:31. Zhu does not settle whether this time is different. New technology can change much while people repeat old biases. Instead of concentrating solely on proving a judgment correct, examine failure consequences and the capacity to absorb them.
His personal recollection is the strongest example. The day before Lehman's bankruptcy, he held a gathering in Hong Kong with many colleagues, none warning him what the next day would bring. Studying risk and knowing professionals do not guarantee recognizing risk in one's own environment. This is more revealing than describing bias as other people's irrationality.
The episode distinguishes financial investment from human-capital investment such as learning. Learning also uses time and opportunities; the distinction cannot prove every course or career change has a high return. Actual choices need goals and constraints.
Finally, Zhu distinguishes experienced happiness from happiness remembered later. This helps restate personal goals without becoming a psychological measurement verified here. External rankings, one profitable outcome, and a valued life are not necessarily the same result.
Exercise: write two pages for an uncertain decision
The first page explains why you believe it. Separate facts, interpretations, and forecasts. Give important numbers sources and dates, add alternative causal explanations, and identify materials repeating the same network or original source.
The second page asks what happens if you are wrong: time and cash committed, difficult-to-reverse promises, consequences for normal delivery, and who reviews the decision when. These checks concern product budgets rather than securities:
| Check | Question |
|---|---|
| Extrapolation | Will the conditions behind recent growth persist? |
| Self-attribution | Which changes came from the environment rather than new team capability? |
| Herding | Do three opinions actually come from one article? |
| Correction | Which evidence would cause narrower scope or stopped spending? |
The aim is not removing every risk, but connecting awareness to a concrete action. Naming every bias without arranging review and adjustment still leaves knowledge separate from practice.
Source boundary
This is a reading of the official introduction and complete transcript, without trading, holding, or allocation proposals. Macroeconomics, employment, historical valuations, research examples, and the four conditions retain attribution without independent verification. Damaged financial names and dates are not reconstructed, and closing music is not treated as argument. Editorial inferences and the exercise concern general product decisions and are marked separately.
Listening navigation
- 01:27–09:57: Three biases and limits to prediction.
- 10:46–19:55: Outcomes, the era, and ability.
- 20:25–30:41: Uncertainty and supposed peak signals.
- 31:15–37:37: Networks, information cascades, and AI illusions.
- 46:25–53:48: Technology, valuations, and narrative resources.
- 54:57–58:58: Necessary but insufficient conditions.
- 59:39–01:09:31: Knowledge, practice, wealth, and life.
Related themes: decision biases, evidence discipline, technology and capital. Further listening: Mo on expectations and delivery and Separating company and product in the Character.AI event.