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NewsMaterial4 min readDisconfirmAI Editorial

Zhu Ning, Translator of '1929': Not Rescuing Guarantees Disaster; What Emerges from Rescue Is Unseen

Zhu Ning, a Distinguished Professor at the Shanghai Advanced Institute of Finance, Shanghai Jiao Tong University, and translator of the Chinese edition of '1929', proposed an asymmetric judgment in a podcast: From the Great Depression and Japan's lost decades, we are certain that not rescuing leads to disaster; but from the market rescue experiences of 2008 and 2020, we have never observed a stable endpoint for continuous rescue. Facing current high valuations driven by AI, he believes investors' core issue is not judging when the bubble will burst, but how to allocate assets amidst this knowledge asymmetry.

2026-10-05 06:00 GMT+8

In a podcast episode of Houxue Changbo (Thick Snow Long Wave) on October 5, 2026, Zhu Ning presented a judgment worth pondering repeatedly. He stated that Ben Bernanke spent his entire life studying the Great Depression, ultimately codifying the lesson that 'not rescuing is unacceptable' into policy during the 2008 crisis. This lesson was indeed executed: after Lehman Brothers' collapse, policymakers, Congress, and the Federal Reserve of the United States resolved to rescue first, resulting in outcomes significantly better than those of 1929. However, Zhu Ning then made a statement that shifted the entire discussion: We have only seen cases where not rescuing failed; we have never seen what problems arise from rescuing.

This statement implies an asymmetric accumulation of knowledge over the past century in economics and policy practice. We know the consequences of not rescuing: In 1929, the Hoover Administration did not decisively rescue the liquidity crisis, compounded by the Federal Reserve's tightening and the Smoot-Hawley Tariff Act, leading to the collapse of over ten thousand banks and a Great Depression lasting more than a decade. Japan's lost ten and twenty years serve as another clear piece of evidence. Thus, economists formed a consensus—that not rescuing is unacceptable. On the other hand, rescues occurred in 2008 and 2020, with the Federal Reserve intervening to stabilize markets, causing asset prices to rise continuously. Investors' faith in the stock market has surpassed even the levels seen during the 2000 dot-com bubble. Zhu Ning asks: What happens one day when the Federal Reserve stops acting? With U.S. Treasury debt already at these levels, market concerns about Treasuries and the dollar could push interest rates higher, which would negatively impact the stock market—these factors are interconnected.

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Zhu Ning attributes this interconnection to the mechanism discussed in his 2016 book Rigid Bubbles: If you provide the market with guaranteed redemption or guarantees, your intentions may be good, but the market will accept them all. Since you are covering my bottom line, I will increase leverage. In 1929, retail investors used 10% margin requirements to trade stocks with ten times leverage; today, South Korea offers single-stock leveraged ETFs with two or three times leverage, and the United States also has broad-based index ETFs with high leverage, sold to households nationwide. The forms of leverage and narratives have changed, but the mechanism remains unchanged. As Mitchell noted back then, if loans can be taken out to buy refrigerators or cars, why not to buy stocks? Today, the narrative shifts to: since you can consume refrigerators and cars via credit, why not consume stocks?

Regarding the relationship between 1929 and today, Zhu Ning offered a clear classification: 1929 and 2008 are more similar, both being credit-driven recessions with broad reach and deep economic impacts. Today's AI bubble differs from 1929/2008 in that it is largely concentrated in China and the United States; other countries might wish for such a bubble but find they lack even the AI infrastructure. In contrast, the shocks of 1929 and 2008 were global. This distinction is crucial for investors: Zhu Ning believes the current AI bubble differs from 1929 and 2008 in breadth and transmission pathways.

So, what should investors do? Zhu Ning’s answer is not market timing. He says, "The time to buy AI companies was yesterday, the day before, and also tomorrow, the day after, and the future. You should not think about whether to buy or sell now." He recommends long-term holding of broad-based indices, dollar-cost averaging, avoiding going all-in or all-out, and maintaining diversification across major asset classes. He cites behavioral finance research showing that retail investors are absolutely wrong in their timing attempts, so much so that institutions view retail investors as reliable contrarian indicators. He concluded with a practical statement more valuable than any bubble prediction: Before thinking about how much money you want to make, first consider whether you can ensure you do not lose money. Especially in the current economic environment, not losing money constitutes a decent investment performance.

Sources

  • xiaoyuzhoufm.com↗
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