Investment Guru Says | Shangya Investment Chairman Shi Bo: Don't race with quant, look for ten-bagger stocks in the physical world. Computing power is the "coal" of the AI era.

By Daily Economic News reporter | Li Na Liu Jinxu By Daily Economic News editor | Peng ShuiPing

In the spring of 2026, investors in China’s A-share market have started to feel helpless.

High-frequency trading, quant funds, algorithmic harvesting… Faced with an invisible net woven by machines, many investors have begun to wonder: in this era dominated by machines, does investing based on fundamental research still matter?

Amid such confusion, Shi Bo, chairman of Shangya Investment, gave a completely different answer.

“ I just got back from a recent on-site research trip,” he said in recent communications with investors, “I went to look at companies that make optical chips and optical modules. I mainly looked at things like the production equipment and inventory, and also learned about the company’s day-to-day management.”

While quant models are keen to capture price spreads in 0.01 seconds, Shi Bo still insists on measuring the industry with his own two feet and using his own eyes to observe details. This seemingly “clumsy” method has allowed him to cross bull and bear markets again and again. Data from a third party shows that in the past five years, Shangya Huoshui No. 1 Fund’s returns have also far exceeded the average level of quant funds.

In his view, real investing has never been about racing against machines, but about moving along with the times.

Image source: supplied by the interviewee

On investing dimensions:

Direction matters more than effort

Over more than thirty years of Shi Bo’s professional career, he has long formed and refined his own investment philosophy; the first is value investing.

There are many paths to achieve value investing. And in his view, buying stocks is buying companies. He emphasizes the importance of learning to “do the math,” comparing a company’s total market capitalization with the value of its actual assets, and identifying companies that are seriously undervalued.

But before judging whether a valuation is high or low, it’s even more important to determine the direction of the times—and that, in itself, is the first lesson of value investing.

“Chinese residents’ wealth mainly comes from real estate, from the dividends of that era,” Shi Bo said. “The first wave of dividends is urbanization dividends, and real estate dividends. The second wave of dividends is the dividends from the internet. Now, we are entering the AI revolution.”

In his view, the AI revolution is not a continuation of the internet. It is a once-in-250-years revolution in cognition and productivity. 250 years ago, when Watt invented the steam engine, humanity entered the industrial era, and physical labor was magnified by tens of thousands of times. And this time is the magnification of intelligence.

“ The internet only solved the problem of production relations,” Shi Bo said. “But the AI revolution can directly generate tokens and create productivity.”

“Compared with the internet revolution, the AI revolution is bigger and faster—one of the fastest revolutions in human history in terms of penetration rate. It is not the internet, and not the computer era. This is a once-in-250-years revolution in cognition and productivity,” Shi Bo emphasized.

In his view, the essence of investing is monetizing cognition. “Direction matters more than effort. Every wave of dividends is the dividends of the era, not of individual ability. What you need to do is identify the direction and then hold on to it; the rest is up to time.”

On research:

From the workshop to the cafeteria, the first focus in research is “people”

In Shi Bo’s investment philosophy, quality investing is the core principle next to value investing. A company whose value is undervalued may not necessarily rise; the key is the quality of management. He insists on investing in companies whose management serves shareholders’ interests and possesses great corporate qualities, and on-site research is precisely to examine “people,” the core asset.

Back in the 华夏 Fund era, Shi Bo was known for “diligence.” He once researched 10 listed companies in one month, condensing years of research effort into 10 pages of notes and 111 indicators. It is this research habit that runs through his career that drives him to keep searching for the next turning-point industry.

Before the gold market took off, he basically visited all the gold companies in China— from Shaanxi to Shandong—going deep into the pits to observe the mines. After that, he also traveled all the way to the Solomon Islands to research the 万国 gold industry in local tribes. It was precisely this kind of in-depth on-site visits that let him see, ahead of time, the investment value of gold. In 2023, he judged that the U.S. dollar’s status was being challenged and that the U.S. was entering a cycle of interest-rate cuts, and that gold would have tremendous room to grow. The subsequent market confirmed his judgment—related gold stocks rose by more than ten times.

Even though he manages private funds now, he still maintains an extremely high frequency of research. “Now I at least need to research four listed companies every month,” Shi Bo said frankly. “And for every stock I buy, I’ve gone on-site to see and examine it.” From his words, you can feel his enthusiasm for research—this isn’t a kind of task; it’s a love that comes from the heart.

But the way he conducts research has its own logic.

“ When I go to research, I must look at the production lines,” he said. “For companies in the computing power industrial chain, you’ll see whether the number of machines is enough, whether the equipment is running at full capacity, whether inventory is high or low—these details are more real than any financial data.”

Besides the workshop, he also pays attention to places that are easy to overlook. A company’s quality of management can’t be seen from the statements. The statements can be polished, but the cafeteria can’t lie. In his view, these details explain more than the income statement. How a company treats its employees often determines how it will treat its shareholders.

He applies this research approach in identifying two types of companies.

One type is turning-point companies—those enterprises that are at the critical point where the relationship between supply and demand reverses, and where an industry moves from 0 to 1 or from 1 to N. Shi Bo believes that identifying turning-point companies does not rely on financial statements, but on visiting the industrial chain. You need to see whether upstream raw materials are sufficient; you need to see whether downstream demand has truly exploded; and you need to see whether competitors’ capacity can keep up.

The other type is pivot companies—companies in turning-point industries that have pricing power. “When the relationship between supply and demand reverses and the industry enters a turning point, you need to look for pivot companies within it,” Shi Bo said. “Pivot companies are those with pricing power; their market share or profit margins are significantly higher than those of peers.”

In his view, differences in management quality ultimately show up in these details. Good management can seize opportunities when the industry is rising and hold the bottom line when the industry is falling. With poor management, no matter how good the track is, you can’t run it out.

On the present:

Computing power is the coal of the AI era

In Shi Bo’s investment methodology, scientific investing is the underlying logic that runs through everything.

His background in investment banking gives Shi Bo a keen ability to understand the industrial chain, business models, and competitive landscape. He divides industry investing into several stages:

The 0 to 1 stage is broad research. At this time, it’s more like venture capital—seeking payoff odds. The industry space is huge, valuations expand rapidly, but there is insufficient certainty in performance. The 1 to 10 stage is deep research, where certainty increases greatly. At this point, you invest in pivot companies and leading companies, pursuing a higher probability of success. Industry barriers and first-mover advantages are extremely important, requiring close attention to changes at the margin, especially being wary of slowing penetration.

Based on his judgment of the AI era, Shi Bo has built a clear investment main line: the computing power industrial chain. And his analysis of this main line also reflects his method of forecasting the direction of industrial evolution using supply-demand relationships, technical pathways, and cost curves.

In his view, the United States, leveraging its advantages in technological innovation, has completed the AI breakthrough from 0 to 1. Meanwhile, China, with its strong manufacturing capability and supply-chain advantages, is expected to capture amplified opportunities in the industrialization stage from 1 to 10. This is the core logic behind his long-term optimism about the computing power industrial chain.

“Computing power is the coal of the AI era—it’s the fuel of intelligence,” he analyzes. “The growth in demand for computing power is exponential. Every breakthrough in applications will trigger an explosion in computing power demand. This is true for language models, and it’s true for video models. And once robots break through, it’s even more so. But the supply side is constrained by the physical world—downstream is a virtual world, iterating every day; upstream is a physical world, where you can only refine step by step. This mismatch between supply and demand is the source of investment opportunities.”

Shi Bo describes how value is distributed across the industrial chain using elastic transmission: “The downstream chips have the highest certainty, but relatively small elasticity; the midstream optical modules have elasticity more than five times that of chips; the upstream optical chips have elasticity ten times that of optical modules; and for resources upstream of that—phosphorus indium, tungsten, rare earths, and the like—the elasticity is the largest. In the industrial era, the most profitable is coal; in the AI era, the most profitable is upstream resources.”

In his view, AI computing power investment is replacing real estate and becoming the engine of the next round of economic growth. Over the next five to ten years, it is the most important investment main line.

“Global supply chains can’t do without China,” he gives an example: “In a computing power rack, you have optical modules, PCB boards, copper foil, and other core components, and then upstream rare metals like tungsten and indium— the entire industrial chain depends on China’s manufacturing capabilities.”

On strategy:

Holding on to positions—that’s the source of excess returns

In Shi Bo’s investment philosophy, the two words “holding on” carry a lot of weight. He knows that only by holding long term can you enjoy the huge returns brought by compounding.

At the discussion site, he repeatedly emphasized the importance of holding on. Most investors miss out on ten-bagger stocks precisely because they lack enough depth of research and thus can’t maintain confidence amid volatility. If you truly research thoroughly and get the direction right, you won’t panic due to short-term drawdowns.

But the “long-term holding” Shi Bo understands is not just holding still. It is a process of dynamic tracking and continuous verification. He sets a benchmark company for the industries he focuses on. If the companies in the related industrial chain, for two consecutive quarters, have quarter-over-quarter growth rates that are lower than the benchmark company’s performance, he will also choose to sell.

Behind this dynamic tracking is also the company’s risk-control system. In his view, true risk control is not about cutting losses after a drop, but about thinking clearly before buying: what could the company’s maximum drawdown be? When it falls 20%, do you reduce position or add to it? If the answer is to add to it, then the investment is worth doing.

Worth noting is that Shi Bo not only embraces the technology wave himself, but also requires the entire team to keep up. In daily work, he asks researchers to use “lobsters” to handle emails and to classify and manage the massive information in research reports, assisting research work. In his view, this is both an efficiency tool and a front line for understanding the industry. In a team that invests in AI, you must use AI first yourself.

“Volatility isn’t risk; getting it wrong is the risk.” Shi Bo said, “Risk control isn’t cutting losses. Cutting losses isn’t risk control; risk control is prediction.”

In this era of quant noise and market confusion, Shi Bo believes investors’ value isn’t in battling machines, but in moving along with the times.

And perhaps this is the underlying code that keeps his thinking clear even as the times evolve.

Cover image source: supplied by the interviewee