MaHui | Source Code Capital's Chang Kaisi: Tech Shifts Reshape Moats, Tech Entrepreneurship Breaks Boundaries

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Tech disruption breaks down barriers

MaGuan

Source Code Capital invests in technology-driven innovation,

in the creation of lasting, real value.

Exploration is at the core of the entrepreneurial spirit at Source Code.

Recently, Source Code Capital partner Kaishi Chang led an internal discussion. Drawing on "first principles" thinking to examine the nature of industry cycles, the team also reaffirmed their original mission — strengthening the consensus around Source Code's commitment to technology investment.

Some fragments of thought, shared with the MaHui ecosystem.

The Cycle of Industry Development

Commerce is ancient. Since China's pre-Shang period, people have bartered goods to complete value exchange. Commerce also has tides. We often speak of "crossing cycles" — but what is this cycle? Markets are cyclical, and so are industries within them.

Reviewing the past yields new insights. Let us first revisit industry life cycle theory.

From the perspective of social supply and demand, the life cycle of most industries passes through four key stages. We use the development of the American beer industry as a case study:

Stage One: Demand begins to explode while supply has yet to catch up. A flood of new companies enters the market. Among them are potential unicorns, but the field is largely mixed — difficult to distinguish who will emerge as the ultimate winner.

At 12:00 a.m. on January 17, 1920, the 18th Amendment to the United States Constitution — Prohibition — officially took effect, suppressing Americans' drinking demand. But in 1933, Prohibition was formally repealed. Previously forbidden activities — friends gathering to drink, drinking in public — were suddenly permitted. Demand for alcohol purchases exploded. Meanwhile, manufacturing, selling, and transporting alcohol became legal again, and hundreds of distilleries of all sizes emerged. In the beer industry alone, in 1934 there were over 800 beer producers across the United States.

Stage Two: Demand begins to slow, but supply continues to increase. "Too many monks, too little porridge" — intensified competition among suppliers suppresses individual companies' profitability, even driving some to bankruptcy. Meanwhile, M&A activity heats up and concentration increases.

From 1935 to 1940, the number of breweries dropped 10%. In 1947, the CR5 and CR10 of the American beer industry (the market share held by the top five and top ten companies by business scale) were 19% and 28.2%, respectively. But these ratios kept climbing. By 1981, the figures were 75.9% and 93.9%. The beer industry had shifted from "a hundred flowers blooming" toward "head concentration." Most companies did not survive Stage Two.

Stage Three: Demand continues to slow, and supply adjusts and contracts. Even leading companies face profit pressure, and industry concentration rises further.

In 1980, the supply-demand relationship in American beer hit an inflection point. First, the demographic dividend faded — there would be no post-WWII baby boom to replicate. Second, the baby boom generation had reached middle age; the gatherings and heavy drinking of earlier years became less common amid family life. Third, social and cultural shifts led people toward healthier, higher-quality lifestyles — less drinking, with a possible tilt toward wine. Multiple factors combined to slow demand for beer.

Meanwhile, the beer industry has strong scale effects, and industry leaders tend to concentrate further. Reality bore this out. In 1981, the fourth-ranked brewer Heileman acquired a Pabst subsidiary. In the 1990s, Stroh — which had previously acquired third-ranked Schlitz — merged with Heileman. Through continuous maneuvering, by 2003 the four largest American beer companies were Anheuser-Busch, Miller, Coors, and Pabst, with a combined CR4 exceeding 98%. Supply had stabilized.

Stage Four: Demand recovers, but supply has already converged. Leading companies control most capacity, dominate supply, and solidify their moats. Latecomers struggle to catch up, and these leaders ultimately become the cycle's winners.

As the children of the baby boomers — Generation Y (born 1981–1995) — came of age, their demand for beer grew again and diversified: fruit flavors, craft brews, and more. Because capacity was relatively concentrated and supply had already "converged," industry leaders tended to develop different product lines to meet this demand rather than re-entering fierce competition with rivals. At this point, companies were spared "competition costs" and found it easier to turn a profit.

Can You Find Good Companies Just by Following the Cycle?

In the traditional sense, good companies are those that endure to Stage Four and complete supply convergence. They adjust themselves through successive supply-demand shifts, iterating across multiple dimensions — scale effects, organizational restructuring, brand marketing — to adapt to the market and continuously build moats.

But note: if supply has converged yet the company still cannot create incremental value for upstream and downstream industry chains, it is not truly "good." Consider the two giants of aviation: Boeing and Airbus.

Around 2020, these two companies' combined CR2 exceeded 80%. Especially as the global pandemic intensified, supply chains and manufacturing workers ground to a halt, pushing more aviation manufacturers to the edge of losses or collapse. This made supply even more concentrated in Boeing and Airbus. Data confirmed that both companies' new aircraft deliveries were rising year by year.

Beyond the supply-demand angle confirming Boeing and Airbus's Stage Four advantages, their comprehensive moats made it extremely difficult for latecomers to catch up. Aircraft manufacturing essentially represents the pinnacle of industrial-era achievement — barriers across technology, talent, and capital expenditure are formidable.

Yet their development remains somewhat arduous.

Consider aviation's upstream and downstream industry chain. The downstream of aviation manufacturing is airlines. With flight routes largely similar, ticket price competition is fierce. When I was studying in the United Kingdom, I once bought a £19 ticket from London to Madrid. Even setting aside pandemic factors, airlines' return on equity (ROE) generally fluctuated between 5–10%, and news frequently broke about airlines worldwide facing severe operational difficulties. In short, the downstream of aviation manufacturing struggles to profit. For the ecosystem to develop in an orderly way, aircraft prices cannot be blindly raised.

So if the entire industry chain cannot make money, there is little lasting meaning in leading companies maintaining their moats. Companies in Stage Four of such an industry may not be ideal after all.

Technological Change Breaks Barriers

From the preceding discussion, companies that successfully cross industry cycles mostly develop formidable moats, making it difficult for latecomers to compete on the same stage.

Yet even the strongest moats may develop cracks over time — through environmental shifts, scale effects, and especially technological change. Such cracks represent opportunities for startups in the primary market.

As a technology investment institution, Source Code Capital focuses on technology-driven innovation. We examine how technology can bring startups the "strongest" new moats and open new horizons.

For example, technology changes the essence of the automotive industry. The internal combustion engine developed over a century, with deep technical accumulation and massive supply chain scale advantages — the culmination of traditional industrial efficiency, with formidable barriers. But with breakthroughs in new energy vehicle technology, the high moats accumulated by legacy automakers were greatly weakened.

Daimler began paying attention to Tesla early on, recognizing that intelligent electric vehicles represented the evolutionary direction of the traditional auto industry. But despite years of effort, including launching some electric models, Mercedes-Benz under Daimler never managed to converge EV supply. Why?

After discussion, we summarized three points:

First, for electric vehicles represented by Tesla and Li Auto, user experience is largely defined by software rather than hardware. What drives these EVs may be software engineers; vehicle functionality relies on software upgrade iterations, and data accumulation and feedback form positive interactions with drivers. What drives Mercedes-Benz, by contrast, is hardware engineers. The existing organization struggles to quickly adapt to software development processes and culture. Traditional advantage moats are weakened or even broken.

Second, the key hardware of new energy vehicles — battery cells, power management, motor drive technology — is completely different from internal combustion powertrain technology. This system renders legacy automakers' long-accumulated technical moats in engines and transmissions irrelevant. We have already seen how the development of the new energy industry chain has brought China self-developed new technologies, and the spillover of new technical capabilities is beginning to feed back into adjacent industries, driving comprehensive upgrading of Chinese manufacturing and creating more entrepreneurial opportunities.

Third, new automakers incorporated autonomous driving into their technology roadmaps from the start. Hardware manufacturing includes cameras, radar, GPS positioning systems, etc.; software data analysis covers road conditions, other moving objects on the road, obstacles, pedestrians, and more, enabling optimal route planning. Massive data and algorithmic iteration gave new automakers enormous advantages even in early development stages. Technology has endowed the electric vehicle industry with new moats.

Consider the biomedical field. In the past, finding roughly 10 potentially effective drugs out of 1,000 was sufficient; patent-holding manufacturers could converge supply and maintain profitability. But as medicine advanced, patients came to expect and need more refined, efficient treatment protocols. Drug researchers needed to invest increasing time and effort in experiments, yet the speed of finding new wonder drugs did not necessarily accelerate linearly with increased investment. The emergence of AI pharmaceutical technology may have changed the essence of new drug development — sunk R&D costs could be drastically reduced, and time to market substantially shortened. Technology broke traditional patent monopolies. Similarly, some cross-disciplinary innovations — surgical robots, for instance — use 3D digital imaging to let doctors see more clearly, improve spatial positioning of lesions, and leverage powerful computing to assist doctors' judgment, while engineering technology standardizes treatment operations. The combination of technology and biomedicine not only creates commercial value more efficiently but may also generate social value — letting patients live with greater quality.

Technology is developing rapidly, and we see startups' opportunities to break giant moats increasing. In a sense, technology transforms many unwritten experiences into systematized institutions, and attempts to find "shortcuts" to narrow competitive gaps become new paradigms. Technology breaks Stage Four industry cycle barriers because it brings entirely new possibilities.

Technology Entrepreneurs Must Break Boundaries and Bring New Possibilities

Excellent founders know how industry moats are built and understand when one has touched the "endgame" of a boundary — they can better strategize and cross cycles. But "many people care about boundaries; few focus on the core." Top-tier entrepreneurs can ascend further, break boundaries, and create value. Consider Amazon, familiar to all — expanding from a consumer-facing e-commerce platform into logistics, enterprise services, and more. What counted as major cost items in 2005 had become revenue items by 2015, testament to Jeff Bezos's foresight.

How to find such entrepreneurs? In our investment learning, we often rely on frameworks for better thinking — such as the industry cycle theory discussed today, or seeking new opportunities to break barriers through technology. The frameworks are not complex, but the analytical process varies enormously from person to person, with significantly different results. Many good investments seem to carry an intuitive insight.

Insight is never accidental. It accumulates massive amounts of data — especially first-hand observational data. Often details that initially seem least relevant may ultimately produce that slight difference in judgment, and that slight difference is precious. So investors must personally get their hands dirty, charge to the front lines, experience the word choices in every user research conversation, feel the most authentic daily market changes — only then can they see the essence of good business, predict technological breakthroughs, and understand the core capabilities of excellent founders. As Kazuo Inamori said, "The divine dwells in the field."

References

[1] An Yaize, Jian Chengguang, China Securities Co., Ltd., Beer Industry Special Research Report: The Path to Profitability Breakthrough, Starting from American Beer History [OL], Future Think Tank, November 29, 2021.

[2] Source Code Capital Internal Research Report [R], Section 2, May 2022.