Editor’s note: Besides following tech developments and innovation, our author is a musical composer (Juilliard-trained). He has provided a musical composition for you to listen to while reading this column. This piece is called “Investor Influence.”
Thirty years ago, outsourcing to China began to accelerate. During the dot-com boom, globalization was in full swing, and hiring overseas labor was a clear way for CEOs to cut costs while expanding the workforce. The rise of globalization, characterized by lower labor costs and trade agreements, made China an attractive manufacturing hub. China’s accession to the World Trade Organization in 2001 further integrated it into global trade, making outsourcing in manufacturing, textiles, electronics, and consumer goods all the more accessible. By the 2000s, tech giants like Apple relied on Chinese manufacturers such as Foxconn, and just-in-time supply chains solidified China’s position as the world’s factory.
Silicon Valley venture capitalists (VCs) began requesting a China Plan from startups in the early 2000s, with it becoming a near-requirement by the mid-2000s. In the 1990s, early movers like Intel and Motorola explored China as an emerging market, but it was not yet standard for startups. China’s 2001 entry into the WTO opened its economy further, triggering a gold rush mentality among VCs eager to tap into its massive growth potential. Between 2003 and 2005, the rise of Chinese tech giants like Alibaba, Baidu, and Tencent showcased China’s parallel internet boom, making it an attractive expansion target for U.S. startups. As a result, VCs—including Kleiner Perkins, Sequoia, and IDG Capital—began pushing startups to develop a China strategy before securing funding, particularly for Series A and B rounds.
A similar phenomenon is happening today with AI, where investors increasingly require startups to have an AI Plan, just as they demanded a China Plan 20 to 30 years ago. In the early 2000s, expanding to China was about cost savings and supply chain efficiency; today, AI is about automation, productivity, and competitive differentiation. Investors want to know how businesses will leverage AI through proprietary models, automation, or data integration to enhance efficiency, reduce costs, or generate new revenue streams. Just as startups once had to demonstrate manufacturing scalability in China, they now need to show how AI can scale operations without increasing costs.
Like China’s transformed global supply chains, AI is reshaping business models across the software, media, finance, healthcare, and manufacturing industries. Companies that fail to integrate AI risk falling behind AI-powered competitors. Investors now expect a clear AI strategy, especially in sectors like SaaS*, healthcare, and finance, where AI is automating processes, improving efficiency, and driving innovations. AI-first startups are securing more funding than traditional firms, with major investments flowing into AI-powered automation, diagnostics, fraud detection, and creative tools, disrupting traditional workflows across industries.
Just as VCs prioritized China-based supply chains in the 2000s, today, they favor AI-native companies or those deeply integrating AI into their operations. Startups lacking an AI strategy may struggle to secure funding and remain competitive. However, AI’s rapid rise also creates regulatory, ethical, and geopolitical concerns, like trade wars and supply chain risks with China. Governments are racing to regulate AI while countries compete for technological dominance. Ultimately, the AI Plan is the new China Plan—startups without a strong AI strategy may soon find themselves too slow, too expensive, and unable to compete in an AI-driven world, shaping each other within their ecosystems or communities.
There is a problem with this follow-the-leader tendency. Just as the dot com boom never made sense to me and to many of my fellow engineers in Silicon Valley, the same is true for many of these AI investments. Here is an example that happened to me. I founded a company called Transonos, a sound-centric computing company where speakers and microphones replaced displays and keyboards so people could keep their hands and eyes free to do other things, driving down the size and cost of computers. Many potential investors said the same nonsensical thing. When asked how long it would take to ship a product, I answered roughly two years with around $2M to make it happen. Several young Ivy League-trained venture capitalists said roughly the same thing: If we give you $5M, can you do it in eight months? I asked them: Have you ever shipped a product? Have you ever built a team? Have you ever filed a patent? None of them had. I lectured them and said if you wanted to have a baby in four months and bought your wife a bigger ring, could she do it? This is the problem with investors who know nothing about their investments. It would be much wiser to invest the majority of your portfolio in the S&P 500 instead.
Yes, AI is real, but most startups in this area will fail, and most investors will lose their money; when someone waves around a checkbook and asks young, inexperienced technical people if they can scale faster, of course, they tend to say yes. The investor influence does not always make sense. Recently, it has been published that companies highly leveraged through AI technologies can scale and grow significantly more rapidly than those unleveraged. Ah, but how do they do this? And is it different in different industries? Is this well understood yet? Before blindly throwing money at AI, smart investors must dive more deeply and investigate whether something proprietary is happening; what about the team, the technology, the track record, the business model, and the market?
Investors have much influence not just on the businesses they wave their checkbooks at, but also on other investors. Not everything can be solved with a checkbook, as many real and supposed businesspeople discover. There are always many other factors. Take the time to do some homework and think about longer-term ramifications. We live in a very interdependent world. Globalization is real. AI is also real but not a panacea. Like other powerful technologies, it still needs to be explored, discovered, and vetted. We are at the beginning of this new age, nowhere near the middle. The S&P is far more vetted and real than AI is now. If you want nonlinear growth, that means risk—but, only risk part of your portfolio on poorly understood, unproven things.
