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How to invest in artificial intelligence

II went It’s been a rough 18 months for tech bettors. SoftBank, the Japanese investment firm that epitomized the 2010s boom in venture capital funding for fast-growing companies, is still wisely out of the transition to a world of high interest rates and low corporate valuations. But there is one area where the company, run by charismatic founder Masayoshi Son, would like to look above the balustrade. It is an investment in artificial intelligence (love).

progress of driving force-love platformchat, etc.GPT, leaving nearly every investor debating how to rate this nascent industry and which companies may turn around. Son sees parallels with the early days of the Internet.driving force love It could provide a new pipeline of initial public offerings and provide a foundation for the next generation of tech giants.

Investors face two questions. The first is which frontier technologies will enrich the market leader. That’s hard enough. The second is establishing whether value will come from venture capital-backed start-ups or from incumbent tech giants, which is at least equally difficult. No one knows yet if it’s better to have the best chatbot or to have more customers. Getting a head start on a dizzying new technology is not the same as being able to make money off it. In fact, much of the value of ground-breaking innovation is often captured by incumbent giants.

Alphabet, Amazon and Meta are three of the seven largest publicly traded companies in the United States, worth a combined $3.3 trillion. They were founded between his 1994 and 2004 years and emerged at a time when Internet technology was new and people were spending more time online. Chinese e-commerce giant Alibaba is a similar example (SoftBank’s early $20 million stake in the company helped cement Mr. Son’s reputation as an investor). By identifying technology trends and developing the best platforms, we have created tremendous value for early and even not-so-early investors. Traditional companies struggled to jump on the bandwagon.

Will it be the same story this time? During the rapid rise of internet giants in the 1990s, the insights of management guru Clayton Christensen, a pioneer of innovation theory, provide a useful guide. Smaller companies often gain traction in low-end or entirely new markets that large incumbents avoid, Christensen said. Incumbents are focused on implementing new technologies for existing customers and lines of business. They are neither incompetent nor ignorant of technological advances, but they seemingly follow the right path in terms of maximizing profits only to find it too late and fatally compromised.

Investors like Son are excited about the future of startups focused on: love, implicitly assumes that a period of disruptive innovation is underway. But most of the recent excitement about generativelove Platforms have focused on their potential as new technologies to be deployed, rather than as companies that can open up entirely new markets. And in the case of other recent technological innovations, the incumbents have triumphed. Venture capitalist Erad Gill points to the value of previous advances in machine learning, a broad category of which is generative. love Almost all of them occur at incumbent companies. Early internet startups have benefited, as have semiconductor companies such as Microsoft, Nvidia and Micron. In the early days of machine learning, there were no publicly traded companies that could be considered niche Amazons or Googles.

Christensen’s insight makes it clear that revolutionary innovations are not necessarily revolutionary from a mere business point of view. But incumbent technology companies are now spend huge sums of money upon love, suggesting that if the technology turns out to be revolutionary, they should be well positioned. An investment in a broad index fund that tracks incumbent publicly traded technology companies could technically outperform a comparable investment in private equity. love– Startups focused on.

Theories about why innovation may or may not be disruptive are more often debated by business and management students than by stock pickers. But the difference between the two possibilities is crucial in assessing whether the next generation of publicly traded technology companies with market caps in the hundreds of billions of dollars can be found among private companies. love enterprise. As it stands, the market value of this technology is likely to become the new string around the already big tech companies.

https://www.economist.com/finance-and-economics/2023/05/17/how-to-invest-in-artificial-intelligence How to invest in artificial intelligence

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