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It took 72 years for the variety of US households with telephones to rise from 10 percent to 90 percent. It took 19 years for the introduction of television to same bowBut the speed at which our latest technologies, comparable to generative AI, are being adopted is breathtaking. Within 15 months of launch 23 percent of American adults have used OpenAI’s chatbot ChatGPT.
The reasons for this acceleration should not difficult to fathom. Generative AI isn’t a single, relatively expensive physical object like a phone or a television, but (mostly) free software that extends existing services.
Huge global digital platforms can deploy this technology at scale almost immediately. This week, Apple announced it might roll out “Apple Intelligence” to greater than 2 billion device users.
However, the supply of a brand new technology doesn’t mean that it might probably be used quickly and productively instantly. It can take years, if not many years, for common technologies to rework the economy.
International donors have long argued that the advantages of development aid are limited by the “absorbency“ of a recipient country. In other words: does a rustic have the institutions and know-how to take a position the cash correctly? Something similar might be the case with generative AI.
If you think the evangelists, AI will turn every thing the wrong way up directly. According to stock market investors, the clear winners are the “Magnificent Seven” of US technology firms, led by Microsoft, Nvidia, Apple, Alphabet and Amazon, which account for about half of the $30 billion market value of the worldwide 960 of the most important publicly traded technology stocks. They provide the models, cloud computing infrastructure and silicon chips on which AI runs.
A significant institutional investor said at a Founders Forum event in London this week that it was “just incredible” to listen to the executives of those AI firms talk concerning the speed of development.
“This has dramatic implications. The seven largest firms made up 10 percent of the (US) market index in 2018. Now it’s 30 percent. There is a concentration risk that’s higher than ever before,” said the investor.
The Magnificent Seven are all hyper-ambitious and need to make use of AI to boil the ocean. Many other firms need to use AI to activate a kettle. For established firms, there are two predominant areas of application: doing things more efficiently and doing things that were impossible before.
Even more exciting, a small army of startups are exploring the probabilities of using the technology to develop entirely recent business models. But the important thing might be to give attention to specific enough use cases to construct sustainable and defensible businesses.
Established firms in most industries which have their very own proprietary data, established brands, and shut relationships with their customers are in an excellent position to leverage the generative AI tools of the large tech firms. Previously, only the chief technology officer of most firms could offer AI services. But the supply of generative AI models now enables all other C-suite functions, from finance to operations to marketing, to make use of them as well.
However, a former founding father of a West Coast AI startup who has spent years working with private and non-private sector organizations to implement AI solutions told me that institutional resistance, particularly within the defense and healthcare sectors, is the largest limitation to using this technology.
As the saying goes, within the US military, there are nine office employees for each trigger puller. This organizational inertia, says the entrepreneur, also applies to many organizations within the private sector. In other words, they lack the capability to soak up data. It will take five to 10 years to get essentially the most out of existing AI models, not to say the more powerful ones to come back.
For this reason, we might even see the emergence of other business models that may speed up this process.
For the past few many years, private equity firms have played a lucrative, if somewhat brutal, game: buying up firms, shedding employees, and moving production to China. With geopolitical tensions between the US and China and a renewed give attention to supply chain resilience, that game may now be over. Perhaps AI might be used as an alternative to rework firms' cost structures.
“Could a personal equity firm buy the fourth largest company in an industry, use AI and make it primary?” asks the entrepreneur. That's an excellent query that may excite some PE managers as much as it should unsettle many employees.
