At the Morgan Stanley Technology, Media & Telecom Conference, the industry’s biggest companies and investors discuss opportunities in chips, software and data from the mass uptake of accelerated computing and generative AI.
Copyright: morganstanley.com – “AI Demand Hits a Tipping Point”
Key Takeaways:
– CEOs are focused on traditional and generative AI solutions for potential cost savings and productivity, and they are looking to technology companies to provide those solutions.
– Investors are analyzing the strong runup in chip designers’ and manufacturers’ revenue and monitoring potential tailwinds in data center needs, hyperscaler partnerships and inference computing use cases that could fuel future growth.
– Investors are also eyeing AI software companies that are disrupting customer service, marketing, financial analysis and general productivity workflows.
– Investment themes for big data companies include consolidated vs. best-of-breed offerings, and technology advancements that improve the efficacy of large language models, including real-time data streaming and synthetic data.
More than a year since generative artificial intelligence became the focal point of every tech company and investor, the pulse of conversation has moved from “platform shift” to meeting mass demand from companies and consumers looking to harness deep learning to be more efficient, productive and profitable.
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A tipping point in AI demand was the overarching theme of Morgan Stanley’s Technology, Media & Telecom Conference in San Francisco—and over 1,600 industry investors and senior executives of more than 350 companies gathered—as businesses hasten to build more intelligent, fast, cost-effective and secure hardware and data platforms. One executive cited an estimated $3 trillion that may be spent on AI between 2023 and 2027, with generative AI spending composing 36% of that by 2027,1 highlighting the immense appetite as well as the opportunity for well-positioned companies and investors.
“Though cost savings and operational efficiencies remain a priority for large enterprises, they are also showing a willingness to spend, especially on generative AI and traditional AI hardware and software that may help reduce costs and increase productivity and revenue,” said Dave Chen, Head of Global Technology Investment Banking at Morgan Stanley.[…]
Read more: www.morganstanley.com
At the Morgan Stanley Technology, Media & Telecom Conference, the industry’s biggest companies and investors discuss opportunities in chips, software and data from the mass uptake of accelerated computing and generative AI.
Copyright: morganstanley.com – “AI Demand Hits a Tipping Point”
– CEOs are focused on traditional and generative AI solutions for potential cost savings and productivity, and they are looking to technology companies to provide those solutions.
– Investors are analyzing the strong runup in chip designers’ and manufacturers’ revenue and monitoring potential tailwinds in data center needs, hyperscaler partnerships and inference computing use cases that could fuel future growth.
– Investors are also eyeing AI software companies that are disrupting customer service, marketing, financial analysis and general productivity workflows.
– Investment themes for big data companies include consolidated vs. best-of-breed offerings, and technology advancements that improve the efficacy of large language models, including real-time data streaming and synthetic data.
More than a year since generative artificial intelligence became the focal point of every tech company and investor, the pulse of conversation has moved from “platform shift” to meeting mass demand from companies and consumers looking to harness deep learning to be more efficient, productive and profitable.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
A tipping point in AI demand was the overarching theme of Morgan Stanley’s Technology, Media & Telecom Conference in San Francisco—and over 1,600 industry investors and senior executives of more than 350 companies gathered—as businesses hasten to build more intelligent, fast, cost-effective and secure hardware and data platforms. One executive cited an estimated $3 trillion that may be spent on AI between 2023 and 2027, with generative AI spending composing 36% of that by 2027,1 highlighting the immense appetite as well as the opportunity for well-positioned companies and investors.
“Though cost savings and operational efficiencies remain a priority for large enterprises, they are also showing a willingness to spend, especially on generative AI and traditional AI hardware and software that may help reduce costs and increase productivity and revenue,” said Dave Chen, Head of Global Technology Investment Banking at Morgan Stanley.[…]
Read more: www.morganstanley.com
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