{"id":15659,"date":"2023-05-30T10:30:57","date_gmt":"2023-05-30T14:30:57","guid":{"rendered":"https:\/\/ifintechworld.com\/markets\/invest-in-nvidia-microsoft-adobe-and-more\/"},"modified":"2023-05-30T10:30:58","modified_gmt":"2023-05-30T14:30:58","slug":"invest-in-nvidia-microsoft-adobe-and-more","status":"publish","type":"post","link":"https:\/\/ifintechworld.com\/?p=15659","title":{"rendered":"Invest In Nvidia, Microsoft, Adobe And More"},"content":{"rendered":"
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Generative AI is all the rage \u2014 particularly since May 24 when chip maker Nvidia forecast mind-blowing revenue growth driven by demand for its chips from consumer Internet companies like Microsoft
\n MSFT
\n <\/fbs-ticker>, Google
\n GOOG
\n <\/fbs-ticker>, and Meta Networks.<\/p>\n

After decades of unrealized hype, could the promise of artificial intelligence finally be here? If so, will it be as big as the Internet or the iPhone \u2014 or bigger? How can investors pick the right stocks and how can business leaders grab a share of the right markets?<\/p>\n

My thoughts: Generative AI has seized the attention of the media and investors and experts predict it will have a huge economic effect and create enormous wealth.<\/p>\n

Those seeking to profit, must understand the entire Generative AI ecosystem \u2014 potentially consisting of seven distinct industries \u2014 and pick the stocks that can win the lion\u2019s share of the most attractive market opportunities.<\/p>\n

Generative AI\u2019s Economic Impact<\/h2>\n

Generative AI \u2014 machines that respond to natural language prompts by \u201cwriting engaging text or painting photorealistic images,\u201d according to TechTarget<\/em> <\/em>\u2014 could create major economic change and spur big growth opportunities.<\/p>\n

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For example, according to Goldman Sachs<\/em>, <\/em>Generative AI could raise global GDP by $7 trillion and create markets for suppliers of technology and services worth hundreds of billions of dollars.<\/p>\n

Boosting Global GDP<\/h3>\n

Generative AI could increase global GDP by 7% \u2014 by nearly $7 trillion \u2014 and boost productivity growth by 1.5 percentage points.<\/p>\n

Goldman Sachs economists Joseph Briggs and Devesh Kodnani \u2014 while noting uncertainty about their forecast \u2014 wrote \u201cgenerative AI\u2019s ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines\u201d could contribute to higher GDP and productivity.<\/p>\n

Generative AI could automate 300 million full-time jobs. Goldman Sachs economists estimated that about 66% of 900 occupations are somewhat exposed to AI-induced automation. While 25% to 50% of these jobs could be replaced by AI, the technology would complement \u2014 rather than replace \u2014 most jobs.<\/p>\n

The analysts also forecast AI to produce demand for new workers. These new jobs would employ \u201cwebpage designers, software developers and digital marketing professionals\u201d as well as service sector workers in \u201chealthcare, education and food services,\u201d noted Goldman Sachs.<\/p>\n

Creating Demand In Supplier Industries<\/h3>\n

Generative AI models must be built and trained \u2014 creating significant growth opportunities for suppliers of technology and services.<\/p>\n

To build a generative AI model, developers must represent things \u2014 such as words, images, sounds, proteins, DNA, drugs, and three dimensional designs \u2014 as \u201cvectors\u201d to analyze patterns. For example, vectors might uncover \u201cwords often used in the same sentence or that mean similar things,\u201d noted TechTarget<\/em>.<\/p>\n

Training generative AI means testing and refining these models by unleashing them on huge amounts of data. For example, \u201ca call center might train a chatbot against the kinds of questions service agents get from various customer types and the responses that service agents give in return,\u201d according to TechTarget<\/em>.<\/p>\n

As Google and Meta Platforms build their own versions of Microsoft\u2019s ChatGPT, many technology providers are supplying the required picks and shovels. For example, application software developers are using generative AI to raise the productivity of knowledge workers, speed up scientists\u2019 discovery of new drugs, and accelerate software development.<\/p>\n

Goldman Sachs Research estimates a $150 billion total addressable market for generative AI software \u2014 representing 22% of the global software industry \u2014 as software providers charge customers higher prices for AI-integrated applications.<\/p>\n

Another significant market opportunity is supplying the technology that cloud services providers \u2014 specifically Amazon Web Services, Google Cloud Platform, and Microsoft Azure \u2014 use to enable the design, training, and operation of generative AI models.<\/p>\n

Andreessen Horowitz estimated these CSPs spend \u201cmore than $100 billion\u201d annually on capital expenditures for hardware such as Nvidia graphics processing units and\/or AMD tensor processing units that control the systems for building, training, and operating generative AI models.<\/p>\n

Mapping The Generative AI Value Network<\/h2>\n

Investors and entrepreneurs should map out the value network \u2014 the chain of industries between the raw materials and the ultimate consumer \u2014 to home in on the best opportunities.<\/p>\n

This is important because each link in the chain has varying levels of inherent profit potential, unique customer purchase criteria, differing capabilities required to succeed, and potential return on investment.<\/p>\n

The Generative AI value network consists 0f five more clearly-defined links and two \u2014 management consultants and capital providers \u2014 which could emerge as independent publicly-traded companies \u2014 as they did during the dot-com boom.<\/p>\n

The Generative AI value network includes the following industry participants:<\/p>\n