Training

ver the course of 2022 and early 2023, tech innovators unleashed generative AI en masse, dazzling business leaders, investors, and society at large with the technology’s ability to create entirely new and seemingly human-made text and images. The response was unprecedented.

In just five days, one million users flocked to ChatGPT, OpenAI’s generative AI language model that creates original content in response to user prompts. It took Apple more than two months to reach the same level of adoption for its iPhone. Facebook had to wait ten months and Netflix more than three years to build the same user base.

“And ChatGPT isn’t alone in the generative AI industry. Stability AI’s Stable Diffusion, which can generate images based on text descriptions, garnered more than 30,000 stars on GitHub within 90 days of its release—eight times faster than any previous package.1”

This flurry of excitement isn’t just organizations kicking the tires. Generative AI use cases are already taking flight across industries. Financial services giant Morgan Stanley is testing the technology to help its financial advisers better leverage insights from the firm’s more than 100,000 research reports.2 The government of Iceland has partnered with OpenAI in its efforts to preserve the endangered Icelandic language.3 Salesforce has integrated the technology into its popular customer-relationship-management (CRM) platform.4

The breakneck pace at which generative AI technology is evolving and new use cases are coming to market has left investors and business leaders scrambling to understand the generative AI ecosystem. While deep dives into CEO strategy and the potential economic value that the technology could create globally across industries are forthcoming, here we share a look at the generative AI value chain composition. Our aim is to provide a foundational understanding that can serve as a starting point for assessing investment opportunities in this fast-paced space. Our assessments are based on primary and secondary research, including more than 30 interviews with business founders, CEOs, chief scientists, and business leaders working to commercialize the technology; hundreds of market reports and articles; and proprietary McKinsey research data.

technology could create globally across industries are forthcoming, here we share a look at the generative AI value chain composition. Our aim is to provide a foundational understanding that can serve as a starting point for assessing investment opportunities in this fast-paced space. Our

Cloud platforms

GPUs and TPUs are expensive and scarce, making it difficult and not cost-effective for most businesses to acquire and maintain this vital hardware platform on-premises. As a result, much of the work to build, tune, and run large AI models occurs in the cloud. This enables companies to easily access computational power and manage their spend as needed.

Unsurprisingly, the major cloud providers have the most comprehensive platforms for running generative AI workloads and preferential access to the hardware and chips. Specialized cloud challengers could gain market share, but not in the near future and not without support from a large enterprise seeking to reduce its dependence on hyperscalers.

Pinpointing the first wave of application impact

GPUs and TPUs are expensive and scarce, making it difficult and not cost-effective for most businesses to acquire and maintain this vital hardware platform on-premises. As a result, much of the work to build, tune, and run large AI models occurs in the cloud. This enables companies to easily access computational power and manage their spend as needed.

• Information technology. Generative AI can help teams write code and documentation. Already, automated coders on the market have improved developer productivity by more than 50 percent, helping to accelerate software development.10
• Marketing and sales. Teams can use generative AI applications to create content for customer outreach. Within two years, 30 percent of all outbound marketing messages are expected to be developed with the assistance of generative AI systems.11
• Customer service. Natural-sounding, personalized chatbots and virtual assistants can handle customer inquiries, recommend swift resolution, and guide customers to the information they need. Companies such as Salesforce, Dialpad, and Ada have already announced offerings in this area.
• Product development. Companies can use generative AI to rapidly prototype product designs. Life sciences companies, for instance, have already started to explore the use of generative AI to help generate sequences of amino acids and DNA nucleotides to shorten the drug design phase from months to weeks.12