Generative AI and Its Economic Impact: What You Need to Know

We entered into the GenAI world in 2017, right when things were getting exciting with generative adversarial networks (GANs). We saw a gold mine in using GANs for image restoration—turning blurry, low-res photos into clear, high-quality images. What has so far been a massively scale-driven upstream evolution, where the economic potential of generative ai the typical corporation is a mere bystander, is evolving into something much more downstream and decentralized, upending the current balance of power in the GenAI industry. GPT-3.5, for instance, was built using more than 175 billion parameters, and that number is believed to have grown to 1.75 trillion for GPT-4.

• In India, the Integrating AI and Tinkering with Pedagogy (AIoT) program was launched last year to upgrade the curriculum at 50 schools.
• They can therefore accelerate time to market and broaden the types of products to which generative design can be applied.
• In working with our clients, we have seen that, depending on the user’s skills with prompt engineering, a chat can easily cumulate to tens of thousands of tokens (or word-parts), costing from a few cents to a dollar or more per query.
• And so we absolutely need to ensure that everyone feels included and that any barriers that exist are being taken down.

While other generative design techniques have already unlocked some of the potential to apply AI in R&D, their cost and data requirements, such as the use of “traditional” machine learning, can limit their application. Pretrained foundation models that underpin generative AI, or models that have been enhanced with fine-tuning, have much broader areas of application than models optimized for a single task. They can therefore accelerate time to market and broaden the types of products to which generative design can be applied. For now, however, foundation models lack the capabilities to help design products across all industries. Generative AI’s impact on productivity could add trillions of dollars in value to the global economy. Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analyzed—by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion.

A bigger impact on an accelerated timeline

A study by the World Economic Forum found that adopting AI could lead to a net increase in jobs in some industries, particularly those that require higher levels of education and skills. However, the report also warned that the benefits of AI could be unevenly distributed, with some workers and regions experiencing more significant job displacement than others. In the entertainment industry, gen AI creates personalized recommendations for movies, TV shows, and music based on individual preferences. This technology can foster the same efficiency and accuracy that it does in other industries, making it a potential cost-saver for media companies. The use of gen AI in finance is expected to increase global gross domestic product (GDP) by 7%—nearly $7 trillion—and boost productivity growth by 1.5%, according to Goldman Sachs Research.

Before assuming his current role, Briggs worked on the U.S. economics team for three years at Goldman Sachs. Put simply, business leaders need to emerge themselves now in generative AI (anyone who can ask questions can use the technology) and be prepared to learn continuously. Interestingly, the range of times between the early and late scenarios has compressed compared with the expert assessments in 2017, reflecting a greater confidence that higher levels of technological capabilities will arrive by certain time periods (Exhibit 7).

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If workers are supported in learning new skills and, in some cases, changing occupations, stronger global GDP growth could translate to a more sustainable, inclusive world. The McKinsey Global Institute began analyzing the impact of technological automation of work activities and modeling scenarios of adoption in 2017. At that time, we estimated that workers spent half of their time on activities that had the potential to be automated by adapting technology that existed at that time, or what we call technical automation potential.

Properly managing the workforce changes posed by generative AI could raise the global GDP by 7% in just 10 years. AI-enabled automation of tasks can empower employees to focus more on highly cognitive tasks, boosting overall output. Simultaneously, many of the new jobs created by the rise of AI are likely to contain higher-level work worthy of higher compensation, further boosting GDP. The speed at which generative AI technology is developing is not making this task any easier. Four months later, OpenAI released a new large language model, or LLM, called GPT-4 with markedly improved capabilities.

Implementation Cost Savings vs. Investment Costs

One method for attaining high-performing specialist models is to build them from scratch, designing them to be small compared to the gargantuan generalist LLMs and LMMs that have captured the public imagination in the last year. One way to do this is by reducing the number of parameters, often by means of distilling (a technique whereby the small model is trained via automated, focused interactions with a larger model). For example, Chinese startup 01.AI recently released a “small” LLM that outperformed peers with more than five times the number of parameters. Microsoft has also taken this approach with Phi, its self-described “suite of small language models,” several of which also outperform much larger peers (despite having as few as 1.3 billion parameters).

Similarly, by May 2023, Anthropic’s generative AI, Claude, was able to process 100,000 tokens of text, equal to about 75,000 words in a minute—the length of the average novel—compared with roughly 9,000 tokens when it was introduced in March 2023. And in May 2023, Google announced several new features powered by generative AI, including Search Generative Experience and a new LLM, called PaLM 2 that will power its Bard chatbot, among other Google products. “Generative artificial intelligence” is set to add up to $4.4 trillion of value to the global economy annually, according to a report from McKinsey Global Institute, in what is one of the rosier predictions about the economic effects of the rapidly evolving technology. Generative AI could still be described as skill-biased technological change, but with a different, perhaps more granular, description of skills that are more likely to be replaced than complemented by the activities that machines can do. Banks have started to grasp the potential of generative AI in their front lines and in their software activities.

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In fact, compute spend is set to soon outrun personnel costs at large tech companies (and there is speculation this could already be the case at Google). That’s why the cost of inference is poised to become (if it isn’t already) the binding constraint on large-scale adoption of GenAI. However, a collaborative approach from government, industry and education providers is essential.

By positioning itself as a platform for others to develop specialized apps based on its foundation models, OpenAI is acknowledging the prospect of fragmented value creation downstream from its state-of-the-art foundation LLMs. It’s also attempting to secure a portion of the redistributed profit pools that are the result of an increasingly modular industry. What many business leaders don’t fully appreciate is that this shift will open up tremendous opportunity even for companies that, today, are not tech players at all—provided they have the right data. That’s why industry leaders of all types should be asking themselves whether their data might put them in a position to become influential players in the GenAI industry, rather than mere consumers of the technology.