What are likely to be the biggest economic applications of the current wave of artificial intelligence technologies? The McKinsey Global Institute takes a shot at answering the question in “The economic potential of generative AI” (June 2023). Their estimate is that these technologies could add $2.6 trillion to $4.4 trillion annually to the global economy; for perspective, total world GDP is about $100 trillion.
As a starting point, what is “generative AI” and why is it distinctive?
For the purposes of this report, we define generative AI as applications typically built using foundation models. These models contain expansive artificial neural networks inspired by the billions of neurons connected in the human brain. Foundation models are part of what is called deep learning, a term that alludes to the many deep layers within neural networks. Deep learning has powered many of the recent advances in AI, but the foundation models powering generative AI applications are a step change evolution within deep learning. Unlike previous deep learning models, they can process extremely large and varied sets of unstructured data and perform more than one task. Foundation models have enabled new capabilities and vastly improved existing ones across a broad range of modalities, including images, video, audio, and computer code. AI trained on these models can perform several functions; it can classify, edit, summarize, answer questions, and draft new content, among other tasks. …
The rush to throw money at all things generative AI reflects how quickly its capabilities have developed. ChatGPT was released in November 2022. Four months later, OpenAI released a new large language model, or LLM, called GPT-4 with markedly improved capabilities. 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.
In what particular areas of these economy are these tools likely to be especially useful? McKinsey suggests that four areas will account for about three-quarters of the productivity gains:
About 75 percent of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and R&D. Across 16 business functions, we examined 63 use cases in which the technology can address specific business challenges in ways that produce one or more measurable outcomes. Examples include generative AI’s ability to support interactions with customers, generate creative content for marketing and sales, and draft computer code based on natural-language prompts, among many other tasks. …
Generative AI has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities.
Current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees’ time today. … The acceleration in the potential for technical automation is largely due to generative AI’s increased ability to understand natural language, which is required for work activities that account for 25 percent of total work time. Thus, generative AI has more impact on knowledge work associated with occupations that have higher wages and educational requirements than on other types of work.
The McKinsey report goes into much more detail. But in broader terms, the discussion makes me think about how people have interacted with computer technology over time. For example, it used to be that only a limited number of high-caste workers could access the computers. When I was in high school in the 1970s, there were only a couple of terminals for the entire class to use–and we felt pretty up-to-date to have those. The personal computer revolution of the 1980s and 1990s, and then the smartphone revolution of the last two decades, have democratized access to the technology. But the specific manner of using the technology still mostly involved using a more-or-less intuitive piece of software or an app. We are now moving into a space where it will be possible to use the technology with “natural language”–not just for searching on a map, but for a range of less structured tasks.
I don’t have a well-developed sense of how this will ultimately affect the tasks we usually do at work, along with wages and inequality. I suspect it will touch us all in various ways. But in particular, those in customer operations, marketing and sales, software engineering, and R&D should have eyes wide open to the evolving possibilities.