Generative AI and Its Economic Impact: What You Need to Know
However, the quality of IT architecture still largely depends on software architects, rather than on initial drafts that generative AI’s current capabilities allow it to produce. In addition to the potential value generative AI can deliver in function-specific use cases, the technology could drive value across an entire organization by revolutionizing internal knowledge management systems. Generative AI’s impressive command of natural-language processing can help employees retrieve stored internal knowledge by formulating queries in the same way they might ask a human a question and engage in continuing dialogue. This could empower teams to quickly access relevant information, enabling them to rapidly make better-informed decisions and develop effective strategies. Some outputs from gen AI models might contain inaccurate information—also called “hallucinations”—that could erode public trust in government services that leverage these technologies.
IBM was unable to stop others from exploiting those APIs to create what began as “IBM-compatible” products and later evolved into PCs assembled entirely from modules built by competitors. The winners were companies focused on modules with inherent scale economies, most obviously Microsoft and Intel. Over time, innovation spurred by modularization enabled massive industry growth and huge improvements in cost and performance. Even if the cost of computation continues to decline, as it has consistently done so far and is likely to continue doing, the volume of inferences demanded by users will grow as companies become better acquainted with, and more comfortable deploying and scaling, GenAI solutions. And that demand will truly explode as autonomous agents begin to deliver on their promise to automate entire end-to-end workflows. As a result, even with declining cost per compute, actual spend on model inference is likely to rise.
“Although the impact of AI on the labor market is likely to be significant, most jobs and industries are only partially exposed to automation and are thus more likely to be complemented rather than substituted by AI,” the authors write. A series of graphs show predicted compound annual growth rates from generative AI by 2040 in developed and emerging economies considering automation. This is based on the assumption that automated work hours are reintegrated in work at today’s productivity level. Two scenarios are shown for early and late adoption of automation, and each bar is broken into the effect of automation with and without generative AI. The addition of generative AI increases CAGR by 0.5 to 0.7 percentage points, on average, for early adopters, and 0.1 to 0.3 percentage points for late adopters.
The online survey was in the field April 11 to 21, 2023, and garnered responses from 1,684 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 913 said their organizations had adopted AI in at least one function and were asked questions about their organizations’ AI use. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP. AI high performers are expected to the economic potential of generative ai conduct much higher levels of reskilling than other companies are. Respondents at these organizations are over three times more likely than others to say their organizations will reskill more than 30 percent of their workforces over the next three years as a result of AI adoption. Describing himself as a “classic European progressive” who wants poor people to have “healthcare and teeth”, he argues that the whole European experiment will be in question “if everyone with a high-value revenue is sitting in America”.
Let’s start with the big question: What is McKinsey doing about generative AI right now?
As companies rush to adapt and implement it, understanding the technology’s potential to deliver value to the economy and society at large will help shape critical decisions. We have used two complementary lenses to determine where generative AI, with its current capabilities, could deliver the biggest value and how big that value could be. While generative AI is an exciting and rapidly advancing technology, the other applications of AI discussed in our previous report continue to account for most of the overall potential value of AI.