McKinsey: gen AI could add $4 4T annually to global economy

The latest generative AI applications can perform a range of routine tasks, like the reorganization and classification of data. But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it. The recent emergence of generative artificial intelligence (AI) raises whether we are on the brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity. McKinsey has found that gen AI could substantially increase labor productivity across the economy. To reap the benefits of this productivity boost, however, workers whose jobs are affected will need to shift to other work activities that allow them to at least match their 2022 productivity levels.

Vicuna, a model trained by fine-tuning Meta’s LLaMa, reportedly achieves 90% of the quality of ChatGPT and Google Bard, with “just” 13 billion parameters and with a total cost of retraining of $300. Microsoft fine-tuned LLaVa to create LLaVa-Med, a conversational assistant for biomedical image processing, in just a single day. Many companies, such as Intuit, have already turned to fine-tuning to deploy GenAI solutions. The research estimates a potential boost to productive capacity of US$621 billion in India, US$1.1 trillion in Japan, and US$79.3 billion in the Philippines alone, with studies ongoing in Malaysia, Indonesia and South Korea.

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This technology uses complex algorithms and machine learning models to memorize patterns and rules from existing data and generate new content similar in style and structure. While AI will automate some portion of jobs, it will also create entirely new occupations and sectors. Nearly 85% of employment growth since that time is due to new occupations created through technological advances. We are already seeing the beginnings of this with the advent of new roles like “Prompt Engineer.” Upskilling workers to be ready for these new roles and to be ready to make use of AI advances, in general, is one way to help maximize the positive impact AI can have on the labor market. Generative AI’s ability to understand and use natural language for a variety of activities and tasks largely explains why automation potential has risen so steeply. Some 40 percent of the activities that workers perform in the economy require at least a median level of human understanding of natural language.

While generative AI will impact a wide variety of industries, 75% of its potential value spans just four sectors. But when it comes down to how we’re working together, we can consciously create space to be together and offer some relief from the external world, rather than allow work to amplify those tensions. If everybody thinks their race is holding them back, that’s a problem in society more broadly. And so we absolutely need to ensure that everyone feels included and that any barriers that exist are being taken down.

Global economist to speak about economic potential of Generative AI

Additionally, some of the tasks performed in lower-wage occupations are technically difficult to automate—for example, manipulating fabric or picking delicate fruits. Some labor economists have observed a “hollowing out of the middle,” and our previous models have suggested that work automation would likely have the biggest midterm impact on lower-middle-income quintiles. As a result, generative AI is likely to have the biggest impact on knowledge work, particularly activities involving decision making and collaboration, which previously had the lowest potential for automation (Exhibit 10). Our estimate of the technical potential to automate the application of expertise jumped 34 percentage points, while the potential to automate management and develop talent increased from 16 percent in 2017 to 49 percent in 2023.

We develop outstanding leaders who team to deliver on our promises to all of our stakeholders. In so doing, we play a critical role in building a better working world for our people, for our clients and for our communities. Looking ahead to the next three years, respondents predict that the adoption of AI will reshape many roles in the workforce. Nearly four in ten respondents reporting AI adoption expect more than 20 percent of their companies’ workforces will be reskilled, whereas 8 percent of respondents say the size of their workforces will decrease by more than 20 percent. Today’s generalist foundation models, like GPT-4, Gemini, and Anthropic’s Claude, require substantial marginal costs to query each system (unlike, for example, search engines).

These country findings are consistent with other global studies—for instance, a recent report by McKinsey estimates generative AI could add up to US$4.4 trillion a year to the global economy. “Generative AI can streamline business workflows, automate routine tasks and give rise to a new generation of business applications,” Kash Rangan, senior U.S. software analyst in Goldman Sachs Research, writes in the team’s report. The technology is making inroads in business applications, improving the day-to-day efficiency of knowledge workers, helping scientists develop drugs faster and accelerating the development of software code, among other things. In addition, jobs displaced by automation have historically been offset by the creation of new jobs, and the emergence of new occupations following technological innovations accounts for the vast majority of long-run employment growth, according to the report. For example, information-technology innovations introduced new occupations such as webpage designers, software developers and digital marketing professionals. There were also follow-on effects of that job creation, as the boost to aggregate income indirectly drove demand for service sector workers in industries like healthcare, education and food services.

It would take only one of these to come good for the field to be transformed from chimera to reality. Based on Access Partnership’s analysis, roles such as biochemists and biophysicists, astronomers, biologists, bioinformatics scientists, and computer and information research scientists are likely to have the greatest share of their tasks transformed by generative AI. Asked what types of AI tools he used in particular, Sukharevsky declined to comment specifically, saying he liked to test new ones out nearly every day. How do you actually create the solution for the world to recover from the worst natural disasters? ” Sukharevsky asked, rhetorically, citing some examples of tasks that could be “augmented” by all kinds of AI. The information contained in this article does not constitute a recommendation from any Goldman Sachs entity to the recipient, and Goldman Sachs is not providing any financial, economic, legal, investment, accounting, or tax advice through this article or to its recipient.

While generative AI has captured the public interest and imagination, McKinsey believes other AI applications and technologies will also play a major role in reshaping the global economy. To construct the report, McKinsey’s analysts examined 850 occupations and 2,100 detailed work activities across 47 countries, the economic potential of generative ai representing more than 80% of the global workforce. As organizations begin to set gen AI goals, they’re also developing the need for more gen AI–literate workers. As generative and other applied AI tools begin delivering value to early adopters, the gap between supply and demand for skilled workers remains wide.

Users could act unscrupulously or illegally by exploiting inherent biases in the data that a specific foundation model was trained on. As a result, some governments—such as those of Iceland and Finland—have chosen to partner with global large language model (LLM) providers to get access to their existing models and augment and customize them to suit their own needs, by adding proprietary data and insights. The potential of technological capabilities in a lab does not necessarily mean they can be immediately integrated into a solution that automates a specific work activity—developing such solutions takes time.

To start, thousands of cell cultures are tested and paired with images of the corresponding experiment. Using an off-the-shelf foundation model, researchers can cluster similar images more precisely than they can with traditional models, enabling them to select the most promising chemicals for further analysis during lead optimization. A generative AI bot trained on proprietary knowledge such as policies, research, and customer interaction could provide always-on, deep technical support. Today, frontline spending is dedicated mostly to validating offers and interacting with clients, but giving frontline workers access to data as well could improve the customer experience.

Humanity can accomplish a lot merely by progressing as fast in the future as it has during peak periods of the past. And at least one Wall Street megabank thinks generative AI technologies such as ChatGPT and Bard can do the trick. Growing demand for GenAI use will create mounting pressure to bring down the cost of inference. And because the industry is becoming increasingly modular, this pressure creates significant opportunity for companies able to develop small, high-performing specialist models.

Dr. Baker leads Baker Lab, which has nearly 80 pre- and post-doctoral students and is part of the university’s Institute for Protein Design. The proteins it designs can be used in a range of ways such as developing drugs and vaccines. Dr. Baker estimates that the pace of innovation in his field has increased by a factor of 10 during the past 18 months, due to a combination of deep learning and laboratory methods used to verify that new proteins are working as expected. For context, such a labor productivity increase would double the long-term Congressional Budget Office forecast of US productivity — and economic growth. François Candelon is a managing director and senior partner of Boston Consulting Group and the global director of the BCG Henderson Institute (BHI).Philip Evans is a senior advisor at the BCG Henderson Institute. As a technology that is democratized—one that doesn’t simply exist in a faraway lab or tech community in Silicon Valley, for instance—generative AI lowers the barriers to participation.

Entos, a biotech pharmaceutical company, has paired generative AI with automated synthetic development tools to design small-molecule therapeutics. But the same principles can be applied to the design of many other products, including larger-scale physical products and electrical circuits, among others. It suggests that generative AI is poised to transform roles and boost performance across functions such as sales and marketing, customer operations, and software development. Some governments may aspire to develop foundation models—the core models on which gen AI applications are built.

At such scale, the cost of using these generalist models—driven by energy use and amortized fixed cost of operating cloud facilities—can rapidly become astronomical, especially if the model is not used strategically. 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. For example, the Japanese government recently announced plans to allow students from elementary to high school limited use of generative AI to facilitate in-class discussions and artistic activities. Taiwan’s Ministry of Education has brought in a generative AI chatbot to help students learn English. In India, the Integrating AI and Tinkering with Pedagogy (AIoT) program was launched last year to upgrade the curriculum at 50 schools.