In the face of a stagnant workforce, rising affluence can only be achieved via higher productivity. We last saw this from 1948-to-1972 when productivity growth averaged 2.8% annually. But since 1973, the United States has suffered from relatively slow productivity growth.

During this period economic growth was largely due to the one-time effects of Baby Boomers entering the economy in the 70s & 80s, followed by markets expanding due to globalization from 1990-to-2007, and a short-lived boost to productivity from 1996-to-2006, driven by the Internet.

However, all three of those forces are vanishing as Boomers retire, businesses embrace “deglobalization,” and the World Wide Web becomes just another utility.

Fortunately, as Trends has been forecasting since at least 2012, artificial intelligence is poised to end this “Great Stagnation” and unleash a new era of rising affluence.

While its ultimate impact on the U.S. and global economy over the next 15-to-20 years is still unclear, some of the best estimates indicate that artificial intelligence could eventually add anywhere between $17.7-and-$25.6-trillion-dollars a year to global GDP.

As Trends has previously explained, this revolution is only taking place now because exponential growth in the price-performance in computing, storage, and networks is converging with major leaps in software effectiveness and the availability of vast oceans of data.

And there is every reason to believe that all three of these trends will accelerate.

Until last year, the focus of this discussion was on what we call non-generative or analytic AI. McKinsey & Company estimates that this kind of technology could ultimately add as much as $18 trillion annually to global GDP.

Non-generative AI algorithms” are highly effective at performing numerical and optimization tasks such as predictive modeling, which continues to find new applications in a wide range of industries.

It’s this non-generative AI technology that makes possible autonomous self-driving automobiles and package delivery drones. It’s also the key to automated drug & materials discovery, medical diagnostic systems, and myriad robotic technologies.

However, the breathless excitement of the past year is primarily driven by so-called “Generative AI.”

McKinsey expects this kind of technology to eventually add a maximum of $8 trillion a year to global GDP.

Generative AI refers to machine learning models, such as ChatGPT, Bing AI, DALL-E, and Midjourney, which are trained on vast databases of text and images to generate new text and images in response to a prompt.

Unlike nongenerative AI which works behind the scenes responding to the world and optimizing activities, generative AI “assists us” by producing visual, spoken, and written content that we can use directly.

Therefore, it has the potential to augment, transform and even replace the jobs of knowledge workers who have, until now, believed themselves immune to “technological obsolescence.”

And it’s because of this potential to disrupt the livelihoods of middle-class workers, that managers, investors and public officials are suddenly obsessing over a technology which the Trends editors have been tracking for over 30 years.

So, what are the implications of implementing a technology like generative AI which could disrupt many millions of jobs, while directly adding $4.4 trillion a year to global GDP and up to $3.5 trillion indirectly?

To arrive at its estimates, McKinsey used an ingenious two-part analysis. The first part identified direct cost saving and revenue enhancement opportunities. The second part identified what we call indirect economic benefits.

In part-one, McKinsey examined “use-cases” which organizations were likely to adopt in order to determine the economic value directly created by using generative AI technology.

For this purpose, McKinsey defined a “use-case” as a targeted application of generative AI to a specific business challenge, resulting in one or more measurable outcomes. 

For example, a use-case in marketing might be the application of generative AI to producing creative content such as personalized emails; potential measurable outcomes for this use-case include reductions in the cost of generating such content and increases in revenue from the enhanced effectiveness of higher-quality content at scale.

For this analysis, McKinsey examined 63 generative AI use-cases across industries spanning 16 business functions.

The estimated total direct value creation was in the range of $2.6-to-$4.4 trillion annually. An exhibit in the printable issue shows the roll-up of generative AI’s potential economic impact on various industries and functions.

Accordingly, the total increase in global economic value created annually by artificial intelligence including generative AI could be 15-to-40 percent higher than the $11-to-$17.7 trillion which McKinsey now estimates nongenerative artificial intelligence could unlock.

This estimate represents a 2023 update of McKinsey’s 2017 estimate for AI’s economic value which said, when fully deployed, it could deliver between $9.5-and-$15.4-trillion annually in new worldwide economic value.

In addition, McKinsey’s research included a second analysis, which evaluated generative AI’s potential impact on the work activities required in 850 different occupations.

It modeled scenarios to assess whether generative AI could perform each of more than 2,100 “detailed work activities,” which make up those occupations across the world economy.

Such detailed work activities could be as simple as “communicating with others about operational plans or activities.”

This approach enabled McKinsey to estimate how the current capabilities of generative AI could affect labor productivity across all work currently done by the global workforce.

As shown in the printable issue, this amounted to between $6.1-and-$7.9-trillion annually. That total captured the benefit of embedding generative AI in software employed outside of the 63 use cases considered in part one.

Obviously, much of this impact overlapped with improved labor productivity identified in the “use-case analysis.”

Assuming the direct cost savings and revenue enhancements ultimately totaled $4.4 trillion a year, the additional economic value added by indirect productivity improvements could be as much as $3.5 trillion a year.

To understand how generative AI could transform business functions and deliver an enormous leap in productivity, consider perhaps the highest impact use-case cited by McKinsey: software engineering.

This use-case involves five well understood functions all of which can benefit from generative AI.

 

Function #1 is Inception and planning. Here software engineers and product managers will use generative AI to assist in analyzing, cleaning, and labeling large volumes of data, such as user feedback, market trends, and existing system logs.

Function #2 is System design. At this stage, engineers will use generative AI to create multiple IT architecture designs and iterate on the potential configurations, accelerating system design, and allowing faster time to market.

Function #3 is Coding. At this point, engineers will be assisted by AI tools that can code, reducing development time by assisting with drafts, rapidly finding prompts, and serving as an easily navigable knowledge base.

Function #4 is Testing. Here, engineers will employ generative AI algorithms that can enhance functional and performance testing as well as generating test cases and test data automatically. And,

Function #5 is System Maintenance. At this final stage, engineers use AI-generated insights on system logs, user feedback, and performance data to help diagnose issues, suggest fixes, and identify high-priority areas for improvement.

This is a big deal because software engineering is a major function in most companies. And it continues to grow more crucial as large companies increasingly embed software in a wide array of products and services.

For example, much of the value of new vehicles comes from digital features such as adaptive cruise control, parking assistance, and connectivity to the Internet of Things.

Generative AI opens new possibilities for software engineering because it treats a computer language as just another language.

For instance, software engineers can use generative AI in “pair programming,” to do augmented coding and to train large language models to develop applications that generate code when given a natural-language prompt describing what that code should do.

According to McKinsey’s analysis, the direct impact of AI on the productivity of software engineering could range from 20 to 45 percent of current annual spending on the function.

This value would arise primarily from reducing time spent on certain activities, such as generating initial code drafts, code correction and refactoring, root-cause analysis, and generating new system designs.

By accelerating the coding process, generative AI could push the skill sets and capabilities needed in software engineering toward code and architecture design.

One study found that software developers using Microsoft’s GitHub Copilot completed tasks 56 percent faster than those not using the tool.

An internal McKinsey empirical study of software engineering teams found those who were trained to use generative AI tools rapidly reduced the time needed to generate and refactor code.

These engineers also reported a better work experience, citing improvements in happiness, flow, and fulfillment.

Importantly the assessed 20-to-45 percent productivity gain did not consider the potential increase in application quality and the resulting boost in productivity that generative AI could bring by improving code or enhancing IT architecture. Both those factors could further improve productivity across the IT value chain.

Notably, we may not have to wait for these benefits to emerge. Large technology companies are already selling generative AI for software engineering. This includes GitHub Copilot, which is now integrated with OpenAI’s GPT-4, and Replit, which is used by more than 20 million coders.

What’s the bottom line?

Artificial intelligence will make a huge difference in our quality-of-life by playing a crucial role in sustaining economic growth even as our workforce plateaus.

Generative AI will be particularly disruptive because it will automate portions of the jobs performed by middle class Americans to an unprecedented degree.

This will make people uncomfortable, even as it opens up huge opportunities and creates enormous wealth. For each of us, the challenge will be accurately assessing the emerging threats and opportunities, despite the noise all around us.

Given this trend, we offer the following forecasts for your consideration.

Business leaders must be especially mindful to engage in critical thinking when information comes from known vectors of technology hype, including consultancies, vendors, and industry analysts.

Claims about the efficacy of new technologies need to be scrutinized for empirical strength. Start with questions that establish a factual basis for the assessment such as, “What do we know?” and “What is the evidence?”

Ask specific questions about how the technology works, how reliable its predictions are, and the quality of other outputs. Beyond that, carefully assess the potential risks of using the new technology in your business. 

Remember, success will require careful oversight, especially where the output from these generative AI systems might go out to customers and open up the company to reputational harm.

Companies also need to recognize that they run the risk of losing control of intellectual property or sensitive information by using systems without oversight; Samsung discovered this when its employees inadvertently leaked sensitive corporate data by feeding it into ChatGPT.

Current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees’ time today.

In contrast, McKinsey previously estimated that technology has the potential to automate half of the time employees spend working.

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.

In an era of worker and skills shortages, the kind of impact McKinsey is forecasting over the next 20 to 35 years is hardly devastating in the aggregate.

The challenge for people in jobs dominated by routine “paper-pusher activities” is to grow their skills and look beyond those types of roles.

Similarly, policy makers need to avoid creating regulatory impediments simply intended to sustain obsolete job categories.

In most industries, retirements and truly new jobs will more than compensate for shrinking demand related to activities replaced by generative AI.

According to McKinsey, about 75 percent of the value that generative AI use cases could deliver will fall across four broad areas: customer operations, marketing and sales, software engineering, and R&D.

Across 16 business functions they identified, 63 use cases exist, 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. Except for three of those 16 functions, the estimated impact is much less than 20 percent.

As a percentage of their revenues, banking, information technology, and life sciences are among the sectors that could see the biggest impact from generative AI.

For example, McKinsey estimates that if all use cases were fully implemented across the banking industry, this technology could deliver additional value equal to between $200 billion and $340 billion annually.

In retail and consumer packaged goods, the additional value is estimated at $400 billion to $660 billion a year.

In 2023, McKinsey updated its adoption scenarios for various economies, taking into account issues related to technology development, economic feasibility, and diffusion timelines.

Its so-called early scenario estimates that over 60 percent of America’s automatable work activities could be automated by 2030. Under that scenario, low-wage countries like India are not likely to reach that level of automation until a decade later.

Notably the adoption curves for both early and late scenarios shown in the printable issue involve generative as well as nongenerative AI. The timing of value creation and job disruption associated with generative AI is most likely to resemble McKinsey’s “Early Scenario.”

As explained in prior issues, we expect the adoption of nongenerative AI to follow a curve resembling that of McKinsey’s “Late Scenario.” 

According to McKinsey, generative AI alone could add up to 0.6 percent annually to labor productivity growth through 2040, depending on the rate of technology adoption and the redeployment of worker time into other activities.

Combining generative AI with all other technologies, work automation could add as much as 3.3 percentage points annually to productivity growth. 

However, workers will need support in learning new skills, and some will need to change occupations.

If worker transitions and other risks can be managed, generative AI could contribute substantially to economic growth and support a more sustainable, inclusive world. And,

Excitement over this technology is palpable, and early pilots are compelling.

But even in the United States and Germany, a full realization of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address.

These include managing the risks inherent in generative AI, determining what new skills and capabilities the workforce will need, and rethinking core business processes such as retraining and skills development.