The "AI era" is here and for the first time "Artificial Intelligence" has been listed as a reason for corporate layoffs. Perhaps, the layoff wave ignited by AI is just beginning.
On June 14, consulting firm McKinsey released a research report titled "The Economic Potential of Generative Artificial Intelligence." Analysts examined 850 jobs across 47 countries and regions (covering more than 80% of the global workforce) to understand the impact AI's exponential growth will have on the global economy. They studied which industries will be most affected and who will face the greatest threat of unemployment. Here are the key findings from the report:
- The timeline for AI replacing human jobs has significantly advanced by 10 years. Between 2030 to 2060 (with 2045 as the midpoint), 50% of jobs will be gradually replaced by AI.
- AI can contribute an annual growth of $26 trillion to $44 trillion to the global economy, increasing productivity by 0.1% to 0.6%. This is equivalent to contributing a GDP equivalent to the UK every year.
- While AI is beneficial for industries overall, it is not beneficial for individuals. The highest-paid and most educated knowledge workers will be impacted the most.
- The value growth brought by generative AI is mainly concentrated (about 75%) in four areas: customer operations, marketing and sales, software engineering, and R&D, indicating these businesses will be most affected by generative AI.
- The development of generative AI and other technologies could automate 60% to 70% of current jobs. The banking, high-tech, and life sciences industries will be most impacted.
- AI will "contribute a UK's GDP" to the global economy each year.
The report found that the use of generative AI in 63 of its studied applications will result in annual growth of $26 trillion to $44 trillion for the global economy. This forecast does not account for all the applications of generative AI. If the yet-to-be-studied applications are included, the economic impact of generative AI may double.
The research primarily covered two aspects: 1. The economic growth potential of more than 60 organizations using generative AI. 2. The labor productivity potential of about 2100 job activities worldwide.
McKinsey pointed out in the report that their research covered 16 business areas. If applied across industries, it can contribute economic benefits ranging from $26 trillion to $44 trillion annually.
The impact of generative AI is equivalent to the annual GDP of the UK (which was $3.1 trillion in 2021). It's estimated that the economic value of non-generative AI will increase from $110 trillion to $177 trillion, an increase of 15% to 40%.
Regarding individual jobs, McKinsey's research covered about 2100 sub-roles within 850 jobs. The report indicated that AI could affect all jobs worldwide across all industries. Over the next 20 years, generative AI could increase labor productivity by 0.1% to 0.6%.
The biggest "losers"? High-paying, educated knowledge workers. McKinsey indicated that while generative AI will impact all industries, high-earning intellectual laborers, who were previously thought to be relatively unaffected by automation, will be most impacted.
McKinsey pointed out that between 2030 to 2060 (2045 as the midpoint), 50% of jobs will be gradually replaced by AI, advancing their previous research by 10 years.
Knowledge workers are most likely to be impacted by automation, especially jobs that involve decision-making and team collaboration:
Previous generations of automation technologies were mainly involved in data collection and processing, so the impact on knowledge workers was small. However, the advent of generative AI aligns the role and tasks of "knowledge workers" with large language models (LLMs).
Since large language models are essentially designed to perform cognitive tasks, we have increased the application ability of large language models in professional knowledge by 34 percentage points since 2017. The potential to automate management and talent cultivation rose from 16% in 2017 to 49% in 2023.
Therefore, McKinsey believes that many jobs involving communication, supervision, record-keeping, and human interaction may be automated by generative AI. This will undoubtedly accelerate the transformation of educational workers and white-collar workers engaged in creative labor.
Meanwhile, McKinsey pointed out that in many previous productivity transformations, highly educated individuals were less affected, but the AI revolution will have a greater impact on highly educated talent:
One explanation for this is that generative AI has increased the potential for technological automation, which is most needed in professions with a high level of education.
Another explanation is that for many years, degrees have been regarded as a skill indicator. This will be challenged by generative AI. In the future, more people will advocate for skill-based methods to drive labor development, creating a more equitable and efficient labor training and matching system. Generative artificial intelligence can still be described as a technological change that prefers skills, but it has more detailed requirements for skills.
McKinsey emphasized that in past waves of automation, middle-income jobs were often the most affected, a trend some economists have compared to the "hollowing out of the middle." However, with the advent of AI, high-paying knowledge workers could see the most significant impact.
In the case of low-wage jobs, the human cost is minimal, negating the advantages of automation. Furthermore, the nature of these jobs, such as picking delicate fruits, make them hard to automate. Yet, these jobs, previously deemed difficult to automate, could be hit the hardest due to advancements in generative AI.
AI is set to shake up a multitude of industries. According to McKinsey, the impact of generative AI is primarily concentrated in four areas, accounting for about 75% of its effects: customer operations, marketing and sales, software engineering, and research and development. The development of generative AI and other technologies could potentially automate 60% to 70% of current jobs. Industries such as banking, high-tech, and life sciences are likely to be most affected:
The banking industry alone could generate an additional $200 billion to $340 billion in output by improving productivity through enhanced customer satisfaction, improved decision-making, and reduced fraud. This corresponds to a 9% to 15% increase in operating profit.
In terms of product development, AI could boost productivity by 10% to 15%. For instance, in life sciences and chemical engineering, AI can produce potential molecules more quickly, accelerating the development of new drugs and materials, potentially increasing profits for pharmaceutical and medical product companies by up to 25%.
In terms of the impact on marketing productivity, generative AI could increase the economic value of marketing productivity by 5% to 15%. Our analysis of AI's potential use in marketing has found that apart from its direct impact on productivity, it can also trigger a domino effect, boosting sales productivity by 3% to 5%.
The incorporation of generative AI into various applications can provide higher quality data insights, generating new ideas for marketing campaigns and better targeting customer segments. Marketing departments could potentially shift resources to produce higher-quality content for their own channels, possibly reducing outsourcing expenses.
In software engineering, generative AI could directly affect 20% to 45% of annual software engineering expenditures. This value primarily comes from reducing the time spent on certain tasks, such as generating initial codes, code corrections and restructuring, root cause analysis, and generating new system designs. One study found that software developers using Microsoft's GitHub Copilot complete tasks 56% faster than those who don't.
An internal empirical study conducted by McKinsey on software engineering teams found that those trained to use AI drastically reduced the time needed to generate and refactor code, and engineers generally felt their work experience had improved, describing it as faster, more convenient, and providing a greater sense of accomplishment.
Looking at product development, we believe that generative AI can speed up the time it takes for products to reach the market, and offer productivity enhancements and operational convenience in two main ways: optimizing product design and improving product quality.
The AI revolution is set to dramatically increase productivity. McKinsey concluded that declining global birth rates and aging populations could hinder global productivity development, but the development of AI and other technologies can compensate for a shrinking workforce, significantly boosting productivity and accelerating the global economy. Developed countries may also adopt AI at a faster pace:
Global economic growth from 2012 to 2022 was slower than the previous two decades. We believe that one factor was the long-term structural challenge of declining birth rates and aging populations.
In many major countries, the workforce has been decreasing year by year. We believe that AI can reconfigure the required labor time, thereby promoting productivity growth.
