Author: Ge Jiaming
The "AI era" has officially arrived, and "artificial intelligence" has been included in the reasons for layoffs for the first time, and perhaps the wave of layoffs triggered by AI has just begun.
On June 14, the consulting firm McKinsey released a research report entitled "The Economic Potential of Generative Artificial Intelligence". In the report, analysts analyzed 850 occupations in 47 countries and regions (80% of the world's The research on the above-mentioned working population) explores the impact on the global economy behind the exponential development of AI, which industries are most impacted, and who are facing the threat of unemployment?
The main content of the report is as follows:
- The time for AI to replace human work has been greatly advanced by 10 years, and 50% of occupations will be gradually replaced by AI between 2030 and 2060 (the midpoint is 2045).
- AI can bring an annual growth of 2.6 trillion to 4.4 trillion US dollars to the global economy, and increase productivity by 0.1% to 0.6%, which is equivalent to contributing to the GDP of the United Kingdom every year.
- On the whole, AI is beneficial to the development of all walks of life, but it is not good for individuals, and high-salary, highly educated mental workers are the most affected.
- 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, which also means that the four businesses are most affected by generative AI.
- The development of generative human AI and other technologies may automate 60% to 70% of current jobs. Among them, industries such as banking, high-tech industries and life sciences have been most affected.
AI will 'contribute one UK's GDP' to global economy every year
The report found that the use of generative AI in the 63 applications it studied would boost the global economy by between $2.6 trillion and $4.4 trillion annually . And this forecast does not take into account all the applications of generative AI. If the applications that have not been studied are included, the economic impact of generative AI may double:
The research mainly includes two aspects: 1. The economic growth potential of more than 60 organizations using generative AI 2. The labor productivity potential of about 2,100 work activities around the world.
McKinsey pointed out in the report that their research covered 16 business elements and concluded that if it is applied in various industries, it will contribute economic benefits between 2.6 trillion and 4.4 trillion U.S. dollars per year . Specifically:
Our metrics include: reducing the cost of generating content, and the revenue generated by improving the quality of content at scale through the use of AI. In marketing, for example, one use case is applying generative AI to generate creative content such as personalized emails.
This increase is roughly equivalent to the UK's GDP for one year ($3.1 trillion in 2021).
We estimate that the economic value of non-generative AI will increase from $11.0 trillion to $17.7 trillion, an increase of 15% to 40%. (In 2017, we believed that artificial intelligence could bring economic value of US$9.5 trillion to US$15.4 trillion)
As for each position, McKinsey’s research covers 2,100 subdivided job functions in about 850 occupations. According to the degree of adoption and implementation of technology, the report points out that AI may affect all current jobs in the world and affect all jobs in the world. The industry has an impact. In the next 20 years, generative AI can increase labor productivity by 0.1%-0.6%.
Biggest "loser"? — Highly paid, highly educated knowledge workers
McKinsey pointed out that although generative AI will affect all walks of life, it will be most affected by high-paid mental workers who "previously were considered relatively immune to automation."
McKinsey pointed out that between 2030 and 2060 (the midpoint is 2045), 50% of occupations will be gradually replaced by AI, which is 10 years earlier than their previous research.
Knowledge workers, however, are most likely to be affected by automation, especially those involving occupations that require decision-making and teamwork:
Previous generations of automation technology mainly involved data collection and processing, so they had little impact on knowledge workers. However, the emergence of generative AI made the roles and tasks of "knowledge workers" just right for the large language model (LLM). match.
Because large language models are fundamentally designed to complete cognitive tasks, our ability to apply large language models to professional knowledge has increased by 34 percentage points compared with 2017, while the potential for automated management and talent training has increased from 16% in 2017. % rise to 49% in 2023.
Therefore, McKinsey believes that many tasks involving communication, supervision, recording, and human interaction are likely to be automated by generative AI, which will undoubtedly accelerate the transformation of educators and white-collar jobs engaged in creative labor :
At the same time, McKinsey pointed out that among the many previous productivity changes, people with highly educated people tend to be less affected, but the AI revolution will make highly educated talents more affected :
We think one explanation for this is that generative AI increases the potential for technological automation, which tends to be most in demand in highly educated occupations.
We think an alternative explanation is that degree credentials have been viewed as a skill indicator for years, and this will be challenged by generative AI, with more advocates for a more skills-based approach to workforce development to create Fairer and more efficient workforce training and matching systems. Generative AI can still be described as a technological change with a preference for skills, but with a more nuanced need for skills.
McKinsey emphasized that it is worth noting that the automation changes of previous generations tended to have the greatest impact on occupations with wage income in the middle. Likely to have the biggest impact on the jobs of well-paid knowledge workers :
For low-wage jobs, low labor costs do not reflect the benefits of automation. In addition, low-wage occupations engaged in labor activities are difficult to automate, such as picking delicate fruits.
However, it is these jobs that were previously considered relatively less automatable will be most impacted due to advances in generative AI technology automation.
AI disrupts every industry
According to McKinsey, the impact of generative AI is concentrated in four areas (about 75%): customer operations, marketing and sales, software engineering and research and development . The development of generative AI and other technologies may automate 60% to 70% of current jobs. Among them, industries such as banking, high-tech industries and life sciences have been most affected:
The banking industry alone could generate an additional $200-340 billion in productivity gains as new technologies improve customer satisfaction, facilitate decision-making and reduce fraud through better monitoring. This equates to a 9% to 15% increase in operating profit.
In product development, AI can increase productivity by 10% to 15%. In life sciences and chemical engineering, for example, AI can generate potential molecules faster, speeding up the process of developing new drugs and materials, which could increase the profits of pharmaceutical and medical product companies by as much as 25%.
In terms of impact on marketing productivity, generative AI can increase the economic value of marketing productivity by 5% to 15%. Our analysis of the potential use of AI in marketing found that, in addition to the immediate impact on productivity, there will be a knock-on effect, increasing sales productivity by 3% to 5%.
The integration of generative AI into various applications can provide higher-quality data insights, bring new ideas to marketing activities, and better target customer groups. Marketing functions can shift resources to producing higher quality content for owned channels, potentially reducing outsourcing spending.
From the perspective of software engineering, generative AI directly affects about 20% to 45% of annual software engineering expenditures. This value comes primarily from reducing time for certain tasks such as initial code generation, code correction and refactoring, root cause analysis, and generation of new system designs. A study found that software developers who use Microsoft GitHub Copilot complete tasks 56 percent faster than those who don't use the tool .
An internal McKinsey empirical study of software engineering teams found that those trained to use AI spent significantly less time generating and refactoring code, and engineers generally reported an improved work experience, saying it made work happier , The process is more convenient and it is easier to get a sense of accomplishment.
From the perspective of product development, we believe that generative AI can speed up the time to market of products , and bring productivity improvements and operational convenience from the following two aspects: including optimizing product design and improving product quality .
The AI revolution will dramatically increase productivity
McKinsey concluded that the decline in the global birth rate and population aging will become obstacles to the development of global productivity, and the development of AI and other technologies can make up for the decline in the employment population, greatly increase productivity, and speed up the global economy. Developed countries adopt The AI could also be faster:
Global economic growth from 2012 to 2022 will be slower than in the previous two decades, partly due to long-term structural challenges – including declining birth rates and an aging population – in our view.
In many large countries, the number of labor force populations has been declining year by year, and we believe that AI can reprogram the required labor time and promote productivity growth.
