👨💻 In this article, I’ll share seven key insights on how ChatGPT and large language models (LLMs) will change the job markets and global economy after reading the most recent McKinsey report on “The economic potential of generative AI”.
Insight 1: Generative AI’s Potential to Infuse Trillions into the Global Economy
The potential economic influence of generative AI is massive, possibly infusing the global economy with trillions of dollars in value.
Most recent studies project that generative AI could inject an annual value of $2.6 trillion to $4.4 trillion across 63 examined use cases. For perspective, this annual value addition equates to the entire GDP of the UK in 2021.
By embedding generative AI in existing software for tasks beyond these use cases, the value could potentially double, increasing the overall impact of AI by 15 to 40 percent.
Insight 2: Four Key Business Domains Garnering 75% of Generative AI Value
Approximately 75% of the potential value from generative AI use cases is concentrated within four domains:
- customer operations,
- marketing and sales,
- software engineering, and
An analysis of 63 use cases across 16 business functions reveal that generative AI can address specific business challenges with measurable outcomes. These include enhancing customer interaction, creating creative content for marketing and sales, and drafting code from natural language prompts, amongst other tasks.
For instance, software developers using GitHub Copilot can complete tasks 56 percent faster than developers without using it:
👨💻 Recommended: AI-Assisted Coding: New Google Research Says How You Should Use It
Insight 3: High-Tech, Banking, and Life Sciences Set to Benefit Most from Generative AI
All industry sectors stand to benefit significantly from generative AI. Industries such as banking, high-tech, and life sciences are predicted to see the most substantial impact relative to their revenues.
For instance, if the use cases were fully implemented in the banking industry, generative AI could generate an additional $200 billion to $340 billion annually.
Similarly, in retail and consumer packaged goods, the potential impact ranges from $400 billion to $660 billion per year.
Insight 4: Automation of Tasks and the Reshaping of Work through Generative AI
Generative AI could revolutionize work structures, augmenting individual abilities by automating certain activities. Current generative AI and related technologies can potentially automate work tasks occupying 60 to 70 percent of employee time, an increase from previous estimates.
This surge in automation potential is largely attributed to generative AI’s advanced understanding of natural language, crucial for work tasks accounting for 25 percent of total work time. As a result, generative AI impacts knowledge-intensive occupations with higher wage and educational prerequisites more than other jobs.
Insight 5: The Accelerated Pace of Workforce Transformation Driven by Technical Automation
The transformation of the workforce is anticipated to quicken, owing to advancements in technical automation.
Some revised projections, taking into account technology development, economic feasibility, and diffusion timelines, suggest that half of today’s work tasks could be automated between 2030 and 2060, with a midpoint estimate in 2045. This is about a decade earlier than our previous forecasts.
Insight 6: Enhanced Labor Productivity and Up To 3.3% Economic Growth through Generative AI Adoption
Generative AI holds the potential to significantly boost labor productivity, contingent upon investments in worker support for transitioning tasks or job roles.
It could foster labor productivity growth of 0.1 to 0.6 percent annually through 2040, varying with the pace of technology adoption and the reallocation of worker time.
Combined with all other technologies, work automation could add 0.2 to 3.3 percentage points annually to productivity growth.
Yet, this requires worker support for skill development and possible job transitions.
Insight 7: Challenges and Opportunities at the Beginning of the Generative AI Era
We’re only at the dawn of the generative AI era. Although the initial excitement around this technology and its early applications is considerable, full realization of its benefits will require time and concerted effort.
The following graphic from the McKinsey report shows how STEM (science, technology, engineering, and mathematics) professionals will likely experience a significant impact due to the generative AI. That’s where the biggest opportunities are for professionals like you to stand out and gain new market share by remaining on the right side of change and learning prompting and LLM technology!
Feel free to check out the following tutorial on LLM technologies that may interest you as well:
My View? McKinsey Is Too Conservative by an Order of Magnitude
Also, allow me to end this article with a small personal note:
McKinsey, in my view, has been far too conservative in their projections. AI will not only increase the GDP by a couple of percentage points — it will increase GDP by hundreds of percentage points in the next decade or two.
Why? Because an economy is only restricted by two factors: labor and capital. Labor can always build more capital, so if you remove the limitation of human labor, the economic potential (output) becomes infinite.
First principles thinking suggests that the resources, i.e., atoms in the universe and energy, are virtually infinite as well, so using infinite labor and virtually infinite resources and energy to significantly reduce entropy in all areas of modern society is the most likely outcome.
At least it’s easy to imagine that AI will double, triple, or even 10x the output of our knowledge economy and make us all significantly richer in the process. 🌞
While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.
To help students reach higher levels of Python success, he founded the programming education website Finxter.com that has taught exponential skills to millions of coders worldwide. He’s the author of the best-selling programming books Python One-Liners (NoStarch 2020), The Art of Clean Code (NoStarch 2022), and The Book of Dash (NoStarch 2022). Chris also coauthored the Coffee Break Python series of self-published books. He’s a computer science enthusiast, freelancer, and owner of one of the top 10 largest Python blogs worldwide.
His passions are writing, reading, and coding. But his greatest passion is to serve aspiring coders through Finxter and help them to boost their skills. You can join his free email academy here.