Economist Michael Webb discusses the impact of AI on jobs, incomes, and inequality. They explore historical patterns, challenge mass unemployment fears, and highlight the potential for explosive economic growth. They also delve into the unique capabilities of language models, the low fixed costs of AI technology, and the challenges of regulating AI. Other topics include the impact on the labor market and GDP, AI's role in research and development, and the risks of AI misuse. They also touch on talent challenges in the education sector and the impact of AI on music.
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Quick takeaways
The exposure of jobs to AI varies, with lower-skilled jobs being more exposed to robots and upper-middle-skilled jobs being more exposed to software.
Technological changes like AI can initially increase inequality, benefiting those with capital investment, but later stages can benefit those who can leverage their skills on top of AI capabilities.
Even without AGI, current AI capabilities are expected to drive significant economic growth for the next few decades, reshaping industries and employment opportunities.
LLMs (large language models) are general-purpose from the start, making them immediately useful for various applications and encouraging widespread adoption across different industries.
LLMs simplify automation processes, allowing for quicker integration and reducing the time and expense traditionally associated with automation projects.
LLMs enable businesses to reimagine processes, leading to significant productivity improvements and the possibility of new ways of doing business.
Deep dives
Impact of AI on Job Exposure
The paper explores the exposure of different jobs to AI based on capabilities observed in 2020. It finds that lower-skilled jobs are more exposed to robots, while higher-skilled jobs are more exposed to software. However, for AI, it is the upper-middle-skilled jobs that are most exposed. The pattern suggests that CEOs are less exposed than lawyers and accountants. Data from a recent study supports these findings, showing that AI has a greater impact on lower-skilled workers and less impact on highly skilled ones.
Effect on Inequality
The impact of AI on inequality is complex. Historical evidence suggests that technological changes can initially increase inequality, followed by a decrease. The initial stage often benefits those with capital investment, while the later stage benefits those who can leverage their skills on top of AI capabilities. There is evidence that low-skilled workers can benefit significantly from AI assistance, while high-skilled workers who are already performing well may not see as much impact. The specific impact on different skills and jobs may vary, and entrepreneurs will have incentives to create new opportunities for displaced workers.
Transition and Economic Growth
Even without AGI, current AI capabilities are expected to drive considerable economic growth for the next few decades. The impact of AI is considered as significant as the internet revolution. AI can streamline processes, increase productivity, and create new opportunities. Skill levels, job types, and the demand for certain tasks may undergo shifts as AI adoption accelerates. The short-term effects of AI will likely be remarkable, reshaping industries and employment opportunities.
LLMs as General Purpose and Low Cost
LLMs are unique in that they are general purpose from the start, making them immediately useful for a wide range of applications. Unlike previous technologies that required specific adaptations and complementary changes, LLMs can be adopted without significant infrastructure or process overhauls. The low cost and ease of adoption allow for quick integration into workflows and encourages widespread use across different industries and sectors.
Easier and Faster Automation
LLMs simplify the process of automation by speaking various languages and enabling effortless transition from old software systems. This is in contrast to the high cost and complexity associated with switching from legacy systems to new technology. LLMs offer the advantage of faster adoption, reducing the time and expense traditionally associated with automation projects.
Greater Flexibility and Redesigned Processes
LLMs enable businesses to reimagine and redesign their processes from the ground up, leveraging the capabilities of AI. They provide the opportunity to create innovative interfaces and customized solutions, previously considered too costly or complicated. This flexibility allows for significant productivity improvements and opens up possibilities for new ways of doing business.
Impacts of AI on the labor market
Some economists believe that the impact of AI on GDP and labor market in the next few years will not be significant, similar to other technologies in the past. They point out that GDP growth has been slowing down since the 1950s, despite the advancements in technology. On the other hand, there are those who believe that AI has the potential to lead to explosive growth by automating the process of innovation and speeding up research. They argue that these advancements could have a profound impact on GDP and productivity growth in the long term.
The role of regulation
Regulation can be a major factor in determining the speed of AI adoption. Some experts believe that regulation from government and interest groups can slow down the progress of AI and lead to slower adoption in various industries. They argue that powerful interest groups, such as unions and professional bodies, can resist change and protect the status quo. In addition, government regulation can also impede the adoption of AI in certain sectors, giving rise to slow-moving decision-making processes and cautious behavior from companies. However, it is also acknowledged that government action can be swift and can heavily influence the direction of AI deployment, depending on the perceived risks and benefits.
Forecasting the future
Given the complexities involved in predicting the future impact of AI, it is challenging to determine the exact trajectory of AI adoption and its effects on the economy and labor market. Academic economists tend to be more cautious about the potential impact, while others are more optimistic, envisioning explosive growth and transformative changes in the coming years. It remains uncertain how AI will shape the economy in the near term, but its potential is widely recognized, and ongoing developments and innovations in AI continue to drive the conversation.
The importance of talent in shaping the future
Talent is consistently highlighted as the key factor in driving progress and solving complex problems across various industries and sectors. The demand for individuals with specific expertise, particularly in emerging technologies like AI and quantum computing, is expected to increase. Developing high-level technical and social skills, along with the ability to build personal networks and establish trust, will be essential in capitalizing on these opportunities.
The evolving role of AI in music and the arts
AI is already capable of generating music and other forms of art that are indistinguishable from human creations. However, the live performance aspect and the human ability to build context, trust, and personal connections remain unmatched by AI. The value of human musicians and performers lies in their ability to provide unique experiences, emotional interpretation, and historical context. As AI-generated music becomes more common, the scarcity and authenticity of human-made artistry may become increasingly valuable.
Addressing talent shortages through innovation in education
Current systems, such as universities, struggle to keep up with the demand for high-level expertise in rapidly advancing fields. The traditional approach lacks the capacity to scale at the required speed. To address talent shortages and foster broader access to knowledge and skills, new educational initiatives and organizations are needed. These initiatives should leverage AI and other technologies to support accelerated learning, innovative teaching methods, and specialized training programs that cater to the evolving demands of the job market.
"Do you remember seeing these photographs of generally women sitting in front of these huge panels and connecting calls, plugging different calls between different numbers? The automated version of that was invented in 1892.
However, the number of human manual operators peaked in 1920 -- 30 years after this. At which point, AT&T is the monopoly provider of this, and they are the largest single employer in America, 30 years after they've invented the complete automation of this thing that they're employing people to do. And the last person who is a manual switcher does not lose their job, as it were: that job doesn't stop existing until I think like 1980.
So it takes 90 years from the invention of full automation to the full adoption of it in a single company that's a monopoly provider. It can do what it wants, basically. And so the question perhaps you might have is why?" — Michael Webb
In today’s episode, host Luisa Rodriguez interviews economist Michael Webb of DeepMind, the British Government, and Stanford about how AI progress is going to affect people's jobs and the labour market.
Whether we’ll we see mass unemployment in the short term
How long it took other technologies like electricity and computers to have economy-wide effects
Whether AI will increase or decrease inequality
Whether AI will lead to explosive economic growth
What we can we learn from history, and reasons to think this time is different
Career advice for a world of LLMs
Why Michael is starting a new org to relieve talent bottlenecks through accelerated learning, and how you can get involved
Michael's take as a musician on AI-generated music
And plenty more
If you'd like to work with Michael on his new org to radically accelerate how quickly people acquire expertise in critical cause areas, he's now hiring! Check out Quantum Leap's website.
Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type ‘80,000 Hours’ into your podcasting app. Or read the transcript.
Producer and editor: Keiran Harris Audio Engineering Lead: Ben Cordell Technical editing: Milo McGuire and Dominic Armstrong Additional content editing: Katy Moore and Luisa Rodriguez Transcriptions: Katy Moore
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