Economic Shifts From AI And Automation

Ironic that AI answered this question for me (the question: what will we do when millions of jobs are replaced by AI?) AI Overview When millions of jobs are replaced by AI, several adjustments will be necessary. Society will likely need to transition to a system where individuals have access to resources and opportunities regardless of their employment status, potentially through Universal Basic Income or similar programs. Retraining and education will be crucial to equip people with the skills needed for new, emerging roles in the age of AI. Additionally, a shift in how we define "work" and "purpose" might be necessary as we explore alternative ways to contribute to society. Here's a more detailed look: New Economic Models: The traditional employment-based economy may face significant shifts. Exploring concepts like UBI, which provides a guaranteed income to everyone, could be necessary to ensure everyone can meet basic needs. This could be coupled with taxes on companies that displace workers with AI, funding the UBI system. Retraining and Education: As AI takes over many routine tasks, people will need to acquire new skills to thrive in a changing job market. This could involve focusing on areas where AI is less effective, such as creativity, emotional intelligence, and complex problem-solving. Lifelong learning and vocational training programs would become increasingly important. Redefining Work and Purpose: The concept of "work" may need to be redefined. Perhaps individuals will contribute to society through volunteer work, creative pursuits, or other activities that don't necessarily generate income but contribute to the well-being of the community. This could also involve exploring new ways to measure success and value individual contributions beyond traditional employment metrics. Focusing on Human Strengths: AI excels at routine tasks and data analysis, but it struggles with creativity, empathy, and complex decision-making. Emphasizing these uniquely human skills could lead to new career paths where AI and humans collaborate to achieve goals. Managing Expectations: It's important to manage both the public's and employees' expectations about AI. Clear communication about how AI will be used, along with rewarding early adopters and providing opportunities for innovation, can help foster collaboration between humans and machines. Adaptability and Flexibility: The job market will likely become more dynamic and fluid. Individuals will need to be adaptable and willing to take on new roles and responsibilities. This could require a more flexible and open labor market, allowing for easier transitions between sectors and businesses.

𝗧𝗟;𝗗𝗥: History shows AI's impact on jobs will follow a familiar pattern of disruption and growth, but on a compressed 10-15 year timeline. Understanding past technological transitions helps us prepare for both the challenges and opportunities ahead. This is part 3 on the #EconomicsofAI. In one of prior posts (https://bit.ly/40tVLRI), I wrote about the history of economic value generation in tech transformations. But what does AI do for jobs? Read on: Looking at 250 years of technological disruption reveals a consistent pattern that will likely repeat with AI, just faster. My analysis of employment data across four major technological waves shows something fascinating: while specific jobs decline initially, total employment ultimately grows significantly – often 2-3x higher than pre-disruption levels. Here's what history tells us about AI's likely impact on jobs: 𝗧𝗵𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝘀 𝘄𝗶𝘁𝗵 𝗘𝗮𝗰𝗵 𝗪𝗮𝘃𝗲: • 𝗙𝗶𝗿𝘀𝘁 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 (𝟭𝟳𝟲𝟬-𝟭𝟴𝟰𝟬): 40% initial job decline, 80 years to full transformation • 𝗦𝗲𝗰𝗼𝗻𝗱 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 (𝟭𝟴𝟳𝟬-𝟭𝟵𝟭𝟰): 30% decline, 44 years to transform • 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 (𝟭𝟵𝟱𝟬-𝟭𝟵𝟴𝟬): 25% decline, 30 years • Digital Revolution (1980-2000): 15% decline, 20 years • 𝗔𝗜 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 (𝟮𝟬𝟮𝟰-𝟮𝟬𝟯𝟱): Projected 20% initial disruption, 10-15 years to transform 𝗧𝗵𝗲 𝗔𝗜 𝗧𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻 𝘄𝗶𝗹𝗹 𝗹𝗶𝗸𝗲𝗹𝘆 𝗳𝗼𝗹𝗹𝗼𝘄 𝘁𝗵𝗿𝗲𝗲 𝗽𝗵𝗮𝘀𝗲𝘀: • 𝟮𝟬𝟮𝟰-𝟮𝟬𝟮𝟲: 𝗜𝗻𝗶𝘁𝗶𝗮𝗹 𝗗𝗶𝘀𝗿𝘂𝗽𝘁𝗶𝗼𝗻 Expect focused impact on knowledge workers, particularly in areas like content creation, analysis, & routine cognitive tasks. Unlike previous waves that started with manual labor, AI begins with cognitive tasks. • 𝟮𝟬𝟮𝟲-𝟮𝟬𝟯𝟬: 𝗥𝗮𝗽𝗶𝗱 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 New job categories emerge rapidly as AI enables new business models. Just as the internet created roles like SEO specialists & social media managers, AI will spawn entirely new professional categories. • 𝟮𝟬𝟯𝟬-𝟮𝟬𝟯𝟱: 𝗚𝗿𝗼𝘄𝘁𝗵 𝗮𝗻𝗱 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 Employment should exceed pre-AI levels as the economy reorganizes around AI capabilities, similar to how manufacturing employment grew 4x during the Second Industrial Revolution. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗳𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝗽𝗿𝗲𝘃𝗶𝗼𝘂𝘀 𝘄𝗮𝘃𝗲𝘀: • Digital infrastructure already exists • Global talent pool can adapt more quickly • Market pressures demand faster adoption This will only happen if we treat AI as Augmented Intelligence! 𝗔𝗰𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗟𝗲𝗮𝗱𝗲𝗿𝘀: The data shows that organizations that invest in workforce transformation during disruption emerge strongest. Focus on: • Identifying which roles will transform vs. disappear • Building internal training using resources from Anthropic Amazon Web Services (AWS) etc. • Creating new job categories that combine human+AI capabilities • Planning for the growth phase

New research from MIT reveals an interesting automation paradox - AI/automation can simultaneously replace experts in one field while creating more expert jobs in another. Let's take a look at two examples from the research - bookkeepers vs. inventory clerks. Both got heavily automated between 1980-2018 but with different outcomes. Bookkeeper employment fell 33% while wages rose 40%. Employment doubled for Inventory clerks, but wages fell 13%. This happened because automation removed the routine parts of bookkeeping (data entry), leaving behind the expert work (analysis, problem-solving). However, for inventory clerks, automation removed the expert parts (price calculations), leaving mostly generic tasks anyone could do. The researchers call this "expertise bifurcation" and it explains why predictions about AI displacement can be so difficult to predict. When looking at the average expertise level of more than 300 occupations over nearly 40 years, they found that when simpler tasks disappeared, jobs became more specialized, and often better paid, even as employment declined. However, when automation removed the more expert tasks, wages tended to fall as more people moved into the role. “Taxi drivers, for example, once relied on deep knowledge of local streets, which was a real differentiator. But with the arrival of GPS, that expertise was automated. The result is a more commoditized taxi service: lower wages, but many more drivers.” The researchers point out that this shift can create opportunities for new professions to open up "because automation removes the hardest parts that used to be out of reach." One of the key takeaways from this research is that it's not about whether your job can be automated - it's about whether AI will eliminate your expert tasks or your supporting tasks. If AI handles your routine work while you focus on judgment, creativity, and complex problem-solving? Your value just went up. If AI can do what makes you uniquely valuable? Different story. The question isn't "Will AI replace me?," but "Will AI make my expertise more scarce, or more common?" So - what do you think this means for sourcers and recruiters? Check out the article and link to the full research here: https://lnkd.in/eHW7zfSp #AI #FutureOfWork #Automation

The latest study from the Council of Economic Advisers, The White House states that ~10% of jobs are vulnerable to AI disruption. That may seem alarming, but let’s take a step back. In 2018, 60% of the jobs Americans held didn't even exist in 1940—created by technologies that emerged over the years (David Autor). Here’s the real concern: Many AI-vulnerable jobs haven’t evolved to match their increasing complexity. Workers in these roles are more exposed to disruption because they haven’t been given the chance to upskill. But this isn't new. Economic evolution is the hallmark of a dynamic economy. Just like we’ve adapted to past technologies, workers and industries will adapt to AI. The key lies in how we approach it. Why businesses should care: Organizations that proactively identify and support employees vulnerable to AI disruption aren’t just doing good—they’re making smart financial decisions. 💡 Investing in upskilling and mobility for these workers could unlock millions in retention and productivity. Mass layoffs due to AI aren’t likely. The real shift? Slower hiring and reduced demand for certain roles. We’re already seeing fewer job postings for writers, coders, and even artists. So, what activities are at risk? Roles involved in processing information, analyzing data, scheduling, and administrative tasks are prime targets. Industries to watch? Architecture, engineering, legal, computer science, and mathematics. Surprising jobs at risk of AI disruption: Airline Pilots, Copilots, and Flight Engineers Nuclear Power Reactor Operators Private Detectives and Investigators Commercial and Industrial Designers These highly specialized roles, which traditionally require significant human judgment, are surprisingly vulnerable to AI-driven changes. Business leaders, what barriers are preventing you from launching upskilling initiatives to future-proof your workforce? The future of work is evolving, but we can shape how it unfolds. #FutureOfWork #AIandJobs #Upskilling #WorkforceTransformation #AI

AI needs to do the equivalent of about 17M jobs to keep the US at historical productivity levels 💪   Even though "AI taking jobs" grabs headlines, the actual problem facing the US economy is low productivity and labor/skill shortages.   The US needs to get more productive. Becoming more productive will not lead to mass unemployment because there is a serious future worker gap to fill.   In manufacturing alone Deloitte has estimated the US will be short ~2M manufacturing jobs by 2033; and even right now ~20% of US plants cite supply of labor/skills as a key constraint to production.   𝐈𝐟 𝐰𝐞 𝐥𝐨𝐨𝐤 𝐚𝐭 𝐭𝐡𝐞 𝐛𝐫𝐨𝐚𝐝𝐞𝐫 𝐔𝐒 𝐞𝐜𝐨𝐧𝐨𝐦𝐲, 𝐭𝐡𝐞 𝐭𝐨𝐭𝐚𝐥 𝐠𝐚𝐩 𝐛𝐲 2040 𝐦𝐢𝐠𝐡𝐭 𝐛𝐞 𝐚𝐬 𝐦𝐮𝐜𝐡 𝐚𝐬 17𝐌 𝐰𝐨𝐫𝐤𝐞𝐫𝐬 𝐚𝐬 𝐩𝐞𝐫 𝐫𝐞𝐜𝐞𝐧𝐭 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐛𝐲 𝐊𝐚𝐫𝐞𝐧 𝐇𝐚𝐫𝐫𝐢𝐬. >>> Her reasoning: 👉 US GDP/capita averaged 2% since 1960. 👉 US productivity growth has ranged between 1-3% where 1% is terrible and 3% is amazing. The US has been oscillating between terrible and good since the mid-1980s 👉 Maintaining a 2% p.a. GDP/capita growth rate will require US labor productivity to improve by about 50 bps structurally; not implausible but without modern precedent 👉 If you estimate the equivalent # of workers that represents that adds up to about 17M workers*   𝐓𝐡𝐢𝐬 17𝐌 𝐠𝐚𝐩 (𝐢𝐧 2040) 𝐢𝐬 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐥𝐲 𝐭𝐡𝐞 “𝐥𝐚𝐛𝐨𝐫 𝐠𝐚𝐩” 𝐭𝐡𝐚𝐭 𝐰𝐨𝐮𝐥𝐝 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐛𝐞 𝐟𝐢𝐥𝐥𝐞𝐝 𝐢𝐧 𝐯𝐢𝐚 𝐚 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐚𝐥 𝐚𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐔𝐒 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐠𝐫𝐨𝐰𝐭𝐡.   And that is just to stay on par with a historical 2% GDP / capita growth, i.e. not decline in your standard of living. If we want to get to "amazing" territory you'd need to structurally add another 50-100 bps.   Every one of us would be well served with the equivalent of that 1-day workshop that Ethan Mollick ran at Procter & Gamble that turned everyone into the equivalent of a 2x worker!   --- Sources   Ethan Mollick's recent research at P&G on how AI boosts performance at real business problems: https://lnkd.in/gWCkQ2KW   Karen Harris' original 2030 automation outlook: https://lnkd.in/g5ZPF7eD Deloitte & SCMR research on manufacturing job/skill shortage: https://lnkd.in/ggSAPhjU ; https://lnkd.in/gNK6Nh7q   *Note: The delta in 20-70 year old population between now and 2040, at current engagement rate and 1.5% productivity