Understanding Automation's Role In Economic Resilience

A new paper from David Autor, in collaboration with Neil Thompson, makes an important contribution to explaining how AI is likely to impact labor markets. Based on a rigorous model, confirmed with an analysis of 40 years of data, they provide a nuanced perspective on how automation impacts job employment and wages. Essentially, this depends on the extent to which easy tasks are removed from a role and expert ones are added, and how specialized a role becomes as a result. When jobs gain inexpert tasks but lose expertise, wages decline, but employment may increase. Think of how taxi driving became less specialized, and well-paid, but more common, due to Uber. In contrast, when technology automates the easy tasks inside a job, the remaining work becomes more specialized. Employment falls because fewer people now qualify, but the scarcity of expertise drives wages up. This is what seems to be happening with proofreading, which is now less about spell-checking and more about helping people to write, leading to lower job numbers but higher average wages. Their model helps us to understand the impacts of AI on labor markets. For instance, why AI tools can raise wages for senior software engineers, but decrease employment, while simultaneously reducing earnings, and increasing employment, for more entry level software engineering roles.

This week I joined Responsible Innovation Labs and Jake Sullivan for a timely conversation about AI-driven workforce transformation and what it means to build enduring companies in this new era. AI-driven workforce transformation is a challenge we need to rise to. AI will dramatically change the nature of jobs. It will likely help people work more efficiently and create higher-value, more enjoyable jobs with less admin work. It will also require us to proactively manage productivity returns, making sure that AI-driven gains are distributed to all stakeholders, not just shareholders, be it through reinvestment in employee retraining programs and education or additional compensation for these higher-value jobs. But we believe that proactive management of this workforce transformation will result in benefits for companies, countries, and society as a whole. Responsible AI development and workforce transformation is strategic: 🔵 Companies will scale more predictably and avoid costly missteps and reactive regulation, and create enduring value by enabling, rather than replacing, human talent. With AI augmenting human capability, companies will find themselves with the ability to simply do more and do it better, be it entering new markets or developing new products. 🔵 Countries will benefit from newly enabled national resilience as an AI-enabled workforce will re-onshore productivity and reduce the reliance on offshore labor. Companies like Crescendo are demonstrating this as they automate repetitive call center tasks. By reducing volume-based labor needs, companies will be able to create customer service delivery jobs domestically, while maintaining similar labor costs. These new business models will enable us to rearchitect supply chains for national resilience and power a new era of domestic job creation, without raising the cost burden for enterprises. 🔵 We believe society as a whole will stand to benefit from productivity gains, as we reimagine the new problems individuals, companies, and entire economies can solve with a workforce less bogged down by administrative and execution-heavy tasks and more empowered by creative, strategic job responsibilities. Appreciate the RIL team and fellow leaders bringing sharp insights to the table. I’m looking forward to keeping this conversation - and the work - going. cc General Catalyst Gaurab Bansal Carin Watson Cecilia Young

As AI systems become more efficient, each individual with AI can accomplish far more work than before. That means fewer people are needed to deliver the same output. Over time, even as the economy grows, the headcount required per unit of GDP shrinks. Couple that with natural attrition—and you don’t need layoffs to see the effect. The workforce simply doesn’t refill at the same rate. The result? 📉 Slower job growth (or even negative growth) compared to equivalent historical periods with similar GDP expansion. 📈 Rising productivity metrics, but concentrated gains. ⚖️ A widening gap between short-term efficiency wins for individuals and long-term systemic shifts in labor demand. This isn’t doom and gloom—it’s a logical implication of technology scaling. The real challenge is not if this happens, but how society, businesses, and policymakers adapt: Do we redesign work to emphasize areas where humans add unique value? Do we rethink education and reskilling cycles? Do we prepare for an economy where growth doesn’t automatically mean more jobs? AI will rewrite the relationship between productivity, growth, and employment. The sooner we start grappling with this, the better positioned we’ll be when the curve bends. Below is a graphic illustration of the historically equivalent effect as internet and computers led to much bigger GDP gains than job growth.