Bonus: If you're an subscriber, you can join David and Richard, alongside's editor in chief, Mat Honan, for an exclusive conversation live on Tuesday, December 9 at 1pm ET about this topic. Sign up to be a part here . Any far-reaching new technology is always uneven in its adoption, but few have been more uneven than generative AI. That makes it hard to assess its likely impact on individual businesses, let alone on productivity across the economy as a whole. At one extreme, AI coding assistants have revolutionized the work of software developers. Mark Zuckerberg recently predicted that half of Meta's code would be written by AI within a year.

The integration of agential artificial intelligence into socioeconomic systems requires us to reexamine the evolutionary processes that describe changes in our economic institutions. This article synthesizes three frameworks: multi-level selection theory, Aoki's view of firms as computational processes, and Ostrom's design principles for robust institutions. We develop a framework where selection operates concurrently across organizational levels, firms implement distributed inference via game-theoretic architectures, and Ostrom-style rules evolve as alignment mechanisms that address AI-related risks. This synthesis yields a multi-level Price equation expressed over nested games, providing quantitative metrics for how selection and governance co-determine economic outcomes. We examine connections to Acemoglu's work on inclusive institutions, analyze how institutional structures shape AI deployment, and demonstrate the framework's explanatory power via case studies. We conclude by proposing a set of design principles that operationalize alignment between humans and AI across institutional layers, enabling scalable, adaptive, and inclusive governance of agential AI systems. We conclude with practical policy recommendations and further research to extend these principles into real-world implementation.

What will likely be the effect of the emergence of ChatGPT and other forms of artificial intelligence (AI) on the skill premium? To address this question, we develop a nested constant elasticity of substitution production function that distinguishes between industrial robots and AI. Industrial robots predominantly substitute for low-skill workers, whereas AI mainly helps to perform the tasks of high-skill workers. We show that AI reduces the skill premium as long as it is more substitutable for high-skill workers than low-skill workers are for high-skill workers.

This article is from Big Technology, a newsletter by Alex Kantrowitz. As soon as artificial intelligence began to read, write, and code, all manner of professions were supposed to automate--fast. And yet, eight months after the release of ChatGPT--and several years since the advent of other A.I. business tools--the fallout's been muted. A.I. is being widely adopted, but the imagined mass firings haven't materialized. The United States is still effectively at full employment, with just 3.5 percent of the workforce unemployed. The usual narrative may say otherwise, but the path toward A.I.–driven mass unemployment isn't simple.

Even as the economic pressures that drove millions of white working-class voters to the right are moderating, the hostility this key segment of the electorate feels toward the Democratic Party has deepened and is less and less amenable to change. "You cannot really understand the working-class rightward shift without discussing what the Democratic Party is doing," Daron Acemoglu, an economist at M.I.T., wrote by email: Many of the trends that negatively impacted workers, especially non-college workers, including rapid automation and trade with China, were advocated and supported by Democratic politicians. Perhaps worse from a political point of view, when these politicians were advocating such policies, they were also viewed as adopting a tone of indifference to the plight of non-college workers. Poll data suggest that Democratic struggles with the white working class are worsening. In "Elections and Demography: Democrats Lose Ground, Need Strong Turnout," an Oct. 22 American Enterprise institute report by Ruy Teixeira, Karlyn Bowman and Nate Moore write: The gap between non-college and college whites continues to grow. For the first time this cycle, the difference in margin between the two has surpassed an astounding 40 points, well above the 33-point gap in 2020's presidential contest.

In this third part of my series on the future of work, I want to deal with the impact of automation, in particular robots and artificial intelligence (AI) on jobs. I have covered this issue of the relationship between human labour and machines before, including robots and AI. But is there anything new that we can find after the COVID slump? The leading American mainstream expert on the impact of automation on future jobs is Daron Acemoglu, Institute Professor at MIT. In testimony to the US Congress, Acemoglu started by reminding Congress that automation was not a recent phenomenon.

AI needs to expand horizons. With the endless promotion of artificial intelligence by analysts, media, and vendors, one can be forgiven for assuming AI is proliferating across and running enterprises far and wide. However, the reality is beyond simply automating narrow applications -- such as credit scoring, upselling recommendations, chatbots, or managing machine performance -- AI still has a limited range, and barely begun to achieve its full potential as a true augment to human intelligence and talent. That's the takeaway from recent panel discussion hosted by New York University Center for the Future of Management and LMU institute for Strategy, Technology and Organization, joined by Daron Acemoglu, professor at MIT; Jacques Bughin, professor at the Solvay School of Economics and Management; and Raffaella Sadun, professor at Harvard Business School. "I am not that excited about the narrow applications of AI," says Sadun. "I'm not excited about the dumb approach of automating one process and them claiming that you are on a different tier of technology."

His 2014 book, coauthored with Andrew McAfee, is called The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. But he says the thinking of AI researchers has been too limited. "I talk to many researchers, and they say: 'Our job is to make a machine that is like a human.' It's a clear vision," he says. But, he adds, "it's also kind of a lazy, low bar.'"

Artificial intelligence and machine learning requires huge amounts of processing capacity and data storage, making the cloud the preferred option. That raises the specter of a few cloud giants dominating AI applications and platforms. Could the tech giants take control of the AI narrative and reduce choices for enterprises? Not necessarily, but with some caveats, AI experts emphasize. But the large cloud providers are definitely in a position to control the AI narrative from several perspectives.

Artificial intelligence (AI) is often touted as the most exciting technology of our age, promising to transform our economies, lives, and capabilities. Some even see AI as making steady progress towards the development of'intelligence machines' that will soon surpass human skills in most areas. AI has indeed made rapid advances over the last decade or so, especially owing to the application of modern statistical and machine learning techniques to huge unstructured data sets. It has already influenced almost all industries: AI algorithms are now used by all online platforms and in industries that range from manufacturing to health, finance, wholesale, and retail. Government agencies have also started relying on AI, particularly in the criminal justice system and in customs and immigration control.