Drawing a line from colonialism to artificial intelligence

+  THREE INSIGHTS FOR THE WEEK

1. Simon Johnson shared a Nobel prize this year for explaining how democratic institutions fuel economic growth, work he first published with Daron Acemoglu and James Robinson in 2001.  

These days, the MIT Sloan professor’s attention is fixed on how economic policy and technology can ease or exacerbate inequality in the U.S. and worldwide. Last week in Stockholm, Johnson, PhD ’89, connected those streams of work in an address at the Royal Swedish Academy of Sciences. 

With the rollout of artificial intelligence, society is at an inflection point — one that’s not unlike the advent of the Industrial Revolution and the maritime colonialism that preceded it, Johnson said. 

“Today … we have processes that dwarf anything that we’ve seen in history,” he said. “But there is an important caveat that looms large over us, particularly in this age of emerging AI: New technology does not necessarily benefit everyone.”

Johnson cautioned against a potential scenario in which technology concentrates power among elites while displacing midlevel and unskilled workers. In that case, AI could exacerbate economic inequalities, even in wealthy countries.

Instead, “we should harness this incredible energy behind AI and the capital that’s being deployed and the investments that are being made to productive purposes — to purposes that help people,” Johnson said.

2. Large language models are a mixed blessing, according to MIT Sloan professor Rama Ramakrishnan. They have immense potential to increase the efficiency of business processes, but they are subject to numerous weaknesses as well. 

Writing in MIT Sloan Management Review, Ramakrishnan makes the case that organizations should take an objective, structured approach to evaluating use cases if they wish to realize a return on their generative AI investments while mitigating risks.

“For any given task, carefully consider the costs included in the generative AI cost equation,” Ramakrishnan writes. “Remember to include the cost of checking and fixing LLM outputs, and consider the consequences of mistakes that may still slip through.” 

Organizations should consider moving forward with a pilot if the cost of the generative AI option appears to be significantly lower than the cost of business as usual. 

Even if a use case fails the generative AI cost equation, Ramakrishnan advises that firms periodically revisit the equation. “Many of the GenAI cost elements are steadily declining, and a task that wasn’t a good candidate last year may become attractive this year,” he writes.

3. Oxford University Press declared “brain rot” to be its word of the year for 2024, and Merriam-Webster added “touch grass” as one of its new phrases. Both choices reflect a year in which the digital media ecosystem felt, to many people, both extreme and inescapable. 

Perhaps in response to the digital firehose, some of the most popular of MIT Sloan’s Working Definitions this year featured a dialed-back vibe. 

During that time, our biweekly Instagram series of words and phrases that define management ideas included low-ego leadership, which describes a quiet style of management; greenhushing, which refers to an intentionally under-the-radar approach to climate advocacy; and financial nihilism, which tracks a trend among Generation Z to opt out of the hype and speculation of investing in the stock market. 

Rounding out this year’s top-five list are glass cliff, a situation in which women and minorities are preferentially appointed to leadership positions in times of crisis, and exaptation strategy, the practice of recombining undervalued assets to create and capture value in transformative ways. 

+  IDEAS THAT MATTER

How AI-empowered ‘citizen developers’ help drive digital transformation

A new book explains how organizations are tapping AI-savvy front-line employees to create applications, mobile apps, automated workflows, and data analyses.

A shortage of software talent and a rise in user-friendly automation technologies have companies cultivating “citizen developers” — nontechnical employees who use domain expertise and creativity to develop apps, configure automations, and build data analyses that can drive value across the enterprise.

Using citizen developers enhances companies’ innovative capacity, minimizes risk, and liberates IT departments to pursue more strategic initiatives, write Babson University professor Thomas H. Davenport, who is a digital fellow at the MIT Initiative on the Digital Economy, and Ian Barkin, MBA ’06, co-founder of 2B Ventures, in “All Hands on Tech: The AI-Powered Citizen Revolution.” 

The book, which was written with AI researcher Chase Davenport, provides examples of how companies have aligned the work of citizen developers with wider organizational goals and offers a framework for integrating citizen development with broader strategy.