What is machine usefulness?
A working definition from MIT Sloan
machine usefulness (noun)
The idea that artificial intelligence should primarily enhance human capabilities rather than replace them.
Current efforts to develop powerful artificial intelligence are mostly based around “machine intelligence” — the idea that machines can think like humans and even outperform them. According to MIT economists and Nobel laureates Daron Acemoglu and Simon Johnson, this framing, combined with a focus on maximizing shareholder wealth, could tip the balance of power toward management, disrupting the workforce and creating greater inequity.
Acemoglu and Johnson argue for a more human-focused version of AI. Shifting the focus from “machine intelligence” to “machine usefulness” would elevate the idea that computers should primarily enhance human capabilities. This would be “a much more fruitful direction for increasing productivity,” Acemoglu and Johnson wrote in a 2023 opinion piece in The New York Times. “By empowering workers and reinforcing human decision making in the production process, it also would strengthen social forces that can stand up to big tech companies.”
Steps toward reframing the relationship between humans and artificial intelligence include asserting individual ownership rights over the data used to build AI systems, pushing back against surveillance capitalism, and establishing a graduated system for corporate taxes, the authors write.
Working Definitions: Artificial Intelligence
MIT Sloan's Working Definitions explore the words and phrases behind emerging management ideas.
AI Executive Academy
In person at MIT Sloan
Register Now
These are the most urgent AI risks, according to 272 experts
Which AI risks could cause the most harm in the next five years? New MIT research shows that businesses should be aware of threats like competitive pressure and dangerous AI capabilities.
5 ways to make agentic AI a competitive advantage
Enterprises looking to make the most of agentic AI will have to rethink how work gets done and how teams are organized, without forgetting the human workers who set their companies apart.
Who will own the AI agent economy?
Here’s what businesses need to know as AI agents move from centralized systems toward a decentralized network of trillions of personal and organizational agents.
5 investments to close the gap between AI wealth and welfare
Transformative technologies like artificial intelligence succeed when societies make parallel social investments to ensure gains are distributed equitably, MIT Sloan researchers find.
Meet the new faculty members joining MIT Sloan in 2026
Debiased machine learning, the currency of invoicing, and training good models with bad data: Meet the new experts bringing their knowledge and skill sets to the MIT Sloan School of Management.
Data liquidity leads to AI success
Three levers — data architecture, data preparation, and data permissions — determine whether data becomes a reusable strategic asset or stays trapped in silos.
Pro-worker AI, explained
Artificial intelligence can make workers more capable and productive, but only if leaders design and deploy it to augment human judgment.
5 things to consider when working with AI
Researchers at the MIT Initiative on the Digital Economy share the latest insights about getting the most from working with AI, such as personality pairing and reorganizing job tasks.
Balance AI innovation and risk with ‘minimum viable governance’
As organizations scale generative AI, traditional governance models prove to be too rigid or too loose. Minimum viable governance calibrates oversight to risk, enabling responsible innovation.
The surprising power of warmth in AI negotiations
In MIT’s international AI Negotiation Competition, “warmer” agents achieved better outcomes in negotiations with other AI agents.