Compliance Stuff

Neuroscientist and AI researcher Vivienne Ming says we are measuring the wrong things, hiring for the wrong traits, and building AI that makes us less capable. The fix, she argues, is not more training—it is a different kind of human. Ming is author of  Robot-Proof: When Machines Have All the Answers, Build Better People.  She doesn’t speak about the future of work the way most executives do. Where they tend to see a straightforward productivity story—AI as a tool that will absorb the tedious tasks and free humans for the interesting ones—she sees a far more unsettling picture. The serial AI entrepreneur argues that the very premise of the debate is wrong, and that the skills companies are currently training for are the ones AI will master first. “We misunderstand what kind of intelligence AI actually is,” she says. “ Agentic AI is a ‘how’ machine. It can learn the rules of coding, legal analysis, or financial modeling and execute them faster and cheaper than any human. If a...

By Michael G. Jacobides, Sir Donald Gordon Professor of Entrepreneurship and Innovation and Professor of Strategy and Entrepreneurship at London Business School; M. Dalbert Ma, PhD Candidate in Strategy and Entrepreneurship at London Business School; Amartya Das, Principal at BCG; and Matt Langione, Managing Director and Partner at BCG. Every major technological shift invites two bad habits: panic and oversimplification. Generative AI has triggered both. On one side are those who insist that white-collar sectors are about to be hollowed out. On the other are those who reduce the whole matter to productivity: faster drafting, cheaper analysis, better search, more efficient workflows. Neither story is entirely wrong. But both are far too narrow. The more consequential question is not whether GenAI makes knowledge work faster. It is whether it changes how knowledge work is organized in the economy: which organizations do which tasks, what remains bundled inside firms, what moves across fi...