We’re Waiting for the Peter Drucker of Machine Intelligence
AI will change everything, yeah yeah. We’ve heard it, and it’s so true we no longer hear it.
Here’s the next big one: how leaders lead companies.
The old way was “leader as genius,” where a company’s job is to find a solution to a problem, and a leader decides on those solutions (mostly other people doing work), and introduces a process to solve the problem reliably (e.g., a workflow to make a car).
That way evolved to empower people “at the edge” to propose their own solutions (and sometimes decide on them), à la how Facebook is run vs. how Ford was run.
Peter Drucker was this way’s prophet, bringing humanity to the center of the organization.
Software accelerated this old way because, instead of people repeating these workflows, machines could automate the workflow’s dullardwork.
Software was merely the translation of people instructions into machine instructions. (Drucker even called computers total morons, because they could not make decisions, only implement them.) Software could spread information, creating an “information-based organization” so people can make better decisions — but, in Drucker’s view the software never decides.
Machine Intelligence changes that. It enables software that remembers not only past data but also the results of past decisions — and distills them into a model. This is the secret concept for future AI management gurus to understand. Models go beyond automating workflows, they automate decisions.
In our fund, where we have been investing exclusively in the future of work from our first check, we see a new kind of company leader emerging. She does not see her role in just the old way — deciding and translating decisions into software and people workflows, or even as nurturing her people to do that. She sees her role in a new way — creating the conditions (in talent and data) to build the right models that can make better decisions than any of us, and — critically — understanding when not to trust models and when to update them. This creates an organization that can make decisions more rapidly (and improve more rapidly) than any pointy-haired boss could through tedious training or tenuous anecdotes.
The previous generation understood how to lead people and how to harness IT. The new generation will understand how to manage models, which are the flux capacitor of making software go beyond workflows to decisions.
If a top 1% CEO today understands how software applications get built and how that changes the way she manages, a top 1% CEO in the near future will understand how models get built and how that changes the way she builds her organization.
A whole way of thinking about leadership, and about the economics of companies, with as many maxims and guidelines as the old way, is to come.
We’re waiting for the Peter Drucker of Machine Intelligence.
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Written with Roy Bahat. For more on how Machine Intelligence is changing management, read (1) our collaborators at the University of Toronto, Ajay Agrawal, Joshua Gans, and Avi Goldfarb on the core economics of AI; (2) MIT’s Andrew McAfee and Erik Brynjolfsson’s recent book Machine, Platform, Crowd: Harnessing Our Digital Future; and (3) Stanford’s Susan Athey on machine learning and economics. Or watch the recent NBER conference on the Economics of AI.
One day, someone might say that Peter Drucker was the pre-Machine Intelligence version of these gifted thinkers.