It never got to the point where the whispers about an impending AI winter got that commonplace, loud, or confident. However, as widespread commercialization of some of the most prominent AI applications—think autonomous vehicles—slipped well past earlier projections, doubts were inevitable. At the same time, the level of commercial investment relative to past AI winters made betting against it wholesale seem like a poor bet.
It’s the technology in the moment’s spotlight. On May 23, it was foundational to products announced at Red Hat Summit in Boston such as Ansible Lightspeed. However, the surprise today would be were AI not to have a prominent position at a technology vendor’s show.
But, as a way to get a perspective that’s less of a pure technologist take, consider the prior week’s MIT Sloan CIO Symposium Driving Resilience in a Turbulent World held in Cambridge MA. This event tends to take a higher-level view of the world, albeit one flavored by technology. Panels this year about how the CIO has increasingly evolved to a chief regulation officer, chief resilience officer, and chief transformation officer are typical of the sort of lenses this event uses to examine the current important trends for IT decision makers. As most large organizations become technology companies—and software companies in particular—it’s up to the CIO to partner with the rest of the C-suite to help chart strategy in the face of changing technological forces.And that means considering tech in the context of other forces—and concerns. For example, supply chain optimization is a broad company business challenge even if it needs technology as part of the puzzle.
AI rears its head
But even if AI was a relatively modest part of the agenda on paper, mostly in the afternoon, everyone was talking about it to a greater or lesser degree.
For example, Tom Peck, Executive Vice President & Chief Information Officer and Digital Officer, Sysco said that they were still “having trouble finding a SKU of AI in the store. We’re trying to figure out how to pluck AI and apply it to our business. Bullish on it but still trying to figure out build vs. buy.”
If I were to summarize the overall attitude towards AI at the event, it was something like: really interesting, really early, and we’re mostly just starting to figure out the best ways to get business value from it.
A discussion with Irving Wladawsky-Berger
I’ve known Irving Wladawsky-Berger since the early 2000s when he was running IBM’s Linux Initiative; he’s now a Research Affiliate at MIT’s Sloan School of Management, a Fellow of MIT’s Initiative on the Digital Economy and of MIT Connection Science, and Adjunct Professor at the Imperial College Business School. He’s written a fair bit on AI; I encourage you to check out his long-running blog.
There were lots of things on the list to talk about. But we jumped straight to AI. It was that sort of day. To Irving, “There’s no question in my mind that what’s happening with AI now is the most exciting/transformative tech since the internet. But it takes a lot of additional investment, applications, and lots and lots of [other] stuff.” (Irving also led IBM’s internet strategy prior to Linux.)
At the same time, Irving warns that major effects will probably not be seen overnight. “It’s very important to realize that many things will take years of development if not decades. I’m really excited about the generative AI opportunity but [the technology is] only about 3 years old,” he told me.
We also discussed The Economist’s How to Worry Wisely about AI issue, especially an excellent essay by Ludwig Siegele titled “How AI could change computing, culture and history.” One particularly thought provoking statement from that essay is “For a sense of what may be on the way, consider three possible analogues, or precursors: the browser, the printing press and practice of psychoanalysis. One changed computers and the economy, one changed how people gained access and related to knowledge, and one changed how people understood themselves.”
Psychoanalysis? Freud? It’s easy to see the role the browser and the printing press have had as world-changing inventions. He goes on to write: “Freud takes as his starting point the idea that uncanniness stems from ‘doubts [as to] whether an apparently animate being is really alive; or conversely, whether a lifeless object might not be in fact animate’. They are the sort of doubts that those thinking about llms [Large Language Models] are hard put to avoid.”
This in turn led to more thought-provoking conversation about linguistic processing, how babies learn, and emergent behaviors (“a bad thing and a bug that has nothing to do with intelligence”). Irving concluded by saying “We shouldn’t stop research on this stuff because it’s the only way to make it better. It’s super complex engineering but it’s engineering. It’s wonderful. I think it will happen but stay tuned.”
The economics
“The Impact of AI on Jobs and the Economy” closed out the day with a keynote by David Autor, Professor of Economics, MIT.
If you want to dive into an academic paper on the topic, here’s the paper by Autor and co-authors Levy and Murnane.
However, Autor’s basic argument is as follows. Expertise is what makes labor valuable in a market economy. That expertise must have market value and be scarce but non-expert work, in general, pays poorly.
With that context, Autor classifies three eras of demand for expertise. The industrial revolution first displaced artisanal expertise with mass production. But as the industry advanced it demanded mass expertise. Then the computer revolution started, really going back to the Jacquard loom. The computer is a symbolic processor and it carries out tasks efficiently—but only those that can be codified.
Which brings us to the AI revolution. Artificially intelligent computers can do things we can’t codify. And they know more than they can tell us. The question Autor posits is ”Will AI complement or commodify expertise? The promise is enabling less expert workers to do more expert tasks”—though Autor has also argued that policy plays an important role. As he told NPR in early May: “[We need] the right policies to prepare and assist Americans to succeed in this new AI economy, we could make a wider array of workers much better at a whole range of jobs, lowering barriers to entry and creating new opportunities.”
