AI Copilots & the Future of Knowledge Work

Authors

Kevin Scott

Bob Swan

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Top Highlights

• I think a similar thing is going to happen now with cognitive work, or knowledge work, which is becoming a bigger and bigger part of what the workforce is spending their time and energy doing.

• We haven’t really had a massive breakthrough in productivity for knowledge workers. The PC was an accelerator and the invention of the internet was an accelerator. I don’t think the smartphone helped much at all in terms of productivity and knowledge work.

• One is you have these big categories of knowledge work where there is no way to do more of the particular type of work because productivity is not getting any better—but you’ve got some kind of deficit in society because you actually could use more of it.

• On the other hand, you have tons of drudgery in a lot of the work that we all do. If you had some kind of productivity mechanism that could come in and do your least favorite part of your job or the most repetitive and most redundant work, I think we all would be delighted to have those sorts of tools in our lives.

• The interesting thing is with some of these big platform shifts that we’ve had in the past, the most valuable things that get done on the platforms are not the things that got deployed in the first year or 2 of the platform change.

• If you think about the smartphone, the place where you spend most of your time on your smartphone is not the SMS app, it’s not the web browser, it’s not the mail client. It’s in the new things that got created on top of the platform in the years following the availability of the platform.

• What are the difficult things that have now become possible that were impossible before? That’s the thing that people ought to be thinking about.

• The thing that I will say is developer productivity is not lines of code produced. It is how you’re measuring a developer’s ability to very quickly deliver things to users and then measure whether or not those users are benefiting from the things that the developer is producing.

• It’s instrumenting that entire feedback loop and making sure that you are able to identify points of friction throughout the entire product lifecycle development.

• The thing that we’re doing inside of the company right now with AI tools, first and foremost, is getting everybody to use GitHub Copilot, which is a really big productivity win.

• The thing that it does, in our observation with developers, more than anything else is it helps keep them in flow state longer than they otherwise would.

• being able to get yourself unblocked in the moment before you’re out of flow state is extraordinarily valuable.

• The other place, too, where we’re using a bunch of AI tools is actually in the deployment of AI. A lot of the testing that we’re doing right now, a lot of the responsible AI work that we do, are all using the AI tools themselves to help do that.

• When folks in these communities have access to very powerful tools, they tend to do remarkable things that create economic opportunities for themselves, for their families, and their communities.

• I think part of the problem in any organization is figuring out how to get yourself to that point of critical mass of adoption where then it just gets easier.

• The unlock that, you have to get to where you can do something on your laptop versus having to go buy millions of dollars’ worth of computing infrastructure to do something really enormous.

• The thing I will say to all of the entrepreneurs in attendance is go find hard stuff to work on.

• Really, really focusing on the fundamentals of product building is important. AI is a model, not a product.