Orbit - An Hg software leadership podcast
Jul 9, 2026 · 33 min · 14 segments
Every December, Rob Toews publishes ten AI predictions in Forbes, then comes back a year later and grades himself in public. That habit of putting a stake in the ground, and owning the misses, makes…
Rob ToewsGuest
John WolkinHost
When was the first list of your 10 predictions published? Maybe explain kind of to the audience what the predictions even are.
And one thing that I do in the column is every year in December, at the end of the year, I publish a set of 10 predictions for the coming year about AI across all sorts of different fields, from the technology to the business side of things, the politics, popular perception, et cetera.
And then as you noted, in addition to publishing that article every December, the following December, I always publish a retrospective that basically grades my 10 predictions and says like, which ones did I get right? Which ones did I get wrong? I try to be like somewhat hard grader.
that's great, by the way, to have the intellectual honesty to put out the predictions publicly and then actually measure yourself at the end of the year.
It's just such a great process, and I'm sure your readers love that transparency.
I think it's so easy to publish predictions and then kind of forget about them and never follow up on them.
And honestly, even just for me personally, it's very intellectually interesting and satisfying to go back and see, like, okay, this is how I was thinking about the world.
Like, what did I get right? What did I get wrong? Obviously, no one can predict the future with certainty, but I think just the exercise of making predictions and then going back and reflecting on them is a great learning experience.
Is there a theme that you found easier to predict? versus harder, for example, the policy side or the pace of change? I feel like you've been pretty good at predicting actually that the rapid pace of change the past few years.
Yeah, I think probably policy and public kind of public perception of AI is easier to predict because it follows a bit more predictable of a cadence.
I think oftentimes the capabilities and the underlying technology just has these jumps that like people wouldn't have expected or seen coming ahead of time.
And so as you thought about the predictions for this year, what were some of the things on your mind when you were writing them, I guess, in December 2025? Yeah.
When was the first list of your 10 predictions published? Maybe explain kind of to the audience what the predictions even are.
And one thing that I do in the column is every year in December, at the end of the year, I publish a set of 10 predictions for the coming year about AI across all sorts of different fields, from the technology to the business side of things, the politics, popular perception, et cetera.
And then as you noted, in addition to publishing that article every December, the following December, I always publish a retrospective that basically grades my 10 predictions and says like, which ones did I get right? Which ones did I get wrong? I try to be like somewhat hard grader.
that's great, by the way, to have the intellectual honesty to put out the predictions publicly and then actually measure yourself at the end of the year.
It's just such a great process, and I'm sure your readers love that transparency.
I think it's so easy to publish predictions and then kind of forget about them and never follow up on them.
And honestly, even just for me personally, it's very intellectually interesting and satisfying to go back and see, like, okay, this is how I was thinking about the world.
Like, what did I get right? What did I get wrong? Obviously, no one can predict the future with certainty, but I think just the exercise of making predictions and then going back and reflecting on them is a great learning experience.
Is there a theme that you found easier to predict? versus harder, for example, the policy side or the pace of change? I feel like you've been pretty good at predicting actually that the rapid pace of change the past few years.
Yeah, I think probably policy and public kind of public perception of AI is easier to predict because it follows a bit more predictable of a cadence.
I think oftentimes the capabilities and the underlying technology just has these jumps that like people wouldn't have expected or seen coming ahead of time.
And so as you thought about the predictions for this year, what were some of the things on your mind when you were writing them, I guess, in December 2025? Yeah.
The rest of this transcript — segmented and speaker-labeled, so you land on the exact moment something was said
Search every transcript — by keyword, by phrase, or by meaning, across every show Radar indexes
Trends — what is surging across podcasts, measured against its own baseline
Alerts — when a name you follow appears in a newly indexed episode
No account is needed to search Radar.