Sep 30, 2026 · 35 min · 10 segments
We are surrounded by new AI-powered devices, many of which seem to have been designed without much thought given to their impact on society. This episode of Utilizing AI features host Stephen…
Olivier BlanchardGuest
Stephen FoskettHost
Guy CurrierGuest
I think the issue here is that AI obviously is a tremendous toolkit that allows us to do a lot of very interesting things, solve a lot of problems, improve systems that need improving.
But the flip side of that coin is that it's also kind of dangerous and it can be used for nefarious reasons.
It can be used improperly and it can ultimately cause more problems than it solves.
And so we find ourselves at this weird intersection with this new technology that's still kind of messy, trying to find its way, trying to find its purpose, trying to justify the tremendous historic sort of unprecedented investments that we're seeing into AI and sort of like form, create some kind of like return on investment for the companies that are backing this push for AI.
But what gets lost, I think, in a lot of this is beyond the investment to ROI pipeline and all of the things that AI could do is what should we use AI for? And also, what shouldn't we use AI for? And one of the ways that it manifests itself for us, the users of AI, dare I say consumers, but also business users, but as human beings who interact with technology on a daily basis, is how it impacts our privacy, how it might impact our freedoms, how it impacts our quality of life.
And I feel like the math on this is still an open question, right? But a lot of the features that I'm seeing sort of popping up every few months in devices specifically.
So smartphones, smartwatches, smart glasses or agenda glasses, PCs tend to be things that nobody asked for.
right there there's a small subsection of experience experiences and features that um that we've been asking for for a long time right voice recognition uh you know wake up on commands the facial recognition to unlock your device things like that that sort of like reduce friction are really great um but we're also seeing sort of a an increase in the number of features that nobody asked for Like devices are always listening, devices that are always recording, devices that track us and follow our faces around a room and interpret what we do.
We're starting to see cameras inside of dishwashers, not because of efficiency reasons, but presumably because they want to track when and how you load your dishwasher and what goes into the dishwasher and what doesn't, which in turn is data that can be sold as somebody who might be trying to understand your behaviors, your habits, the timing of what you do, what you fed your family that week, et cetera.
And so I think that tension between what we would like AI to do and the things that we would like to opt into, as opposed to what we don't want AI to do and the things that we may want to opt out of and may not be able to, that tension is at the center of pretty much every new discussion that we have about emerging features in AI.
Well, I think that's a terrific perspective, Olivier, and I think there's a question in your, let's say, soliloquy about the situation we're in, the question that you are asking without asking.
which is how the heck do these things come into existence in the first place? Why are we getting the features we didn't ask for, as you put it? And I think that's actually a really critical question when it comes to AI, because you know where they come from? Well, they come from human beings, but they also come from human beings using AI.
Give me 10 ideas for new product development or the dishwasher product manager or what have you.
We are, as knowledge workers at least, I'm hard-pressed to find any corner of anything that we do that we can't at least use a quick AI chat for.
And I'm mindful while I'm doing this, and I'm mindful while I'm speaking on podcasts or whatever to remind myself, to remind everybody, that AI is simulative.
It is trained to spit out something that looks original, creative, and new, and yet it has been trained based on original and synthetic inputs that are based in turn on the current human corpus.
I think the issue here is that AI obviously is a tremendous toolkit that allows us to do a lot of very interesting things, solve a lot of problems, improve systems that need improving.
But the flip side of that coin is that it's also kind of dangerous and it can be used for nefarious reasons.
It can be used improperly and it can ultimately cause more problems than it solves.
And so we find ourselves at this weird intersection with this new technology that's still kind of messy, trying to find its way, trying to find its purpose, trying to justify the tremendous historic sort of unprecedented investments that we're seeing into AI and sort of like form, create some kind of like return on investment for the companies that are backing this push for AI.
But what gets lost, I think, in a lot of this is beyond the investment to ROI pipeline and all of the things that AI could do is what should we use AI for? And also, what shouldn't we use AI for? And one of the ways that it manifests itself for us, the users of AI, dare I say consumers, but also business users, but as human beings who interact with technology on a daily basis, is how it impacts our privacy, how it might impact our freedoms, how it impacts our quality of life.
And I feel like the math on this is still an open question, right? But a lot of the features that I'm seeing sort of popping up every few months in devices specifically.
So smartphones, smartwatches, smart glasses or agenda glasses, PCs tend to be things that nobody asked for.
right there there's a small subsection of experience experiences and features that um that we've been asking for for a long time right voice recognition uh you know wake up on commands the facial recognition to unlock your device things like that that sort of like reduce friction are really great um but we're also seeing sort of a an increase in the number of features that nobody asked for Like devices are always listening, devices that are always recording, devices that track us and follow our faces around a room and interpret what we do.
We're starting to see cameras inside of dishwashers, not because of efficiency reasons, but presumably because they want to track when and how you load your dishwasher and what goes into the dishwasher and what doesn't, which in turn is data that can be sold as somebody who might be trying to understand your behaviors, your habits, the timing of what you do, what you fed your family that week, et cetera.
And so I think that tension between what we would like AI to do and the things that we would like to opt into, as opposed to what we don't want AI to do and the things that we may want to opt out of and may not be able to, that tension is at the center of pretty much every new discussion that we have about emerging features in AI.
Well, I think that's a terrific perspective, Olivier, and I think there's a question in your, let's say, soliloquy about the situation we're in, the question that you are asking without asking.
which is how the heck do these things come into existence in the first place? Why are we getting the features we didn't ask for, as you put it? And I think that's actually a really critical question when it comes to AI, because you know where they come from? Well, they come from human beings, but they also come from human beings using AI.
Give me 10 ideas for new product development or the dishwasher product manager or what have you.
We are, as knowledge workers at least, I'm hard-pressed to find any corner of anything that we do that we can't at least use a quick AI chat for.
And I'm mindful while I'm doing this, and I'm mindful while I'm speaking on podcasts or whatever to remind myself, to remind everybody, that AI is simulative.
It is trained to spit out something that looks original, creative, and new, and yet it has been trained based on original and synthetic inputs that are based in turn on the current human corpus.
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.