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AI is the biggest technology shift of our lifetime. This show is about how to profit from it together.
Each week I talk with the founders and CEOs closest to AI and Content, the ones figuring this out in real time.
I’m also building an AI content business myself and share the lessons I learn along the way.
WHAT WE COVER
The Titans -- How companies like OpenAI, Anthropic, Google, Meta, and xAI are moving, and why their decisions matter.
The Incumbents -- How content giants like Disney, News Corp, Universal Music Group, and Reddit are responding to AI, and what it means for creators and publishers.
The Playbook -- Real lessons on AI business models, content strategy, creativity, IP licensing, distribution, and getting paid.
Family & Our Future -- Every episode ends with me asking my guest what AI means for our jobs, our families, and the next generation.
ABOUT YOUR HOST
Rob Kelly has interviewed Steve Jobs and Bill Gates, helped pioneer early web content licensing, and built multiple companies with more than $100 million in total sales. His work has appeared on CNBC, CNN, TIME, and Entrepreneur.
Thanks!
-Rob Kelly
China, for example, have data collection cities. Jd.com, the Amazon of China, is essentially trying to create a city where they would employ a 100,000 people in that city to collect their everyday lives to train robots. They're having just everyday citizens go and pick up headsets to do their daily routine.
Rob Kelly:Welcome to Media and the Machine. I'm Rob Kelly. AI is the biggest technology shift of our lifetime, and this show is about how to profit from it. I'm a three time founder and CEO of content driven businesses, and each week I talk with the people on the inside of AI and content, the ones building this new world and learning what works firsthand. I'm an optimist about AI, but I'm also a realist.
Rob Kelly:So every show ends with me asking our guest what AI means for our jobs, our families, and our future. My guest is Say Pike, VP of AI growth and solutions at TELUS Digital. She built and sold two companies. Ernst and Young bought her digital consultancy, Citizen, and ADT bought her smart home company, Iotas. Say now has her sights set on physical AI and robots.
Rob Kelly:We break down Google's recent breakthrough where its new Gemini four Argon taught a robot to play cornhole, and it made seven shots in a row. But what makes data skills like that possible, and who's gonna supply it? We get specific about what AI labs want, and why robotics companies like Figure AI are getting into the data collection business. We discuss why robots might need a billion training hours, plus our forecast of a $45,000,000,000 market by 2035. That's just collecting data for physical AI.
Rob Kelly:Then we explore China's data collection cities. Imagine a 100,000 people recording their everyday lives just to train robots, and why she thinks China, despite being behind The US and LLMs, can be number one in physical AI. Plus, a robotic centaur with chainsaw hands. As always, we close talking about AI's impact on our families and jobs, including advice for her kids, their artists, and they're rebelling against their AI optimism. Please enjoy my conversation with Say Pike.
Rob Kelly:This Gemini 4 Argon playing cornhole? Yes. And it didn't just play cornhole, it sunk seven shots in a row.
Sce Pike:I think one of the things that was very nice about that demonstration is going from real to simulation. So taking a real video of a human doing a shot into a cornhole, so watch that video and then put it into a simulation and simulated the physics of that of the... What do you call it? The bag, the hacky bag or whatever, right, going
Rob Kelly:into the The bag.
Sce Pike:Right? And so it simulated it and then it actually took it into its own practice and learn over and over again after, like, how much force it needed, what's the trajectory. Right? And so it watched itself do that and learned over a period of time to get to understanding how to make that shot. What it didn't show was the robot picking up the beanbag and then throwing it.
Sce Pike:Contact physics, meaning, like, how hard it had to pick that bean bag up, how soft it had to hold that bean bag when it threw that bean bag.
Rob Kelly:So it's not quite the alpha go deep blue type moment for physical AI?
Sce Pike:The ideal scenario, the end game is like, I have a robot at my house, I show it how to like sort my laundry and put it into the laundry machine and push the buttons and then like take it out, put it in the washer. You show it once, how you want it done, and it'll just perfectly mimic you. I think that would be the breakthrough moment, if it could mimic anything that you did. Mhmm. I mean, that would be the end game, because it's not just household chores, you're talking factory floors, you're talking retail, you're talking hospitality, right?
Sce Pike:It's just if it could actually watch you do something once, twice, three times, whatever, and then actually do the task, that would be the end game.
Rob Kelly:And the overall definition just of physical AI, is it simply that we're going from using AI for digital reasoning to physical action?
Sce Pike:So physical AI, when I say physical AI, it encompasses both robotics as well as automotive, autonomous vehicles as well. So physical AI to me encompasses anything that is action oriented in the physical world.
Rob Kelly:So physical models versus LLMs. I just want to contrast the two because most people now are familiar with learning language models, LLMs. My layman's take on this is LLMs learned from the Internet. Let's just kinda simplify and say that's the corpus where it learned from. Right?
Rob Kelly:And what's the closest thing to the Internet, in air quotes over here? What's the closest thing to the Internet type corpus that robots will train on?
Sce Pike:So the closest thing doesn't exist. This is the issue with physical AI is that it has already ingested all of the Internet. It can't learn from that because that's not how the real world works. So it needs data to train on, and unfortunately, that data doesn't exist, so traditionally different companies tried lots of different things, which is like simulations, you know, synthetic data, and over and over again research has shown that the best type of data for it to learn from is real world human collected data. And so this is why you're seeing this uptick of people maybe wearing headsets and collecting themselves doing household chores, washing clothes, doing laundry, or in factories, in like garment factories where they're recording themselves sewing and other things.
Sce Pike:And so this is where China, for example, have government citywide initiatives, and they call themselves, like, data collection cities. And so this is where it gets really fascinating. There's just not enough corpus, as you will, to train from.
Rob Kelly:And these data collection cities, are they... Can you just describe what you mean by those? Like, what would it look like?
Sce Pike:Yeah. So JD.com, one of the largest ecommerce groups in China.
Rob Kelly:Sort of the Amazon of China?
Sce Pike:Yeah. The Amazon of China is essentially trying to create a city where they would employ a 100,000 people in that city to collect their everyday lives to train robots. They would also send out robots to do tasks in people's homes so that it could learn from it and train from it as well. But they're having just everyday citizens go and pick up headsets to do their daily routine, and that's a state sponsored thing and jd.com is part of it as well, and just it's just fascinating to me how, like, the whole government, I think last year, China's government said, hey, Physical AI is something that we're gonna win at. Right?
Sce Pike:Because I think maybe they saw that with OpenAI, Anthropic and other kind of US based, what I would call like AGI companies win the race, they're like, we're gonna win the Physical AI race. And so they're just mobilizing all of their initiatives just the way they did with EVs when they wanted to get EVs adopted. There's tax breaks for the citizens. There's incentives. There's just a huge mobilization going on right now over there.
Rob Kelly:And the gear you're describing that they would wear, this is the egocentric kinda data collection devices?
Sce Pike:Yeah. So they're like headsets. It's like the halo around your head, have several cameras that have a field of view of at least like 150 degrees or more. Sometimes when we do collection, we wear wrist cameras as well, so people are wearing cameras on their wrist that show kind of the dexterity of your fingers, because really that's the hardest thing again for robots is just understanding grasp, understanding contact friction.
Rob Kelly:Well, it's interesting. I mean, if we kind of call it, if we won the LLM race, I mean, in the lead Mhmm. The physical AI race, I mean, that is really up for grabs, and you would kinda have to bet on China because of its robotics experience and its volume of people. Right?
Sce Pike:You nailed it.
Rob Kelly:What are the top physical AI models right now?
Sce Pike:Physical intelligence, releasing their APIs. So that... I thought that was really fascinating today. DynaRobotics is also very interesting to watch because they're also producing a lot of data as well, and that might get them ahead of the game. And then Figure AI, which I thought was fascinating, they are also a robotic company, but they also went into the data collection business as well.
Sce Pike:So some of these companies that are robotic companies are switching over to data collection, data creation, again because that's where the revenue is right now, because there's just not enough ground truth data out there to advance the models forward.
Rob Kelly:Let's talk about training data needed for physical models, physical AI. We touched upon it a little bit, but I just had a few key questions here. How many hours do you think are needed to really make this thing sing?
Sce Pike:So until recently, the number that was commonly shared by all the labs is like hundreds of millions of hours. Most recently at IROs in Pittsburgh, which is a fairly large and old conference for robotics and autonomous vehicles and whatnot, till this time around it was all robotics. I heard a billion hours being thrown around.
Rob Kelly:Who said that?
Sce Pike:I can't say, but I'll say that they should know because they themselves are in the simulations and physics business, so physics engine business, and if a sim and physics engine business is telling you that, they know what it takes to make their simulations more accurate, right? And they also work with all the foundation labs, hyperscalers as well.
Rob Kelly:Yeah. Can you list out... Let's just walk through sort of the types of data that physical AI needs. So first question is, assumption here, they use the equivalent of the corpus of the Internet, sort of like what ChatGPT and Claude and Gemini already know?
Sce Pike:Yes. They do. And, you know, the numbers vary all over, but by 2035, we may be looking at like a 45,000,000,000 or more market size, maybe bigger.
Rob Kelly:This is for the data collection market?
Sce Pike:Just the... Yeah. Just the data collection market.
Rob Kelly:For physical AI or is that included
Sce Pike:Physical AI. Just for physical AI. Just for physical AI. With egocentric data, which we've talked about, which is people wearing headsets and wrist cameras and whatnot, there's definitely something like that that's necessary. But there's also teleoperation, right, where somebody is wearing controllers for the actual robot model, right, and so mimicking all the tasks using controllers for the model and teaching it that way.
Sce Pike:So there's a combination there, but it's not just the data collection, there's also the quality control that's needed of that data. Right? If you're gonna generate a billion hours, how do you actually check that for quality that it's actually usable for training? And then on top of that,
Rob Kelly:and I would guess AI is what's used to check a billion hours.
Sce Pike:You would have to use some level of AI and this is actually where
Rob Kelly:Or another city in China?
Sce Pike:Yeah. But so... But there is a lot of human annotation and labeling that goes on, so somebody has to on the video itself label the task of like, this is somebody putting on a shoe exactly at this time in seconds and milliseconds, right, And then tying the shoelaces next. But going back to just the data creation, you still need three d assets, mapping of an environment, so like Niantic Spatial is doing that quite effectively out there.
Rob Kelly:So just on the data collection math side of things, like, billion... You know, I I had the CEO of CuriosityStream and the CEO of Trove, two companies sitting on a bunch of video between, call it, three to eight million hours that they've got, the two companies. Just doing some basic math here. Let's just say we need to get to a billion hours. I mean, it would seem like it would have to be setting up these data collection cities or companies fully.
Rob Kelly:So I I doubt The United States will be able to pull off a data collection city like China.
Sce Pike:The way we're doing it is through crowd collection as well. Right?
Rob Kelly:Mhmm. Tell me more.
Sce Pike:Yeah. So in our crowd, we have probably over a million people that we have utilized in the past to train AGI, Gen AI LLMs. So we constantly survey the crowd to understand whether or not they are capable and willing to do physical AI data collection as well.
Rob Kelly:And this is TELUS?
Sce Pike:This is TELUS Digital. Yeah. Mhmm. So it's not us employing a million people or setting up a data collection city, but it's us reaching out to this community that we've built to train LLMs previously to now train physical AI or collect data for physical AI. It's actually just doing your normal tasks that you would do on a daily basis.
Sce Pike:Go to your job and wear a headset and just record what you're doing on your job, and we will essentially take care of the rest kind of thing. Right?
Rob Kelly:And then you pay them?
Sce Pike:Yeah. You're getting paid on your job and getting paid by us to record that job.
Rob Kelly:For
Sce Pike:us, it's using the collective power of the crowd, the gig workers, to be able to generate this. So will it come to a point where, you know, I think people were dubious when Uber came out, this whole gig economy, right, initially when it first started happening, I don't think anyone understood how many people it would employ. This is gonna be very similar. I think this is gonna be very similar.
Rob Kelly:Help me picture what types of jobs, like common types of jobs are folks doing where they're wearing headsets and getting paid to collect this data for physical AI?
Sce Pike:So right now, are asking for scenarios within retail, hospitality, farms, factories, homes as well. So right now, those are the kind of highly in demand work, right? But seriously, like you could be a barista, you know? You could be in the fast food industry, you could be working in a hotel lobby, right, or you could be working in a data center, or you could be a nurse, but I think those are the areas where we see a tremendous need. Anything that's also hazardous, you know, firefighting, like, there's this really funny robot out there in the market that is designed for firefighting in the wild, right?
Sce Pike:So in the woods, so firefighting in the woods, and it's stuff like that, I think, hopefully, that will take off, that will make it easier for us to do some of these more hazardous jobs.
Rob Kelly:And are companies paying TELUS for this data right now? Is that how you subsidize it? Okay. Are there things that you don't wanna show robots in terms of training data?
Sce Pike:Oh. Yeah. Probably, like, we would never have a robot be trained on anything that a human did where, let's say they pushed aside a cat or a dog or a pet not so gently, like we would not want the robot to be trained on stuff of that nature.
Rob Kelly:Right?
Sce Pike:So
Rob Kelly:because you don't want them to learn it.
Sce Pike:No. We don't want them to learn it.
Rob Kelly:Yeah. Right? So there'll be some sort of, like, constitution or values built in for the dataset for robots depending on what company is building what. Right?
Sce Pike:Exactly. And I think this is where LLM companies, for better or for worse, built their constitution after they built the LLM. Right?
Rob Kelly:Whoops.
Sce Pike:I would hope that robotic companies or physical AI models would build their constitution and before as well as into their training material because I think this is kind of critical, especially for things that are gonna be deployed to be working alongside us and have physical presence.
Rob Kelly:Can you talk about just use cases of what robots will do, maybe interesting ones outside of business? For instance, I just heard Kevin Ryan, founder of a lot of companies, he thought robot massage was gonna be huge because people want their privacy, and it just can learn to apply the right pressure in the right spots.
Sce Pike:I'm skeptical about the robot massage, honestly, because, like, again
Rob Kelly:You're not signing up?
Sce Pike:Yeah. Not gonna sign up for that because the contact friction is probably one of the hardest things. I do think some better usage of it would be robots in dangerous situations that humans might not want to necessarily be in.
Rob Kelly:Fighting As I said wildfires, as you talked about.
Sce Pike:Yeah. Yeah. Or fires in homes.
Rob Kelly:I know you mentioned the goat head centaur. Can you just describe that, by the way?
Sce Pike:Yeah. So that one again is for fighting wildfires, which is basically a quadruped. It's a centaur, but it's also instead of Quadruped
Rob Kelly:just means it's on four sort of legs. Legs.
Sce Pike:Yeah. Four legs, but instead of hands, it's chainsaw hands. Right? So it's like your worst nightmare come to life. So half of the body is humanoid up top, legs are like quadrupeds, centaur, and instead of hands, it has chainsaw as a hand.
Rob Kelly:Why does it have chainsaws when fighting fire? Just to knock down trees?
Sce Pike:Yeah. To create a fire barrier. Right? So...
Rob Kelly:Wow. Wouldn't wanna meet one of those in a dark alley.
Sce Pike:But it probably was really useful if it could actually do the job.
Rob Kelly:Any other wild stuff that you've seen in that category?
Sce Pike:I don't think it's, like, that wild, but it's mainly for security purposes. So a lot of quadrupeds in construction sites.
Rob Kelly:Humanoids. What's a humanoid capable that far exceeds that of a human right now, if anything?
Sce Pike:I mean, a few did beat Usain Bolt's speed record recently in the Chinese Robotic Olympics, you know. So I guess in that sense...
Rob Kelly:Yeah. That didn't get as much publicity as I thought it would.
Sce Pike:No. No. Because the Chinese media kind of hit that amongst like robots, like running into walls and being silly, which is a whole different thing.
Rob Kelly:And you brought up that you had a theory about why they were being extra silly about those Because I
Sce Pike:I believe that the Chinese media and, you know, don't hate me if you're a Chinese media controller or not, but I think really they're doing a great job of trying to make robots not seem scary by making them look silly. So I think it's a way to make sure that their population will adopt this technology versus our way of marketing something, I mean, like Optimus and Figure AI, they do look scary. They're not the cutest looking things or the most approachable looking things at all, like, the design of them are not friendly.
Rob Kelly:What are your thoughts about how people will pay for robots, like, the consumer side?
Sce Pike:Yeah. So Optimus supposedly is going to retail for 20 to 30,000, and I'm guessing Tesla's gonna do a lease model. And I think most companies will probably end up doing a lease model.
Rob Kelly:So it'd be like a car, you can buy it or you can lease it.
Sce Pike:Exactly.
Rob Kelly:So lease might be a couple 100 a month or something like that?
Sce Pike:Exactly. Couple $102,103 100 a month. Right? And if... I don't know about you, but like I pay more than that for my housekeeping on a monthly basis.
Rob Kelly:Mhmm.
Sce Pike:So to me, if it actually could do all the tasks that my housekeeper does, it would pay off. Right?
Rob Kelly:Are we gonna see humanoids traveling in planes and cars? What are your thoughts
Sce Pike:about that? I guess, why not? Definitely cars, I'm sure.
Rob Kelly:You know someone's gonna put it in the front seat of those places where you can drive in the fast lane to commute.
Sce Pike:Of course. People do problems with mannequins. Right? So the question is like, will it be able to drive? Right.
Sce Pike:Right?
Rob Kelly:That's
Sce Pike:a I weird mean, like, it actually need to be able to drive or does it just take over the, you know, autonomous system kinda thing?
Rob Kelly:Like a backup to the self driving autonomous vehicles too. Yeah. Like a backup copilot. Uh-huh. That is crazy.
Sce Pike:Yeah. Yeah. Because my 16 year old won't drive.
Rob Kelly:Your 16 year old won't drive? Will they Mhmm. Let a humanoid drive?
Sce Pike:I mean, it might be better than my 16 year old who hasn't learned how to drive. Right? Or any 16 year old.
Rob Kelly:Have they been in an autonomous car yet?
Sce Pike:No. Not yet.
Rob Kelly:K. That'll be fine.
Sce Pike:But I would trust an autonomous vehicle more than I would trust an Uber driver. Yes. Transport my kids around. It's a weird mental thing.
Rob Kelly:What do you think about when I just bring up cyborg, the word, and are we gonna have this hybrid, us humans with gear that is powered by things like physical AI?
Sce Pike:I'm probably an exception, but people used to ask, would you get a chip in your brain? And I'm like, yes. I would totally get a chip in my brain.
Rob Kelly:Mhmm.
Sce Pike:So you know, there's companies out there again, Neuralink is something that Elon Musk has been working on for a while. Yeah. Essentially putting a chip in your brain to be connected to the internet all the time.
Rob Kelly:I heard a guy had a chip just under his skin of his... Like his hand, and he's using it to open up his door in his San Francisco apartment.
Sce Pike:Yeah. I'm not surprised. Some of the things that we're getting asked for a lot right now in terms of data is natural language conversation. So what I mean by that is like, you know, just from labs, they were asking us for like more audio content because you can't go up to a robot and type into a screen to tell it what to do, you gotta like yell at it or you know, talk to it and it has to be able to talk back to you. But that's not just for robots, that's for everything around you, right?
Sce Pike:So I think there'll be a lot more natural physical interaction, and so user experience will move away from screens to more physical interactions in the natural environment. You worked at Palm. I loved Palm, but we didn't make smartphones happen.
Rob Kelly:Mhmm.
Sce Pike:IPhone did, and you know, we were also busy trying to make the keys angled certain way and competing against Blackberry, did not think that a keyless smartphone would be the thing that dominated. Right?
Rob Kelly:Interesting. What's your thought on the impact of physical AI on jobs and AI in general?
Sce Pike:I'm very personally conflicted about this because I flip flop between like, oh, no. This is gonna be great for humanity. I'm like, to moments of like, oh, this is not gonna be great for humanity. I think the concern is that right now the jobs that the AI is going after are junior level jobs, entry level jobs, and it's really impacting the younger generation that are entering the market. I have kids who are going be entering the market too soon, and it does worry me.
Sce Pike:But I'll say this, like my kids are creatives, they're artists, and I do think that art is the one thing that AI will never actually truly replace. Like, technology has never replaced artists as long as we've had technology going back to fire.
Rob Kelly:Who's the little person who appears to be walking on water in your LinkedIn profile? You know the backdrop picture?
Sce Pike:Yes. That is my son when he was very little. He was about three or four. Yeah. He does look like he's walking on water, it's like...
Rob Kelly:So he's not a robot?
Sce Pike:No. Not a robot. Not a robot. But yes, that would be my son, Kian, who's now taller than I am. So...
Sce Pike:And he calls me short all the time. How old are your kids? 17 and 13. So...
Rob Kelly:17 and 13. What are you telling them in general about this new era of AI?
Sce Pike:Art has always defined and artists have always defined the human experience, and that's I think something that's always gonna be wanted and needed, even if it's using AI to define that human experience.
Rob Kelly:And are they on board? No. Yeah. So they're like many others of their peers.
Sce Pike:That's totally Luddite.
Rob Kelly:Yeah.
Sce Pike:This is how they rebel against me. Very much Luddite.
Rob Kelly:Hilarious, Who would have thought that the older members of the family are the ones who are more optimistic about AI than the younger?
Sce Pike:Yep. Yep. I think it's a major backlash.
Rob Kelly:We'll say thank you so much for investing the time today.
Sce Pike:Yeah. No. Thank you. This was fun.
Rob Kelly:Well, that's it for this episode of Media and the Machine. Before you go, a few quick things. If you wanna hear the next episode, hit follow on the show in your podcast app. If you like something about this episode, the guest, the topic, even one part of the conversation, please leave a rating or review and tell me what. And if something didn't land, email me.
Rob Kelly:I wanna hear that too. Either way, it helps me make the show better for you. If you wanna go deeper, head to mediaandthemachine.com and subscribe to my newsletter. You'll get my favorite highlights from the show, plus essays I write about AI. That's also where you can email me or recommend a guest.
Rob Kelly:If they make it on the show, I'll give you a shout out. And if this episode made you think of someone, please pass it along. That's how people find us, and I'm genuinely grateful. Thanks for listening, and see you next time.