Steve Grubbs:
Okay, our guest today is Courtney Harding, and she has a new book coming out October 7th called The Spatial Race. I’ve known Courtney for quite a while because, as CEO of VictoryXR, we work a lot with Meta, and Courtney was the person that sort of shepherded us through that whole Meta process—which is a big organization. You need somebody to do that.

So I know that Courtney is an expert in virtual reality, spatial learning, all of those things, and we’re excited to talk to her about her new book. So Courtney, why don’t you start us off by just giving us a quick overview of what the book is about?

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Courtney Harding:
Sure. So The Spatial Race is about spatial computing and artificial intelligence, although it does fold in other frontier technologies like blockchain. But the idea is really that we’re going to move from a world where this technology right now is what I call “lean forward”—so you have to actively put on a headset, you have to actively prompt an AI—to a “lean back” experience where you put on glasses just like you look at your phone every day, or every minute of every day at this point, and the AI is just running in the background all the time, serving you contextual information without you actually having to do anything.

So it’s really setting out this vision for the next 10 years, and then telling people how to actually think about it and build for it and make something so that they’re prepared for when it actually happens.

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Steve Grubbs:
And why is October of this year the right moment to release this book?

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Courtney Harding:
Um, so I had this idea for this book for a long time. I had a folder of ideas and drafts and things, and I fussed around for a long time. And finally the publisher said to me, “You have six months—write this thing.” And so I did. I responded to that pressure.

I think October is a good time because people are coming back to school, they’re coming back to work, they’re kind of over their summer break, and I think they’re ready for sort of Q4 work and then Q1 of 2026 planning. So it’s a good time to set this up and set these ideas up so that people can then start thinking about them and building them into next year.

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Steve Grubbs:
Great. And so in the book you argue that spatial computing will reshape work, and learning, and daily life—the whole thing. Can you think of one near-term use case that people might underestimate? And what gives you the data behind thinking that this will come to pass in the near term?

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Courtney Harding:
So I think it’s very interesting right now to look at the return to office and how more and more organizations—Microsoft most recently—are doing return to office, when there is a great solution right there with using spatial computing for work productivity and collaboration. So I think that is probably one of the most undersold solutions this technology provides.

I have a Vision Pro that I use, if not daily, most days—for calls, for collaboration, for work. I use my Quest headset for the same thing. And I really think the future of work in the next several years, and starting pretty soon, will be anchored around working together in a spatial environment.

And what that does is it opens up opportunities for a lot of new talent. I have a friend who is an expert in her field—a true expert—and she’s had two interviews with two big companies recently. And at the end, they’ve said basically some form of, “You’re perfect for this. When can you relocate?”

Now she can’t. She has a family, she owns a home, she has a spouse who has a job, she has children who are in school. It’s not easy to pick up and move across the country when you are an established professional. And yet these companies are cutting off their noses to spite their faces when it comes to talent retention and talent acquisition.

So using this technology to open up a new world of work and collaboration will solve a lot of those problems. And the technology is pretty much there—it’s just that people need to start really thinking about it and using it.

Steve Grubbs:
Yeah, you know, that’s a really important point. VictoryXR—we are headquartered in Iowa, founded in Iowa—and I knew from the very beginning we would not be able to find the very specific skill sets that were needed to build a virtual reality and AI company.

And so, our people are all over the place, and it works well. Some people think that you can’t pull off a remote office, that people just won’t work. And maybe it helps that we’re only 25 people, but nevertheless, we have—I think, if not the best team in the world, one of the best teams in the world—for learning and spatial learning. And that’s because we follow that exact example.

To the extent that we can use these technologies, we do. Obviously, we’re in the space of learning and education. Where do you see that whole space going, and particularly with the integration of AI and spatial learning?

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Courtney Harding:
So I have an entire section of the book on this and the need that we will have to radically rethink education—especially K–12 education, but also university education.

K–12 education, unfortunately, has become very rote, and you have a lot of people that are teaching to the tests. This is not anything negative about individual teachers—they do amazing work. I come from a family of educators and I respect what they do very, very much, and they are working under very hard conditions.

But the idea that you have to just teach to a test, that you have to teach everyone the same curriculum that’s outdated—that needs to change.

So the model that I’m most interested in is the Texas Alpha School. And the Texas Alpha School—basically, every morning there’s two hours of instruction. So every student has to know the fundamentals. They need to know reading and writing and arithmetic.

But after that instruction, the students have their own self-directed projects, and a lot of those are powered by AI. So they will work with AI tutors, they’ll work with different programs to build a business, or to come up with an idea for a spaceship. Whatever their passion is, they can use AI to build around that.

Teachers are still very much in place—they’re controlling the classroom, they’re making sure everyone stays on task, they are giving advice and feedback. So the teachers are almost more like docents.

It’s kind of the Oxford University model. It’s funny—I hear people talk about how Texas Alpha School is this brand new thing, and then I’m like, “No—one of the oldest universities in the world kind of does the exact same thing,” maybe not with as much technology focus.

And I think that sort of way of thinking about things is going to really have to be infused throughout education in the next 10 years. Because just training students to do rote tasks—those are all going to be replaced.

Training students to be critical thinkers, to be builders, to work for things they’re passionate about and interested in—that is going to be really the future of education. And that’s really going to be a big transition, but it’s going to be an exciting transition as well.

And the same for university. Students need to be self-directed. In the age of AI, being self-directed and curious and coming up with problems that you need to solve—those are going to be very, very valuable skills. So we really need to reorient education around learning those skills as opposed to basic memorization.

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Steve Grubbs:
Yeah, I think obviously the work we’ve done with HoloTutor, which is our AI tutor platform—we think that hopefully that will give every school the opportunity to have two hours of instruction with a personalized AI tutor that adapts on the fly.

And so that—I’m glad you brought that up, because that’s exactly the model. But it can’t just be text-based. It has to be spatial and 3D, and include video, and two-way conversations, and all of that.

So, you know, the next five years are going to be transformative. Glad you’re at the front of it, and we hope to be at the front of it as well.

So your earlier work emphasized virtual reality for social change. How does The Spatial Race extend that—perhaps around ethics, accessibility, that type of thing?

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Courtney Harding:
So one of the things that I’m very committed to is making sure that this technology is available to everyone. And one thing that we absolutely have to avoid is the uneven distribution of this technology.

I’m on the World Economic Forum’s AI Governance Committee, and that’s a lot of the work that I’ve done in that space—making sure that this is equally distributed. So I don’t want it to just be people in Silicon Valley, or New York, or London, or Shanghai—whatever tech center you want.

I want the technology to benefit everyone, to make everyone’s lives better.

And in the book I talk about sort of the VR-powered robotics revolution. The way I see that playing out is: having AI-powered robots right now is still pretty far out in the future. We still need a lot more training data, we still need a lot more work to do that.

But in the short term—and you are seeing this particularly in Japan—it is possible to power a robot while wearing a headset. It’s basically like playing a video game. And thinking of that:

A) that can create a lot more job opportunities for people in different markets, and
B) that’ll create a lot more worker safety.

There was an article in The New York Times last week about delivery drivers in Rome—delivery cyclists—and they actually had to stop working because it was too hot. And those jobs are dangerous, those jobs are unpleasant, and they are often the only way a lot of people can survive.

So if we look at the social change aspect there, having those people in an air-conditioned room with a headset on—they still get to keep working, they still have the jobs that they need, but it’s a lot safer, it’s a lot more comfortable.

And if a robot gets hit by a bus—I mean, that’s a bummer for the robot company and whoever doesn’t get their delivery order, but who cares at the end of the day—versus a human being, which obviously is a huge loss.

So I think really sort of looking at that social impact side of things and thinking about how we can incorporate this technology to make everyone’s lives better—and make sure it’s equally distributed—is going to be a really important thing going forward.

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Steve Grubbs:
Yeah, you know, people don’t realize, but one of the most dangerous—if not the most dangerous—jobs in the United States is agriculture. You have a lot of young people, and people who may not be paying attention, working with heavy equipment and these things.

And so a lot of that—we’re seeing byproducts from John Deere and others—are being roboticized.

You know, it’s interesting—you said that the AI robots are a long ways off. I actually expect to see them start showing up in the workplace doing basic tasks in the second half of ’26.

I was looking at the video of Optimus being able to do laundry, for example. If I can get a robot to vacuum and do laundry and all those menial tasks, I’ll—you know, it’ll be like the 1950s, people buying televisions.

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Courtney Harding:
Well, it’ll be like The Jetsons, right? That was Rosie.

Yeah, I think for basic, simple tasks, we are close. I think for tasks that are a little bit more complicated—where your friend is navigating a city, having to pay attention…

Right? I mean, Waymo is coming to New York very soon. Waymo is already active in a couple of cities. So I think we’re seeing growth, but I think in terms of overall—it’s still a ways off.

Courtney Harding:
And part of that—and I have a friend who’s starting a company around this, actually, and I’m helping him out—is just this training data. We don’t have enough training data for these robots yet.

But people in headsets piloting the robots can:
A) still be employed, and
B) be building out that training data.

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Steve Grubbs:
Yeah, you know, that’s really, really interesting because we’re a little bit off topic here from healthcare. But it’s interesting because in our other company, Victory Store, we have these quarter-million to half-million dollar printing machines and cutting machines. I think there’s going to be a marketplace for someone to take a robot—an AI robot—and train them how to use the HP 5000, or the Gerber cutting machine.

And then that software—because somebody’s got to train it. We know that Tesla or whoever is building the robots isn’t going to train all of these. So, I think there will be a whole cottage industry of training robots with whatever data—or actually, I think, potentially just showing them how to do it, saving the data…

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Cortney Harding:
Well, we’ve been doing this forever. And I know this is a real thing, even though sometimes I feel like I hallucinated it. But back in the early 2000s, there was a service called Google 411. This was pre-smartphone. What you could do with Google 411 was dial it on your flip phone and ask for information—like “Is this business open? What are their hours? What is the address?” Just basic info you’d normally get from a phone book or a directory.

Now, I used it a few times, and I kind of thought it was curious, but I didn’t really think too deeply about it. Until someone told me this wasn’t just a public service on Google’s behalf. It was a way for Google to collect pronunciation data, so that when they built Google Maps and other services, it could give you directions that accurately pronounced local street names.

Like in New York City, it’s not “Houston Street,” it’s “HOW-ston Street.” In Portland, Oregon, it’s not “Couch Street,” it’s “Cooch Street.” You can see how people just using the service were training the data. I mean, how many of us have done CAPTCHAs asking us to select images with birds in them? We’re already training the data. The entire internet is just a field of training data for these platforms.

And yeah, as we move into more robotics, it’s going to be about building up that data.

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Steve Grubbs:
That is so fascinating. You’re exactly right. You know, we have Nevada, Iowa instead of Nevada, and Milan, Illinois instead of Milan. So many of those. Vista College is pronounced Avista, etc.

Okay, almost my last question. Let’s think in terms of practical implementation. If a Fortune 500 company and a startup each wanted to win the spatial race—thinking in terms of a short-term playbook, like a 90-day playbook—how would they execute on getting up and running and moving forward in this space?

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Cortney Harding:
So, the first thing any company needs to do is take a step back and come up with a problem statement.

I’ve been in so many rooms where people say, “We need to do VR. We need to do AI.” No, you don’t. You need to solve a problem using those technologies where they are appropriate.

There’s a data point being talked about a lot recently—95% of corporate AI projects are failing. I don’t like the word “failure.” People are learning. They’re building. That’s not failure. However, many projects don’t work or don’t work in time because they weren’t scoped correctly. People either misunderstood the technology’s capabilities, or misunderstood the problem they were trying to solve, or they just built something to say they built something.

So, the first step is to find a real problem that the technology can help solve. Then, build from there. Come up with solid KPIs and measurement metrics. Stay agile as you build and start shipping, learning, and iterating. Eventually, empower others in the organization to do the same.

There’s also this strange dissonance in corporate America. On one hand, leadership is saying “AI is the future,” and they’re spending tons of money on it. But when someone says, “Great! I used AI to write our newsletter this week,” people get scandalized. That’s a perfectly valid use of AI—just fact-check it. But we’re at this weird inflection point where people are both excited about AI and terrified of it. They both overestimate and underestimate it.

Companies need to break down those barriers to usage and focus on solving measurable problems rather than throwing ideas out and seeing what sticks.

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Steve Grubbs:
What you said about the 95% failure rate—I read that too. My first thought was, when Edison was testing filaments for the lightbulb, were each of those failures? I suppose they were, but really it was just another brick in the wall. Every moment of learning is another brick in building your house or company.

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Cortney Harding:
Exactly. The first chapter of my book covers the first .com bubble. I’ve been writing about this a lot because there are lessons to be learned.

Three of the biggest “failures” from that era were Pets.com, Webvan, and Cosmo.com.

• Pets.com is now Chewy, a totally mainstream business.
  
• Webvan is now Instacart, Fresh Direct, and other grocery delivery services we use all the time.
  
• Cosmo.com was a delivery service with no minimum order fee—I abused that in college. Now we have Uber Eats, DoorDash, GoPuff, etc.
  

So all these “failures” turned into multi-billion dollar mainstream businesses a decade later. The key is: if you try something and it doesn’t work, but you learn from it—it’s not a failure. It’s only a failure if you learn nothing.

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Steve Grubbs:
My favorite story about that is when the U.S. Department of War decided to build their own airplane in 1908. They tested it, and it dropped right into the ocean. The New York Times wrote an editorial saying humans wouldn’t fly for a thousand years and the government should stop wasting money.

Less than 90 days later, the Wright brothers flew their first plane—for pennies on the dollar.

Same idea with the Apple Newton vs. the iPad. A lot of it is timing. Sometimes it’s about having the right people with the right view tackling a problem. But it starts, like you said, with solving the right problem.

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Cortney Harding:
Exactly. Google Glass was ridiculous-looking—no one’s arguing otherwise. But it led to better things.

It’s about timing, execution, compute power. Early VR headsets didn’t work because the compute power wasn’t there. Now, it is. My hackles go up when people are just reflexively anti-technology.

Don’t get me wrong—there are real issues around AI: copyright, data privacy, protecting young people. Just this morning, The New York Times Daily episode was about people getting lost in AI hallucinations. We need guardrails. We need smart legislation.

But let’s not act like AI hallucinations are totally new. I had a boyfriend in high school who listened to midnight radio shows where the host claimed aliens built the pyramids. We’ve had strange beliefs and hallucinations forever. AI is just part of that long human story.

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Steve Grubbs:
Absolutely. Some conspiracy theories even turn out to be true. So Courtney, help us out—where can we pre-order your book, and where will it be available?

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Cortney Harding:
The book comes out October 7th, and you can pre-order it on Amazon right now. I’ll send you the link. I’m also doing several events:

• October 8th: Speaking at Stanford Law School
  
• October 10th: At the Fast Future Executive Summit in Los Angeles
  
• Later this fall: Events in New York City and possibly the Bay Area
  

I also do a lot of speaking and consulting—so if anyone is interested, they can reach me through my website: courtney-harding.com, or connect on LinkedIn, Instagram, etc. Just search for The Spatial Race on Amazon, and it should pop right up.

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Steve Grubbs:
Courtney, thank you for joining us today on the VictoryXR Show. We appreciate it, and I’m really looking forward to seeing great things from your new book.

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Cortney Harding:
Thank you so much! It was great to be here.