Could Bringing AI Into the Physical World Make It Profitable?

Summary

WSJ What’s News Summary

Episode: Could Bringing AI Into the Physical World Make It Profitable?
Release Date: June 15, 2025
Host: Alex Osola
Guest: Bel Lin, AI and Enterprise Technology Reporter, The Wall Street Journal

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Introduction

In this episode of WSJ What’s News, host Alex Osola delves into the burgeoning integration of artificial intelligence (AI) into both digital and physical realms. As businesses increasingly adopt AI technologies, the discussion pivots to whether embedding AI into the physical world—through devices and automation—can become a profitable venture for developers and companies alike. Joined by Bel Lin, an expert covering AI and enterprise technology, the conversation explores significant investments, future applications, monetization strategies, and the challenges of achieving a return on investment (ROI) in this evolving landscape.

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Massive Investments in AI Infrastructure

Alex Osola opens the discussion by highlighting the substantial financial commitments tech giants are making towards AI infrastructure:

• Meta allocated up to $65 billion in 2025.
• Microsoft committed $80 billion.
• A data center startup collaborating with OpenAI secured nearly $12 billion.

These investments underscore the industry's belief in AI's potential to drive future innovations and market growth.

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Evolution of AI: From Chatbots to Physical Embodiment

Bel Lin outlines the current and future phases of AI development:

• Current Phase: Dominated by chatbots like ChatGPT, where interaction is primarily text-based.

  "It's still in some of the earliest phases of AI, where AI is still chatbots... you have to type in something." [01:48]

• Next Phase: Introduction of AI agents—software that can perform tasks autonomously, such as ordering a cab or making restaurant reservations.

• Future Phase: Physical AI—embedding AI into hardware to automate physical tasks in warehouses, factories, and potentially in consumer devices like humanoid robots for home use.

Bel emphasizes that physical AI signifies AI’s transition into tangible applications, moving beyond virtual interactions to impact various sectors physically.

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Leading the Charge in Physical AI Development

The collaboration between OpenAI and Jony Ive’s company emerges as a frontrunner in the physical AI space:

"They have this really great heritage in the whole Apple ecosystem and the design aesthetics that Jony Ive has put out." [03:54]

This partnership aims to develop a new family of devices that integrate seamlessly into users' daily lives, serving as an ambient third core device alongside traditional tools like MacBooks and iPhones. The synergy between OpenAI’s advanced models and Ive’s design expertise positions them as potential leaders in this next wave of AI innovation.

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Monetization Strategies for AI Technologies

Bel Lin discusses various pathways AI developers are exploring to monetize their advancements:

• Software Licensing: Selling access to AI models on a per-word or per-bit basis, allowing integration into other services and technologies.
• Hardware Sales: Marketing AI-embedded devices and offering software upgrades as additional revenue streams.
• Adoption Focus: Initially prioritizing user adoption in consumer markets to establish a foothold before scaling monetization efforts.

In the industrial sector, monetization hinges on ROI through cost savings:

"That's where ROI becomes a lot more important because you can shave a lot of costs by automating human labor away." [05:14]

Automation in warehouses and logistics aims to reduce operational costs, presenting a clear financial incentive for businesses to adopt AI technologies.

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Corporate Adoption and the Existential Imperative

Bel Lin asserts that AI integration is becoming an existential necessity for many companies:

"We need to embrace AI now or else we won't be around." [07:19]

Citing a McKinsey survey, 78% of companies are already utilizing at least one AI function. Sectors like law firms are evaluating the impact of AI on traditional business models, such as the billable hour, as AI can efficiently handle tasks like reading, summarizing, and synthesizing information—roles traditionally filled by paralegals.

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Applications and Demand for Physical AI

The episode explores existing applications of physical AI that demonstrate its value:

• Military Training: Utilizing AR and VR devices for training armed forces.
• Medical Training: Assisting in surgical training through immersive technologies.
• Skilled Trades: Enhancing the training of plumbers, HVAC technicians, and jet engine specialists with augmented reality tools.

These examples illustrate that physical AI is already making tangible impacts, suggesting potential areas for accelerated adoption as the technology becomes more refined and accessible.

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Cost Challenges and Future Outlook

A significant concern addressed is the high cost of developing and maintaining AI infrastructure:

"The cost of hardware in a really general sense is still quite high." [09:16]

Bel Lin explains that while advancements in AI model efficiency can reduce the computational and energy demands of data centers, the hardware required for physical AI applications—such as AR headsets and humanoid robots—remains expensive. Achieving cost reductions will be crucial for broader adoption and profitability.

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Monetization Challenges for AI Developers

AI startups, often funded by venture capital, face pressure to demonstrate profitability within limited timeframes:

"They have to show their mettle... until they have to show their mettle." [10:36]

The sustainability of these startups depends on whether their technologies gain consumer traction. Without clear monetization pathways, many may struggle to transition from funded projects to profitable enterprises.

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Measuring ROI and Economic Incentives

For companies adopting AI, measuring ROI remains a complex challenge:

"Productivity of workers has gone up, but it's hard to measure." [11:20]

While AI can enhance productivity and potentially boost sales, quantifying these benefits is not straightforward. Nonetheless, economic incentives persist, driving both AI developers and adopters to seek ways to ensure profitability and cost savings.

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Conclusion

The episode concludes with a reflection on the critical juncture AI technologies face in transitioning from innovative concepts to profitable, widely adopted solutions. The collaboration between leading tech entities, the strategic focus on monetization, and the imperative for companies to adopt AI are pivotal factors shaping the future of AI in both digital and physical realms.

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Production Credits:
Produced by Charlotte Gartenberg
Supervising Producer: Michael Kosmides
Deputy Editor: Chris Sinsley

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This summary encapsulates the key discussions and insights from the WSJ What’s News episode on AI's transition into the physical world, providing a comprehensive overview for those who haven’t listened to the original podcast.

Transcript

[Bel Lin]

In business, they say you can have better, cheaper or faster, but you only get to pick two. What if you could have all three? You can with Oracle Cloud Infrastructure. Try OCI for [e-mail] Wallstreet hey, what's news, listeners?

[Alex Osola]

It's Sunday, June 15th. I'm Alex Osola for the Wall Street Journal. This is what's News Sunday, the show where we tackle the big questions about the biggest stories in the news by reaching out to our colleagues across the newsroom to help explain what's happening in our world. On today's show, as businesses are finally starting to find ways to integrate artificial intelligence into their operations, developers are already working on future iterations of AI, including ways to embody the technology in the physical world. But the question can the developers or companies make money from AI? One of the biggest stories in tech over the past six months is the huge investments tech companies are making in data centers needed to power artificial intelligence. In January, Meta said it was allocating up to $65 billion this year. In the same month, Microsoft committed $80 billion, and in May, a data center startup that works with OpenAI secured almost $12 billion. These developers have big plans. They see one of the next steps in artificial intelligence as bringing it out of the cloud and into the physical world, like consumer devices and humanoid robots for manufacturing spaces. But will this future phase of AI finally earn a return on investment for these users and developers? To dig more into the AI industry's future plans and whether they'll make AI profitable, I'm joined by Bell Lin, who covers AI and enterprise technology for the journal Bel. What do developers say is the next phase of AI what's coming?

[Bel Lin]

It's an interesting question because it feels like we're still in some of the earliest phases of AI, where AI is still chatbots and you have to interact with ChatGPT in order to get some something back. You have to type in something. But the wave after chatbots is supposed to be AI agents, and those are technologies or software that can basically do things for you, like order a cab when you're arriving home from the airport or to make a restaurant reservation. And then after that is physical AI. And some tech watchers, and certainly Jensen Huang, the CEO of Nvidia, has talked about this phase as being where AI enters our physical world. And and that has a lot of meanings. But in the corporate sense it can mean that you're bringing automation to warehouses and bringing automation to factories. And then maybe in our daily lives that's something like bringing humanoid robots to our homes. So broadly, it's the idea that AI is entering our devices, whether in our homes, in wearable devices that we wear, or in the factories and the warehouses where our products and goods are made.

[Alex Osola]

I'm curious how that actually would work, because right now I think about AI as a chatbot, essentially. How does that then become something that is embodied in the physical world, whatever that may mean.

[Bel Lin]

There are some examples of wearable devices and these AI pins and devices that already came to fruition in this sort of first few phases of AI. There are things like AR and VR goggles that we've all heard of, the Apple Vision Pro, there's the Meta quest, smart glasses like from Meta and Snap. And so these are examples of AI that is embedded within these devices that we interact with, usually by voice or with gestures. Sometimes there's a more physical button that we might press or something that we might toggle. But the idea is really that AI gets embedded within the hardware itself rather than the human, the user, us being tied to some screen or some interface that we're used to seeing as a laptop or a phone.

[Alex Osola]

Who is leading this trajectory? Who's leading the pack?

[Bel Lin]

What we've seen from OpenAI and Jony I've's company is this collaboration called IO in which Jony I've and his team will serve as the creative brain behind this new device that OpenAI will release, this sort of family of devices. And they've been pretty tight lipped about what the device will look like and what it will do, but they've said a few things like it'll be ambient, it'll be this third core device that you put on your desk after your MacBook and your iPhone. And so you could say that they're leading the pack because they're promising a lot of what has yet to come. But they have this really great heritage in the whole Apple ecosystem and the design aesthetics that Jony I've has put out. And also they have the models, they have the fantastic models that OpenAI has pioneered so far that are still state of the art. So when you combine these two technology powerhouses right now, you get a bunch of promises, but they seem to pretty, pretty promising.

[Alex Osola]

You know, it sounds like there are a bunch of different kinds of applications, consumer facing, more heavy industry, kind of something in between in the form of self driving cars. Do we have a sense of which of these might sort of come first and how the developers of AI are thinking about monetizing those phases?

[Bel Lin]

Monetization questions are always front and center because so many of these startups are funded by venture capital firms who need to see a return. And there's so much cash that's being injected into to AI right now. Some of the ways in which they're monetizing are in the software side, on the models themselves. So you could sell on a word or bit basis the ability to use OpenAI's models in other services and other technologies in the wearable side, the selling of the hardware itself, plus the software upgrades. But at this point, it's still really about adoption and figuring out which areas in the consumer world really stick. And then if we're talking about the heavy industry side, that's where ROI becomes a lot more important because you can shave a lot of costs by automating human labor away. And so that's where a lot of the warehouse and logistics companies are hoping to have an impact on their bottom lines.

[Alex Osola]

Coming up, AI developers may already be making the next generation of artificial intelligence, but if they build it, will the customers come? Stay with us.

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[Alex Osola]

Bell we've been talking a lot about the developer side, how AI gets made and what form it'll be in. But now I want to talk about the people who are going to be buying it and using it. Lots of companies have started using AI. According to a survey by McKinsey, 78% of companies say they use at least one AI function. So it seems like companies need to show they're integrating AI into their operations. Would you say this is an existential need for companies right now?

[Bel Lin]

Oh, absolutely. There are really existential questions for categories of companies like law firms that have questioned what is the value of the billable hour. Because so much of what AI is really good at automating away right now is reading and summarizing through texts and being able to provide synthesis of answers. And that's kind of early stage paralegal work. So if Companies don't embrace AI, there's the question of will we still exist in 10 years time frame, Nevermind questions of will we be using AI, pins and devices. We need to embrace AI now or else we won't be around.

[Alex Osola]

So that kind of brings me back to this other existential question about physical AI. Who actually wants this?

[Bel Lin]

Well, there is, if you look at examples of where physical AI exists. Now, I know we talked about warehouses and factories, but there are also great examples of where wearable headsets like the Apple Vision Pro and the Meta Quest and many others that have been around for a while have huge applications in the military, for instance, for training the armed forces and in training for surgeries in home services, where you have skilled trades like plumbers and air conditioning technicians learning how to build the physical engines that keep homes running, as well as jet engines technicians learning and figuring out how to troubleshoot them. So there's great examples of where physical AI and augmented reality, which is a really early version of bringing AI into the real world, already have a lot of value. And so you might see more acceleration in areas where AI in the real world are already having an impact. But once it becomes much more useful, you could see things like basic knowledge work becoming a lot more augmented because the ability to stream someone's virtual presence into a meeting room makes it that much more better. And there's no longer a need to have an in person meeting.

[Alex Osola]

One of the things that is in the news cycle about AI right now is just how unbelievably expensive it's been. Companies are shelling out billions of dollars to build these data centers because they are doubling down on AI being the future. Is there enough demand in all of these different applications for physical AI that we've talked about that will bring down those costs of the data centers, or will they just keep skyrocketing?

[Bel Lin]

A lot of this goes back to the AI models and the software layer, because as they become more efficient, then the promise is that they require a lot less gpu, compute and power going into the data centers. And so when the models become more efficient themselves, even though they are quite large and unwieldy, they can be trained much more efficiently. From that point of view, costs will certainly start to come down in terms of the infrastructure, but at the same time, other costs will need to come down as well. The cost of hardware in a really general sense is still quite high. The chips required to basically power Apple Vision Pro, or to power a humanoid robot, or to power self driving cars, those are not quite commoditized, they're still quite expensive.

[Alex Osola]

So as developers make these devices and software, and as companies figure out how to use them, whose responsibility is it going to be to figure out how to actually make money off of this?

[Bel Lin]

Yeah, a lot of the AI developers and the AI startups will be hard pressed to come up with an answer on how to actually monetize what they're building right now. A lot of them are funded by VC dollars, are backed by research or other types of grants and funding. And so there will be this sort of inflection point where either their technologies, their devices, their robots, their cars catch on with consumers or they don't. Because as we look at some of the other waves of technology that were funded by VC dol, like the Ubers and the Lyfts of the world, there's this limited time frame in which they can be funded by venture capital dollars until they have to show their mettle.

[Alex Osola]

And how about for the companies using the products for the companies?

[Bel Lin]

That's already a really pressing question. ROI has been challenging since the dawn of the ChatGPT AI era that we're in now about three years ago. Companies have been investing heavily in AI models and AI technologies, but there's really not a clear way to determine whether or not they're paying off. So you could say that productivity of workers has gone up, but it's hard to measure. You could say that sales have gone up, but that's also hard to measure. So measuring AI's value has been a question for tech executives for the past several years and continues to be. But there's a lot of economic incentives that are aligned in trying to make sure that the AI companies are profitable and that companies are saving on the bottom line and generating top line revenue that the market forces kind of end up working out in some way.

[Alex Osola]

That was WSJ reporter Bel Lynn. Thank you so much, Bel.

[Bel Lin]

Thanks for having me.

[Alex Osola]

And that's it for what's new Sunday for June 15th. Today's show was produced by Charlotte Gartenberg with supervising producer Michael Kosmides and deputy editor Chris Sinsley. I'm Alex Osola and we'll be back tomorrow morning with a brand new show. Until then, thanks for listening.

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[Alex Osola]

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