AI Is a Tool for Augmenting People, Not Replacing Them
Episode Summary
Sean White, then chief executive of Inflection AI, frames AI as a tool for augmenting people rather than replacing them, and says what is different this time is the direction of travel. Instead of us going to the technology and learning its language, it comes to us and uses ours, which means anybody can use it. For enterprises he wants the option to own your own intelligence, running models in a virtual private cloud or on premises so a company controls its own data rather than trusting a large corporation with it, and he is explicit that the interest is not only regulatory, since firms protecting a trade secret want the same thing. The second half goes to augmented reality and wearables. His ethical rule, carried over from Mozilla, is no surprises, and his worked example is earbuds that can read EEGs, where he wants personal data encapsulated for one user while federated learning still lets aggregate patterns help.
Key takeaways
His framing is deliberately narrower than the discourse around it, and he says so first. Some of the conversation about AI has been about replacing humans or AGI; for the most part he thinks about AI as a tool, a fantastic one, and a way to augment how we do things
His analogy for that is mechanical rather than cognitive. We originally created mechanical devices and pickup trucks to help us move more than we could as humans, and we never mistook those things as humans
What he calls different this time is the direction of travel, and everything else he argues rests on it. For the first time in his history, instead of us having to go to the technology, learn its language and figure it out, it is coming to us, and it is using language, which means anybody can use it
The conversational quality of Pi was intentional rather than incidental. Over hundreds of thousands of years people built the ability to learn from each other and interact with each other, which he calls the first interface with things outside us
On scale he gives two markers, both as of February 2025. Inflection AI was one of six or seven signatories of the White House commitments on AI and had done the same on the Seoul and UK agreements, and was one of the few companies that could converge a 350 billion parameter model and larger
He counts scale on the inference side too, and gives the only other number in the episode. More than 14 million people had come through and used Pi, which he treats as knowing what that scale means at both the consumer and the enterprise end
He answers the difference question across technology, business model and philosophy, and the business-model half is the one about transparency. They were discussing licensing their source with enterprises, so those companies could see what is there and understand where it comes from, or run it on premises so they control and own their own intelligence
His reason for on-premises is trust rather than latency or cost. He wants enterprises not to have to just trust that some large corporation is going to always do the right thing with the data that they have
The corporate structure was part of his answer on differentiation. Inflection AI was a public benefit corporation, focused on making whole solutions for people rather than one piece of the pie such as model building or fine-tuning
Asked which enterprises fit best, he names the ones that want to own their own intelligence, in a virtual private cloud or on premises, and includes regulated organizations such as banks, insurers and the medical and health field
He is explicit that regulation is not the only reason, which is the more useful half of that answer. The same interest comes from companies that simply believe something is their own trade secret, or want to control their privacy or their own data, and he says he understands and respects that
The second group he names wants dialogue rather than answers. Rather than a declarative or interrogative question that returns a book report, they want the collaborative dialogue built into the system, and API users push the edges of that into their own agentic flows
Asked whether healthcare is where the privacy requirement and the empathetic interface meet, he agrees, calls it a great sweet spot and then widens it. Almost any enterprise that wants tools which do not feel like drudgery, and let people work at a higher cognitive level, is in the same position
His augmented reality work started from a belief about posture rather than about graphics. He did not think the systems we use to interact with computation would stop at staring at a screen, or something in our hand, or a keyboard, but should let us look up and be present with each other
He draws a line between two things that share the name augmented reality. Holding up a phone and seeing cute little graphics overlaid is not what he means; he means enhancing the things we see all around us in the world
On enterprise wearables he says it is early days and splits the field in two. Glasses, where AR has been identifying things in enterprise settings for a while, and simpler devices such as earbuds, which he points to in the room during the conversation
What makes wearables interesting to him is not the display at all. It does not have to be AR in glasses; it can be an ongoing Socratic dialogue in your head while you approach tasks in the world around you, which he says changes how we think about work and keeps us in flow
He puts agentic flows on the same track rather than treating them as a separate subject. The next step is not just the cognitive discussion but getting things done, which he thinks we all care about
His ethical rule comes from Mozilla and he states it in two words. No surprises: you should not suddenly find out that something has happened you did not expect, in terms of your data, your life or your experiences
His worked example is material science turning into an ethics problem. Advances in the material science for earbuds mean they can now function as EEGs, reading the electrical patterns in your brain and telling you about attention, health or relaxation
He names the discomfort before proposing anything, and hedges it as he goes. It does sound a little scary, he says, and something that kind of knows a little bit about what you are thinking does not feel good
His answer is not to stop collecting but to change how the data is held. Encapsulate and encode the personal information so it is only for the context and personalization of that individual user, while still aggregating across the many millions of people who might be wearing those earbuds so the data can identify someone having a stroke or starting to get dementia
His closing ask is a thought experiment rather than an instruction. Think creatively about what AI can be beyond today’s chatbot, engage with everybody working in the space because it will not be one company, and think about how you would augment your own human experience
About Sean White
Sean White was chief executive of Inflection AI at the time of this conversation in February 2025. Before that he was at Mozilla, and his research background is in augmented reality, going back to PhD work that put wearable systems in the field to identify plant species. On this episode he argues that AI is best understood as a tool for augmenting human experience rather than as a step toward replacing people, and that what is new is the direction of travel: the technology now comes to us and uses our language, so anybody can use it. He takes the same argument into wearables and augmented devices, where his rule for personal data, carried over from Mozilla, is that nobody should be surprised by what a device knows about them. Everything he says here about Inflection AI, its models and its Pi assistant describes the company as it was in February 2025.
In this episode
| 00:42 | Welcome, and who Sean White is |
| 01:09 | Bursting with questions about the vision for Inflection AI |
| 01:24 | A question from the host: augmented human experiences and the enterprise |
| 01:31 | AI as a tool, not a step toward replacing people |
| 01:54 | Pickup trucks, and the first time the technology comes to us |
| 02:24 | It uses language, which means anybody can use it |
| 02:59 | Why the conversational interface is intentional |
| 03:32 | What the competitive advantage was in early 2025 |
| 04:15 | Scale: the White House, and the Seoul and UK commitments |
| 04:36 | A 350 billion parameter model, and why scale mattered |
| 05:13 | Scale on the inference side, and 14 million people through Pi |
| 06:01 | How they differed from other LLM companies |
| 06:28 | Licensing the source, running on premises, owning your own intelligence |
| 07:18 | A public benefit corporation building solutions rather than pieces |
| 08:39 | Which enterprises were the best fit |
| 09:08 | Owning your own intelligence, on premises or in a private cloud |
| 09:41 | Trade secrets and privacy, not only regulation |
| 10:10 | API users pushing the edges, and their own agentic flows |
| 10:37 | A question from the host: is healthcare where privacy and empathy meet |
| 11:07 | A great sweet spot, and working at a higher cognitive level |
| 11:43 | How early augmented reality work led here |
| 11:58 | Not stopping at a screen, a keyboard or something in your hand |
| 12:29 | The far future of augmented reality, and being present |
| 13:30 | How enterprises use AI in wearable and augmented devices |
| 13:36 | Early days: glasses, and the earbuds in the room |
| 14:30 | The Socratic dialogue in your head, without glasses |
| 14:54 | The next step: agentic flows, not just cognitive discussion |
| 15:27 | A question from the host: materials, sensors and training on behavior |
| 15:42 | The ethical considerations put to him |
| 16:10 | The Mozilla rule: no surprises |
| 16:53 | Material science, and earbuds that work as EEGs |
| 17:21 | What an EEG earbud can read about attention and flow |
| 17:44 | EEG earbuds within a year or two |
| 18:06 | Encapsulating personal information for that user alone |
| 18:40 | Aggregating it to catch a stroke early, and federated learning |
| 19:07 | An example from the host: earrings that monitor hormones and temperature |
| 19:40 | What to explore next: Pi, the website, Google Scholar |
| 20:26 | What listeners should take away |
| 20:32 | Think creatively, and engage with everyone in the space |
| 20:56 | His ask: augment your own human experience |
In Sean’s words
“For the most part, I tend to think about AI as a tool.”
— Sean White (01:31)
“Instead of us having to go to the technology, learn its language, figure it out, it’s coming to us.”
— Sean White (01:54)
“so that they control and own their own intelligence.”
— Sean White (06:28)
“Something that starts to let us be in the flow, that lets us look up from the things that we are doing and really be in the world.”
— Sean White (11:58)
“At the simplest level, no surprises.”
— Sean White (16:10)
“Something that kind of knows a little bit about what you’re thinking doesn’t feel good.”
— Sean White (17:44)
“My ask, think about how you would augment your own human experience.”
— Sean White (20:56)
Resources
Sean White
Sean White on LinkedIn: His LinkedIn profile
Inflection AI: The company where he was chief executive at the time of this conversation, and the address he gives on air at 19:48. The company has changed materially since the recording
Named on air
Pi: The Inflection AI assistant at the time of this conversation, which he discusses throughout and points listeners to at 19:40
Mozilla: Where he worked before Inflection AI, and the source of the no surprises rule he gives at 16:10
Stanford: Named at 05:37 as a block away from the office, which he describes as a way to pick up and collaborate on how the field is evolving
Google Scholar: What he points to at 19:48 for anyone who wants the deep academic material rather than the popular press
The White House commitments, and the Seoul and UK agreements: The AI commitments he names at 04:15, of which he says the company was one of six or seven signatories as of February 2025
Ideas and terms discussed
Augmenting human experience: His framing for the whole conversation. AI as a tool that extends what people do, set against the parts of the discourse about replacing people or reaching AGI
Owning your own intelligence: His phrase at 09:08 for running a model in a virtual private cloud or on premises so the company controls the model and the data rather than trusting a supplier
No surprises: The rule he carries over from Mozilla at 16:10. Nobody should suddenly discover that something has happened they did not expect with their data, their life or their experiences
Encapsulation: His answer to personal data from wearables: encode it so it serves only the context and personalization of that one user
Federated learning: The technique he names at 18:40 for getting aggregate benefit, such as early identification of a stroke, without the personal data leaving the individual system
Related AI Realized episodes and events
Cognitive Capital: The Advantage Nobody Is Protecting: John Sviokla on organizations where people and AI work as one system, which is the same argument from the organizational side rather than the interface side.
When Restoring the Painting Costs More Than the Painting: Alex Kashkin on AI extending human craft rather than replacing it, which is this episode’s argument carried into a field where the craft is centuries old.
AI Governance as Code: From PDF Policies to Pipelines: Ken Johnston and Bob Rapp on making a data rule enforceable in the pipeline, which is what a rule like no surprises needs before it means anything.
Frequently Asked Questions
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Augmenting human experience with AI means treating the system as a tool that extends what people can do, rather than as a step toward replacing them. Sean White, then chief executive of Inflection AI, sets that against the parts of the discourse concerned with replacing humans or with AGI, and reaches for a mechanical comparison: we built machines and pickup trucks to move more than we could carry, and never mistook them for people. What he says is new is the direction of travel. Instead of people going to the technology and learning its language, the technology now comes to us and uses ours, which is why anybody can use it.
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Enterprises run models on their own premises so they control and own their own intelligence rather than trusting a supplier with their data. Sean White, then chief executive of Inflection AI, described discussing licensing the underlying source with enterprises in early 2025, so those companies could see what was in the model and where it came from, or run it inside their own environment. His stated reason is trust rather than cost or latency: he did not want customers to have to rely on a large corporation always doing the right thing with the data it holds.
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Regulated industries fit first, and the ones named are banks, insurers and the medical and health field. The more useful half of the answer Sean White, then chief executive of Inflection AI, gives is that regulation is not the only driver. The same interest comes from companies that consider something a trade secret, or that want to control their own privacy and their own data for reasons of their own, and he says he understands and respects that. He treats an enterprise customer the same way he would treat a consumer on that question.
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A public benefit corporation is a company structure that commits the business to a stated public purpose alongside its commercial one, and Inflection AI was one at the time of this February 2025 conversation. Sean White, its chief executive then, raises it as part of what made the company different rather than as a legal detail, and pairs it with a focus on building whole solutions for people rather than one piece of the pie such as model building or fine-tuning. Anything about the company here describes it as it was in February 2025.
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Enterprises use AI in wearables in two forms, and both were at an experimental stage in February 2025. Glasses, where augmented reality has been used in enterprise settings for a while to identify something and tell you what it is, and simpler devices such as earbuds. What interests him is not the display. He describes an ongoing dialogue about the meaning of what is around you, or a Socratic exchange in your head as you approach a task, and puts agentic flows on the same track: not just the cognitive discussion but getting things done.
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The central risk is that a device learns something about a person that the person did not expect it to know. Sean White, then chief executive of Inflection AI, gives the rule he took from Mozilla as no surprises, and his worked example is material science: advances in earbud materials let them function as EEGs, reading electrical patterns in the brain and revealing attention, health or relaxation. He is direct about how that feels, saying something that knows a little about what you are thinking does not feel good, and he expects earbuds that do this within a year or two of the recording.
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Federated learning lets patterns be learned across many people’s devices without the personal data leaving any one of them. That is how Sean White, then chief executive of Inflection AI, resolves the tension he has just described: he wants personal information encapsulated and encoded so it serves only the context and personalization of that individual user. He also wants the aggregate benefit, because data gathered across many millions of earbud wearers could identify someone having a stroke or beginning to show dementia early. Federated learning is the technique he names for having both.
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[00:42] Christina Ellwood: Welcome to AI Realized Podcast for Enterprise Executives Leading AI Deployments. From addressing security, data, and operations challenges, to managing the organizational and management changes, AI deployment presents the opportunity to redesign our organizations from the inside out. I’m Christina Ellwood, your host for today’s episode, and we are talking today with Sean White. Sean, welcome to AI Realized.
[01:08] Sean White: Thank you, Christina. Thanks for having me.
[01:09] Christina Ellwood: It’s lovely to have you, and I, as the CEO of Inflection AI, I’m just bursting with questions to understand where you are and where you’re going, and to learn a little bit more about the vision that you have for AI, or for Inflection AI. So let’s s- let’s just start by talking about augmented human experiences with AI and what that means to the enterprise.
[01:31] Sean White: Oh, great. Yeah. I think there’s a lot in there to unpack, so let me start with a couple of things. The, the first is augmenting human experiences, and I think this is an important framing because some of the discourse around AI has been about replacing humans or AGI, and for the most part, I tend to think about AI as a tool. It’s a fantastic tool, and it really is a way for us to augment how we do things. In the same way that we originally created mechanical devices and pickup trucks to help us move more than we could as humans, but w- we never mis- mistook those things as humans. I think the thing that is perhaps a little different here is that for the first time in, uh, our history, certainly my history, instead of us having to go to the technology, learn its language, figure it out, it’s coming to us. It’s using language, and that, that’s a, a great interface for us ’cause that means anybody can use it, and, uh, it, it’s a great way for us to actually think about how we augment the human experience
[02:37] Christina Ellwood: Yeah, I agree. I think that is, is very well said. And, uh, of course, one of the hallmarks of Inflection AI’s technology is your very personable and high IQ interface, your chat bot called Pi. So that really fits nicely in with what you’re saying, that when we’re talking with Pi, we almost feel like we’re talking to a human, don’t we?
[02:59] Sean White: Yeah, that’s right. Um, and that’s intentional. The idea is that we have built up, again, over hundreds of thousands of years, a, an ability to learn from each other, ability to interact with each other, really the first interface with things that are outside of us. And so you don’t want something that’s just has a high IQ, super smart, knows all the things in the world. You really want a, a tool, a thing that you can interact with that it takes into account how we naturally interact best, and that really is through language.
[03:32] Christina Ellwood: Yeah, for sure. You recently joined Inflection AI as the CEO. What are a few of your key observations about the, um, competitive advantage that Inflection AI has in the marketplace today?
[03:42] Sean White: Yeah. First, uh, it is a competitive space and, and, and I think that’s a great thing. It, it reminds me in some ways of when the internet was first getting started up in the ’90s, and it was exciting because it wasn’t just a couple of people focusing on a couple of areas. It was a giant surface area of everything that was happening, and, and that’s exactly what’s happening in AI today. Even though we tend to focus on some of the things like we are doing with large language models, um, but really there, there are many different kinds of flavors of AI that are coming out into the field. Um, for us, I think there are a couple of competitive advantages that matter. One of them is that we really deal at scales that most companies just have not come close to. We’re one of the six or seven signatories of the White House commitments on AI, and we’ve done the same with the, the Seoul and UK, uh, agreements on AI. And then in part that’s because we’re one of the few companies that can actually converge 350 billion parameter model and larger. And so you’ll hear lots of companies that say, “Oh yeah, we can, we can train our own model or we can fine-tune our own model.” Um, but those tend to be smaller, and in this case, scale really matters. It really has impact. It’s what made the difference between something that just felt like it was a little broken or a little gibberish and something that really feels like it is starting to understand that language. I think another thing is that it’s not just scale there, it’s also scale on the inference side. That is the usage, the runtime side And one of the advantages of having Pi that you mentioned earlier, and, and I hope folks get a chance to try Pi, it’s really a great experience, is that it’s not just a couple hundred people using it. We’ve had over 14 million people come through and use Pi, and so we really understand what it means to have that kind of a scale, both at the consumer and the enterprise, uh, scale. And maybe that, the, the, the last piece is that we just keep on building. So we’ve had this incredible booster rocket, and there are so many new things coming out on AI. We- we’re a block off of, uh, the Stanford campus. It gives us a great way to actually pick up and collaborate and engage on how this is evolving, ’cause it really is evolving fast
[06:00] Christina Ellwood: Yeah, for sure. There are many LLM companies out there now, so how do you differ from those companies? Do you differ both in your technology and in your business model, or in your philosophy or your approach to the market? What makes you different from other LLM companies?
[06:17] Sean White: And, uh, across all of those really, I already mentioned in terms of the technology, the scale and the experience is really something that most other companies just haven’t been able to achieve yet. And i- in some ways this is a little bit like alchemy, which is to say information is passed on from a small group of people, and e- eventually it gets out into the world and everybody understands it. But r- right now, early on, it’s just not the case. I’d say that our business model is also a little different i- in that we are, we in some ways are thinking more openly about what we do with this, so that when we talk to some of the enterprises, we’re having discussions about licensing our source so they actually have transparency, so they see what’s there, they understand where it’s coming from, or even for them to be able to run this on premises so that they control and own their own intelligence, so that they don’t have to just trust that some large corporation is going to always do the right thing with the data that they have. I’d also say our philosophy is a little different. That is, we are a public benefit corporation. We are really focused on how we don’t just make tools here, but that we actually make solutions for folks. And so it’s not just one piece of, one piece of the pie where you are doing, uh, model building or fine-tuning or some pieces, but overall systems. And I think that system building, that complexity is really important because it’s a little bit like the early web days where, you know, when we were first starting out, you would build something and that was basically a server. And so when we ping the server, you send something back, but eventually we would then start to actually make these really complex systems where you might fail over to multiple devices and servers, and you have database systems and app servers and all this other stuff. That’s really happening here very quickly. And our philosophy is really about listening to our users, making those systems benefit our users. And between the, the technology that we have, the, the difference in the business model, and just our general philosophy, um, including the public benefit corporation aspect of it, it feels differentiated than s- some of the companies that are really focused on one piece of that
[08:39] Christina Ellwood: Yeah, I, I, I can see that. So just to summarize that, you’ve got a high EQ LLM, you’ve-- with huge scale. You’ve got a philosophy that includes your public benefit corporation structure and approach, and then you’ve got the opportunity for, uh, companies to license or put your, your model behind their firewall. So which enterprises are most ex- ideal or excited by the Inflection AI model or approach?
[09:08] Sean White: Yeah. I’d like to say all enterprises, but I’ll, I’ll break it up into a couple of different groups. For the folks who really care about owning their own intelligence, the idea of being able to either in a virtual private cloud or on-prem manage and control this, we see a lot of excitement there. And that, uh, in some ways is, for instance, some organizations are regulated, like banks and insurers and the medical and health field, but also, um, that comes from folks who just believe that it’s their own trade secrets or it’s just something they wanna control. And we understand that and respect that. They wanna control their privacy or th- or they wanna control their own data. Just as we would treat a consumer, we, we wanna treat the enterprise in that same way. But the other folks who are, uh, excited and interested in, in this do come for the sense that, that these systems shouldn’t just be a declarative question or an interrogative question where you get a, a book report back, but it’s really a dialogue, and that collaborative dialogue that we’ve built into the system is from the user interface point of view. And when we have API users who are building things off of this, they really are pushing the edges of that and experimenting with that, where sometimes it is that they are creating their own agentic flows from it. Sometimes it is that they want something that keeps that dialogue going so you can learn more and work with somebody or, or help them out in some way, that those two ends of the spectrum really take advantage of the things that we’ve built out in the system
[10:37] Christina Ellwood: Would it be fair for me to imagine that an intersection of those two would be in healthcare, where doctors and healthcare practitioners dealing with patients and other healthcare practitioners would benefit from that, from both of those aspects, the private data and, and the protection of that private data because of the regulatory requirements and the privacy requirements, but also the very empathetic voice and the sense of connectivity between the u- the, the person asking the question and the answers that they’re getting back?
[11:07] Sean White: Yeah. That’s right, Christina. But I, I, I think that’s a, a great sweet spot for it. It’s also true that really almost any prize, uh, any enterprise that cares about creating tools that help the folks who are working there, that, that don’t feel like drudgery, but actually f- feel like they are getting ahead in the things that they are doing, let them work at a higher cognitive level, that feels just as important. ’Cause it, it’s, it, it’s not just that you have the high IQ and not just that you have the high collaborative intelligence, but really it’s the two of them together
[11:43] Christina Ellwood: Yeah, that’s beautiful. You’ve been working for, um, let me start over there, I just heard a, an indicator. You’ve been working with bleeding edge technologies for a long time. How did your early work in augmented reality pave the way for your current work?
[11:58] Sean White: Uh, that’s a great question. One of the reasons I started working in augmented reality was my belief that the systems that we use to use computation to interact with our systems and with each other and with the world wasn’t just going to stop at staring at a screen or a tab in our hand or a keyboard, but really should be something that starts to let us be in the flow, that lets us look up from the things that we are doing and really be in the world, be present with each other. And with, uh, augmented reality research, and, and I wanna distinguish that from something where you, you hold up a phone and you look through it and you see some cute little graphics overlaid. Um, but, but really the, the far future of augmented reality where you’re, where we are enhancing the things we see and all around us in the world, that kind of change in the way that we, uh, engage with technology, uh, feels like it, it, it changes our behaviors, right? That we’re not all bent over looking at things. And there’s a, a thread of that, this sort of human-centric technology development design that I think really shows up in AI, which is to say that if, if we can actually do some of the same things, some of the same work or even better work, but not just be sitting in front of our computers to do that, that feels like a, a win for individuals. It feels like a win for society. It feels like a win, really a win for enterprise as well.
[13:30] Christina Ellwood: How are enterprises using embedded AI in wearable devices or augmented devices?
[13:36] Sean White: It, it is early days. Mm-hmm. I, I’d say there are a lot of experiments, and it’s, it’s worth breaking out, um, augmented reality, say in glasses and devices like that, and then in some ways simpler devices like just the earbuds, like the ones that you’re wearing right now With glasses, there’s a lot of interesting work, and you’re seeing this released on some of the main consumer devices, but it’s been showing up in, in enterprise devices for a while, where you use AR to identify something, to know what it is. This is a bit like some of my early PhD work where you were wearing this out in the field and you could identify a plant species or the things around you. But it becomes much more interesting when there is an ongoing flow about the semantic and cultural and even process and operational meaning of these things around you. And so it doesn’t just have to be AR in terms of glasses, it can just be the conversation that is in your head. And so, for instance, having something that has an ongoing Socratic dialogue where you are learning or asking as you are approaching tasks in the world around you, that, that actually changes the way we think about our work, keeps us in flow more. And the next step in a lot of this then is not just on these other platforms, but also in different agentic flows. So it’s not just that cognitive discussion, but also getting things done, which I think we all care about. And so we’re just starting to see some of that in some of the interfaces and some of the enterprises. And frankly, some of the enterprises that have been using glasses, like manufacturing and construction, things like that, are realizing that they can add quite a lot in by adding in AI together with these systems.
[15:27] Christina Ellwood: Yeah. Breakthroughs in materials are also very important in that, aren’t they? In terms of the sensors, but also in terms of the materials for forming, uh, devices that are natural for humans to wear or to interact with in their environment. So there’s, uh, some breakthroughs over in that area too. What do you think are some of the ethical considerations when companies are using LLMs and augmented reality, um, and train them on employee behavior? A kind of intelligence that we don’t today have a way to measure, um, but we know it’s there. It’s not just the data, it’s not just the business process, it’s also people’s behavior. How do you-- what do you think are the ethical considerations related to that?
[16:10] Sean White: Yeah. I, I’ll go back a little bit to the, the way we tended to think about it at Mozilla, which I think is important, which is at the simplest level, no surprises, right? Which is you don’t want to suddenly find out that something has happened that you didn’t expect in terms of your data, your life, your experiences, any of these things. And so there’s a lot of good work now both in terms of how the models are built, how they’re used, but also how we take and personalize that data. And you can encapsulate that and really keep that private on a per user basis, but also try and figure out what that means if you want to share that data in some aggregated or collective way to improve a system. Let me give an example, and it’s a little related to, um, the wearable devices and material science you’re talking about. Um, there are a number of companies that have advances in the material science for earbuds such that they can actually now function as EEGs, which is, say, look at the electricity and electrical patterns in your brain, and you can tell all kinds of things about th- uh, like attention or health or relaxation in those systems. And pardon me. On the one hand, this is really exciting on an individual basis ’cause it helps to understand the state of somebody, and you can do everything from whether they are in flow, and so they, they’re really able to do the things that they’re focused on, but if they’re breaking out of that flow and if that is problematic in some way, if there’s something environmental that has happened around them. And so that material science enables... And I, I promise you, you will start to see earbuds in the next year or two that do this, have let you read your EEGs that open up lots of possibilities. And that does sound a little scary, right? That is something that kind of knows a little bit about what you’re thinking doesn’t feel good. And so the set of work around that then is how do we take that personal information, encapsulate it, encode it in ways such that it really is only for the context and personalization of that individual user. There’s another ethical issue around there, which is also you can imagine that aggregating that, let’s take the many millions of people who might be wearing those earbuds, you could actually do some really beneficial things in terms of, for instance, identifying if someone’s having a stroke or if they are starting to get dementia, any of these other things. And you would like to actually aggregate that data so that you can help people, so you can do early identification, benefit, all these other things. And this is actually where things like federated learning really come into play. There’s, there’s a lot of good work on how we do privacy preserving for the individual systems that also can take data and in aggregate Help build models that benefit everybody.
[19:07] Christina Ellwood: Yeah, that’s a really good example. There’s also earrings that monitor your, uh, for women, that monitor their hormone levels and temperature and things like that, with similar ethical considerations. So I think that the earbud example is an excellent one, um, but we’re gonna see more and more of them as time goes on. So I think it’s important that we all keep those ethical considerations in mind. So what resources would you recommend to our listeners who want to learn more about Inflection AI or augmented reality or any of the other topics that you’ve covered today?
[19:40] Sean White: Yeah. Uh, first thing is download Pi. Ask Pi. Pi knows all sorts of, uh, things about this. You can also go to our, um, website, inflection.ai, and learn more. And then I would encourage them to s- start learning. If they have a ... If they really want that deep academic bent, they can always go to Google Scholar. But if they’re looking for more sort of popular press, there are a lot of great outlets that are starting to focus on this as well. And of course, we will occasionally be posting on some of this ourselves as we start to grow, not just from today, but in the next 5 and 10 years into where AI can take us, making sure that as this moves so quickly, we remain a positive actor in the space.
[20:26] Christina Ellwood: Great. Okay. So as we wrap up, what would you like our listeners to take away from our conversation today?
[20:32] Sean White: I, I hope they think creatively about what AI can be, not just today, not just the, the chatbot, but how it really augments the human experience. And engage with us, engage with everybody who’s working in the space, because, you know, it’s not going to be just one company. It will be all of us working together and sharing those ideas and iterating through. My ask, think about how you would augment your own human experience.
[21:00] Christina Ellwood: Sean White, CEO of Inflection AI, thank you for talking with me today on AI Realized.
[21:07] Sean White: Thank you, Christina.