Machine Speed Innovation: DeepInvent Founder Marcus Weller is Revolutionizing the IP Economy

Marcus Weller is the visionary founder behind DeepInvent—an AI-powered platform that’s revolutionizing how intellectual property is created, scaled, and commercialized. By slashing the time and cost of invention, DeepInvent is accelerating innovation across industries and delivering transformative value to the global economy.

With a background in cognitive science and data science, Marcus has driven innovation across sectors including AI, semiconductors, consumer electronics, transportation, and augmented reality. His multidisciplinary experience led to a breakthrough insight: AI isn’t just a tool for productivity—it can be a catalyst for invention itself.

Now, Marcus is on a mission to empower a new generation of inventors, entrepreneurs, and companies with the AI engine that could reshape the future of innovation.

Transcript

Jeanne Gray: I am Jeanne Gray, publisher of American Entrepreneurship Today and host of the podcast series Experience Voices, where I talk with highly accomplished people who share the critical elements that led to their success.

Innovation is the lifeblood of a thriving economy. And few understand that better than our guest today on experienced voices. Marcus Weller. He is the founder of Deep Invent, an AI powered platform revolutionizing how intellectual properties created and advanced by dramatically reducing the time and cost of invention.

Deep Invent is accelerating innovation across industries. And delivering transformative benefits to the innovation economy and to society at large. Before founding Deep Event in 2025, Marcus applied his expertise in cognitive science and data science across a wide range of sectors, including ai, semiconductors, consumer electronics, transportation, and augmented reality, making him uniquely equipped to lead this next wave of intelligent innovation.

Welcome to Experience Voices. Marcus. 

Marcus Weller: Thank you. It's a pleasure to be here. 

Jeanne Gray: Well, I'm very curious about Deep Invent, so let's start off with you explaining to listeners what Deep Invent does. 

Marcus Weller: Deep Invent is an AI innovator. It's first AI that can actually invent.

New intellectual property, new inventions, and then draft the resulting patents from that innovation process. 

Jeanne Gray: What did you bring into the model that has made it so innovative? 

Marcus Weller: Well, the model, it's a compound architecture, and basically the summary is that it uses a set of cognitive heuristics derived from cognitive science that human innovators use, and it stacks those in a certain sequence.

And by doing that. It allows users to actually generate new innovations. Essentially, it's looking into the future, figuring out what those critical innovations might be, and then bringing them into the present to work on them. 

Now with a concrete product definition and patent draft that comes from the system.

Jeanne Gray:

I understand that has very particular strengths. You're using the scientific method, there's cross-disciplinary insights. You're building an algorithm. Share a little bit about those elements. 

Marcus Weller: You can think about it like the application layer to innovation. It algorithmically takes you through the steps of innovation that allows you to derive insights and inventions directly from the primary data.

It will take an idea and then it'll pull in all of the world's scientific literature related to that idea and pull that into a knowledge graph for you and then it'll do the same for the existing patents and the same for the existing industry data.

Then it predicts where those trends are going in a composite cloud of data, right? and looks for those low-density regions in that cloud or that knowledge graph that's the fertile ground for innovation. It generates new patents, new intellectual property in that future white space.

And that allows you to basically grab those ideas, bring 'em into the present and work on them now. 

Jeanne Gray: So how tied is this to the patent and trademark office or existing intellectual property? 

Marcus Weller: It extracts data from the USPTO and we also have our own proprietary IP databases and it is highly linked to that because it's important to stand on the shoulders of giants.

The whole point of the USPTO and why it exists and why it's important to patent IP is that it allows innovators in our innovation ecosystem in the United States abroad to publish the constituent parts of their innovation, of their inventions. Then that allows other inventors to cite those patents and stand on the shoulders of giants and invent their own things.

We find that to be very important. The USPT O'S stance is very pro ai. I met with Jerry Ma at the USPTO. He's the Chief AI officer there. I met with him last year actually and we got a little bit of insight about where their future policy heading with respect to ai.

They want to encourage the use of AI if it increases American innovation and, and innovation throughput. But they want to make sure that the human has a significant contribution to every invention, which is why we start with the idea of the human. It always starts of the idea of the human first.

Jeanne Gray: Now anyone using Deep Invent, their inquiries are kept confidential. 

Marcus Weller: That's absolutely right. It's protected by NDA. It's encrypted in our system, and all ideas are those of the inventors. 

Jeanne Gray: So who are the, target users in terms of a broad spectrum? You have the general public people who end up coming up with something unbelievably innovative of humble background, and then you have people who have been doing r and d for 30 years.

So how does Deep Invent touch their process? 

Marcus Weller: Right. So that's what's so special about Deep Invent is it, it democratizes this process of innovation, innovation as a concept. It's kind of amorphous, it's difficult to define, it happens sporadically. , You have to basically rely on serendipity for these big, , step function improvements technology, right?

What this allows any user to do, whether you're a startup or an enterprise. Or an IP attorney, or an IP practitioner is it allows you to simply start with an idea and then generate profound innovations, validate those with scientific literature, and then all in one seamless flow, generate the patent drafts directly from that process, and it takes basically months or years of r and d and compresses that into minutes.

So what we see initially is a lot of demand from the startup ecosystem, and I think the reason why. Is as a multiple founder myself, I'm very familiar with this friction and struggle, which is the need to get IP on file and how that competes with the need to execute and achieve product market fit.

But the problem is that it's super valuable to get your IP on file. As a startup, each patent is worth. A million dollars or more according to Forbes, to your valuation as you're going through and getting your IP filed. So, startups cannot afford not to do it. And , in addition to that, it prevents you from going down the wrong paths.

If you have well conceived r and d and you've got a pipeline and a suite of ip, it allows you to know what the landscape is and ensure that you're working on the right things at the right time. 

Jeanne Gray: So has all of the earmarks of being highly disruptive to the innovation process. Can you share a little bit about the, the feedback that you've gotten?

Is this been demoed? Do you have a team behind yourself, some advisors as to how do they put this in context of where it will change innovation? 

Marcus Weller: Great question. We actually shipped to 153 companies about two days ago. And so the product is live and in production and anyone can get access to it now.

You have to apply to receive the free trial and users get seven days of access. It's at Deep Invent ai. Basically the feedback has been incredible. People have said that it's accelerating , their roadmap dramatically. And also helping them prune what all the different r and d paths that they were going down.

And we're also seeing people who they were, they, they were working in a corporate innovation role. One example was an Amazon lab, 1 26 engineer who was able to come up with a new type of e-reader that was an augmented reality e-reader that was platform agnostic, and it would modulate the speed of the text.

Depending on the complexity of the text that it would auto detect the complexity. So if it was a neuroscience textbook, it would go slower, and if it was a news article, it would go faster. And , that allowed it to be platform agnostic, meaning it could go on any augmented reality glasses. And it wouldn't need any special hardware , to assess reading comprehension as a brilliant idea.

He was able to file a patent in minutes and then develop a prototype. VI because of vibe coding, what came outta the system and he ended up quitting his job at a Amazon lab 1 26 to pursue it full time. So, and that wasn't the first time that that's happened when we were enclosed beta. That happened two other times.

So what I think is happening here is , it's like. putting your idea in. It's something you're passionate about, you're thinking about, and it's like having a genius at your fingertips that's just as passionate about your idea as you are, but it knows all of the world's information all at once and brings that to bear on helping you bring it to market.

I. 

Jeanne Gray: what features does Deep Invent also offer? We've basically been talking about the fact that it accelerates innovation. It can accelerate the intellectual property process, but are there other , key elements that a user would like to know about? 

Marcus Weller: Yeah. What we're seeing in our, usage data is users are deriving a lot of value from going through the different innovation phases , in the steps through the system.

So it starts out with a discrete analysis of all of the scientific literature up to the minute, right? And so you get to understand , the global landscape of scientific literature. All related to your idea. And it's interdisciplinary. So it looks across disciplines, not just within one domain.

So , that's a major strength, is that you start out with this scientific grounding around your idea or concept. And to achieve that by looking through and trying to. Fine tooth comb. The scientific literature is extremely difficult , and basically impossible to do that from a cross-disciplinary perspective, even if you're a PhD, because you only know what you know as a human.

And then secondly is , it does a white space analysis. So when it pulls in the patent data and it brings that and combines that with what's happening in the industry. And it also combines that with the scientific literature, you're able to understand this holistic view of what is the state of the art.

And having that white space analysis is so critical for founders, entrepreneurs, and enterprises that are trying to understand , where they should be focusing their, capital, their resources, their engineering time, and RD. 

Jeanne Gray: So let's take a step back. We will cover actually in our intro, your extensive tech background in cognitive science.

But you explained to me and you can share with the listeners your aha moment in which you saw the potential. Maybe we'll start with that point is how did you come up with the idea for Deep Invent? 

Marcus Weller: So I had a friend he had raised some capital from Peter Thiel for his Health 3.0 company, and he was going out for a second round of funding.

And I asked him how it was going and he said he had some initial conversations, but the evaluation wasn't good. And I, I was surprised by that. So I said, what patents do you have? , What's your tech? Angle. And he's like, we don't have any, and I said, you're raising without filing any patents and you're trying to get a tech multiple, you're gonna have a down round and you're gonna, you're gonna lose control of your company.

This is not good. We have to get some patents on file for you immediately. So I asked him to send me what he had as far as like what was going on, what was underway with the company and what were they doing from a technology perspective. And I volunteered to kind of. , Create a suite of IP outta that, , some actual patents from it.

Just by hand, , I filed a number of patents in my career, over 20 patents, and I was happy to do that because I had some heuristics of my own. I knew that I could get to something patentable by just going through the steps that I usually do to be able to do that. And so I sat down and I started working on them, and then I just had this realization.

Why don't I actually build an AI that will help me draft these patents and I'll teach it my heuristics, my cognitive heuristics of how I would do this, and then it will draft it. And I ended up doing that. I made the little prototype. I didn't save myself any time at all. , It still took me the whole week.

But what happened was that then I was left with . A system that I could continue to file new patents. and so that was kind of this aha moment that I was like, okay, this is something that's infinitely scalable and if I keep going with this r and d and this keeps getting better and better, it's essentially an AI that innovates it's impact is potentially unbounded.

You know, if I was building some augmented reality glasses, the most that invention could ever be. Would be, , everybody's wearing the augmented reality glasses and it's just that, and maybe it's a new platform, but if you reinvent invention itself, , that's unbounded. Everybody has access to that and now everybody around the world is innovating with that system.

And so that was kind of a realization for me that this is the most important thing that I could possibly work on. And that was about a year ago and it's been about a year that we've developed the architecture to the state it's at now. 

Jeanne Gray: So when you were at that moment. And you are an entrepreneur and you're starting up just like every other entrepreneur.

Give me a sense of the, say three aspects of your background in the area of tech, because I think other entrepreneurs, whether they're tech or non-tech, are sitting here saying, do I have the skills to do something like Marcus? So when you sat down and you began this journey at the very beginning, what were the three things that you self-assessed that said.

I can do this or I can get other people to help me do it. 

Marcus Weller: To be honest, I don't think I would've ever come to the conclusion that I'm the one that should build this and , I should be the reason why this thing exists. In fact, it was the opposite. It's constant imposter syndrome. Am I actually capable of being the one that brings this to market?

That being said. When I look backwards, there were clues that I might have some relevant skills to bring to bear on this, , which were that when I was working on my PhD as a grad student, I was studying innovation and that was the thing that I was most curious about.

And also the nature of cognitive ability and how that leads to innovation and. The cognitive science behind it, . But also the societal stuff, , the impact of innovation at the societal level and, , the leadership styles that elicit innovation in global companies , and those kinds of things.

And as part of that, you know, and as part of my education, I started learning, , more deeply about cognitive science and data science. And those worlds were really. Truly very separate at that time. You know, this is in the early 2010s, and so , what happened is the world sort of converged on those things , as we started approaching, 20 15, 20 16, 20 17, cognitive science and data science started merging into this field of ai.

And then you started seeing these really dramatic leaps when they would incorporate certain, cognitive structures or cognitive approaches, or at least those were the ones that were most interesting to me. And so, leading up to today, I. And then also kind of along the way, , I'd done some startups and I, taught myself engineering , in a bunch of different domains to be able to help us build augmented reality stuff.

And that kind of gave us sort of the, beginning lattice to build on which were, just really understanding from a psychological perspective what a system might look like. What an artificial system might look like. And then from a data science perspective, understanding the data structures that , we might capitalize on or exploit in these knowledge graphs that could use these heuristics to generate new insights.

To put it more simply. , It's basically certain thinking structures and thinking strategies that come from the, human brain can be used on data sets to then predict things about the future through a process called regression.

Jeanne Gray: I am absorbing what you've described and sounds like to me. You've laid out the visionary model and the next step is the execution. And going back to my original thought was you are not gonna be able to do this all by yourself. And you're gonna need some early buy-in , from the techies who are going to get you through the first three or six months or proof of concept.

So maybe you could share a little bit about what you defined as your proof of concept and the team or individuals that brought the skillset from vision to execution. 

Marcus Weller: Yeah, I couldn't have done this without my amazing co-founders Ali, our CTO, and Mitch who's my brother and perpetual co-founder. And so those two really enabled this in a deep sense of the word because.

What we did is we started from that initial prototype, right? But then we started doing a lot of AI research to figure out where relative to the frontier we were and if we were going down the right path. But the big moment I. Happened when Lio and I were developing this thing day and night, figuring out how to get to the next level of the architecture, make it better, smarter, faster, safer, cheaper.

And eventually we got to a point, LIO and I, where. , , the thing was generating new inventions at a level that was starting to saturate our metrics. So we had a grading rubric where we would compare human drafted patents versus our AI drafted patents. And we would have subject matter experts in those domains that would evaluate them blindly, ?

And so when the system started. Exceeding the human ratings , for the human patent drafts, we realized that we might have something here that other people could use , that they would be excited about. So what we did is in order to determine if we wanted to, , really pursue this in earnest, raise some capital.

And go to market with it. We wanted to get some deeper certainty. And so we said, what is the one thing that we are subject matter experts on that we know better than anyone else in the world? And that one thing was the architecture itself, the thing that we had built, right? So what we did is we gave our AI deep invent.

Its own product definition, its own architecture description. And we asked it how would you innovate on yourself and what it came up with. Was exactly the same, about 85%, the same as what was in my handwritten engineering notebook and nowhere else. That was the product of eight months of thinking and research and falling asleep, thinking about it and waking up, thinking about it.

And it was this concept called recursive evolutionary regression. What it called, it was recursive evolutionary inference. But it was a method for enhancing the patentability of the concept as it would generate by breeding the best ideas and then injecting genetic diversity through multidisciplinary data and then doing that recursively.

And it was a concept that we were really proud of that was, derived from a lot of research and. The crazy part about that was that it was clear to us in that moment that it wasn't just coming up with, , great inventions. It was coming up with the right invention. And that was a very, , qualitative difference that , we had observed.

And so we just went through the rest of the process in the app on this test run where it then generated a patent draft. Based on this, and then as a good steward of its innovation, I filed it on its behalf. And that was our very first patent. And that, as far as we know, was the first AI to innovate on its own architecture and then patent itself.

Jeanne Gray: So how excited was your team? 

Marcus Weller: I am still excited about it. Now, if you can't tell you know, I had goosebumps. We took screenshots of those moments when we were doing that, patent run. And it was just, it was astonishing. And, we've seen that now echo through our users where they send us these examples and screenshots where they have this.

, This extremely, peak experience moment. Where they feel the same emotion. And I think it's just, it's remarkable. like. , There's something to this that, a system that can, give you this aha moment about your own idea and help you see , what the idea is capable of becoming.

And it's just, it provides so much motivation to go from zero to one and take that first step. And I think that's one of the subtle things that's not necessarily scientific, it's kind of psychological and emotional, but it matters. Inspiration matters when it comes to innovation. 

Jeanne Gray: And information is power , and AI is letting us have access to that information faster and at a higher quality.

So taking this point, one step further, , it's sort of a project manager perspective as an entrepreneur, is you and your team had to lay out the milestones to progress and a lot of it is done, , sweat equity, kitchen sink, you know, in the basement many hours pulling your hair out. Then you get to your proof of concept.

So can you describe in terms of the investment, you've put the sweat equity in, you've brought in the talent that compliments your own, but at some point you are writing checks or you're trying to get to a point where someone else will write a check. Share a little bit about your thinking on that. 

Marcus Weller: Well from a development perspective, we suspected that what we were working on. Was going to have a lot of meaning for society and potential for impact. And there could be an enabling technology to innovators everywhere. So we felt a deep sense of responsibility to get it to market and to get to market as fast as possible and in the United States so that we were the ones that brought it to market, the United States and that it wasn't China.

And in order to do that, we knew that we needed to. Look at what could we accomplish in the next year, and then try to do that. In a week and then try to do that every single week. And we knew that just with the rate and pace of innovation in ai, that there was no other option that we needed to do this.

And also every month that it's not in the market is another month that's gone by, that people have not been able to use it to innovate. So that was our, , main objective was have the societal impact accelerate American innovation and. To get it to market as quickly as possible to be able to have that impact.

Fortunately for us, that resonated with some of the investors that we had been in contact with. And that brings us to antler who we were working with a partner there Tyler Norwood who had heard about what we were working on. I met with him. I told him what it was. And what I thought it could be, and he tried it for himself and.

He messaged me that he was blown away and that it had given him some reason to even consider branching into a new career pursuing one of the ideas that came from the system. And so we said, don't do that. Keep. Working in VC and cut us a check first. and fortunately he did, and that was our first check into the company and that allowed us to really start accelerating.

And that was about two months ago. And then after he cut that check, then we were able to dramatically accelerate our time to market until about two days ago when we shipped for the first time. 

Jeanne Gray: So would, would that be considered a seed round? 

Marcus Weller: That was actually our pre-seed round. 

Jeanne Gray: explain a little bit to listeners to how you separate out pre-seed from a seed round. 

Marcus Weller: Yeah, for our pre-seed, it's a time where you're trying to establish the viability of the company and the product and the team. And it's really an exploratory phase and you're looking to capitalize that to do a sufficient rate of speed and inertia to establish viability so that you can, , progress to to the seed stage where you're establishing product market fit and you're getting.

Your product into the market. But because we had such an accelerated clip and trajectory, , we end up shipping a few weeks raising our first check of our pre-seed round And so it's a little bit atypical I think, of a pre-seed company to be, , live in production.

Already, but for us, it's not an option. We, we just wanna accelerate this as fast as possible, and now we're able to actually focus more on revenue than raising. And I think that's kind of the ideal state in the age of AI is, , build quickly , and try to get it. Into the hands of the people that need it as soon as possible and get paying customers so that you can actually validate and not just hypothesize whether it has market viability.

Jeanne Gray: Share a little bit about your concept of bringing it to market. , What does that mean? You, you have a live website, you offer demos. You also just shared that you have quite a number of companies embracing it, so , what's your marketing arc? 

Marcus Weller: yeah, so we're live in production, meaning that any company or innovator , can come in and sign up now and get a free trial.

And the arc of. Sort of what we wanna accomplish from a marketing perspective is through product-led growth. It's really not marketing. , If we have to market it, then , it doesn't have the, potential scale and impact that we think it has. So we want people to experience this for themselves, have those aha moments, and then share that with.

Other innovators that they know, and we think that that's the best possible way to grow. And what we're seeing is without doing any marketing, that it just continues to grow. The signups are growing every single day. And I think that, , in the age of ai, it doesn't hurt to, , talk to press and talk to media. I think that's a great approach. But as long as you're focused on, you know, what the mission is and, and educating people rather than trying to generate demand for something that , is only incremental. I. 

Jeanne Gray: Now AI is all about disruption and displacement, so share a little bit about those involved in the intellectual property process that are now having their roles disrupted.

Marcus Weller: Look, I think in our conversations with a number of law firms and, and patent practitioners, IP practitioners is they're excited to self disrupt, to adopt ai, to accelerate their ability to add value to the innovation ecosystem. The vast majority, I would say. And we want to be the tool for them to do that.

And so we see ourselves as a absolutely a complimentary component to , that part of the innovation ecosystem, that critical part of the innovation infrastructure. So we want patent attorneys to be able to use this to increase their throughput, to be able to add more value to the innovation.

Process with the entrepreneurs and enterprises that they work with so that they can do deep white space analyses that are future oriented rather than just present day. And to be well conceived, to understand the scientific literature and how that fits into the white space analysis rather than just, the patent landscape.

And so we wanna be the go-to tool for them to add more value. And I, and I think that a lot of. Patent attorneys and IP practitioners, they're bandwidth constrained. It's, extremely laborious to generate new intellectual property. And it's, super cognitively loaded.

It's really complex work, and this is a tool that allows them to do their very best work and compress months into minutes , and ultimately that can only be good for the 

Jeanne Gray: Do you have concerns about emerging competition? 

Marcus Weller: I think that we are the emerging competition, if I'm honest right now.

But I want more players in this innovation ecosystem , to bring things to market , and to partner with us. , Like for example cursor. Windsurf , and V zero. , These vibe coding and, IDE platforms, they're a perfect compliment as partners for what we do because you're able to export directly a well conceived product definition, product description, innovation description, and and intellectual property and, and patent draft directly into these IDs.

And then you can vibe code that. So that's an extremely powerful combination is 'cause you know, for a fact. That you're working on the right idea at the right time, and that it's as well conceived as it can possibly be based on the current state of the art. And then you're able to immediately bring that into the real world, into a prototype, especially if it's a software application , and actually test it with live users within an hour.

I mean, we're not talking about months or even days. We're talking about within an hour. This Amazon Lab 1 26 engineer did this within one hour. So I think that's incredibly powerful. And , we see just an enormous range of possibilities for partnerships. And not only that, but these other companies in the space, they can use our platform to advance their own intellectual property and their own suite of ip.

, To continue growing and figuring out do they grow and scale their companies? At the end of the day, what Deep Invent is about is solving your most intractable problems. By innovating first, then , it drafts patents second, so it really does both. You know, it allows you to innovate on the things that need to be solved most urgently.

And then once you clear that work in progress, you're able to then start looking towards the future and build out a suite of IP that's forward-looking and future proofs your company. 

Jeanne Gray: , as we wrap up our conversation, Marcus and we've covered so much, most innovators, especially when they've, taken their product to market also.

Or seeing something two to three years down the road that they have their toe in the water or they've had their first launch. So how would you see Deep Invent a year or two from now, or how do you see the AI landscape that it fits in a year or two from now? 'cause it's going so quickly. 

Marcus Weller: I think if you look at the levels of AI framework that OpenAI released a while back they talked about, , these, five different levels of AI and, , level one are chatbots level two are reasoners, level three are agents, and then level four are innovators, AI innovators, and that's kind of where we're at.

And so people aren't. , That aware right now that that's what's coming. But I think that's what's gonna define the next, the next couple of years, is our ability to leverage AI to do, to advance us and bend the curve of human progress through better, more programmatic approaches to innovation so that we're not relying on, , hope and serendipity , and coincidence and lucky accidents to actually.

Innovate and progress us forward. As important as it is to progress as a humanity, we need systemic serendipity. We need, engineered serendipity. Right. And that's where I think that, , the most progressive models are. And if you, if you take a step back and you think about, you know, what is a GI, it's difficult to define, it's kind of amorphous right now.

But what I think we can assume is on the way to a GI. Being able to innovate and be truly creative is a necessary step toward that goal. And, you know, and that's, that's really our position , in the history right now that's being created in ai. think where I see Deep Invent today.

I. 

Jeanne Gray: It sounds like it's an unleashing of an enormous amount of broad potential that's not being tapped into. And you briefly mentioned China in, in your comment. How much does that come into play when you're assessing your intellectual property and knowing. What has already been transpired with the Chinese presenting their own ai platform?

Marcus Weller: Well, China's relationship to intellectual property , is an interesting one, especially when it comes to , the international relations dynamics between the US and China. China has not had a history of respecting US intellectual property rights, and we do see that as , an inhibitor , of global innovation.

You know, what we want to do is make sure that we've got appropriate mechanisms in place for us to accelerate. And to allow people to stand the shoulders of giants because ultimately the reason why the US PTO exists is it allows, and it incentivizes people to publish the nature of their innovations transparently.

And when they're able to publish that and have it be protected. For a certain period of time, it gives them a financial incentive , to make that transparent. Why is it important to be transparent? Well, it's important to be transparent because then it allows other innovators to cite those innovations in their patents and stand on the shoulders of giants and continue, and it creates this virtuous cycle of innovation that's good for the ecosystem and creates transparency and it creates.

Open competition, right? If we don't have those infrastructures in place then and people just are stealing the intellectual property, then what happens is it disincentivizes people to be transparent about their innovation. Then it makes it more difficult to cite that, to understand what's happening in the Frontier Labs, and then.

With that lower research transparency, r and d, transparency. It inhibits actual competition and the acceleration of innovation. So we need framework that we can believe in, that we can have confidence in to accelerate and that's why I see it's, so important and why I say American innovation is, so key.

We have a hundred years, a century of evidence of almost all of the world's most innovative companies. Being created in the United States because of our culture around innovation and the way that we created the US PTO and these incentive structures to allow entrepreneurs and innovators to bring us forward a humanity, and as a society.

So we wanna make sure that we continue to augment our capability to do that as a country and to allow us to really accelerate the trajectory of humanity. 

Jeanne Gray: Well, very excited for you, Marcus. This has been a very enlightening conversation for me as to what you've created and where it is is going.

So, very much appreciate you being a guest on Experienced Voices, and we will keep our eyes on you as. Things unfold in the coming months. And I actually would like you to come back on, in the near, not in the near, maybe near future. 'cause we don't know what the word near means anymore when it comes to ai.

 so this is a great story that's unfolding and I look forward to chatting with you again. 

Marcus Weller: Likewise. It was a pleasure. Thank you for having me. 

Jeanne Gray: You have been listening to the podcast series, experienced Voices. To hear more and subscribe, visit american entrepreneurship.com/podcast. Where you will also find a form for listener feedback.