This Professor-Turned-Entrepreneur Raised $50 Million To Enable Businesses To Create 10x Faster, Real-Time AI Applications
Some founders stumble into the frontier. Others live there by instinct. Stefano Ermon, an Italian-born scientist, Stanford professor, and co-founder of Inception, is firmly in the second camp.
Stefano was working on the foundations of generative AI long before it became a phrase on every investor deck and conference stage.
Well before diffusion models became the engine of image and video generation, he was theorizing, building, and publishing in a niche corner of AI research that few believed would matter.
Today, that corner has become the epicenter of a new technological revolution, and Inception, Stefano’s company, is betting on a radically different future for large language models. This is the story behind it.
A Childhood in the Alps and an Early Love for Building Things
Stefano grew up in a tiny mountain village in northeastern Italy, a world away from Silicon Valley. With just a few hundred people and small local schools, it was the kind of place where you spent your afternoons outdoors, skiing, exploring, and tinkering.
Stefano attributes his curiosity to his father, who loved science, experiments, and building things. Together they mixed chemicals, played with circuits, and constructed small projects from scratch.
That early exposure became a blueprint: touch the world, shape it, build it.
It wasn’t just learning; it was problem-solving as a way of life. Even back then, Stefano was keenly interested in studying, artificial intelligence, coding, and building computers.
The Leap From Italy to U.S. Academia
Though passionate about computers, Stefano chose electrical engineering in college because he wanted to understand computing at the hardware level. “Building everything from scratch has always been my thing,” as he puts it.
A turning point arrived during his master’s thesis in Italy. His advisor, Gianfranco Bilardi, who had spent years at Cornell, told him bluntly: “If you really want to be at the forefront of research, you need to go to the United States.”
That comment changed everything. Although he had never traveled to the US before, Stefano applied to several PhD programs, took the TOEFL and the GRE, and submitted applications. He was accepted to multiple programs and ultimately chose Cornell.
Stefano was strongly influenced by his advisor’s experience and the strength of the program. Thus, he boarded a plane for his first-ever trip to the US. His destination: Ithaca, New York. What started as curiosity became a path.
The Research Mindset Fueled by Curiosity - Defining Artificial Intelligence
As Stefano recalls, he had always been curious, wanting to learn and learn some more. He wanted to figure out how to do things himself and solve problems no one had ever solved, ranging from puzzles to computer games and math problems.
Stefano loved challenges, which drew him to research, where there’s “no shortage of problems,” as he puts it. Specifically, he had always believed that artificial intelligence was the biggest problem humanity had ever worked on.
From Stefano’s perspective, nothing is more important than building a machine that effectively imitates human intelligence. That seemed like the ultimate kind of research question.
Since high school, Stefano had always gravitated toward neuroscience, understanding how the brain works, what intelligence is, and how to build something as intelligent as a person. Asked how he would define artificial intelligence, he admits that’s a tricky question.
Stefano quotes, “There’s a saying in the field where we say, if it works, it's not AI.” It's almost like the goalpost keeps shifting, and that’s probably the most natural definition. It’s an imitation game, and if you cannot distinguish the machine or artificial intelligence from a person, you’ve achieved your goal.
Whether it’s communication with the entity or getting it to do work, it’s about building something that generates value compared to a human, in terms of its behavior and performance, as Stefano says.
Cornell to Stanford: Catching the Startup Fever
Cornell nurtured his academic foundation, but 10 years at Stanford shaped his entrepreneurial mindset. At Cornell, he immersed himself in research. People here were not necessarily thinking about the implications, impacts, and business opportunities that technology could create.
Stanford was the opposite, leaning more toward commercialization. The entire ecosystem buzzed with ambition, ideas, and the belief that anything could be turned into something world-changing. Undergrads, grad students, professors—everyone was thinking about impact.
“The energy and culture here are incredible,” Stefano says. “You can see why innovation flourishes. There’s no bureaucracy and red tape. Things move fast.” At first, he didn’t consider building a company. But the longer he stayed, the more the startup mindset seeped in.
His time at Stanford turned Stefano from an academic researcher into an accidental entrepreneur. “The startup fever is contagious, and anyone who spends enough time at Stanford eventually ends up doing a startup,” he opines. You just can’t avoid it.
In retrospect, Stefano talks about the culture shock he experienced when he moved from Italy to the US. In Italy, people usually end up becoming lawyers or doctors, though things are changing today.
However, on US campuses, people just get into a cafe and start brainstorming the future together and how to change things. Stefano loved the dynamism and how people are open to taking risks, trying new things, breaking things, and thinking big. Particularly, he loved how they moved quickly.
Ahead of the Curve: The Early Days of Generative AI
Stefano traces the sequence of events that led him to take action and make an incredible career shift. He had been at Stanford for 10 years, working on what they called generative models, which is now called generative AI. This was years before diffusion models became mainstream.
At the time, the field wasn’t glamorous. Funding was limited, and publishing was difficult. Most people didn’t understand why anyone would care about machines generating images or text, Stefano recalls. But he did. He saw the future before the world caught up.
As Stefano points out, to build real-world AI systems, they needed to leverage as much data as possible. And it had to be done without supervision because that was the only way to make things more scalable.
Stefano considers himself lucky because he was early in the field and his research group at Stanford has already made a few key technical contributions. They had developed a bunch of algorithms and models that are now being widely deployed and used in the industry.
More specifically, in 2019, Stefano’s lab introduced a breakthrough: diffusion models, the backbone technology now used by Sora models, MidJourney, OpenAI, Stable Diffusion, and virtually every cutting-edge image- and video-generative system.
The Big Breakthrough in Text Generation
Since creating the initial technology, Stefano had been trying to get the diffusion models to work, not just on image generation and video generation, but also on text and code generation. Or, the kind of things that can be accomplished with a typical large language model, like ChatGPT, Gemini, or Claude.
Last year, they discovered a way to adapt diffusion methods to text generation, and the performance matched GPT-2-scale autoregressive, less-than-a-billion models, but with major advantages. They were 10x faster with parallel generation, efficiency on GPUs/TPUs, and delivered high-quality outputs.
Traditional LLMs (like GPT, Gemini, or Claude) generate text one word at a time. That makes them inherently slow, sequential, and expensive. As Stefano points out, it’s a structural bottleneck that can’t be avoided. Diffusion language models take a different approach.
They are built to be parallel and take advantage of GPUs and TPUs, much like parallel hardware. The neural network no longer generates one word at a time. Instead, it starts with a rough guess and refines it in parallel, updating multiple words simultaneously.
This speed advantage creates new possibilities—especially in enterprise use cases where latency matters. That breakthrough became the spark for Inception. Stefano quickly realized that doing this in academia was very challenging because of the resources needed to train bigger models.
They would also need engineering teams and massive volumes of data to build a production ready system. They also needed much more coordination.
The Birth of Inception: A New Stack for LLMs
Stefano was excited about the technical results and saw a huge opportunity. He realized that LLMs are at the core of the Gen AI revolution. Nearly every LLM company was building the same autoregressive architecture, and investors were hungry for AI breakthroughs.
Stefano was convinced they had something better. He could do it the Stanford way--take the risk, and go out and build it himself. Thus, Inception was born from the desire to create a different foundation for AI. Stefano and his team spent the initial six to nine months just developing the technology.
Initially, it was all about research and development, scaling up their ideas and building a commercial-scale diffusion language model. The Inception flagship model, Mercury, became the world’s first commercial-scale diffusion language model.
This model is a direct alternative to ChatGPT, Gemini, and Claude, built on a fundamentally different technology stack.
The Business Model: Enterprise AI at the Speed of Thought
Inception sells access to Mercury through an API platform. Its business model involves marketing to enterprise customers and developers worldwide who are building Gen AI applications on top of these diffusion language models. Stefano’s pitch is simple but powerful. Mercury is
Faster than today’s LLMs · Cheaper to run · Higher quality in latency-sensitive tasks · Perfectly built for enterprise-grade deployments
Use cases are already gaining traction, and their customer base includes Fortune 500 companies:
Code autocomplete & AI-assisted vibe coding for faster, better coding. Inception models can suggest code completions and suggest changes that need to be done to the code base. · Developer tools and IDE integrations. They are already deployed as the default model across a variety of coding IDEs because they deliver superior performance. If you need to give a developer a quick answer, diffusion-alloy lamps are the way to go. · High-volume, low-latency enterprise apps that run much faster and deliver higher quality than competing autoregressive models.
Developers don’t want to wait for an answer. Businesses don’t want to pay for inefficiency. Inception sits perfectly at the intersection of both. Mercury is already the default model in several coding environments, chosen for its better performance and faster response time.
As Stefano explains, developing the Inception technology is something he has been working on at Stanford for a while. It wasn’t much different in terms of R&D and experimenting with other approaches, GPUs, and algorithms. Only the scale was much larger.
The technology no longer involves a single or a couple of graduate students writing PhD-level code or research-grade code that breaks all the time. Here, the expectations, in terms of the quality of the code and the way the systems are developed, are very different.
Inception now has a large team working toward the same goal, rather than a typical academic or lab structure where each student works on their own thing. The team is laser-focused on a single goal.
A $50 Million Seed Round and the Backing of Titans
Building next-generation AI models isn’t cheap. Training runs cost millions. Scaling infrastructure requires world-class engineers and GPUs. Thankfully, investor appetite matched the ambition.
Stefano reveals that securing the right resources and capital hasn’t been too difficult, thanks to his Stanford background. He had several connections with venture capitalists and other entities that recognized the uniqueness of his models and the value proposition he offered.
Stefano partnered with Navin Chadha at Mayfield, who has deep ties with Stanford and understands the Inception model. Now, Inception’s cap table includes Menlo Ventures (lead), as well as other key VCs and strategics, including Microsoft, Nvidia, Databricks, and Snowflake.
This is not just capital; it’s a coalition of strategic powerhouses that see significant potential in the Inception technology and a future where fusion language models will become the standard for LLMs and generative AI solutions.
The $50M seed round positions Inception among the most heavily funded AI startups at this early stage. Stefano considers himself fortunate to be surrounded by high-caliber individuals.
Storytelling is everything that Stefano Ermon was able to master. The key is capturing the essence of what you are doing in 15 to 20 slides. For a winning deck, take a look at the pitch deck template created by Peter Thiel, Silicon Valley legend (<a href=" target="blank" rel="noopener noreferrer">see it here</a>), where the most critical slides are highlighted.
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The Future They’re Building: AI That Feels Like Magic
Stefano has a bold vision: “AI should be as fast as human thought.” He envisions a world where AI is super efficient and available to and empowering every business. AI should be high quality, robust, reliable, and almost feel like magic.
If he could go back one year and speak to the version of himself preparing to start Inception, Stefano would say, “People are the most important thing. Surround yourself with great people and everything becomes possible.” In the end, the technology matters—but the team determines how far it goes.
Anyone can test the model through the Inception playground at: chat. inceptionlabs.ai
Conclusion: From Alpine Curiosity to AI’s Next Frontier
Stefano’s journey is a reminder that innovation doesn’t always come from predictable places. As his vision becomes reality, the future will be faster, smarter, and more magical than anything we’ve experienced so far.
Stefano Ermon pioneered generative AI long before it became mainstream, laying the foundations for the diffusion models used globally today. · His journey from an Alpine village to Stanford reflects relentless curiosity, deep problem-solving instincts, and a love for building things from scratch. · A decade at Stanford transformed him from a pure researcher into an entrepreneur ready to commercialize groundbreaking AI. · His team’s breakthrough—adapting diffusion models to text—enabled 10x-faster, parallel language generation that outperforms autoregressive LLMs. · Inception’s flagship model, Mercury, offers enterprise-grade speed, efficiency, and quality, already powering Fortune 500 developer tools and coding environments. · A $50M seed round backed by Microsoft, Nvidia, Databricks, Snowflake, and top VCs underscores the market’s belief in Inception’s radically different AI stack. · Stefano’s greatest lesson: surround yourself with exceptional people because world-changing technology only becomes reality through the right team.
Original Version
Alejandro Cremades: All right. Hello, everyone, and welcome to the DealMakers show. So today we have an amazing founder, you know, joining us. Be prepared to have the battle of accents because we're going have the Spanglish and also the Italian, you know, kicking in too. So be ready.
Alejandro Cremades: But again, we're going be learning a lot, you know, on the building, the scaling, financing. i mean, they have something exciting to announce today. You know, there's a lot of... generative AI you know by talk going on. Our guest today, you know he's he's been at it. He knows it very well before it even became trendy, as it is today.
Alejandro Cremades: But you're going find the conversation today quite inspiring. So without further ado, let's welcome our guest today, Stefano Ehrman. Welcome to the show.
Alejandro Cremades: So originally from Italy. So give us a walk through memory lane. How was life growing up for you, Stefano?
Stefano Ermon: Yeah, so I grew up in a small village in northeastern Italy in the Alps. So it was a very small community of a few hundred people. i went to elementary school there and yeah, I went all the way through through high school in very small schools.
Stefano Ermon: ah But, you know, a a lot of fun, spend a lot of time outdoors, so you know, grew up skiing. So that's one of my passions.
Alejandro Cremades: And what ah what about the problem solving?
Stefano Ermon: The other one was studying and artificial intelligence. You know, I was always very excited about computers and coding and building stuff.
Stefano Ermon: I actually studied electrical engineering and in in college because I really wanted to build the computers, figure out how they actually work, build everything from scratch has always been my thing.
Stefano Ermon: And yeah, and after that, I went to ah came to the States for my PhD.
Alejandro Cremades: And before that, before that, how did you get into the whole, you know, problem solving, engineering and the geeky side of you? How how did that flourish? You know, what where did that come from?
Stefano Ermon: I don't know exactly. um My dad, I think, was also you know very into science, very much into like doing experiments. And we would have a lot of fun doing chemistry stuff and building things.
Stefano Ermon: So think it's just something that I grew up in and always got me interested, like building with my hands, building stuff from scratch.
Stefano Ermon: I don't know where it came from, I think from my dad, but but I'm not really sure.
Alejandro Cremades: So you you studied the college there. I mean, you did engineering in Italy. And, you know, it's interesting because Italy is very similar to Spain where I'm from, where ultimately coming to the U.S. is a really big deal.
Alejandro Cremades: um You know, I mean, just like in Spain, people in Italy, they live with the parents, you know, for quite a while too, no? And at what point do you decide, um hey, I think maybe it makes sense for me to take a look at what's going on in the U.S. and and go to to do my PhD, you know, there?
Stefano Ermon: Yeah, that was actually by luck. um So when I did my master thesis in Italy, I worked with a professor, Gianfranco Bilardi, and he had actually studied in the US.
Stefano Ermon: He had been a professor in the US for a while at Cornell University, and he was the one that kind of opened my eyes and told me that, you know, the the the The most exciting research is happening in the US. like If you really want to be at the forefront of something, you've got to go to the US.
Stefano Ermon: And I had never been in the US. had never traveled to the US before, but I just applied a bunch of PhD programs. I did my TOEFL. did the kind of like the English test. I did my GRE. I did all the things that I had to do and just applied to a bunch of places.
Stefano Ermon: And yeah, luckily, I was admitted. I don't know if I would be admitted again today. Like now the um it's so hard to get into a PhD program, but I think back then it was a little bit easier.
Stefano Ermon: And ah eventually, yeah, I got into a few places and I decided to go to Cornell just because, yeah, my ah advisor had been a professor there. He was telling me good things about the program.
Stefano Ermon: And yeah, at the time i I took the plane to the US, that was my first time in in the in the country. And then I landed in Ithaca, New York, and you know I started my PhD.
Alejandro Cremades: So obviously we're going to talk about now what you're up to with your with your business. But I wanted to ask you, obviously after doing the PhD, you got into this academia path.
Alejandro Cremades: um I mean, what what really got your curiosity, you know, wanting to go after that, you know, to Stanford um and really following that track, um what really sparked that chapter for you?
Stefano Ermon: Yeah, I've always been very curious and i I always want to learn. I want to learn more. i want to try to figure out, you know, how to do things myself, how to solve problems that nobody has ever solved before, whether it's, you know, like from puzzles all the way to computer games, all the way to math problems. Like I always want to solve them. I want to, I like challenges and and research has been amazing for me because, you know, there's no shortage of problems.
Stefano Ermon: you know very hard problems that nobody has ever solved before. And that has always been my like passion. That's what gets me excited. And specifically, I've always thought that artificial intelligence is probably the biggest problem that that humanity has ever worked on. i mean, what's more important than the building um ah machine that that effectively imitates human intelligence, right? That is probably the the ultimate a kind of like research question.
Stefano Ermon: So that's I've always gravitated around kind of like neuroscience, understanding how the brain works, understanding what is intelligence, figuring out how to build something that is as intelligent as a person.
Stefano Ermon: and That's always been kind of like what has fascinated me since basically high school.
Alejandro Cremades: Out of curiosity, based on everything that you know and that you've been exposed to, I mean, you were alluding to it. What is intelligence? like How would you describe it you know in a non-technical way for all of us ah in a way that we get it?
Stefano Ermon: yeah it's It's very hard to define. right That's the thing. and that's that's ah you know There this saying and in the field where we say, ah if it works, it's not AI. right so it's It's almost like you always keep shifting the goalpost. then and and I think you know probably that the the most natural definition is it's it's kind of like the the imitation game. right so If you basically cannot distinguish this machine this artifact, this artificial intelligence from from a person, ah then you've probably achieved your goal. and So you know whether it's communicating it with this with this entity or whether it's
Stefano Ermon: getting this entity to do work, the way a person would do it or create economic value. There's many different ways to think about it, but ultimately I think it has something to do with building something that is quite similar in terms of the way it behaves, the way it performs, the way it generates value ah compared to to to a human.
Alejandro Cremades: And in Stanford, you were there for 10 years. I mean, that's a hell of a lot of time, right? And I'm sure that you had a blast. So that's why you stayed there for for quite a bit. But we're talking about being there a professor in one of the most renowned universities and also in in one of the universities where some of the biggest innovations and companies have been built.
Alejandro Cremades: um How was that experience for you as well of of being exposed to all of that and just being in it?
Stefano Ermon: It's been amazing. um you know Before coming to the to to the to the Bay Area, I had not experienced this culture, like the the the startup, the entrepreneurship.
Stefano Ermon: ah It was not really a thing at at Cornell, I think. Cornell was much more academic and people were not necessarily thinking about the the implications, about the impact, about the business opportunities that all these technologies could could lead to.
Stefano Ermon: Stanford has been completely different. like the Everybody is kind of like thinking about the impact, how they can change the world, whether it's undergrads or grad students or professors, it's kind of like the culture, it's what you do what you feel the moment you are on campus.
Stefano Ermon: And it's just, the it's been it's been amazing.
Stefano Ermon: like the The energy that you see every day is just the just incredible. And I think, you know, Initially, I was not thinking about starting a business, about doing a startup, but you know after a little bit, you just get into it and then it it just and was just just contagious. right You can't avoid it.
Stefano Ermon: I think everybody that spends enough time at Stanford eventually ends up doing a startup.
Alejandro Cremades: Was it like a big of a, was it for you like a big of a culture shock? Because I mean, obviously is it being used to um Italy where you're either a lawyer or a doctor, I mean, obviously things are are changing nowadays, but then all of a sudden you're like in this campus where, you know, people just get into a cafe and they start brainstorming, you know, the future together and and and and how to change things. And I mean, I'm sure that that was quite a exciting too.
Stefano Ermon: Yeah, please come to our website inceptionlabs.ai. There you'll find links to our platform if you want to use our models, if you want to build next-generation AI solutions powered by best-in-class diffusion language models, please come to our platform.
Stefano Ermon: There's a playground if you want to chat with our model. And we are hiring, we're actively growing the company. So please visit our job section and apply to come and work with us on what's going to be the future of generative AI.
Alejandro Cremades: Amazing. Well, Stefano, thank you so much for being on the DealMaker Show today with us. It has been an absolute honor to have you.
Stefano Ermon: Thank you so much for having me. Yeah, it's been a really fun conversation.
Stefano Ermon's net worth: what is known and what is not
There is no verified public net worth figure for Stefano Ermon. Inception, the company he co-founded, is private, so its cap table is not public and no founder holding has been disclosed. The company has raised around $50 million, but that capital belongs to the company, not to its founders. The value attached to him is best described by its sources rather than by a number:
Founder equity in Inception , illiquid and priced only at the last round. · A Stanford faculty position in computer science, with the salary and research funding that accompanies it. · Research output that predates the company , including foundational work on diffusion models that is now cited across the generative AI field.
Stefano Ermon at Stanford, and the Ermon Group
Ermon is a Stanford professor whose research group works on generative modelling, probabilistic inference, and machine learning for scientific and sustainability problems. His group's work on diffusion-based generative modelling became one of the technical foundations of the current generative AI wave — which is why the search term "Ermon group" and his name appear together in AI research contexts far more often than in company contexts.
Inception applies that research line to language: diffusion-based language models, which generate text through a different mechanism than the autoregressive models that dominate today, with the claim being materially faster real-time generation.
What the Inception raise shows about research-to-company transitions
The research is the moat and the pitch. When a founder authored the technique, the diligence question shifts from "can they build it" to "can they commercialise it." · Speed is a sellable axis. Inception's pitch is not that the model is smarter but that it is faster in real time — a claim that maps directly onto customer cost and latency budgets. · Academic founders should price the operator gap early. The $50 million round buys the team that turns a research artefact into a product with uptime commitments, and investors will look for that hire plan in the deck.
