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Cohere's Aidan Gomez: The Canadian Founder Taking AI Models To The World, Not Just Big Tech
Aidan Gomez says AI can revive economic growth, while democracies need independent AI suppliers to strengthen resilience.
Upstarts · Alex Konrad
- Aidan Gomez is co-founder and CEO of Cohere.
The best minutes
- 30:18Cohere chose independence over the easier growth path5 min
- 21:48Gomez sees AI-driven growth as a defense against scarcity4 min
- 25:13AI sovereignty depends on a diverse supply of providers4 min
Also worth watching
- 36:31Cohere keeps humans involved in sensitive AI decisions3 min
- 4:13North lets staff build automations without creating a new product3 min
- 18:49Cohere began with a bet on training a model on the whole web2 min
- 8:17Rural internet scarcity led Gomez into hands-on AI research5 min
- 39:33Gomez wants Cohere to IPO for broader ownership and scrutiny2 min
Skip the opening company overview, the Transformer explainer, and the Mercury sponsor read.
Summary · 3 min read
Aidan Gomez is Cohere’s co-founder and CEO and co-authored the Transformer paper Attention Is All You Need while at Google. He grew up in rural Ontario, studied computer science at the University of Toronto, went to Oxford for a PhD, and founded Cohere with co-founders Nick and Ivan.
Gomez says Cohere is entering a takeoff phase as enterprises adopt AI and seek privacy, secure deployments, and sovereign options. He describes Cohere’s North platform as infrastructure for secure enterprise agents that lets nontechnical workers build automations. He argues that AI could restore economic growth and support democratic resilience, and that countries need diverse technology suppliers rather than dependence on one country. He says Cohere has stuck with B2B, privacy, security, and independence rather than chasing consumer products or hyperscaler deals, and sees an IPO as a way to widen ownership and scrutiny.
Minute by minute
- 1:24Enterprises push Cohere into takeoff
Gomez says Cohere is in a takeoff phase as enterprises adopt AI across their organizations, with demand centered on privacy, sovereignty, and critical industries. He says use cases range from morning email summaries to monitoring ship sensors, and that some customers need on-premises or air-gapped deployment.
- 5:23North makes enterprise automation accessible
Gomez says vibe-coding tools can get technical users 90% or 95% of the way to an application, but closing the last mile often still takes technical skills. He says North lets nontechnical users build automations by chatting with a model and connecting tools, without creating a new codebase for each task.
- 8:17Rural internet scarcity led to coding
Gomez says unreliable internet in rural Ontario made computers compelling and led him to coding and a small website business. At the University of Toronto, he joined deep-learning research around Geoffrey Hinton, then worked on music transcription and later joined Google and the Transformer team.
- 14:35The Transformer scaled beyond translation
Gomez says the Transformer was designed to improve machine translation by modeling language sequences efficiently, not to take over AI. Its scalability made it a strong fit for larger models and later applications to images, audio, and other data. He credits the wider research community for much of its impact.
- 19:05Cohere began by modeling the web
Gomez says Cohere grew out of an idea he and his co-founders developed to train a model on the web, after seeing language models produce increasingly plausible text. He says they chose B2B within the first year because they wanted to bring AI into the economy and support productivity and growth.
- 22:35Gomez sees AI as a growth engine
Gomez says slowing growth in developed democracies encourages zero-sum thinking and contributes to xenophobia and conflict. He argues that AI could restore broad-based growth and help sustain liberal democracy by improving efficiency and living standards.
- 25:43AI sovereignty depends on diverse suppliers
Gomez says technology sovereignty has declined outside the US and China, leaving countries more dependent on a small number of suppliers. He says building Cohere in Canada is an intentional contribution to resilience, and argues that democracies need diverse suppliers without every country having to build every technology itself.
- 30:38Cohere preserves cloud independence
Gomez says Cohere considered selling a large stake to a hyperscaler but did not pursue it, preserving independence from any one cloud ecosystem. He argues that enterprises need options to negotiate with providers and countries need diversified suppliers in case one becomes unavailable.
- 33:09Cohere stayed on its chosen path
Gomez says founding in Canada made the path harder, but Cohere stuck with privacy, security, independence, and B2B rather than chasing consumer AI. He says the market later moved toward those priorities and points to Jensen Huang’s long conviction in Nvidia as an example of a commitment that can take decades to pay off.
- 37:05Human oversight matters in sensitive AI
Gomez says AI will enter public and private sectors, including defense, healthcare, and finance, where deployments can have serious consequences. He says Cohere works with customers on readiness and safeguards, builds human oversight into North, and chooses which customers and governments it serves.
- 39:57Gomez wants an IPO for public access
Gomez says he wants Cohere to IPO so more people can own a stake and the company faces greater financial scrutiny. He says the company’s success should be measured by its impact on countries’ sovereignty and democratic resilience, not just its valuation.
Published Apr 16, 2026.
Transcript · 31 min read
Upstarts, hosted by Alex Konrad, with Aidan Gomez.
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
There's been a sort of slowdown in growth for these developed democratic economies. When that starts to taper off, you see the emergence of things like xenophobia, like that immigrant is taking my opportunity, they're taking my slice of the pie. A lot of people talk about like AI destroying the world. I really don't think AI is going to destroy the world. It might be like our best shot at saving the world. Welcome to the Upstarts Podcast, our weekly show where we talk to emerging startup leaders about their upstart moment. Upstarts are challengers who punch above their weight and take on the status quo to improve the world, all while building a big business too.
I'm your host, Alex Konrad, founder and editor of Upstarts Media. And I'm delighted to be joined today by Aidan Gomez, co-founder and CEO of Cohere. Thanks for joining, Aiden. Thank you for having me. This podcast is brought to you by Mercury, banking redesigned from the ground up. Aiden, you have run Cohere now for six and a half years. You guys have raised $1.7 billion. You work with some big governments, some big companies. When you think about sort of what Cohere looks like today, is it early innings? Is it everything you hoped for six years ago? I think we're in the takeoff phase. So we've been around for a while, but this past year was really the breakout year for us in terms of enterprises just adopting AI at scale across the entire company and the importance of all the things that Cohere is good at really sort of hitting the front of mind of executives at companies all over the world.
So privacy, sovereignty, and this focus on critical industries. And so last year, all of that came together and we sort of started our takeoff. And then this year, we expect it to get even bigger as enterprises and the broader global economy really, truly adopts AI at scale. So with some of these big companies or G7 countries that are working with Cohere, what is the problem that they want you to solve? Or where is your technology actually helping them in sort of a simple overview? view. Yeah. Well, it's super broad because we're working with a really diverse range of customers from different industries, from like manufacturing to public sector to financial services, energy.
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And it's as simple as like, you know, get the model to every morning at 6am, check my unread emails and send me a text with a summary of like the most important one. that I need to know. So that's like a super general, you know, any company would want that to a hyper specific, you know, within a specific ship. I'm going to plug north into the telemetry within that ship, do continuous monitoring on different sensors on board and build automations around if sensors cross certain thresholds, what to do, who to notify, what sort of remediations. But I know it's a wide range of use cases, but generally speaking, why often do they need Cohere to exist?
Like, why is that an unsolved problem for them? Yeah, I think it's the privacy piece. I think the secure deployment of this tech, of this platform, is something that our customers couldn't do without. It's something that is a hard requirement in their settings. They can't send data out into a cloud. They're highly regulated. They're in these high security settings. And so they need something that's deployed on-prem or even air-gapped on their infrastructure. There's a deployment-level differentiation, and the other piece is within the product itself. It's super easy to use. It's very customizable.
There's tons of administrative enterprise controls that are unique to the North platform. So should we think of Cohere as sort of the base layer or this infrastructure layer that then the company is either building internal apps on top of or it's running these agents doing sort of all sorts of other work with sort of the info that your horsepower is providing?
“for a lot of these applications, you don't need to create a product. You don't need to create a whole new piece of software to solve it.”
Yeah, I don't know. I don't know. I definitely appreciate my own privacy as an individual, but I try to be open. I think for people to trust you, they have to know you. And so I talk about where I grew up, how I grew up, my family. So I try to be somewhat open, but I'm certainly not the type, I'm not a great marketing or sales guy. Like, I'm not out there screaming, you know, my own name or Cohere's name constantly. My background is, like, I'm an academic, right? Like, I came into this through research. And I think there's a very different type of personality that thrives in research than in company building.
And I've been, you know, doing my best to adjust. to being a CEO and having to be spokesperson and a salesperson for the company. But you can't run from your nature, which is like, I'm a, you know, shy nerd. So that doesn't go anywhere. It's a very tall, it's tall, shy nerd. Yeah, got it. Well, so when you were in academia, you were privileged or you had earned your way to be at sort of the epicenter of some of the earliest work with LLMs, you know, the, this famous transformer paper, could you talk a little bit about how you first you know got into this field why it was exciting to you and sort of how the
“and i had to write kernels in metal which was like apple's gpu language for forward and backward propagation”
I met my co-founders when I came back from the Valley to Toronto, ran into Nick and Ivan. Nick was working for Jeff at Google. It was like Jeff's first report at Google. And then I went off to Oxford to do my PhD. And then from Oxford, I called up Nick and Ivan and just said, guys, we have to start something. I saw the first few results of language model scaling. and it was very much this zero to one, you know, we could barely get these models to string a sentence together to this was like plausible, fluent, ostensibly human-written text coming out of a machine. And now we take that for granted.
It's so normal. It's like this is kind of not surprising. But back then it was like it was surreal reading a machine. I think people sort of take for granted the fact that like we're talking to some rocks and metal like we're literally talking to our pet rock at this stage which is it's so insane yeah it's normalized we do it all every day but good god like back then it was just mind-blowing and so like what else could you want to work on we talked about the transformer paper which was called attention is all you need i don't know what the best comp would be maybe the Magna Carta of AI research. Very seminal paper that you were a part of. For folks who are not deep in AI world, what would be the TLDR of what this paper was? Yeah, well, it was done when I was at Google. And in particular, it was done as a collaboration between Google Brain, which was like the AI research department under Jeff Dean and Google Translate. And the two teams came together and really the objective was to improve translation, improve the state of the art of translation. The architecture itself was designed to better and more scalably model language. And what I mean by that is You know, language is a sequence. It's a sequence of characters, it's a sequence of words, in order.
The order matters, and you want to be able to model that or represent that data in the most efficient way possible. So the transformer was, that was the problem we were trying to solve. How do we just make this a more efficient model for language? Turns out, the transformer became extremely effective beyond language, and you can phrase many different data modalities, like video, etc. as a sequence. The fact that the model was so efficient made it a very good option as we were going through this process of scaling up models. And they're called large language models because they're really big. The transformers and architecture just scaled better than the alternatives because it was so efficient. And so as we went through this process of the field, building bigger and bigger models, training on larger and larger computing clusters, it was the best thing available and so people poured effort into optimizing chips for it and coming up with better uh you know libraries for training these models and then over time the transformer just kind of took over everything took over the domain of images took over the domain of audio it took over pretty much uh the whole field but it certainly was not designed for
that it was just designed for this one narrow problem of like we want to make translation three percent better and now people are saying what's comes after the transformer now i've been hearing that for a long time now yeah i i don't know like i i've gotten so excited before i i thought um ssm state space models which was like this thing that came out three or four years ago i thought that was going to kill the transformer i get excited discreet would that be like killing your own baby if no no not at all no like if you're a researcher you really hope humanity comes up with something better than what you've created.
Otherwise, progress has ended. When you published the Transformer paper, the group of you, did you think this is going to be, like, we know this is going to be huge. We're all going to end up rock stars in this category. No. I mean, we're like the world's most niche celebrities. There's like 13 people that know us. But no, we didn't know the impact it was going to have. Or at least I didn't. I don't want to speak on behalf of all the Transformer paper authors, but we can't take credit for what happened with the Transformer. Like, we put it out there. Our effort was around improving translation performance.
That's the thing that we set out to solve and to come up with an architecture that was better suited to language and sequence modeling, and I think we did that. But then the community took it and applied it to everything, and they've taken it so far. They've optimized the hardware for this specific architecture. We sort of set the ball in motion, but the work has been done by thousands of others who have actually turned this into what it is today. We still call all these models Transformers. I think, I'm not trying to be like overly modest but if it wasn't us, something else would have come out that kind of looked and felt the same.
There were ideas along these lines with like WaveNet and ByteNet and all these different architectures that were emerging at the time. I'm so grateful that the community leaned in on the Transformer But someone else would have done it, you know, three months later. I do think that sounds kind of modest to me, but I respect it. I think it's true. I think it's true. Well, okay. Modest, but true.
“what if we treat the entire internet, like all the pictures, the videos, etc. As a data set to train on”
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Issuing an invoice, sharing routing numbers, and processing the wire with just a couple clicks. No phone calls, no paperwork, no learning curve. We're still figuring plenty of stuff out. When it comes to our banking, Mercury's already got it all figured out for us. Visit mercury.com to learn more and apply online in minutes. Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column NA members FDIC. What was so exciting about the idea or the mission of Cohere that you were like, let's commit a lot of time and opportunity here? Because you predate and saw, for example, the team leave OpenAI and start Anthropic, and then all of a sudden Anthropic is growing super fast and sort of Cohere is continuing along the journey.
“i really don't think ai is going to destroy the world it might be like our best shot at saving the world”
“i actually think it's existential to democracy if you love democracy if you love people um having self-governance”
feel like maybe more people have come around to see this? Or what is it like to share these principles with your peers in AI right now? I think some people see it. I don't think this is a particularly broadly held view i don't if you ask other people about these sorts of questions i don't know if they view it as actually that significant i think there's more ideology behind it or sort of political awareness than i think you know at least some very you know growth obsessed ai startups today really worry themselves with it seems like it's very intentionally intertwined with Cohere to be thinking about this, you know?
We're also, like, just uniquely positioned to be able to contribute to that notion of resilience, right? Like, being a Canadian company, like, we're one of a very small group that is building this capability outside. And so we're uniquely positioned to be able to deliver on that mission. I don't know if folks here are positioned to deliver on that mission. I don't think they are.
“we're not looking for like the easiest path and we're looking for the right path”
He appeared out of nowhere four years ago. No, no, he was just, he knew this was useful. like it's it's hard to describe but he knew that there was there was a need and he could have just done cpus and he could have just like folded and done rolled into like everything intel was doing and whoever the you know whatever the latest fad was and i'm sure he got told a million times this is irrelevant this is never actually going to take off it's niche it's niche it's not going of scale. Um, people don't need this, but you know, it's the biggest company in the world and, uh, well-deserved. Like no one has had as long a conviction and has like worked harder for this than Jensen has. And I think he deserves all the success he's gotten. And that sort of conviction to an ideal, that sort of conviction to his beliefs over decades is
“Human oversight is a key component of it. The ability to say there's a certain category of activities or actions that we're never going to fully automate.”
“People can see your finances, you know, you have to report them, you have no other choice, you're way more, you're better governed because you're under higher scrutiny.”
Awesome, well I hope that the impact you're having with these customers makes its way into the public eye more and more so maybe if it's not Cohere being the household name you know the products or the infrastructure run off of Cohere models is still having that huge impact as well. Yeah, well we sponsored
an F1 team so Aston Martin. Was that just you could go to the race? That was so that I could go to the race. But our sticker is there. So people are going to be seeing it now. So maybe
