On March 19, 2026, a threat actor with the handle TeamPCP exploited a single misconfigured GitHub Actions workflow inside Aqua Security’s Trivy repository, one of the most trusted open-source vulnerability scanners in the world. They compromised a service account, force-pushed malicious code to 76 of 77 version tags, and quietly embedded a credential stealer into a tool that security teams around the world were actively using to protect themselves.

The payload harvested SSH keys, cloud tokens, Kubernetes secrets, and npm credentials from CI/CD environments. Those stolen tokens fueled CanisterWorm, a self-propagating worm that cascaded through 66+ npm packages using blockchain-based command infrastructure that couldn’t be conventionally taken down. Within eight days, the same campaign had compromised GitHub Actions, Docker Hub, npm, PyPI, and the VS Code extension marketplace. One misconfiguration. Five ecosystems. An estimated 300 GB of exfiltrated data. Over 500,000 stolen credentials.

The attacker didn’t write a zero-day. They didn’t break encryption. They turned a trusted security tool into a weapon, and most organizations never saw it coming.

This Isn’t Your Father’s Supply Chain Attack

The industry has been talking about software supply chain risk since SolarWinds in 2020. But something fundamental has shifted in the last twelve months.

The attack surface is no longer human-scale.

The average application ships with over 1,100 open-source components. Most of those were chosen by nobody on your team: they’re transitive dependencies, packages that your packages depend on. And increasingly, even first-order dependency decisions are no longer made by humans.

AI coding agents (Cursor, GitHub Copilot Workspace, Claude Code, and their descendants) are now writing code, selecting libraries, and opening pull requests without a human ever touching the keyboard. A study of over 117,000 dependency changes found that AI agents select known-vulnerable dependency versions 50% more often than humans. They do this at speeds that compress the security review window to essentially zero.

The same week as TeamPCP, attackers also compromised Axios (the HTTP library downloaded over 100 million times a week) by adding a malicious dependency that ran a remote access trojan on install, then self-destructed before anyone noticed. The industry average time to detect a supply chain breach is 267 days. On 135 monitored endpoints, that malware executed and phoned home to the attacker’s server within 89 seconds of install.

As a16z put it bluntly in their April 2026 analysis: “We are building a world where machines write the code, machines choose the dependencies, and machines ship the updates.” If security doesn’t keep pace, the AI agents are cooked.

The Three Attack Surfaces Nobody Is Defending

There are now three distinct attack surfaces that most security teams have almost no visibility into.

The developer machine. Developer machines are running MCP servers, AI models, IDE extensions, and browser plugins that directly influence what code gets written and committed. Credentials accumulate across dotfiles, .env files, and environment variables. Security teams typically have no real-time inventory of what’s running.

The coding agent. AI coding agents don’t just suggest code: they act. They install dependencies, invoke external tools via MCP servers, execute builds, and push commits. Most security policies were written for humans. There is no category in your existing tooling for “approve this MCP server plugin before the agent can use it.” Attackers are beginning to exploit this gap explicitly.

AI-generated code. LLMs regularly invent package names that don’t exist. Nearly 20% of AI-recommended packages are fabrications, and attackers register these hallucinated names in advance with malicious payloads. The technique is called “slopsquatting.” One researcher uploaded a dummy package with a commonly hallucinated name and watched it accumulate 30,000 downloads, largely from AI-driven workflows, in weeks.

Traditional security tooling is blind to all three surfaces. Most software composition analysis tools work by checking dependencies against CVE databases. But a newly planted backdoor doesn’t have a CVE. Running npm audit on the compromised Axios version returned a clean bill of health because the malware had already self-destructed.

Securing AI Development at the Source

This is the problem that Montreal-based Boost Security is built to solve. And why we at Amiral Ventures are proud to have backed them.

Boost Security’s core insight is deceptively simple: the right place to stop a supply chain attack is not the CI/CD pipeline gate. By the time code reaches your scanner, credentials may already be exfiltrated and your developer machine may already be compromised. You need to move protection upstream: to the moment a prompt is sent to a coding agent, to the moment a dependency is suggested, to the moment a plugin is installed.

Their newly launched Developer Endpoint Security platform gives security teams centralized visibility and governance across the full AI development workflow:

• Developer Endpoint Visibility: A real-time inventory of every coding agent, MCP server, AI model, IDE extension, and package running across your developer fleet, the exact visibility gap that made TeamPCP possible.

• Coding Agent Safety: Governance controls ensuring agents only run with approved MCP servers and plugins, with configuration drift flagged before it becomes an incident.

• Supply Chain Security: Behavioral analysis of packages and extensions: evaluating what code actually does, not just checking it against a CVE list.

• Secure Agentic Code Generation: Guardrails embedded into the coding agent workflow so generated code follows organizational secure coding guidelines before being committed.

• Data Leakage Prevention: Outbound prompt scanning to detect and mask credentials and API keys before they reach external LLMs.

“AI coding agents are fundamentally changing how software gets built, but security has largely remained focused on scanning code after the fact,” said CEO and Founder Zaid Al Hamami. “Developer Endpoint Security moves protection upstream. It secures the developer machine, governs the coding agent, and ensures safer code is generated from the start.”

Why We Invested

A few things stood out when we evaluated Boost Security at Amiral.

The team. Zaid co-founded the company alongside Rajiv Sinha, who built Oracle’s first Application Security program and later worked at Cigital, the leading North American AppSec consulting firm. This is Zaid's second software security startup. His first, Immunio, built the first RASP technology, and was acquired by TrendMicro. Between them, two decades of seeing every era of software security from the inside. Their VP of Security Research, François Proulx, is one of the founders of NorthSec and a veteran AppSec researcher who has discovered 0-days in Terraform providers, AWS Helm Charts, and major GitHub Actions; he’s a recognized voice in the supply chain security community long before most people knew it was a category.

The timing. The threat landscape shifted materially in the past 12 months. AI coding agents have moved from prototype to production infrastructure at thousands of organizations. The attack surface they create, developer machines running MCP servers, agents making autonomous dependency decisions, LLMs hallucinating package names, didn’t meaningfully exist two years ago. Boost Security is building for the world that actually exists now.

The market. After SolarWinds, Log4Shell, XZ Utils, TeamPCP, and the Axios attack, the CISO conversation has shifted from “should we care about this?” to “how do we actually defend against it?” The specific category of AI-native developer security (securing the coding agent workflow, not just the code it produces) is still early. There are very few credible solutions addressing all three attack surfaces described above. Boost Security is one of them.

What This Means for You

If you’re a CISO or AppSec leader, three questions worth asking your team today:

1. Do you have a real-time inventory of every AI tool, MCP server, and IDE extension running on your developer machines?

2. Do your security policies govern what AI coding agents can connect to and what packages they can install?

3. Are you checking packages for behavioral signals, or just CVE matches?

If the answers are uncertain, this is exactly the gap Boost Security is built to close.

Canada faces a choice: step forward into the world, or be left behind.

There is a narrative about Canada that has been repeated so often it has started to feel like fact. That we cannot win. That our ambitions must go south to be taken seriously. That world-changing ideas need to be built somewhere else. History says otherwise.

There is a call for the brave, for the self-believing, and for those willing to swim against stagnancy. There is a chance to prove that Canadians can win.

In our roots, there is resilience. Canada was not built on excess. It was built on persistence.

There Is a Pattern in Our Roots

In the winter of 1921, a young surgeon named Frederick Banting walked into a University of Toronto lab with an idea that most of the medical establishment thought was wrong. He had no research budget, no proper lab space, and no track record in endocrinology. He had ten dogs, a borrowed room, and eight weeks before the facility needed it back. Despite everything, he discovered something that changed the world forever.

One year later, a 14-year-old boy named Leonard Thompson was dying of diabetes in Toronto General Hospital. His blood sugar was sky-high. He had weeks, maybe days. The clinical team administered Banting’s discovery, insulin, for the first time in human history. Within hours, Leonard’s blood glucose fell to normal. He lived for another 13 years, long enough to see the discovery that saved him earn a Nobel Prize.

That story is not an exception in Canada’s history. It is the pattern: a small team, limited resources, enormous ambition, and a result that echoed across generations.

Canada tends to narrate its own story with excessive modesty. We celebrate our politeness and our stability. These are real virtues. But we have grown apologetic, underselling our capacity for bold, world-changing invention. We have lost our own self-belief, and with it, our capability.

The truth is more interesting. Canada has been building at the frontier, in medicine, space, computing, and energy, for centuries. Not as a follower. As a pioneer.

Building at the Frontier

In 1950, while researching hypothermia treatments, electrical engineer John Hopps at the National Research Council made an accidental discovery: a stopped heart could be restarted mechanically or electrically. He built the world’s first cardiac pacemaker. It was large, external, and needed to be plugged into a wall, but the principle was correct. Today, over three million people worldwide carry a direct descendant of Hopps’ invention inside their chests. Canada gave them another heartbeat.

In 1975, NASA invited Canada to contribute to its Space Shuttle program. What Canada brought was a robotic arm, the Canadarm, a 15-metre articulated manipulator that became the most complex construction tool ever deployed. Built by Spar Aerospace (now MDA Space) in Brampton, Ontario, it flew on 90 Shuttle missions between 1981 and 2011. It helped assemble the International Space Station. It serviced the Hubble Space Telescope five times. When astronaut Scott Parazynski needed to repair a torn solar array in 2007 while standing at the end of an extended boom 90 feet above the ISS, it was Canadian technology holding him there.

Canadarm2, launched in 2001, has been a fixture on the ISS ever since. It has captured over 50 visiting spacecraft, including SpaceX’s Dragon and Northrop Grumman’s Cygnus. MDA Space is now deep into building Canadarm3, an AI-powered autonomous robotic system destined for Gateway, NASA’s lunar-orbiting outpost. Canada’s reach is getting longer. We are going to the Moon.

These are not stories of Canadian ingenuity waiting for American permission. They are stories of builders called upon to solve problems no one else could and who delivered.

The Long Revolution in AI

In the 1980s, when virtually every major research institution in the world had abandoned neural networks as a dead end, a British-born researcher named Geoffrey Hinton chose Toronto. He came because Canada was willing to fund speculative, long-horizon research through the Canadian Institute for Advanced Research. Work that the rest of the world had decided wasn’t worth the risk.

For decades, Hinton in Toronto, Yoshua Bengio in Montreal, and Richard Sutton in Edmonton worked in what felt like academic obscurity. They were studying the mathematics of how the brain might learn. Their colleagues thought they were chasing ghosts. The field was called “deep learning,” and virtually no one believed it would amount to anything practical.

In 2012, Hinton’s lab at the University of Toronto entered the ImageNet competition, the world’s most-watched test of machine vision. They won by a margin so large that it didn’t just beat the competition, it ended the competition. Neural networks weren’t a dead end. They were the beginning. The modern AI revolution, ChatGPT, Gemini, Claude, and every large language model you’ve heard of, runs on architectures descended from work done in Toronto, Edmonton, and Montreal.

In 2018, Hinton, Bengio, and Yann LeCun shared the Turing Award, the Nobel Prize of computer science, for their foundational contributions to deep learning.

Canada did not stumble into AI leadership. It invested in it patiently, for decades, when no one else would. Mila, the Quebec AI Institute co-founded by Bengio at Université de Montréal, is now the largest academic deep learning research centre in the world, with over 1,400 specialized researchers. The Vector Institute in Toronto, co-founded by Hinton, hosts over 1,000 researchers. Together with Amii in Edmonton, these three institutes form a national AI infrastructure that other countries are still trying to replicate.

This is what Canadian patience, ambition, and public investment can produce. Not a fast flip. A foundation that changed the world.

From Research to Runway

Canada’s innovation has not stayed in the lab. Shopify began in Ottawa in 2006 when Tobi Lütke, frustrated by the state of available e-commerce software, built his own. It now powers approximately 5 million stores globally, with a market capitalization of over $200 billion. Lütke, when asked why he hasn’t moved to the United States, replied simply: “Because I like Canada.” In a period when Canada’s sovereignty is under real external pressure, those four words became a rallying cry.

Cohere, founded in Toronto in 2019 by Aidan Gomez, Ivan Zhang, and Nick Frosst, has become the country’s flagship enterprise AI company. When a large US firm offered to acquire them at a nine-digit price, the founders refused. Today, Cohere is valued at over $7 billion, with revenue growing more than three times over in 2025. CEO Aidan Gomez was direct about why: “I really feel that our country is under threat. I feel that the box of Canada’s sovereignty has been opened, and it can’t be closed.” That is not the language of a startup pitch. That is the language of a builder who understands what is at stake.

Waabi, founded by Raquel Urtasun, who previously led Uber’s self-driving research, is taking a simulation-first approach to autonomous trucking, training its AI in virtual environments rather than running millions of potentially dangerous road miles. Backed by Uber and Khosla Ventures, Waabi began deploying fully driverless trucks in 2025.

Then there is Reaction Dynamics, a Montreal-based rocket propulsion startup building Canada’s capacity to reach orbit independently. A small team, few resources, a hard technical problem, and a mission tied directly to national sovereignty. Canadian Dynamism in practice.

The Unifying Thread

Banting worked on borrowed time in a borrowed lab. Hopps was researching something entirely different when he found the pacemaker. The Canadarm was Canada’s entry ticket to the Space Shuttle program, pragmatic, collaborative, and technically extraordinary. Hinton and Bengio funded deep learning research through Canadian institutions for decades before the world recognized what they had built.

Canadian innovation tends to be long-term. It tends to be patient. It tends to emerge from people willing to work on hard problems, in unglamorous conditions, for timelines that nobody else believes in. It does not tend to be loud. That is both its strength and its weakness.

The strength: Canadian builders build things that last. Insulin. The pacemaker. The Canadarm. Shopify. The foundation of modern AI.

The weakness: We have been better at invention than ownership. We discovered insulin and sold the patent for one dollar each. We conceived the light bulb and sold it to Thomas Edison. We built some of the most important technologies of the 20th century and watched the commercial value accrue abroad. The world benefited, but Canada did not always capture what it created. A sapling that dies before it has the chance to develop its own strength is lost potential. We forced our greatest inventions to move to survive.

Canada needs to start owning, protecting, supporting, and being proud of what makes it great.

The Moment We Are In

The external pressure Canada faces, economic, geopolitical, and technological, is real. But it is also an invitation. An invitation to stop outsourcing our ambition. To stop equating “not losing” with winning. To remember that we have grown world-changing inventions in the fields where no other crops could survive.

The resources were never lavish. Capital has been leaving quietly for two decades. In 2005, Canada’s largest pension fund held 74% of its investments at home. Today it holds 12%. The builders have noticed.

The next generation of builders in AI, in space, energy, defence, biotech, and climate, are not starting from scratch. They are standing on the shoulders of Canadian resilience. On Banting, Hopps, Hinton, and Bengio. They are inheriting a tradition of patient, rigorous, and frontier-facing work that has quietly shaped the modern world.

They need capital that leads, not follows. They need investors willing to sit with hard problems and long timelines. They need an ecosystem that treats ambition as a Canadian value, not an export. Building here is not a consolation prize. It is the point.

There is an opportunity to triumph. We have struggled long enough. Winter is over.

What Canadian Dynamism Means

American Dynamism is explicitly about the national interest, building for America, strengthening America, and making America more competitive. That is a worthy and coherent philosophy.

Canadian Dynamism is something different. It has always been oriented outward. Banting didn’t sell insulin to Canadians. He gave it to the world for a dollar. The Canadarm didn’t build a Canadian space station. It built humanity’s. Hinton and Bengio didn’t create AI for Canada’s benefit. They published their research openly, and the entire world built on top of it.

That is the Canadian instinct: to build things so good and so fundamental that the world cannot do without them. Not to win at the expense of others. To build in a way that makes everyone better off and to be indispensable because of it.

It means backing founders solving hard problems. It means recognizing that resilience of supply chains, infrastructure, sovereignty, and health systems is not a niche thesis. It is the defining challenge of the next decade. It means understanding that the country which built the tools that assembled the International Space Station, gave the world insulin, and quietly invented the foundation of modern AI, is not a country that needs to apologize for its ambitions.

It means building flagships. Not subsidiaries. Not branch offices. Not features inside external platforms. Companies of global scale, owned and led from Canada, solving problems that matter and keeping the value at home.

The Canadian Moat

Dynamism is resilience, a deep patience for difficult problems, combined with a certainty that through hard work, the problem will yield. Patience is the Canadian moat.

Banting didn’t give up when the university laughed at his proposal. Hinton didn’t give up when the field of neural networks was effectively declared dead. MDA didn’t stop building after the Canadarm. They built Canadarm2, then Dextre, and now Canadarm3.

That is the spirit. The one still standing when the problem finally yields.

Canada has always been a nation of builders. We built at the frontier of medicine when it was dangerous and speculative. We built at the frontier of space. We built the intellectual foundations of AI when the rest of the world thought it was a waste of time. We are now building autonomous systems for the Moon, enterprise AI companies with billion-dollar valuations, and rocket propulsion for sovereign access to orbit.

The pattern is clear. The capacity is real. The question is whether the next generation of builders will be supported, with capital, community, and conviction, to build at the scale Canada is capable of.

The future must be claimed. There are flagships to be built.

Ethan Marcoux is a writer and researcher focused on Canadian technology and defence innovation, and the founder of The Canadian Explorers publication.

Amiral Ventures is a Montreal-based seed and pre-Series A venture fund backing Canada’s most ambitious founders in AI-native enterprise software, resilience, and sustainability.

Welcome to the first edition of the Amiral newsletter.

We started Amiral to back founders who see what others miss and build where others won’t.

For our first issue, we’re starting with Patrick Murphy, founder of Maket. He’s making residential architecture accessible to anyone, no CAD, no $10K design retainer, just describe what you want. It’s a massive opportunity hiding inside one of the largest, most fragmented, and least digitized industries in the world.

Patrick is exactly the kind of founder we built Amiral for.

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A warehouse on the outskirts of Montreal was the not the kind of place a father takes his 14-year old son.

Patrick Murphy sat in the passenger seat, watching the city’s polished neighbourhood dissolve into industrial grey, trying to keep his nerve. He asked for this trip himself, explained his idea with enough conviction that his father had listened, driven across town, and walked through a an industrial factory that looked like it was a hideout for criminal activity. Inside, among bolts of fabric and racks of blank shirts, Patrick picked out his production run while his father paid for it. The logo was already finished, as Patrick had built it himself, on Illustrator.

He called the brand Ambassador Apparel, because they would be ambassadors for Christ. Even at 14 he understood that a name should carry meaning and weight. He took the shirts to school and sold them in limited drops, manufacturing scarcity before he had a word for the strategy. He never lost money, but never really made any either. Each run broke more or less even.

This was Patrick’s first entrepreneurial experience, and although it was not a success story by any conventional measure, it proved something else. That he could originate an idea, build it into something physical, find the people who wanted it, and moved it from his hand to theirs.

Childhood

Otterburn Park sits on the South Shore of Montreal, a quiet city beside the hiking trails and rocky face of Mont-Saint-Hilaire, close enough to feel the mountains and far enough from downtown to breathe. Patrick was born there in 1994, the youngest of five children. In a large family, birth order shapes personality as surely as anything else does, and the youngest child often arrives already partially formed by the weight of the personalities stacked above them. In Patrick’s case, the defining characteristic of being last was that everyone left him alone.

“They always allowed me space to just be Patrick,” he said. “I was doing entrepreneurial stuff, I had a ton of ideas, and they were always really supportive.” He was not pushed toward medicine, law or any of the respectable professional tracks that families sometimes mistake for ambition. Whatever path opened in front of him, he would have to find it himself.

His father had grown up in California, studied theology in the United States, and then completed his PhD in Quebec, arriving in Francophone Canada in the 1980s to run a master’s-level training program for pastoral students. His father spoke no French. He was, Patrick would later say, about as locally intelligible as someone who had just landed in rural China. And yet he stayed, built a ministry, raised a family, and ran his life with the independence of a man who reports to no one. Patrick did not see the word “entrepreneur” written anywhere in his house growing up, but he was watching one in real time. His mother was an artist, moved by the Group of Seven deeply and permanently. She painted and created throughout Patrick’s childhood, and her aesthetic sensibility became something she passed on without trying.

Growing up in a house with five children on one salary and a mother working part-time as a teacher and artist, comfort was not the operative word. They never felt poor, however. The Lord always provided, as his father would say, and Patrick tends to agree. But there was not a lot of extra. There were five kids, one income, and a life in ministry that demands the soul in ways it does not always reward financially.

Patrick watched his father navigate this with the specific equanimity of someone who has made peace with uncertainty, and something about that lesson settled into him deeply. “The shared experience of an entrepreneur and a missionary is actually really close,” Patrick said. Both are trying to make something work with insufficient resources and an audience that isn’t always there. Both go through long stretches where nothing seems to be growing and where the natural response is to wonder whether they should have done something else entirely.

What Patrick discovered at 14, running Ambassador Apparel out of his school locker, was that he was a communicator and a salesman in the sense that he liked the work of persuading people, the social texture of it, the reading of what someone wanted and then offering it to them. He was not a scientist or a mathematician, and rather than spending years fighting against his nature, he leaned into it. He built the brand because he could build it. He sold the shirts because he was good at selling. He understood the mechanics of scarcity before he understood the term. And when each run broke even, he didn’t view this as failure. He viewed it as confirmation that the machinery worked, and that the next iteration would improve.

What also became clear, in those same years, was that his sense of design was not casual. He had built the Ambassador Apparel logo himself because it had not occurred to him to outsource it, and because he had opinions about fonts, spacing, and a hunch on what a good logo communicated versus what a sloppy one did. He was the kind of kid who cared about what things looked like, genuinely cared, the way his mother cared about her paintings.

University

After high school, rather than enrolling in a traditional university program, Patrick went to CEGEP for graphic design. He wanted the technical vocabulary of design so that he could do contracts, generate income, and develop skills that would make him useful rather than dependent. He was not interested in a degree for its own sake. He wanted tools.

The three-year program gave him those tools, and after CEGEP, he went to Concordia University for communications, specializing in sound production, rounding out a creative education that was broader than most people would have chosen.

During those years, while completing coursework, he was also working full-time at a performance marketing agency. He had come through an unexpected door, where Patrick had started a hockey blog called Hockey Busts, covering draft prospects with earnest obsessiveness. The agency needed someone to run a hockey blog for their clients. As matches go, it was almost embarrassingly convenient.

He joined as the one of the first employees. He was in his early twenties, recently out of design school, and knew very little about digital marketing. His boss, James, knew this and hired him anyway. Patrick would later describe this as the defining professional gift of his early career, a boss who extended responsibility before it had been earned, who let mistakes happen, who trusted Patrick with increasingly important pieces of the business and then stepped back to watch what happened.

“James just gave me a shot,” Patrick said. “I was just coming out of university. I did not know anything about anything when it came to digital marketing. And he allowed me to run a very important part of his business.” The lesson Patrick absorbed was less about marketing specifically and more about what good leadership looks like from the receiving end. A mentor who extends trust, even when the trust is not yet fully warranted, creates a different kind of professional than one who makes people earn every inch of responsibility.

At the agency, Patrick learned the mechanics of performance marketing in ways that would serve him a decade later, on how to drive inexpensive traffic, how to measure conversion, and how to think about customer acquisition costs in relation to customer value. He worked across clients in different industries, including a shoe company obsessing over return on ad spend, a solar company needing high-quality B2B leads, a fintech application fixated on cost per install. Each client was a case study in what a different kind of business needed to grow, and Patrick was taking notes on all of it. He also became certain that he did not want to be in the services business forever, and that he wanted to build something of his own.

COVID

The transition came through ThirdBridge, a software development shop with ambitions to build an office management platform. Patrick joined as head of commercialization, a role that suited his skills and gave him real exposure to the work of building software. He had ideas about products, always had, but he did not know what it cost to build them, how long it took, what testing looked like, what deployment meant, and how things broke when real customers got their hands on them. ThirdBridge gave him that education.

He worked closely with the company’s CEO, Pierre-Étienne Bousquet, who trusted Patrick with access to leadership conversations that a typical employee at his level would not have seen. Patrick was thinking alongside the founder, not just executing for him. He learned what it meant to make a product decision, commit to a technical direction, and discover mid-build that the thing you were building wasn’t quite right.

ThirdBridge’s platform was aimed at a problem that COVID rendered immediately irrelevant, the friction of office check-in solutions, conference room booking, and shared physical space. When the pandemic hit, the company cut the initiative. Patrick found himself, for the first time, genuinely unemployed and free.

There was someone he had been thinking about for a while. Years earlier, back when he was still at the performance marketing agency, a man named Stéphane had reached out to him. Stéphane had grown up in Montreal, moved in overlapping circles with Patrick without the two of them ever becoming close, and been running his own architectural design agency.

Stéphane had an idea for a SaaS platform and was looking for a co-founder. Patrick told him no, or something close to no. The timing was wrong, and Patrick needed more experience, learn more about software, and become more useful before he could be useful to someone else. They stayed loosely in touch. After ThirdBridge went quiet, Patrick called Stéphane.

A month after he had stopped going into an office, ThirdBridge called Patrick back. They wanted him to return. He sat down with his wife, who had been with him since university and who understood, what kind of person she had married, and told her what he was thinking. He wanted to build a company, but he wasn’t sure it would work and if he was ready. She listened and then asked, “What’s the worst that happens? You try this and it doesn’t work, you can go do something else. And the best that happens is that you try this and it works and you’re really happy.”

He describes her as having kicked him off the ledge. He was standing at the edge of something, hesitating, and she pushed. He turned ThirdBridge down and called Stéphane back.

Maket.ai

They started the company during COVID, working remotely from the beginning. Neither of them was an engineer. They were two business-oriented founders with ideas, relationships and the ability to sell, but without someone on the team who could build the underlying technology. They were, in fact, constructing a pitch without the product to back it up. They got into NEXT AI, one of Quebec’s most prestigious startup accelerators, on the strength of a personal relationship Patrick had with the program director. By their own admission, they did not belong. “We had no idea what we were doing,” Patrick said. “We’re two founders. We’re not technical. You had to have a technical founder and a business founder to get in. We’re both business guys.” They got in anyway. The program introduced them to someone at Mattamy Homes, Canada’s largest home builder.

Mattamy had been thinking internally on whether AI could generate floor plans.

The question landed at a moment when Patrick and Stéphane were open to a new direction. Patrick looked around at the housing stock in Otterburn and told Stéphane, “Dude, everything is ugly. I hate these houses, they look terrible. Everybody’s living in terrible garbage boxes that all look the same. Why does this space around us not look nice?”

Mattamy’s suggestion aligned almost perfectly with that irritation. And yet the pivot was not easy. The technical challenge of using AI to generate floor plans was, as Patrick and Stéphane would quickly discover, much harder than it appeared. “Nobody’s doing this,” they had thought when Mattamy posed the question. “It must not be that hard.” It was, in fact, incredibly hard.

The argument over whether to make the pivot happened on a phone call while Stéphane was hiking up a mountain, the signal cutting in and out, both founders raising their voices across a dropping connection. Stéphane wanted to stay the course on their original thesis of an online home sales platform. Patrick wanted to go all in on generative architectural design, on the harder, stranger, more exciting path. You do not know who your business partner really is until you have disagreed with them about something that actually matters. They resolved it by following the money, or the prospect of it. Mattamy was willing to be their first paid pilot. A real first customer changes the economics of almost any argument. They took the generative design path, secured the contract, and used that traction to get into Techstars.

Distribution

What Patrick understood before Maket had a good product was that good products need customers to become good, and customers do not appear on their own. He had been watching this mechanic for years, at the agency, at ThirdBridge, in his own early ventures, and he had come to believe that distribution was not a problem you solved after the product was right. Distribution was something you built alongside the product, sometimes ahead of it, because without the signal that real users generate, you are building in the dark.

Rather than following the conventional early-stage advice of cold calls, warm introductions, and careful qualification of prospects, Patrick and Stéphane spent money on Facebook ads. They set up landing pages, tested messaging, drove traffic to a waitlist, and watched what happened. What happened was that people signed up. A lot of people. By the time the V1 of the product was mature enough to support real users, Maket was seeing roughly 1,000 sign-ups per day.

The aggregate spend over the course of a year came in part by a commercialization subsidy from the Quebec government that Patrick deployed. “The government is giving us free money,” he reasoned, “we’ll spend it on ads.” The marketing drove organic search, which drove more sign-ups, which made attribution complex, but it also generated data that would be more valuable than the numbers. Feedback forms, customer support logs, live chat transcripts, and churn exit surveys were the holy grail of the government money spent.

Olivia, who joined Maket before she had finished her university degree and would still be with the company three years later as she completed her final semester, managed the customer-facing edge of this feedback loop with the specific thick-skinned patience of someone who has absorbed a great deal of complaint on behalf of a product that was not yet what it needed to be. She knew the customer the way someone knows a difficult family member, deeply and sometimes painfully. What came out of all those complaints, feedback forms and support emails was a portrait of what the platform needed to become, a portrait that would eventually shape the architecture of V2 in ways that a hundred curated customer interviews could not have produced.

One of the key lessons Patrick absorbed at the performance marketing agency was less about marketing specifically and more about what good leadership looks like from the receiving end. A mentor like James who extends trust, even when the trust is not yet fully warranted, creates a different kind of professional than one who makes people earn every inch of responsibility. Patrick, now in the mentor seat, would extend that same trust to Olivia, building a knowledge base of the customer that made her indispensable.

Fundraising

The fundraising process lasted 14 months, produced 100+ rejections, and came within weeks of ending the company.

Patrick began preparing for the seed round in Q3 of 2024, at a point when Maket was on an upward trajectory but carrying a structural vulnerability that any experienced investor would identify immediately, that the product was not good. The V1 needed a lot of work, but the initial concept was presentable and functional. The team was two business-oriented co-founders without a strong technical leader, which meant that Maket had distribution and market validation but could not fully capitalize on either. Patrick understood this, but decided to run a process anyway for survival.

He compressed the meetings into the shortest window he could manage, stacking them back to back in August and stretching through November, creating the artificial urgency that a tight timeline produces in any serious negotiation. Inbound came from tier-1 investors. Conversations went deep, some progressing all the way to investment committee presentations. And then came the rejections, one after another, patient and varied in their specific wording but consistent in their underlying message: a product that wasn’t good enough and a founding team without a technical founder.

In July of 2024, a message arrived from someone Patrick had not been expecting to hear from. The man was a successful entrepreneur who had sold his company and was now looking to deploy capital into things he understood. He was interested in Maket, and they talked several times over the following weeks. The conversations went well, which is to say they went slowly, because the man seemed to move at his own pace and respond on his own schedule.

In August, Patrick was on a flight to Japan. He was taking a real break, the kind that founders rarely allow themselves and that Patrick had learned, partly through experience and watching his father manage exhaustion through decades of ministry, to actually take. The message came through during the flight and the investor was in. He would be the lead, the round would be anchored, and the long process would be over.

Patrick landed in Japan, spent the rest of the trip doing data room and term sheet work between stretches of rest. When he flew back to Montreal, he told his team they were good to go.

And then the man stopped responding.

Patrick followed up. He reached out to mutual contacts, people who knew the investor and had vouched for him. They described him as a great investor, someone who would be terrific on a board. He just wasn’t responding. A few weeks passed, then a month. It became clear, slowly and then all at once, in the way that a funding round falls apart, that the yes had been a no that hadn’t yet been delivered. Maket had been operating on the assumption that the round was closing while the round was, in fact, dissolving.

“We were about to shut down the company,” Patrick said, “because we almost ran up our runway.”

He met with Amiral in January of 2025. Patrick had first encountered Nectar about 2.5 years earlier at a mixer in Montreal’s startup ecosystem, when the fund was still in the process of being raised. He had stayed loosely in touch. Later, Fred had become a kind of informal mentor through a Quebec Tech program. Patrick met Fred for coffee and told him the truth, all of it, including the ghost investor, the near-shutdown, and what had gone wrong. At one point, Patrick told Fred directly not to invest. “I’m talking to you as someone I consider a close friend. I don’t think you guys should invest.”

Fred never forgot that. The honesty of it, the willingness to prioritize a relationship over an opportunity that might have closed a desperate gap, was the kind of signal that stood out as something durable. When Amiral was capitalized and the timing was right, that meeting was still sitting in the ledger of things Fred knew about Patrick.

The round eventually came together. Blitzscaling Ventures closed on their commitment in January of 2025, while Patrick was back in Japan, but this time closing their now largest customer. Their partners applied what Patrick described as a formula rather than a gut feeling, that the company had distribution, the ability to blitz a market, and disrupt technology within the space. Jeff, the partner who championed the deal from Blitzscaling’s side, had a single quality that Patrick would describe as the thing that mattered most after hundreds of rejections. He asked thoughtful questions and understood what he was hearing. The conversation that closed the round was, Patrick said, “very, very easy.” Amiral Ventures ultimately led the $3.7 million seed round alongside Blitzscaling, Hidden Layers, BYVP, StartUp in Residence, Spatial Capital, and others.

Bruno Morency, an advisor and investor through Techstars, had been with Patrick through much of the navigation, helping him think through the negotiations and the moments of uncertainty. “Bruno really, really helped me a lot during fundraising,” Patrick said. The visible part of fundraising is the pitch; the less visible part is the person in the corner helping you figure out what to do next, and Patrick is someone who is honest enough about what he doesn’t know to go find those people.

“It’s like a long sales cycle,” he said, “and you’re knocking on hundreds of doors and you just have to knock on one and find somebody who has conviction. It’s all like dating, right? You meet somebody for 15-30 minutes, and you know if they’re leaning in.”

Ski Crash

Three years before the seed round closed, in 2022, Patrick crashed on a ski slope and fractured his femur badly enough that paralysis seemed possible. He recovered. The bone healed, and continues to ski on the weekends.

But something recalibrated in the accident’s aftermath, in the weeks of recovery and the months that followed. Patrick had started Maket in 2020 and had been building it for two years when it happened. He was the same person after the crash that he had been before it, curious, optimistic and energized by design and by the problem he was trying to solve. But the crash gave him a counterweight to the ambition, a second truth to hold alongside the first. “Every day is like a plus-one bonus for me,” he said.

What he means by this, in the context of building a company, is nuanced enough to be worth unpacking. He is not saying that the accident made him less ambitious, or that the brush with paralysis made him indifferent to outcomes. He is saying that it forced him to hold two truths simultaneously, the truth that what he is building matters and demands everything he has, and the truth that no amount of what he has can guarantee the outcome, because the fragility of life does not negotiate with ambition. “No matter how hard you build,” he said, “there’s a certain element of just luck and right place, right time and blessing, whatever you want to call it, that determines some of the paths.”

His father’s advice during the dark periods of building Maket would draw directly from decades of ministry experience, to give yourself three-month marks. Set a start time, check in at three months, see where you are. If you still feel like quitting, allow the feeling but don’t act on it. Then do it again. “It would be the same thing of when he was in ministry,” Patrick said. “He would just always take a reassessment.” The advice sounds simple but it is structurally very good, because it takes the chaos of a difficult stretch and turns it into a data collection exercise. You are not deciding whether to quit; you are deciding whether the evidence at a specific checkpoint supports continuing. That reframe saved Patrick more than once.

Fun as a Moat

Patrick has been building Maket since 2020, which is a long time to be doing something that started, by his own admission, as a guess. The guess was that AI could do something for architecture, interior design and floor plan generation that had never been done before, that there was a market of homeowners, builders, real estate developers and agents who were underserved by existing tools, and that the combination of distribution and technology could unlock something large. Five years in, with a V2 about to launch, a seed round closed, 30,000 sign-ups per month, and 50,000+ users waiting for the next version of a product they were already using, the guess looks increasingly like a thesis.

His competitive advantage, as he sees it, is that he is having fun. He quoted a tweet to that effect, couldn’t remember exactly who wrote it, but felt it described him accurately enough to repeat. As competitors have multiplied in the generative design space, the fun has acquired a sharper edge. He is nice, he said, but also very competitive, and when he sees a competitor doing something, his instinct is to figure out how to counter it. The fun and the competitiveness are not in tension; they are the same thing, because he is playing a game he has chosen and cares about, rather than one he has been assigned.

He has resisted the pull of San Francisco, not without understanding what it offers, but with a specific argument against it that a mentor once made to him, that building in Montreal, with less access to capital and therefore a slower ramp, meant that when the technology matured enough to match the ambition, the company had not yet spent all its money chasing a vision the market wasn’t ready for. The slower ramp had been, in retrospect, a form of protection.

He believes Montreal is due for a rise on the global stage, that the concentration of AI talent and the quality of the institutions feeding it create the conditions for something significant.

His father lives nearby, and discovered recently, that AI can do extraordinary things. He uses Claude to write theology books, gets them approved through an e-publisher, creates songs with Suno, generates images. Patrick watches this with the specific delight of someone who has spent 6 years trying to build something what his father is now using, and who sees, in his father’s creativity finally flowing freely through the right tools, a version of what he is trying to give to everyone. “People who thought that they weren’t creative, now they can be creative.”

Maket is building the platform for generative architectural design. If you’re a homeowner, home builder, real estate developer, or agent who needs to create and modify floor plans, check out Maket.ai.

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Designing a home has always been a process of vision meeting constraints, a dance between creativity, regulations, and technical feasibility. Maket is reimagining that process.

Founded in Montréal by Patrick Murphy, Stéphane Turbide, and Simon Vallée, Maket empowers anyone, from architects and builders to homeowners, to generate and refine architectural floor plans in seconds. Users can describe what they want in plain language (“a three-bedroom house with an open kitchen and lots of natural light”), and Maket instantly generates multiple, code-aware design options ready to explore in 2D or 3D.

What Canva did for graphic design, Maket is doing for architecture: democratizing access to creation.

From Vision to Platform

The idea began with a simple question: What if designing a home could be as easy as sketching a thought?

Patrick and Stéphane, both passionate about architecture and user experience, started developing the core generative engine in partnership with Mila, Montréal’s world-leading AI institute. Patrick brings a background in design and growth, shaping Maket’s path from product to market. Stéphane spent over a decade as an architectural technologist, witnessing firsthand the cost and complexity faced by non-architects. Their third co-founder, Simon Vallée, a veteran product leader and three-time founder with exits to Groupon (OpenCal), Slack (Spaces) and Figma (Flight), joined to help shape that vision into a world-class product.

Today, Maket’s platform enables users to instantly create, edit, and visualize realistic floor plans. Behind the simplicity lies a complex generative AI system that learns from every project created, improving with each iteration. With over a million users and millions of plans generated, Maket is building a powerful data flywheel and a deep technical moat.

The Next Chapter: From Consumers to Builders

As Maket grows, its mission of democratizing architectural design extends to professionals, empowering builders and developers with the same intuitive AI tools that made design accessible to everyone.

Its proprietary models, refined through millions of user interactions, now serve as the foundation for a professional-grade platform that can compress weeks of front-end design into minutes. Builders can generate compliant plans, adapt them to local codes, and instantly visualize options for clients all while reducing costs and accelerating time-to-market.

This evolution positions Maket not only as a creative tool but as a core design infrastructure for the construction industry, bridging the gap between design intent and build execution.

AI Meets Architecture

Maket’s core innovation lies in its AI-powered design engine, which combines deep learning, architectural rules, and reinforcement learning to generate realistic, buildable layouts. The system understands adjacency constraints (like placing bedrooms near bathrooms), respects local zoning and building codes, and can even suggest style variations on demand.

By compressing weeks of early-stage design work into minutes, Maket delivers tremendous efficiency for professionals and creative freedom for individuals. For architects and home builders, it’s not just a productivity tool; it’s a new way to think about design.

A Team Building for the Long Term

Maket’s founding team brings a rare blend of expertise:

• Patrick Murphy (CEO) blends creativity, product vision, and storytelling.

• Stéphane Turbide (COO) grounds the company in deep architectural and technical rigor.

• Simon Vallée (CPO) brings two decades of experience building intuitive and category-defining products with global reach..

Together, they’re shaping an entirely new category at the intersection of architecture, design, and AI.

Why We Invested

At Amiral Ventures, we back founders using AI to reinvent traditional industries. Maket represents that vision perfectly: a category-defining company born in Québec, tackling one of the world’s most entrenched, analog sectors, architecture and construction.

We believe the next generation of design software will be conversational, data-driven, and generative. Maket is leading that shift. The company’s technology, user growth, and ambition position it to become the global reference point for AI-assisted design, a new standard for how spaces are imagined and built.

CFOs at every mid-market and enterprise company are constantly seeking to better understand the operational and financial performance of their business; which product SKUs are less profitable? Why is my inventory cost higher in certain areas? Why am I paying more overtime in specific teams? The answers to these questions are well hidden within their multiple Enterprise Resource Platforms (ERPs) and other financial and operational systems of record: NetSuite, Oracle, Salesforce, ServiceNow, Quickbooks, SAP... There are thousands of those systems and it is common for an enterprise to use several if not dozens of them.

In order to answer these daily questions, CFOs and FP&A professionals end up spending their days navigating the complexity of multiple systems, extracting complex, disjointed data from several disparate ERPs, and attempting to join them in Excel. This is time consuming, error prone, and incredibly inefficient. ERP providers’ BI and analytics solutions are siloed and proprietary, and don’t move at the speed of business, simply put: it sucks.

Maxa was founded in 2019 to help solve this problem hiding in plain sight. Maxa enables the complete automation of a company’s ERP data, across all systems, so that FP&A teams have a single source of truth and can deliver insights in a snap.

Building a magical solution

Maxa automatically unifies multiple ERP and data systems into a data product. A Maxa Data Product is enterprise data that is standardized, made relevant with pre-computed insights ready to be consumed by non-technical users and reusable across tools and algorithms, without the business logic being locked into a visualization layer. All these insights are generated in seconds, not weeks.

It’s all delivered via the Snowflake Data Cloud as a Snowflake Native App. Maxa created the universal enterprise data schema which is essentially an intermediate mathematical language; they built a single data model across ERPs. This allows them to take siloed systems and make their data available in an efficient and unique way to advanced analytics and artificial intelligence models.

Founders on a mission​

Raphael Steinman and Alexis C. Steinman are the quintessential founders. Smart, gritty, humble, not afraid to take risks and looking to make a big dent in the world. They just raised a US$21M Series A in which Amiral Ventures participated - we couldn’t be happier. The company is on an incredible trajectory and is poised to become a Canadian flagship.

Why now?

The underpinning of Maxa’s solution depends on storage and compute becoming extremely cheap and infinitely scalable which only really became a reality in the past few years. Their solution hinges on the ability to crunch billions of rows of data in seconds. This allows for Maxa to create massive value for their customers. Their algorithm also becomes highly reusable. Hundreds of templates now become available for all their customers and insights available at the click of a button. In essence, they are trying to make advanced financial analytics a commodity; taking something that used to be really hard and allowing their customers to move much faster and run their business much better.

A scalable and robust model

Success comes from making good decisions (and learning from bad ones). One of the good decisions they made was to not try to reinvent the wheel. Instead of building a reporting front-end, Maxa simply leverages existing tools like Power BI, Tableau and Looker. In the current age of LLMs, this was quite prescient. One of the new opportunities for Maxa is to use this new form of computer communication instead of legacy dashboard reporting. The promise of generative AI only works if the data can be trusted and the single source of truth for companies is their ERP.

Moreover, it’s a usage based business model which is super scalable. Customers get hooked since it solves a major problem and decide to use Maxa for more & more analytics needs (read: strong LTV). Ultimately, this gets people out of crunching data in Excel and frees up executives’ time to work on the business. Demand planning, forecasting, procurement, inventory management all generate tons of data; what’s missing is insights. This is where Maxa excels. For example, live performance per SKU was not imaginable a few years ago. This drives automated insights which can be tied directly to financial impact for their customers. To top it off, their native app captures Snowflake’s capabilities all while protecting sensitive customer data.

The global opportunity

The ERP software market is substantial and rapidly expanding. An estimated $250B is spent on systems each year. it’s projected to exhibit a compound annual growth rate exceeding 24% over the next several years. This growth is fueled by the escalating need for operational efficiency, transparency in business processes, and the urgent requirement for data-driven decision-making. There are 4M companies globally running an ERP. Additionally, there is an estimated $1T staff automation cost savings potential with companies increasingly seeking manual task automation. It’s a limitless opportunity for Maxa.

Building the next Canadian Flagship

Maxa demonstrates exceptional potential for growth in a massive global market. Their innovative solution addresses a clear pain point which demonstrably saves an FP&A team significant time while demonstrating rapid return on investment for their customers. This creates a lot of value to their users with a robust and scalable business model. Maxa was also selected as startup of the year by Snowflake as well winning the 2024 Data Driver’s award which has engendered significant customer interest. The founding team possesses both deep industry expertise and a track record of execution, bolstering our confidence in their ability to drive Maxa’s success. This combination of product-market fit, scalability, and a strong team positions the startup exceptionally well for rapid growth.​

Maxa fits squarely into Amiral’s vertical AI thesis: using innovative data-first technology to create significant customer value. There is a clear imperative for businesses across the world to increase their productivity and we believe that tackling ERP data to derive real-time financial insights is a huge unlock to allow each business to focus on what really matters. It is clear that running LLMs on financial ERP data has huge potential if you can trust the results, and Maxa has demonstrated that running AI on the Maxa universal model provides better results than directly on the ERP’s complex databases.

Congratulations to Alexis, Raphael and the entire Maxa team. You are only getting started!

In this candid episode, the three partners trace the origins of the fund: how Fred's entrepreneurial journey (including co-developing LTE at Nortel and a successful Series A & B raise at Mnubo) first brought him together with Dom, a veteran M&A banker who went on to launch Xpnd Capital and deliver two IPOs. Nectar rounds out the team, coming off years of post-investment value creation work at PNR and crossing paths with Fred through this very podcast.

The conversation gets real about what it actually takes to raise a fund from scratch: nearly three years in the trenches, no product to show, just a slide deck and conviction. They share what they've learned, the primacy of trust-building, the importance of giving candid feedback to founders (even on a "no"), and the gap they're filling in the Canadian ecosystem at the seed-to-Series A inflection point.

The team also lifts the curtain on their Prosperity Decoded thesis, backing AI-native companies driving enterprise productivity, sustainability, and resilience; and spotlights their first two portfolio companies: Maxa (AI for CFOs and FP&A) and Maket (AI for residential architecture and design).

They close on what excites them most: the caliber of Quebec founders, the community of successful exits now back as LPs and mentors, and the long game of building Canada's next generation of flagship technology companies.

We talk about his journey from building robots as a kid to launching startups while still in university, what he learned from failing his first company, and how General Magic is rethinking the way humans interact with software using natural language.

We also dive into their recent $7M seed round, going through a16z Speedrun, and why the future of computing might move beyond the traditional graphical interface.

I really enjoyed this conversation, Anthony is thoughtful, ambitious, and building something genuinely interesting. Let's get into it.

Tyler shares his journey from space-obsessed kid to Stanford researcher to venture-backed space founder, and explains how Zona is building a next-generation positioning system designed for centimeter-level accuracy, stronger signals, and built-in security. We unpack how modern infrastructure depends on timing and location, why GPS is vulnerable to jamming and spoofing, and what new satellite constellations unlock for autonomous systems, defense, data centers, and everyday devices.

We also go deep on building a hard-tech startup: raising capital for space infrastructure, launching satellites, assembling the right team, and navigating the long road from research to orbit.

If you're interested in space tech, deep tech startups, autonomous systems, or the new space economy, this episode is for you.

Topics covered:

• Why GPS needs an upgrade

• How next-gen satellite navigation works

• Centimeter-level positioning and resilient timing

• Space infrastructure and low-Earth orbit constellations

• From PhD research to space startup

• Lessons from building a hardware + space company

• The coming wave of space innovation and lunar missions

Topics covered include: Early exit offers vs long-term ambition Scaling from startup to global platform Fundraising strategy and cap table discipline Hiring executives and leadership evolution Crisis moments and near failures Building and keeping tech champions in Canada AI, moats, and the future of company building Recorded live at North Star — Inspiring Entrepreneurs in Montréal.

Across all three segments, one theme dominated: AI is moving faster than any tech cycle before it, and Canada has a once-in-a-generation chance to lead.

VC Panel, Navigating the AI Wave

Investors from Inovia, White Star, and Amiral Ventures explored the chaos and opportunity of today's market. The AI cycle is "psychotic" in speed and scope, but the fundamentals still matter: strong teams, sticky products, real customer value. The best startups will use AI as an enabler, not the product, and differentiate through privileged data, deep domain expertise, and the ability to deliver ROI, not hype.

Founder Panel, Building Real AI Companies in Montréal

Budpress, Maxa, and Wrk shared candid takes from the front lines: why traditional SaaS is being rewritten, how open-source models and reasoning engines are changing the game, and why Montréal remains one of the best bases in the world for AI talent and cost-efficient scaling. The founders stressed that Canada must shed its "small market" mindset, big outcomes are possible here.

Keynote with Sam Ramadori, AI, Climate, and Responsibility

Sam recounted his unlikely journey from private equity to leading BrainBox AI through an acquisition, and why he's now dedicating himself to Law Zero, a nonprofit effort led by Yoshua Bengio to build safer, more reliable AI systems. His message was clear: Canada has extraordinary AI talent, but must rally around sovereignty, responsible innovation, and deeper collaboration to avoid being squeezed between global superpowers.

The night ended with a message to the community: this is our moment. We have world-class research, world-class founders, and a fast-maturing ecosystem. If we choose ambition, and support one another, Montréal can be one of the defining AI hubs of this decade.

Étienne opened up about the lessons he learned along the way, the value of resilience, trust, and playing the long game in both building and backing startups. He also spoke about his partnership with Luis & Varun at Telegraph Hill Capital and their shared mission to support founders in Quebec.

Real talk, honest stories, and practical takeaways, this one's for anyone thinking about their next chapter, whether it's starting up or stepping into VC.

We discuss his career path, touching on Etienne's background in management consulting, transition into VC, and current role at a prominent firm. We explored investment trends, particularly in AI and cybersecurity, and shared insights into their firm's differentiators and investment strategies. The conversation wraps up with advice for aspiring VCs on the importance of networking, building a personal brand, and developing essential skills like context switching.

The conversation covers the importance of aligning founder priorities, the unique challenges of creating hardware, and the innovative techniques used in field testing. George also shared insights into how Rozvelt has quickly gained traction and their ambitious plans for the future.