Tell us about yourself and your role in the startup landscape.
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I鈥檓 the founder and CEO of Convoy, an enterprise webhook gateway that helps engineering teams deliver events reliably at scale, with an open-source core that鈥檚 been adopted by teams across fintech, healthcare, and developer tools. We went through Y Combinator in the W22 batch.
Alongside Convoy, I work on product engineering at Speakeasy, building features that help companies understand and control how they use AI. Most recently I built a cost and budget feature that gives teams visibility into their AI spend and the controls to keep it in check. Most of my career has sat at the infrastructure and developer-tooling layer: distributed systems, APIs, and the products engineers rely on. Put simply, I build the unglamorous plumbing that other builders depend on, which gives me a particular appreciation for founders doing the same.
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As the founder of Convoy and an engineering leader working with enterprise AI deployments, what lessons from your experience most influence how you evaluate startups today?
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Three things stand out. The first is speed, or really the rate at which a team learns. We live in a fast-paced world, so I want to see how quickly founders learn, move, and iterate. I鈥檇 rather they index on speed over perfect correctness, because I鈥檝e seen companies spend a very long time polishing a feature or a problem that turns out not to matter to customers. I feel this one personally. Convoy actually applied to YC twice. The first time we were rejected, but the feedback from the partners was incredibly valuable. They told us honestly what they felt was lacking, including that our original idea wasn鈥檛 going to scale across markets, and once we sat with it, they were right.
We took that feedback, dug in, realised the problem we should be solving was a real one, and came back with a completely new product, which became Convoy. We submitted it in the next batch and got in. One of the things a partner told us afterwards was that we鈥檇 shown a very high learning rate, and I鈥檝e believed ever since that the ability to learn and iterate quickly is one of the strongest signals a startup can give.
The second thing I look at is how close the team is to their users. The more they鈥檙e talking to users, the more confident I am that they鈥檙e building something genuinely useful and identifying the real pain rather than an imagined one. The third is distribution, and specifically whether the team cares about product and distribution in equal measure. One isn鈥檛 more important than the other; you need both to succeed. So when I evaluate a startup, I鈥檓 less interested in the pitch and more interested in whether the team learns fast, stays close to its users, and takes getting the product to people as seriously as building it.
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What makes the United States鈥� startup landscape different from other parts of the world?
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As a Nigerian founder who got into YC, a few things stood out immediately. The first was access to capital. The second was access to customers who are actually willing to pay. But the one I didn鈥檛 expect was less tangible. Going through an accelerator like YC puts you in the room with very successful founders and operators, and as a young founder that gives you real confidence. You stop feeling like you don鈥檛 belong, and you start to feel like you have a seat at the table, that you can contribute and build something great too.
That shift in self-belief is one of the most underrated things about the US ecosystem. I got into YC right at the tail end of COVID, so we didn鈥檛 actually fly out to the US until much later, and the difference was stark. Being physically in San Francisco, meeting investors, selling, and talking to customers in person was a completely different experience from doing it remotely. The density of capital, customers, and people who have done it before is all concentrated in a way that鈥檚 hard to replicate anywhere else
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What advantages do startups based in the USA have over startups located elsewhere?
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Access to capital at every stage is the obvious one, but I鈥檇 point to two others that matter just as much. The first is a huge, relatively homogeneous market, with one language and one broad regulatory environment, so a product that works in one city can scale nationally without being rebuilt. The second is a customer base of early adopters, including large enterprises, who are genuinely willing to try new tools from young companies. That combination of capital, market size, and adoption appetite lets US startups compound quickly.
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What challenges do startups based in the USA face compared to startups from other parts of the world?
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The flip side of all that opportunity is noise and competition. Almost every good idea already has several well-funded teams chasing it, so differentiation and speed matter enormously. Talent and operating costs are high, which pushes up burn. And there鈥檚 a subtler risk: when capital is easy, it can mask weak fundamentals for longer than it should. Founders building under tighter constraints, which is common outside the US, are often forced to be more capital-efficient and closer to revenue earlier, and that discipline is something a lot of US startups have to learn the hard way.
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Having worked across developer tools, API infrastructure and AI systems, what do you find most exciting about the US startup ecosystem right now?
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We鈥檙e living through one of the greatest times to be building. We鈥檙e essentially rebuilding the infrastructure for knowledge work, and the US ecosystem is at the very center of it. In the industrial revolution the breakthrough was the assembly line; in this era we鈥檙e building what you might call software factories, laying the foundation for how knowledge work itself gets done.
The US is moving very fast to figure out where the edges of this technology are: what large language models can really do, where the durable advantages lie, and how far we can push the way we work. A huge amount of that momentum is concentrated there, with companies like Anthropic, OpenAI, and Nvidia building the underlying infrastructure that everyone else gets to build on top of.
The work I do at Speakeasy sits right in the middle of this. Over the past year companies have been moving incredibly fast, throwing AI agents at all kinds of work. The natural next step is measuring it: understanding the compute, the cost, and where the real return on investment actually is. Not to slow anyone down, but so they can keep deploying these models intelligently and effectively. The cost and budget feature I built is a critical piece of that, giving teams visibility into their AI spend so they can put it where it genuinely pays off. I love the dynamism and velocity of the US ecosystem right now, and it feels like one of the most exciting times in a generation to be building.
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What are you looking forward to seeing in US50 entries?
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I鈥檓 hoping to see founders solving specific, real problems rather than chasing whatever鈥檚 trending, and, importantly, showing evidence that people actually use what they鈥檝e built. I鈥檓 drawn to technical depth paired with commercial clarity: teams that can explain both how their product works and why someone pays for it. I have a soft spot for companies working on unglamorous infrastructure, because that鈥檚 often where the durable businesses are hiding.
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From both a technical and commercial perspective, what qualities immediately tell you that a startup has strong long-term potential?
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Technically, I look for sound architectural judgment: founders who understand the trade-offs they鈥檝e made and can defend them, and who treat reliability as a feature rather than an afterthought. Commercially, I look for a clear picture of who the customer is, real signs of retention and usage rather than vanity sign-ups, and a wedge that can expand into a larger market over time. Above all, I look for a team that ships quickly and learns from what they ship. The quality of a startup鈥檚 iteration loop tells you more about its future than any single metric.
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What can US50 entrants do to stand out from the crowd?
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Be concrete, and be honest. Showing traction helps, but it isn鈥檛 enough on its own, and inflating it is a mistake, because experienced investors and judges can spot fake traction from a thousand miles away. What often stands out more, especially when you鈥檙e early and don鈥檛 yet have the revenue to point to, is depth of insight. Show that you understand your market, your customers, and how buyers actually behave better than anyone else, and make a clear case for why you specifically will win. That kind of unique, hard-won understanding is harder to fake than a chart, and it tells me far more about a founder than a polished deck ever could. So state the problem crisply, avoid hiding behind buzzwords, and let genuine depth do the work.
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As someone who spends time building and deploying technology in real-world enterprise environments, what鈥檚 your biggest piece of advice for founders entering the competition this year?
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Stay ridiculously close to your users, and don鈥檛 let your own assumptions stand in for talking to them. I learned this the expensive way at Convoy. Early on we built a ton of infrastructure because we assumed that was what customers wanted, and we shipped a lot of it, but we weren鈥檛 seeing any real growth. So I stopped and went on a spree of just talking to people: current users and prospects, reaching out on LinkedIn, not pushing for a sale, just asking questions of people who had actually lived the experience of working with webhooks.
It became clear that the real problem wasn鈥檛 the infrastructure at all. It was the developer experience of serving webhooks. That was the turning point for us, and once we focused there we started to see real business impact. So it鈥檚 fine to have a point of view about what your customers need, but you have to spend serious time talking to them to find out where the pain actually is. Everything else, including how you raise and how you grow, follows from getting that right.
