In the rapidly evolving landscape of venture capital, technology serves as the primary catalyst for innovation. Few understand this better than Gautham Buchi, the Chief Technology Officer at AngelList. With a rich background that includes senior roles at Coinbase and founding a Y Combinator-backed startup, Gautham brings a unique perspective on leveraging cutting-edge tools to solve complex financial problems. In this conversation with host Nataraj, Gautham dives deep into the operational core of AngelList, a platform dedicated to building the infrastructure for the startup economy.
He shares how AngelList is harnessing Generative AI to automate fund formation, provide deep, actionable insights for investors, and accelerate capital deployment. The discussion also explores the integration of crypto primitives, such as stablecoins and tokenization, to create new pathways for liquidity in private markets, a critical component for fueling the next wave of innovation. This episode is a masterclass in how modern technology is reshaping the world of startup investing.
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Nataraj: To set the context of the conversation, can you give a quick introduction of who you are and what your journey was before joining AngelList as a CTO?
Gautham Buchi: My journey has largely revolved around key levers that can personally change someone’s life, which is largely education and access to financial tools. It started at Coursera, where we tried to democratize access to good education and then moved on to my own company, furthering the journey. Then to Coinbase, which democratized access to better financial tools using crypto as a methodology. Now I’m continuing on the path to democratize access to capital. Access to capital is probably the single best innovation hack we could do to create more startups. AngelList is in the business of creating more startups, creating more tools for the founders and builders. And I’m really excited to continue that journey there.
Nataraj: Talk a little bit about for those people who are not aware of AngelList. What are the different products on AngelList and what are the core business drivers among those products? You have rolling funds, venture funds, syndicates. Talk a little bit about that.
Gautham Buchi: A good mental model that I use is if you think of a triangle where one corner is the founders, the other corner is the GPs, like the Sequoias of the world, and the third corner is LPs, people who want to invest in early-stage venture or venture more broadly. AngelList is smack in the middle of the triangle. Our sole purpose is to make sure that the sides of this triangle are getting stronger and stronger because these three are the pillars of the innovation economy. The first thing you have to believe, to believe in the mission of AngelList, is that startups are good for the world. The creation of more startups is the way we innovate and is the way we accelerate innovation. Now post that, we need to identify how do we really strengthen each of these pillars: the founders, the fund managers, and people who want to invest in early-stage venture.
If you’re a founder, and maybe this is surprising to a lot of people, Robinhood’s first check was on AngelList. Many companies through our product have been able to come in and say, ‘Okay, I’m looking to access good capital, not just dumb money, but good capital on the platform.’ I can go to AngelList and start a company. We are creating an ecosystem for founders to take the mental gymnastics around starting a company and really focus on building the product. We will build the rails for you to get the capital that you need.
Moving on to the other corner of the triangle, which is you are a fund manager. Let’s say you have a unique hypothesis, a unique insight into where you think you can be investing to accelerate this innovation. You need two pieces: access to good founder opportunities and access to good capital that is looking to be invested. That is our core fund admin product, the core GP product. This is probably the one that AngelList is well-known for today. You get a lot of tools so you don’t spend time doing the gymnastics of how to raise and deploy capital, but really focus on what you can do to add maximum value to the founders.
The third corner of that pillar is the people looking to invest in early-stage venture more broadly. This is probably the one that most people have historically known AngelList for. Wherever you’re in the world, if you want to invest, get access, and believe in the startup economy, you can write a $1,000 check to a $100,000 check. You want to be an angel, you go to AngelList. This is the thing that Naval envisioned: how do I really democratize access to early-stage venture across the world? So we provide a number of tools for people who are looking to get their toes wet in the world of angel investing. To sum it up, the way I would think about AngelList as a business is to really think about the triangle between founders, GPs who are looking to run the fund, and then angels. The speed with which we can spin the triangle is essentially innovation.
Nataraj: You joined AngelList this year or last year, and you’ve worked in different companies. How is working at AngelList different from working at Coinbase?
Gautham Buchi: Very different. Right off the bat, crypto in 2017-2018 was very different than crypto right now. To give a specific anecdote, if you and I met in 2017 and you told me that by 2025 we would have a Bitcoin ETF or we would have stablecoins, most people in crypto would have laughed. The pace of innovation is so constant, so relentless, and quite frankly, very uplifting. But there is always this overhang of regulation on top of you. There were many times, especially over the last four years, where being at Coinbase felt very much like you’re fighting a big institution, a regulatory battle. That is not something that we face at AngelList. You don’t spend time thinking about regulation in the way that you would in the crypto world. You’re really thinking about how do I accelerate capital deployment? How do I bring more efficiency to how capital is being deployed? Which is a very different problem space.
Second, the pace of innovation in crypto is insane. We had a joke at Coinbase that one year in crypto is like 10 years. There’s a popular meme where after five years in crypto, somebody has this white beard and gray hair. It’s very true. I can personally attest to it. And so is the eternal optimism. The crypto crowd is probably one of the most optimistic crowds that I’ve ever worked with. It’s different in capital products. While innovation does happen, it’s not at the same pace at which it’s happening in crypto. So the way you think about product, you’re thinking more from a reliability lens, you’re thinking longer-term, which is very different. As for the companies, particularly, AngelList is much smaller, much more early stage. We are about 150 people. Coinbase, when I joined, was probably a few hundred, but it’s now a much bigger company. So that definitely has its own pros and cons.
Nataraj: There’s one through-line I see between Coinbase and AngelList: both were involved in major regulatory changes. Naval and the team were involved in the JOBS Act earlier to change and make AngelList and crowdfunding happen. Now we are seeing that happen in real-time with some of the crypto legislative changes. I want to pivot towards what I wanted to talk about most in this conversation, which is about AI. Post-ChatGPT, we saw you could do a lot more with this current technology. In my career, this feels like a game-changing moment. I wanted to quickly get your thoughts on what you think of Generative AI and this current AI hype cycle.
Gautham Buchi: Let me dial back the clock a little bit. I don’t know if your audience is familiar with Coursera; it was an education platform that started in 2011. Our first major success was a machine learning course by Andrew Ng. A lot of people, especially in deep learning, probably got their start with Andrew. At Coursera, we were incredibly excited about it, not just from a pure technology perspective, but also the audience and the learning; these were the most subscribed courses on the platform. The thing that was different back then was it was still largely a research problem. It was harder to think about what an actual go-to-market version was.
Even when I was starting my own company in 2016-2017, there was a running joke within YC that all you had to do was attach .ai to your domain and you automatically would raise a bunch of money. So there was definitely hype cycle number two happening in 2017. What’s different about this particular iteration is one, it moved from being a research problem to an engineering problem. You could take the model in a box, assume somebody has already done all the heavy lifting, and now you’re just trying to figure out what other things in the ecosystem you need to connect to make sense out of this. That’s been incredible to see.
Second is the utility of it. The utility back in the first cycle of my experience, 2012-2013 machine learning, 2016-17 AI, you really had to squint your eyes. There was always a human in the loop. The utility was not obvious. You had to bet that one day this thing would actually be at a point where you will see real feedback loops. But we are in a world where you can parse a PDF instantly in a couple of seconds, or you could do voice translation. So now you bring these two ideas together: it’s an engineering problem, and the utility is instant. That means you have a very fast feedback loop. You and I can spend the next 20 minutes and literally build something, put it out in the world, and see how people are interacting with it. And that is very powerful.
Nataraj: What do you think of the use cases that are most exciting for you as a CTO, and how are you at AngelList adopting AI in different ways?
Gautham Buchi: There are two interesting questions there: what is very exciting to me, and what is very exciting to the business. To me, it is so interesting to see the blurring of the roles. Even three years ago, if you wanted to build an MVP, you’d ask, ‘Who’s going to be the designer? Who’s writing the PRD? Who’s going to be building this project?’ That’s a lot of overhead. Today, our chief legal officer builds an end-to-end product himself. That includes the design, the spec, the PRD; he releases it, he’s tracking analytics. Our designer is building end-to-end products. An intern is building end-to-end. We really went from a role in a box to a product in a box. We have this full spectrum of skills that are very much available to you. The conversations become so much sharper on a day-to-day basis. This idea that you have to go through multiple iterations to even define what you’re doing will become so outdated, and the roles become so blurry. It is increasingly becoming hard to define the role of a product person versus an engineer.
Second is your ability to deploy and get the boilerplate out of the way has been huge. The hard problem in most companies is working with legacy code, not greenfield code. The moment you are able to put things in place that can abstract the legacy away from you or even better, intelligently retool the legacy for you, you’re taking a ton of work out of the way. We are able to now see folks join and start deploying the same day. It used to be aspirational, but now it’s almost an expectation because of all the tooling available.
The third thing goes back to the base-level expectation. I have this view that it will be increasingly hard to see a good role for yourself if you don’t become very quickly AI-native, meaning being able to understand which tools create maximum leverage. It almost feels like, ‘Am I late?’ You’re not late, but now is a good time to start. I can clearly see the difference between teams that have adopted AI and the teams that are still lagging behind. The difference is so clear, so obvious, that we now have a default expectation that everybody’s trying out these tools.
Nataraj: What are the blockers for teams that aren’t AI-native?
Gautham Buchi: I don’t think it is a philosophical stance. It is more of an inertia and momentum thing. You could also be skeptical. For what it’s worth, I was skeptical at one point as well. If you are an engineer today and you have not tried one of the IDEs like Cursor, CodeWhisperer, or Copilot, you are already behind. So inertia could be a big component. Second, there are some good reasons not to do it, depending on which team you’re in. For example, if you’re in security or a very critical path, you want to spend that extra time and attention. At Coinbase, we had a lot of concern internally around what we might accidentally expose because a lot of these are also primitives that are being built right now.
Nataraj: I always call AI right now ‘draft AI’ because it gets you the draft pretty fast. But if I’m reporting business numbers to my leadership, I want to depend on myself to review each line, even if I use AI to write it. You still need that 5% manual intervention, but that 95% is a really big time saver. Can you talk about some examples of how you’re using AI in your own products at AngelList?
Gautham Buchi: Let’s talk about our customer type. On a typical fund deployment, there are a lot of workflows you go through that are sequential, whether it’s legal, boilerplate, or dependent on internal movements. One of the metrics we track religiously is how long it takes for you to deploy your fund, raise your fund, or get set up for the fund. We are increasingly using a lot of AI and automation to do that. One thing we do is doc parsing. In a fund formation, there are tens, if not hundreds, of docs. We can parse the docs, provide the information that is very relevant to you, and automate your deployment. This is integral to how we simplify fund formation.
The second thing is operational. Once you have your fund deployed, the thing that AngelList is known for is the venture associates and the quality of service. We want to enable our internal teams to very quickly get access to data at their fingertips. A couple of years ago, pulling up a specific legal term for a GP would be half a day’s task. Now we have built internal tooling where our customer support and venture associate teams can, in most cases, auto-resolve issues. We can pull information, make sense of it, and spit out exactly what the customer is looking for. This feeds into the cycle of closing feedback loops and becoming more efficient.
The third bucket is AngelList is sitting on a gold mine of data. Some of the hardest resources to get to is early-stage venture data. There are hundreds and thousands of companies on the platform raising money. There is a tremendous opportunity here where we can drive deep insights into what’s happening with your portfolio. We can tell you exactly where you’re invested, opportunities you might be missing, and how your fund is performing compared to the rest of the funds on the platform. We are now able to start doing some of that using AI.
Nataraj: Talk a little bit about your crypto integration as well. I know AngelList was one of the first adopters of Circle a couple of years back. Is tokenizing shares on a blockchain a path that AngelList is looking towards?
Gautham Buchi: This is one of the best opportunities for AngelList. One thing we have done very concretely today is we enable USDC funding. If you’re a startup that is raising money with USDC, AngelList allows you to do that at no fee, and we have seen pretty significant adoption. The second opportunity is distributions. For a lot of crypto companies, distributions happen through crypto tokens. Us being able to support that means if you’re a crypto company that has an exit, your investors are able to get and keep those tokens on the AngelList platform.
Moving on to stablecoins, I think it’s one of the most exciting areas right now because they’re instantaneous, near-instant settlement. This drastically simplifies cross-border wires, which is a massive pain. This is something we are seriously thinking about: how do we make capital deployment more efficient? We are seriously thinking about how we can make stablecoins a primary citizen on the platform, potentially enabling digital wallets for all customers and LPs.
The second bucket is tokenization. What has changed over the last seven or eight years is companies are increasingly choosing to stay private. Stripe, OpenAI, Anthropic are examples. This means your capital is locked for much longer than historically seen. While you’re happy the valuation is going bonkers, at the end of the day, this is on paper; it’s not liquidity yet. And liquidity is really important because it fuels the next generation of startups. One of the things we are seriously thinking about is how do we create liquidity for the GPs and investors on the platform. On the technology side, tokenization is a reality. We are seriously thinking about how we can bring the regulatory framework, tokenization framework, and KYC/AML together to create liquidity for the funds on the platform and create good incentives for founders to participate in it.
Nataraj: As the lines are blurring, what skills should product managers invest in building?
Gautham Buchi: The thing that has changed is your ability to go from an idea to seeing it in the world has dramatically changed. The most powerful thing product managers have today that they didn’t have before is an ability to take their product idea, put it out in the world, gather actual data, and then come back to the table. They can say, ‘Here’s an MVP that I was able to build for myself. I’m not stuck in multiple rounds of prioritization. Here are 10 people I have shown this to, and here’s the information I received.’ That is so powerful and empowering. The classic role of a product manager as an information router is quickly disappearing. If you are purely serving the purpose of routing information and doing prioritization, you’re in trouble. We have moved to a world where product managers are empowered to very quickly generate these prototypes and take them to market. That’s what I would invest in right now.
Nataraj: Thanks, Gautam, for coming on the show and sharing your insights and time.
Gautham Buchi: Likewise, thank you for having me and nice to meet you all.
Gautham Buchi provided a clear look into how AngelList is pioneering the future of venture capital by integrating AI and crypto. This conversation highlights the tangible benefits of these technologies in making startup investing more efficient, accessible, and liquid for founders, GPs, and LPs alike.
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