Category: Podcast Episode Transcript

The world of digital advertising is a complex, rapidly evolving ecosystem that powers much of the free content we consume online. At the heart of this system is programmatic advertising, a technology that automates the buying and selling of ad space in real time. In this conversation, Nataraj sits down with Jared Siegal, the founder and CEO of Attitude, a company at the forefront of empowering publishers to maximize their revenue in this competitive landscape. Jared shares his unexpected entry into the ad tech industry, demystifies concepts like header bidding and ad exchanges, and explains how his company’s SaaS model is disrupting the status quo. They also explore the seismic shifts caused by AI in search, the ongoing debate around Google’s market dominance, and what the future holds for content creators and publishers trying to navigate this intricate digital world.

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Nataraj: So I think a good place to start would be how did you get into this ad business?

Jared Siegal: Totally by accident. I don’t think anyone grows up saying, “I’m going to serve ads on the internet” or even really understands that this part of the economy exists. I went to school for econometrics, which is applying economic theory to math problems and vice versa. I couldn’t even tell you how I got into that, but as I was getting ready to graduate, I really wanted to work in the car industry. I reached out to every graduate from my university who worked at Ford, Chevy, and all the major brands here in the US. Couldn’t get a job.

I went to the head of our school’s entrepreneurship program and said, “Hey, I got a cool idea for a class I wanna teach here.” I pitched it to him, and he said, “This is a great idea, but I really think you should meet this guy, a former graduate from our school. He runs a company called Answers.com.” I met him, and frankly, I had no idea what they did. I didn’t understand it. He offered me a job and I said sure. And that’s how I got into online advertising. I had a choice of working on the revenue side of the business or the cost side. I was always taught growing up to always be a revenue driver, so I chose the revenue side. That forced me into Ad Ops, and very quickly, within a few months, I fell in love with this industry.

Nataraj: So what were you doing? Was it trying to grow revenue or grow traffic?

Jared Siegal: It was twofold. One was actually trying to grow revenue, and one was trying to grow traffic, which obviously indirectly and directly grows your revenue. On the revenue side, this was right when DFP, now GAM, was created. So I was literally learning how to integrate DFP on a website, figuring out how to get away from this concept of a waterfall auction into something a bit more programmatic and real-time, and creating a bunch of different layouts and page types to understand which ad units, sizes, and arrangements make us the most money.

Nataraj: Can you explain DFP and the waterfall concept?

Jared Siegal: Yeah, DFP, which is now called GAM, is Google’s ad server. It’s used by almost every website on the planet to host the final auction of that ad on your website. Before that, people were hard-coding ads on their websites and hoping they made money. The creation of the ad server meant that you as a publisher could host an auction, get a bunch of people to compete for that ad, and choose the highest winner. Waterfall is this idea of, let me call Google, if Google doesn’t fill, let me call partner B, if partner B doesn’t fill, let me call partner C. Where we are today in programmatic is, let’s get Google, partner A, B, C, D, E, F, G, all to compete in real-time. They all bid at the same time, and whoever wins, wins. It’s a little bit faster, a little bit more efficient, and it’s far more accurate in terms of valuing your audience.

Nataraj: So let’s explain the lay of the land today. For example, I go to a site like verge.com and I see display ads. What are all the players involved when I’m seeing that ad? Who’s the publisher, who’s the bidder, who’s the exchange, and what is Google’s role versus Attitude’s role?

Jared Siegal: Okay, cool. So you go to that website; the website is the publisher. They’re the one that is publishing the content, and you’re on that site because you like their content. When that ad gets served, 99% of the time that publisher probably uses Google Ad Manager as their ad server. It’s what eventually makes the final decision of who had the highest bid from all of these exchanges. Google is also an exchange, but there are hundreds of exchanges that work with publishers directly and tens of thousands behind the scenes. All of these exchanges need what’s called a wrapper to host this auction and pass all the different bids and ad creatives into Google Ad Manager so it can make a decision. And that’s what Attitude does. There’s a handful of companies that do that part of the business. You have the ad server, you have the advertisers, and you have the company that is connecting the advertisers to the publishers. Attitude is that connection.

Nataraj: So you collect the different bids for the ad spot. Where are you collecting them from?

Jared Siegal: It’s all happening in the browser in real-time. Publishers basically load our code in the head of their page. On page load, boom, we instantly start pinging all these different advertisers they have relationships with to find the highest bids and send them along. It’s happening in milliseconds. For that one ad to be served to you on that one website, there were probably millions of different agencies, brands, and companies that got pinged in a matter of milliseconds to say, “Do you have something for me?”

Nataraj: Why do customers choose Attitude versus just using Google? Because it feels like Google is a competitor here.

Jared Siegal: To some extent. You could go directly with one exchange, and they’ll probably be able to serve most of your ads. But what happens when you only have one exchange is it’s no longer really an auction. They can pay whatever they want for that ad because they don’t have to beat out anyone else. Where a company like Attitude comes into play is we say, don’t let Google or Facebook dictate the value and price of that ad. Have a bunch of people compete and let the highest one win. In an auction, you want as many bidders as possible. You don’t want one person bidding because then they’ll just bid a dollar and they’ve won.

Nataraj: So it’s better to use Attitude to create a neutral playing field.

Jared Siegal: Yeah, you need some piece of technology to do that because Google Ad Manager and most ad servers don’t natively integrate all of the other exchanges. They’re limited to their own exchange. So if you want a bunch of exchanges to compete, you need this third-party tech to layer on your page. How we separate ourselves is our business model and the fact that we are agnostic. Everyone else in our space takes a percentage cut of the publisher’s business. We don’t have ulterior incentives to let one exchange win more because they pay us a higher rev share. It’s irrelevant to us who wins. We just want the publisher to make as much money as possible. We built a pretty big name for ourselves as the first SaaS pricing model in this space.

Nataraj: Let’s talk about Google’s role. Do you have a view on the whole trial of Google as a monopoly?

Jared Siegal: Let me preface this by saying we’re a really good partner of Google’s, and Google’s a great partner of Attitude. But there’s a reason why companies like mine exist, and that is because Google has historically had the last look at every auction. If they’re the ad server being used, they see all the other bids that come in, and after they see all that, they can say, “Hey, do I want to bid one penny more and steal that impression?” That starts getting into this idea of, is it really a fair auction? Companies like mine have been coming up with creative ways to make it fairer, whether it’s through setting price floors or creating our own ad server. With all of the recent news about monopolization, if you’re in our space, you’re kind of sitting back saying, “Yeah, obviously this has been going on for 20 years. Everyone knows this.”

Nataraj: How did you start as a consulting firm and transition to a full-fledged product company?

Jared Siegal: I started this company by accident. I quit my job and just wanted to do something on my own, so I started consulting for a bunch of publishers I had become friendly with, charging them by the hour. I did that for about 12 to 14 months and got the business up to close to a million-dollar run rate. Back then, auctions were second-price, meaning the winner pays one penny higher than the second-highest bid. I made a career for a year of trying to figure out the gap between the first and second bids and setting minimum prices to capture more revenue. Then Google said everything’s moving to a first-price auction, and my whole business model was gone. At that time, a lot of my clients were using the same header bidding company and having a lot of issues. They were paying me an hourly rate to communicate those issues to this third-party company. I realized, why am I helping someone else grow their business? I should build this piece of tech myself, do a better job, and sell it to my existing clients. I gave it away for free for six or seven months to grow the tech, and eventually, I converted all my clients. At that point, I got an offer to buy the company from an ad exchange. I was blown away. I sat down with my wife and some friends, and they all said, “Don’t sell, grow the business.” So I called up my best friend, who’s now our CTO, and said, “Quit your job. Come over here. Let’s build something.” And the rest is history.

Nataraj: Post-ChatGPT and Google’s AI search results, how is that affecting publishers?

Jared Siegal: For sure. The fact that Google rolled out AI in its search results radically changed SEO. If you’re a website where the majority of your content is easily answerable in one sentence or a yes/no manner, AI is going to crush your business because the answer appears in the search results and the user never clicks through to your website. If you’re a site that has opinionated, long-form content, or things that are not a simple question-answer relationship but more like thought pieces, you’re probably much safer, at least for now. AI inside of search results has made the internet worse. I think most publishers would agree. Every piece of tech developed in our industry has always been to help the biggest players—advertisers and search engines—not publishers. AI has a huge impact on traffic for a lot of publishers.

Nataraj: Internet traffic seems to be shrinking or consolidating, but Google and Facebook are still increasing ad revenue. How is that possible?

Jared Siegal: To some extent, it’s pricing control, but also an important piece of information is that any search engine probably makes more money from the ads served in their search results than the revenue share they get on ads they help serve on publisher websites. If you search on Bing and click on one of the paid search results, they probably made a dollar. If you click a link to a publisher’s website, they might make a few pennies. There’s a huge asymmetry and a conflict of interest here. It behooves them to not send you traffic and to keep you within the search results page. They make more money that way.

Nataraj: What’s a common misconception about running a company that you’ve found not to be true?

Jared Siegal: There’s this concept that was hot a few months ago about founder-led versus employee-led businesses, and many people were anti-founder-led. I am very involved in the day-to-day of Attitude, from cutting checks to talking with publishers to running A/B tests to negotiating deals. I love it and I do it all. I think a successful entrepreneur and leader is someone that actually understands all aspects of the business. People say, “Just hire smarter people and have them handle all that.” 100%, have them handle it, but you better understand what they do better than they do. If you want to run a successful company, you need to understand every penny that comes in and every penny that goes out. We’re very much a founder-led business, and I think it’s what has allowed us to scale up as quickly as we did.

Jared’s insights reveal the intricate balance of power in the ad tech industry and the critical need for solutions that champion publishers. As AI continues to reshape content discovery, the strategies discussed in this conversation offer a valuable roadmap for navigating the future of digital monetization.

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The global waste crisis is a staggering $1.6 trillion problem, with mountains of discarded materials ending up in landfills and oceans. But what if we could see this “waste” not as trash, but as a valuable resource? This is the mission of Ambarish Mitra, co-founder and CEO of Grey Parrot. After a successful journey in augmented reality with his previous company, Blippar, Ambarish pivoted to tackle a more tangible and pressing global issue. Grey Parrot uses sophisticated AI and computer vision to analyze and sort waste streams in real-time, bringing unprecedented intelligence to the recycling industry. In this conversation, Ambarish discusses the technological challenges of deploying AI in harsh industrial environments, the importance of building a cost-effective hardware and software solution, and how data is key to unlocking a truly circular economy where materials are recovered and reused, not discarded. It’s a fascinating look at the intersection of deep tech and environmental sustainability.

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Nataraj: What is Grey Parrot, and how did the idea start?

Ambarish Mitra: Grey Parrot is a waste intelligence platform that uses computer vision-based AI blended with material sciences to recognize large-scale waste flows. When people throw away rubbish, it ends up in material recovery facilities where it’s processed and sorted for recycling, landfill, or incineration. Grey Parrot uses analyzer boxes to recognize 100% of the waste flowing through these plants, helping to sort it more efficiently. It’s a large and complex problem because humans generate garbage at such a massive scale that it can’t be solved with just human or mechanical interaction alone. It requires a large amount of vision-based processing and was almost waiting for the AI era to kick in to address it. We saw a large, unaddressed opportunity. Plus, waste is a global crisis that impacts lives and the planet, so we decided to address this issue head-on.

Nataraj: Was the initial idea to do what you’re doing today, or was it different?

Ambarish Mitra: It was different. My co-founder and our initial team came from my previous company, Blippar, where our mission was to build the world’s first visual search engine. We built a large-scale vision model, but we realized our revenue model led to recognizing brands that often ended up in the bin. This got us thinking. Everyone has mapped the consumption world—Amazon, DoorDash, Instagram all know what you’re about to purchase. But after that $23 trillion of annual consumption ends up in the bin, there was almost no digitization. I call it the shadow economy. One reason waste remains waste is that no one is doing enough digitally to value and recover it. That’s why so much value is lost. So the idea came: why don’t we use our vision expertise to do something more impactful and circular? We call it waste, but we see it as paper, aluminum, and different types of plastic. We think of ourselves as a material asset recovery company rather than a waste company.

Nataraj: What is the actual product that you’re selling to companies in the recycling ecosystem?

Ambarish Mitra: Let me give you a brief intro to how waste works. Waste is thrown in bins, collected by trucks, and taken to Material Recovery Facilities (MRFs). It’s tipped out, piled onto conveyor belts, and goes through layers of mechanical processes. There are large leakages in that process, and a majority of that leakage ends up in landfill. Our goal is to reduce that leakage. We built hardware we call the analyzer. The job of the Grey Parrot analyzer is to analyze 100% of the waste flow in real time. These are rivers of waste on belts two meters wide, moving at three meters a second, processing up to 1,500 tons of waste per day.

When the camera recognizes 100% of the waste flow, it helps plant owners understand the unit economics of their business—what material comes through and what its financial value is. Secondly, it provides waste analytics to show if the plant is efficient or inefficient because every percentage difference is a revenue opportunity. The last thing is quality control—the purity of the materials. The more single-stream a material becomes, the more a buyer will pay for it. Finally, we’re integrating a brain into these mechanical machines, much like Waymo makes existing cars into self-driving cars. We are making these plants semi-automated by applying intelligence to existing mechanics, sending signals from one gate to another to ensure everything is sorted as purely as possible. The plant owner sees a dashboard where all this data is available, showing if the plant is working optimally.

Nataraj: What are the architectural and structural issues specific to this industry that you had to navigate? It sounds like you’re shipping hardware and software into environments that are not known for being tech-savvy.

Ambarish Mitra: That’s a great question. This is not a category where you can grow at any cost. It’s a cost-prohibitive industry where every cent matters. Unlike growth-oriented industries like e-commerce or advertising, you can’t have a variable cost architecture where revenue compensates for growth costs. Here, we have to recover more waste and create value from it. The tonnages are massive. So, we had to build an architecture where a lot happens locally on the machine. Our deep learning models sit locally so our costs don’t go up as we process hundreds of millions of images. We process images at the scale of social networks, but we’re processing trash, not people.

It also needs to be near real-time, because the system has to react within 30 milliseconds to trigger a robotic arm, an optical sorter, or stop the plant for hazardous materials. The system cannot rely solely on internet connectivity. We came up with an architecture that requires the internet periodically, but a lot of the processing is on the edge. A huge amount of the vision processing actually happens on the camera itself to normalize images, because lighting conditions in every plant are different. We built one platform that works in every plant. It was an interesting challenge to consider everything from image capture to model building to ensure it works with 99% efficiency, 24/7.

Nataraj: Can you talk a little bit about customer acquisition? How did you approach your first five to 10 customers and how do you scale now?

Ambarish Mitra: As an outsider, we had to learn the hard way. We came from a background of large-scale, vision-based compute, but we didn’t understand waste. So, in the first days, we did something smart: we built the first version of the product *with* the waste industry. We asked waste management companies what problems they were trying to solve, like counting for audit trails or quality control. We learned from them and released our first version by talking to seven or eight customers, giving them the intelligence for free for the first two years while we built our larger model.

We also didn’t build it in just one geography. We spread out across Europe, America, and South Korea to get diversity of data. Commercially, we started with a direct sales model, hiring people from the industry. Then we learned there’s a whole middle tier of specialized salespeople who are plant builders. They were already aggregating multiple technologies to build a plant, so it made sense to partner with them. In the last two years, we partnered with Bolograph, the world’s biggest plant builder, and Van Dyke Recycling Solutions in the US, America’s largest. We disintermediated our direct sales model through these strategic partnerships, which made us more cost-efficient and allowed us to scale effectively.

Nataraj: Which countries are doing the best when it comes to waste management?

Ambarish Mitra: Japan and Korea are very good. Germany is very good. The society is very conscious, and it’s designed to collect waste in many forms, not just from bins. Germany has a direct deposit scheme where people can return bottles for vouchers, for example. I would say there are four components to solving this. One is the manufacturer, who can take more responsibility through standardization, like how USB cables were standardized. Then you have the government’s role, which can enforce regulations. Then you have the waste management side, which can optimize and digitize with AI. And the last quadrant, which has a lot of power but often doesn’t use it, is the consumer making choices that are more circular in nature. Today, consumers are making some choices, governments are doing something, and a few brands are doing a few things in fragments, but a perfect storm hasn’t happened yet.

This conversation with Ambarish Mitra offers a compelling look at how advanced AI can be applied to solve one of the world’s most fundamental environmental problems. Grey Parrot’s innovative approach not only enhances the efficiency of recycling but also provides the critical data needed to build a sustainable, circular economy for future generations.

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The Challenge of Modern Observability

In the rapidly evolving world of cloud-native technology, observability has become a cornerstone for maintaining reliable and performant systems. Yet, as companies shifted to containerized environments like Kubernetes, traditional monitoring tools struggled to keep up with the scale and complexity. Martin Mao, co-founder and CEO of Chronosphere, experienced this problem firsthand while leading the observability team at Uber. He witnessed the explosion of data and costs associated with monitoring microservices at a massive scale. This challenge became the crucible for a new idea. Martin joins us to share the story of how he and his co-founder turned their internal solution at Uber into Chronosphere, a leading observability platform. He delves into the nuances of building for a containerized world, the strategy behind competing with cloud giants, and the future of observability in the age of AI.

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The Genesis of Chronosphere at Uber

Nataraj: How did Chronosphere start? When did you decide you had to stop working at other companies and start your own?

Martin Mao: The story goes back to when my co-founder and I worked at Uber, where we led the observability team. We faced many of the challenges internally at Uber that we’re now solving for our customers at Chronosphere. We ended up creating a bunch of new technologies in that solution and open-sourcing many of them. That showed us that the observability problems we were solving for Uber were also being seen by the rest of the market as they started to containerize their environments. Ultimately, that led us to decide we should create a company to bring the benefits of this technology to the broader market.

Nataraj: What was the specific problem you faced at Uber that wasn’t being solved by available tools at the time?

Martin Mao: If you think about observability, it’s about gaining visibility and insights into your infrastructure, applications, network, and business. The concept isn’t new; we’ve had observability software, previously called APM or infrastructure monitoring software, for a long time. What happens when you start to containerize and modernize your environments is twofold. First, you’re breaking up larger monolithic applications into smaller microservices. You have more tiny pieces running on containers, which are running on VMs. There are just more things to monitor, which generally produces a lot more observability data. The first problem you’ll find is either there’s too much data for your backend, or it costs you too much.

Second, the types of problems you’re trying to solve on monolithic apps running on a VM are different from the causes of problems in a distributed, containerized environment. A lot of APM software focused on how software interacted with hardware and the operating system. In a containerized world, you often don’t have access to that level, and a cause of your issue is more likely a downstream dependency, a deployment, or a feature flag change. The causes of problems have changed, so you need a tool optimized for these new types of issues. Those were the two big problems we saw at Uber: too much data, too much cost, and it wasn’t the ideal tool for these new environments. When we looked at the market at the time, there was nothing we could buy, so we were forced to build our own solutions.

Nataraj: What services were available at that point? There’s a lot more competition in the observability space now.

Martin Mao: There was still a lot of competition back then, but different types of companies. Tools like AppDynamics and New Relic were very popular. Even Datadog was a series C company when we were looking at this problem space. There were many solutions, but none were targeting containerized environments. In 2014, when we were solving this at Uber, the majority of the market had not containerized. It was pre-Kubernetes becoming the de facto platform. Most folks were running on VMs, and an APM-style piece of software was probably the right solution.

Nataraj: You mentioned open source. Was this the M3 database that you open-sourced?

Martin Mao: Yes, it was multiple solutions. One was M3, the backend, which was a time-series database great for storing metric-based data. Jaeger, for distributed tracing, was created by the same team and is a CNCF project today. We also open-sourced various clients and other pieces.

Acquiring the First Five Customers

Nataraj: So you saw a gap in the market and decided to start the company. What were those initial days like? Talk to me about getting your first five customers.

Martin Mao: We saw the gap in the market later, around 2018-2019, especially after KubeCon in Seattle when all the major cloud providers announced they were going all-in on Kubernetes. It was only then that we realized there was a real gap in the broader market. In the beginning, it was quite difficult. Just like every other startup, nobody knew who we were. There was no brand recognition. For the first one or two customers, there was a bit of trust because we had worked with people at those companies when we were at Uber. They knew us as the observability team at Uber and had used the technology before, which gave us some credibility. Honestly, the rest was just typical outbound efforts. I was on LinkedIn every day sending 500 messages to various VPs and CEOs, saying, ‘Hey, this is us, this is the problem we’re trying to solve. Can I get you on a call?’ A lot of outbound emails and messages to get those opportunities.

Nataraj: Observability is mission-critical, used to find and fix live issues. It must be hard to convince a company to adopt a new mission-critical technical product. Were your initial customers transitioning to Kubernetes and saw it as a good time to test a new solution?

Martin Mao: Initially, it was a lot of companies that had already transitioned. These were tech-forward companies running mostly containerized environments at scale in 2019-2020. Being mission-critical probably didn’t help us as a startup. You’re trying to convince a company to replace a mission-critical piece of software they’re likely purchasing from a big public vendor with a well-known brand name. As a one or two-year-old startup, the benefit of switching had to be so large that it would outweigh the risk. For us, early on, the benefit was on the scale and performance of the backend, but also on cost efficiency. It was so much more cost-efficient than other solutions. We’re not talking 20% more cost-efficient; we’re talking four to five times more cost-efficient. The gap had to be very large.

The Chronosphere Platform: Differentiating on Cost and Capability

Nataraj: Can you give a high-level overview of the products Chronosphere offers today and talk a bit about the business model?

Martin Mao: We offer two products. One is our observability platform, which can ingest and store logs, metrics, traces, and events from your infrastructure and applications. We then provide analytics capabilities on top to help you debug issues. Compared to others, it differentiates in two main ways. The first is cost efficiency. We realized there’s a lot of waste in observability; you store and pay for a lot of data you may not need. Most observability companies charge you for the more data you produce, so they aren’t motivated to help you reduce it. As a disruptor, we had to do something different. We created features that show the customer what is and isn’t useful, giving them tools to optimize the data so they only pay for what’s useful. This not only reduces costs but guarantees that every dollar is well spent.

The second differentiator is that you need a different tool optimized for modern environments, where the probable cause of an issue is a downstream dependency, a new rollout, or a feature flag change. Our platform looks for those changes and correlates them with issues. Our customers have found they reduce their time to detect and resolve problems by around 65%.

Separately, we have a solution called an observability telemetry pipeline. You can install this in your environment in front of an existing tool like Splunk or Elastic. It can route and transform the data it collects to those backends, but it can also reduce and optimize data volumes. For instance, you can route subsets of data to cold storage like S3 to reduce costs. You don’t have to use it with our observability platform, but it provides a similar benefit without a full migration.

Nataraj: So customers using competitors’ observability products think about cost predictability?

Martin Mao: In the last two to three years, as the economy has changed, they care about it a lot. It’s not just the absolute dollar amount. Our customers ask what fraction of their revenue or operating expense is spent on observability. The predictability and knowing the relative percentage of cost matters. If your business grows 2X, but your observability costs grow 3X, that’s a bad efficiency model. Being able to see and control that is key. We provide tools that show them where their spend is going and how data is being used, giving them the ability to make decisions and stay within their budget.

Competing in a Crowded Ecosystem

Nataraj: All the big three clouds—AWS, Azure, Google—have their own observability products like CloudWatch and Azure Monitor. How do you compete with them, especially with bundled pricing advantages?

Martin Mao: I look at this in a few ways. First, what’s unique about observability is that it’s meant to tell you if your infrastructure is up or down. If your observability service runs on the same infrastructure you’re monitoring, there’s a problem. For example, AWS’s observability services depend on S3 and Kinesis. When S3 goes down in a region, your infrastructure is likely impacted, but the thing meant to tell you that is also down. It’s in that moment you need observability the most. There’s a huge advantage in decoupling your observability from the infrastructure it monitors. Our architecture is purposely single-tenanted, allowing us to ensure we are not on the same public cloud infrastructure as our customers.

Another angle is that cloud providers are really good at providing building blocks—the underlying infrastructure—but historically less great at building end-to-end SaaS products. Their observability services are decent for storage, but they lack advanced capabilities for data efficiency, root cause analysis, or anomaly detection. If you look at the leaders in the observability market—Chronosphere, Splunk, Datadog—none are cloud providers. To compete, you need to differentiate on the product side, not just on underlying storage and unit economics, because you’ll likely lose that game against the cloud providers.

Product Philosophy: Building for the Bleeding Edge

Nataraj: What’s your philosophy on deciding what to build next?

Martin Mao: We listen a lot to our customers. Tech-forward companies are generally containerizing first and doing it at scale, so we get to work with companies at the bleeding edge of their technology stack. They are constantly pushing us on what’s next and inform a lot of our innovation. Targeting early adopters gives you significant input on product innovation, versus targeting the laggards or the majority. We’re lucky that we target innovators and tech-forward companies who provide us with a lot of input.

Nataraj: Who are some of these tech-forward customers today?

Martin Mao: When we first started, it was large, digital-native companies like DoorDash, Robinhood, and Affirm—companies that grew up in the 2010s in the public cloud. They were the first to containerize and were pushing technology. Today, we see more of the majority of the market containerizing. Big enterprises like JP Morgan Chase, American Airlines, and Visa are containerizing at a large scale, often because they have a hybrid and multi-cloud strategy. If you have two or three different pieces of infrastructure, you need a common layer like Kubernetes to avoid implementing your infrastructure three times. Now, we see a lot more demand from those companies. And of course, the latest are the AI companies. Everyone starting an AI company today is running on modern, containerized infrastructure from day one, which is our sweet spot.

Observability in the Age of AI

Nataraj: You mentioned AI. How does observability change for AI companies, especially for LLM-based applications?

Martin Mao: We noticed that even with LLM technologies, you still have application logic and CPU-based workloads. But it added new use cases, like monitoring GPUs for inferencing. At the infrastructure level, monitoring a GPU cluster isn’t too different from a CPU cluster. As you go up the stack, we found that the basic observability data types—metrics, distributed traces, and logs—still map very well for debugging what’s happening in an LLM application. Because the data types map nicely, the features and tools we’ve built work quite well for these new apps. So far, we haven’t had to create a new solution; it’s just been more data and more use cases.

Nataraj: How are you thinking about leveraging AI for your own product?

Martin Mao: We’ve been playing around with it a lot. Initially, like everyone else, we put an LLM trained on our docs to create a chatbot. But we found that a lot of our data is numerical or unstructured in a way that’s not typical for LLMs. When we try to apply a foundational model to the raw observability data, it’s not very effective because it wasn’t trained on it, and this data is unique to each company. However, for years, we’ve been building knowledge graphs and structuring this data to power our analytics engine. When you feed these structured knowledge graphs into the models, they become much more effective. We were lucky to have already been doing the hard work of data scrubbing and normalization for our product, and now it’s beneficial for AI models. Still, I’m not sure a chat interface is the right starting point for observability. When you get paged, a visual interface with graphs feels more natural than a chat box asking, ‘Tell me what’s wrong’.

Founder Reflections

Nataraj: We’re almost at the end of our conversation. What do you know about starting a company that you wish you knew earlier?

Martin Mao: Early in my career, I assumed that to be a CEO, you needed an MBA and executive experience. I found that not to be true. I don’t have an MBA or experience as a big executive. I was an engineering manager at Uber before this. There’s probably less of a barrier for someone to become a founder and CEO than one might think from the outside.

Nataraj: What are you consuming right now that’s influencing your thinking? It can be books, audio, or video.

Martin Mao: A lot of conference talks, especially on AI-related topics where things are evolving so fast. By the time a book comes out, it might be outdated. So, things like podcasts and conference talks are better for accessing what’s happening live. Historically, even a research paper takes a while to be released, and a book takes even longer.

Nataraj: Martin, thanks for coming on the show and looking forward to what Chronosphere does in the future.

Martin Mao: Thank you. Thanks for having me. I enjoyed the conversation, and hopefully, we can do this again sometime.

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Conclusion

Martin Mao’s journey with Chronosphere offers a compelling look into solving complex technical challenges born from real-world, large-scale operations. His insights on product differentiation, customer acquisition in a mission-critical space, and the evolving landscape of AI-driven observability provide valuable lessons for founders and engineers.

→ If you enjoyed this conversation with Martin Mao, listen to the full episode here on Spotify, Apple, or YouTube.

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In the fast-paced world of tech startups, building a great product is only half the battle. Getting noticed by the right people—investors, customers, and top talent—requires a strategic approach to communication. This is where public relations comes in, but for many founders, PR remains a mysterious and often misunderstood discipline. To shed light on the subject, we sat down with Emilie Gerber, the founder and principal of SixEastern, a PR firm dedicated to helping startups and tech companies navigate the media landscape.

With a background that includes corporate communications at Uber and product communications at Box, Emilie brings a wealth of experience to the table. In this conversation, she demystifies the world of startup PR, drawing a clear line between earned media and paid marketing. She offers a practical framework for when early-stage companies should consider hiring a PR agency, how to set realistic expectations for coverage, and the art of crafting a pitch that resonates with today’s journalists and content creators.

→ Enjoy this conversation with Emilie Gerber, on Spotify, or Apple.

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Nataraj: A lot of my audience is tech-heavy—people working in tech who are trying to start companies, founders, operators, and they’re usually unaware of the PR industry. A good place to start is if you can set a context about what a PR company or person does in general, and then we can narrow it down to tech specifically.

Emilie Gerber: The biggest misconception I see when chatting with founders, especially first-time founders that haven’t done PR before, is conflating marketing and public relations. Marketing involves a lot of paid methods: paid advertising, sponsorships, that sort of thing. There’s also owned content, stuff that you post on your blog, doing webinars, and the social channels that you post to. PR is really neither of those things, though there’s obviously always going to be a little bit of overlap.

PR is anything that’s earned media. So earned is when you are able to get that speaking slot or get that interview with a reporter or get on a podcast without necessarily needing to sponsor or pay. You’re getting it because of your credibility. The value in that is that because you’re not paying, there’s supposed to be this sort of objectivity to it where you earned the spot because of your credibility or the business you’re building or what you have to share with the reporter. It’s held in a different regard than other kinds of marketing, and it’s an important part of the puzzle. But for startups, because they’re usually small and new, there’s not going to be the same sort of interest necessarily in the business as the companies that are further along.

The other big misconception is that you launched your company, now let’s go get that big TechCrunch feature or that big Wall Street Journal feature. Most of those publications have maybe one or two relevant reporters to your business and they’re in charge of covering your entire space. So that’s not always necessarily what you can get right off the bat. There are other things that we can go into that you can get, but that’s usually what I find from the first conversation.

Nataraj: At what point in a startup’s stage is it worth having an internal or an external PR engagement?

Emilie Gerber: For a lot of seed-stage companies, it does not make sense to have a PR agency on retainer. There are exceptions to that rule. We’re working with a seed-stage company right now that is doing some really wild stuff. They have an AI tool being used for a class at Harvard Business Review and every student’s taking that course. To me, that’s a big enough story where it doesn’t matter how much funding they have; reporters are going to be interested regardless. But if you’re building a more infrastructure AI tool or software, chances are unless there’s something that’s really, really unique—and the bar for unique is super high—you don’t need to have an agency on retainer yet. What you can do is potentially still make a one-off announcement announcing that the business exists and that you’ve raised funding, especially if you have a relatively large seed round or some great investors. You just have to be more realistic with what you’re going to get for that piece.

Generally speaking, when we work with a company that’s early, we’re trying a lot of different things. We’re being really creative with the outlets we go after and we will get something, but you shouldn’t bring on a PR agency if you’re expecting a really top-tier piece of coverage in The Wall Street Journal, because that’s not realistic. But in a project capacity, seed-stage companies can do something, but I wouldn’t have someone on retainer. I think by the time you’re Series A, there’s more that can be done and it can make sense. There are some really great consultants out there too; you don’t necessarily need to bring on a full-fledged agency. We’re kind of in the middle where we act a little bit more like consultants, but we are an agency. But by then, you’re still not going to be getting the huge stories, but there’s going to be podcasts to go on, awards and lists you can submit to, and speaking opportunities at conferences. So there’s going to be stuff that you can be doing and find value out of the engagement. But really, the longer you wait, the more you can end up doing, and you’re going to get higher ROI from the engagement. So even then, some companies wait till they’re closer to Series B, I would say.

Nataraj: How do you cater expectations? Because every startup will see your previous success story and come to you saying, ‘I also want a TechCrunch or Wall Street Journal coverage when I raise my seed round.’ How do you gauge or set those expectations?

Emilie Gerber: I try to really dig into the details with them of their story versus what they’re comparing themselves to. Maybe they are the same caliber and we can go pitch something similar to something else we landed for another client. Even when we are able to do that, it often just comes down to reporter bandwidth. So I explain that. Sometimes you could have the coolest story in the world, but if it’s happening at the wrong time or you just have bad luck with pitching it—part of it’s luck—then you might not get the same win. The first thing I try to do is emphasize how much of it is not in our control.

Another thing to emphasize is that reporters are not paid by us; their only job is to report on the news and to tell stories they think their audience will find interesting. They don’t owe us anything. They don’t owe the startups that they cover anything. And then if they’re comparing themselves to a unicorn story that’s not similar to what we’re telling for them, I try to go into the details: ‘Well, this company shared that they just reached $100 million in ARR,’ or ‘This company has celebrity investors. What are we bringing to the table that’s similar?’

It’s a balance because you also don’t want to shoot down a founder who is super excited about what they’re building. So it’s a balance of showing them that we’re equally excited and that we’re going to try to get them the best possible outcome, but it’s just a tough world out there with media.

Nataraj: For podcasts specifically, do you advise founders to craft their message? Do you help with that? Because not every great founder is a great storyteller.

Emilie Gerber: It’s a fine line. I think a lot of the larger agencies spend so much effort crafting messages that the execution piece gets lost and they’re not even focused on pitching. I think it’s easy for founders to get too in their head if they’re going off of talking points. Those can be more valuable for traditional media interviews where you really do want to land the headline and one or two specific quotes. For podcasts, I’m a fan of going at it a little more casually.

If we can get the questions in advance, which some podcasts do share, that can be useful. We’ll say, ‘Hey, look these over, see if there’s any that you think are alarming or you want to discuss.’ But because it’s not really a product pitch most of the time—it’s talking about their journey and their story—I prefer they don’t spend too much time on specific talking points because they usually end up sounding really canned.

One thing that can be really great for prepping for podcasts is having a couple of stories or anecdotes in your back pocket that you always just use. Those can be useful to think of in advance; otherwise, they might not occur to you on the spot.

Nataraj: I always tell founders to start a document to note down their thoughts or the highlights they want to make. You can use it as a starting doc for future interviews. People see successful thought leaders and think it’s coming off the hip on a podcast. It’s not. They have running notes of ideas and sometimes a team of people bringing in interesting statistics.

Emilie Gerber: That’s why I like the stories. And a good point you raised that I forgot is having in your back pocket the stats that you can share, whether it’s customer names you’re able to disclose, the latest stats on the business, or any market or industry stuff. Those are not going to be top of mind for you unless you have them prepped in advance. And if you’re at a startup, you do want to make sure you’re being consistent with what you’re sharing and you’re not just riffing with company metrics. That’s another area where it can be really useful to have something written down.

Nataraj: There’s also this trend of founders going direct and not engaging with a PR filter. Every founder wants to be a persona on Twitter. Is that where the PR industry is going?

Emilie Gerber: It’s funny you brought that up. I’m actually doing a survey of startup founders, and so far, I think 96% put that it’s important to build up your founder’s social profiles, which is way higher than I expected. So the general sentiment is yes, you should be doing this. Personally, maybe this is a contrarian view, but I don’t think it’s realistic or scalable for that to be the case for everyone. Not every founder is going to have it come naturally to them. For some, it’s going to take a lot of time, especially if they’re not willing to just outsource their social presence.

I don’t know that it’s going to be possible for every founder to build up a huge social following where it’s actually worth the time investment. I just don’t know if it’s always realistic. Within our community right now, it’s definitely the hot new comms approach. I do think there’s tons of value in it, especially for the right founder. But for others, I just think it would be distracting them from the business and other marketing they can do. The work that we’re doing, the more traditional approach, is that if a client goes on your podcast, there’s a built-in audience. You’re able to tell the same story but without having to do the work of building the audience.

Nataraj: People say traditional media is dead, but we’ve been talking about TechCrunch, Wall Street Journal, and CNBC. Why does it still matter for startups to be on traditional media?

Emilie Gerber: It definitely is smaller. One of the biggest benefits is the trust that you get from being in a traditional outlet. There’s just a certain brand cachet that comes along with having your startup in a publication that people know and respect. I think it helps with trust with customers and with potential candidates. It’s a validation piece that companies still look for.

But I should also flag that beyond traditional media and podcasts, there’s this whole world of new media. Alex Konrad from Forbes just launched Upstarts. Eric Newcomer has Newcomer. Some of those are more open to startup stories and conversations. I think those are kind of blurring the lines. I really value those as well. There’s this third bucket that I think is very helpful right now too.

Nataraj: A lot of PR firms I see usually have a marketing wing. How do you think about that PR plus marketing service offering?

Emilie Gerber: It’s interesting because I’ve gotten asked about this a lot with how much media is changing. We basically had a waitlist for the past six months. We can’t take on new clients. We’ve been so busy that I haven’t felt the pressure to explore that yet. I’m sure it’ll happen eventually because media is going to continue to change, but it’s almost like, don’t mess with a good thing. For us, we’re busy with our current client base and we can’t take on new work, so adding new services doesn’t sound appealing to me right now.

Nataraj: What do you know about PR now that you wish you knew before starting your career?

Emilie Gerber: It has changed so much. A lot of publications overall have moved away from doing funding stories, period. Even TechCrunch and Axios, which covered them a lot. I think I would have maybe changed our model sooner to not be as focused on those. This is a lesson that I’m currently learning as we speak, but I think that the playbook is changing there and I don’t know what the new playbook is. But it’s one that I think I should have given more thought to maybe earlier.

Nataraj: You were at Uber during a period of interesting PR challenges. Are there any crisis mode situations you were involved in that you can talk about?

Emilie Gerber: I joined right when a lot of that stuff had started. My role at Uber was focused on comms for Uber for Business and their business development team, so any company partnerships. I wasn’t on the corporate comms team where we were focused on the actual crisis. If anything, it was a lesson for me to try to figure out how to pitch and land positive stories amidst a world where all this negative stuff was happening. I got some really great hits during that time, and I think it was about being very creative with who we worked with, doing the due diligence on them, and then pitching stories in a very specific way. It was a unique challenge trying to get them positive press during that time.

Nataraj: What type of positive press did you get?

Emilie Gerber: I launched Uber Health, which was HIPAA-compliant patient transportation. We went after health tech reporters, who could not care less about the ride-share side of the business, and got tons of product features on that. We put customers forward, we put a spokesperson forward that was the GM of that part of the business so it wasn’t anyone involved in anything else going on. We got some really straightforward hits that way. Some of these folks are just excited to get a unique opportunity to chat with Uber about how they’re thinking about healthcare, so they want to write a story that’s really focused on that.

Nataraj: Which niche or sector of startups is ignored by the PR industry right now?

Emilie Gerber: With all the focus on AI, a lot of those reporters that used to cover enterprise software more broadly are not anymore. If you’re not doing AI, there are not the right reporters out there for you right now. Those are the companies I struggle with the most in getting the right folks interested because everything is so all-consuming in AI right now. If your company doesn’t have that angle, you’re kind of left out to dry. I would say enterprise software, non-AI, is the answer.

Nataraj: Emily, thanks for joining the show. It was very insightful.

Emilie Gerber: Awesome, thank you so much. It was a great conversation.

This conversation with Emilie Gerber provides a clear and actionable playbook for any founder looking to leverage the power of public relations. Her insights cut through the noise, offering a realistic perspective on what it takes to build a strong narrative and earn valuable media attention in the competitive tech industry.

→ If you enjoyed this conversation with Emilie Gerber, listen to the full episode here on Spotify, or Apple.

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In a world saturated with fleeting trends and disposable products, what does it take to design something truly meaningful and lasting? We explore this question with Todd Bracher, an award-winning industrial designer and the founder of BetterLab. With a portfolio that includes partnerships with iconic brands like Herman Miller and Issey Miyake, Todd has been honored twice as the International Designer of the Year. In this conversation, he delves into the powerful intersection of design, science, and technology, revealing how this synergy drives innovation. Todd shares his philosophy on human-centered design, the critical importance of sustainability, and his journey building a successful design firm. He also gives us a look inside BetterLab, where his team is creating game-changing products, from UVC light sanitizers to glasses that can reverse childhood myopia. This is a deep dive into the mind of a designer who is shaping a more responsible and thoughtful future.

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Nataraj: We haven’t had many industrial designers on the podcast. We usually talk about growing companies and designing technology products, so I think it would be interesting to get a more design-centric perspective on bringing products to market. To start, could you give a quick background about your entry into design and your career so far?

Todd Bracher: I’m not surprised that designers aren’t usually spoken with regarding business or startups, because designers often aren’t part of that process, strangely enough. That’s a source of my frustration. What brought me into design was applying to art school in the 1990s. I applied to Pratt Institute in New York, and to get in, you had to do a visual exam. The topic was to design a breathing device for a hypothetical future where we couldn’t survive in the open because of pollution. As I was drawing it, I started thinking through the design process: does it work? If you’re wearing it all the time, it has to look good, be comfortable, and work for men and women at work or at parties. When I submitted the drawing, they asked what it was because it wasn’t illustration; I had created a solution. They said, ‘Well, that’s called industrial design, but that’s not what you’re applying for.’ That’s the moment I switched to industrial design.

Nataraj: Were you always good at drawing? What made you gravitate towards design?

Todd Bracher: Drawing has always been a part of my life. It’s the lowest barrier to entry for seeing your ideas. When my brother and I were kids, we used to build little plastic model planes. He always said he wanted to be a pilot, and I was always in love with the form of the plane—how it’s very purpose-built, but beautiful. We had two different points of view on the same subject. Interestingly enough, he became a pilot, and I became a designer. It shows two ways to look at the same thing very differently and have very different experiences.

Nataraj: To crystallize the idea of industrial design, can you talk about a couple of examples of projects you’ve worked on and brought to market?

Todd Bracher: By definition, industrial design means really understanding how to manufacture at scale. You see a lot of design objects, but that doesn’t mean they’re industrially designed. Someone might make five chairs in their garage, and that’s design for sure, maybe a version of art or craft, but industrial design is about things that are repeatable and manufacturable at scale. My expertise is in understanding manufacturing, materials, processes, and the whole orchestration around supply chain and engineering. It’s really A to Z. I see myself as the representative of the market or the end user, and at the same time, the representative of the business manufacturing it. I’m the translator between the two. The products I work on can range from furniture to beauty products—I do fragrance bottles for Issey Miyake—to glasses or even a water dispensing machine. There’s a whole host of things, which is what’s cool about industrial design.

Nataraj: I want to shift to your perspective on technology products. What are some tech products you admire that have a strong design element, constructed in a way that you as a designer appreciate? And please, no Apple products—that’s the go-to answer for all designers.

Todd Bracher: And rightfully so, to be honest. Apple is incredible. What’s most interesting to me is when I see design in the world that leverages a certain aspect of science. I recall seeing things like color blindness correction. One example is a project we worked on with a gentleman who had invented a device that distributes a specific spectrum of UVC light. He developed it for NASA and the space station. I was part of the team that helped deploy it into architecture. What’s so incredible is that we weren’t just making a lamp. This UVC light is a germicidal light that deactivates pathogens—bacterial, viral—on surfaces or in the air, while being safe for humans in the environment. This gentleman figured out the science, engineered the light engine, and created a device we can afford. The designer’s job is to package it and deliver it to the market. These types of solutions are fascinating to me.

Nataraj: In the world of industrial design, what trends are you noticing? What’s in, what’s out, and what might an average person not know about?

Todd Bracher: The trends I see in design tend to be unfortunate in my opinion. They’re not going in the direction I would like, as they’re often very cosmetic. However, one trend that’s quite important is sustainability. You will see designers using less material and reaching for materials that are recyclable or come from recycled sources, like ocean-bound plastic. Various companies are collecting this material from waterways and reprocessing it for designers. This is a really wonderful trend. So on one hand, we have this incredibly responsible trend happening that most people don’t see. On the other hand, we still have the old trend of making consumable products, which has been disappointing. I think we’re in a transition point as an industry.

Nataraj: What’s disappointing about the consumable products?

Todd Bracher: I think they’re made a bit irresponsibly, without considering circularity or sustainability. A colleague and I once looked at a 30-story apartment building in New York City and wondered how many hammers were inside. If there are 100 apartments, there are probably 90 hammers. Why would there be even 50? Shouldn’t there just be two hammers in the building that people can share? This communal mentality could solve some of these problems. Instead, everyone is consuming things they don’t really need. It’s funny that as someone who creates products, I’m sort of anti-consumerism in that way.

Nataraj: What’s your take on Ikea? It’s mass-market, attainable, and brings designs that might otherwise be inaccessible to a wider audience, similar to how Zara operates in fashion.

Todd Bracher: It’s funny because they copied one of my lamps, and they did a terrible job at it. It’s not a well-executed version. However, I had a friendly argument with a friend about the drug industry—you can get a prescription for $80 a pill or the generic for $1. I think having a generic option is fantastic. I see IKEA in a similar light. I welcome that they copied my design. If someone enjoys it and can’t afford or access the original, that’s fine. I don’t know enough about their sustainability practices given their huge volume, and I imagine there’s a lot of waste because their products are so accessible that people tend to throw them away quickly. But as a business, I think they make pretty good design very accessible, and that’s a good thing. Design shouldn’t be expensive.

Nataraj: What are some brands, in furniture or fashion, that you admire as a designer?

Todd Bracher: One brand in particular is a Swiss brand called VITSOE. They make a shelving system designed by Dieter Rams around the 1950s. He’s often considered the founding father of Apple’s design DNA. It’s a very simple extruded aluminum rail you screw on the wall with a simple folded metal shelf. What I love is that these products look incredible nearly 70 years later. They function perfectly and last forever. They’re beautiful. That’s what I strive for in my work—creating something that stands the test of time in the truest sense.

Nataraj: Is that a big aspect of well-designed products—longevity? And does that contribute to their cost?

Todd Bracher: Yes, at least that’s how I like to live my life. I have a few things I really need and like, and they last forever. I don’t have to replace them every few years, which feels irresponsible. I go to these huge furniture fairs in Milan, and it’s an enormous amount of new stuff coming out every year. The question of where it all goes at the end of its life is a big one, and our industry doesn’t handle that very well.

Nataraj: You run BetterLab. Tell me about the business of running a design firm and the types of products you’re building.

Todd Bracher: I have two businesses. One is Bracher, my design consultancy, which is inbound—I work with clients. The other is Betterlab, which is my outbound venture platform. I started Betterlab because after serving clients for two decades, I wanted to do what I actually want to do. With client work, I don’t own it and don’t get to make 100% of the decisions. With BetterLab, it’s different. We have three ways of engaging. First, we do a diagnosis. Like going to a doctor, we first understand what a company needs rather than just taking a design brief. We provide a recommendation for treatment. The next phase is opportunity discovery, where we figure out what we’re trying to solve and if it aligns with business goals and market needs. The final phase is execution—the design portion—and then the rollout and marketing support.

Nataraj: What are some of the products that came out of BetterLab?

Todd Bracher: I’m quite in love with science, physics, and optics. I helped build a lighting business for 3M, and it was a realization that design and science fit beautifully together. BetterLab spun from this thinking. I had a beer with a scientist friend and asked him about his fears for the world. He mentioned myopia. Myopia is when the human eye doesn’t fully develop through childhood. He was one of the guys credited with inventing the commercialized LED, and he explained that modern LEDs are value-engineered to only emit the visible spectrum of light, ignoring the rest that the human eye thrives on. Now, kids spend more time indoors with LED lighting and screens, so they aren’t exposed to the full spectrum of light. The World Health Organization has identified myopia as the largest threat to eye health in the last hundred years. So, we developed a pair of glasses. In the frame, we attach a glow-in-the-dark material. When the child steps outside or the glasses are near a light source, they passively charge—no electronics. This material delivers the healthy spectrum of light to the eye. It also actually reverses myopia, unlike traditional treatments.

Nataraj: I think you’re also working on another sustainability project using light. Can you tell me about that?

Todd Bracher: Yes, back to the UVC light. Around 2019, I was helping put UVC light in architecture to mitigate the spread of COVID by sterilizing environments. But I realized a vaccine was coming, the technology was expensive, and people didn’t understand it. Meanwhile, I saw my young kids constantly using gel hand sanitizer and I wondered about the chemicals they were putting on their hands every day. On one hand, I had this chemical problem, and on the other, a technology that uses light to stop pathogens. I thought, what if we merge the two? So we developed Lightwash, a hand device using UVC technology. You put your hands under it, and within three to four seconds, they are sterilized. Light gets into all the crevices of the hands where liquid sanitizer doesn’t. Later, I learned that gel sanitizers are responsible for 2% of the global carbon footprint due to transport, storage, and maintenance. Our solution displaces that completely, which makes me incredibly happy.

Nataraj: You also advised startups at Antler, a pre-seed firm. What was that experience like?

Todd Bracher: My role there was interesting because they don’t make physical products, which is my expertise. I was a design advisor, asking questions from a design lens that they might not have considered. My role was to represent the end users. For financial or legal software, for instance, I’d ask, ‘Have you considered this? Does this experience feel trustworthy when you’re dealing with legal documents?’ I brought the soft side to their hard business, focusing on what really resonates with people.

Nataraj: Are there any day-to-day products you use because their design and utility are so good?

Todd Bracher: The first one that comes to mind is Leica cameras. They make what’s called the Leica M. The design has been roughly unchanged since it was first introduced, maybe in the 1930s. It’s an all-manual camera—no autofocus, no video. What it does is provide a real connection with capturing an image. It’s like the difference between driving a 1960s air-cooled Porsche and a modern Honda Accord. The Accord is great, but it doesn’t have the spirit, the feel of the machine and the connection to the road. The Leica is like that. It’s an inferior camera in some ways, but the experience is so superior that it makes you deliver your best work.

Nataraj: What’s your take on modern design aesthetics, like the trend where many luxury brands have adopted very similar, minimalist iconography?

Todd Bracher: I think one or two brands spearheaded it with success, and others followed quickly. I welcome it. I think design is late in this country. Apple helped unlock some of that, but the rest of the world, like Japan and Scandinavia, is light years ahead of the U.S. in areas like furniture design. I think globalization is helping improve design here. While it can get a little sanitized or washed out, I think it’s for the better. When you create simpler things, you have nowhere to hide. You’re delivering things that are more honest, which fits the contemporary culture we need, rather than hiding behind flashy noise.

Nataraj: What’s your take on digital design? Is the tech world doing it well?

Todd Bracher: I think it’s gotten better. I do fault Apple for some of their earlier choices, like the digital leather notebook with stitches. In my opinion, you should embrace the technology and its material rather than creating an image of a yesteryear material. But I do think digital design today has gotten quite good, even a bit experimental, which I welcome. I’m seeing more personality. The new codebases allow for more adventurous things. Designs are becoming less static, more engaging and interactive in a beneficial way. You can customize and adapt things much more, and I’m happy for that.

Nataraj: Anytime a designer talks, Japan is always mentioned. What is it about Japan that is so interesting in terms of design?

Todd Bracher: That’s a very big conversation. I have my own take. My partner is Japanese, so we have a deep appreciation for this. There’s a really deep connection to the experience of something and being truly present in what you’re doing. To me, that’s the anchor of what makes their design so good. In the Western world, we’re more interested in the cosmetics—is it the right shininess? In Japan, I feel they ask, ‘Are we really meeting the soul of what this thing needs to do?’ Take a traditional tea ceremony: the materials, the smells, the lighting—everything is considered for very specific reasons. It’s a true attention to the deepest meaning of what you’re doing.

Nataraj: We’re almost at the end. What are you consuming right now, be it a book, podcast, or show, that you’re inspired by?

Todd Bracher: I’ve been watching Lex Fridman’s podcast since he started. I enjoy his long-form interviews, usually on subjects I know nothing about, like a recent one with a former Russian spy. He also covers machine learning and other topics. He keeps it very neutral and is just there to share information. I’m also that weird guy who loves watching old MIT physics lectures on YouTube. I’m not a physicist, but after years of watching them, I feel like I have been trained. It’s fascinating how much you can learn, and it’s my way of switching my brain off.

Nataraj: Who are your mentors?

Todd Bracher: I don’t necessarily have a mentor, but one personality that keeps cropping up, strangely, is Charles Darwin. His thesis on the finches on the Galapagos—how different species had different shaped beaks based on what they were eating—really helped formulate my philosophy for design, which is designing in context. I’m making the solution most appropriate for its situation. I’m not imposing my opinion. The finch’s beak doesn’t have a random shape; it’s designed for function, but it’s still beautiful and logical. It’s absolutely designed for purpose. So I would say Darwin is my mentor.

Nataraj: What do you know now about being an industrial designer that you wished you knew when you were starting out?

Todd Bracher: The business side of design. For some reason, designers are often inserted at the end of a process to ‘make it look better.’ When I get things like this, I often ask, ‘Why are we making it? Did you talk to your market?’ You quickly find holes in the system. As a young designer, I wish I knew that we should be inserted at the beginning of the process to help identify the full context. That way, when the design arrives, we can deal with it relative to that context and not in isolation.

Nataraj: Todd, thanks for coming on the show. This has been a fascinating conversation, and I’m looking forward to seeing what BetterLab creates next.

Todd Bracher: Thank you, I really appreciate this. Thanks so much.

Todd Bracher’s insights offer a powerful reminder that great design goes beyond aesthetics; it solves real-world problems with intentionality and responsibility. His work at the crossroads of science and design highlights a future where products are not only beautiful and functional but also sustainable and deeply human-centered.

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In this episode, Nataraj is joined by Zach Lloyd, the founder and CEO of Warp, a company developing an intelligent terminal to modernize the command-line experience for developers. A former principal engineer at Google who worked on Sheets and Docs, Zach brings a wealth of experience to his mission of reinventing a tool that has remained largely unchanged for decades. The conversation delves into the evolution of the terminal, the profound impact of AI on software development, and Warp’s vision for a future where developers interact with their computers through natural language. Zach shares insights on moving from ‘coding by hand’ to ‘coding by prompt,’ the challenges of building a sustainable business model around LLMs, and his bottoms-up, product-led growth strategy. This discussion is a must-listen for anyone interested in the future of developer tools and the practical applications of AI in coding.

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Nataraj: Zach, welcome to the show.

Zach Lloyd: Thanks for having me. I’m excited to be here.

Nataraj: I was really excited to have you on the show because after the ChatGPT moment broke out, the LLM companies were everywhere. I think that’s the first line of value that has been captured. I was excited about the new types of applications that we will see, and the most bullish use case for me was developer productivity. The reason being, anyone who studied compilers will know that LLMs are actually looking a lot like compilers in terms of text completion and autocomplete. Then there’s this aspect of code being very deterministic. I can say something in English and it could mean different things for the same person in different contexts, but code is already logical. So if you are feeding a logical structure to the LLMs, it’s more likely that it performs better on code than on English language. That was my thesis. I think in some format, we are seeing the biggest use cases are around developing new products, especially for software developers. So I think a good place to start is if you can talk about what Warp is and how you came up with this idea of an intelligent terminal.

Zach Lloyd: Cool. So yeah, Warp is an intelligent terminal. The terminal, in case folks aren’t familiar, is one of the two most important tools that developers use every day. They use a terminal and they use a code editor. The terminal is basically the place where you tell the computer what to do. That could mean building your code, running your tests, writing internal tools, or interacting with your production system. So it’s a very ubiquitous and important tool for developers. It’s also a tool that’s kind of stuck 40 years ago from a usability perspective. It’s something that really has not evolved much from an experience point of view. When Warp started, our goal was to modernize this interface, make it more usable, and make it work more like a modern app. Even really simple things, like make the mouse work in the terminal. But as the LLMs have matured and come out, the product has become vastly different. At this point, Warp is a place for developers to talk to their computer and tell the computer what to do. Because they’re doing this through the terminal, there’s this huge array of tools that already exist in the form of these command-line apps that can take what a developer says in English and turn it into a series of app calls that do what the developer wants. That could mean setting up a new project, debugging something in production, or increasingly just writing code, which is obviously the biggest developer activity. So that’s where we’re at today. We think Warp and the terminal are an amazing interface for a developer to tell AI what they want to do and essentially have it done.

Nataraj: Developers are really unique; everyone is picky about their stack of tools. They have their own slightly different version of terminals or command lines they use. How does a developer use Warp now? How does the existing behavior of the terminal change by installing Warp?

Zach Lloyd: Great question. If you’re a developer, you can just go to warp.dev, download Warp—it’s a native app. If you’re running on Mac, Linux, or Windows, you just open it up and use it instead of whatever terminal you were using. Whether it was iTerm, the stock terminal app, or the VS Code terminal, you just use Warp. Despite being an AI-native experience, Warp is backwards-compatible with your existing stack. The way this works, really big picture, is a terminal is the app you run, and then within the terminal, you run a shell. Think of the shell as a text interpreter, so when you type a command, it’s the shell that figures out what program to run. Warp works with all the existing shells. A big product emphasis for us is to meet developers where they are and not make them take a step backwards in order to get all the extra benefit of doing this incredible stuff with AI.

Nataraj: So basically, you allow developers to bring in their existing nuances into Warp.

Zach Lloyd: That all basically works. For 98% of the stuff that developers have set up in iTerm or wherever, you can just open up Warp and it should just work the same, but also be better. At least that’s the goal.

Nataraj: A terminal is generally a little restrictive. Usually, they’re not intelligent in the sense that while some terminals let you easily reuse a previous command, you have to know the exact command. This becomes really hard when a developer is in the early stages of their career because you have to remember all those commands, or you’re constantly going to ‘help.’ If you’re using Git and trying to commit or do different things, you’re struggling to find the right command to do the right thing. What intelligence is Warp adding?

Zach Lloyd: Yeah, you’re absolutely right. One thing that’s really frustrating for beginners and experts is you open up the terminal, and it’s just a blank screen. If you want to get something done, you better remember what the command is. And these commands can become quite complicated. Let’s say you want to set up a brand new Python project. You have to install the Python toolchain, and then you might have to clone some Git repo. When you go to clone the Git repo, you might find that you don’t have SSH keys, and then you’re going to start Googling or go to Stack Overflow to figure out how to recreate your SSH keys to authenticate to GitHub. That’s annoying. That’s not what developers want to do. Developers want to build things; they don’t want to deal with all this incidental complexity. So what Warp does is you don’t have to remember the commands at all. You just need to know what you want to accomplish and you tell the computer to do it literally in English. So instead of typing a command in Warp, you would type ‘help me set up a new Python tool chain, clone this repo, make a new branch for me, make sure it all compiles and runs,’ and that’s it. You would hit enter. And then what the LLM does is it tries to figure out its context. The LLM might run ‘ls,’ it might run ‘git status,’ it will try to run ‘git clone.’ When it hits that SSH error, it’ll say, ‘We had an SSH error, do you want me to generate these SSH keys for you?’ As a user, you’ll say yes, and then it will remember the command to generate the SSH keys. It will basically do this with you until you get to the spot that you want to be at. That’s a way better workflow than switching context out of the terminal and looking this up on Google every time you hit some error.

Nataraj: There’s also been this huge integration between IDEs and terminals. Does that change how you think about Warp? Does Warp have to also now work on the IDE?

Zach Lloyd: Great question. A lot of people use the terminal in the IDE, and there are definite benefits to that. What’s interesting that’s happening in the world of AI-based development is that I think neither the IDE nor the terminal actually makes sense as the primary tool for the future of code. What makes the most sense is some sort of workbench where you as a developer just tell the computer what you want to do. The standard workflow for someone using an IDE today is you’ll open up all the files that might be relevant to building a feature, and then you’ll start writing a function or a class definition. You’re basically doing what I call ‘coding by hand.’ The world that we’re moving towards is one where, rather than doing anything by hand from the outset, you’re going to work by prompt. You’re going to describe the feature that you want to build in English, and the AI, with increasing autonomy, is going to solicit whatever information it needs from you and your environment, and then it’s going to go do that task. My hypothesis is that the IDE is not actually the right place to do that. It’s much more of a place for having a bunch of files open and doing hand editing. What you see in all of the AI-based IDEs, like Cursor, is that they are guiding users over to a chat panel where the user can, through conversation or prompting, build their feature. That chat panel is starting to look more and more like a terminal in its interactions. Warp’s approach is not to build an IDE, but to build something where a developer can ask for anything they want done and build the interface around showing the work that’s being done directly in that linear fashion. My vision is that these traditional IDE and terminal boundaries are going to blend into something oriented around what the best workflow for development should be in the future.

Nataraj: Historically, we’ve moved up the level of abstraction in development. We used to write HTML, then we came up with WordPress. For e-commerce, we went to Shopify. We’ve moved to a layer where we no longer use HTML directly. The end output is the same, but what you’re doing to get it has changed.

Zach Lloyd: Totally. Another good analogy is that back in the day, developers used to work in assembler language, which was very low-level. Then you moved up to a language like C, where you still have to know how memory works but it enabled faster productivity improvements. Then you moved up to a language like Python or JavaScript where you don’t have to worry about so much of the underlying system architecture. This is a bigger step because you can basically do it through English, but you’re lessening the barrier to working with code. I do think, for now and for the next couple of years, you’re going to need that programming expertise to build things of high complexity. It becomes more important that you know what’s going on because a lot of times, with this method of developing by prompt, the AI will do 80% of something and then get stuck or have bugs it can’t resolve. If you don’t know what’s going on, you’re going to be stuck with it. But the level of abstraction is definitely changing for developing software.

Nataraj: How has the feedback been from developers? And how is adoption coming? Are developers discovering it and then forcing engineering managers to buy your product, or is it coming from the top down?

Zach Lloyd: We’re mostly building for developers, so our go-to-market motion is bottoms-up, product-led growth. It’s going really well from a user adoption standpoint. We’re well into the multiple hundreds of thousands of developers actively using Warp, and that’s growing really fast. We have some people who are using it because they want a better terminal UX, and some are using it because they’re AI early adopters. Our strategy is to get a lot of developers using it, spread it wide, and get them paying for it. When we have enough concentration at a company, we end up having conversations with engineering leaders. We do have enterprise contracts with pretty good companies, but the primary motion is bottoms-up product growth. What gets people to pay us is getting them to an ‘aha’ moment in the app where the AI did something that blew their mind. It could be something as simple as fixing all of their dependency issues. A big part of what’s helped us grow is inserting ourselves into developers’ existing workflows in ways that are low friction but surface the value of AI with them doing almost no work.

Nataraj: What are some examples of workflows you’ve inserted yourselves into?

Zach Lloyd: A prime example is you try to build your code, you get a compiler error, and Warp just pops up a fix for it. As a developer, all I need to do is accept this fix. That’s very different from expecting the developer to know to type in, ‘Hey, please fix my compiler error.’ To the extent that we can hook into someone’s workflow, guess what they’re trying to do, and surface the AI as a fix, that’s the best way to get an ‘aha’ moment. I think that’s one of the reasons the first modality that really caught on is autocomplete—it’s just there, it’s no work, and it’s really low cost if it’s wrong.

Nataraj: Are you creating your own model or leveraging other LLM models? Which models are doing the best job for your use cases?

Zach Lloyd: The best model for developers right now is Claude 3.5 Sonnet. We offer it in Warp. We’re also offering for more complex tasks, users have the option to do a two-step execution where first they use one of the reasoning models to come up with a plan, and then we switch them to a standard LLM to actually execute the plan.

Nataraj: How do you think this will evolve in the next two to three years in terms of development? There’s this new phenomenon we’re calling ‘agents,’ where we are using high-reasoning models with traditional LLMs.

Zach Lloyd: The way I look at it, there are three main modalities that are important for developers right now. One is completions. The second is chat, where you’re pairing with an agent in an interactive mode. The third is a true agent with real autonomy. I think this is coming. In this world, you have to change the user experience to be based around higher latency interactions. What does that mean? It means if I’m asking an agent to build a feature for me, I don’t want to sit there and watch it do it. It might take five minutes to get a plan and another 10 to execute and test. That points towards a different interaction modality, essentially some sort of workflow management software, kind of like GitHub Actions. You start a job, it tells you when it finishes or if it hits an error, and you can have multiple running at once. I think another really important property is that when the agent fails, it’s not a pain for the developer to hop in and work with it to fix the issue.

Nataraj: Can you talk a little bit about cost? LLMs are costly, and the per-query price is not yet cheap enough to make a sustainable business. How are you seeing that play out?

Zach Lloyd: It’s a great question. Our pricing is based on a couple of plans for individuals and small teams at the $15 and $40 price points, differing mainly around AI requests. It’s a hard thing to price because the underlying price of these models is based on tokens, but pricing by tokens is too close to the metal and too far from the value to a developer. For all of our paid users, we have a pretty healthy positive margin, around 30 to 60%. However, it’s hard because the underlying models change both their costs and how much context they want to gather. We give all of our free users some amount of AI because we want them to understand the value and get to a moment where they want to pay us. I definitely think there’s a path to a sustainable business here, but it’s a bit of an open question exactly what will happen with model costs.

Nataraj: I always thought this dependency on LLMs could change completely if you adopt an open-source model and host it in your own cloud, then start to fine-tune your own models.

Zach Lloyd: Totally. If we were to take DeepSeek or Llama and host it, it’s a totally different level of control over the costs. You’re not paying a model provider. If you look at who’s making money on AI, it’s the chip makers, then the hyperscalers, then the model providers. There are a lot of people taking margin before you get to the app layer. We don’t do that right now because the quality difference of the models is such that our number one concern is getting users to realize the power of this stuff and convert them to paying customers. From a unit economic standpoint, we’re trying to stay breakeven and see what the right way to optimize costs is.

Nataraj: Is there any metric that you really focus on for your product? For example, how much time does it take for a new customer to decide to pay?

Zach Lloyd: One really interesting metric we’ve just started looking at is what percentage of things done in Warp are either done by AI or being asked of AI. In a normal terminal, it’s 0%. For Warp, it’s around 10% of things happening in the terminal right now are either the user asking in English or the AI doing something. AI engagement is the leading indicator for monetization for us. It would be a cool spot for us to get to where more than half of the interactions in Warp are happening in English or autonomously because of the AI. We’re trying to flip our users’ perception of this being a terminal that has AI to an AI interface where you can fall back to using the terminal if you want.

Nataraj: Can you talk about your go-to-market motion? Marketing for developers is a particularly interesting problem.

Zach Lloyd: About 80% of it is organic. We spend some money on sponsorships at GitHub repos and a little on Google ads, but the primary thing is organic. The biggest driver by far is developers telling each other about it. We’ve experimented with viral loops, like a referral program, and the ability to share cool things you do in Warp via a link. A really big thing is social media. The best thing for us is when someone does something super cool with our product and shows it to the world on Twitter or YouTube. For a product like Warp, you have to see it to get it.

Nataraj: How are you using AI, and did it change the way you are building your own startup?

Zach Lloyd: It’s an interesting question. We’re building an AI product, we’re all developers, and we all use our own product every day. There is a virtuous cycle: as the AI gets better in Warp, we do more of our coding, debugging, and DevOps tasks just by talking to our own product. Outside of our own product, there are a few AI tools I use. For example, I use a tool I really like called Granola, which is an AI meeting note-taker, and I just don’t take notes in meetings anymore. That’s cool, but it’s not like some of the stories you hear. I saw a tweet from the president of YC that in the latest cohort, for 25% of the companies, 95% of the code was written by LLMs. That’s not how it’s been with Warp. But we are adopting AI tools, and the primary one we adopt is the one we’re building.

Nataraj: Do you think we have hit a productivity level where we need fewer developers versus more?

Zach Lloyd: Not at all. We’re trying as hard as we can to hire developers. I think there’s probably a class of relatively simple front-end apps where you can maybe start to not hire developers. But for professional software development at a tech company, the impact of these AI tools is that it makes your existing developers more productive. The other thing is there’s basically infinite demand for software. Developing software is becoming more efficient, and there are benefits to that. Every company I know is trying as hard as they can to hire awesome software developers right now. I haven’t seen a negative impact at all in the type of development we do.

Nataraj: I actually think AI is at a stage where it’s sort of ‘draft AI.’ It gets you to 80-90%, but not 100%. That’s where the narrative versus reality is. You still need a developer to do that last 15-20%.

Zach Lloyd: I agree. I think it’s a mistake to think of it only as a function of the progress in the models. The models are only as good as the context that’s provided. Getting all the right context and knowledge into these things is a challenge. And then, the likelihood of succeeding at a task depends on the ability to specify it correctly. English is ambiguous, and people assume a lot of context that the LLM does not know. The fallibility of humans and how they communicate is still going to create work around this.

Nataraj: We’re almost at the end. What are you consuming right now? Books, podcasts, Netflix?

Zach Lloyd: I’m reading a totally different non-tech book called ‘Traveler’s Guide to the Middle Ages.’ It’s like, imagine you were traveling in the Middle Ages, what would that experience be like? It’s about people going on religious pilgrimages or traveling to the Far East. I like it because it’s an interesting reminder of how different an individual’s experience of the world was not that long ago. It’s history based on how people lived, not major historical events.

Nataraj: Are there any mentors in your career that helped you?

Zach Lloyd: I’ll call out a guy who’s kind of legendary in the tech industry, he’s now the CTO of Notion. His name is Fuzzy. He was my manager at Google on Google Docs, and he was one of the creators of Google Sheets. Most of what I learned about how to create incentives for engineers, give feedback, and get a team functioning at a high level, I learned from him.

Nataraj: What do you know about starting a company now that you wish you knew before?

Zach Lloyd: I’m on my second company, and I can tell you things I learned from the first to the second. The first one I failed at, but learned a lot. Really focus on team. Really try to hire great people, even if it makes you go a little slower, and hold that high bar at the beginning. Also, really try to work on as big of a problem as you can, which is counterintuitive to a lot of startup founders. Counterintuitively, the bigger swing you’re taking, the easier it is to get people to fund you and attract awesome people to work with you. People want to work on something really meaningful.

Nataraj: That’s a good note to end the conversation. Zach, thanks for coming on the show and sharing all about Warp.

Zach Lloyd: Cool, thank you so much for having me.

Zach Lloyd’s insights provide a compelling look into how AI is not just enhancing but fundamentally reshaping developer tools. The conversation highlights the shift towards more intuitive, AI-driven workflows and the exciting future for software creation.

→ If you enjoyed this conversation with Zach Lloyd, listen to the full episode here on Spotify, or Apple.

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Karthee Madasamy, a seasoned investor in the deep tech space, joins the Startup Project to discuss what he calls the “golden age” of foundational technology. As the founder of MFV Partners and the former Managing Director at Qualcomm Ventures, where he led investments in groundbreaking companies like Waze and MapmyIndia, Karthee brings a unique perspective shaped by decades of experience. In this conversation with host Nataraj, Karthee shares his unconventional journey from engineer to venture capitalist, the critical differences between corporate and financial VC incentives, and the challenges of launching his own fund. He provides a masterclass on evaluating deep tech opportunities, explaining why the sector is moving beyond selling technology to tech companies and is now being embraced by traditional industries, unlocking massive new markets in robotics, AI, and quantum computing.

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Nataraj: How did you come into venture investing as a career?

Karthee Madasamy: It wasn’t planned at all. In fact, people who knew me from a very young age would have been surprised that I’m an investor or even a business person. When I was younger, the plan was to do a PhD and then go develop new technologies. That’s what I pursued for the first 10 years of my career: building new things in semiconductors and wireless communication.

I think it was circumstances where I was getting pulled up, going in front of customers, and leading people. I basically learned that maybe more than my technical capability to build things, I have other skills, which is what led me to do an MBA. I had a very clear thought that I wanted to come back to technology, but maybe more as a business person.

I spent a summer doing venture capital. Until then, I was a startup guy, just doing startup stuff, and then suddenly I’m evaluating startups. That was a lot more interesting because I could look at several different startups. Even then, I just jumped in, thinking maybe I’ll try this for a couple of years. There wasn’t any planned path to get into venture capital. If I had to rewind, would I just go to venture capital? I don’t know. I might have just done product management and been an operator.

Nataraj: What was your first job in venture?

Karthee Madasamy: My first job in venture was at JK&B Capital in Chicago, where I was doing my business school. In venture, unlike other areas, you have to basically go fight for your job. I reached out to them saying, “Look, I’ve done a lot of stuff in semiconductors and wireless communication, and it seems like you guys are starting to look at that. I can add value.” I told them about reconfigurable semiconductors, new architectures, and so on. They liked that because it was an area they were spending time on but didn’t have deep expertise in. So I joined to look at new electronics, microelectronics, and semiconductors. I was basically doing sector analysis and reviewing companies in those areas.

The one key thing in venture capital is that you have to define a job opportunity and a job description, telling them, “You need this, and this is what I can come and help you with”—even as an associate, an entry-level person. In venture, it’s not just about saying, “I’m a smart person who can do software.” You have to create an opportunity and then say, “I can go fill that opportunity.”

Nataraj: Once you joined the firm and started understanding venture capital, what were some of the early deals that you worked on?

Karthee Madasamy: One of the early ones was using wireless communication for tracking things. This was in 2005, 20 years ago now. They were creating a proprietary wireless communication stack for tracking objects or assets across the country. This was in the early days of 3G, so most data communication was pretty low-key. Think of it like an early incarnation of Apple’s AirTags, but for big assets. It was interesting because I had a wireless and microelectronics background. We didn’t end up investing, but it was interesting to understand how to solve business problems using technology.

The key learning there was that you always try to solve a business problem with underlying technology, but you have to be aware of how quickly that technology gets commoditized. One of the early companies I evaluated at Qualcomm was a navigation app. This was 20 years ago, before Google Maps. You paid $10 a month for an app which gave you phone-based turn-by-turn navigation. It was a business need because you were driving and didn’t want to buy a separate navigation device. It was a good business opportunity, and some companies were doing good revenues.

But the underlying technology was getting commoditized. MapQuest was saying, “I can do it for $4 a month.” And then Google said, “It’s free.” Suddenly, these companies’ business models just went away in one day. So it was good to understand both the business problem and the underlying technologies. That’s the thing about deep tech, or new technologies—they get commoditized very quickly as well. Whatever I built 25 years ago as a startup is now a five-cent chip. It’s a commodity. Understanding the technology curve is extremely important to figure out which is going to last longer and which is going to just go up in flames.

Nataraj: Is there any pattern you’ve identified to figure out if a new technology is getting commoditized and whether to invest or not?

Karthee Madasamy: It’s a lot of heuristics. You try to see the path of commoditization and who could still retain value. In the turn-by-turn navigation stack, the navigation part went from $10 to zero when Google came along. But we felt the underlying map data—the actual data of where all the points of interest are—is not easy for anybody to just go build. At that time, NAVTEQ and Tele Atlas were spending $50 to $100 million every year to make sure they had updated data. I never felt that was going to get commoditized down to zero based on technology alone.

That’s what led to two investments: one in the US called Waze, and the other in India called MapmyIndia. You have to figure out what could get commoditized and what could not. For example, would Nvidia’s GPU get commoditized down to nothing? There are a lot more barriers there. First of all, to get to that level of performance, but more importantly, they have built the stack on top—the middleware, the software, the application layer. They have created a moat around everybody using CUDA and their software middleware to build applications. So they’ll be able to preserve quite a bit of that. You look at all possibilities to see if they have any other protection or if they are just at the whim of the technology curve.

Nataraj: What was your lens behind investing in MapmyIndia?

Karthee Madasamy: We looked at the whole stack of location-based services and felt that the biggest value was in the underlying map data and points of interest data, which is not easy to get. Waze was doing it in a crowd-sourced way. MapmyIndia was more traditional, but they were going after a market which is very, very unstructured. In the US and most Western markets, addresses were standardized in the mid-1900s. India is still completely unstructured.

To give context, when a parcel of land is assigned, there’s a plot number. Then there’s a numbering system, maybe a road, and finally an official street name. You have at least three different versions of an address, but most of the time, there are five or six. Different people will use different ones. Mapping this data and routing it is very unstructured. Navigation in India is more about, “You’ll find this particular place, turn left on that.” It’s never about turning left on a specific street; it’s turning left at a point of interest, which itself will have six different names. It’s a much harder data problem, which is why we felt that data would be very valuable. We invested in them in 2009. They went public two or three years ago, and even today, nobody can match the quality of data they have built.

Nataraj: Talk about the differences in incentives. When you work at an early-stage fund, you get carry and salary. In a corporate venture firm, it’s a salary plus some equity component. For people early in their careers who want to get into venture, what should they maximize for?

Karthee Madasamy: Early on, there’s not as much difference. You’re learning to build your network of entrepreneurs, build relationships, and figure out which are good, investable startups. You’re curating deals and developing your own framework for what makes a good investment. When you start in venture capital, those are the first things you’re focusing on. Frankly, there’s not as much difference between a corporate venture and a financial venture because, in the early years, you’re learning the craft.

Some of the bigger financial VC firms have a brand that attracts entrepreneurs. Corporates also have that. For anything related to semiconductors or wireless communication, Qualcomm is a well-known name. To have someone from Qualcomm validate your technology or company is very interesting for a startup. At Qualcomm Ventures, we were given reasonable autonomy to chase investments. If we made an investment, we would be on the board and responsible for it.

On the compensation side, again, early on, it doesn’t matter as much. In a financial VC firm, you get some carried interest plus bonuses and salary. In a corporate firm, some are now instituting carry, but even if you didn’t have it, you probably had bonuses, stocks from the corporate parent, and a salary. It was almost the same.

The difference comes in the later years. Once you’ve made these investments and one has a good outcome, if it had been in a financial VC firm, the compensation would have been different. It starts to matter once you are five or six years in because an investment takes six or seven years to exit. With corporate VC, your downside is protected, but your upside is capped. You live within a band, which is okay in the early part of your career but not later. If you believe you’re a very good VC making very good investments, that’s when you start to feel the difference.

Nataraj: So you made a couple of interesting bets, like Waze and MapmyIndia, and then decided this wasn’t enough and started your own firm. What was that journey like, and how challenging was it to raise your first fund?

Karthee Madasamy: It wasn’t the compensation alone. I became a corporate VP and felt that I was hitting the roof, both in terms of learning and other things. And also, if I’m making good bets, I should be compensated accordingly. The transition to starting a firm was probably one of the hardest experiences of my career.

Raising money from LPs is very different from raising money as a startup. The main reason I did this was that in 2017-2018, software was ruling the roost. Software was eating the world. We had the cleantech bust in 2010-2011, so nobody wanted to touch anything that was remotely hard technology. Most of the big VC firms were retiring their hardware VCs. It was clear that if you talked to entrepreneurs in core technology, they had very few VCs to go to, even in Silicon Valley. We felt there was a missing gap, and that was my DNA in terms of evaluating new technologies. So we felt there was a gap we could fill.

The biggest thing was fundraising. As a corporate VC, I didn’t have to fundraise; we invested off the balance sheet. Investing as an LP is a trust-based thing. People invest based on trust, which means you can really only raise money from people that know you or at least know of you. It’s much harder to go beyond those circles. Coming from a corporate background, I had to learn that from scratch, build those relationships, and build that network. It was much slower and harder, but it’s not a short-term thing. We felt there was a need to do this for the very long term, so we were able to go through all the ups and downs. Now we’re investing out of our fund two. It’s much better than how it was when we started.

Nataraj: Based on your experience raising two funds, what are two quick lessons you can share?

Karthee Madasamy: There’s a statement that everybody has a plan until you get knocked in the face. I don’t think I could have done any more analysis. The only thing I would have said is that there were so many people I felt could have invested in the fund who didn’t. Separating people from their capital, even as an investment, is the hardest thing to do. We started off thinking we were going to raise a bigger fund; maybe we should have started by thinking we would raise a very small fund. Assume that everyone you think could invest is not going to, and then maybe start that way. But I don’t think the end game would have been any different. Maybe we would have had fewer disappointments.

Nataraj: Let’s talk about deep tech. The term has become pretty common now. VCs used to fund hard problems, but then we went a bit haywire. You probably found the right time when everyone was focused on software. Where do you think deep tech is right now, and which areas are you interested in?

Karthee Madasamy: You’re right, VCs used to fund hard problems. Not necessarily R&D science research, but when something was proven out and ready to be built, even though it was still hard. Then the internet started, then mobile, then elastic compute and cloud. All three came together. The concept of deep tech or core infrastructure has been there for the last 50-60 years of our technology evolution. You come up with a microprocessor, which starts the personal computing era, then you get a bunch of applications. You start with internet infrastructure, and you build on top of that. In that cycle, nothing has changed. The next one was robotics, automation, and AI.

What happened was the internet, mobile, and cloud provided this era of cloud, internet, and mobile applications. It coincided with the hard landing of cleantech and the emergence of China as a semiconductor hub. A lot of the slightly easier semiconductor work moved to China. You couldn’t compete. So, people investing in hard technologies found that the opportunity threshold was much, much higher. The institutional knowledge was completely gone. The new VCs hired into firms were all doing software.

But I’m very bullish because technology used to be bought by technology companies; now other verticals are buying technology and getting disrupted. We are in a golden age of this core deep tech. We’re seeing things around robotics and automation, synthetic biology, a new generation of computing like quantum computing, and core AI solving classification and generation problems. We are in the golden age of new technology solving problems. I’m sure once this infrastructure gets built, you’re going to see a variety of application layer companies.

Nataraj: I want to talk about one of your portfolio companies, PsiQuantum. You invested before Quantum was really in the narrative. What was your bet on PsiQuantum, and what is the company really doing?

Karthee Madasamy: We are hitting the limits of computing. We’ve gone to one-nanometer, two-nanometer semiconductor chips. There’s no more room to pack things in, but our computing needs are not going away. We need a different form of computing architecture, and quantum provides the best alternative. Today, if you want to push the edge of computing beyond what we can do with our current technologies, the best option is quantum.

The basics of quantum draw from quantum mechanics, where anything can be in multiple states at one time. Current digital computing is either a zero or a one. With quantum, a bit can take multiple states. That has exponential qualities. If you have eight bits, it can stay in 2^8 states at one time. If you can use that to do arithmetic, you can solve complex exponential problems much harder to solve with conventional computing.

The first applications are all things that require this. Think about drug discovery. We can’t simulate the full interactions in your body on a computer because it becomes an exponential problem. In a quantum computer, we could get most of that simulation done. This means we could get a drug to market much faster. The pharma and computational biology applications are big. Same for computational chemistry. Those are all going to be the first applications, and they have huge impacts on business, industries, and humanity. It may take a while before you have a quantum computer in your laptop, but solving these big problems for industries is going to happen much sooner than people realize. We are a few years away from significant breakthroughs using quantum computers.

Nataraj: What do you know about investing that you wish you knew when you started your career?

Karthee Madasamy: It is not a monolith. Investing in the stock market is very different from investing in a Series Seed company versus a seed company versus a pre-seed company spinning out from a lab. They are all different, and they require different levels of gut instinct versus being data-driven. I’ve come to the conclusion in the last 10 years that I’m more comfortable in the early stage because it involves intuition and gut as well. When you’re starting your career, you want to figure out what you are more comfortable with. Are you comfortable with data, with processes, or with technology and instinct? Depending on that, you probably belong in different parts of the spectrum.

Nataraj: That’s a good note to end the conversation. Thanks for coming on the show and sharing all the insights about what you’re investing in deep tech.

Karthee Madasamy: I enjoyed the conversation. You asked a lot of interesting questions that I typically don’t get. It was fun.

Karthee Madasamy’s insights reveal a pivotal moment for deep tech, where foundational technologies are not just advancing but creating entirely new industries. His journey underscores the long-term vision and conviction required to invest in companies that are solving the world’s hardest problems.

→ If you enjoyed this conversation with Karthee Madasamy, listen to the full episode here on Spotify or Apple.
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The landscape of startup investing has dramatically shifted, with companies staying private longer than ever before. This extension of the private lifecycle has created a significant challenge: a lack of liquidity for early employees, founders, and investors who have poured years of effort and capital into building these businesses. How can they access the value they’ve created without waiting for a distant IPO or acquisition? Atish Davda, founder and CEO of EquityZen, created a solution. In this conversation with Nataraj, Atish breaks down the world of secondary markets. He shares the personal story that led him to start EquityZen, explains how the platform standardizes and simplifies private share transactions, and details the company-friendly approach that has earned them the trust of major late-stage startups. He also dives into different investor strategies, the outlook for the IPO market, and why building a trusted brand is the ultimate long-term play in this evolving space.

→ Enjoy this conversation with Atish Davda, on Spotify, Apple.

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Nataraj: So let’s start. A good place to start would be, for a lot of folks who don’t know about EquityZen, what is EquityZen and how did it get started?

Atish: EquityZen is a marketplace for private company shares. We have been around for about 13 years and our mission is to build private markets for the public. This is what we do. We work with founders, employees, and early investors of late-stage private companies—large companies that 20 years ago would have been public but are still private today. We help them get some liquidity, meaning they can sell their shares, and we match them up with investors on the other side that want to invest in these companies but can’t find them on their Fidelity account or their Vanguard account because they’re not a publicly traded firm yet. So EquityZen works with the company to get their shareholders cash and to get new investors access before the company actually goes public. And we’ve been around, like I said, since 2013. How the company got started? Well, this is in some ways a very personal story for me and not unsurprising for many folks. I had a personal need. I’ll just give you my quick background. I started my career at a quant hedge fund. I studied computer engineering and mathematics. Basically, if anything had to do with numbers, it made sense to me. I was fortunate. I worked at a quant hedge fund called AQR Capital. My work there was excellent, and it was a multi-billion dollar hedge fund where a lot of the entrepreneurship occurred maybe 10 years before I had joined. And so I wanted to be an entrepreneur, so I joined a startup as its first employee to learn. My equity in that company and a few other companies I had just been consulting for ended up being worth a little bit of money. And because I wasn’t getting paid the startup share, the hedge fund money anymore, I wanted to liquidate some of my private company stock. Well, because I wanted to liquidate $25,000 worth of stock or $50,000 worth of stock and not $25 million worth of stock, I was effectively out of options. I didn’t really have any brokers I could go to, didn’t really have any bankers I could go to. And so that was really the genesis behind what has now become EquityZen. We serve these private company shareholders that are big believers, supporters that have been there for the value creation, supporting the private companies. It’s just that if you work at Google, you can just sell your Google stock and pay for the house, or in my case, an engagement ring, which is what I wanted to sell my stock for. And if you’re a private company, you can go to EquityZen and allow your shareholders to do that in a regulatory sound manner and with the company’s blessing.

Nataraj: Before EquityZen existed, how did people find liquidity in the market? Startups existed forever, for the last 30-35 years. Was there no market at all? Or was it more like happenstance that you knew someone who was looking to buy these shares? I was working at early Yahoo, and I know some investor who is looking to buy. He probably doesn’t have a venture fund, but he’s trying to find late-stage company shares. Is that how it was working back then?

Atish: That’s a great question. Look, there’s been about three stages of evolution in the private market. Stage number one, companies used to go public when they were three, four, five years old. Amazon went public famously as a four-year-old company. Today, most companies that go public in the tech sector are teenagers, especially if you think about over the last three years, how closed the IPO window has been. Basically, if you’re an 11-year-old company and you wanted to go public in 2022 and you missed that window, you’re now a 14-year-old company and probably you’re not going to go public for another six to 12 months. And so before you know it, your shareholders have had to wait 15 years, your investors have had to wait 15 years to get liquidity. What used to happen before in phase one is effectively companies would just go public. And then you saw what happened in the dot-com boom. A lot of companies went public. Most of them didn’t survive. Some of them have survived since then. And it was the public investors that effectively took on the risk of providing liquidity. Phase two of the market is when there were basically six or seven companies. You have Facebook, LinkedIn, Groupon, Pandora, Pinterest. I mean, these small number of companies had grown to be multi-billion dollars but they were still private. So larger, sell-side capital markets desks were trading their stock. Goldman Sachs very famously conducted a lot of secondary transactions for Facebook, but it wasn’t $25,000 shareholders or $30,000 investors. It was $25 million worth of blocks, and hedge funds and family offices invested.

Nataraj: In a lot of ways, it’s like a version of selling your IPO stock because that’s sort of what happens when a company is going public, right? You have a roster of your clients who want to invest and then you allocate shares to that roster of clients, and pretty much very high net worth.

Atish: Yeah, it’s totally a heavily negotiated and heavily brokered transaction. That’s what phase two was about. And Facebook got arm-twisted into going public actually because there were too many secondary transactions the way Goldman was doing them, Goldman and other firms. And what ultimately ended up happening was that Facebook was forced to go public. That kind of put a chill on the venture secondaries market until EquityZen came along. And what EquityZen has done is effectively done two things. One, we have standardized the process of conducting these transactions. Look, when you’re transacting $50 million worth of shares, it makes sense to heavily negotiate each contract. Those economics don’t work if you want to trade $50,000 worth of stock. And so what we’ve done is the traditional technology company thing, which is build up the infrastructure and then amortize the paperwork and the cost over thousands of transactions. And by doing that, we can reduce the artificial minimum of conducting these transactions. Now, you don’t need $10 million to transact in private company stock. You can do it for $10,000, thanks to EquityZen. So that’s one thing we did was standardize the process. And the other thing we did was by making it available to accredited investors via our website, via technology. And this was right around the time that AngelList was coming up and crowdfunding was becoming more popular and people were getting comfortable actually deploying capital into an investment online. And while AngelList focused on the early stage of venture and Schwab continued to service the public companies, EquityZen kind of filled that hole in between. If you’re a series C but not yet a public company, you can now invest via EquityZen. So those are the two things that we kind of, I would say, reignited the secondaries market back in 2012, 2013.

Nataraj: EquityZen and AngelList have a lot of similarities from what I can see as a pure consumer or someone who has seen EquityZen and participated in one deal on EquityZen and plenty on the early-stage side on AngelList. And AngelList, I think, has a different set of products. It has sort of an early-stage fundraising product, which is slightly different than what EquityZen does, although there are some secondary transactions that happen on AngelList as well. Can you quickly talk about what type of standardization you brought? Like what are the couple of important terms that investors or even early employees who are selling the stock are looking at? Like I know for early stage the stage counts, if it’s a convertible or direct equity, when it matures, what is the discount? Is it a SAFE or a non-SAFE? Those are the terms that I’m usually familiar with, but for EquityZen, what are the two or three terms that are actually making or breaking the deal?

Atish: Yeah, great question. Before I say that, let me just draw a quick parallel to early-stage investing, which I think a lot of folks are generally familiar with. In early-stage investing or crowdfunding or the things AngelList became really popular doing initially, it’s the actual company that’s raising money. We call that in our parlance a primary transaction. So this is a transaction where the company is issuing new stock, whether it’s in the form of a note or SAFE or new common stock or preferred stock, they’re issuing stock. The company experiences dilution and all the money that’s raised goes to the company in order to fund operations, pay salary, rent office space and what have you. What the secondary market does, which is what EquityZen does is again, conduct secondary transactions. In this situation, when we conduct a transaction in some company, Instacart, it’s a public company now and so I can talk about it. When we conducted transactions in Instacart, Instacart wouldn’t actually get any money. Let’s say we do anywhere from $10 million or $100 million of transactions in Instacart. Instacart itself doesn’t actually get operating capital from them. It’s the shareholders who already own a piece of Instacart. They sell their shares, get money back, and new investors now get to basically own equity in Instacart. So that’s just one key difference between early-stage and late-stage. And because of this, there’s a difference in asset class returns. Early-stage investments are very famously power-law investments. You’re going to make 30 investments. You’re going to lose your money on 15 of them. You’re going to return your money on 10 of them. And if you’re lucky, five of them will earn you back all the money that you’ve basically invested and lost. Late-stage investments are very different. These are doubles and triples. You’re not just swinging for the fences for the home run. And you’re getting a lot more established businesses. And so now to answer your question, what are the things that matter? What do people look for when they’re thinking about investing in this asset class? Well, first of all, you should look at your whole portfolio and you say, I have a certain amount of money in public stocks, a certain amount of money in bonds, a certain amount of money in early-stage venture maybe. Do I have anything in between where on a risk-adjusted basis it’s a more established company, but there’s still a lot of value to be created? So you should think about allocation of how much of your portfolio you want to put in. Then you should think about what your sophistication level is. Do you want to invest in one of EquityZen’s multi-company offerings? Meaning I write one check, I’m going to write a $50,000 check or $10,000 check, and I’m going to get access to 20 companies. Or do I want to invest in individual companies? I’m going to write 10 $10,000 checks or $10,000, $50,000 checks and make my own portfolio. So I think that’s kind of the next level of making a decision of do I want to buy an ETF, if you will, or do I want to pick a single stock? Then the next level of deal evaluation is what series of stock am I buying? Am I buying preferred stock? Am I buying common stock? What is the discount to the last round of capital or premium to the last round of capital? And of course, because these are more established businesses, you can do a little bit of research on what the revenue stream looks like, what the management team looks like, who the other investors are. So Sequoia just puts money into this company. Sequoia has, first of all, a fantastic track record, but also way more resources than the average individual investor does. And so Sequoia and Andreessen Horowitz, Benchmark, you kind of have your top list of investors. If they have recently put money in, they’ve kind of done their work and established a price point. Well, now it’s a lot easier for you to all of a sudden say, well, it’s a private company. I don’t have public stock information. How do I get comfortable with it? And in this way, it is similar to earlier stage investments, where usually in early-stage investments, there’s a VC that puts money in, does all the diligence. And then you have a bunch of angel investors who are basically tagging on and saying, yes, I will also put money in. So in an essence, you’re kind of borrowing from the diligence that institutional investors have done and knowing which institutional investors’ investments fit your return profile. That’s probably where a lot of investors ought to spend a bit of time understanding like, yes, I understand Benchmark’s portfolios, they invest in marketplaces, they’re excellent at it. This company is a marketplace. Maybe this is a good opportunity for me, or maybe this is not a good opportunity.

Nataraj: Talking about the multiple products, in terms of your business, which is the more successful product for you? Is the portfolio offering a bigger business or are individual transactions a bigger business for EquityZen?

Atish: Yeah, for us as a business, certainly the single-company transactions, that’s what we’re known for. That’s a larger business. I think if you’re an investor learning about EquityZen for the first time, the real question I would ask is, what is your goal? Is your goal that you want to build your own portfolio? You’re familiar enough with the technology industry, you do your own research. I’ll give you an example. If you’re a security engineer that works at a marketing firm, you’re probably a pretty great person to be able to determine the difference between cybersecurity company A and cybersecurity company B. If you’re a marketing executive at an engineering company, maybe you’re great at determining which new marketing tech company is better than the other. So within certain sectors, people are going to get more value out of establishing their own portfolios by choosing single names. And in other sectors, they may say, you know what? I don’t know anything about artificial intelligence. I can’t tell. I can’t keep up with which company’s beating which other company. I just want to invest in the sector. Fine. So maybe you can make a thematic investment. So it’s more a matter of what makes more sense for the user. From EquityZen’s perspective, certainly the bulk of our volume happens on the single-company side. And I think that’s more a function of where the market is right now. We are still very much in the early days of this market forming. So if you take a look at the typical customer lifecycle, we’re in the early adopter phase. The folks that use the beta version of a product, not wait until the final version is released. And so even though we’ve been in business for 12 years, 13 years, and even though we have this fantastic track record, I would say we’re still probably in decade one of three before this entire cycle continues to grow. And the next 10 years are actually going to bring that next segment, the folks that really say, I don’t know this company from that company, but I know I need an allocation here. And so I think for the last 10 years, brokerage of individual companies has been the bigger segment. And over the next 10 years, if I were to fast forward, no doubt that more structured products will be the larger business segment.

Nataraj: The way I always thought of secondary transactions is like betting on your unique knowledge in a lot of ways, where it is less risky than early stage, because even if you have a lot of knowledge in a specific sector, it’s a very hard bet in early stage. Like the security engineer example, you cannot really identify a Wiz or something like that when the team is three people. But when the team is 100 people, when a couple of well-known VCs have invested, you can probably convince a Wiz employee to sell some of the equity to you. So I always felt like secondary transactions are for these sophisticated investors or sophisticated individuals who want to acquire equities in companies they are super confident about. That’s how I always saw secondary transactions.

Atish: But first of all, let me just say you’re spot on. And if you look at history, typically you have sophisticated folks doing something, and then people realize that’s where the sophisticated money is going. So then people come up with products that are versions of the sophisticated strategy, more for the less sophisticated investors in the space. This is exactly what happened in the liquid alt movement. You had institutional investors… one of my first projects at the hedge fund, a strategy that $250 million check writers get access to, was to convert this into a mutual fund that my mom with $2,500 in a Vanguard account can access. So I think in that way, you’re spot on, and that’s candidly what we’re doing. Institutional investors have invested in late-stage venture for 20 years, 30 years. What EquityZen is doing is bringing that access, making it available to smaller check writers first, and then eventually the folks that don’t even need to be in the ins and outs of technology every day. Their financial advisor will actually just find our ETF equivalent or mutual fund equivalent for them and say, we’re going to put 1% of your portfolio in this growth bucket, next.

Nataraj: Yeah. So companies in the sector usually, at least on the early stage, where they’re doing primary transactions, they’re always taking some amount of carry in their compensation or in their business model. They have a standard, like an X amount of cost structure plus an equity component of it. Is EquityZen also taking some equity component of it to have a stake on the upside or is it just purely per-transaction capital cost?

Atish: Yeah, so I mentioned we have two products. Product one, we operate exactly like a marketplace. We do not take carry on top. Frankly, I think that would dissuade a lot of people from putting money in. And therefore, what we do is we effectively only charge a commission to the buyers and sellers. In this product line, we do take carry. This is more of a traditional kind of managed fund product. And so people put money in, we charge a small management fee, and we charge a carry. And all of this is less than the two and 20 kind of products. However, there are two different product suites designed for two different use cases. And so in one case, what we’ve learned is a lot of clients don’t really want to pay carry on individual names. Frankly, AngelList convinced people to do that, and that’s a phenomenal trick that they pulled and it’s fantastic for them as a business model because even if you build a portfolio of 10, if one of them ends up doing well, the carry pays off. Later-stage investors, a little more sophisticated, don’t really want to do that. But on this side, they’re only portfolio-based kind of carry calculation.

Nataraj: I also think carry works when they have a syndicate-like product where you are basically incentivizing a lead to bring a good deal. And like, why should he offer his work to you? It’s that sort of alliance. I think that’s why it still works. Even though a lot of competing products are trying to reduce the carry component, and I think we’re trending down towards zero eventually. But at least that’s the reason it worked in my view.

Atish: I’m sure that’s a big part of it. And look, in any market, as the level of familiarity grows, the animal spirits tell us that people will just arbitrage away inefficiency.

Nataraj: So today, what is the state of business? Give us a sense of how many transactions happen on EquityZen, how many assets are under management.

Atish: Yeah, so we’ve conducted almost 50,000 private placement transactions. We’ve conducted transactions in 450, close to 500 of these large private companies. About a third of these companies have already gone public or gotten acquired in M&A, and therefore we’ve returned capital to investors. And a bunch of capital that we’ve returned just gets recycled. The other thing I’d like to point out is we have around 700,000 households on our platform. But it’s heavily skewed to the investor side, right? The majority of the users on our platform are investors, meaning they’re actually trying to access these investments for the first time. It kind of makes sense. Most people don’t work in technology, and most people don’t invest in early-stage venture so that they become late-stage. And so a smaller segment of our user base is shareholders. But when shareholders get liquidity, usually it’s their first or at most second time coming into money. At least a portion of those actually end up becoming investors too, just because they say, again, like the examples we talked about, I know the difference between this tech company and that tech company, I’d like to participate. A couple of other stats that might be relevant, just to give a size idea. We manage over 2,000 special purpose vehicles right now. So it’s not just one-to-one transactions we conduct. We also spin up SPVs, people invest in SPVs, the SPVs sit on the cap table of all these companies. And we have between one and a half and two billion of active investments, not counting all the stuff that we’ve already processed. And of course, for the last few years, we haven’t seen that many exits, but again, in like 2020, 2021 and prior, there were a lot of exits that we processed, so all those returns are not counted in the one and a half to two billion estimate.

Nataraj: What are some of the interesting stories or examples of some of those exits that happened in 2021, which you can probably talk about?

Atish: Yeah, well, I guess one thing that’s worth saying out loud, maybe this is more of a nuance of the secondary market compared to the primary market, is EquityZen operates as a broker, right? We’re a matchmaker between buyers and sellers. But unlike most marketplaces, we operate a three-sided marketplace. So we have a seller of stock, we have a buyer of stock, which is what most marketplaces have. But then we have the issuer, which is the company in which the stock is. And EquityZen is really the only platform in our industry that very much gives the issuers, the companies, a seat at the table. Issuers, we don’t charge them anything. We’re not beholden to them about anything. But we have taken the approach that because it’s their company’s stock, they should have visibility into who’s buying, who’s selling, and to actually get permission to be able to say yes, I approve this transfer restriction, or I waive my right of first refusal. Maybe it sounds obvious, but that’s not always the case. We’re the only ones that very much put companies at the top in terms of the decision-making tree. And what that allows us to do is really establish a relationship with the company. So for example, sometimes we will have companies say, hey, EquityZen, we’re in the middle of raising financing. We don’t want a secondary transaction to be price-setting right now, even though people are willing to pay a crazy amount of money. It actually hurts our negotiations with VCs or with private equity firms. So we’re going to put a one-month block on these transactions. And so that’s the kind of dialogue that we establish with these companies. So they kind of tell us, Hey, look, there’s a blackout window coming. We’re pursuing an IPO. Those are the types of dynamics that exist because we’ve taken a very company-friendly approach. And that’s just one example of how this is different from the rest of the peer group, perhaps.

Nataraj: But what if the company… so you mentioned right of first refusal, often referred to as ROFR, which means that a company can deny a secondary transaction because they have the first right to purchase that stock at that price, right? So if an early employee wants to sell a stock and the company doesn’t have a ROFR, EquityZen would still block the transaction? And wouldn’t that drive away certain business to your competitor, and they might execute that transaction elsewhere because technically the company can’t stop it if they don’t have ROFR rights?

Atish: Let me clarify a couple of things you mentioned. First, I have not come across a company that does not have a right of first refusal in the 13 years I’ve done this. And as a private company owner myself, I want a right of first refusal on my stock. And there are very legitimate and sensible reasons for that. However, a right of first refusal is not the same as a blocking right. So a right of first refusal is effectively the company saying, I don’t want that investor on my cap table. That investor is actually funded by my competitor. I don’t want that person on my cap table. Or that investor is from a different geography and for regulatory reasons, I’m not allowed to have that investor in my cap table. So what I will do, shareholder, is I will buy your shares. Effectively, it’s a matching right. It’s a right that says, shareholder, you will still get your liquidity, but that investor is not allowed to come in at this price. That’s separate from the blocking rights that I think you’re describing. And so you’re absolutely right. What some of my competitors do that we will not do is they will effectively conduct a transaction without actual share certificates changing hands. They will conduct what are called forward contracts, which are effectively IOUs. If you were a shareholder and I was an investor, and I was using some other company because EquityZen does not do this, one thing that could happen is you could say, okay, yes, I agree to sell you 100 shares at $100 a piece. Cool, no problem. I give you the money. In theory, I have the equity exposure. But if I’ve entered into this forward contract with this funky SPV with multiple other SPVs or whatever, and then the company actually ends up being Uber, and you regret selling your stock. In 2025, you took my money. And in 2028, you might have $10 million from your 100 shares because the company just blew up in a great way. And you might say, you know what? I don’t want to sell all 100 of my shares. In that scenario, what I have done is not only illegal from the standpoint of I violated the company’s restriction. What you’ve done is illegal because the company has changed restrictions for a good reason. But now I have no recourse to this. Like if you move to Singapore and basically change your phone number, I’m not saying you would ever do that. But of course there are people who would do that. As an investor, I’m completely exposed. And from a company standpoint, they don’t like that. There are companies today that are dealing with this and they’re going out of their way to educate their shareholders and they’re saying, hey, there are brokers out there that are claiming to sell you shares and claiming to trade your stock. Let me be very clear. We only work with a small number of preferred partners like EquityZen, and outside of those partners, you should be careful about what it is that you think you’re buying or you think you’re selling because we are not endorsing that. And that nuance is just something I want to bring up because it’s not a real concern when the counterparty is an early-stage company.

Nataraj: I think there is one company whose secondary shares keep trading higher and higher and are traded everywhere. I think I know which company this might be. This is SpaceX. I’ve been seeing this company’s secondary offers everywhere from every Indian WhatsApp group to every online portal. Someone is offering a SpaceX secondary offering. How many of these are legit versus how many of these are like the second category of IOUs that you’re talking about?

Atish: I certainly cannot speak to generalities on that front, certainly not for a specific issuer. What I can say very clearly is, EquityZen conducts transactions with the issuer’s knowledge and with the understanding that we give the issuer basically the visibility, hey look, here are the shares, here are the investors, we want to trade, are you okay with this or not? And time and time again, we have walked away from revenue that we could have just kind of skirted, but that’s not how EquityZen operates. It’s not how we want to operate unless the broker that you’re using can say that, unless the fund manager that you’re using can say that. I always try to caution people about what it is they’re buying. Cause at the end of the day, you are the one parting with your money. So it’s your responsibility to make sure that you understand what it is that you’re buying and whether or not there’s a trusted platform. There’s a reason in the 13 years that we’ve been in business, we’ve seen, I don’t know, half a dozen to a dozen platforms come and go. Some of them because the government told them to stop and some of them because they made so much money by doing some of these things that they didn’t need to work anymore. And our approach the entire time has been, we want our name to be synonymous with trust. And that means we’re going to have to say no to a lot of things. And in the long run, it’s going to pay off and we’re seeing the effects of it now. EquityZen is a referred platform for many companies who say no to most other brokers out there. In fact, we sometimes get assigned a right of first refusal. A company says, Hey, I received this transfer notice from this other broker. I don’t want that broker or their client on my cap table, but we don’t want to execute a right of first refusal at this price. So if you can do this, we have an assignment right. We will assign a right of first refusal to you to do this. EquityZen will then get tagged in, in order to be the broker of choice. And candidly, that doesn’t just happen. That happens because of years and years of basically being a trusted partner with a lot of issuers and effectively sometimes gritting our teeth and doing the thing we don’t want to do, which is to say no to revenue at the end of the day. But again, long-term perspective, it’s a no-brainer decision.

Nataraj: You also have some interesting data I feel because you also gauge demand or interest in different companies when you go to your platform. How do you use that data either to create new products or do you use that data to approach companies and say hey there’s a lot of demand for transactions, how are you thinking about secondary transactions and providing liquidity? How do you leverage that data?

Atish: Yes to all of the above. The only way we don’t leverage the data is we don’t actively sell or license the data like many of our peers do. And our view on that candidly is there’s no philosophical reason why. It’s pure and simple. The market’s early. So anyone—I’m a former quant, so data is very pure to me when it comes to utilizing data in a statistically significant way. Not everyone does that, that’s okay. And so our view on it is the market is still nascent. It does not make a ton of sense to use the data unless it’s a tiny portion of a larger model. Using private company transaction data should be one factor out of many factors in your investment decision. And unfortunately, a lot of companies out there are trying to sell their data and pretend like this is equivalent to public market data. Public market transactions are orders of magnitude more often than private market transactions. Private market transactions are actually more similar in terms of frequency to house sales compared to public company stock sales. And many of our peers are not drawing the distinction and papering over it. And so the only ones I think using data well, maybe as intended, let’s say, are institutional investors and sophisticated individuals who are saying data is one of my inputs. It really informs on the margin. It’s going to inform whether I’m going to pay 12 bucks or 13 bucks. It’s not a yes or no decision for me on data. And I think I would draw a parallel to the crypto world where you have a lot of supporters of various cryptocurrencies who are effectively acting like day traders. The data says the stock is going up, so I’ll invest or it’s going down, so I’ll sell. And we just are not that place where people would make purely speculative bets. This is more of a buy and hold investment in your portfolio from an allocation perspective. Again, the very first thing I told you when you asked me is, the very first decision to make is, what is my allocation to the space? Then determine all the other things. And so how do we use our data? We use it to talk to issuers. We use it to inform investors. We use it to inform shareholders. We publish cap tables. We give our users a range in which transactions are happening. And all of this we’re happy to provide to the issuer because ultimately we want the issuer to be a partner with us and that’s true. What we don’t do is use our data to kind of reel in investors who don’t really know what they want to do. We want people who know what they’re buying basically, if they’re going to buy.

Nataraj: And I don’t know what the regulation allows you or doesn’t allow you, because any company has to market itself. So how does EquityZen market itself to a future employee or early investor who wants to sell or a future investor who’s thinking about maybe expanding or diversifying their portfolio? How do you market EquityZen? Because it’s a very regulated industry, right? And these are very niche transactions for a lot of individuals. So how does marketing for EquityZen work?

Atish: Heavily regulated with good reason, I would argue. How does EquityZen market itself? We stay away from individual company marketing. So we will not market a specific offering. That’s just not our world. What we want to do is we want to educate. And then we want investors to self-select in. We want to put forth a knowledge base that we provide, one of the more detailed FAQs I’ve seen that we provide. We want people to read, spend the time to read, and not just make TikTok videos and kind of help people very quickly get brought in. Our view on this is, this is a serious thing you’re doing. Your money is an important asset. You should be careful with it. And so we almost build in a little bit of friction into this process. And I think my product and my marketing team are not always happy with me about that. But the way we do this candidly is we say, look, we’re in this for the long term. Let’s focus on educating people. Let’s help them make a decision about whether or not they want to invest. Then if they decide they want to invest, then they can go through and ask us, what do you recommend? And at that point, we said, we will not recommend individual companies or investments. But we will make the data available to you for you to make your own decision. So we always go out of our way to only educate people that this option exists and here are the benefits and risks of this option. And then sometimes clients will say, hey, I read all this stuff, but I still don’t know whether to invest in company A or company B. And so what we will do is we will say, look, we are not going to give you a recommendation. But we have a financial advisor that if you don’t have one, we’ll refer you to one. And because their buyer base has to be accredited, there’s a wealthy household that’s a typical investor. A lot of times, the vast majority of our users don’t actually have a financial advisor. So we’ll actually connect the dots and say, you should talk to someone who can give you more holistic advice. That’s not something we’re going to do. And so it’s very much about content-driven, education-driven, mission-oriented stuff as opposed to individual deal and transaction oriented.

Nataraj: So for both early-stage and secondary markets, the exit is IPOs. Obviously, the next secondary transaction might be another exit, but IPOs are measured at the exit points. Are there any exit differentiations when it comes to traditional IPOs versus direct listings? Is there any difference that the stakeholders see in terms of an exit perspective?

Atish: Yes, and let me add, if you invest through one of EquityZen’s SPVs, chances are good that you’re actually going to be eligible to sell your holding before the underlying company goes public. And we’ve seen this in the last three years. As an example, for the last three years, the IPO window has been pretty tight. So very few exits have taken place. However, if you bought five years ago or eight years ago and your goal was to hold for five to 10 years, you’d just have to wait until the IPO happens, right? Unless you bought it through us, in which case, chances are good that what you may be eligible to do is actually say, you know what? I hit my return target and I hit my holding period. If there’s a buyer for this, I’m willing to list it. And investors can go and kind of seek liquidity for their own holdings. So that is an exit avenue for the individual investor that is decoupled from the actual company going public. Your question was about a direct listing versus an initial public offering. There’s just the concept of a lockup. Typically in an initial public offering, what ends up happening is, as we talked about earlier, you have insiders who list, existing shareholders who list, and they get some liquidity maybe. Usually, the vast majority of liquidity they get is after the lockup window. And the only people that really get to buy and sell are the newcomers. This is pretty typical in a share registration. It’s in there for a reason. It’s there to prevent fraud and all sorts of other investor protection reasons. In a typical IPO, most of the stock is restricted. It’s locked up until usually a six-month window. It’s different for every listing. And so if you invest in something, and the company goes public using an IPO, usually you’re not allowed to sell until after the IPO lockup expires. And so you’re taking six months of additional public market risk. Now for a lot of EquityZen’s clients, it’s irrelevant because they’re long-term holders. And I think the way a lot of them look at it is, well, this is going to be in my portfolio for 10 years. What’s the difference whether it’s nine and a half years or 10 years or 10 and a half years? And so from that standpoint, there’s a lot of a buy-and-hold perspective. There are some folks who have more of a ‘buy today and sell in two years’ mindset. And they might care more about direct listing versus IPO. It’s not something you can influence, but if the company chooses a direct listing, then the shareholders typically can sell right after the lockup window, which is actually very short or non-existent. And so the next day, or the same day, you can actually start to sell. That is a key difference between an IPO versus a direct listing. And again, depending on the product that you invested in with EquityZen, you might actually have the ability to get liquidity separately from whether the company goes public. We’ve seen a lot of that over the last few years. Candidly, I think we’ll continue to see a lot more of that over the next couple of years as well until the IPO window fully opens and the business cycle hits that stride.

Nataraj: You mentioned the IPO market being frozen for the last couple of years, and 2019 to ’21 was when everything was hyped up. I think that was also the peak for secondary market sales if I remember. How do you see the next couple of years? That’s when I did my first EquityZen deal. It didn’t work out that well.

Atish: Yeah, it was just peak venture in every way. There you go. Then you should do another one now while the market’s come down quite a bit to dollar-cost average.

Nataraj: Yeah, I was keenly looking at the platform over the last year. But what is the outlook for the next couple of years looking like? Are the animal spirits coming back? It feels like it, that we can expect some IPOs to come back. What are your thoughts?

Atish: I absolutely believe there’s going to be more IPO activity. I think there’s actually going to be a lot more M&A activity, which is fine. Company liquidity is liquidity. This is the thing that, when you just read TechCrunch, it’s easy to forget that at the end of the day, an IPO, whether it’s M&A or IPO, anything worth buying should have a long-lasting impact. And so whether it’s a financing round, whether it’s an IPO event, whether it’s an M&A, it’s just one more milestone in the journey. It’s like you graduate high school, you graduate college, you rent your first apartment, maybe get married, maybe have a kid, maybe buy a house. It’s just one more event. I think when you actually look at it from a zoomed-out perspective, what I think is going to happen is there’s going to be a lot more liquidity that’s been built up, and the pressure valve will be released. A lot of this pressure comes not only from VC funds that have been able to make money with continuation funds but private equity sponsors. You have traditional hedge funds and private equity sponsors that put a lot of money in over the last six or seven years in the zero-interest-rate environment. There’s a lot of pressure for them and they actually control the businesses so they can maneuver the businesses to either sell or get sold to a corporate or to another sponsor. So I think that activity is going to help a lot. Anytime there’s that kind of activity, even without the cost of capital coming down, even without interest rates coming down, you’re going to see external prints. As soon as you see external prints, secondary transaction activity goes up. Because again, like I said, whether it’s Sequoia, which is the example I used earlier, or Francisco Partners, like a sophisticated investor that’s conducting diligence and setting a price, boom, now you have a benchmark off of which to go. And so whenever that activity goes up, secondary transaction volume will go up. And I think further what’s likely to happen is because there is, whether it’s in the next 12 months or certainly by 18 or 24 months, interest rates are expected to come down a little bit further. As soon as the cost of capital comes down, venture activity can really start to take off and private equity investment can start to take off. So not only are you going to have M&A, you’re also going to have IPOs and you’re also going to have financings and no matter what type of deal event takes place, it’s generally a positive sign for venture secondary transactions. And so from EquityZen’s standpoint, we’re really excited. We’re not really trying to figure out whether it’s this month or last month, or this quarter or last quarter. From a zoomed-out perspective, we think we’re kind of finding the bottom and we can’t wait for the growth that’s ahead of us. And at EquityZen in particular, we’ve actually been unique in our peer group; we’ve raised almost no outside capital. The last time we raised capital was eight, almost nine years ago. And so from that standpoint, the last few years that have been a little more lean, that’s what we were built for. And so we’re actually really excited and ready to go and not feeling beaten down about where the next few years are going to be. Unlike some of our peers that have raised a ton of money and are trying to figure out what they are going to do because venture activity is not yet at those crazy levels. And so from our standpoint, we’re just really pumped about where the market opportunities are going to be for the next really two and a half, three years at a minimum.

Nataraj: I think the reality of this industry, whether it’s early-stage or secondary-stage, is that you have to build long-term trust. And I think AngelList has done that in the early-stage space. When I looked at the secondary transaction, I saw a bunch of companies, but I can’t remember any other name other than EquityZen. I think that’s sort of a testament to the trust that you’ve built in the ecosystem.

Atish: I really appreciate you saying that. I think you and my mom are the two people that have told me that before, so I really appreciate that.

Nataraj: I think that’s a good note to end the conversation on. I know I want to be respectful of your time. Atish, thanks for coming on the show.

Atish: Hey, this was a fantastic conversation. Thanks for shedding a light on an area that obviously I’m really pumped about. And I think everyone who’s an investor should at least evaluate. So thanks again.

Atish Davda’s insights provide a clear roadmap for understanding the secondary market’s crucial role in the modern startup ecosystem. This conversation is a must-listen for anyone interested in pre-IPO investing, startup equity, or the future of private market liquidity.

→ If you enjoyed this conversation with Atish Davda, listen to the full episode here on Spotify, Apple.

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In this episode of the Startup Project, host Nataraj sits down with Aviel Ginzburg, a central figure in the Pacific Northwest’s tech scene. Aviel’s journey is deeply intertwined with Seattle’s startup evolution, from founding Simply Measured (acquired by Sprout Social) to his roles as Managing Director at Amazon’s Alexa Accelerator and now General Partner at Founders Co-op. He offers a candid look into the world of early-stage investing, sharing how his perspective has shifted from focusing on product to understanding the founder’s core motivations—often finding success in those who are ‘irreparably broken’ and driven by an innate need to build. Aviel also discusses the recent volatility in the venture market and introduces Foundations, his new community-focused initiative designed to be an anchor for Seattle’s startup ecosystem, aiming to foster the collaboration and serendipity needed for the next wave of innovation.

→ Enjoy this conversation with Aviel Ginzburg, on Spotify or Apple.
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Nataraj: Hello everyone, welcome to Startup Project. I am your host Natraj. Today my guest is Aviel Ginzburg, founder of Simply Measured, which was acquired by Sprout Social. He was managing director at Amazon’s Alexa Accelerator, he’s also a general partner at Founders Co-op, and he’s now co-founder of Foundations, which is a shared workspace and accelerator, an anchor to Seattle’s VC ecosystem, or trying to be.

Aviel Ginzburg: Exactly. The way that you’re answering it shows the work that we need to do. We’re defining it right now as an invite-only community of founders in the startup ecosystem. It has one part that looks like an accelerator and one part that looks like a co-working space, and we’re going to do a better job of explaining what it is as we figure it out ourselves in 2025.

Nataraj: We’ll get to Seattle Foundations and everything about it. A good place to start this conversation would be with Chris DeVore, your partner from Founders Co-op. He was on one of the best episodes of Startup Project. We had a really great conversation, and I’ve been following Founders Co-op’s work and Chris DeVore for a while. How did you get to start working with him?

Aviel Ginzburg: For better or worse, my entire career has been wrapped around Founders Co-op and has involved Chris. I graduated college in ’07, at the beginning of the Great Recession. I knew I wanted to do something in tech, maybe tech and finance. I was from the East Coast, and the plan was to go to New York. Then everything just went to shit. All of my peers who were graduating were having their jobs pulled. So I had been building small website businesses to pay my bills in college, and I just said, okay, let’s go all in and do this.

So a buddy and I decided to found a company. Before we closed on the friends and family money, I realized very quickly that we were about to lose all of it because we had no idea how to actually start a startup. We were reading TechCrunch and thinking, let’s do that, but neither of us had worked at one. We hadn’t even really talked to someone who had been there and done that. So I said, I have to go West and see what this is like. I didn’t know anyone in SF, but I had one good high school friend at Microsoft, so I wound up in Seattle not knowing anyone.

Less than a week after I started, there was a startup weekend. Back then, everybody would get in a room and build together. There were 150 people from early Amazon, Microsoft, and the budding Seattle startup ecosystem. It was like, okay, let’s pick an idea, form divisions, and all build together. It’s insane to get 150 people to work on the same thing at once. It’s a complete shit show. But I raised my hand and said, I’m a designer, I’ll lead the design department. Was I a designer? I don’t know, but I was more confident than everyone else who said they were.

So I found myself rubbing shoulders with what eventually became the who’s who of the Seattle tech community. Out of that, I landed a job at a company called Aperture, which was funded by Founders Co-op and Madrona. Within two weeks of arriving in Seattle, I was right in the middle of that community, so I got to know Chris. He knew my plan was always to start a company. I was clear with the founders that this was for me to learn and there was nothing they could do to keep me.

I was lucky enough to watch that business work. I think so many people start their career at a startup that goes nowhere or at a big company where you’re stuck in a tiny hole. I was able to write code, design products, talk to customers, and act as a product manager. When I felt I had enough confidence, paired with the fact that I think what makes founders founders is that they just can’t not start something, I started working on nights and weekends with my best friend, hacking together anything we could with Twitter data and Facebook data. So I said, it’s time to go off and start a company, but I don’t know exactly what it’s going to be. Chris and his then partner, Andy, said, we like you, you should do it, we’ll just write you a check. We incorporated as Untitled Startup, which was the precursor to Simply Measured. Chris made me put together a pitch deck that he ripped to shreds and made me feel like he wasn’t going to invest, but he did anyway.

So I transitioned from an early employee to founding CEO, which then transitioned into head of product and engineering. But I was finding that I was much more interested in the zero-to-one of things—going from idea to MVP to customer traction. So I was naturally spending more time with Chris and Founders Co-op. I found myself wrapped up as a pseudo-venture partner, which turned formally into a venture partner in 2014. Then, when we sold Simply Measured, I transitioned into a GP role. It has been 17 years of working every possible seat inside of the Founders Co-op portfolio. Now, it’s just Chris and I working together and investing in new companies.

Nataraj: So talk to me a little bit about what Founders Co-op is doing right now. Are you actively investing? At what stage and what type of checks are you writing?

Aviel Ginzburg: We are at the tail end of our fifth fund, which is a $50 million fund. The average check size is about a million to a million and a half, investing in pre-seeds and seeds in the Pacific Northwest mostly. COVID sort of screws up what geography really means, but we find that we gravitate towards founders who are culturally Seattle. That means you learned how to build at an Amazon or a Microsoft and you gravitate more towards unsexy business workflow problems rather than flashy consumer products. We’ll be doing the first close of our next fund within the next month and are actively deploying. The thing about us is neither of us are finance people. We’re both founders ourselves. We never worked at a larger venture firm. We have no associates. We act very much like a startup itself. Our process feels more like interacting with a former founder who’s deploying his own capital into really early-stage things that they find interesting.

Nataraj: One thing you mentioned on a podcast was that you did these things overlapping—you were an investor while you were a founder. I can relate to doing multiple things. I invest, I work at a big company, and I do a podcast. Talk to me about how your thesis of investing and finding companies evolved.

Aviel Ginzburg: When I was starting to make investment decisions as part of Founders Co-op, I was still actively operating. I was anchoring too much on product and on what I would do if I was running the company. I was thinking way too much about the product, the customers, and the opportunity rather than the founder. That was a hard lesson to learn because I invested in a couple of companies and watched as their direct competitor became a monster. I knew that was going to be a thing, but what I really messed up is that the only thing that really matters is the people. I just did not yet have the pattern matching to know what behaviors and motivations make a great founder. So much of success is about luck and upcoming challenges that you aren’t expecting. I was bringing my perspective of, I know what good product looks like, I know how to take things to market. Frankly, that initially made me a bad investor.

Nataraj: Right now in AI, you see certain types of companies will succeed, but now you have five competitors in the area. Who are you going to invest in?

Aviel Ginzburg: As a fund, I think you can run a strategy if you are a thematic fund with enough dollars and access. You can see everything in a space and pick the winner. It’s hard to do that at the pre-seed and seed stage. Ultimately, as a seed-stage investor, your job is to get really good at identifying a very specific type of founder and be good at knowing how to help them. Focus on those folks. Don’t feel FOMO about missing out on other things.

It’s embarrassing to look at what a lot of folks do. They’ll put out a whole thesis on AR/VR, then they’re a crypto fund, then an AI fund. Then you learn pretty quickly that a company did a 180-degree pivot after they invested, so the whole reason why it was in the fund is completely wrong. Ultimately, early-stage investing is like casino-level risk in people. The reason why it works is that you make enough investments that something does work and takes care of the rest. How you become good at it is through having enough experience with enough founders and seeing the movie play out enough times. I was part of the selection committee for Techstars Seattle from 2010 through 2020 as well, so I have seen thousands of companies from pitch to exit. Ultimately, when I look at a founder, I look at how they’re tackling an opportunity, the shape of the market, and the market opportunity. I can see all the different directions this can go. What you are underwriting is really just the human and the market they’re going after. That’s it.

Nataraj: You mentioned pattern matching. Are there any specific things that are deal-makers or breakers when you look at certain types of founders?

Aviel Ginzburg: Motivation matters a lot to me. Why are you doing this and where does your energy come from? A lot of times, the folks that have that infinite, renewable energy resource are those who are building something because there’s something broken in them. They’re just filling a leaky bucket for the rest of their life with creating something new. They find happiness and satisfaction in the work, in building, not in a specific technology or product. They just have to be putting something into the world or they’re falling apart. You work with crazy people, and I love that.

Mental health is a big factor. Depression is real, I’ve lived this as a founder—high highs, low lows. But your job is to ride that wave. You don’t want to find someone who gets one-shotted by finding peace and happiness and is no longer motivated. I’ve seen it happen where someone says, ‘I discovered the love of skiing, and now I have a family and I ski, and I’ve found balance in life and I no longer care if my company succeeds or not.’ You want to find founders who are irreparably broken, so they’re just going to keep charging forward and build. My bond with them is that I’m the same way. I’m not here giving you money trying to take advantage of what’s broken about you. I’m saying, I’m broken too, and living this path fulfills me. It can fulfill you too. Let’s go on it together.

Nataraj: That’s a pretty good way to put it. I grew up in India, and when I came to the US, I noticed a similar dynamic. Entrepreneurship in the US is so de-risked in a lot of ways. The only risk you’re taking is mostly on time. If you’re a developer at a big tech company, you should technically be doing a startup because it is de-risked. You will find some seed money eventually. The Indians who come here are a bit more broken by their childhood. They have more motivation. If you have a really stable childhood, that motivation doesn’t exist. They’re happy doing what they’re doing.

Some funds invested in narrow categories like AR/VR during the 2020-2022 period. It was a sign of so much capital that there were funds with inexperienced fund managers being created left and right. We created a lot more new funds which will never be successful.

Aviel Ginzburg: Money became functionally free. So you’re looking at, if money is free, where do I find alpha? When you run out of ideas, you say the best place to put that money is in the unknowable, in things that don’t exist yet. That led to an outrageous amount of first-time funds. A first-time VC is bringing their network with them. They haven’t yet saturated it. It’s all these amazing, smart people I know from my operating days, now let’s give them capital. Somebody can get $20 million and immediately deploy it into all these awesome-looking companies because they know the right people. An outsider looks and thinks, this person is a great investor. But you realize this isn’t really a venture capitalist; this is just someone with a great network who now has capital and just deployed it right into their network. They were not being thoughtful about if there is a real business. All that was also masked by markups.

The fun part is the excitement you get around a new idea. Then you invest, and it’s all downhill from there. Nothing ruins a good story like data. In the beginning, you’re going to the moon. Then you learn, this business is terrible, what was I thinking? You have this high, and then it goes to shit. That first investment meeting, you’re just wondering, what is this? You come into that first meeting thinking, how bad is it going to be? Then you build yourself out of the hole into a great company. That’s where the real work is. A lot of folks will not raise future funds. They’ve realized this isn’t that fun or enjoyable. The side effect is there are a lot of companies that raised money that shouldn’t have, and we’re still going through that pain.

Nataraj: I think the biggest distortion was the opportunity size. Every idea was exaggerated to be a billion-dollar opportunity.

Aviel Ginzburg: I would take that even further. People were saying there are $10 billion opportunities because unicorns went to decacorns. That started to make the model work. But if the company can only be worth $1 billion and you started at a $50 million pre-seed valuation, your fund is fucked at inception.

Nataraj: There’s also this winner’s bias. The winners became the Mag 7 and these trillion-dollar companies. So now everyone will say ‘trillion,’ and then you can justify a seed round of a billion dollars, which is happening with AI companies.

Aviel Ginzburg: There are some areas where there could be winner-take-all market opportunities. But I’ve also come to see over time this illusion that great companies have an arc that’s constantly up and to the right. Things move so fast these days that there are amazing companies where their enterprise value is high and then it goes to zero because the world moves. Part of your job is to invest in the company and know when to get out.

For example, we invested in a seed round of this company called Ally, which ended up selling to Microsoft. It was OKR software. We invested right before OKRs took off and before COVID, so there was this insane accelerant. We got approached by Microsoft for an acquisition. At the same time, we had a term sheet from a major fund offering $100 million at a billion-dollar valuation, even though we had single-digit ARR. We had many conversations with the founder about what’s the right way to go. We ended up selling to Microsoft.

Now, fast forward two and a half years. Microsoft acquired the product, put it into Microsoft Viva, and made it Viva Goals. Then they announced they’re just shutting it down because their strategy has shifted to co-pilots. They just don’t care anymore. Those two other competitors are functionally screwed. So, was that a bad investment? I underwrote a category that never came to exist. But one investment returned nearly a billion dollars and another similar one will return zero. Trying to reconcile that is crazy. You have to think about that sometimes, even in a great business, the local maximum may be the global maximum.

Nataraj: Let me ask you this, was that acquisition sort of a fund returner for your fund? So it was an easier decision for you from the perspective of the fund?

Aviel Ginzburg: As a seed investor, I was very aligned with the founder. It was a meaningful return rather than just another turn on capital.

Nataraj: This reminds me of Clubhouse, which got an acquisition offer of $4 billion from Twitter. They went and raised a similar amount from A16Z at a similar valuation. When COVID dropped, the hype for audio products dropped, and everyone featurized Clubhouse. At that time, the rational choice, even if you’ve only spent two years on the company, is to sell.

Aviel Ginzburg: I think it is. That’s the point I’m making. Just because Clubhouse got featurized and there was no long-term residual enterprise value does not mean it should be viewed as a failure. You created something of value that there were buyers for. Something does not have to exist on its own in perpetuity to be successful. We sort of lost sight of that over the past decade. It almost became a bad thing to sell your company, like you sold out. Instead, the chip on your shoulder was to build a unicorn. Now we have hundreds of unicorns that are going to go to zero.

Nataraj: One more point and we’ll shift to Foundations. This was also the time where secondaries were huge. Founders who were shrewd enough to make the rational choice of taking more secondaries were also winners.

Aviel Ginzburg: You need to be able to keep founders properly incented, so I expect some semblance of secondaries to continue. But it got nutty.

Nataraj: Moving to Foundations, I was a Techstars mentor for the last two batches before it got shut down in Seattle. I saw this story closely and can relate to how important that space was. It was one of the anchor points for the Seattle startup community. It got shut down, and I felt that Seattle is probably second or third after SF in terms of talent, but we are all somehow not living up to that potential. Talk to me about why you started Foundations, what it is going to be, and what its goal is.

Aviel Ginzburg: The general thesis is that Seattle should be a much better place to be a founder than it is. Coming out of COVID with the rise of large language models, it was embarrassing to watch the phoenix of SF proper with YC leading the charge. The difference is insulting. I went on a listening tour with founders in the area and found this desire to work and build around others, but also a desire to leave Seattle, which was not a good thing for our ecosystem.

I went back in reflection to why Techstars came to Seattle in the first place back in 2010. At that time, it was the rise of cloud and the SaaS business model. There was this new wave of founders excited to build things they couldn’t before. But there were no best practices. The magic came from people working in real-time around each other and sharing knowledge. That rhymes exactly with what we’re experiencing with large language models right now.

Every startup ecosystem needs an anchor point plus rhythms that allow the earliest stage folks, those who are actively building, and those who have been there and done that to get into the same place at the same time. The idea for Foundations was, can we do that and kickstart a flywheel? We need to anchor this around a physical space because that’s something all three of those categories need right now. We created all these different rhythms anchored by events and an entrepreneur-in-residence program to get people together. Our goal is to help people quickly on their way. Success is having a founder who went down and did YC, while others who were thinking about leaving Seattle have instead found a home and a community here. So far, it’s been a great success. Our mission is to serve the founder community in Seattle and make this a much better place to start a company.

Nataraj: So you’re doing Foundations, but have you thought about what other things should exist in Seattle to live up to its potential?

Aviel Ginzburg: There is a very big need for everything from founder matching to the incubation required to get people out from big tech companies. It’s weird about Seattle that we have more venture studios than venture funds. I think that’s because for a lot of people who have been at big companies, it’s a more comfortable path. But we should have another product here. Going to AI tinker events, you see all these people who are not quite founders, but the rise of large language models has been enough of a catalyst for them to say, I want to go out and start my own thing. There’s room for something more structured that could look like an accelerator.

Nataraj: One thing I also think is missing is that we don’t have enough pre-seed funds that write quick, small checks, considering how much talent we have. We have more studio models where it takes longer to get that first check.

Aviel Ginzburg: It’s not that hard to raise that money from the Bay. What we don’t have, and this will take time, is tons of folks in the Bay Area who made their money on startups and write 50k, 100k checks to people who remind them of themselves. The people who made money in Seattle made it by never leaving Microsoft or Amazon. They buy boats and houses on lakes. Their hobby is not investing in startups. We are starting to see those exits and people have that money, but we don’t have one-hundredth of what there is in the Bay Area. We just need to keep investing in our startup community and building things. Five to 10 years from now, there will be a lot more folks who have had those exits and can write those really quick, easy checks where they’re not asking for a business model. They’re just looking at the human and saying, I’ve been where you are, I want to be part of your journey.

Nataraj: What are you consuming right now—books, podcasts, Netflix—that is influencing your thinking?

Aviel Ginzburg: As someone who has multiple jobs and an eight-year-old, you would be surprised at how much I can consume. For podcasts, I listen to All-In, Rogan, and Jordan Peterson, and then I just pick up random other things. I love sci-fi, so I pretty much watch every sci-fi show that is out there. It’s been a year since I’ve sat down with a good book, but I was reminded to reread ‘The Courage to Be Disliked.’ I would highly recommend that book to anyone who’s ambitious.

Nataraj: What do you know about investing that you wish you knew when you first started out?

Aviel Ginzburg: The feedback loops on investing are really long. I am a builder; I started my career as a software engineer. As an investor, if you try to do more than you should, you mess things up because you are not the co-founder. If you are the type of person who really needs the dopamine hit of having an impact, you need to find other places in your life to place that energy, hopefully in ways that are accretive to your work as a VC. And that doesn’t look like just shit-posting on Twitter, because that’s what I see a lot of people do with that energy.

Nataraj: I think that’s a good note to end the conversation. Thanks for coming on the show and sharing your thoughts. I hope Foundations will become an anchor for the Seattle ecosystem.

Aviel Ginzburg’s insights offer a valuable look into the mind of an experienced founder and investor. His focus on founder psychology and his mission-driven approach to rebuilding Seattle’s tech community provide a compelling roadmap for the future of the ecosystem.

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In this conversation, Nataraj sits down with Jacob Bank, the founder and CEO of Relay, a startup building AI agents to revolutionize how we work. With a rich background in AI and productivity from his time at Google and as the founder of Timeful (acquired by Google), Jacob offers a unique perspective on the intersection of automation and artificial intelligence. He shares the winding journey of Relay, from its initial concept as a cross-product collaboration tool to its current form as a powerful AI agent platform. Jacob dives deep into the challenges of building in a rapidly evolving market, the importance of robust integrations, and the product-led growth strategies—particularly his success on LinkedIn—that have propelled Relay to early product-market fit. This discussion is a masterclass in modern company-building, navigating the AI landscape, and understanding the future of automated workflows.

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Nataraj: What is Relay and how did it get started?

Jacob Bank: Relay.app is a platform to build AI agents. The journey has been a bit winding to get here. I started the company in 2021. My background is in academic research in AI and then building productivity tools. When I started the company in 2021, we had two founding premises. The first was that we all use a lot of tools to get our work done. The builders of those individual tools spend a lot of time figuring out how to make experiences within those tools better, but probably not enough time figuring out how those tools interact with the others in your ecosystem. For example, I used to be the product lead for Gmail and we would agonize about every single pixel when you were archiving, replying to, or starting an email. But if you said, ‘Hey, I really need to get data out of Gmail into Notion and then also Salesforce and then send a Slack message,’ we’d say, ‘Sorry, good luck. Use the API.’ When we looked at what knowledge workers are actually doing, a lot of what we do is take stuff from one tool, do some stuff with it, and then stick it in another tool. We thought there was an opportunity where people were underestimating the importance of cross-tool coordination. And second, it sounds so silly to say this now, but it was not obvious in the summer of 2021 that AI was going to be important in doing this. The original name of the company was Collab AI, and we didn’t know exactly what product we wanted to build, just that it was going to help with cross-product workflows and that it was going to use AI somehow to do that.

For the first year and a half of the company, we wandered in the desert, as they say. We built eight or nine different product prototypes that all fit that theme, but none were quite right. We built an automated to-do list, a contextual knowledge base, a stand-up tool, and an employee onboarding tool. Eventually, we landed on a workflow tool. A workflow tool that captured repeated tasks that had an element that can be automated and an element that required human judgment. That’s why we named the company Relay, because we were thinking so many things we do should be a relay race where the computer does some stuff and the user does some stuff. We announced our beta at the end of 2022 and ran it in 2023. By the end of the beta, we realized that maybe there was a category to be created there, but it wasn’t us who was going to create it. It just wasn’t right. When you’re an entrepreneur and you’re just muscling something through that’s fundamentally not right, there’s too much friction.

So in the summer of 2023, we decided we were going to build an automation tool. We would focus on the market that Zapier is the leader in: cross-product horizontal workflow automation for a non-technical audience. But we were going to try to build the modern version of it. What makes it modern is that it’s way easier to use for non-technical people, it has AI better integrated into the workflows, and it has human-in-the-loop capabilities so you can correct your AI when it gets stuff wrong. In 2024, we were an AI-powered automation product, positioning ourselves as the modern alternative to Zapier. We got to initial traction and then early product-market fit. But we realized that by positioning ourselves as an automation product, there were two major limitations. One, you limit the audience of people that think you’re the tool for them. We want to tackle the much bigger opportunity of helping every business get more work done with AI. And second, we didn’t want to be perceived as a duct tape product that only exists to temporarily glue two products together. We want to be a transformative tool. So the evolution we’re making now is transitioning from an AI automation platform for no-code workflow builders into an AI agent building platform for everyone.

Nataraj: When I see a product similar to yours, the problem is we are using so many tools and so much data is spread across different things. How are you prioritizing which tools to bring into the platform?

Jacob Bank: Right now we have about 120 native integrations. The way we think about it is that there are about 12 categories of tools that pretty much every business needs. Every business has an email client, a calendar client, a messaging tool, a CRM, an email marketing tool, an e-signature tool. There are these 12 to 15 categories that are quite ubiquitous. In each category, there are three to 20 players that have material market share. We’ve basically just tried to work our way down that list for our target audience, which skews towards small and medium-sized businesses. If you’re a regular SMB using modern tools, you’ll probably find everything you need with Relay. Zapier has 7,000 integrations; I don’t think you need 7,000. I think that’s a vanity metric. The number we need to get to is probably somewhere between 300 and 500 for the product to really feel complete. I believe integrations are skilled labor. I don’t think this is something you can just outsource. Building a really good Salesforce integration is really hard software engineering. Second, I believe agents will only be as useful as the robustness of their ability to interact with the tools you use. There are two big schools of thought: do everything in the browser or build on top of APIs. I think every serious player will need to do both, but if an API is available, that’s almost certainly a more efficient and robust way for the AI to interact with the product. We have focused entirely on robust API-based integrations.

Nataraj: What has the traction been like? Give us a little bit of insight in terms of the scale of the company right now.

Jacob Bank: We’re now at 440 paying customers. We have about 1,200 weekly active teams. That’s up from essentially zero when we launched at the very end of 2023. I’d call it early product-market fit. My personal definition of product-market fit for a product-led self-serve business is: could I go on vacation for a week and come back with more users, more customers, and more revenue? That is now true of our business. With 440 paying customers, there’s enough there to say that you’re not just a bespoke consulting shop for one or two companies.

Nataraj: What are you doing to drive this adoption further? I think you’ve done very well in terms of product-led content growth, especially on LinkedIn.

Jacob Bank: That’s super recent; we only figured that out in the past month. We’ve been building from the back of the funnel to the front of the funnel. Meaning we started with retention and depth of engagement, then moved to activation, and now have moved to working on top of funnel. In my first company, Timeful, we got 300,000 downloads the first weekend and retained none of them. I was scarred by that experience. So for this company, I would rather have 10 rock-solid, retained customers and then figure out how to bring more in. The strategy that makes sense for our kind of product has to be facilitated word of mouth, facilitated by content, community, and partnerships. I spend a lot of my time on content creation. One type is LinkedIn posts, which are teasers that illuminate a use case for an AI agent. I’ll post something like, ‘I just built a cool AI agent to synthesize insights from my customer calls.’ I’ll make a six-second GIF about it and pair that with long-form YouTube tutorials that show you how to actually build it. On LinkedIn, I now have 15,000 followers, and I’m getting about 150,000 impressions a month. Our YouTube channel will cross 10,000 views for the first time this month. It’s an order of magnitude fewer views, but super high intent. About 25% of our paying customers come from YouTube. Now that we have a robust community, many of them are writing LinkedIn posts or building templates, which helps solve the problem of people figuring out what to use a horizontal product for.

Nataraj: How come there’s no LinkedIn integration when you post so much on LinkedIn?

Jacob Bank: We’re actually in the review process from LinkedIn right now. We’re waiting for them to flip the bit to accept us, but it’s coming very soon.

Nataraj: What integrations are coming in the next couple of months?

Jacob Bank: We have a public roadmap. LinkedIn is at the top. WhatsApp is coming—that’s a really highly requested one. Xero, the accounting provider, is another. We have a few more social media integrations to build, like deepening our YouTube integration and building an Instagram integration. We need to deepen our integrations with website builders like WordPress, Squarespace, or Wix. We just have Webflow at the moment. The drumbeat of integrations will go on forever, but we really want to cluster around the use cases where we’re seeing the most traction: content creation and marketing, research use cases in sales, and general back-office operations.

Nataraj: There’s a lot of overlap with Make.com and Zapier. Do you see them as competition, and how are you differentiating?

Jacob Bank: When people are deciding between Relay and other products, they typically consider two categories. One is the traditional automation players: Zapier, Make.com, N8n. The other is the new AI agent builders: Lindy, Gumloop, Relevance. With respect to Zapier, our main differentiations are a way easier product experience for non-technical users, AI is integrated much more natively, and we have human-in-the-loop support. For the AI agent builders, they typically have good AI primitives because they’re AI-first, but they don’t typically have the depth and robustness of integrations or perfect usability. We all have our strengths and flaws, but we’re all kind of circling around the same opportunity.

Nataraj: This is your second company, and you sold the first one to Google. What didn’t work in that company, and what are you trying to avoid doing in this company?

Jacob Bank: Two big lessons from that company. The first is that we let ourselves be a little seduced by top-of-funnel numbers when we should have focused on having 10 super high-value retained users. The second was we just didn’t understand business. It was like, oh, we’ll just build a mobile app like Instagram and eventually get bought for a billion dollars or monetize it with ads. It turned out there was this other business model of subscription B2B SaaS, which totally existed in 2014, but it wasn’t in our DNA. So for this company, I wanted to make sure it provided rock-solid value, high engagement, and high retention to a small set of customers before we expanded. And second, that from the very beginning, the business model made sense.

Nataraj: You are at the edge where you might be called automation, but if you push, you’ll be called agents. How do you think these will look in two years?

Jacob Bank: I think there are three principles that are going to be really important in agent building. One, agents will need a robust and reliable mechanism to interact with all your tools, likely through APIs in the next two years. Two, agents will need to give you some sort of intermediate representation of what they plan to do before they do it and give you the ability to give feedback. And third, you need a great human-in-the-loop mechanism to correct things and help the agent learn. I think we will primarily use natural language to instruct an agent, then iterate on its plan, and then work with it to get the final output. This pre-compiled version of the flow chart is more understandable to users and will run more reliably and faster in practice.

Nataraj: What’s next in terms of scaling? Are you planning to fundraise?

Jacob Bank: We don’t feel any need to fundraise at the moment. We have plenty of runway, and our revenue is growing quite quickly. It looks like we’d be able to make it to profitability if we had to. That said, we will likely want to raise additional capital because it’s such a big opportunity. But my philosophy on company building has changed. It used to be you raise a round and triple the company size. For the kind of business we’re building—we have nine people right now—I can see us needing 12 or 15, maybe 20, but I don’t see a near-term future where we need 200. Modern company building is going to look very different.

Nataraj: This was an amazing chat. Thanks for coming on the show and sharing all your insights.

Jacob Bank: Thanks so much, it was a blast.

This conversation with Jacob Bank highlights the incredible potential of AI agents to transform business operations. His journey with Relay provides a clear roadmap for building a modern, lean, and highly effective company in the age of AI, focusing on real customer value over vanity metrics.

→ If you enjoyed this conversation with Jacob Bank, listen to the full episode here on Spotify or Apple.
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