How Simon got into investing

I had a bit of a meandering path into early-stage investing. I worked in consulting out of undergrad, but it wasn’t a good fit for me. I didn’t mind the weekly travel, but it felt weird to return to the home office on Fridays and barely know anyone. I ultimately wanted a role that had a stronger sense of camaraderie, so I moved to a big tech firm to work in various growth and strategy roles. 

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One of the advantages of being at a big tech like Google is getting to be “in the room where it happens,” especially around product development and prioritization. I worked in product strategy at Google Cloud, for example, and saw how AWS Lambda pushed us to enhance our GCP serverless offerings to stay competitive. Google was a great place to work, but I missed the fast-paced, client-facing nature of consulting. So, toward the end of my time there, I decided to give VC a try, thinking it might be the best of both worlds. I helped Valia Ventures set up their first fund and was their first associate while also applying to business school. 

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My experience with early-stage investing at Valia was great, but I remember thinking that early-stage investing wasn’t for me because I love numbers and analysis, and early-stage companies don’t have numbers to analyze! I used to do competitive math when I was younger, and I thought my quantitative skills should be my edge in investing. Some friends suggested that banking might be a good path into later-stage, growth investing, where you actually have historical data to dig into. So, post-MBA, I joined Qatalyst Partners, focusing on selling enterprise software companies. To tell the truth, investment banking gets a bad rap, but I loved it. Over the four years I was there, I was involved in about $100B worth of announced M&A deals. That said, the itch to get back into investing never went away. 

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After having kids, I made a wishlist for my ideal investment firm, and two things topped it: (1) working with nice people and (2) working with investors who truly understand technology, not just finance. Vertex checked all the boxes, especially my top two. I’ve been with the team since early 2024 and couldn’t be happier – I hope to be here for the long haul!

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Key opinions

* Early-stage firms are well-served by a concentrated portfolio. Vertex has a very concentrated portfolio: Every investor is expected to make just 1-2 investments per year. This approach is appropriate because there's so much company-building to be done at the earliest stages. Pre-seed, seed, and Series A investors should be sounding boards to founders, and that requires genuine knowledge of the company and its industry. I can’t serve any company well if I’m on 40 other boards. I like that human aspect of high-conviction, early-stage investing, by the way. Whether it’s more optimal or not from a portfolio risk theory perspective is somewhat beside the point, it’s more satisfying and genuine to be a trusted confidant for founders at the beginning, when the company is little more than a vision and some code.

* As a sourcing strategy, start with the best teams at high-growth companies. When identifying promising entrepreneurs, we generally look first to the companies we respect. But, while a lot of VCs look for individuals within those organizations, I like to think about teams. In almost every company, there’s an elite team that’s particularly strong relative to others. The supply chain team at Apple, for example, or the developer experience team at Stripe. These teams are incubators of tremendous ideas or the training ground for amazing future founders. So who are the aspiring and talented entrepreneurs currently working on those high-performing teams? I like to use Harmonic filters to surface them; those filters are my favorite Harmonic feature by far.

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Key outlooks and predictions

* VCs are turning their attention to vertical applications of AI. Over the past two years, VCs have focused on foundational model work and infrastructure tooling, investing in companies developing core technologies and fundamental models in AI – the “picks and shovels.” More recently, though, I think there’s been a broad shift towards AI use cases. The key question now seems to be: "What real-world problems can we solve with these powerful AI tools?" This change reflects a maturing of the field, where the emphasis is increasingly on delivering tangible value rather than just showcasing technological prowess. Firms are looking at applications—specifically vertical applications—and asking where agents can actually provide value. From my POV, this is an exciting time to invest in AI applications. It’s popular to say AI is wildly overhyped in VC circles, and yes, some of these valuations are crazy high. Still, I’m more on the side that AI will change everything fundamentally, and I’m willing to bet AI will change things faster than most expect.

* A VC’s deep industry experience and expertise will become less important to founders. Plenty of smart people disagree with me here, but I think this is a natural consequence of AI. Platforms like Perplexity now allow VCs to learn about a space exponentially more quickly than they’ve ever before. As accelerated learning becomes the norm—especially in technical investing—an investor’s extensive experience in the industry will become less relevant to founding teams. One driving force here is that the gulf between yesterday and tomorrow widens more rapidly today than ever before. Put more simply, the future will look very different from the past. 

Psychology is at play here too – if I’ve been in an industry for a long time, I somehow feel that I have earned the right to opine and predict, but ironically, that “authoritative” knowledge works against you in a rapidly changing world. Better to have thoughtful, naive commentary from your VC than well-informed, outdated perspectives from a VC unwilling to leave history (and its warning signs) behind.

* Firms’ thematic focus will broaden going forward. I remember a time not that long ago when VCs were adopting hyper-specific brand identities: “I'm the micromobility VC” or “I only invest in alternative blockchains.” I believe this trend was largely driven by “VC tourism,” people getting into the space more for the hype or clout rather than as true practitioners of the craft. History suggests that success in early-stage VC is more about seeing than picking. Intentionally limiting one’s investment focus is an unnecessary constraint that I expect will become less common going forward. I’m a fan of the “prepared mind” approach in early-stage investing as popularized by USVP and Sequoia. I think this approach allows investors to remain open to diverse opportunities while still leveraging specific expertise in identifying promising ventures – especially in early-stage investing.

My earliest investing challenges

Early-stage investing—as opposed to, say, public market investing—comes with unique challenges. Everybody in the VC world knows about the power law: Empirical evidence suggests that only a small percentage of a firm’s portfolio provides the returns that LPs pay us for. That profoundly impacts how VCs perceive and pursue investment risk. 

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Early on, when conducting due diligence, I struggled to distinguish between companies making fundamental, market-shifting improvements and those simply adding incremental changes. Historically, the biggest hits for early-stage VCs have been investments in companies leading market shifts or even creating new markets. In practice, identifying these potential opportunities typically requires extensive research, a nuanced understanding of the relevant market dynamics, and investment discipline. 

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To help overcome this struggle, I like to adapt a Bobby Fischer-like approach to developing a prepared mind. Fischer famously focused on how players lost rather than how they won. In VC, this translates to analyzing why prior promising startups fail, not just blindly seeking the next big winner. By understanding historical pitfalls and blind spots, I think you gain valuable insights into what may be different today. My working hypothesis is that ideas based on market changes, regardless of root cause, tend to be more transformative in nature because they are more free from past constraints. Like Fischer's edge in chess, this approach has helped me sharpen my ability to spot truly transformative ventures amid a sea of incremental improvements.

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Coming from the banking world, I was used to relying on substantial data and publicly-traded comps to make decisions. Transitioning into early-stage investing was like traveling to the other end of the spectrum: In the absence of quantitative data, you really have to think deeply about what the market might look like in five to ten years. I find this pure, undiluted exercise in critical thinking incredibly intellectually satisfying. Talking to other smart early-stage investors and founders about what the future might look like is really so much fun!

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One investment I’m excited about 

One of Vertex’s most recent investments was in a company called SPRX, a platform that streamlines R&D tax credit claims for mid to large-sized companies. While most businesses still rely on outdated practices to claim their R&D credits, SPRX leverages AI and machine learning to reduce the process from months to days, or even hours. This is what I mean by market disruption! We led SPRX’s Series A, and Dom Vitucci, the founder, is exceptional. He has a rare combination of skills: He’s conversant in AI, is a programmer himself, and was a tax consultant for years. That cross-section is a rare find! Dom is the perfect founder for SPRX.

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SPRX is aligned with one of my recent focus areas, services-as-software. By our estimates, the shift from SaaS to services-as-software presents an enormous opportunity, thanks to breakthroughs in AI. Our projections have the market opportunity at roughly $4.6 trillion globally, from higher sales efficiency to massive cost-reduction gains.

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Personally, I love debating the broad economic implications as AI companies begin to reshape and disrupt service provider. What does this wave mean for pricing? What will happen to market valuations and how will white-collar workers adjust to a world where AI displaces so many jobs? So much is still up in the air and unknown, but in any case, it will be fun to see how everything unfolds.

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How I’m using AI in my day-to-day workflows

AI is very well integrated into a variety of daily tasks for me. I think the area in which I leverage AI the most right now is company diligence. When I'm engaging with a company that’s in an industry or field I'm not particularly knowledgeable about, it’s remarkable to be able to get up-to-speed both quickly and thoroughly. I typically use Perplexity for initial diligence. The platform not only allows for rapid education on a space; it also serves up high-quality, first-party sources that I can read through if I need more complete understanding of a topic. 

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I also frequently use Anthropic’s Claude Sonnet when writing investment memos. It’s unbelievably powerful on so many dimensions. Claude now has a new project feature; and when I'm diligencing things, I'll drop key data and intelligence into a Claude project, then use Claude to test different aspects of the investment or the market in which that investment lives. I’ve found that by dropping information into a project and telling Claude to specifically base its responses on those artifacts, I get high-quality and thoughtful responses that allow me to iterate very quickly.

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The advice I’d give to a new investor

I'm no expert, but here's something I've learned about networking in VC: be true to your personal style. Sure, networking is crucial in this field, but it's got to feel natural to you. If I could go back and give my younger self some advice, I'd say, "Skip those VC happy hours." Not that there's anything wrong with them! But as an introvert in this hyper-extroverted job, I've found I'm much more in my element in smaller gatherings with nerdy founders.

Look, time's always tight, and there's a ton to do each day. Just because every VC in town is flocking to an event doesn't mean you have to be there, too. And let's be real - are you really going to get any worthwhile intel by chatting up your competitors?

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Here's another tip for new investors: try to tune out the competitive consensus - you know, all that VC deal gossip. It's so easy to get caught up in the hype. You hear Sequoia or Benchmark is sniffing around a company, and you may have the natural instinct to drop what you’re doing and check it out. Of course, you're not going to develop a non-consensus perspective that way, and worse, you may start justifying deals without really thinking from first principles.

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We’re co-invested with some of the big names, and I can tell you from experience that interest from another VC firm - even a prestigious one - is a very weak signal of enterprise value. Here's a thought experiment: if I told you I had an engine that could spot unicorns more accurately than the best VCs, you'd probably pay a fortune for it, right? But what if that "engine" was just a notecard saying, "pass, this is not a unicorn"? You'd probably have second thoughts. Sure, that notecard might be more statistically accurate than most VCs, but is there any real signal there? Some food for thought.