Hi, Vignesh. Can you tell us a little about your career and path to joining Sierra Ventures?
Sure, I started my career as an analyst at a mid-market investment bank called AGC Partners, where I focused on M&A transactions for enterprise software companies. During one of the CEO’s trips to the West Coast, he brought me along to meet with a number of venture capital firms, including Sierra Ventures. Afterward, I sent a note to one of Sierra’s GPs telling him I was interested in working on the buy side and was looking for some career advice. Sierra happened to be hiring at the time, and they wound up bringing me on as an analyst in 2013.
Over the decade since joining Sierra, my niche has been in health tech. I also focus more broadly on the application layer, which is general enterprise software that business users rely on universally, and the vertical application layer, by which I mean enterprise software that’s developed for specific industries.
What is Sierra Ventures’ investment thesis, and what differentiates it from other early-stage venture firms?
Sierra stands out for three main reasons. First, we’re highly disciplined and only focus on B2B software at the early stage.
We also go after deals in non-traditional markets. The majority of our portfolio is outside of the Bay Area in places like Seattle, Boston, and Philadelphia, as well as in the UK, Israel, and Canada. They’re all markets where there’s tremendous talent but where early-stage founders often don’t have access to Silicon Valley. We’re able to be a bridge for them by making connections and helping them understand how investors here think and how they should be presenting themselves. We see it as a unique way of helping our portfolio companies get to their next stage of growth.
And the third thing?
The third thing is that we only work with founders who have had ring-side seats to greatness, meaning they either already had a small exit themselves or have witnessed great exits first-hand. Having had a taste of success, but not yet been at the steering wheel of a billion-dollar business, they’re hungry and have the passion it takes to build something special. Plus, they bring fresh perspectives and valuable experience, both of which are necessary to make it. All of that’s exactly what we want from our founders.
One of the areas we know that you’re particularly interested in is generative AI and large language models. How important is the technology, and where do you see it heading?
The fact that large language models work as well as they do — which is a lot better than most people expected — is really meaningful. To me, it signals that we’re at the start of a generational shift. And while there obviously have already been a lot of dollars chasing the space, I don’t think it will be long before we see some very compelling, generation-defining companies emerge.
To understand where things are headed, let me remind you where we’re at today. Many of the companies we see right now are good at information retrieval. A lot of chatbot companies have nearly perfected specific tasks like answering questions or generating text, for example. But that’s really just the start of where this tech can go. The next step is for those same chatbots to be able to take a user’s input and actually use it to drive actions.
So what might that look like?
Imagine telling an HR chatbot that you need to onboard a new employee. Your typical bot today could provide some helpful information, answer questions, or point you to resources. Not too long from now, however, when you tell an HR chatbot you want to onboard a new employee, it will not only understand what it all means but also be able to do something with it.
For example, based on the function of the new hire, it will know what equipment, access, and approvals that person will need to do their job. More impressively, it will actually be able to orchestrate all of that end-to-end on the HR team’s behalf. We’re already looking at companies that are working on these kinds of next-generation chatbots, and it’s really exciting to see what they’re able to do.
What are the biggest benefits of generative AI, and are there other implications of the technology we should be aware of?
The productivity gains will be huge. In the past, founders would have to get really deep into a particular area so that they could design software that someone highly trained in that space could use to perform specific tasks. Now founders are basically just asking what their customers are trying to accomplish and then delivering software that can be plugged in to do a person’s entire job. That will allow companies to completely reimagine how they run their processes, leading to a massive increase in productivity.
The other big implication will be a shift in the cost structure. We recently invested in a company that’s using generative AI to replace e-commerce agents. After coming in, training its software on a company’s data, and integrating with all of its systems, the software is able to manage every aspect of processes like selling to customers and handling their requests. From a cost structure perspective, that means that rather than selling software or seats, they’re actually selling software-enabled agents that can augment the capacity of their existing team. That’s a pretty big change.
In your view, how should companies be thinking about adopting generative AI and large language models? Are there any best practices you can share?
First and foremost, now is the time to figure out how to use the technology, how it will impact your industry, and what to do with it. Founders who aren’t already thinking about this stuff are doing their company and their shareholders a disservice.
More specifically, founders should be figuring out which models to use, what the cost-performance implications are for their business, and how it will all fit into their overall tech stack. They should also figure out where they can find ways to scale nonlinearly within their organization. If they can increase their sales team’s productivity by 10x just by using some of the generative AI sales tools, for example, they should do so. Companies need to look and act as generative AI native as possible. It shouldn’t appear to be an afterthought.
Can you tell us about any of the other trends you’re seeing that are driving Sierra’s investment activity?
There are really two underlying trends that shape the way we think. The first goes back to the emerging agent model we were talking about earlier and its implications on how people think about buying software.
We just finished a deal with a company doing AI-powered QA testing. Like most companies today, their customers are in a consolidation phase and don’t want to buy more tools for their team. Instead, they want solutions that can help them run workflows end to end. Of course, being able to run a playbook like that will have major implications for how products get positioned and sold and how people will think about buying software. We’re effectively transitioning from a per-seat model to a per-agent or a per-bot model.
The other interesting trend is the emergence and importance of small data instead of big data. That’s especially true in individual verticals, where you can own and collect data that allows your models and software to perform tasks better than anyone else. So it’s really about having access to and capturing the right kind of data to develop a competitive advantage.
Last question: If you were to pursue a totally different career path, what would it be and why?
I’m a big NBA guy, so I’d love to work in an NBA front office. Finding the best talent and figuring out where and how they fit into your team would be a lot of fun.