Decoding Investment Impact: What To Look for Before, During, and After Investing in AI Companies with Pramod Gosavi 

In the rapidly evolving landscape of technology investment, the allure of AI companies has captured the attention of venture capitalists seeking high-growth opportunities. As the world embraces artificial intelligence to solve complex problems and drive innovation across industries, investors are keenly attuned to the potential impact and returns offered by these ventures. However, navigating the intricacies of investing in AI companies requires a nuanced understanding of what to look for before, during, and after making an investment decision. In this discussion, we’ll decode the investment impact by exploring key considerations and strategies for evaluating AI companies, maximizing investment potential, and mitigating risks throughout the investment lifecycle.

To give us insights from an investor perspective we have with us Pramod Gosavi who brings a wealth of experience in product management and engineering to his role as an investor at 11.2 Capital. With a passion for technology and innovation, Pramod is dedicated to identifying and supporting the next generation of enterprise software companies. Leveraging his operational background and domain expertise, he empowers entrepreneurs to build and scale innovative solutions for the enterprise market. Pramod’s previous roles include positions in corporate venture capital, strategy, and corporate development at VMware, where he led investments and acquisitions in cloud computing, information security, and SaaS. With a deep understanding of the tech landscape and a commitment to driving meaningful impact, Pramod is poised to shape the future of technology.

In his upcoming discussion, Pramod will shed light on his journey from engineering roles at Sun Microsystems to venture capital in Silicon Valley, emphasizing his focus on funding startups in the AI and data science space. Drawing from his extensive experience, he will delve into the critical factors VCs consider when discerning between hype and sustainable potential in AI companies, highlighting the importance of evaluating founders and the scalability of technologies. Additionally, he will offer insights into navigating the dynamic landscape of technology trends and entrepreneurship, providing valuable advice for aspiring VCs looking to embark on this career trajectory.

AIM: Can you share insights into your journey transitioning from Sun Microsystems to venture capital in the Valley, particularly focusing on funding startups in the AI and data science space? For those aspiring to enter the VC realm, what pathways or strategies would you recommend to embark on this career trajectory?

“Venture capital was a unique industry where you would get funding and then build the product. I needed to help founders out, particularly in the zero-to-one journey, making a feasible and valuable product.”

Pramod Gosavi: I started my career at Sun Microsystems as an engineer there. So, most of my product and engineering journey has been building zero to one product. When I joined Sun Microsystems, they worked on a new product. I was one of the early new engineers on the products. I worked on the product for about five or six years and then worked in venture-backed startups. After that, one of the reasons that I moved on to the venture side is a gap in helping founders. At that time, about 10 to 15 years ago, venture capital was a unique industry where you would get funding and then build the product. And I needed to help founders out, particularly in the zero-to-one journey, making a feasible and valuable product. That’s how we move on the venture capital side. 

So, I would say there are probably two to three ways to get into venture capital. One is, if you’re from a product background, if you’ve been a product manager, particularly if you’ve been in one of the hot companies or very successful companies like a PM at say, Uber, now OpenAI or ScaleAI, I think that is a skill set that VC’s should look for. The second way to get in is to have a banking and consulting background, like financial services. The third background is if you have been an entrepreneur, founder, or executive, let’s say a CEO, CPO, or CEO of a big company; those skill sets are also very valuable.

AIM: Do you need some kind of capital from the start to be investing, to be a partner at a VC firm, or do you start off without any initial investment and work your way up?

“You don’t need much to start in venture capital because if you join a fund that has already raised money, you can start investing from that fund.”

Pramod Gosavi: Venture capital today is very institutionalized. However, 15 to 20 years ago, it was characterized by successful individuals who had sold their companies and invested their own money. Then, they could invest someone else’s money, but primarily, they mostly invested their own money. Where it is now is fully specialized, meaning nobody invests their own money. So, all the money comes from limited partners, such as pension funds, endowments, and family offices. You don’t need much to start in venture capital because if you join a fund that has already raised money, you can start investing from that fund. But, one way to get into venture capital is through angel investing. I also encourage people to do a bit of that because you’ll learn along the way, on what works and what does not work. So, when you get on the institutional side, you won’t make mistakes. You need some money, and you don’t need to invest a lot. You could invest 5K to 20K in checks to get started.

AIM: How do venture capitalists discern between hype and sustainable potential when investing in AI companies, given the current hype surrounding AI? What criteria do you use to evaluate the longevity and scalability of AI technologies, considering past examples like Blockchain, AR, and VR?

“You could be the first in the market, but if the barrier to entry is not large enough, you can have fast followers, and even large companies build that product and commoditize your product.”

Pramod Gosavi: Yes, one of the most important things when we are investing is, we are looking to see if customers want to buy this product. Is this useful? They could use it internally within their IT, or they could use it to support their business like a SaaS product. So even before we invest, we get a lot of potential customers and we ask them if they would buy this product. So that goes into the diligence. That’s the most important thing. 

The second thing we look for is if this team can actually build a product. Do they have the experience? Do they have the right attitude to build a product? The third important thing is having what they call a mode, a long-term mode. You could be the first in the market, but if the barrier to entry is not large enough, you can have fast followers, and even large companies build that product and commoditize your product. But the mode can be different. It could be a technology mode where only you have it, and you find some patents on it. It could also be on the customer side where you’re still the first mover, you got a lot of customers, and you got a lot of data, and you learned a lot about the customer; that also can become a mode.

AIM: How do venture capitalists conduct checks to assess whether a technology, such as blockchain, is worth the hype and investment, considering factors like its fundamentals, market demand, regulatory landscape, and ecosystem dynamics?

“One of the reasons we didn’t end up investing is the barrier to entry.”

Pramod Gosavi: One of the reasons we didn’t end up investing is the barrier to entry. So, if you look at many of these projects, they’re all sitting on top of barely a few hundred or thousand lines of code. So, mostly, it became like projects that anybody could build.

So it became more of a go-to-market, where you’re trying to sell it. And then what ended up selling is the financial products, what they call underrated coins, like Solana and what they call “Shitcoins.” Those are financial products. They ended up selling quite well, but in terms of real utility, I think there was no real utility. And also, with blockchain, I think what people wanted to do was something massive that requires huge levels of disruption. They talk about Bitcoin becoming the next reserve currency, displacing dollars and Chinese currency. That is something that takes decades to disrupt. It will happen eventually, but it does take a long time, and that is something. Also, as we see, you just have a horizon of 10 years, and you need to exit this investment in  10 to 15 years. So, to me, if any company were to become successful in blockchain, I think you just needed a very long horizon.

AIM: How do venture capitalists differentiate between Gen AI companies with potential for scalability and those that may not, given the prevalence of AI-related pitches? What critical factors do you prioritize when evaluating companies for scalability?

“Gen AI has been a bit difficult because it is one thing where many companies will see short-term success, but they might not succeed in the medium and long term.”

Pramod Gosavi: Gen AI has been a bit difficult because it is one thing where many companies will see short-term success, but they might not succeed in the medium and long term. And that’s always very painful because many of these companies will raise a series A or B, but eventually, they’re unable to scale. And the reason is as an investor, what you have to do is take a bet. So what I do daily, and I discuss that with my co-investors, customers, and others, is how this technology will evolve. Three to five years from now, they will have 10 or 20 new Gen AI applications, and all existing companies will have some Gen AI. What does a tech stack look like? What tools will companies use at that time? What usage will people have? And then you have to back it up to the current day. You could be wrong, and that’s where, as an investor, it’s not an easy job because you could be very pessimistic or optimistic. But in Gen AI, I think that’s what I do daily. When I look at this company, let’s say it’s a testing or evaluation company or a dev tools company, I start thinking that in three to five years, when this whole thing becomes a bit mature, will people use this product or some other product? And can this product sell to hundreds and thousands of customers, or will this become like a niche product? So you have to take that bet; you have to think about it, and you can be wrong. And I’m pretty sure I am wrong, and many businesses will be wrong, but you have to take that bet because if you invest based on short-term traction, you will be wrong.

AIM: As a VC, how involved do you typically become in decision-making regarding strategic pivots to different technologies or products for startups, especially considering the evolving landscape and business needs in the tech industry?

“I’m a big fan of winding that down and starting new”

Pramod Gosavi: I’m a big fan of pivoting early on. A couple of the founders that have done well recently in my portfolio were initially working on two or three different products internally and looking at where they found traction. So, you are raised like a pre-seeder if you’re in the early stages. Keeping an open mind and being very flexible about the product is healthy.

They say that you should be more passionate and worry about the problem, not the solution and what the customers want versus what you think they want. So, in the early stages, it’s very healthy to pivot. And that is something we chat about a lot in the board meetings. As a board member and investor, you must be open to that. Many investors are not open to that because they see pivots as failures. If you take a hard pivot, it changes the whole thing altogether. But it’s a slight pivot, mainly based on understanding where the customer is. One of our companies, initially, wanted to focus on something about crypto, like they wanted to focus on some of the neo-financial systems. Crypto was hot then; crypto companies wanted that product more than your banks. Then, they pivoted on the crypto side and found a lot of traction. Having that sort of small pivot regarding the customer and product is healthy, and the investors should be okay with that. 

I’m not a big fan of a hard pivot. When you start something and don’t get traction, something goes wrong there. You either could have done a better job sort of validating it. And in that case, I’m a big fan of winding that down and starting new. Because if you don’t wind it down, there’s just a lot of baggage, in terms of cap table and you want to raise more money. And now when you’re raising more money on a discretionary business plan, and you have different investors. So when you invest, you may ask why should I benefit the whole investor. They invested in a different thesis. So there’s a lot of complications. Even in terms of the product, a lot of times, you want to think from a clean slate, where you don’t want to take what you have and move it towards other things, so I’m a big fan of winding it down somehow and then starting again.

AIM: What is your perspective on the importance of evaluating the founders and leadership team of AI companies, especially considering that many of them may not have direct hands-on experience in data science or AI technology, despite having embarked on their entrepreneurial journey several years ago?

“Focus on a broad mass market”

Pramod Gosavi: If you look at data and AI over the last 10-15 years, the founders that have been very popular and sought after are the individuals who worked in some of these giant companies like Facebook, Netflix, Amazon, and Google, and also some of the leading consumer companies like Uber and Lyft. The whole thing was, these companies, the consumer companies, they just had a very different set of problems. They needed to create their own infrastructure and a lot of the new open-source projects actually originated from those companies. So Confluent came out of LinkedIn and TechTown came out of Uber. So these individuals internally, and also they hire really good people, very strong people in these companies like Uber and Netflix and they build those platforms internally. But the caveat we look for is, they bring this product internally and they think this is what the whole world should use and a lot of times actually it’s not useful at all. So what we look for is the best-ever founder is someone like that who has built really massive scale and scale a product internally and knows how to build it very cleanly and has good architecture. But at the same time, focus on a broad mass market. What I mean by mass market is, focus on enterprises like SMEs. That has been a challenge with data and AI. A lot of those products actually are very high-end products. They build a product for people like them, like in other companies, and there are not many companies like that. So the challenge has always been sort of dumbing down a bit, like making it slightly different for a broad set of customers rather than what they think are the customers.

AIM: What resources do you utilize to stay updated on the latest technology trends and news, including identifying popular founders within niche platforms? As a VC, how do you ensure you’re well-informed and connected within the ever-evolving landscape of technology and entrepreneurship?

“There are four to five newsletters and then there’s a lot of  sub-stacks. And you get to know about them through LinkedIn.”

Pramod Gosavi: I would say that the biggest source of learning comes from the founders. So, you have founders coming and pitching you every day, they’re doing a lot of research. You are getting a lot of learning just by listening to one of these pitches. But at the same time, you also need to supplement that with other information. So, what I’ve done in Gen AI last year is I’ve subscribed to four or five newsletters. And those guys do a phenomenal job. One of the newsletters, they highlight the top ML papers of the week. And that has been really good. I spend my Sunday actually going through it. 

There are four to five newsletters and then there’s a lot of  sub-stacks. And you get to know about them through LinkedIn.And the other one who runs a newsletter on all the new products that are coming in AI. 

The third source, I think it used to be a big source, but not that much anymore is Twitter. So Twitter used to have a lot of these founders, practitioners, sharing their own insights. And you can actually train Twitter, so that you can just see it like an AI sheet and then you can get a lot of information. And the last one is to go out to conferences and talk to customers on a regular basis. There are also some very institutionalized like some customer research companies like GMG and others where you can actually talk to customers on a daily basis to learn more.

AIM: What advice would you offer to aspiring VCs who are looking to enter the field? Do individuals actively aspire to become VCs, or is it more common for people to stumble into this career path?

“If you cannot appreciate a new technology and a new thing, you cannot be a good investor, because if you stick to what you know, you cannot be a career master.”

Pramod Gosavi: There are a lot of aspiring VCs because what happens from outside the job looks very glamorous. So, you’re sitting on top of this huge billion-dollar fund, and you’re in a very powerful and enviable position where you’re writing checks, you’re deciding which companies get funded, and also it looks very glamorous from outside. But the advice I give to aspiring VCs is that the hardest part of the job is really you have to stay on top of all of the current events and current technologies. That is something that people misunderstand. A lot of times, for example, like right now it’s the Gen AI wave. So if you’re a Gen AI person, you want to get into VC, you can get into VC. But two years from now, will Gen AI be the thing? It could be something else. Like in 2022, we were looking at crypto. So I had to learn about crypto so much. And then one year later, I have to learn about Gen AI. And this year, I’m learning about something else. So in order to be a successful VC, you have to learn a lot. You have to be very open-minded. That is something most people don’t understand before they want to get into VC, that how tough it is to just evolve on a regular basis.

If you cannot appreciate a new technology and a new thing, you cannot be a good investor, because if you stick to what you know, you cannot be a career master.