Luca Giani

A new era of deep tech has emerged. First time funds will raise “unheard of” amounts of capital to fuel next gen deep tech startups - producing outsized, superior returns for LP’s compared to the rest. Prudent investors will act on this data and shift investment strategy as LP’s or risk being left behind savvy wealth managers and CIO’s / FO’s who saw this trend begin 4 years ago.

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I recently had the pleasure of speaking with and learning from several software execs from our portfolio companies and industry experts during Thoma Bravo's AI Summit in Miami. It was great to share our insights into trends and practices we see in our portfolio and how we manage opportunities and risks. At TB, we have a long history of guiding enterprise software companies through digital transformation, and we’re already navigating the transition driven by generative AI. Our focus is on managing this new innovation by absorbing the technology to bring meaningful benefits to our portfolio companies and indirectly to their customers. And it’s evolving fast! We see our portfolio companies today using gen AI in generally three ways: first, to create new products and provide customers with incremental use cases. Second, to defend against cyber attacks that have become much more sophisticated by bad actors. And third, as an enabler of talent to make business operations run more efficient and productive. In our view, AI presents a significant growth opportunity, but we also believe that to really maximize that potential, companies should carefully evaluate the risks and be thoughtful about governance, trust, security and data privacy. AI is poised to accelerate innovation and it’s hard to predict the full, transformative potential of AI, but we are confident that some of the advantages of incumbent knowledge and data will be key to unlocking its value.

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I finally figured out the simplest framework to what I’m looking for at seed. As a generalist investor, this question has always been difficult to answer because I can’t use sector or layer of the stack to answer. If you look back at the publicly-announced investments, I’ve led at Susa from Antares to Shinkei to Hanover, it’s very hard to draw a clear throughline. After too many fumbling conversations where I hemmed and hawed my way through an answer when other VCs, LPs, or founders asked me this question, I’ve finally synthesized my answer to a simple framework: Spiky founders building category-defining companies 12-36 months before it would appear on a market map What does that mean? Link is in the comments below (this is how ChatGPT visualizes a spiky founder)

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If you are in the VC space, you should know one of the early drivers of "Moneyball for VC". That's Gopinath Sundaramurthy, Ph.D Gopi partnered with the Kauffman Fellows Fund on implementing these concepts in their investment process. The results were staggering. 50% of the investments the fund made became unicorns. In Gopi's words 👇🏻 "We knew something was working, so we thought: Let's build this a little bigger." What did that look like in practice? 1️⃣ Gopi's firm uses a single ‘source of truth’ platform to centralize information, improving decision-making and consistency. 2️⃣ They maintain an even 50/50 split between data engineers and investors to ensure they leverage data to gain insights into teams, markets, and products. Want to learn more about what Gopi and the team at Ensemble VC has implemented at their firm? Check out the case study that our sponsor Harmonic published about the firm -->

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