
Bain Capital Venturesâ Kevin Zhang: The âUnlockable Potentialâ in Lending, Investing, and Insurance
Kevin Zhang, partner at Bain Capital, assess the technological tailwinds for emerging players in lending, investing, and insurance....
Jown Cowgill ~quoteblock
I look for contrarian bets on applications of data in the enterprise, built by founders who have off-the-charts founder-market fit. Namely, they get their space better than anyone else.
Over the years, this has meant I've invested in everything from vertical SaaS auto software companies (Roadster), to creating the internet in space (Kepler), to securing the SaaS applications that power modern enterprises (AppOmni).
I'm not wedded to one market. But, given my focus on data in the enterprise, it's impossible not to be inspired by the rapid growth of foundational models like GPT-3 and Stable Diffusion and what this trend means for the application of AI in the enterprise.
I love this space because there are huge debates about where it's going and where it's adding value to how we live our lives and build businesses. How much will innovation be driven by startups or incumbents? Where does the value really live: the model, the data, the tuning, the orchestration?
I back founders who have an extremely deep understanding of their market and an unconventional take on what's missing or needed.
I like sectors and trends where even the experts disagree about what the future will look like. Therein lies the opportunity to skate on where the puck is going and not just focus on what people are talking about today.
Q: Which applications or use cases are you paying  special attention to?
A: While image generation gets the eyeballs, I think an order of magnitude more value is created with text generation, and applications of LLMs on the tech backend, like search optimization. I'm fairly skeptical of image-generation startups at this point. It's extremely crowded and the experts applying it to obvious use cases - like design, such as those at companies like RunwayML - have been at it for years by now. I think we're still in the early innings of applying LLMs to all the unsexy and thorny language-related problems in the enterprise.
Just as an example, I see a huge opportunity in code translation â rewriting and updating legacy code to more modern languages and stacks. Consider that 95 percent of Fortune 500 enterprises have some reliance on COBOL, and the average COBOL programmer is 50+ years old.
Q: What do you believe is commonly misunderstood by other participants in this space?
A: Â For any horizontal app built on a foundational model, I'd worry how they will maintain competitive advantage as their underlying model or models improve. Does the fine tuning you've done on GPT-3 remain as valuable when GPT-4 comes out? And what will your unit economics really look like as so much of your product is build on another company's model?
The emerging analogy describing 'foundational models as the new public cloud' is compelling, but I think this is where it falls down a bit. While the storage and compute you bought from AWS got cheaper and more performant over the last decade, improvements in cloud-native infrastructure did not necessarily have a strong relationship to your product's differentiation. In contrast, improvements in foundational models may turn out to have a large impact on the applications built on them.
Q: What is your view on whether applications will augment human tasks or serve as replacements?
A: While I'm bullish that generative AI can automate a significant portion of creative and white-collar work, perhaps as much as 80% in some cases, I think the net of it is that it just makes that last 20% of fine-tuning work that only humans can do well, all the more valuable.
With that in mind, I'm interested to revisit âAI Agency" businesses, which leverage generative models, but with some human-in-the-loop component which takes the output â be it text or image â to the final level. As an example, I'd be more interested in investing in a 'next-generation Ad Agency that uses generative AI combined with top ad talent to create better content faster than anyone else,' than in a company that says, "let's use generative AI to automatically generate ads for brands," at this stage.
Itâs still undetermined whether SaaS business models will apply to the AI space. âAt the application layer, we're still figuring out what AI-native products really look like in the enterprise,â says Cowgill. âThat said, most of the early winners of generative AI still look a lot like the good SaaS companies of the 2010s. Their business models and products are mostly just web apps operating as a thin layer on top of a foundational model.â That said, given how reliant these companies are on foundational models and the open questions around how those models are priced, it remains to be seen whether the typical AI-native business is an 80% gross margin, high recurring revenue business (like most SaaS companies) or perhaps lower margin with a higher percentage of revenue that is transactional based on 'jobs done'."
Adjacent opportunities will include deep-fake detection. âIn terms of next horizon bets, we're going to have to get better at detecting text, image, and video that is generated by AI. A lot of very weird and unpleasant things are going to be unleashed by this stuff. I may already be too late to make a Seed bet on deep-fake detection but I'm convinced a great company will get built there.â
AppOmni, Cohere.ai, Kepler, Roadster, RunwayML, Stability.ai
For McKinsey, The State of AI in 2022-and a half decade in review, see here.
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Technology, innovation, and the future, as told by those building it.