AI Personalization and Data Infrastructure Drive 2026 Consumerization

Key Quotes

"a lot of people look at the like dbt five tran merger and like talk about the end of the modern data stack and i think that is like a fundamentally wrong take both of these companies were growing you know very healthily both of these companies and you funded dbt we funded dbt so so like they both of the companies were actually like beating their revenue targets i think what you're more seeing is a you know ipo environment wherein companies are expected to have far more than you know like a hundred million revenue and so well just say to barry's now 300 no like about 600 600 yeah yeah and the combined company is 400 uh i believe that they'll actually be close to 600 i don't have the exact number but they're totally just getting ready for ipo so so you know basically like the merger was a way to accelerate that path to liquidity"

Sarah Catanzaro argues that the merger of DBT and Fivetran does not signify the end of the modern data stack. Instead, Catanzaro explains that this move is a strategic step towards an IPO, driven by the market's expectation of higher revenue thresholds for public companies. She highlights that both companies were already performing well individually, indicating their strength within their respective categories.

"crazy looks like raising upwards of a hundred million dollars seed like upwards of a hundred million dollars in a seed round where you have a long term vision but not a near term roadmap this is something that i'm seeing happening not just occasionally but quite frequently yes and it definitely makes me anxious because firstly like when founders are asking me you know how much should i raise i'm typically saying like three like five well like what do you need to do like what are your milestones for the next let's call it 12 to 24 months"

Sarah Catanzaro expresses concern about the current funding environment, describing it as "crazy." She notes a trend of startups raising over $100 million in seed rounds without clear near-term roadmaps, which makes her anxious as an investor. Catanzaro emphasizes the importance of founders having defined milestones and resource needs for the next 12-24 months when determining how much capital to raise.

"i think one of the things that has actually uh pleasantly surprised me um and this speaks to again the symbiotic relationship between you know data and ai many of the big frontier labs are actually using both dbt and five tran i recall talking to folks at um thinking machines like within weeks of the company's formation and dbt was already an important part of their stack it's certainly like training data sets need to be managed we need insight into what users are doing on these platforms and in fact like the way in which you would analyze interactions with an agent or analyze interactions with an llm is even more complicated"

Sarah Catanzaro highlights a surprising synergy between data infrastructure tools and AI development. She observes that many leading AI research labs are utilizing both DBT and Fivetran, even early in their formation, for managing training data and analyzing complex user interactions with AI agents and LLMs. Catanzaro points out that this demonstrates the continued relevance and integration of data management tools in the AI frontier.

"i think like it is possible it's just we're not there yet today a theme that i've been spending a lot of time thinking about is uh memory management and continual learning i work with a lot of okay the i think i know what startup you're you're thinking about is as well but i actually like i see i see like a lot of market potential for uh memory management and continual learning my interest in this is actually more driven by conversations with uh practitioners personalization is so important right now i think what we're seeing is that like a lot of ai application companies they're growing really quickly but they suffer from you know relatively low retention relatively high churn"

Sarah Catanzaro identifies memory management and continual learning as significant themes with substantial market potential, driven by conversations with practitioners. She notes that many rapidly growing AI application companies struggle with low user retention and high churn. Catanzaro suggests that improved personalization, achieved through better memory and continual learning, could be a key unlock for addressing these retention challenges.

"i think the best rl environment is is you know the the real world why would i you know want to buy a doordash clone when like i can just use logs and traces from you know doordash itself it doesn't mean that we don't need to yeah i mean i think like using the real world using real apps as like our rl environment is in fact like the best thing and this is what cursor does like they actually do use you know real user activity on their platform to you know significantly like improve both their coding agents as well as tab and i think it's one of the the approaches that has like made the platform so compelling"

Sarah Catanzaro expresses skepticism about the long-term value of dedicated RL environments, stating her belief that they are a "fad." She argues that the "real world" and actual user data from existing applications, like those used by Cursor, are superior to synthetic clones. Catanzaro explains that leveraging real-world activity provides richer, cheaper, and more generalizable insights for improving AI agents.