Episode
[State of AI Startups] Memory/Learning, RL Envs & DBT-Fivetran — Sarah Catanzaro, Amplify
- Published
- Dec 30, 2025
- Duration seconds
- 1722
- Processing state
processed- Canonical source
- https://www.latent.space/p/state-of-ai-startups-memorylearning
Actions
POST https://stenobird.com/v1/public/podcasts/latent-space-ai-engineer/episodes/state-of-ai-startups-memory-learning-rl-envs-dbt-fivetran-sarah-catanzaro-amplify/transcription-requests
Idempotently request low-priority transcript generation for this episode.GET https://stenobird.com/podcast/latent-space-ai-engineer/state-of-ai-startups-memory-learning-rl-envs-dbt-fivetran-sarah-catanzaro-amplify.md
Read the agent-friendly Markdown representation of this episode resource.
Summary
From investing through the modern data stack era (DBT, Fivetran, and the analytics explosion) to now investing at the frontier of AI infrastructure and applications at Amplify Partners , Sarah Catanzaro has spent years at the intersection of data, compute, and intelligence—watching categories emerge, merge, and occasionally disappoint. We caught up with Sarah live at NeurIPS 2025 to dig into the state of AI startups heading into 2026: why $100M+ seed rounds with no near-term roadmap are now the norm (and why that terrifies her), what the DBT-Fivetran merger really signals about the modern data stack (spoiler: it’s not dead, just ready for IPO), how frontier labs are using DBT and Fivetran to manage training data and agent analytics at scale, why data catalogs failed as standalone products but might succeed as metadata services for agents, the consumerization of AI and why personalization (memory, continual learning, K-factor) is the 2026 unlock for retention and growth, why she thinks RL environments are a fad and real-world logs beat synthetic clones every time, and her thesis for the most exciting AI startups: companies that marry hard research problems (RAG, rule-following, continual learning) with killer applications that were simply impossible before. We discuss: * The DBT-Fivetran merger : not the death of the modern data stack, but a path to IPO scale (targeting $600M+ combined revenue) and a signal that both companies were already winning their categories * How frontier labs use data infrastructure : DBT and Fivetran for training data curation, agent analytics, and managing increasingly complex interactions—plus the rise of transactional databases (RocksDB) and efficient data loading (Vortex) for GPU-bound workloads * Why data catalogs failed : built for humans…