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Observations from conversations with healthcare payors indicate a shift in focus from AI models to data readiness, PHI handling, and integration across disparate systems.
An interview with Pierre Zemb, staff engineer at Clever Cloud, discussing his work building data layers on FoundationDB and his previous experience at OVHcloud.
A Twitter thread highlights key takeaways from a Latent.Space podcast episode with Databricks co-founders, covering why Databricks beat Snowflake, the rise of metaharners, Neon's success, HTAP via LTAP, MosaicML's fate, and maintaining startup culture in a large company.
Robotics teams are rebuilding the data stack from scratch to overcome the 'data layer tax' that slows down iteration and scaling in robot learning, as existing infrastructure doesn't handle multi-rate and multimodal data.
Anthropic's science blog argues that AI progress in biology lags behind coding because biological data infrastructure is not designed for agents. A case study shows that adding a deterministic retrieval layer (gget virus) boosts accuracy to nearly 100%.
Despite $2.5 trillion in projected global AI spending in 2026, MIT's NANDA Initiative reports 95% of enterprise generative AI projects deliver zero measurable ROI, with a practitioner's first-hand analysis of 14 engagements pointing to misallocated budgets favoring model work over data infrastructure as the root cause.
Jerry Liu, CEO of LlamaIndex, discusses on the Venture with Grace podcast why data infrastructure is crucial for the agentic AI boom, emphasizing that AI agents need access to the right data at the right time.
Judgment Labs is launching today with a $32M funding round, providing infrastructure to improve AI agents using production data.
The article argues that AI inference poses unique challenges to cloud data infrastructure, likening its demand to high-concurrency OLTP systems rather than traditional human-speed applications. It emphasizes the need to optimize storage and data access layers to handle the 'AI data tsunami' driven by autonomous agents.
Anthropic researcher Laura Luebbert argues that biological data infrastructure needs to be redesigned for AI agents, using a case study where even strong models failed to reliably retrieve sequence data from NCBI Virus until a deterministic retrieval layer was added.