The San Francisco-based startup, which builds AI systems that identify cause-and-effect relationships rather than mere correlations, is using a significant portion of the capital to deploy what it claims is one of the fastest privately owned supercomputers ever built — an Nvidia NVL72 superPOD that will power its enterprise-grade causal AI models.
When I first wrote “Vector databases: Shiny object syndrome and the case of a missing unicorn” in March 2024, the industry was awash in hype. Vector databases were positioned as the next big thing — a must-have infrastructure layer for the gen AI era. Billions of venture dollars flowed, developers rushed to integrate embeddings into their pipelines and analysts breathlessly tracked funding rounds for Pinecone, Weaviate, Chroma, Milvus and a dozen others.
Deep insights for enterprise AI, data, and security leaders
Artificial intelligence agents powered by the world's most advanced language models routinely fail to complete even straightforward professional tasks on their own, according to groundbreaking research released Thursday by Upwork, the largest online work marketplace.
In the winter of 2022, as the tech world was becoming mesmerized by the sudden, explosive arrival of OpenAI’s ChatGPT, Benjamin Alarie faced a pivotal choice. His legal tech startup, Blue J, had a respectable business built on the AI of a bygone era, serving hundreds of accounting firms with predictive models. But it had hit a ceiling.
Managing and maintaining AI systems remains a challenge for many enterprises, particularly with the potential for agentic sprawl to expose businesses to risky entry points.
AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated.
Despite new methods emerging, enterprises continue to turn to autonomous coding agents and code generation platforms. The competition to keep developers working on their platforms, coming from tech companies, has also heated up.