DataDecisionMakers Author
Amit Verma is the Head of Engineering/AI Labs and founding member at Neuron7, a Bay Area based AI product company backed by Nexus Ventures and Battery Ventures. Amit has published multiple scholarly articles as well as business articles that have been featured on Forbes amongst others. Neuron7’s AI product is used by customers such as AMD, Abbott, Qualcomm, Swisslog, Ciena amongst others.
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.
For three decades, the web has been designed with one audience in mind: People. Pages are optimized for human eyes, clicks and intuition. But as AI-driven agents begin to browse on our behalf, the human-first assumptions built into the internet are being exposed as fragile.
Welcome to 2024, where if you're not riding the generative AI wave, you might as well be stuck in 2022 – practically ancient history in the AI timeline. Every organization has an AI roadmap now, from AI pillows to AI toothbrushes, and if you still have not hurriedly put a plan together, let me suggest a three-step roadmap for you.
In an audacious move that defies conventional wisdom, generative AI companies have embraced a cutting-edge approach to quality assurance: Releasing large language models (LLMs) directly into the wild, untamed realms of the internet.