The early Pinecone team founded the company with the goal of giving every company access to the same sophisticated AI tools and infrastructure as tech behemoths. They bring deep domain expertise from years building similar technologies at Amazon, Couchbase, Databricks, Splunk, and Yahoo. The unbridled growth of AI and the need for vector-based databases attracted our attention, but we became more enthusiastic about investing in Pinecone after meeting its founder and CEO, Edo Liberty, as well as Bob Wiederhold, President and COO, and the entire Pinecone team. The newer, fast-growing slice of AI-generative AI-is expected to grow 35.6% per year and reach $109 billion by 2030, according to Grand View Research. International Data Corporation says global spending on AI, including software, hardware, and services for AI-centric systems is expected to double in three years to $300 billion in 2026. Meeting the growing demand for AIĪccording to McKinsey, as of 2022, 50% of surveyed organizations reported having adopted AI in at least one business unit or function. With Pinecone, engineers and data scientists can build vector-based AI applications that are accurate, fast, and scalable, and get them into production quickly. Pinecone is a core component of the infrastructure stack underpinning AI-powered applications that search and rank results based on similarity.

Along with Andreessen Horowitz and previous investors Menlo Ventures and Wing Venture Capital, as well as experienced operators we have been fortunate to partner with in the past, including Bret Taylor (former President & Co-CEO, Salesforce), Bob Muglia (former CEO, Snowflake), and Olivier Pomel (Co-founder & CEO, Datadog), ICONIQ Growth is investing in Pinecone’s $100 million Series B. So that is why today, ICONIQ Growth is proud to announce our partnership with Pinecone, the vector database company providing long-term memory for AI. And in order for that to happen, more of the world’s data must be expressed in the language of AI: vector embeddings. In order for generative AI to optimize its potential, more people and more organizations - not just the largest tech companies in the world - must be capable of building their own AI applications. The ability to build AI applications is still limited to a relatively small number of machine learning engineers whose work is inhibited by a myriad of constraints, including the inability to find and use the vast majority of an organization’s data to inform and train their models. However, while most individuals can marvel at the enormous opportunities ahead, not everyone can yet wield the truly awesome power of these tools today.

One of the most awe-inspiring stories of our day is how breakthroughs in Artificial Intelligence - especially generative AI applications based on large language models - have recently achieved almost-incomprehensibly magical results in art, coding, healthcare, music, science, and more.

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