From: redpointai
Pinecone, a prominent vector database company, has emerged as a fundamental tool for building AI applications [00:00:11]. With over 750 million valuation, Pinecone plays a crucial role in the evolving vector database landscape [00:00:03].
The Rise of Vector Databases
Before the generative AI craze and the launch of ChatGPT, vector databases were largely underutilized despite their potential for semantic search, candidate generation, ranking, feed ranking, and recommendation systems [00:00:54]. Large companies like Google and Amazon used them internally, but educating the broader market was challenging [00:01:18]. Many investors initially misunderstood the concept, often confusing it with ML Ops products [00:02:11].
The launch of OpenAI’s ChatGPT significantly elevated the discussion around AI to a wider audience [00:02:27]. While the underlying technology for practitioners didn’t change drastically, the increased capital and energy behind AI made it accessible to non-AI engineers [00:02:41].
A particular moment that caused usage to spike was the emergence of AutoGPT, an open-source precursor to AI agents [00:05:15]. This tool’s popularity led to a surge in Pinecone sign-ups, reaching up to 10,000 new users daily [00:03:20]. This demand forced Pinecone to rethink its architecture for scale and efficiency, leading to the development of its serverless solution, which is two orders of magnitude more efficient [00:03:26].
Pinecone’s Scale and Efficiency
Pinecone now truly shines at scales of hundreds of millions to billions of vectors [00:07:18]. Companies, often SaaS providers, leverage Pinecone to build deep AI solutions like Q&A and semantic search for their own customers’ data [00:07:37]. For instance, a company with 10,000 customers, each having millions of documents, might need to handle 10 billion embeddings across thousands of partitions [00:07:48].
The new serverless architecture allows for highly cost-effective multi-tenant workloads, with costs for a single paying customer potentially as low as a dollar or even 50 cents per year [00:08:31]. This focus on unit economics is crucial for companies building true products rather than just lab experiments [00:08:14].
Internal Challenge of Serverless Transition
The transition to a serverless model was financially painful for Pinecone, impacting revenue growth as existing customers saw significant cost reductions (sometimes 70-90%) [00:37:29]. However, it was deemed the right decision for customers and for fitting snugly into the cost structure required for mass AI adoption [00:39:28].
Applications of Vector Databases
Common applications leveraging vector databases include:
- Q&A and Semantic Search: Widely understood and deployed, such as Notion’s AI Q&A features [00:07:33].
- Chatbots and Support Bots: Special flavors of Q&A systems [00:09:42].
- Discovery and Analytics: Including legal discovery, medical history discovery, and other forms of analytics that can be thought of as specialized search [00:09:54].
- Multimodal Data: Applications involving images, audio, video, anomaly detection, security, and even Pharma and Drug Discovery [00:10:05]. However, text and images remain the “meat and potato” [00:10:19].
While multimodal AI shows amazing potential in research labs, its widespread adoption by mainstream technology developers is unlikely in the next year or two [00:12:30]. Even today, many companies struggle to train any deep learning model, let alone large foundational models [00:11:47].
Addressing Hallucinations with RAG
One of the biggest barriers to building effective AI apps is the issue of “hallucinations” in Large Language Models (LLMs) [00:13:04]. LLMs are designed to generate language, and when compelled to write about unknown subjects, they will produce text that may contain inaccuracies [00:13:15].
Retrieval Augmented Generation (RAG) is a critical approach to mitigating hallucinations [00:16:31]. RAG, fundamentally based on semantic search, ensures that LLMs retrieve relevant, factual information from a vector database before generating a response [00:10:26]. This process enhances the “faithfulness” of the output to the data it was trained on or provided with [00:16:07].
Overcoming Hallucinations
Measuring hallucinations accurately is a complex challenge, as a model that always responds with “I don’t know” won’t hallucinate but also won’t be useful [00:15:31]. The goal is to measure usefulness, correctness, and faithfulness [00:16:05].
Loading large amounts of data into a vector database and running an LLM with even “not incredibly sophisticated RAG” can outperform more advanced models like GPT-4 [00:17:32].
RAG also addresses issues related to data security, governance (e.g., GDPR compliance for data deletion), and the practical limitations of large context windows offered by model companies [00:16:40]. While larger context windows allow models to ingest more data, they are slow, expensive, and often don’t provide better results, especially when dealing with massive datasets like entire company documentation [00:27:28]. Using a vector database for retrieval can achieve similar or better performance with significantly lower costs, even for smaller-scale use cases [00:29:02].
Industry Landscape and Future Trends
The vector database market is experiencing an “explosion of startups” and incumbents adding vector support, as people recognize vectors as a fundamental data type for AI [00:19:01]. However, merely adding vector support to existing databases doesn’t make them true vector databases [00:19:45]. A genuine vector database uses the numeric array (vector) as the primary lookup and data organization mechanism, leading to highly optimized performance [00:20:06].
Regarding the “RAG stack,” while OpenAI is a common default for models, smaller, cheaper, and open-source models often provide good performance without significant cost [00:21:05]. Partnerships with companies like AnyScale for bulk transformations and data movement, and Cohere and AI21 for models, are common [00:21:37]. A key missing piece is robust evaluation technology [00:22:24].
Hardware Sustainability
The current reliance on GPUs for AI is unsustainable due to high costs [00:48:03]. Future shifts in hardware are expected, potentially involving more CPUs, specialized servers optimized for training or serving, and distributed infrastructure [00:48:26]. Additionally, current data pipeline and management tools are insufficient, and changes are needed to reduce operational headaches, costs, and waiting times [00:49:03].
Strategic Considerations for AI Application Developers
Most companies currently focus on simply getting AI applications to work [00:30:01]. Few have advanced to iterating over specific embedding models, retrieval methods, reranking, and filtering [00:30:14]. Building a robust AI solution is not magic; it requires significant effort from scientists and engineers, involving continuous learning, iteration, and customization for each use case [00:31:00].
A common failure mode for potential AI application builders is incorrect cost estimations [00:35:02]. Miscalculating infrastructure costs can prevent companies from even starting projects that would otherwise be feasible [00:36:10].
Overcoming Inertia
The most common mode of failure in the AI field is “doing nothing” [00:55:46]. Developers are encouraged to focus on building something exciting, and if a vector database like Pinecone becomes necessary, then use it [00:55:27].
The Future of AI and Agents
The AI stack is still in its early stages, with many early adopters just reaching production [00:39:54]. Eventually, almost every company is expected to utilize a vector database [00:40:13].
There is significant excitement in the application and solution space for AI, much more so than in the infrastructure layer, which tends towards a “winner take all” phenomenon [00:41:51]. New companies are emerging to disrupt established players, while incumbents are trying to reassert themselves as AI natives [00:42:27]. This creates a “conveyor belt of innovation” from startups to enterprises [00:42:54].
One area of particular interest for unconventional applications is human communication data (emails, slack, Twitter, meeting transcriptions), where there’s a vast amount of knowledge to be extracted [00:43:46].
Regarding AI agents, it’s believed they are already functional, approaching human levels of task completion probability [00:54:02]. While their mistakes might be “silly” or uncharacteristic for humans, their overall performance is improving [00:54:37].
The speaker finds foundational models to be overhyped, as they haven’t seen significant qualitative progress for a long time [00:50:54]. Conversely, coding assistance and code generation are considered exceedingly useful and underhyped applications of AI [00:51:14].