Winning in AI means mastering the new stack

The AI landscape is rapidly evolving, with new technologies like LLMs and foundational models transforming expectations from AI. In the past decade, AI has moved from big data and ML to chatbots and knowledge hubs powered by RAG. Looking ahead, agents and co-pilots are expected to take center stage in 2024 and beyond. Despite this innovation frenzy, infrastructure components powering AI solutions have remained constant, including model training and hosting, pretrained foundation models, vector databases, model pipelines, data operations, labeling, evaluation frameworks, and AI application hosting. The challenges facing AI applications include multimodal data and models becoming the norm, increasing data complexity, changing hardware ecosystems, more AI-centric application development, transitioning model training, and cloud centrality. To address these issues, companies are focusing on foundational models, model training and deployment, vector databases, AI application hosting, LLM developer toolkits, and LLM Ops. The new AI stack is built around components like AI21 Labs' highly optimized LLMs for embedding and generation, Anyscale's Ray for distributed computing, Pinecone's cloud native vector database, Next.js and Vercel's infrastructure for GenAI companies, and LangChain's LLM toolkit. These tools are designed to be future-proof, flexible, iterative, and generally applicable. CEOs of the companies involved in building this new AI stack pledge their commitment to future-proofing users' applications by constantly improving the building blocks, integrations, and evolving alongside other AI products and services.