Case Made for Ruby in AI Application Development
Engineer Carmine Paolino argued that Ruby is an optimal language for the product engineering of AI applications, distinguishing it from the Python-dominated field of model training. He cited Ruby's strengths in rapid iteration as a key advantage for building agentic apps.
- The argument for Ruby in AI product development is bolstered by its token efficiency; one analysis shows equivalent code in Ruby can be up to 40% more token-efficient than Python, which is a significant cost and performance advantage when making frequent LLM API calls. - For building agentic workflows, the Ruby ecosystem offers dedicated tools like `langchainrb`, a port of the popular LangChain framework, and `agentic`, a gem for creating plan-and-execute style AI agents. The official OpenAI Ruby SDK is also gaining traction for its direct alignment with the latest API features. - Venture capital investment in AI agents is experiencing explosive growth, with the market projected to reach $12-15 billion in 2026 and AI agents capturing 33% of total global VC funding. Top investors in the space include Andreessen Horowitz (a16z), Sequoia Capital, and Founders Fund. - Y Combinator has heavily invested in the AI agent space, with agent-focused startups making up nearly half of its Spring 2025 batch. This trend highlights a strong belief in the "picks and shovels" approach, funding tooling for agent testing, identity, and reliability to build out the foundational infrastructure for agents to operate effectively. - In the real estate sector, AI startup Ridley is using an AI-guided platform to unbundle traditional agent commissions, allowing home sellers to pay a flat fee for services. The company has already processed over $150 million in listings, with sellers saving an average of $25,000. - Prominent AI agent startups are attracting massive valuations, indicating intense investor interest in companies that move AI from analysis to action. Sierra, an enterprise customer service agent startup from former executives of Salesforce and Google, reached a $10 billion valuation, while the developer-focused agent Cognition AI (Devin) is valued at $2 billion. - While Python has historically dominated AI due to its extensive academic use and mature libraries for machine learning like TensorFlow and PyTorch, Ruby's strengths are in rapid web application development, a key factor for building user-facing AI products. Carmine Paolino argues that since most AI applications are primarily API calls, Ruby's "slower" execution speed is less important than its high developer productivity. - The fitness and human performance sector, a key interest for endurance athletes, is also seeing a surge in AI applications. AI agents are being used to create personalized training plans, with startups like April, a YC-backed voice AI agent, demonstrating the potential for AI to manage productivity and personal workflows.
