River AI raises $1.1B led by General Catalyst and AMP PBC to build open AI stack. Founded by xAI co-founder Igor Babuschkin, River AI gives developers and enterprises the tools to train, tune, and own custom AI models PALO ALTO, Calif.-(BUSINESS WIRE)-River AI, the full-stack AI company founded by xAI co-founder Igor Babuschkin, today announced $1.1 billion in funding, led by General Catalyst and AMP PBC with strategic investment from NVIDIA and AMD Ventures. Additional investors include Y Combinator and Temasek. The round will accelerate River AI's mission to build powerful personal AI and to give developers and companies the tools to train, tune, and serve their own AI models, putting ownership of AI directly in the hands of the people and organizations who use it. Today, most companies using AI are running general-purpose models trained on the entire internet and designed for the broadest possible audience. They are powerful, but they are not built for any one organization in particular. Until now, building a custom model required a dedicated infrastructure team, specialized hardware, and months of work, putting it out of reach for most companies. With River's API, any enterprise can complete a complex reinforcement learning training run in 15 to 20 minutes with no infrastructure team required, at two to four times the cost savings relative to closed-source alternatives. "The way AI is built today is not how it will be built in the future," said Igor Babuschkin, co-founder and CEO of River AI. "AI should be open, freely available, and affordable. It should feel like it is working for the person using it, not the lab that trained it. We started River to allow people and companies to own their intelligence." River's API delivers state-of-the-art LoRA fine-tuning and reinforcement learning for frontier open weight models. The platform handles the underlying infrastructure complexity, including fast weight transfers, sampling-training consistency, and elastic compute, so developers can focus on improving their models rather than managing infrastructure. Trained models can be deployed instantly to production, and billing is strictly metered on tokens used for training and inference, eliminating the cost of idle GPU capacity. River is building a full, integrated stack: new hardware that lets personal AI live close to the people it serves, training infrastructure that makes fine-tuning accessible to any developer, and products built around personalization and continual learning. Rather than aligning one model to billions of users, River aligns AI directly to each user. Today, that means giving developers and enterprises the tools to build and control their own models; River's long-term goal is to extend that same control to every individual. "American leadership in AI urgently requires leadership in open weight models, while maintaining a lead in closed frontier models," said Hemant Taneja, CEO of General Catalyst. "Igor and the River AI team have the experience to make this happen, and we view their agenda as a priority for American resilience. The core philosophy of putting ownership of intelligence in the hands of the people using it will prove to be on the right side of history for the open weight ecosystem." Babuschkin previously worked on generative modeling and reinforcement learning at Google DeepMind and spearheaded large-scale training efforts at OpenAI before co-founding xAI. River's founding team brings hands-on experience from xAI and Tesla at the leading edge of deep learning and reinforcement learning, with the rare ability to execute at speed across the entire AI stack. "There is a gap between what AI can do and what most companies actually experience," added Marc Bhargava, Managing Director at General Catalyst. "Until now, companies have lacked a cost-efficient way to train, tune, and own custom AI models. River closes this gap, helping any company build models on their own data, tailored to how they actually work." About River AI River AI is a full-stack AI company founded by Igor Babuschkin to create personal, continually improving AI. Its platform allows any developer to train, tune, and serve custom models without a dedicated infrastructure team or expensive hardware. Through its API, River delivers state-of-the-art LoRA fine-tuning and reinforcement learning for frontier open weight models, with token-metered billing and instant deployment to production. River AI is headquartered in Palo Alto, California, and backed by General Catalyst, AMP PBC, NVIDIA, AMD Ventures, Y Combinator, Temasek, and others. Learn more at river.ai River AI. Release Versions

Anthropic has raised $65 billion in Series H funding led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital.

OpenRouter raises $113M Series B. OpenRouter · 5/28/2026 Today Flyingcat is announcing its $113M Series B, led by CapitalG (Alphabet's independent growth fund), with participation from NVentures (NVIDIA's venture capital arm), ServiceNow Ventures, MongoDB Ventures, Snowflake Ventures, Databricks Ventures, AMP PBC, and Pace Capital, alongside its existing investors Andreessen Horowitz and Menlo Ventures. Where Flyingcat is. Over the last six months, weekly volume on OpenRouter has grown from 5 trillion to 25 trillion tokens. Flyingcat is on pace to process over a quadrillion tokens this year and serve 8M+ developers building across 400+ models. AI is rapidly shifting from experimentation into critical production apps and agents, and that transition requires infrastructure that works reliably at scale, across providers, across modalities, and across use cases. This growth reflects the simple fact that developers love building on OpenRouter. Why this round matters. The composition of this investor group is deliberate. CapitalG, NVentures, ServiceNow Ventures, MongoDB Ventures, Snowflake Ventures, and Databricks Ventures are more than financial backers; they are the infrastructure and platform companies that enterprises already depend on. Their participation reflects a shared view: as organizations move from single-model pilots to multi-model production systems, they need a routing and gateway layer purpose-built for that complexity. OpenRouter is that layer. Flyingcat sit between agents and model providers, handling the routing, reliability, cost optimization, and compliance that production AI demands. The breadth of strategic investors in this round signals that the market has converged on this as a critical piece of the stack. What Flyingcat has been building. The past year has been focused on expanding what OpenRouter can do for production workloads: * Multimodal inference: Beyond text, OpenRouter now supports image, audio, speech, transcription, embedding, and video models. * Enterprise controls: Workspaces, spend management, guardrails, and zero-data-retention policies for organizations deploying AI at scale. * Intelligent routing: Provider-level failover, cost and latency optimization, and quality-aware routing that goes beyond simple load balancing. What's next. Flyingcat'll use this funding to continue scaling its infrastructure, deepen enterprise capabilities, and continue to invest in intelligent routing; teams need help finding the right model and provider for every request. Flyingcat is grateful to its customers, partners, investors, and the developer community building with Flyingcat as Flyingcat continue scaling the infrastructure layer for the multi-model AI era.