NeuralStackTech startup funding in the past two months has skewed heavily toward AI infrastructure, enterprise software, and security, according to Techbible’s weekly tracker. The round list spans January and February 2026 and is led by unusually large checks into companies such as Anthropic, CoreWeave, Databricks, and Cyera.
Among the biggest disclosed raises, Anthropic closed a large Series E with a post-money valuation north of $60 billion, while CoreWeave secured a $2 billion investment from Nvidia. Databricks also reported a massive late-stage capital injection alongside debt, and said it was preparing for IPO readiness with a much larger revenue base and a new focus on Lakebase, its serverless database for AI agents.
The tracker also highlights a cluster of companies building the plumbing around AI adoption. Deepgram raised fresh capital to expand beyond speech-to-text into real-time conversational AI, while Parloa tripled its valuation in months as voice automation moved further into enterprise customer service. Inferact, the commercial successor to the vLLM inference engine, landed a large seed round as companies look for faster and cheaper ways to run models in production.
Security and governance were another major theme in the list. Claroty added another round to reinforce its position in cyber-physical infrastructure security, Upwind raised a Series B focused on runtime protection, and Cyera secured a major Series F as organizations confront expanding data risk in agentic AI environments. Goodfire also raised for interpretability work, reflecting growing demand for tools that can explain how models behave rather than just improve what they output.
Several smaller but strategically interesting bets point to where founders think the next wave of AI tools will land. Emergent is using natural language to let users ship software faster, Flora is recasting design as a more modular system, and VoiceRun is building a developer-first platform for voice agents. Ricursive Intelligence is targeting the chip-design layer itself, using AI to help design and improve the silicon that powers AI systems.
Why this matters
The funding pattern suggests investors are still rewarding companies that sit close to enterprise production needs: compute, inference, data security, voice, and model reliability. That is a meaningful shift from hype-driven app layers toward the infrastructure and controls organizations need before they can deploy AI broadly. It also shows that capital remains available for startups with clear technical moats and direct ties to operational pain points.
Just as important, the list shows that AI is spreading into adjacent categories, not only core model development. Software creation, design, industrial security, and chip tooling are all being reworked around AI-native workflows. If this pace continues, the next funding cycle is likely to favor startups that make AI more usable, auditable, and economical inside real businesses.