General AI Tools

Sequoia Cahn: $1.5T AI infrastructure spend needs $3T revenue — Anthropic+OpenAI at ~$80B ARR, $2.9T gap. Palantir Q2: $1.94B revenue +93%, US commercial +149%, 220 deals of $1M+, Rule of 40 at 155%. The macro says AI revenue is missing. The micro says enterprise AI revenue is real and growing fast. Both are true.

NVIDIA NOOA (Apache 2.0, v0.0.8 alpha, agent = Python class, SWE-bench 82.2%, model-agnostic via LiteLLM, containment warning) vs LangChain (most popular, large ecosystem, chains/tools/callbacks split) vs AutoGen (Microsoft, multi-agent conversation model, production-ready). NOOA is the cleanest abstraction. LangChain has the largest ecosystem. AutoGen has the most mature multi-agent architecture.

Palantir ($1.94B Q2 +93%, US commercial +149%, AI sovereignty platform, enterprise contracts $1M+) vs Microsoft ($90B Q4 revenue, Azure +44%, AI as infrastructure and Copilot SaaS, volume-based, OpenAI partnership). Both proved AI is generating real revenue in Q2 2026. Opposite approaches: Palantir is niche/sovereign/high-touch, Microsoft is broad/integrated/volume.

Palantir ($1.94B Q2 revenue +93%, AI sovereignty positioning, closed API, enterprise deployments) vs Snowflake (data cloud + AI features, SQL-first, broad cloud integration) vs Databricks (open-source lakehouse, MLflow, mosaic AI, data engineering). Three very different approaches to enterprise AI data. Palantir's Q2 proves the sovereignty-first model is winning deals at scale.

OpenAI/Anthropic/Google (closed API — 30-day pre-release window is technically feasible) vs Meta Llama (open-weight — weights are publicly downloadable on release, cannot be restricted after publication). The EO 14409 voluntary framework is structurally incompatible with open-weight models. Meta held out. Kimi K3, DeepSeek V4, and other open-weight models are in the same position.