New from Ramp data: the latest threat to the AI trade. AI companies' revenues are heavily dependent on a small set of customers. 80% of OpenAI and Anthropic's enterprise revenues come from 1% of their customers, and it's not getting better. This is a level of concentration risk unseen in any other software category we track. The companies in the top 1% skew heavily toward the tech sector and AI products and services. What happens in a market correction? All these companies are highly correlated, and an increasing share of our economy is invested in them. Especially as we approach blockbuster IPOs for OpenAI and Anthropic.

the request-level evidence is the interesting part: Default baseline: $0.001219 Flex actual: $0.000610 same model, roughly half the cost. this is the first test that gave me a concrete reason to use Router instead of calling Luna directly. Nice work, @tryramp , @vral and team! next thing I’d love: aggregate baseline vs actual spend, flex share, fallback count, and why each tier was selected.

the frontier just got more affordable. we teamed up with @OpenAI: GPT-5.6 Sol is 50% off on router through September 18. one endpoint, every model, each request routed to the best fit. so every AI dollar goes further. start saving in a couple minutes at

We tested NVIDIA NeMo Switchyard’s stage router for coding agents in Ramp SWE-Bench. Routed agents showed comparable performance to single-model controls while substantially reducing costs and runtime.

There is no substitute for looking at the data. Manually inspecting failure modes is how you discover the shape of the problem. Great AI products are built by teams who set up their infra + instrumentation to make looking at data easy. @austospumanto @bcherny @rahulgs

What’s the best open source version of Claude Cowork that a) allows you to plug in @OpenRouter or @bittensor b) works with local models and c) is optimized for the average knowledge worker (Hermes and OpenClaw too complex for normies) Right now Perplexity computer is my best choice (doesn’t do local models yet)

Every week, the price-intelligence-latency frontier shifts, and we expect this trend to continue Across 100+ use cases in our product, keeping all up to date with the right model is a challenge - Either we're losing out on intelligence for the dollars we spend, or we're spending too much money for the intelligence the feature needs Ramp Router lets you benefit immediately without rewriting your application. We cut our LLM costs by 30%, while also making our features smarter and faster. We built it for Ramp. Now we’re opening it up to everyone. get access here:

AI is extremely good at spending your money very quietly. our own token spend went from a rounding error to more than 10% of payroll in a year. one week in May we burned through $1.5 m. our CFO didn't love telling me that number, and he really didn't love telling the internet. but every finance leader we talk to is living the same story. the bill keeps going up and teams can't answer basic questions about it. which team is driving it? which models they're using? what changed this month? whether a cheaper model would do the same job? so finance gets two bad options: keep paying and hope, or cut broadly and slow down the work that's actually compounding. we built a third one. Ramp now connects to OpenAI, Anthropic, Gemini, Cursor and pulls it all into one place. see it, understand it, control it - down to a single API key. built with 1,000+ companies managing 100T+ tokens a month, and now spending less than they expected too! try it today at