Andrew

Y Combinator Verified account @ycombinator · 3d

Weave (W25) helps engineering teams understand whether their AI coding tools are actually making them more productive. Its models measure the work humans and agents produce and compare that output with AI spending.

The company recently raised a $13.5M Series A led by Standard Capital and launched Weave Router, which selects models for different coding tasks.

In this fireside, @WorkWeave co-founders @adambcohen93 and @AndrewChurchiII join YC's @snowmaker to share how the company evolved alongside the rapid rise of AI coding, from measuring the impact of tools like Cursor to helping teams build software factories that can work autonomously.

They explain how the best engineering teams are learning to measure the real output of their agents, spread the habits of their strongest AI users, and optimize for a future where much more software is written without a human at the keyboard.

00:00 — Optimizing AI Coding With Weave
04:02 — Getting More From Your AI Budget
08:39 — Measuring Work Beyond Lines of Code
12:30 — From AI Adoption to Optimization
16:47 — Measuring AI ROI and Coaching Engineers
20:16 — Building Software Factories
25:38 — The Future of Model Routing

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Justin Schroeder Verified account @jpschroeder · Sep 26

Model routers are a bad idea.

1. Intelligence is jagged.

At its core, routing treats intelligence as a smooth gradient. “Hard” problems go to “smarter” models, easy ones to “dumber” models. This is a terrible way to dish out tasks, but the problem is exacerbated by smaller models. Small models (which are typically a heavy mix in routers—after all, the goal is to save money) are necessarily more domain-specific. Hy3, for example, is incredible at 3D work but doesn’t know anything about history. Gemini 3.1 Pro tops @theo’s skate bench, but no real engineers ever used the model successfully.

2. Task difficulty is unsolved.

I don’t mean models aren’t good at this…nothing is good at this. Some tasks are just verifiably “easy,” but often even simple tasks are complicated, and complex tasks are simple. It’s the halting problem rearing its ugly head again. Old news, but like perpetual motion, it is a sweet siren that just keeps luring engineers to throw their pearls before swine.

3. It’s more expensive.

The sales pitch is you’re farming out work to cheaper models. And it’s true that cheaper models are…cheaper. But honestly, you would get better, faster inference just using the cheaper model. Sometimes it’s even cheaper to use the expensive model because routers add a whole extra layer of inference and often take longer to complete.

4. It breaks the cache.

Model cache is critical to performance and cost. It works by matching your input (the whole conversation) from start to end, chunk by chunk. As soon as there is a divergence in the content vs. what it’s seen before, it charges you the full input rate for the rest of the conversation. Moving between models as the conversation continues is constantly breaking cache. Yes, there are “cache-aware” routers, but these are entirely self-defeating.

5. It breaks chain of thought.

Models are best when they can make a plan and stick to it over several “steps.” To do this effectively, thinking data is fed back into the model on every step, so it can remember why it just took the last action and assist it in making the next right choice. These “brainwaves” are encrypted on models from major labs and do not transfer when the router decides to switch models. Not only that, but even when thinking can be preserved, models generally do not like the output of other models. This causes the models to constantly second-guess their own work. They churn through gobs of tokens just trying to understand why they previously thought this direction was a good idea.

So what is the solution? Either pay for frontier intelligence (if you need it) or use domain-specific agents. Instead of dividing work by “difficulty,” divide it by “domain,” and have an entire agent dedicated to that domain. This lets you use smaller models only for what they are provably good at.

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Theo - t3.gg Verified account @theo · Sep 26

I stayed up til 2am and spent $1,000 benchmarking Jev Router so you don't have to.

Performance on DeepSWE was roughly the same as GPT-6 Astra on low. It costs slightly more, and it took almost 5x longer to run.

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Adam Cohen Verified account @adambcohen93 · Jul 28

We’re announcing our $13.5M Series A led by @Standard_Cap.

AI has taken over engineering and companies are wasting millions. Weave gets you the most out of every dollar spent on tokens.

Have fun watching a man attempt to conquer his fears in the video.

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