Weave Raises $13.5M Series A to Kill Tokenmaxxing with Engineering-Intelligence Platform

Weave, the engineering-intelligence layer for the AI era, today announced a $13.5 million Series A led by Standard Capital, along with YCombinator, Moonfire, Burst Capital, IrregEx and the Agent Fund. Weave builds ML/RL models that understand engineering work, they help companies get the most out of every dollar they spend on tokens and humans. The funding will accelerate Weave’s mission to become the system of record for how engineers and AI impact business outcomes.

Engineering Leaders’ Problem: Uncontrolled Tokenmaxxing Costing Billions
As companies of all sizes pour tens of billions into AI coding tools, they are fueling a dangerous new phenomenon: tokenmaxxing. When an AI can generate thousands of lines of code in seconds, teams inadvertently optimize for sheer volume over genuine engineering progress. Because legacy metrics like commit counts, lines of code, and DORA / SPACE frameworks were built for human output, they actively reward this artificial bloat and create a false illusion of velocity. Executives are left flying blind, paying for generated tokens instead of tangible business value, and completely unable to calculate the true ROI of their AI spend. Weave was built to kill tokenmaxxing and solve this exact crisis.

“Our goal is to ship the highest quality product as quickly and efficiently as possible for our customers. We use Weave to get objective measurement of our teams and agents as well as ways to optimize” said David Casem, Co-Founder & CEO at Telnyx.

Weave’s Solution: Engineering-Intelligence that Drives Business Outcomes
Weave is a comprehensive engineering-intelligence platform that normalizes all human and AI engineering work into a single, objective unit. Powered by its proprietary code output model, the platform attributes every pull request, review, and deployment to either human or AI contribution. Already analyzing 2+ million PRs from 20,000+ engineers across 500+ organizations, Weave gives C-suites and engineering leaders an undeniable picture of what their teams, and their AI tools, are actually delivering.

Weave’s Core Features:

• Code Output: Normalizes units of work based to deliver a fair and accurate view of productivity by team or by individual engineer.
  
  
• AI ROI: See how many dollars you spend for every hour of human work you’re completing.
  
  
• AI Skills: Show engineers how to improve their ability to utilize agents
• Prompt Router: Use all of your production data to feed into a routing model that will pick the cheapest model without sacrificing quality or speed.
  
  

“The era of tokenmaxxing is over”, said Adam Cohen, Founder and CEO of Weave. “Every engineering and finance leader is now asking the same question: what is our AI spend actually returning? Lines of code are dead as a metric. Weave gives leaders one objective measure of real output, human and AI, so they can finally manage engineering like every other part of the business.”

“AI spend is the most powerful force in the world, and right now there is not an easy way to measure it” said Dalton Caldwell, General Partner at Standard Capita. “lThe opportunity for Weave is to enable every organization to effectively track and route their spend, and is thus is a critical piece of infrastructure for any organization embracing AI.”

Companies can try today at weaveos.com

About Weave
Weave is the engineering-intelligence platform built for the AI era, enabling organizations to objectively measure both human and AI engineering output. Weave gives C-suite executives and engineering leaders unprecedented visibility into true development velocity, code quality, and the real ROI of their AI tooling investments. The platform is already measuring 2M+ human & AI code contributions across 20,000+ engineers at 500+ companies including Robinhood, Reducto, and PostHog. Headquartered in San Francisco, Weave is backed by Standard Capital, along with YCombinator, Moonfire, Burst Capital, IrregEx and the Agent Fund. Learn more at weaveos.com.

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