Human-quality workflows need human-quality data, an axiom that has only grown truer in the AI-first enterprise. However, access to complete, high-signal data remains a limiting factor, given steep data provider fees, inflexible schemas, AI hallucinations, and scattered, inconsistent, and mutating sources. Customers don’t need to be data scientists to recognize shovel-ready datasets, but if they need to be data scientists to generate them reliably, data will always be rate-limiting.
It’s widely agreed that GenAI will transform software development, and GenAI dev tools have emerged as cornerstones of 8VC’s portfolio and broader AI productivity thesis. Up to now, however, hard data on the scale and specifics of this shift have been missing from the equation. In competitive industries, the speed and efficiency gains promised by GenAI coding tools could well mean the difference between market leadership and obsolescence. Companies can’t afford to select the wrong tools and end up on the wrong side of the AI adoption curve.
Ponder’s mission is to make data teams more productive. They empower data practitioners to get insights faster with tools they already know and love.
.png)
