Wirestock raises $23M to supply multimodal data to AI labs

AI training companies like Mercor are actively acquiring internal communications and code from startups, offering payments up to $300,000 for Slack threads, GitHub records, and meeting transcripts. Warmly's CEO received four such acquisition offers within days of the company's HubSpot acquisition announcement. The practice highlights how internal startup data has become a commodity for AI model training, even as acquirers may not want the same datasets.

A MIT Technology Review Insights report based on a survey of 300 data and technology executives finds that legacy data systems are a major blocker to AI agent adoption and effectiveness. Organizations with mature data infrastructure, termed 'data leaders,' report significantly higher trust in agent decisions and fewer scaling constraints than 'data laggards.' The research suggests that without modernizing data systems, enterprises will struggle to realize ROI from agentic AI despite widespread adoption plans.

Meta is deploying its in-house coding agent MetaCode to thousands of engineers to improve the coding capabilities of its AI models and close the gap with Anthropic and OpenAI. VP Maher Saba has asked engineers to submit at least one code change per week for review and integration. The feedback loop has already improved Meta's latest model, Muse Spark 1.1, and will be used to train an upcoming model called Watermelon.

AI is accelerating drug discovery by enabling predictive design of candidates and hit identification at scale, but the technology is exposing critical gaps in data quality and lab infrastructure. Drug companies are hitting a 'data wall' where publicly available datasets lack the structure and diversity needed to train accurate models, while lab teams struggle to validate the growing volume of AI-generated compounds. Success depends on closing the loop between computational prediction and experimental validation through better data collection and integration.