Meta Deploys Thousands of Engineers to Train Coding AI

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.

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.

Frontier physical AI models are moving beyond video training data to incorporate multiple camera angles, dense annotation, and brain wave readings as training inputs. The shift reflects growing recognition that traditional video datasets alone are insufficient for training AI systems that interact with the physical world. Brain wave data represents an emerging frontier in multimodal training approaches for robotics and embodied AI.