Around the middle of last year, Pim de Witte started reaching out to a handful of prominent AI labs to see if they'd be interested in using data from Medal, his popular video game clipping platform, to train their agents. Within weeks, it became clear that Medal's data was more valuable to the labs than he expected. "We received multiple acquisition offers very quickly," he told me.
As the number of foundation models proliferates and enterprises increasingly build applications or code on top of them, it becomes imperative for CIOs and IT leaders to establish and follow a robust multi-level due diligence framework, Shah said. That framework should ensure training data transparency, strong data privacy, security governance policies, and at the very least, rigorous checks for geopolitical biases, censorship influence, and potential IP violations.
The AI stack has become increasingly confusing and complex. We've gone from two major players (OpenAI and Anthropic) in 2023 to over 200 providers, dozens of vector databases, and a constant stream of new "AI-native" tools launching weekly. AI applications are no longer in the experimental phase. These technologies have now matured to production-ready applications that enterprises can deploy at scale.