Why future applications of AI will need higher quality data

What if the real AI revolution isn't about better models—but about unlocking the data we've been sitting on?Mike McLaughlin—cybersecurity and data privacy attorney, former US Cyber Command—joins us to discuss something most people miss in the AI conversation: we're building the infrastructure for a completely new asset class.The conversation moves past today's headlines and LLM limitations into what becomes possible when we solve the data access problem:Research acceleration at unprecedented scale. Imagine biotech startups accessing decades of pharmaceutical failure data, every null result, every experiment that didn't work. That's years cut from development cycles. That's drugs to market faster. That's lives saved.Universities as innovation accelerators. Right now, research institutions pay to store petabytes of data collecting dust on servers. Mike argues they're sitting on billions in untapped assets to fuel innovation.Beyond synthetic training. The next generation of AI won't be trained on Reddit threads and scraped websites. It'll be trained on high-quality, provenance-verified research data from institutions that have incentive to participate in the ecosystem.Mike's vision isn't just about compliance or risk mitigation. It's about creating the conditions for AI to actually deliver on the promise everyone keeps talking about. The compute exists. The capital exists. The models are improving. What we need now is the mechanism to turn decades of institutional research into fuel for the next wave of moonshot innovation.MentionedGoogle licensing deal with RedditPoisoning Attacks on LLMs Require a Near-constant Number of Poison SamplesMIT researchers discover new class of antibiotics using machine learningReducing bacterial infections from hospital catheters using machine learning