The credibility economy: How AI will transform the measurement of value.

      A growing sense of discomfort is influencing how professionals interact with artificial intelligence, especially as its capabilities expand in information creation and execution. Dan Pratl, the founder of Quadron, believes this feeling of anxiety highlights a deeper systemic issue that goes beyond mere automation and into the recognition of value itself.

      “We’ve reached a stage where the advancement of AI has left nearly everyone feeling insecure,” Pratl states, highlighting a broader disconnect between technological progress and the systems meant to acknowledge human contributions. He argues that current recognition and reward systems have either stagnated or devolved into speculative or game-like scenarios, citing trends in cryptocurrency markets and retail-fueled trading environments.

      Pratl contends that AI is hastening a shift that has been occurring for years. “AI excels at commoditizing knowledge and its execution,” he notes. “What becomes scarce is the final expertise, judgment, and the deployment of that judgment.” As knowledge becomes more readily available and execution increasingly automated, he asserts that it becomes much tougher to differentiate high-quality work from inferior output, especially for those who are not experts.

      This situation creates what Pratl describes as a “meta problem,” where the amount of information available increases, but the means to verify its credibility have not kept up. “For non-experts, all high-quality work appears similar,” he emphasizes, pointing out that current systems struggle to distinguish between accurate insights and confident yet unverified claims.

      Within this context, Pratl claims that visibility often takes precedence over credibility. In his view, social media platforms tend to reward attention rather than accuracy, allowing what he refers to as “the loudest voices” to overshadow more rigorous but less visible expertise. “There’s no system in place to reward correct information,” he explains. “No way to quickly verify individuals and empower non-consensus voices to participate.”

      Pratl warns that as AI-generated content becomes more common, the lack of trustworthy credibility signals could jeopardize decision-making across various sectors, including business and healthcare. Research indicates that online misinformation and disinformation cost the global economy around $78 billion annually, underscoring the gravity of the issue.

      In response, Pratl proposes a credibility economy, essentially a system aimed at measuring, verifying, and rewarding expertise in a more organized and scalable manner. This approach shifts focus from output alone to judgment and trust, facilitating mechanisms that attribute value to individuals based on the quality and impact of their choices.

      Quadron, the company he founded, is designed to create the necessary infrastructure for such a system. Pratl identifies three core components involved in this vision.

      The first is an enterprise layer that provides a cohesive finishing touch for work within organizations. “I use several productivity platforms, but I often find a key finishing layer is missing for final comprehensive utilization,” he explains. This layer is meant to ensure that individuals are recognized for exhibiting sound judgment and achieving validated results, rather than merely contributing to ongoing workflows without proper attribution.

      The second component is a verification layer aimed at modernizing the structure and sharing of knowledge. Pratl describes existing intellectual property systems as outdated and inadequate for the speed and scale of present-day knowledge exchanges. In their place, Quadron is developing methods to expose and evaluate insights while safeguarding appropriate security levels.

      The third element consists of what Pratl calls credibility markets, which differ from traditional prediction markets by emphasizing domain-specific expertise. “It’s not about speculative betting. You’re not wagering on external events where the odds are unclear,” he clarifies. Instead, these markets are intended to assess credibility in real-time, linking individuals with relevant expertise and allowing their judgment to be evaluated within suitable contexts. He observes, “Organizations need context and structure, which requires a different methodological approach. Individuals need incentives and rewards to arrange their information accordingly. We are creating systems to provide both.”

      Pratl's viewpoint is shaped by a career that includes law, open-source software, crowdfunding, and cryptocurrency, each of which, he argues, has revealed shortcomings in how systems encourage and sustain meaningful engagement. Reflecting on these experiences, he remarks, “Many systems lacked the structural integrity at the incentive level to endure beyond their initial creators and often fell out of alignment once initial motivations weakened.”

      A more personal motivation arose during a medical emergency involving his mother, where access to critical information was inconsistent, even though it was technically available. “The information was centralized but not truly accessible,” he explains, highlighting a system where incentives did not align with the necessity of surfacing actionable knowledge.

      The eventual resolution, he notes, relied on informal networks rather than structured systems, a reality he believes is unsustainable given the tools currently available.

      In the coming years, Pratl asserts that the ongoing development of AI will exacerbate these challenges unless new systems are implemented to tackle them. Without mechanisms that reward accuracy and highlight credible expertise, he warns that decision-making processes could increasingly rely on visibility or chance rather than informed judgment.

      “We’re all experts,” he states. “Our expertise is valuable if it's organized and presented