The credibility economy: How AI will transform the way value is assessed.

      An increasing sense of discomfort is influencing how professionals interact with artificial intelligence, especially as its capabilities grow in areas of information generation and implementation. Dan Pratl, the founder of Quadron, suggests that this unease highlights a deeper structural concern that goes beyond automation and touches on the recognition of value itself.

      “We have arrived at a stage where the development of AI has led to widespread feelings of insecurity,” Pratl asserts, noting a larger disconnect between technological progress and the systems meant to acknowledge human contributions. He believes that current recognition and financial reward structures have either failed to adapt or deteriorated into what he describes as speculative or game-like environments, citing instances in cryptocurrency markets and retail-driven trading systems as examples.

      Pratl argues that AI is hastening a transformation that has been in motion for some time. “AI excels at commoditizing knowledge and its execution,” he explains. “The crucial resource now lies in the last mile, such as expertise, discernment, and the application of judgment.” As knowledge becomes more abundant and execution increasingly automated, he points out that it becomes much harder to distinguish between high-quality work and lower-quality output, particularly for those who are not experts.

      This situation fosters what Pratl labels a “meta problem,” where the growing amount of available information outpaces the tools for verifying its credibility. “To non-experts, all high-quality work appears similar,” he highlights, emphasizing that current systems provide limited capability to differentiate genuine insights from confident yet unverified claims.

      In this context, Pratl argues that visibility often replaces credibility. He believes that social platforms tend to prioritize attention over accuracy, allowing “the loudest voices” to overshadow more rigorous expertise that lacks visibility. “There’s no system to reward correctness,” he states. “There’s no way to quickly verify individuals and allow non-consensus voices to participate in the discourse.”

      Pratl warns that as AI-generated content becomes increasingly common, the lack of dependable credibility indicators could jeopardize decision-making in various sectors, including business and healthcare. Research indicates that online misinformation and disinformation may cost the global economy approximately $78 billion annually, underscoring the seriousness of the issue.

      To address this, Pratl proposes establishing a credibility economy, which aims to create a system for measuring, verifying, and rewarding expertise in a more organized and scalable fashion. Instead of concentrating solely on outputs, this approach shifts focus toward judgment and trust, thereby establishing mechanisms that confer value on individuals based on the quality and impact of their decisions.

      Quadron, the company he founded, aims to build the infrastructure required for such a system, which he believes comprises three primary components.

      The first component is an enterprise layer that adds a finishing and cohesive aspect to work within organizations. “I use several productivity platforms, yet often find a lack of a finishing layer for final, comprehensive use,” he notes. This layer is intended to ensure individuals are acknowledged for exercising sound judgment and achieving validated outcomes, rather than merely contributing to workflows without clear attribution.

      The second component is a verification layer intended to modernize the structuring and sharing of knowledge. Pratl views existing intellectual property systems as outdated and inadequate for the current pace and scale of knowledge exchange. In their place, Quadron is creating mechanisms that will allow insights to be evaluated while safeguarding necessary levels of security.

      The third element, which Pratl refers to as credibility markets, differs from traditional prediction markets by targeting domain-specific expertise. “It’s not about general speculation. You’re not wagering on external events where the odds aren’t clear,” he clarifies. Instead, these markets aim to assess credibility in real time, linking individuals with relevant expertise and evaluating their judgment in appropriate contexts. He emphasizes, “Organizations require context and structure, which necessitates a different methodological approach. Individuals need incentives and rewards to organize their information accordingly. We are developing systems to provide both.”

      Pratl's perspective is shaped by a career that spans law, open-source software, crowdfunding, and cryptocurrency, during which he argues he identified weaknesses in how systems motivate and sustain meaningful participation. Reflecting on these insights, he shares, “Many such systems lacked the structural integrity at the incentive level to persist beyond their original creators and often lost alignment as initial motivations faded.”

      A more personal catalyst arose during a medical crisis involving his mother, where access to crucial information was inconsistent, even though it was technically available. “The information was centralized, yet not truly accessible,” he explains, pointing to a system where incentives were misaligned with the necessity to highlight actionable knowledge.

      Ultimately, he observed that the resolution relied on informal networks rather than structured systems, a situation he believes is unsustainable given the tools available today.

      Pratl asserts that without the introduction of new systems to tackle these issues, the ongoing advancement of AI will exacerbate these challenges. He cautions that without mechanisms that reward accuracy and bring credible expertise to light, decision-making may increasingly rely on visibility or chance rather than informed judgment.

      “We’re