Dan Pratl: Quadron Pushes a 'Trust Economy' for Valuing Expertise in the AI Era

AI is rapidly eliminating scarcity in knowledge production: text, analysis, code and even decisions are getting cheaper and more accessible. But a different shortage is emerging: figuring out whom and what you can trust when good and weak results increasingly look the same on the surface. Quadron founder Dan Pratl believes this is where the next big market will form: not automation as such, but a "trust economy", where value is determined not by the volume of content produced but by a person's proven ability to make the right decisions.

Pratl starts from a simple premise: AI is good at turning knowledge and execution into commodities. Where a specialist's value was once often measured by access to information or the ability to quickly do routine work, those advantages are now eroding fast. The scarce resource becomes the "last mile": expertise, judgment and the ability to apply them in a specific context. The problem is that it is getting harder for a non-expert to tell a genuinely strong conclusion from a confidently delivered but weak or wrong answer. Hence the unease in the market: tools are getting more powerful, while mechanisms for recognizing quality cannot keep up with their speed.

According to Pratl, digital platforms themselves widen this gap. Social networks and media systems usually reward attention, not accuracy: the winner is whoever is louder, faster and more visible. In such an environment, visibility easily replaces authority, and correctness earns no separate reward. In practice this hits not only online debates but also business, medicine and any field where decisions rest on information from many sources. The scale of the problem is already measurable: studies put the annual damage to the global economy from online disinformation at about $78 billion.

Pratl's answer is a "trust economy": a system in which expertise can be measured, verified and rewarded in a structured way. The focus shifts from the mere fact of producing a result to the quality of judgment and the level of trust in it. The idea is that value should be built not around an endless stream of material but around the confirmed impact of decisions: who turned out to be right, in what context, how consistently that repeats, and whether an organization can rely on it. For the AI era this is an important shift: when almost anyone can produce content, what becomes more expensive is not generation but reliable calibration of competence.

Pratl describes his company, Quadron, as an attempt to build infrastructure for this approach. The first layer is corporate: not yet another productivity system, but a kind of closing loop that helps assemble a result into a coherent whole and record who exactly applied sound judgment and carried the work to a verified outcome. The second layer is knowledge verification.

Pratl considers traditional models of intellectual property and knowledge exchange too slow for the current pace, so Quadron wants to give companies tools to surface and evaluate insights without losing security. The third layer is trust markets. Unlike prediction markets, the point is not speculation on external events but an environment where the reputation of subject-matter experts in specific domains is calibrated in real time.

Pratl arrived at this position through more than his experience in law, open source, crowdfunding and the crypto industry, where, by his account, he repeatedly saw broken incentives and systems that lost stability without the will of their creators. A medical situation in his family was also a personal trigger: during a crisis involving his mother's health, important information formally existed and was even centralized, but in practice remained hard to access and was poorly converted into action. In the end, informal connections, not formal processes, played the decisive role. For Pratl, this is a sign that current systems for organizing knowledge and trust no longer match what the technology can do.

If Pratl is right, the next stage of the AI market will be built not only around models, agents and automation but also around trust infrastructure: attribution of contributions, verification of competence, contextual reputation and mechanisms that reward accuracy. For companies, this means one simple thing: in a world of content glut, the winners will not be those who produce the most, but those who best show whom to trust and why.