Dan Prattle: Quadron Advances Trust Economy for Value Assessment in the AI Era

AI is rapidly eliminating the scarcity in knowledge production: texts, analysis, code, and even solutions are becoming cheaper and more accessible. But alongside this, another shortage emerges — understanding who and what to trust when high-quality and poor results increasingly look identical on the surface. Quadron founder Dan Pratl believes this is where the next major market is forming: not a market of automation per se, but an "economy of trust," where value is determined not by the volume of content produced, but by a person's proven ability to make correct decisions.

Pratl starts from a simple observation: AI is excellent at turning knowledge and execution into a commodity. If in the past a specialist's value was often measured by access to information or the ability to quickly execute routine work, these advantages are now rapidly eroding. What becomes scarce is the "last mile" — expertise, judgment, and the ability to apply them in a specific context.

The problem is that for a non-expert it is increasingly difficult to distinguish a genuinely strong conclusion from a confidently delivered but weak or incorrect answer. Hence the sense of anxiety in the market: tools are becoming more powerful, yet quality recognition mechanisms are not keeping pace with their speed. According to Pratl, digital platforms themselves amplify this gap.

Social networks and media systems typically reward not accuracy but attention: those who speak louder, faster, and more prominently win. In such an environment, visibility easily substitutes for authority, while correctness receives no separate reward. In practice, this impacts not only internet discussions but also business, medicine, and any field where decisions are made based on information from multiple sources.

The scale of the problem is already measurable: studies estimate the annual cost of online misinformation to the global economy at approximately 78 billion dollars. Pratl's answer is the "economy of trust" — a system in which expertise can be systematically measured, verified, and rewarded. 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 created not around an endless stream of materials, but around the proven impact of decisions: who turned out to be right, in what context, how consistently this repeats, and whether an organization can leverage it. For the AI era, this is an important shift: when nearly everyone can produce content, what becomes 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 another productivity system, but a kind of closing loop that helps consolidate a result into a coherent form and fix who exactly applied correct judgment and brought the work to a verified outcome. The second layer is knowledge verification. Pratl believes traditional intellectual property and knowledge-sharing models are too slow for the current pace, so Quadron wants to give companies tools that allow them to uncover and evaluate insights without compromising security.

The third layer is trust markets. Unlike prediction markets, this is not about speculation on external events, but an environment where the reputation of relevant experts in specific domains is calibrated in real time. Pratl's position was shaped not only by his experience in law, open source, crowdfunding, and the crypto industry, where he says he has repeatedly witnessed broken incentives and systems losing stability without the will of their creators.

A personal trigger was also a medical situation in his family: during a health crisis involving his mother, important information formally existed and was even centralized, but in practice remained difficult to access and poorly translated into action. Ultimately, informal connections played the deciding role, not formal processes. For Pratl, this signals that current systems of knowledge and trust organization no longer match technological capabilities.

If Pratl's idea is correct, 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, competence verification, contextual reputation, and reward mechanisms for accuracy. For companies, this means one simple thing: in a world of content abundance, those who win will not be those who produce the most, but those who best demonstrate who and why can be trusted.