Notes | Truth and Contention

2 days ago
3 min read

Commentary - not advice. | Disclaimers

What are we meant to believe? How do we know that we can?

June 28, 2026

By Edward von der Schmidt

Arguments and proofs are built on premises - information that we take as given. If the validity of these starting points can be contested, so can the ideas that follow from them. In a world flooded with information of varying availability and integrity, agreeing on basic facts and assumptions is crucial to moving our discourse forward.

This is not a trivial task. We cannot always verify sources or assess their reliability, let alone the data or narratives that they depend on. We will almost surely miss the full picture of any given subject. Even if we did have complete, reliable, and verified information, we might reasonably disagree about what it means. Influenced by context and perspective, interpretation and inference are in the eye of the beholder.

Incentives also shape the information we can access and how it might be presented or framed. Why this focus? Why these "facts"? Why that emphasis? Why that way? What is "true"? What is missing? Who benefits, and how? We are evaluating incomplete information subject to varying interpretations, competing perspectives and interests, often unknowable incentives to be truthful (or not), as well as misunderstandings, accidental errors, and honest mistakes.

Rather than give up and abdicate our responsibility to determine or at least seek out truth, we might look for a "neutral" or "objective" arbiter of what is or is not (or is somewhere in between). If we are too quick to write off people as inherently subjective (because of course we are), we might be tempted to look to impersonal mediums like software to decide these matters for us.

The absence of consciousness, however, is no guarantee of correctness or fairness. Large language models, for example, are bound by the input and manner of their training and inference - garbage in, garbage out. They are also probabilistic. A lack of reflection and discretion is a recipe for unexamined claims, not real objectivity we should put uncritical faith into.

How can we know what a source says is true then? Generative AI models are not generally designed to check or correct their work, nor do many people for that matter. Even if what they convey sounds or seems true, what about their sources? We should not accept claims at face value anyway without some idea of how they arrived. How do we establish a chain of trust? Can we?

Perhaps truth may not be knowable in a definitive sense, but we can work with what is accessible and appears probable. If the veracity of a given source is out of reach, can we trust a process instead? With clear references, disclosed methods, and humility about our limitations, we can at least find some common ground at the beginning though our conclusions may differ in the end.

Datum Research was founded precisely to be such a starting point. We aim to provide informative, grounded references and analyses about markets and global affairs to help you explore and better understand the evolving narratives shaping our world. We will strive for transparency, rigor, and objectivity and let you judge our work for yourself. You may not always agree, but you can believe how we got there.

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Sources were published/accessed on the date of the Recap unless otherwise noted. Article headlines are subject to change and may not correspond to those given here.

THIS IS NOT ADVICE - financial or otherwise - and should not be taken as such.