What Your Model Will Never Admit - Konrad Wojnowski

Konrad Wojnowski

SubscribeSign in

What Your Model Will Never Admit<br>Better living through honesty — or the lie at the heart of all large-scale models

Konrad Wojnowski<br>May 17, 2026

Share

What I am proposing in this text is a philosophical speculation — but one that tries to stay as close to the ground as possible. In fact, I am less interested in speculation itself than in a very practical question: is the current trajectory of artificial intelligence really the only possible one?<br>Today, nearly the entire technology industry behaves as if the answer were obvious. Larger and larger models. Larger and larger data centers. Deeper and deeper dependence on the cloud. More and more computation performed somewhere far away from the user — inside infrastructures they neither see, control, nor increasingly even attempt to understand. Google’s recent Android presentations make this logic especially clear: more cloud integration, more centralization, more ubiquitous intelligence operating above and beyond the device itself.<br>Thanks for reading! Subscribe for free to receive new posts and support my work.

Subscribe

And yet another direction is possible. More local. More finite. More material.

These ‘AI-generated-images’ emerged through 3-year-long process of fine tuning to reach a coherent aesthetics that imagine probabilistic latent spaces bounded by resources.<br>A few months ago, designer and entrepreneur Michal Malewicz pointed out something that until recently seemed like an insignificant detail of the AI market: unlike most major technology companies, Apple has largely refused to participate in the race toward the largest frontier language model.1 Instead, it focused on local models, dedicated neural hardware, and the MLX framework, which allows CPUs and GPUs to share memory directly without the costly copying of data between them. The consequence is simple, yet potentially profound: an increasing amount of AI computation may soon move away from gigantic data centers and back onto the user’s own device.<br>This is not really about Apple itself. Similar effects could emerge from open-source initiatives, grassroots hardware projects, or entirely different actors. The problem is that grassroots development is no longer the dominant logic of technological progress. That is precisely why the Apple example matters: it demonstrates the possibility of an alternative emerging from within the very center of the existing technological order.<br>This looks like a limitation. But not all limitations were created equal. I really like my roof, for example — a very limiting invention, from rain’s standpoint especially. Let’s remind ourselves that architecture is a system of limitations. We cannot tread on endless possibilities. We cannot hide under them. We cannot securely build on voids, however abundant the virtualities they foster.<br>This will not, however, be merely a philosophical manifesto against “The Cloud.” Over the past two years, I have been working on a formal mathematical framework — VOID Theory — built around finitude, computational cost, and the materiality of cognition. What this framework suggests is that the architecture of AI is not merely a matter of engineering or business strategy. It is also a matter of mathematics.<br>And once the mathematics changes, the very “materiality” of machine intelligence changes with it: the way a system knows, fails, consumes resources, and arrives at answers.<br>Perhaps these are precisely the kinds of systems we are beginning to need most: more finite, more local, but above all more honest about their own limitations. Especially in situations where the stakes cease to be a marketing demonstration of a model’s capabilities and become something brutally real instead: a medical diagnosis, a court decision, a risk assessment, a human life. Increasingly, contemporary AI systems seem not so much to “make mistakes” as to systematically produce the appearance of knowledge in situations where they should be capable of remaining silent. The problem is visible even in perhaps the most intuitive example imaginable: autonomous vehicles. The central issue is no longer whether a car can technically drive itself, but whether the system can honestly recognize the moment at which it should no longer trust its own judgment. Tesla, Waymo, and Mercedes have spent years developing autonomous driving technologies, yet we remain strikingly far not only from fully autonomous transport itself, but even from the cultural acceptance of such systems. People can tolerate a system that is limited. What they struggle to trust is a system incapable of understanding its own limits.<br>A similar pattern is beginning to emerge across nearly every domain into which generative AI is being introduced. A 2023 JAMA study involving 457 clinicians across 13 U.S. states found that systematically incorrect AI suggestions degraded the quality of human judgment even among experts....