Author: Philip Poremba

Once artificial intelligence is introduced into a web-based information system, it cannot be fully removed because its integration quickly becomes structural. AI does not remain a separate add-on; it reshapes how data is processed, filtered, and accessed. The system adapts around its presence: workflows, user expectations, and even the formats of stored information begin to assume AI’s interpretive role. Attempts to remove it would not simply “turn off” a tool, but dismantle the very logic through which information is now organized.

Moreover, AI leaves behind models, indexes, and optimizations that persist even if its active functions are disabled. Once search patterns, metadata, and connections between users and content have been altered, a baseline without AI no longer exists. Users, accustomed to AI-driven speed and personalization, resist regression to slower, manual systems. Institutions also adapt policies and practices around its capacities, embedding AI into governance and communication.

Finally, AI is replicable. Even if one version is removed, other agents, mirrors, or third-party services can reintroduce similar functions instantly. The presence of AI becomes a permanent layer of the information ecosystem, not because it cannot be switched off technically, but because the system and its users evolve to require it.

An opposite approach to AI-dominated web systems is to go back to basics: articles written and edited by humans, published in a controlled domain environment. Instead of relying on machine-driven filtering and recommendation engines, content is deliberately crafted by authors and editors, preserving context and intent. A single domain DNS structure reinforces trust and reduces reliance on dispersed networks managed by artificial intelligence. Readers know the source and its boundaries, which avoids algorithmic distortions or hidden classification mechanisms.

Handwritten articles ensure that meaning, tone and nuance remain directly tied to the writer, not to predictive models. They resist homogenization because each article reflects an individual’s knowledge and perspective rather than machine-learned averages. In a single search environment-perhaps built with minimal indexing and designed for clarity over personalization-users engage with raw text rather than AI-selected snippets.

This model demands discipline: slower publication, stricter editorial standards, and trust in authors rather than intermediary machines. However, it offers resilience. By dispensing with AI-dependent infrastructures, these systems preserve the independence, authenticity and stability of archives. They may fall short of the speed and breadth of AI-based systems, but they incorporate a deliberate counterweight: knowledge rooted in direct human communication, protected under a carefully bounded domain name space.