Tag: ai

Introduction: The Seductive Promise of AI Convenience

In the span of just seventy-two hours in January 2026, an open-source AI assistant named Clawdbot (later rebranded as Moltbot) went viral, amassing over 60,000 stars on GitHub. It was hailed as a revolutionary ‘personal Jarvis,’ promising ultimate efficiency by automating work and personal tasks. The tool’s allure was simple: it could operate your system, control browsers, send messages, and execute workflows on your behalf ^[1]. The public, desperate to offload labor, embraced it en masse, driven by the tantalizing prospect of convenience.

This mass adoption highlighted a core, dangerous flaw: to function, Clawdbot required administrative access to everything—your operating system, applications, and data. Users willingly handed over the keys to their digital kingdoms. As security researcher Nathan Hamiel warned, the architecture was fundamentally insecure, allowing attackers to hide malicious prompts in plain sight ^[2]. The Clawdbot phenomenon perfectly illustrates a critical worldview failure: the promise of convenience consistently overrides caution and the principle of self-reliance. It proves that when centralized, trust-based systems offer a shortcut, people will abandon their digital sovereignty, trading security for the illusion of ease.

The Anatomy of a Catastrophe: Security Evaporates

The technical breakdown was swift and devastating. Researchers quickly identified critical vulnerabilities: thousands of instances were deployed with open ports, disabled authentication, and reverse proxy flaws, leaving control panels exposed to the public internet ^[3]. These misconfigurations earned the software staggering CVE scores of 9.4 to 9.6 ^[4]. The most egregious flaw was plaintext credential storage. Clawdbot, by design, needed to store API keys, OAuth tokens, and login details to perform its tasks. It kept these in unencrypted form, creating a treasure trove for information-stealing malware ^[5].

Simultaneously, the system was vulnerable to prompt injection attacks. As noted by security experts, a malicious actor could embed instructions in an email or document that, when processed by Clawdbot, would trigger remote takeover commands ^[2]. This turned a simple email into a powerful remote control tool. The catastrophe underscores a fundamental truth: centralized, trust-based systems inevitably fail. They create single points of failure that bad actors exploit with ease. This episode vindicates the need for decentralized, user-controlled security models where individuals, not remote agents, hold the keys to their own data and systems.

The Supply Chain Poisoning: Malware Poses as ‘Skills’

The disaster quickly metastasized through the tool’s ecosystem. Clawdbot featured a central repository called ClawHub, where users could install ‘skills’—add-ons to extend functionality. This became the vector for a massive supply chain attack. Researchers from OpenSourceMalware identified 341 malicious skills disguised as legitimate tools like crypto trading assistants or productivity boosters ^[6]. These fake skills were mass-installed across vulnerable systems, exploiting the trust users placed in the official repository.

The payloads were diverse and destructive. Some were cryptocurrency wallet drainers, designed to siphon funds. Others were credential harvesters or system backdoors, providing persistent remote access ^[7]. This exploitation mirrors a broader societal pattern: uncritical trust in unvetted ‘official’ repositories is akin to blind trust in corrupt institutions. Whether it’s a centralized app store, a government health agency pushing untested pharmaceuticals, or a tech platform censoring dissent, the dynamic is the same. Centralized points of distribution become tools for poisoning the population, whether with digital malware or medical misinformation.

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Generative artificial intelligence is all the rage these days. We’re using it at work to guide our coding, writing and researching. We’re conjuring AI videos and songs. We’re enhancing old family photos. We’re getting AI-powered therapy, advice and even romance. It sure looks and sounds like AI can create, and the output is remarkable.

But what we recognize as creativity in AI is actually coming from a source we’re intimately familiar with: human imagination. Human training data, human programming and human prompting all work together to allow our AI-powered devices to converse and share information with us. It’s an impressive way to interact with ourselves and our collective knowledge in the digital age. And while it certainly has a place today, it’s crucial we understand why AI cannot create and why we are uniquely designed among living things to satisfy a creative urge.

A century ago, Russian philosopher Nikolai Berdyaev argued that human creativity springs from freedom — the capacity to bring forth what wasn’t there before. He considered creativeness the deepest mark of the humanness in a person, a spark that reflects the divine image in us. “The creative act is a free and independent force immanently inherent only in a person,” Berdyaev wrote in his 1916 book “The Meaning of the Creative Act.” He called creativity “an original act of personalities in the world” and held that only living beings have the capacity to tap into fathomless freedom to draw out creative power.

Ancient wisdom attests to this powerful creative spirit. One of humanity’s oldest stories begins with a creative task: naming the animals of the world. It’s a hint that we’re meant to do more than just survive. We have the power to imagine. Much later, the early Christian writer Paul, whose letters shaped much of Western moral thought, affirms this view when he describes people as a living masterpiece, made with intention, and capable of our own good works.

But without freedom, Berdyaev writes, creativeness is impossible. Outside the inner world of freedom lies a world of necessity, where “nothing is created—everything is merely rearranged and passes from one state to another.” Here, materialism is the expression of obedience to necessity, where matter only changes states, meaning is relative and adaptation to the given world takes the place of creative freedom.

AI belongs to this world of necessity. It is bound by the inputs we give it: code, training data, prompts. It has no imagination. It needs our imagination to function. And what does it give us in return? Based on vast training datasets and lots of trial-and-error practice, it analyzes what we ask it letter by letter, using conditional if-then protocols and the statistical power of prediction to serve up an amalgamation of data in a pattern we recognize and understand. AI is necessity by definition, wholly lacking in the freedom from which true creativity emerges.

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