ChatGPT on your iPhone? The four reasons why this is happening far too early

This phrase demystifies AI by explaining its actual mechanism versus what people perceive. AI systems like ChatGPT identify patterns in their training data and generate responses predicted to satisfy users, rather than understanding concepts or possessing knowledge. They’re “designed to please” in that their optimization targets user satisfaction metrics, not truth or accuracy. This fundamental architecture explains why they can confidently produce wrong answers—they’re matching patterns and generating plausible-sounding responses without “knowing” anything in the human sense. The characterization challenges anthropomorphization by emphasizing AI’s mechanical rather than cognitive nature.

Google Gemini generating images of Black World War II German soldiers demonstrates how attempts to counteract training data bias can backfire when “crowbarred in.” The AI was likely overcorrecting for historical underrepresentation of people of color by inserting diversity into contexts where it’s historically inaccurate, creating “ahistorical ignorance.” This reveals the difficulty of fixing bias through post-hoc adjustments rather than addressing foundational training data problems. It also shows how bias and its correction both produce “unreliable results”—the original bias was problematic, but the ham-fisted fix created new problems by sacrificing historical accuracy for diversity optics.

While acknowledging Apple’s private cloud compute strategy as “a positive and convincing way to head off concerns,” the author argues it addresses the wrong problem. Privacy protections ensure “no one, not even Apple itself, can snoop in on conversations,” but this doesn’t fix technological immaturity, lack of genuine intelligence, biased training data, or questionable public demand. The author states “it’s not the reason people haven’t been signing up to AI services in their droves. It’s because appetite has been low.” Privacy is a legitimate concern, but solving it doesn’t make premature deployment appropriate—it’s addressing one objection while ignoring four more fundamental problems.

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This article is rated Intermediate because while it discusses technical AI concepts, it maintains accessibility through conversational tone and clear explanations. The author explicitly positions himself as translating between tech insider knowledge and general readership. Vocabulary like “multimodal,” “anthropomorphise,” and “hallucinate” requires some technical literacy, but concepts are explained contextually. The piece assumes basic familiarity with AI news but not deep technical understanding, making it appropriate for educated general readers interested in technology criticism. The argumentative structure is straightforward—four numbered reasons—making the logic easy to follow despite the technical subject matter.

The fastest-growing-app status establishes ChatGPT’s initial explosive popularity, making the subsequent stagnation more significant. It demonstrates that early curiosity-driven adoption—people trying the novel technology—differs fundamentally from sustained engagement. Reaching 100 million users in two months proved novelty appeal, but the figure not changing “substantially” since then reveals limited staying power once the novelty wore off. This trajectory supports the author’s argument about the gap between tech industry excitement and genuine public appetite: rapid initial growth suggested revolution, but plateau suggests many users tried it without incorporating it into regular usage, undercutting claims that mass deployment meets actual demand.

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