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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →AI can look almost free to the person using it while carrying costs that are harder to see: electricity demand, pressure on local communities, labor disruption, environmental burdens and security risks. In her Cybernews editorial, Chief Editor Jurgita Lapienytė argues that those costs deserve attention—but that difficult-to-test predictions of catastrophe deserve scrutiny, too. Her point is not that AI is harmless or that every warning is credible; it is that public debate should distinguish measurable present-day effects from claims that are hard to prove or disprove.
What the title means
“Let’s make AI way harder than it needs to be” is the title of an opinion editorial by Jurgita Lapienytė, identified on the article page as Cybernews Chief Editor. It is not a technical study, a product guide or a finding that AI should literally be made more difficult to use. The title introduces a critique of the way AI’s apparent convenience can obscure its wider costs and of the way some predictions about its future are argued.
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Lapienytė opens, ironically, with “I love the thrill of thinking the world is about to end.” The line sets a skeptical, darkly humorous tone; it is not a literal endorsement of anticipating catastrophe. The argument holds two ideas together: real concerns about AI’s effects merit attention, while dramatic predictions should not be treated as established facts merely because they are alarming.
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Why AI’s low personal cost can be misleading
A user sees the immediate price of a query, subscription or generated image. The editorial asks readers to consider costs that may not appear on that bill: electricity demand, environmental strain, disruption to work, effects on local communities and security exposure. It also raises concern about books being scanned for AI training and distrust of large technology companies. These are concerns the editorial lists, not a set of independently verified findings presented in the piece.
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As one personal example, Lapienytė writes that an “‘80s-style picture of myself just cost me 4 cents in tokens.” That is the author’s reported cost for one image in 2026, not an average price for generating images or a measure of the broader cost of AI.
The distinction between a national trend and a local effect matters especially for energy and infrastructure. A separate Cybernews article about data centers discusses that distinction, but its reported figures should be checked against the original sources before being quoted as established measurements: Cybernews coverage of data centers and electricity.
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Why the editorial questions AI-doom arguments
Lapienytė’s other concern is how some catastrophic forecasts are presented. She writes that many are “hard to argue against because they sound and function just like conspiracy theories: impossible to prove or disprove, leaving claims to be judged solely on the speaker’s authority.” That is her assessment of the debate’s framing, not a demonstration that catastrophic risks are impossible, or that every forecast lacks evidence.
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The distinction is useful: evidence about a present-day electricity demand, a documented security incident or a specific labor change can in principle be examined against records and methods. A far-reaching prediction—such as a claim about enormous future loss of life—requires its own argument, assumptions and evidence. The editorial mentions public figures and such claims, but does not provide a full evidence review of those forecasts. Readers should therefore treat the passage as criticism of how the claims are debated, rather than as a verdict on their probability.
How to read the examples without overstating them
The editorial links to reporting about energy prices and data centers, an alleged AI-agent access incident, and book scanning. Those links supply context for the author’s concerns; the underlying claims are not independently established by the editorial itself. The associated Cybernews stories can be read as reporting, not as substitutes for original records or primary-source verification:
- Cybernews reporting on an alleged AI-agent access incident
- Cybernews reporting related to book scanning
That qualification does not make the issues irrelevant. It keeps the strength of a claim proportionate to the evidence available in the editorial: a reported allegation remains an allegation, a personal anecdote remains one person’s example, and an argument about future danger remains an argument unless supported by further evidence.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.The article’s central tension
The title works because it points to a mismatch: using AI can feel simple and inexpensive, while questions about who bears its costs and how its risks are described are complicated. Lapienytė wants readers to resist both easy reassurance and easy apocalypse. Electricity, work, environmental impact, security and data-use concerns should be discussed on their merits; predictions that cannot readily be tested should not be mistaken for proven outcomes—or dismissed solely because they are unsettling.
Read the original editorial for the author’s full argument: Jurgita Lapienytė’s Cybernews editorial.
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