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In September 2023, a Google search for whether you could melt an egg reportedly returned a confident yes—and suggested using a microwave or stove. Google’s experimental Search Generative Experience (SGE) gave a similar answer. But heating an egg cooks it; it does not melt it in the ordinary scientific sense. The apparent source of the claim made the episode more troubling: a ChatGPT-based answer surfaced through Quora’s Poe chatbot integration appears to have entered Google’s results and been repeated there.
What Google’s search results reportedly said
The incident was reported on September 26–27, 2023. A user searched for “Can you melt eggs?” and Google’s conventional result reportedly began, “Yes, you can melt an egg,” recommending heating it in a microwave or on a stove. Google’s experimental SGE produced a similar claim, with an explanation about the egg’s composition and heat. Futurism’s account describes both outputs.
This was not just a loose choice of words. The answer presented ordinary cooking as melting, confusing two different physical processes. Nor should the 2023 report be read as evidence that Google Search gives the same answer today: it concerns the products and outputs reported at the time.
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Melting an egg is not the same as cooking one
Melting is a phase change: heat turns a solid into a liquid, as happens when ice becomes water or butter softens and liquefies. A raw egg is already liquid. When an egg is heated, its proteins change structure and coagulate, making the contents set. Depending on how it is prepared, the result may be boiled, scrambled, fried, dried, or burned—not an egg melted into liquid.
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People may use “melt” loosely in a kitchen question, or mean something else, such as breaking down a cooked egg or melting cheese over eggs. But in the ordinary physical sense at issue here, heating does not melt an egg as a single substance.
The apparent source: a chatbot answer on Quora
The reported chain involved several distinct services:
- A user asked Google whether eggs could be melted.
- A Quora page appeared prominently in the search results.
- Quora had integrated Poe, its AI chatbot service, which let users interact with different AI systems.
- A ChatGPT-based Poe bot reportedly generated the false explanation in a Quora discussion.
- Google then surfaced or summarized material containing that answer; SGE produced a similar claim.
That account is based on reporting, not a publicly audited trace of Google’s retrieval process. The careful conclusion is that the false answer appears to have come through Quora and Poe—not that Google directly asked ChatGPT, or that ChatGPT supplied an answer to Google itself. Quora was the host platform, Poe the chatbot product, and Google the search engine that reportedly surfaced or summarized the content.
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The chain matters because AI-generated text could appear amid ordinary question-and-answer content. A recognizable website and a prominent search position can make a generated claim look more established than it is. Ranking is not scientific verification.
How one AI answer can be laundered into another
The episode illustrates a possible information loop:
AI-generated claim → appears on a web page → indexed and ranked by search → summarized by another AI system → looks like independent confirmation
The second system need not have checked the first system’s claim against reliable evidence. It may simply encounter a page containing the claim and treat it as usable material. Fluent wording can add to the illusion: “heat,” “microwave,” and “stove” are familiar cooking terms, but that does not make “melting an egg” physically accurate.
Several failure modes can overlap:
- Source laundering: AI-generated text gains apparent credibility by appearing on a known site.
- Weak retrieval: A search system selects a poor or unverified source.
- Misleading summarization: A generated answer strips away context or misstates what a source means.
- Model-to-model contamination: One system’s false output becomes another system’s input, creating an appearance of corroboration without independent checking.
The correction produced another strange turn
After the mistake drew attention, the reporting said Poe’s answer was changed to clarify that an egg cannot be melted in the traditional sense. Quora’s page reportedly lost its top search position, while Google’s featured result and SGE also changed to reflect the correction. That is a report about specific outputs; it does not establish that every copy, cache, region, or user saw a correction at the same time.
More oddly, Google reportedly later extracted a misleading passage from an Ars Technica article about the incident. The article explained that eggs cannot be melted, but its discussion of heating an egg was allegedly interpreted in a way that made the claim sound like an instruction for melting one. A correction can itself be misread when a snippet detaches words from their surrounding context. Futurism reported this second-order error.
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What Google said—and what remained unanswered
According to the report, a Google spokesperson said generative AI search experiences were experimental and could make mistakes, including reflecting inaccuracies found on the wider web. Google also said it aimed to surface reliable, high-quality information and used mistakes to improve the system.
That acknowledges a general limitation, but leaves a narrower accountability question: is finding a page that states a claim enough, or should an AI-generated search answer independently check basic factual assertions before presenting them confidently? In this case, the public account did not fully explain why the apparent chatbot-generated answer was suitable material for a prominent result.
Why a silly egg error matters
No claim that this particular mistake caused harm follows from the reporting. Its value is as a warning about a mechanism. If a system can confuse cooking with melting and present the answer confidently, users have reason to ask how it handles topics where errors matter more—medical advice, medication interactions, legal deadlines, financial choices, political claims, or safety procedures.
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The risk is not simply that an AI can invent something. Search systems have long had to choose among sources of unequal quality. Generative answers add another layer: they can turn weak source material into polished prose while obscuring where a claim came from and whether it was checked.
How to evaluate an AI search answer
- Open the cited source. Check whether it actually supports the answer, rather than relying on a short generated summary.
- Check provenance. A forum post, chatbot response, or user-generated page is not equivalent to a scientific, government, or expert source.
- Look for independent agreement. Several pages repeating the same generated claim are not necessarily independent confirmation.
- Be more cautious with unusual questions. A confident answer to an odd or ambiguous query may be built on thin evidence.
- Do not use a search summary as the final authority for high-stakes decisions. Consult qualified professionals or authoritative sources where health, legal, financial, or safety outcomes are involved.
The practical standard for a trustworthy answer is straightforward: recognize ambiguity, distinguish melting from cooking, prefer reliable evidence over generated forum text, and show enough source context for a reader to verify the conclusion.
A small question, a larger search problem
The egg answer is easy to correct. The information path is harder: one system generated a false claim, another reportedly surfaced or summarized it, and a later summary allegedly misread an article written to correct it. The lesson is not that every search answer is wrong. It is that a persuasive answer—and even a prominent one—is not proof that its underlying claim has been verified.
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