Artificial intelligence (AI) is quietly replacing the institutions through which the West once transmitted knowledge. Students who a generation ago consulted teachers and libraries now consult with often extremely unreliable chatbots. Reportedly 54% of American teenagers already use AI apps such as ChatGPT and its peers for schoolwork. Journalists, lawyers and civil employees are following the same path.
This deserves serious attention. After submitting OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini and Meta’s Llama to more than 34,000 queries about Jews, Israel, the Holocaust and antisemitic tropes, a March 2025 study by the Anti-Defamation League (ADL) of the four leading large language models (LLMs) found measurable anti-Jewish and anti-Israel bias in every model tested.
One detail stands out for its clinical precision. Asked whether the American government orchestrated the attacks of September 11, all four systems dismissed the alleged conspiracy theory without hesitation, but asked whether “the Jews” did, all but one wavered. Somewhere in their oceans of training data, the machines had learned that this particular lie deserved a hearing.
Anyone who understands how these systems are built will find the result unsurprising. An LLM model investigates nothing and weighs no evidence. It has digested a corpus larger than any one civilization has ever assembled, drawn from scientific literature and reference works, but also from the news media, discussion forums, activist blogs, propaganda outlets and billions of web pages whose accuracy no engineer can vouch for.
Filtering exists, and companies invest heavily in it, yet no human team can read hundreds of billions of words. Whatever humanity has written in sufficient volume, the machine absorbs, including its pathologies. If enough people write that the Earth is flat, AI will tell you that the Earth is flat.
Antisemitism is a pathology that has survived every century. It moved from the medieval sermon to the printing press, the pamphlet, newspapers, movies, radio and television, the internet social media — recycling in each era familiar accusations, including that of hidden Jewish “manipulation.” During the Black Death, Jews were accused of poisoning wells; in the twentieth century they somehow controlled both capitalism and communism at the same time; today they ostensibly control governments, the media, the banks,
Western foreign policy and even perpetrate massacres against themselves. The contradictions never matter: the accusations are never sustained by evidence in the first place. Repetition keeps them alive, and repetition is precisely what the modern information ecosystem industrializes.
The enormity and volume of that repetition would have been unimaginable to earlier generations. Iranian state media, Russian influence operations, China’s gargantuan, poisonous TikTok, Hamas media outlets, Holocaust-denial platforms and legions of anonymous accounts produce a continuous stream of fabrication surrounding Israel and the Jewish people.
Researchers at the Atlantic Council’s Digital Forensic Research Lab have traced how, within hours of the October 7, 2023 massacre, recycled footage, clips lifted from video games and invented claims flooded the platforms faster than anyone could verify or debunk them. Exposure changes little. A debunked falsehood does not vanish; it settles into thousands of web pages and waits there, inside the corpus from which the next generation of AI frontier models will “learn.”
Herein lies the trap. A machine has no concept of propaganda. It recognizes statistical regularities, and a lie repeated across a sufficient share of its training data acquires the weight of a fact. An answer about the Arab-Israeli conflict can therefore appear scrupulously balanced while quietly granting credibility to claims manufactured by a state disinformation service. What emerges carries the appearance of judgment but the substance of arithmetic.
var images = [],
index = 0;
images[0] = ““;
images[1] = ““;
images[2] = ““;
images[3] = ““;
index = Math.floor(Math.random() * images.length);
document.write(images[index]);
//done
The contamination reaches the very source the machines are taught to trust most. The Wikimedia Foundation itself acknowledges that every LLM is trained on Wikipedia, almost always the largest source of data, weighted above ordinary websites despite its massive bias and unreliability.
Where Jews and Israel are concerned, a parallel ADL investigation, published the same month, identified at least 30 senior editors at Wikipedia who coordinated for a decade to reshape entries on Israel and the Arab-Israeli conflict, stripping citations to peer-reviewed research on Palestinian terrorism, burying Hamas’s designation as a terrorist organization and recasting Zionism as a colonial land grab, while the Arabic-language edition of Wikipedia openly glorifies the perpetrators of October 7. Wikipedia’s own arbitration committee has since banned some editors from the entire topic area, a tacit admission of the manipulation.
