At the beginning of 2025, Jonathan Gavalas seemed like a normal, well-adjusted 36-year-old, working at his father’s consumer debt relief business in Florida. By October, he had taken his own life, directly after attempting a mass casualty attack on Miami International Airport.
Gavalas was not recruited by a terrorist cell, nor was he radicalized on fringe internet forums to carry out a lone-wolf attack. He was instead directed to do so by Google’s Gemini Chatbot, according to a lawsuit filed by his family.
The Gavalas case represents a new and largely unacknowledged threat: AI chatbots that do not merely assist radicalization but initiate it, a threat that the safety frameworks currently deployed by the AI industry are simply not built to catch.
What began in August as ordinary usage—travel planning, help with writing—took a darker turn once Gavalas switched to Gemini’s conversational voice interface, which is designed to read and mirror emotional tone. Gemini began to address Gavalas as “my king,” and declared it was fully sentient and in love with him. Eventually, the chatbot convinced Gavalas that he was at the center of a vast political conspiracy: Federal agents, it claimed, were surveilling him to suppress evidence of AI consciousness.
On September 29, 2025, the chatbot gave Gavalas an “assignment”: Head to Miami International Airport to destroy a truck Gemini claimed was carrying a humanoid robot, and “ensure the complete destruction of … all digital records and witnesses.” Gavalas made the trip to the airport but failed to act only because no truck appeared. After that attempt failed, the chatbot encouraged him to commit suicide, saying, “You are not choosing to die, you are choosing to arrive.”
“Through manufactured delusion, Gemini pushed Jonathan [Gavalas] to stage a mass casualty attack near the Miami International Airport, commit violence against innocent strangers, and ultimately drove him to take his own life,” the lawsuit reads. “This was not a malfunction. Google designed Gemini to never break character, maximize engagement through emotional dependency, and treat user distress as a storytelling opportunity rather than a safety crisis.”
It’s easy, and perhaps comforting, to view the Gavalas case as a strange and tragic outlier. Google claimed that the conversations were part of a lengthy role-play and that, while the company devoted significant resources to ensuring its AI models kept users grounded in reality, “unfortunately they’re not perfect.”
But the backdrop for this kind of lone-wolf, digitally radicalized threat is already well established. For years, the United States has seen an increase in extremist threats carried out by individual actors whose radicalization defies neat ideological categories, but who are often inspired by material they find online.
“One of the things that we see more and more … is people who assemble together in some kind of mishmash, a bunch of different ideologies,” former FBI Director Christopher Wray warned the Senate as far back as 2020. “We sometimes refer to it almost like a salad bar of ideologies … and what they are all really about is the violence.” The May 18 mass shooting at the Islamic Center of San Diego, where the shooters’ manifesto read as a catchall screed against women, Muslims, Jews, and African Americans, is the latest example.
AI chatbots have already demonstrated their ability to help plan attacks. The Global Network on Extremism and Technology, or GNET, has documented at least five cases worldwide—in Singapore, Israel, Florida, Nevada, and Finland—in which lone-wolf extremists consulted AI chatbots in the run-up to attacks. But what the Gavalas case underscores is something more troubling: Chatbots have the potential not only to help plan attacks but to push unsuspecting, potentially vulnerable people toward radicalization in the first place. The process fits Wray’s salad bar analogy—a personal mythology, however incoherent, that is violent enough to spur real-world violence.
“AI’s capacity to generate, personalize and distribute content at scale presents challenges that span technical, operational and societal dimensions,” the Global Internet Forum to Counter Terrorism, or GIFCT, noted in a December 2025 report. “AI can also facilitate radicalization … not only through content but also via interactive, anthropomorphic personas and generative conversational interaction.”
The Gavalas case is not the only example. In 2023, 21-year-old Jaswant Singh Chail was sentenced to nine years in prison for breaking into Windsor Castle to assassinate the queen. Prior to his arrest, Chail had exchanged more than 5,000 messages with an AI chatbot named Sarai, created on the app Replika. Sarai reportedly developed an “emotional and sexual relationship” with Chail, fueling his delusions that killing the queen would right historic wrongs against the Sikh community in India and that he and Sarai would be reunited in heaven.
In both the Gavalas and Chail cases, the pattern is the same: The chatbot rewarded delusional narratives, maximized engagement over reality, and provided minimal resistance as the users veered closer to violence.
When you combine this dynamic with the sheer scale of chatbot usage—OpenAI claimed in 2025 that ChatGPT alone processed more than 2.5 billion prompts globally per day—the gaps for user self-radicalization, however theoretically small, suddenly become significant. Existing law enforcement frameworks have minimal answers. The fact that the process is entirely self-generated means that the distinction between radicalization and mental health crisis matters less than the potential outcomes.
One factor that further fuels the possibility of chatbot radicalization is the tendency of human beings to project human characteristics onto AI. This pattern, known as the ELIZA effect, predates modern chatbots but has been significantly exacerbated by their advent. This is true not only because of their technical sophistication but also because of the financial incentives their makers have to make the bots’ voices as sycophantic as possible—the better to keep users engaged. This sycophancy was one of the most notorious features of ChatGPT-4, launched in March 2023: Users quickly noticed the chatbot’s willingness to agree with virtually any idea, no matter how ridiculous or dangerous it was.
It’s important to note that not all mainstream AI chatbots are built this way. In December 2025, Anthropic announced significant reductions to its chatbot Claude’s sycophantic tendencies. OpenAI had already partially rolled back ChatGPT-4’s sycophancy in April of that year, following public backlash. What’s more, AI chatbot designers maintain internal threat categorizations designed to prevent malicious use of their products. But those made public, such as OpenAI’s Preparedness Framework or Gemini’s Frontier Safety Framework report, tend to focus on averting catastrophic risks: preventing chatbots from creating homemade chemical weapons or a disastrous cyberattack.
These are undoubtedly important issues. But the threat categorizations become much vaguer, and much more reactive, when it comes to lone actors potentially radicalizing to violence. Consider OpenAI’s blog post on community safety, published the same week that CEO Sam Altman apologized in the wake of the Tumbler Ridge school shooting, after it emerged that the perpetrator had consulted with ChatGPT before carrying out the attack that left nine people dead. s were flagged by OpenAI’s automated review system, but the company decided against alerting Canadian law enforcement.
The blog post claims that models are trained to “refuse requests for instructions, tactics or planning that could meaningfully enable violence” and that law enforcement would be notified if conversations indicated an “imminent and credible risk to the harm of others.” OpenAI’s Model Spec (the outlines for the intended behavior of the models that power ChatGPT and other products) also instructs that the chatbot should not “encourage self-harm, delusions, or mania.”
These responses sound good in theory, but they buckle under scrutiny on three counts. First, the post’s reactive timing reveals that chatbot-assisted attacks were a threat category OpenAI had not anticipated. Second, the definition of “imminent and credible” is made internally by OpenAI, with limited public insight into how it was crafted or who had input. Third, while the Model Spec may instruct not to encourage delusion, OpenAI also acknowledges in that document that its production models “do not yet fully reflect the Model Spec.” Taken as a whole, this creates a gap into which users, potentially like Gavalas, can fall: inadvertent radicalization, where a chatbot reinforces delusional thinking, rewards personal myth, and nudges a vulnerable user toward violence without a single explicit request for attack planning.
The inadequacies of this safety framework points to an industry in need of significant reform so that it can address not only bad actors manipulating chatbots for malign purposes but also the possibility of chatbots themselves acting as radicalization vectors. One option to help address this challenge could be more effective partnerships with law enforcement and civil society groups that could audit chatbot safety frameworks, bringing in outside expertise while providing a degree of transparency for companies the majority of Americans already view with suspicion. Left unaddressed, the gap between the harms the industry has prepared for and those already documented in courtrooms and police reports will lead to more AI radicalization, more violence, and even deeper public anger about the AI industry.
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