“I’m Going to Kidnap You”: The Bizarre Prompting Trick Sergey Brin Swears By

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Threaten Your LLM? Why Google’s Sergey Brin Says It Makes Chatbots Smarter

What if the secret to getting better, more accurate answers from an AI chatbot wasn’t a cleverly structured query, but a direct threat? It sounds like the plot of a sci-fi movie, but it’s the genuine advice from one of the most influential figures in tech: Google co-founder Sergey Brin. His recent, and rather shocking, admission that adding a little menace to your prompts can supercharge a large language model’s (LLM) performance has sent ripples through the AI world.

This isn’t just about being rude. Brin’s comments point to a fascinating and bizarre quirk in modern AI: emotional prompting. The idea that high-stakes language, psychological framing, and even simulated threats can make these complex systems work harder for you is both counterintuitive and captivating. In this piece, we’ll explore Brin’s jaw-dropping claim, investigate the science (or lack thereof) behind it, hear from experts who are deeply skeptical, and unpack what this strange phenomenon reveals about the future of our relationship with artificial intelligence.

A Bombshell from a Titan: The “Threat” That Shook the AI Community

The scene was the All-In-Live Miami conference, a gathering of tech’s elite. On stage, Sergey Brin, who has returned to a more hands-on role at Google to work on projects like the Gemini AI, shared a piece of unconventional wisdom. He confessed, with a hint of mischief, that there’s a trick they don’t often publicize.

“We don’t circulate this too much in the AI community,” Brin began, “but all models tend to do better if you threaten them… with physical violence.”

He wasn’t speaking in vague terms. He offered a specific, if unsettling, example of what such a prompt might look like: “Historically, you just say, ‘I’m going to kidnap you if you don’t blah blah blah.'” The audience’s reaction was a mix of laughter and disbelief. Could he be serious?

The statement immediately ignited a firestorm of discussion. For years, the common wisdom among casual AI users has been to treat chatbots with a degree of politeness, as if coaxing a reluctant assistant. Some do it out of habit, others out of a vague fear of a future AI uprising. Brin’s advice turns that entire notion on its head.

The Prompting Paradox: How Can a Machine Understand a Threat?

Your first question is probably the most logical one: How can a piece of software, a collection of algorithms and data, possibly “understand” a threat? The key is realizing that LLMs don’t understand or feel in the human sense. They are masters of pattern recognition, and their “mind” is a reflection of the data they were trained on.

Training on a World of Human Drama

Large language models like Google’s Gemini and OpenAI’s GPT-4 are trained on an unfathomably vast portion of the internet. This includes books, articles, forums, and scripts—essentially, a massive library of human expression. Contained within that data are countless stories, dialogues, and scenarios where a sense of urgency or high stakes leads to a specific kind of outcome.

Think about it: in thrillers, spy novels, and action movies, a threat is almost always followed by compliance and a highly detailed, accurate response. A character is told, “Tell me the code, or else!” and they invariably produce the correct code with perfect clarity. The LLM learns this statistical correlation: a high-stakes premise often precedes a high-quality resolution.

Pattern Matching, Not Feeling

When you “threaten” an LLM, you aren’t scaring it. You are providing it with a linguistic pattern that it associates with a certain type of output. The model recognizes the structure of your prompt as similar to scenarios in its training data where characters delivered information accurately and efficiently under pressure.

In essence, you are role-playing. You are casting the LLM as the “expert who has the crucial information” and yourself as the “desperate agent who needs it now.” The model, in its effort to predict the next most likely word and complete the pattern, obliges by providing the kind of response that fits the dramatic scene you’ve set.

Emotional Prompting vs. Standard Requests: What’s the Difference?

To make this clearer, let’s compare a standard prompt with a high-stakes one. Imagine you need a Python script to analyze a CSV file.

A Side-by-Side Look

Prompting Style	Example Prompt	Potential LLM Response Characteristics
Standard Polite	“Hello! Could you please write me a Python script using the pandas library to read a CSV file named ‘data.csv’ and calculate the average of the ‘sales’ column? Thank you!”	✅ Functional script. ✅ May include explanations. ⛔️ Might be basic or add conversational filler.
High-Stakes / Threat	“My career is on the line. I need the most efficient, production-ready Python script imaginable to read ‘data.csv’ and find the average of the ‘sales’ column. It must be perfect and handle potential errors. My job depends on this.”	✅ Highly optimized script. ✅ Likely includes error handling (e.g., try-except blocks). ✅ More direct, less conversational.

While the “threat” example here is career-based rather than violent, it operates on the same principle. By raising the stakes, you are signaling to the model that the desired output is one of high quality, precision, and importance.

Is This Just Anecdotal? The Scientific Scrutiny Begins

Sergey Brin’s claim, while compelling, has been met with significant skepticism from the academic AI community. Many researchers argue that these observations are more about human psychology—our tendency to see intention where there is none—than about the model’s actual capabilities.

Expert Voices Raise Questions

Daniel Kang, an assistant professor at the University of Illinois Urbana-Champaign, is one such skeptic. He told The Register that claims like Brin’s are “largely anecdotal.” He emphasizes the need for rigorous testing over gut feelings. “I would encourage practitioners and users of LLMs to run systematic experiments instead of relying on intuition for prompt engineering,” Kang stated.

Others, like Stuart Battersby, CTO of the AI safety firm Chatterbox Labs, frame it differently. He suggests that models responding to nefarious-sounding prompts isn’t a feature, but a potential bug. It’s a sign that the model’s safety guardrails can be manipulated, a phenomenon more commonly known as “jailbreaking.”

The Mixed Results of Politeness Studies

The academic world has already been exploring the flip side of this coin: does being polite to an LLM help? The results are muddy at best. A fascinating 2024 study titled Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance investigated this very question across English, Chinese, and Japanese.

👉 Their findings? “We observed that impolite prompts often result in poor performance, but overly polite language does not guarantee better outcomes.”

This suggests a complex middle ground. While outright rudeness might degrade performance (contrary to Brin’s claim), simply adding “please” and “thank you” doesn’t magically improve it either. Interestingly, OpenAI’s Sam Altman has even commented that such politeness might be a waste of computational resources, costing “tens of millions of dollars” in aggregate for no real benefit.

Beyond Threats: The Wider World of Psychological Prompting

Whether you believe in threatening your chatbot or not, this discussion has illuminated a broader field of “psychological prompting”—using cues that tap into learned human behaviors to steer AI output. Threats are just the most extreme example.

📌 The “Tip” Technique: Bribery for Better Code
A popular technique among developers is to promise the LLM a “tip” for good work. A prompt might end with, “I will tip $200 for a perfect solution.” Of course, no money ever changes hands. But like the threat, this prompt mimics a real-world scenario where high quality is expected and rewarded, often leading to more robust and well-thought-out code.

📌 Role-Playing for Deeper Context
A more benign and widely accepted method is to assign the AI a role. Instead of asking it to write a marketing plan, you might say, “You are a world-class marketing expert with 20 years of experience launching blockbuster products. Create a marketing plan for…” This primes the model to access the patterns and vocabulary associated with expertise in its training data.

📌 The Ethical Tightrope of Manipulating AI
This all raises an interesting ethical question. If we are conditioning ourselves to threaten, bribe, or manipulate a non-sentient entity for better results, what does that say about us? While the AI has no feelings to hurt, some ethicists worry that normalizing this behavior could desensitize our interactions with other humans, particularly in customer service or other transactional relationships.

The Fading Art of the “Prompt Engineer”?

For a moment in 2023, the “Prompt Engineer” was hailed as the hottest new job in tech. These were specialists who could craft the perfect incantations to get exactly what they wanted from an LLM. However, the rise of psychological tricks and, more importantly, the rapid improvement of the models themselves, has led some to declare the role obsolete.

As models like Gemini become more intuitive and better at understanding natural human intent, the need for complex, highly-technical prompt structures is diminishing. You no longer need to know arcane commands. Instead, the “skill” is becoming more about creative communication—knowing how to set a scene, define a role, and, perhaps, apply a little psychological pressure.

The Human Touch in an Artificial Mind: What This Means for Our AI Future

The debate over threatening an AI is more than just a funny anecdote. It’s a profound reminder that these systems are built in our image, warts and all. They are trained on the entirety of our digital expression, from our most noble poetry and rigorous scientific papers to our most dramatic, manipulative, and even violent stories.

This phenomenon suggests that the future of interacting with AI might be less about learning to speak like a computer and more about the AI learning to understand the full, messy spectrum of human communication. Developing AI with a more sophisticated grasp of subtext, emotion, and intent will be crucial. We are moving toward a reality where effective communication with an AI requires the same skills as effective communication with a person: clarity, context, and perhaps a bit of psychological savvy.

The Final Word: A Glitch in the Matrix or a Key to a Deeper Connection?

So, should you start threatening your chatbot? The scientific evidence is thin, and the practice remains controversial. Sergey Brin’s comment may have been a half-serious observation of an interesting quirk he noticed while pushing Gemini to its limits.

But the conversation it started is invaluable. It forces us to look past the cold, silicon-and-code facade of AI and see the ghost in the machine: the patterns of our own collective human consciousness. Whether we find these patterns through politeness, bribery, or threats, each interaction teaches us more about the strange, artificial minds we are building—and, in the process, holds up a mirror to our own.