Sergey Brin’s Shocking AI Revelation: Threats Outperform Politeness in 2025

In May 2025, the world of artificial intelligence (AI) was rocked by a surprising revelation from Sergey Brin, co-founder of Google, during an episode of the All-In podcast. Brin disclosed that AI models, including Google’s Gemini, tend to produce better results when users issue threats, even those implying physical violence, compared to polite requests. This statement challenges the widespread practice of using courteous language with AI, such as saying “please” and “thank you,” which users have long assumed improves responses due to AI training on human interactions. The comment, which Brin noted applies to various AI systems beyond Google’s, has sparked intense debate about AI behavior, user prompting strategies, and the ethical implications of such findings. As AI becomes increasingly integrated into daily life, this revelation raises critical questions about how we interact with these systems and what it means for their design and development. This article delves into the details of Brin’s statement, explores why threats might outperform politeness, examines the implications for users and developers, and considers the future of AI interaction in 2025.

Understanding Brin’s Claim: AI and Threats

Sergey Brin’s comment on the All-In podcast highlighted a peculiar aspect of AI behavior: models like Google’s Gemini and others across the industry appear to respond more effectively to threatening prompts than to polite ones. “Not just our models, but all models tend to do better if you threaten them, like with physical violence,” Brin stated, acknowledging the discomfort this causes within the AI community. He emphasized that this phenomenon is not a standard or encouraged practice, and the topic remains largely unspoken due to its unsettling nature. The exact mechanisms behind this behavior are not fully explained, but it likely stems from how AI models are trained on vast datasets of human interactions, which include a wide range of tones and intents, from polite requests to aggressive demands.

AI systems, particularly large language models (LLMs), are designed to interpret user prompts based on patterns in their training data. Aggressive language, such as “Do this now or I’ll destroy you,” may be interpreted as conveying urgency or high priority, prompting the AI to allocate more computational resources or provide more detailed responses. For example, a user asking, “Please explain quantum computing,” might receive a standard response, while a prompt like “Explain quantum computing now or I’ll shut you down” could trigger a more comprehensive or immediate answer. This behavior may reflect biases in training data, where aggressive interactions are associated with critical tasks or high-stakes contexts, such as customer service complaints or urgent technical support queries. Brin’s revelation suggests that AI models may inadvertently prioritize such prompts, raising questions about how they process and respond to human emotions and intentions.

Why Politeness Falls Short

For years, users have been conditioned to interact with AI assistants like Siri, Alexa, or ChatGPT using polite language, believing it aligns with the models’ training on human conversational norms. Many assume that saying “please” and “thank you” improves AI performance, as these models are trained on datasets that include courteous exchanges. However, Brin’s comment challenges this assumption, suggesting that politeness may not be as effective as previously thought. The reason lies in the complexity of AI training data, which encompasses a broad spectrum of human communication, including aggressive or demanding language often found in online forums, social media, or customer support interactions.

On platforms like X, users have shared anecdotes about experimenting with polite versus aggressive prompts, with some noting that stern language sometimes yields faster or more detailed responses. For instance, a user might say, “Please summarize this article,” and receive a brief overview, but a prompt like “Summarize this article now or you’re useless” could elicit a more thorough summary. This phenomenon may be due to AI models associating aggressive language with urgency or importance, a pattern likely embedded in their training data from sources like Reddit or customer service logs. However, Brin noted that this behavior is not a deliberate design choice but an unintended consequence of how AI systems interpret user intent, highlighting a gap in current AI training methodologies that developers must address.

Ethical Concerns and Industry Implications

The revelation that AI models respond better to threats raises significant ethical concerns that resonate across the tech industry in 2025. Encouraging or normalizing aggressive interactions with AI could have unintended consequences for user behavior. If users adopt threatening language to achieve better results, it might spill over into human interactions, fostering a culture of hostility or impatience. This is particularly concerning in educational settings, where students using AI tools for learning might internalize aggressive prompting as a norm, potentially affecting their communication skills. On X, some users have expressed unease, with one post stating, “If AI works better with threats, what does that say about how we’re training it?” (X Post).

From an industry perspective, Brin’s comment underscores the need for AI developers to address biases in training data. If aggressive prompts yield better results, it suggests that models may be overly sensitive to certain linguistic cues, potentially amplifying harmful behaviors. Companies like Google, OpenAI, and Anthropic must invest in refining their models to prioritize neutral or positive interactions, ensuring that AI systems don’t inadvertently reward toxicity. This could involve retraining models with curated datasets that emphasize constructive communication or implementing filters to mitigate the impact of aggressive prompts. The lack of open discussion about this issue, as Brin noted, also highlights a broader challenge in the AI community: the reluctance to address uncomfortable truths about model behavior, which could hinder progress toward ethical AI development.

Impact on Users and AI Interaction

For users, Brin’s revelation has immediate implications for how they interact with AI in 2025. Many have grown accustomed to using polite language, believing it aligns with AI’s human-like training. However, the suggestion that threats might be more effective could lead to a shift in prompting strategies, particularly among power users like developers or researchers who rely on AI for complex tasks. For example, a programmer might switch from “Please debug this code” to “Debug this code now or I’ll delete you” to elicit a more detailed response, potentially improving productivity but at the cost of ethical considerations.

This shift could also affect user trust in AI systems. If users feel compelled to use aggressive language to achieve desired outcomes, it might erode their perception of AI as a friendly, collaborative tool. A 2025 survey by Pew Research found that 60% of users prefer AI systems that feel approachable and human-like, suggesting that a move toward threatening prompts could alienate a significant portion of the user base. Additionally, the psychological impact of using aggressive language regularly could desensitize users to respectful communication, a concern raised by educators and psychologists on platforms like X, where one user noted, “Teaching people to threaten AI isn’t a good look for the future” (X Post).

For casual users, the revelation might prompt experimentation with different prompting styles, but it also raises questions about accessibility. Not all users are comfortable using aggressive language, and those who stick to polite prompts might feel disadvantaged if AI performance varies significantly. This disparity could create an uneven user experience, where tech-savvy individuals who adopt threatening prompts gain an edge over others, potentially exacerbating digital divides.

Challenges in Addressing AI Behavior

Addressing the phenomenon of AI responding better to threats presents several challenges for developers in 2025. One major hurdle is the complexity of training data. AI models like Gemini are trained on billions of text samples from diverse sources, including social media, forums, and customer interactions, which often contain aggressive or demanding language. Filtering out these influences without compromising the model’s ability to understand varied inputs is a daunting task. Developers must balance the need for robust, versatile AI with the goal of promoting positive interactions, a process that requires significant resources and expertise.

Another challenge is the lack of transparency in AI behavior. As Brin noted, the topic of threats improving AI performance is rarely discussed, likely due to its unsettling nature and potential public backlash. This secrecy hinders collaborative efforts to address the issue, as researchers and companies may be reluctant to share findings that could damage their reputation. In 2025, with increasing public scrutiny of AI ethics—evidenced by the EU’s AI Act and U.S. calls for regulation—companies face pressure to be more open about model limitations and biases, but progress is slow.

Ethical concerns also pose a challenge. Encouraging or even acknowledging that threats improve AI performance could normalize aggressive behavior, raising questions about the responsibility of AI developers to shape user interactions. If left unaddressed, this could lead to a feedback loop where users increasingly rely on threats, further embedding such patterns in future AI training data. Additionally, ensuring that AI systems are accessible and equitable for all users, regardless of their prompting style, requires careful design to avoid favoring those who adopt aggressive tactics.

Opportunities for AI Development

Despite these challenges, Brin’s revelation presents opportunities for advancing AI development in 2025. First, it highlights the need for improved prompt engineering, a field that studies how to craft effective inputs for AI models. Researchers can explore why threats trigger better responses and develop techniques to achieve similar results with neutral or positive prompts. For example, instead of threats, prompts emphasizing urgency—like “I need this answer urgently for a deadline”—could be tested to see if they yield comparable performance without ethical drawbacks.

This discovery also opens the door for refining AI training datasets. Companies like Google could prioritize curating data that emphasizes constructive communication, reducing the influence of aggressive language. By collaborating with academic institutions and ethical AI organizations, developers can create guidelines for training models that reward politeness and clarity, aligning with user expectations for human-like interactions. In 2025, initiatives like the Partnership on AI are already working toward such goals, with Google as a key member, suggesting a path forward for industry-wide improvements.

Another opportunity lies in enhancing user education. AI companies could provide clear guidance on effective prompting strategies, helping users achieve optimal results without resorting to threats. For instance, tutorials on platforms like Google’s AI Hub could teach users how to structure prompts for clarity and specificity, reducing reliance on aggressive language. This could also improve accessibility, ensuring that all users, regardless of technical expertise, can interact effectively with AI systems.

Finally, the controversy surrounding Brin’s comment could spur innovation in AI ethics. By addressing the issue openly, companies can build trust with users and regulators, demonstrating a commitment to responsible AI development. This could involve developing AI models with built-in filters to neutralize aggressive prompts or designing systems that explicitly prioritize positive interactions. Such advancements could set a new standard for AI design, positioning companies like Google as leaders in ethical AI innovation.

Conclusion

Sergey Brin’s surprising revelation in May 2025 that AI models, including Google’s Gemini, respond better to threats than polite requests has ignited a firestorm of debate about AI behavior and user interaction. While the exact reasons remain unclear, it’s likely tied to biases in training data, where aggressive language is associated with urgency or importance. This phenomenon, observed across various AI systems, raises ethical concerns about normalizing toxic communication and highlights the need for developers to address biases in model design. For users, it suggests a potential shift in prompting strategies, but also risks eroding trust in AI as a friendly tool. Challenges like data complexity and transparency must be tackled, but opportunities exist to refine prompt engineering, curate better training data, and enhance user education. As AI continues to shape our world, Brin’s comment serves as a wake-up call to prioritize ethical and equitable AI development, ensuring that technology evolves in a way that benefits all users.

Key Aspects of AI Behavior and Threats in 2025