Bidding Farewell to “Please” and “Thank You”? Google Co-founder Sergey Brin Has a Wild Idea!
Hey everyone, John here, back with another dive into the fascinating world of AI! You know how sometimes you catch yourself saying “please” and “thank you” to your smart speaker or even to an AI chatbot? It’s a common thing. We treat them a bit like people, hoping a little politeness will get us better results. Well, what if I told you that one of the absolute giants of the tech world, the co-founder of Google himself, has a completely different, and frankly, a bit shocking idea?
Get ready, because today we’re talking about Sergey Brin and his suggestion that instead of being nice to AI, we should… well, kind of threaten it!
The Usual Way: Being Polite to Our Digital Pals
For a long time, the advice floating around about talking to AI, especially those chatty ones like ChatGPT, has been to be polite. People would add “please” at the beginning of their requests and “thank you” at the end. Why? A few reasons:
- Habit: It’s how we talk to other humans, so it feels natural to extend that courtesy to intelligent systems.
- Superstition: A little part of us wonders if being nice might somehow make the AI try harder or understand us better, even if we know it doesn’t have feelings.
- Testing the Waters: Some just like to see if politeness has any impact, even a tiny one, on the AI’s responses.
Most AI experts, however, would tell you that these large language models (LLMs) don’t actually understand politeness in a human sense. They don’t feel appreciated or offended. Their responses are based on patterns in the vast amount of text they’ve been trained on.
Enter Sergey Brin: The Unconventional Approach
Now, let’s shake things up! Sergey Brin, one of the brilliant minds who created Google, has recently shared an intriguing perspective. He suggests that we might get better results from these generative AI models, not by being polite, but by introducing a sense of “threat” or consequence into our prompts.
Lila: “John, wait a minute! ‘Threatening’ AI? What does that even mean? Are we supposed to tell it, ‘Answer this or else… what?’ That sounds a bit scary, and I thought AI didn’t have feelings!”
John: “That’s a fantastic question, Lila, and it’s important to clarify! When we say ‘threatening’ AI in this context, we’re absolutely not talking about real-world threats or making the AI ‘feel’ scared. AI doesn’t have emotions like humans do. What Brin is likely referring to is a specific way of structuring your instructions – what we call a ‘prompt’ – to make the AI understand the *importance* or *stakes* of getting the answer right. Think of it less like threatening a person, and more like setting a very clear, high-consequence condition for a computer program. For example, instead of just asking, ‘Please summarize this article,’ you might phrase it like, ‘Summarize this article accurately; if the summary is incorrect, it will lead to severe consequences for the next step of the project.’ This isn’t about making the AI *fear* consequences, but rather about nudging its internal ‘thinking’ process to prioritize accuracy and thoroughness because the ‘cost’ of failure (within its operational framework) is highlighted.”
Why Would This “Threat” Method Even Work?
This idea might sound counterintuitive, but there’s a logical (though still evolving) explanation for why it could be effective. It boils down to how these complex AI models process information and generate responses.
- Increasing “Stakes”: By adding a “threat” or a negative consequence if the AI fails, you might be implicitly telling the model that the task is of very high importance. This could make the AI’s internal algorithms work harder to find the absolute best, most accurate, or most comprehensive answer, as if it’s trying to ‘avoid’ the stated negative outcome.
- Clarity and Focus: Sometimes, adding a condition like “if you fail, X will happen” can make the AI focus more intensely on the core request and less on generating generic or overly cautious responses. It provides a clearer boundary for what constitutes a “good” answer.
- Prompt Engineering Evolution: This concept suggests that our understanding of how to “talk” to AI is still very much in its early stages. What we thought was intuitive (politeness) might not be the most effective way to optimize AI performance.
Lila: “So, it’s like we’re giving the AI a very strict deadline or a warning if it messes up, but without it actually understanding what a deadline or warning feels like?”
John: “Exactly, Lila! You’ve got it. It’s not about emotional understanding, but about setting up the ‘rules of the game’ within the prompt itself. When you tell a human, ‘If you don’t finish this by 5 PM, the whole project is delayed,’ they feel the pressure. When you tell an AI, ‘Complete this task perfectly; anything less will be considered a critical failure for this simulation,’ it’s about providing a strong signal within the data and parameters it understands, pushing it towards a higher quality output. It might be interpreted by the AI’s algorithms as a higher weight or priority for accuracy and completeness.”
What This Means for How We Talk to AI
This suggestion from Sergey Brin is a fascinating development for how we interact with AI, particularly in the field of:
- Prompt Engineering: This is a growing area where people learn and experiment with the best ways to phrase questions or instructions to AI models to get the desired results.
- Challenging Assumptions: It forces us to question our preconceived notions about AI. We often project human qualities onto AI, but their “thinking” processes are fundamentally different.
- Experimentation: It encourages more experimentation with various prompting techniques beyond simple instructions, exploring what truly unlocks the best capabilities of these powerful models.
Lila: “John, you just said ‘prompt engineering.’ What’s that? Is it like, building engines for prompts?”
John: “Haha, good guess, Lila! While it sounds a bit like building machines, ‘prompt engineering’ is actually the art and science of crafting the absolute best instructions or questions – what we call ‘prompts’ – to give to an AI model so it produces exactly what you want. Think of it like being a chef with a very specific, incredibly powerful oven. You don’t just throw ingredients in; you need to know exactly how to set the temperature, time, and specific steps in the recipe to get the perfect dish. Prompt engineering is about knowing those ‘settings’ and ‘recipes’ for talking to AI, whether it’s asking for a story, summarizing an article, or generating code.”
My Two Cents (John’s Perspective)
This whole idea from Sergey Brin is truly mind-bending and a fantastic reminder that our understanding of AI is constantly evolving. It makes me wonder just how much more we have to learn about effectively communicating with these systems. It’s not about being ‘mean’ to AI, but about discovering the most efficient way to guide its complex internal mechanisms to produce the best possible outcome. It certainly challenges the polite facade we’ve sometimes built around AI interactions!
Lila’s Takeaway
“Wow, this is so different from what I thought! So, AI isn’t really ‘listening’ to our ‘please’ or ‘thank you,’ but it *does* respond to how we explain the importance of a task. It’s like, I need to be super clear about the ‘stakes’ of what I’m asking for, not just polite. My mind is officially blown!”
This article is based on the following original source, summarized from the author’s perspective:
Google co-founder Sergey Brin suggests threatening AI for
better results