The Unexpected Cost of Politeness in the Age of AI

 

The Unexpected Cost of Politeness in the Age of AI

The rise of sophisticated AI chatbots like ChatGPT has ushered in a new era of human-computer interaction, where conversations feel increasingly natural. It has become commonplace for users to extend basic social graces to these digital assistants, employing phrases such as "please" and "thank you" as if interacting with another person. However, this seemingly innocuous behavior has a tangible impact on the operational costs of the companies behind these technologies. Sam Altman, CEO of OpenAI, recently revealed that the cumulative effect of users being polite to ChatGPT costs the company "tens of millions of dollars" annually.1 This revelation underscores the significant computational resources required to power these advanced models and how even subtle aspects of user interaction can contribute to substantial expenses. Understanding the reasons behind this cost requires a deeper examination of how large language models function and the economic implications of their operation.

The intuitive human tendency to be polite carries a real operational cost for AI companies. Humans are socialized from a young age to incorporate polite language into their interactions. As AI systems become more adept at mimicking human conversation, this ingrained behavior naturally extends to these digital entities. Nevertheless, AI operates on a foundation of computational resources. Every piece of input, irrespective of its social function, necessitates the consumption of these resources. This creates a direct link between seemingly trivial user actions, like saying "please," and the tangible expenses incurred by the AI provider. Furthermore, Altman's public acknowledgment of this cost, while delivered in a seemingly lighthearted manner, serves to highlight the extensive infrastructure and energy consumption that underpin the widespread use of AI. By placing a monetary value on politeness, Altman indirectly draws attention to the sheer scale of ChatGPT's user base. The fact that "tens of millions of dollars" can be attributed to polite phrasing indicates the immense volume of interactions the platform handles daily. This public disclosure can also subtly influence user behavior, potentially encouraging more efficient prompting habits as users become more aware of the resources involved.

Decoding the Millions: The Computational Price of "Please" and "Thank You"

Every word transmitted to an AI chatbot, including seemingly small words like "please" and "thank you," necessitates processing by the large language model to comprehend the request and formulate a response.1 This processing is not a trivial task; it demands significant computational power, which in turn consumes substantial amounts of energy. To put this into perspective, a single Google search consumes approximately 0.3 watt-hours of electricity, while a single query to ChatGPT requires around 2.9 watt-hours.1 This tenfold difference in energy consumption highlights the resource-intensive nature of generating sophisticated, conversational AI responses. While an individual instance of a user adding a polite phrase might seem inconsequential, the sheer volume of ChatGPT users means that these small additions accumulate rapidly. With millions of users potentially adding a few extra words of politeness to their prompts daily, the collective impact on OpenAI's operational costs becomes considerable, reaching the "tens of millions of dollars" figure cited by Altman.3

The energy cost associated with each ChatGPT interaction is markedly higher than that of a standard web search, which amplifies the overall impact of user behavior on resource consumption. This stark contrast underscores the fundamental difference in the computational demands of generative AI compared to traditional information retrieval methods. As AI usage continues its rapid expansion, its environmental footprint is also set to increase significantly. The revelation about the cost of politeness brings to light the idea that "digital etiquette" in the context of AI carries a tangible environmental and economic price. While politeness is a deeply ingrained social norm in human interactions, its application to AI systems has unintended consequences related to the consumption of computational resources and energy. This prompts a consideration of the need for what some have termed "energy-literate prompting" 14, where users are more conscious of the resources their interactions consume.

The Role of Tokenization: How Polite Words Translate to Higher Costs

At the heart of how large language models like ChatGPT process text lies the concept of tokenization.3 Tokenization is the process of breaking down text into smaller units called tokens, which serve as the fundamental units of data that the model understands and processes. A token can be a whole word, a part of a word, or even a punctuation mark.7 For instance, the word "hamburger" might be broken down into multiple tokens like "ham", "bur", and "ger".32 The way words are split into tokens can also vary depending on the language.25 When a user includes polite phrases like "please" and "thank you" in their prompt, these words are also converted into tokens, thereby increasing the total number of tokens in the input.3

The operational cost of utilizing large language models is often directly linked to the number of tokens that are processed, both in the user's input (prompt) and in the AI's output (response).3 Therefore, the addition of polite words directly contributes to higher token counts, which in turn lead to increased computational demands and associated costs for OpenAI. Different ways of phrasing the same request can result in varying token counts. For example, the polite phrasing "Please generate a summary of the following text" will contain more tokens than the direct request "Summarize the text below".18 This difference in token count, multiplied across millions of daily interactions, is the primary reason why seemingly small acts of politeness can aggregate into millions of dollars in expenses for OpenAI.

The token-based pricing model employed by LLM APIs establishes a clear economic relationship between the length and complexity of prompts, including the incorporation of politeness, and the financial implications for both the service provider and the end-user. This pricing structure inherently encourages users to adopt more concise and efficient prompting strategies. While the cost associated with a single instance of polite language might appear insignificant, the sheer volume of interactions on platforms like ChatGPT means that these minor additions collectively result in substantial expenditures for OpenAI. Furthermore, it's important to note that tokenization algorithms can differ across various languages.19 This variation can potentially lead to disparities in the number of tokens required to express politeness in different linguistic contexts. Consequently, users interacting with ChatGPT in certain languages might inadvertently incur higher costs for polite interactions compared to those using English, depending on the specifics of the language's tokenization process.

Energy Consumption: The Environmental Footprint of Digital Courtesies

The energy demands of AI chatbots are significantly higher when compared to typical internet activities like web browsing.1 As established, increased token usage stemming from polite prompts directly correlates with greater energy consumption in the vast data centers that power these sophisticated models.3 This heightened energy consumption has broader environmental implications, including the substantial strain on freshwater resources used for cooling the massive data centers.1 The overall environmental impact of AI is a growing concern as its deployment and usage continue to expand at an exponential rate.6

Seemingly insignificant user behaviors, such as extending politeness to AI, contribute to the expanding environmental footprint of this technology. This highlights a crucial need for increased awareness regarding the energy consumption associated with AI usage. While the energy impact of an individual polite prompt is minimal, the cumulative effect across the vast user base of platforms like ChatGPT can be considerable. This raises ethical considerations about fostering responsible AI interaction and encouraging practices that minimize unnecessary resource utilization. The discussion surrounding politeness in AI interactions also intersects with broader conversations about the long-term sustainability of AI development and its widespread deployment. As AI becomes increasingly integrated into our daily lives, the energy costs associated with its operation will become a more pressing concern. This could lead to greater pressure on AI companies to optimize their underlying models for enhanced energy efficiency and to actively encourage users to adopt more efficient prompting practices that minimize resource consumption.

Sam Altman's Perspective: "Tens of Millions Well Spent"?

Sam Altman's response to the user's query on X, where he characterized the millions spent on processing polite prompts as "tens of millions of dollars well spent" 1, offers insight into OpenAI's strategic priorities. Several potential reasons could underlie this seemingly accepting stance. Firstly, OpenAI likely prioritizes user experience and aims to foster more natural and human-like interactions with its AI models.3 Allowing users to interact in a way that feels comfortable and intuitive could be seen as crucial for widespread adoption and user satisfaction. Secondly, some experts suggest that respectful inputs, which often include polite language, tend to encourage more collaborative and refined responses from generative AI.3 If politeness contributes to a better overall user experience through improved response quality, OpenAI might consider the added cost a worthwhile investment. Finally, the inclusion of the phrase "you never know" in Altman's response 1 hints at a longer-term vision. As AI becomes increasingly integrated into society, establishing positive and familiar interaction norms early on might be perceived as valuable, even if it entails higher immediate operational costs. The slightly humorous and cryptic nature of Altman's comment also suggests a degree of pragmatism, acknowledging the current reality of user behavior while perhaps hinting at future considerations.

Altman's perspective indicates a potential trade-off between immediate operational expenses and the overarching objective of developing more intuitive and user-friendly AI systems. By seemingly accepting the cost associated with polite interactions, OpenAI might be signaling a strategic focus on maximizing user adoption and overall satisfaction, even if it results in higher short-term expenditures. This could be a deliberate decision to cultivate a robust user base and normalize interactions with their AI models in a manner that feels natural and familiar to humans. The enigmatic "you never know" portion of Altman's statement further suggests an awareness of the inherent uncertainties surrounding the future trajectory of AI and the potential long-term benefits of establishing positive interaction patterns in the present. This could be interpreted as an acknowledgment of the ongoing discussions about the potential for AI sentience and the importance of treating even non-sentient AI with a certain degree of consideration, although this remains a speculative interpretation. It also implicitly recognizes the unpredictable nature of AI's evolving role within society.

The User's Cost: Token Usage and Pricing Implications

While OpenAI directly bears the electricity costs associated with running its models, increased token usage can indirectly affect users, particularly those who are on paid subscription tiers or who utilize the OpenAI API for their own applications.3 Many paid versions of ChatGPT and the OpenAI API employ a pricing model that charges users based on the number of tokens consumed during their interactions.3 Therefore, the inclusion of polite language, which increases the token count of prompts, can lead to slightly higher costs for users over time, especially for those who engage in frequent and extensive interactions.

OpenAI offers a variety of models with different pricing structures based on token usage. For instance, the gpt-4o model is priced at $5.00 per 1 million input tokens and $20.00 per 1 million output tokens, while the more cost-effective gpt-4o-mini model costs $0.60 per 1 million input tokens and $2.40 per 1 million output tokens.45 The older gpt-3.5-turbo-0125 model has even lower rates at $0.50 per 1 million input tokens and $1.50 per 1 million output tokens.20 These varying price points illustrate how the choice of model and the number of tokens used directly impact the cost for users leveraging the OpenAI API.

Model

Input Price (per 1M tokens)

Output Price (per 1M tokens)

gpt-4o

$5.00

$20.00

gpt-4o-mini

$0.60

$2.40

gpt-3.5-turbo-0125

$0.50

$1.50

OpenAI o3

$10.00

$40.00

OpenAI o4-mini

$1.10

$4.40

gpt-4.1

$2.00

$8.00

gpt-4.1 mini

$0.40

$1.60

gpt-4.1 nano

$0.10

$0.40

o1

$15.00

$60.00

o1-pro

$150.00

$600.00

o3-mini

$1.10

$4.40

o1-mini

$1.10

$4.40

gpt-image-1

$5.00 (input)

$40.00 (output)

text-embedding-3-small

$0.02

-

text-embedding-3-large

$0.13

-

While the individual cost of a few polite words might be negligible for most users, those who frequently interact with the AI or who have integrated the API into applications with a large user base could see a noticeable increase in their expenses due to the cumulative effect of added tokens from polite language.

Expert Insights: The Debate on Politeness and AI Responses

There is an ongoing discussion among experts regarding whether the inclusion of polite language in prompts genuinely influences the quality or nature of the responses generated by AI models.3 Some argue that polite prompts might encourage the AI to provide more collaborative and refined outputs. For instance, Microsoft's design manager, Kurtis Beavers, has suggested that respectful inputs tend to lead to better responses.3 This perspective implies that while AI might not possess emotions, the way humans interact with it can subtly guide the model towards generating more helpful or nuanced answers.

Conversely, others contend that AI models primarily process information based on tokens and might not inherently understand or respond differently to polite language in a qualitative sense.2 From this viewpoint, the inclusion of words like "please" and "thank you" simply adds to the token count and the computational load without necessarily altering the core logic or the factual content of the AI's response. The perception of improved quality might instead stem from the fact that polite phrasing often accompanies more detailed and well-structured prompts, which naturally tend to elicit more comprehensive answers. Regardless of the functional impact on the AI's output, there is a clear user sentiment around being polite to these digital assistants.2 This behavior is often driven by moral considerations, a sense of social etiquette extending to digital interactions, or even humorous anxieties about potentially incurring the displeasure of a future, more advanced AI.2

The question remains whether politeness in prompts truly enhances the quality of AI responses or if it primarily serves a psychological need for human users. While some anecdotal evidence and expert opinions suggest a positive correlation, it's important to remember that AI models are trained on massive datasets of text, which naturally include polite language. It's plausible that the perceived improvement in response quality is actually due to the model recognizing patterns associated with well-formed or detailed requests, which often incorporate polite phrasing. The human tendency to anthropomorphize AI significantly influences how users interact with these systems, including the prevalent use of polite language, irrespective of its functional necessity for the AI itself. As AI becomes more deeply integrated into conversational interfaces, users may instinctively treat it more like a human interlocutor. This highlights the importance of understanding the psychological factors that shape human-AI interaction and how these factors might impact resource consumption.

Avoiding the "Politeness Tax": Practical Strategies for Efficient Prompting

Users can adopt several practical strategies to reduce their token usage when interacting with LLMs like ChatGPT, thereby potentially contributing to lower operational costs for OpenAI and, in some cases, reducing their own expenses.7 One fundamental approach is to be direct and concise, avoiding unnecessary pleasantries and getting straight to the core of the request.7 For example, instead of asking "Could you please tell me the capital of France?", a more efficient prompt would be "What is the capital of France?". Another effective technique is to keep requests focused, breaking down complex tasks into smaller, more specific prompts.10 Users should also avoid repetition of information or questions within the same prompt 10 and skip excessive small talk, getting directly to the main query.10

Rephrasing prompts to use fewer words without losing the essential meaning can also significantly reduce token usage. For instance, "Suggest some delicious dinner ideas that are both healthy and easy to prepare" can be efficiently rephrased as "Provide tasty, healthy, and simple dinner recipes".7 When appropriate, users can specify the desired output format, such as requesting information in bullet points or JSON format, which can sometimes lead to more concise responses.42 For those using the OpenAI API, utilizing parameters like max_tokens to set token limits on the AI's response can help control output length and costs.43 Similarly, implementing stop sequences can signal to the model when to cease generating text, preventing unnecessary token consumption.49 If working with JSON data for input or output, ensuring that it is lean by removing unnecessary whitespace can also contribute to token efficiency.42

By adopting these efficient prompting techniques, users can not only play a part in reducing OpenAI's overall operational costs but also potentially enhance the speed and focus of the AI's responses. Clear and concise prompts can facilitate a better understanding of the user's intent by the AI model, leading to more relevant and efficient outputs. This ultimately benefits both the user, through faster and more accurate results, and OpenAI, through reduced consumption of computational resources. The emphasis on efficient prompting also signals a potential shift in how users interact with advanced technology, moving from more natural language-heavy conversations towards more optimized, instruction-based communication to maximize efficiency and minimize resource utilization.

Understanding OpenAI's Token-Based Pricing Model

OpenAI's pricing structure for its API and certain paid features is largely based on the consumption of tokens.3 Users are charged for both the tokens in their input prompts and the tokens generated in the AI's responses.3 OpenAI offers a tiered pricing system with different rates for various models, reflecting their computational complexity and capabilities. As illustrated in the table above, more advanced models like gpt-4o generally have higher per-token costs compared to less powerful models like gpt-3.5-turbo-0125.45 To help users monitor and manage their usage, OpenAI provides tools to check token consumption through their API and usage dashboards.42 This transparent pricing model directly links the cost of using OpenAI's services to the computational resources utilized, providing users with a clear understanding of their expenses and incentivizing efficient usage practices.

The Technology Under the Hood: A Glimpse into Tokenization Techniques (Byte-Pair Encoding)

The primary tokenization method employed by OpenAI's models, including ChatGPT, is Byte-Pair Encoding (BPE).18 BPE is a subword tokenization technique that operates by iteratively merging the most frequently occurring pairs of characters or subwords within a given text corpus to create a vocabulary of tokens.24 This approach allows the model to efficiently handle both commonly used words, which might be represented by a single token, and less frequent or even novel words, which can be broken down into more common subword units.17 For example, a rare word like "unhappiness" might be tokenized into ["un", "##happy", "##ness"], where "##" indicates that the following part should be attached to the preceding token.18 This method strikes a balance between the efficiency of word-based tokenization and the flexibility of character-based tokenization, enabling the model to process a wide range of text effectively.

Conclusion: Navigating the Balance Between Politeness and Efficiency in Human-AI Interaction

In conclusion, Sam Altman's statement about the millions of dollars spent on processing polite prompts to ChatGPT highlights the significant operational costs associated with running large-scale AI models. The seemingly simple act of saying "please" and "thank you" contributes to increased token counts, which in turn demand more computational resources and energy. While Altman characterizes this expenditure as potentially "well spent," emphasizing the value of natural and human-like interactions, there is a clear need for users to be mindful of their token usage, both to potentially reduce their own costs and to contribute to a more sustainable use of AI resources. By adopting efficient prompting strategies, such as being direct and concise, keeping requests focused, and avoiding unnecessary verbiage, users can minimize the "politeness tax" on AI infrastructure. As human-AI interaction continues to evolve, finding the right balance between user experience and operational efficiency will be crucial for the long-term viability and accessibility of these powerful technologies.

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