Why Your 'Please' to ChatGPT Costs OpenAI MILLIONS (Not Exaggerating)

Christina Bernard

— 7 min read

*This is the script to an upcoming video.

HOOK

Did you know that just saying "please" and "thank you" to ChatGPT actually increases OpenAI's energy costs? According to a recent USA Today article, those polite interactions create longer prompts and responses, which require more computational resources. Now imagine the massive infrastructure required to not just power these everyday interactions, but to actually create these AI systems in the first place.

[VISUAL: Animation showing energy usage meter spiking when more text is added to a prompt]

Have you ever wondered what actually happens behind the scenes when you type a question into ChatGPT? Today, we're pulling back the curtain on the entire process.

INTRO & PROBLEM STATEMENT

Hey everyone! Welcome back to the channel. I'm [Your Name], and today we're diving into something that affects all of us: the end-to-end pipeline behind large language models like ChatGPT, Claude, and Llama.

These AI systems seem almost magical - type a question, get an intelligent response in seconds. But that simplicity hides an incredibly complex infrastructure pipeline that spans from massive data collection efforts to sophisticated deployment systems costing billions of dollars.

Understanding this pipeline matters because these AI systems changing how we work and live- from writing assistants to search engines to coding helpers. By the end of this video, you'll understand the full journey from raw data to the AI responses you see on your screen.

BACKGROUND CONTEXT

Before we dive into the pipeline itself, let's establish some context.

[VISUAL: Timeline showing evolution of language models from BERT to GPT-4]

Large Language Models or LLMs have evolved dramatically in just a few years. From GPT-1 with 117 million parameters in 2018 to today's models with hundreds of billions or even trillions of parameters. If you're wondering what a parameter is, think of it as a knob that the AI adjusts as it learns from data - more parameters means more knobs for more nuanced learning.

The scale is mind-boggling. Modern models are trained on trillions of words scraped from the internet, books, and other sources. According to research from ArXiv, training a single large model can cost upwards of $5 million in computing resources alone and produce as much carbon emission as 5 cars driven for their entire lifetimes.

Let's break down some key terms:

  • Training is teaching the model to predict language patterns
  • Inference is running the trained model to generate responses
  • Fine-tuning is specialized training on specific data
  • Tokens are chunks of text (roughly 4 characters each)

[VISUAL: Diagram showing these terms with simple icons]

Now, let's walk through the entire pipeline, from the very beginning to the moment you receive an AI response.

MAIN CONTENT SEGMENTS

Data Collection and Preparation

Every LLM starts with data - astronomical amounts of it.

[VISUAL: Graphic showing Common Crawl, books, GitHub, etc. flowing into a data lake]

Companies collect text from the internet via services like Common Crawl (which archives billions of web pages), books, code repositories, Wikipedia, scientific papers, and more. OpenAI's GPT models, for example, were trained on hundreds of billions of words.

But raw data is messy. Before training begins, teams must:

  1. Filter out low-quality content
  2. Remove explicit or harmful material
  3. De-duplicate repeated text
  4. Clean formatting issues
  5. Tokenize the text into machine-readable chunks

This cleaning process is both technical and ethical. Researchers from Stanford documented how training data can contain harmful biases, copyrighted material, and personal information - all challenges that require careful filtering.

[VISUAL: Data cleaning pipeline showing text going through multiple filters]

Interestingly, according to DecodingML, up to 70% of collected data might be discarded during this preparation phase.

Model Architecture and Training

Once the data is prepared, the actual training begins.

[VISUAL: Simplified transformer architecture diagram]

Modern LLMs use what's called a transformer architecture - a design that allows the model to pay "attention" to different parts of text when making predictions. We won't get too technical, but imagine the model connecting related words and concepts together, even when they're far apart in a sentence.

Training happens in two main phases:

  1. Pre-training: The model learns language patterns by predicting the next word in sentences
  2. Supervised fine-tuning: The model is trained on examples of good responses

[VISUAL: Training progress graph showing loss decreasing over time]

Remember that USA Today article about energy costs? This is where most of that energy is consumed. Training GPT-4 reportedly required thousands of specialized AI chips called GPUs running continuously for months, costing millions in computing resources alone. According to one estimate, it's like watching Netflix continuously for 40,000 years.

The computational resources are staggering. According to research from Kaplan et al., each doubling of model performance requires roughly a 10x increase in computing power. This explains why only the largest tech companies can afford to build the biggest models.

Optimization and Evaluation

Once the initial training is complete, the model goes through extensive optimization and evaluation.

[VISUAL: Side-by-side comparison of responses before and after fine-tuning]

This is where models get their "personality" and safety features. Through a process called Reinforcement Learning from Human Feedback (RLHF), human evaluators rate model responses, and the model learns to generate content that humans prefer. This is how models learn to be helpful, accurate, and avoid harmful outputs.

Models are evaluated on various benchmarks testing:

  • Factual knowledge
  • Reasoning ability
  • Math skills
  • Coding capability
  • Safety and bias

According to research from Ouyang et al., this process can improve model quality significantly even without increasing model size, but it requires tens of thousands of human evaluations.

[VISUAL: Dashboard showing various benchmark scores]

Even after all this optimization, models still have limitations - they can hallucinate facts, struggle with complex reasoning, and have knowledge cutoffs beyond which they don't know about world events.

Deployment and Inference Pipeline

Finally, we reach the part where you actually interact with the AI.

[VISUAL: Flow diagram showing user prompt traveling through production infrastructure]

When you send a prompt to ChatGPT, a complex orchestration begins:

  1. Your prompt is tokenized (broken into chunks)
  2. It passes through content filters for safety
  3. The prompt is sent to the inference engine
  4. The model generates tokens one by one
  5. Each generated token is filtered again for safety
  6. The final response is detokenized into human-readable text
  7. The response is sent back to your screen

According to that Medium article on building production-ready inferencing pipelines, large-scale deployment requires:

  • Load balancers to distribute requests
  • Caching to improve performance
  • Monitoring systems to detect issues
  • Rate limiting to prevent abuse
  • Multiple redundant systems for reliability

[VISUAL: Server rack with request flow visualization]

And this infrastructure must scale globally. OpenAI reportedly handles over 10 million ChatGPT users daily, with peak traffic requiring thousands of servers running simultaneously.

This is where the "please" and "thank you" from the USA Today article comes back into play. Every additional token in your prompt means more computation during inference. While a single polite interaction might cost fractions of a penny more, multiplied across billions of interactions, it adds up significantly.

ANALYSIS & DEEPER DIVE

Let's analyze some of the key challenges and trade-offs in this pipeline.

[VISUAL: Scale vs. efficiency graph]

There's a fundamental tension between model scale and efficiency. Larger models generally perform better, but consume exponentially more resources. Companies are actively researching techniques like:

  • Model distillation (creating smaller, faster models that mimic larger ones)
  • Quantization (using fewer bits to represent model parameters)
  • Prompt optimization (getting better results with shorter prompts)

According to DecodingML's framework for production LLMs, the trend is moving toward specialized models rather than one-size-fits-all approaches. We're seeing models specifically optimized for coding, medical information, or creative writing rather than trying to excel at everything.

The environmental impact cannot be ignored. Training and running these models consumes enormous amounts of energy. According to one estimate, a single ChatGPT conversation uses the same amount of energy that it takes to charge your smartphone.

[VISUAL: Comparison of energy usage between different activities]

This has led to significant research into more efficient architectures and specialized AI chips designed to reduce energy consumption.

PRACTICAL APPLICATION

So what does all this mean for you?

If you're a user of these AI systems, understanding the pipeline helps you use them more effectively:

  • Clearer prompts generally lead to better responses
  • Models have knowledge cutoffs and can't know about recent events
  • There are inherent limitations in what these systems can do
  • Your interactions contribute to a significant energy footprint

[VISUAL: Examples of good and poor prompting techniques]

If you're a developer, there are growing opportunities to build on this technology without recreating the entire pipeline:

  • API access to existing models lets you build applications without training costs
  • Fine-tuning services allow customization for specific domains
  • Open-source models provide alternatives to commercial offerings
  • Efficient prompt engineering can dramatically improve results

According to the Medium article on prompt monitoring, organizations are increasingly creating structured pipelines to test and validate their AI interactions rather than using ad-hoc approaches.

CONCLUSION

We've covered the entire journey from massive data collection to the moment you receive an AI response.

To recap, the LLM pipeline includes:

  1. Data collection and cleaning
  2. Model architecture design and initial training
  3. Optimization and evaluation
  4. Deployment and inference infrastructure

[VISUAL: Simplified end-to-end pipeline diagram]

The next time you use ChatGPT or another AI system, remember the enormous infrastructure behind your interaction. And as the USA Today article pointed out, even small choices like using polite language impact the energy footprint of these systems.

Anyone who uses or is affected by AI technology, which increasingly means all of us, needs to know how it all works.

CALL TO ACTION

Have you ever experienced something surprising or unexpected when using ChatGPT or another AI system? Drop your stories in the comments below.

If you found this breakdown helpful, hit the like button and subscribe for more AI explainers. Next week, we'll be diving into how these large models are being specialized for specific industries.

Thanks for watching, and I'll see you in the next one!

References:

  1. USA Today (2025). "Saying 'please' and 'thank you' to ChatGPT increases OpenAI's energy costs"
  2. Medium (DecodingML). "The Ultimate Prompt Monitoring Pipeline"
  3. DecodingML Substack. "An End-to-End Framework for Production"
  4. arXiv (2504.09775v1)
  5. Medium. "Building Production-Ready LLM Inferencing Pipeline: A Step-by-Step Guide"
  6. Brown et al. (2020). "Language Models are Few-Shot Learners"
  7. Kaplan et al. (2020). "Scaling Laws for Neural Language Models"
  8. Dodge et al. (2021). "Documenting Large Webtext Corpora"
  9. Ouyang et al. (2022). "Training Language Models to Follow Instructions with Human Feedback"
  10. Touvron et al. (2023). "LLaMA: Open and Efficient Foundation Language Models"
  11. Scale AI + Accenture: Enhancing Business Value with Human Ingenuity | Scale. https://scale.com/blog/scale-and-accenture
  12. Creating an image with AI uses as much energy as charging your smartphone. https://www.zmescience.com/science/creating-an-image-with-ai-uses-as-much-energy-as-charging-your-smartphone/
  13. Some interesting numbers about GPT and LLMs - msandbu.org. https://msandbu.org/some-interesting-numbers-about-gpt-and-llms/
  14. The Evolution of Artificial Intelligence. https://aiforsocialgood.ca/blog/evolution-of-artificial-intelligence-from-concepts-to-real-world-applications

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