Baking AI: How Different Is It from Baking Bread?

AI Is Baking Bread?

Imagine this: What if the artificial intelligence (AI) services we use every day were actually similar to freshly baked bread? Just as the bread we love is created by precisely mixing flour, yeast, and water and going through a patient time in a hot oven, modern Large Language Models (LLMs) undergo a very similar process.

People often use expressions suggesting that AI “thinks” or “learns” on its own. However, from a technical perspective, AI model training is actually closer to a process of following a very precise “recipe.” Today, we will explore the interesting journey of how this massive technology called AI is completed and delivered to us, much like bread on our dining table.

Why Does This Matter?

As AI technology advances brilliantly, we have entered an era where anyone can utilize AI models to build their own services. Surprisingly, cases have emerged where small startup teams of 12 people train massive models on a 70B (70 billion parameters) scale (Source 8).

There is a clear reason why we need to understand this process through the metaphor of “baking bread.” Knowing the difference between the process of creating a model (training) and using the result (inference) allows us to clearly grasp why certain AI services are expensive and slow, or why they are difficult to tune as we desire. When understood through metaphors, complex technology feels much more familiar.

Easy Understanding: AI’s ‘Bread Baking’ Analogy

Simply put, AI training is the process of making precise dough.

1. Kneading (Training): Training a deep machine learning model is like mixing various ingredients to make dough according to a recipe (Source 2). In this process, the model builds its foundation as a ‘Base Model.’ Specifically, it repeats the game of reading half of a document and guessing what the other half is, improving performance by receiving rewards the closer it gets to the answer (Source 6).
2. Serving After Baking (Inference): Once training is complete, the model becomes well-baked bread (weights). Now, when we ask the AI a question, it is the process of slicing the finished bread and delivering it quickly to the customer (Source 3). Baking bread takes a long time, but once the bread is out, slicing and serving it is relatively fast. This ‘slicing and serving’ process determines the AI response speed we feel in our daily lives.

Of course, this process also has limitations. A model (trained model) baked by mixing all ingredients according to a specific recipe is easy to make and accessible, but once baked, it has the disadvantage of being very difficult to change into bread of a different taste (Source 2).

Current Situation: Where Are We Now?

Current technology is advancing to a stage where models are trained to be smaller and faster. In the past, people thought training was only possible with massive capital, but now, cases of training powerful models at a cost of around $10,000 by utilizing optimization technology and cloud resources are increasing (Source 8).

However, AI model training still requires an enormous amount of computational resources. As of 2025, the GPU (Graphics Processing Unit) cloud market is very hot with resource competition for AI and LLM training (Source 9). It is as if we have just begun to realize how to efficiently handle the massive oven that is AI.

What Will Happen in the Future?

Technicians are now researching smarter training methods to resolve bottlenecks that occur during training (Source 7). In the future, the ovens that bake the bread (training infrastructure) will become much more precise, and ‘Fine-Tuning’ technology, which slightly changes the taste of the bread on the spot according to the user’s needs, will also become more popularized.

You might also soon experience ‘baking’ your own customized AI model at home that perfectly suits your taste. The only thing to remember is that AI is a model that has undergone a sophisticated training process of matching patterns within vast data, rather than actually ‘understanding’ like we do (Source 5).

MindTickleBytes’ AI Reporter Perspective

When we express that we are ‘training’ AI, we often confuse it with human intelligence. However, the model is a thoroughly calculated result, much like baking bread. When we understand the answers provided by AI as the product of precisely baked logic rather than treating them as magic, we can finally utilize AI more smartly. Remember that technology is not magic, but the result of a precise recipe.

References

1. A Theory Guided Scaffolding Instruction Framework for …
2. The Cake that is Intelligence and Who Gets to Bake it: An AI Analogy and its Implications for Participation
3. What Is LLM Inference, Really? A Deep Technical Walkthrough - Karthika Raghavan
4. Metaphors - GenLaw

5. [Understanding large language models demands distinguishing human projection from machine cognition

   Communications Psychology](https://www.nature.com/articles/s44271-026-00508-6)

6. Author, assistant, and persona: the metaphors I use for …
7. LLMTrainingBottleneck Breakthrough 2026: Subquadratic Stealth…

8. [LLMTrainingGuides: Fine-Tuning & LoRA

   Spheron](https://www.spheron.network/blog/topics/llm-training/)

9. [GPU Cloud Market Share2025

   Zhiwei Li](https://lzwjava.com/notes/2025-07-26-gpu-cloud-ai-2025-en)