AI Buzzword Olympics: Topic #3

“Let’s Build Our Own LLM”. Just because your company has data does not mean it needs its own Large Language Model. Every AI journey seems to follow the same script.

Week 1: “We should use ChatGPT.”

Week 2: “We need an enterprise model.”

Week 3: “Our data is unique.”

Week 4: Someone says it:

“Why don't we build our own LLM?” At that moment, GPU manufacturers smile. Cloud providers start calculating revenue. And somewhere, an AI researcher quietly asks: “Does anyone actually know what they are proposing?”

The First Question Is Simple

Whenever someone says: “Let's build our own LLM.” I ask one question: Why? Not because building one is impossible. Because it will be an extremely expensive answer to the wrong problem. Your Data Does not Mean You Need Your Own Model. A common enterprise argument goes like this: “We have 20 years of proprietary documents.”

Therefore, “We need our own LLM.” Having data doesn't automatically justify building a foundation model. And most enterprise data is not even ready for it. How much is:

• Current?
• Accurate?
• Duplicate-free?
• Consistent?
• Legally usable?
• Relevant?
• Properly structured?

Much of it exists because someone once had to fill out a form. That is not the same thing as having a world-class training dataset. LLMs Are Not Magic. Large Language Model simply means a model trained on enormous amounts of data to learn patterns in language, code and other content. The difficult part is not just writing the model architecture. It is everything around it: Data. Compute. Training. Evaluation. Safety. Alignment. Infrastructure. Monitoring. Retraining.

And doing that reliably at enormous scale. Building a foundation model is not a weekend hackathon. It is an industrial-scale engineering program. The Real Asset Is not Your Model. Here is the uncomfortable truth: Your competitive advantage is probably not the model. It is your: Knowledge. Processes. Domain expertise. Customer understanding. Business rules. Proprietary data. Those don't necessarily require a new LLM. They require giving existing models the right information at the right time. That is a very different engineering problem. Build The Car. Don't Necessarily Build The Engine.

If someone gives you a world-class Formula One engine, you don't spend five years designing another engine. You build the fastest car around it. The same principle increasingly applies to AI. Foundation models are becoming infrastructure. The opportunity is in what you build around them:

• Enterprise knowledge
• Agents
• Workflows
• Retrieval
• Tool use
• Governance
• Domain intelligence
• AI-native products

The model is the engine. Your business solution is the car. So When does building your own model make sense? It can. If you:

• Have massive, genuinely unique datasets
• Operate at extraordinary scale
• Have deep ML research capability
• Need capabilities existing models cannot provide
• Can afford years of investment
• Have a compelling reason to control the entire model stack

Then yes. Build it. If you are OpenAI, Anthropic, Google, Meta, DeepSeek or a national research organization, the conversation is different. For everyone else, start with a simpler question: What problem are we actually trying to solve?

Need company-policy answers? You probably need retrieval.

Need consistent document generation? You probably need better prompting and context.

Need domain-specific answers? You probably need domain knowledge and evaluation.

Need an enterprise chatbot? You almost certainly don't need to reinvent language.

The Engineering Question: Good engineers have always asked: “Does this already exist?”

We don't build our own operating systems every time we build an application. We don't manufacture our own processors. We don't create a database engine for every product. We use proven foundations and innovate where it matters. LLMs are increasingly becoming part of that foundation. The innovation is not always in building the foundation. It is in what you build on top of it.

Questions For Every Technology Leader: Before approving an “own LLM” initiative, ask: What problem that existing models can not solve? What unique capability are we creating? What makes our data genuinely different? Who will operate and maintain the model? How will we evaluate it? How often will we retrain it? What is the five-year total cost?

If those questions don't have convincing answer, You are probably not looking at an AI strategy. You are looking at an AI aspiration. 

The next time someone walks into the room and confidently says: “Let's build our own LLM.” Don't immediately agree. Don't immediately disagree. Just ask: “What business problem becomes impossible to solve unless we build our own?” If the answer is clear, measurable and compelling, Maybe you are onto something. If the answer begins with: “Because everyone else is doing it”

Congratulations. You have just won another gold medal in the AI Buzzword Olympics.

Stay tuned for Next Week's Topic

"We will Just Add RAG."