AI for founders: the core concepts you need to know

AI is changing products and business models in every sector. Even if you are not building an AI startup, knowing the concepts helps you make good decisions and speak the same language as investors, customers and your team.

Core concepts

• Artificial intelligence: systems that perform tasks needing human intelligence (understanding, predicting, deciding, generating)
• Machine learning: learning patterns from example data instead of hand-written rules
• Deep learning: machine learning with multi-layer neural networks; the basis of today's progress in vision, speech and language
• Large language model (LLM): a model trained on huge amounts of text that can understand and generate text; see our LLM guide
• Generative AI: models that produce text, images, audio or code

Training and inference

• Training: the model learning from data; very compute-intensive for large models.
• Inference: a trained model answering a new input; in products, each use costs money here; see our AI costs guide.

Language-model terms

• Token: the chunks of text a model processes; pricing and limits usually use token counts.
• Context window: how much text the model can consider at once
• Prompt: instructions and context given to the model; see our prompt engineering guide
• Hallucination: fluent but false output; see our hallucination guide

What it means for founders

Most startups do not train their own foundation models; they create value by combining hosted models with their own data, workflows and domain knowledge. Lasting advantage usually lies in data, distribution and customer relationships rather than the model; see building an AI startup.

Conclusion

Knowing the concepts helps you tell real opportunities from hype. Next, think about where AI can add value in your product; see our generative AI integration guide.