AI is often seen as a cost-saving solution, but the hidden costs can quickly add up. While the model might be the most visible part, the real expenses come from hardware, labor, infrastructure, and oversight. Running AI on a laptop might work for a demo, but scaling it for real-world use requires careful planning. From failed attempts and retries to the need for human supervision and unexpected errors, AI projects can become expensive if not managed properly. This episode breaks down the often-overlooked costs and offers insights into how to avoid common pitfalls. Whether it’s cloud costs, data preparation, or the risk of bad outcomes, understanding these hidden expenses is key to building a successful AI strategy.
