It’s not the tools!

Many AI initiatives fail from the start. Some sources put the number at 80% or higher. We'll talk plenty about the why and how in this newsletter, but today, I want to talk about one thing that's not it: the tools.

Practitioners in any discipline have strong opinions about their preferred tools and tech stacks at just about any altitude:

• Which editor/IDE is best?

• AWS, GCP or Azure?

• MySQL or PostgreSQL?

• Pytorch versus Tensorflow

• React versus Angular versus whatever the new sexy frontend framework is (Svelte?)

Our engineering mindset encourages us to look for the elusive "optimal" choice, but...

Here's the thing

There is often an inverse relationship between the fierceness of the debate over which tool is best and its ultimate impact on the project outcome. That's because the things that matter can be discussed (relatively) objectively. The critical decision lies in the category:

• Microservices or monolith

• Relational database or NoSQL

• Classical ML, special-purpose neural network, or GenAI LLM?

Once those have been made, the rest does come down to personal preferences and tastes.

Keep it simple

Your project won't go up in smoke just because you selected Django over Ruby on Rails, your infrastructure runs on Azure rather than AWS, or you picked TensorFlow instead of PyTorch. So choose one, move forward, and concentrate your energy on the choices that truly matter.