It’s been a little over a year since the AI gold rush was ignited by the launch of ChatGPT. We’ve seen countless, excellent market maps emerge, documenting in detail how the AI landscape is exploding.

These market maps typically outline a few key areas: compute, AI models, infrastructure and apps.

The problem with the expansive market map is that it visually represents the market as if all sections of the map have equal weight. In reality, market momentum and buyer appetite varies considerably both within and across sections. It’s important to consider the traction of key use cases, and especially important to consider the buying ideal customer profile (ICP), in order to identify the products that have the best chances of taking off and becoming long term winners.

Fortune 5,000 companies (with legitimate budget, headcount, and renewal potential) have begun adopting AI technologies for both their IT stack and their product offering with incredible speed. From an investment standpoint, we thought it would be helpful to understand how these companies are starting to build their AI stacks.

The New “AI Simple Stack”

Although products will emerge to serve every AI use case you can think of, we’re starting to see the emergence of the “AI Simple Stack,” the core areas that every company will soon be evaluating — if they aren’t already. The staples of the new, streamlined stack will include at least three core components:

1. An AI model

• The role of the AI model is to be the best, fastest, and most accurate input/output machine:
• the end user enters a command or question
• the AI model must interpret the question and work on a solution
• the AI model produces the best output

2. A vector database

• Vector embeddings + vector search will make it easy to classify all product or enterprise data so that pulling the relevant information to a generative query will be faster, more accurate, and more complete.

3. A data retrieval mechanism

• The role of the retrieval mechanism is to access all potential data (including both internal data and application level data) that might be needed for problem solving, gain permission to that data, and grab that data so the best output can be delivered. Without a retrieval mechanism, an AI model will be largely limited to knowledge that it has been pre-trained on, which would severely inhibit the enterprise use cases. And without the vector database, the retrieval mechanism won’t know exactly which data to pull.

This stack will help business leaders and technology decision makers to supercharge both their employees, as well as their products. These are the two key areas of investment, and these executives are likely to pursue a minimum viable stack at first, as opposed to dozens of products that could be costly and time intensive to deploy.