The technology world is currently undergoing a massive shift fueled by investors who are moving their money away from traditional companies and into new ventures focused completely on Artificial Intelligence (AI).

The Great Divide: Old Tech vs. New Tech

The tech landscape is splitting into two clear groups because of a move toward an “AI-first” approach:

• “Old Tech” (Pre-AI): This is traditional tech such as Software as a Service (SaaS) or similar software development. These companies typically raised money from large investors and planned to sell their stock to the public. We are seeing some of these companies conducting layoffs and framing it as pandering for AI adoption. For these companies, everything now needs to be totally redone into an AI-first paradigm.
• “New Tech” (Post-AI): These are brand-new companies that were created and funded specifically to develop AI. Investors started pouring money into these new AI companies around June 2023, which is also when the stock value of NVIDIA (a key chipmaker for AI) began rising very quickly.

The Investor-Driven Shift

This massive change is primarily being driven by investors who have essentially stopped funding anything they consider “Old Tech”. Because of this, many older companies attempting to reach for an IPO saw their prospects diminish around 2023, causing them to hold on and make pivots before considering a liquidity event. Instead, investors are now putting huge amounts of money into “New Tech” companies that are intensely focused on hiring developers to lead the advancement of AI-focused technologies.

Understanding the AI-First Paradigm

The AI-first movement is seen as a market inefficiency that will be progressively suppressed over a two- to six-year transition.

The fundamental basis of this paradigm is that AI is machine learning; it’s brute-forcing statistical regression and describing reality purely in math. This mathematically driven process has resulted in two primary machine learning categories: computation and simulation-based learning.

It is important to understand that large language models (LLMs) and chat are only a temporary starting point and the first example of extraordinary effectiveness. The true potential lies in describing reality in numbers, feeding large amounts of numbers to a machine, and enabling it to recognize patterns and scale beyond what is humanly possible. Beyond text, major categories like video and images are rapidly seeing applications of artificial intelligence applied to brute-forcing math at scale.

The Race Between Human and Artificial Intelligence

We are currently witnessing a race between organic intelligence (what humans can do) and artificial intelligence. The ultimate goal is for AI to be trained to successfully handle every single thing that human intelligence can do. Taking a snapshot of this comparison in early 2026, we expect to see this discrepancy between organic and artificial intelligence diminish significantly.