The AI boom looks historic on the surface. Underneath, the structure tells a very different story.

In this episode, Neo examines what happens when capital flows in exactly the wrong direction. Drawing on analysis of more than 4,000 AI and machine learning startups and over $400 billion in disclosed funding between 2020 and 2025, she shows how a handful of mega-rounds came to dominate what we now call “innovation”.

Nineteen companies absorbed the vast majority of AI capital in 2024, while thousands of others competed for what remained. This rant unpacks why that kind of concentration feels exciting in real time, why it distorts behaviour across the ecosystem, and why the damage often only becomes visible years later.

Using a real marketplace case from the UAE, Neo traces how AI narratives collide with growth-stage reality in markets where Series B capital is scarce, infrastructure costs are high, and ownership of technology matters more than demos. From there, she zooms out to reveal a global pattern: capital clustering around foundation models and infrastructure, application-layer companies being starved of scale capital, and entire regions pushed into performative AI just to stay visible.

This is not an argument against AI. It is an argument about allocation, incentives, and endurance. If you are building, backing, or expanding AI companies and wondering why the boom feels loud but fragile, this episode explains why.