The Numbers Behind AI

The Numbers behind AI's substrate independence:

Market Size and Growth:

• The global AI market was valued at around $62.35 billion in 2020 and is projected to reach over $312 billion by 2026, with a compound annual growth rate (CAGR) of around 30%.
• The substrate-independent AI market segment, which focuses on creating AI systems that can seamlessly run across various hardware platforms, is expected to see significant growth within this overall market.

Device Diversity:

• By 2025, it is estimated that there will be over 75 billion Internet of Things (IoT) devices connected worldwide. Substrate independence could allow AI to efficiently run on these diverse devices, ranging from sensors and wearables to industrial machinery.

Resource Efficiency:

• Cloud computing is a major component of modern AI deployment. Substrate independence could lead to better resource utilization, potentially reducing cloud server costs by up to 40% according to some estimates. This cost reduction could make AI applications more accessible and affordable.

Performance Scaling:

• AI models have been growing in size and complexity. The number of parameters in state-of-the-art models like GPT-3 is in the hundreds of billions. Substrate independence could enable these models to seamlessly scale and take advantage of different hardware's capabilities for faster and more efficient processing.

Edge Computing:

• Edge AI involves running AI algorithms on local devices, reducing the need for sending data to remote servers for processing. Substrate independence would enable AI models to adapt and run effectively on edge devices, enabling real-time processing for applications like autonomous vehicles, robotics, and more.

Interoperability:

• The ability of AI systems to run across diverse hardware platforms and environments could lead to greater interoperability among applications. This could result in a more connected and efficient ecosystem, fostering innovation and collaboration.

Challenges:

• While substrate independence holds immense promise, achieving it comes with challenges. For instance, ensuring that AI algorithms perform consistently across various platforms can be complex. Researchers estimate that achieving 100% efficiency and adaptability might not be possible, but substantial progress can be made.

Security:

• As AI becomes more substrate-independent, the need for robust security measures increases. AI models should be able to protect sensitive data and adapt to different security protocols across various environments.

These numbers help underscore the potential impact and challenges of substrate independence in the world of AI. As technology continues to advance, realizing the vision of substrate-independent AI will likely have a transformative effect on various industries and aspects of our lives.