The second problem is related to Amdahl's Law, which discusses the diminishing returns of parallelization in massively parallel machines.

The demand for computing power is growing rapidly, and we can't ramp up spending fast enough to keep up with use cases.

Moore's Law indicates that computing power is increasing rapidly, allowing for more complex computations in smaller devices, which is essential for AR.

I think it's very unlikely that the scaling laws will just stop. If they do, it might be due to not having the right architecture for the task.

There is ongoing debate in the AI community about whether we have reached the limits of scaling laws, as the latest models have become larger and more expensive, yet their capabilities seem to be plateauing.

What you want out of hardware and software is changing quite rapidly, and if people knew what we could do with more compute, they would want way, way more.

We're building hardware that will be of a very high quality, which takes time, as current computers were designed for a world without AI.

Moor's Law indicates that semiconductor technology improves by a factor of two every year, which is essential for our business model.

If the old scaling laws are beginning to lose their edge, we need to consider what comes next for AI development.