p.b.'s Shortform

My bear case for Nvidia goes like this: 

I see three non-exclusive scenarios where Nvidia stops playing the important role in AI training and inference that it used to play in the past 10 years: 

1. China invades or blockades Taiwan. Metaculus gives around 25% for an invasion in the next 5 years.
2. All major players switch to their own chips. Like Google has already done, Amazon is in the process of doing, Microsoft and Meta have started doing and even OpenAI seems to be planning.
3. Nvidias moats fail. CUDA is replicated for cheaper hardware, ASICs or stuff like Cerebras start dominating inference, etc.

All these become much more likely than the current baseline (whatever that is) in the case of AI scaling quickly and generating significant value. 

No one really knew why tokamaks were able to achieve such impressive results. The Soviets didn’t progress by building out detailed theory, but by simply following what seemed to work without understanding why. Rather than a detailed model of the underlying behavior of the plasma, progress on fusion began to take place by the application of “scaling laws,” empirical relationships between the size and shape of a tokamak and various measures of performance. Larger tokamaks performed better: the larger the tokamak, the larger the cloud of plasma, and the longer it would take a particle within that cloud to diffuse outside of containment. Double the radius of the tokamak, and confinement time might increase by a factor of four. With so many tokamaks of different configurations under construction, the contours of these scaling laws could be explored in depth: how they varied with shape, or magnetic field strength, or any other number of variables.

Hadn't come across this analogy to current LLMs. Source: This interesting article.