More like "Amazon's Blindlingly Obvious Weapon In Chip Design Is AWS".
The difference is, Amazon's bet on commodified ML Compute infra largely paid off due to a mix of developer advocacy and the fact that there is a large existing market of users somewhat adept with AWS.
In fact, it could be a case study of how an incumbent can lose the ball - back in the 2014-18 period Tensorflow was THE framework, and Google absolutely could have used it as a killer app to market then new GCP (and they did try), but Amazon was able to outcompete GCP on both Containerization and Cloud ML Compute because of their strong developer advocacy and training programs.
Don't be dismissive about Amazon's historically strong developer advocacy motion. Peak Microsoft, Intel, Cisco, VMWare etc all placed similar bets, and Nvidia has done something similar since the mid-2010s in the ML space. At the end of the day, buyers are somewhat technical.
GTM strategy is just as important as technical and product strategy.
By designing their own chips and partnering with a foundry to manufacture them, Apple can create customized solutions that meet their product's specific requirements, distinguishing themselves from other PC manufacturers that use Intel/AMD chips.
Amazon recognizing it as an opportunity to stand out from the competition in the cloud services market by offering its own chips.
If they're doing better than the others, good for them. It still blindingly obvious that having the biggest cloud is a huge advantage for chip design and to successfully exploit savings because of that scale, not some sort of super amazing secret just now being revealed by IEEE.
Google had the advantage of owning the entire ML and Infra stack (TensorFlow, K8s, BERT, CNCF) and Microsoft had an inbuilt advantage in research communities thanks to MS Research's outsized impact in fundamental ML research.
At the time, the Annapurna Labs acquisition was seen as a massive coin-toss because IBM went down a similar path a decade before and failed.
Tbf, Apple transitioned to Samsung by the early 2010s for their SoCs. The MacBook on Apple Silicon was a recent transition after the kinks in the iPad Pro (which is laptop specced) were ironed out.
> Amazon recognizing it as an opportunity to stand out from the competition in the cloud services market by offering its own chips
Exactly, and evangelizing earlier than other cloud providers.
I thought for a few minutes and I could not come up with an example of an ML technology that originated at MS Research and then spread outside MSFT. Care to give some examples? Thanks!
In the 2010s they were the leader in NLP and the precursor of LLMs like GPT3/3.5/4/4o
Machine Translation with Human Parity (2018) - https://arxiv.org/abs/1803.05567
MT-DNN (2019) - https://arxiv.org/abs/1901.11504
MASS (2019) - https://arxiv.org/abs/1905.02450
VALL-E (2023) - https://arxiv.org/abs/2301.02111
VALL-E 2 (2024) - https://arxiv.org/abs/2406.05370
While OpenAI was the first to monetize an LLM at scale via ChatGPT, it's still the early stages of this field, and there is a lot of innovation that can still be leveraged, especially in non-English language modeling, machine translation, text-to-speech, etc.
It's in this segment that Microsoft Research shines moreso than even Google Research let alone other organizations because of their strong NLP background in Chinese (Microsoft Research Asia), South Asian languages (Microsoft Research India), Arabic (Microsoft Research's older work during the Iraq War), etc.