Ben Thompson had a good post this week looking at a report that
We examined this subject in depth a few months back when we did some back-of-the-envelope math around Apple’s M1 CPU. Our conclusion there was that building a chip internally was at best a break even proposition in terms of replacing a merchant silicon solution. Instead, companies need to build chips that convey some strategic advantage. For Apple, this advantage comes in the form of a tight binding of the processor to the device’s operating system (OS) which leads to noticeably better performance in their phones and laptops. For Google, its TPU and VCU likely yielded billions of dollars of capex savings.
Seen in this light, Meta building an AR chip makes little sense. As Thompson points out their metaverse strategy is highly confused, and building a chip was not going to solve any particular problem.
That being said, we remain highly curious as to why Facebook has not built a chip for their own data centers. They are rumored to be building their own AI accelerator, although this product seems to be taking an inordinately long time to reach the market, perhaps due to abandoned attempts with past partners. We have to imagine that Facebook could use as many such chips as Google does. Facebook does a huge amount of image recognition, a task well suited for AI chips. It is possible that Facebook’s software stack is not as homogeneous as Google’s. At its core, the vast majority of Google’s compute is used to execute a single algorithm – search (albeit an incredibly complicated algorithm). By contrast, Facebook has to perform multiple other tasks around connecting everyone with their network and ranking news items for engagement. It is possible that this diversity of task alters the math around designing an AI chip internally because they would need multiple chips and thus any advantage is muted by much higher expense. It is also possible that Meta does not have a chip design team capable of building such a chip, or more likely its best chip designers were instead told to prioritize the ill-fated AR chip. Finally, it is also possible that Meta’s propensity for building data centers with the cheapest parts, premised on higher failure rates, leads them to believe that they already have the cheapest processors. If true, this would mean they are performing the wrong calculation, looking at chip price, rather than strategic benefit.
To be fair, Meta is not alone with this problem. As much as it seems that everyone is building their own chips, the truth is for most companies an internal solution is not the right path. Designing chips is expensive and risky. Many companies may find that their compute needs are too diverse, with no single chip capable of meriting the effort required to build internally.
As a result, chip design is coming out of companies that have the ability to differentiate with these chips, or defending from competitors who are doing so. Thus the mobile phone makers and some of the big Internet companies. This makes sense for other companies making hardware – Cisco being the best example of this, they are the grandfather of these chips, having rolled their own for decades. But the other big category is clearly going to be the auto makers. As we have chronicled a bit recently, this is the next major battlefield for chip design. Some will argue that auto company that designs its own chip will be the one that dominates the future. We are not convinced of this, far more important will be the software that controls autonomous vehicles. Whoever does build that software will likely want to design their own chips, and that combination may make them unstoppable. But this analysis contains a mountain of “if”s, with much of the auto software stack still filed under To Be Determined.
The final segment to consider is what the large public cloud service providers are going to do. They consume a massive amount of semis, but are taking a variety of approaches to designing their own. We will return to this topic soon.