Like almost everyone in semis today, we have spent the past year trying to wrap our heads around “AI”. What it is, how it works, and what it means for the industry. We are not sure that we have any good answers, but a few things have been clear.

Part of the difficulty in writing this piece is that we are stuck in something of a dilemma. On the one hand, we do not want to dismiss the advances of AI. These new systems are important technical achievements, they are not toys only suited for generating pictures of cute kittens dressed in the style of Dutch masters contemplating a plate of fruit as in the picture here (generated by Microsoft Co=Pilot). They should not be easily dismissed. On the other hand, the overwhelming majority of the public commentary about AI is nonsense. No one actually doing work in the field today who we have spoken with thinks we are on the cusp of Artificial General Intelligence (AGI). Maybe we are just one breakthrough away, but we cannot find anyone who really believes that is likely. Despite this, the general media is filled with all kinds of stories that conflate generative AI and AGI, with every kind of wild, unbased opinions on what this means.

Setting aside all the noise, and there is a lot of noise, what we have seen over the past year has been the rise of Transformer-based neural networks. We have been using probablistic systems in compute for years, and transformers are a better, or more economical method, for performing that compute. This is important because it opens up the problem space that we can tackle with our computers. So far this has largely fallen in the realm of natural language processing and image manipulation. These are important, sometimes even useful, but they apply to what is still a fairly small piece of user experience and applications. Computers that can efficiently process human language will be very useful, but does not equate to some kind of universal compute breakthrough.

This does not mean that “AI” only provides a small amount of value, but it does mean that much of that value will come in ways that are fairly mundane. We think this value should be broken into two buckets – generative AI experiences and low-level improvements in software.

Take the latter – improvements in software. This sounds boring, it is, but that does not mean it is unimportant. Every major software and Internet company today is bringing transformers into their stacks. For the most part, this will go totally unnoticed by users. Security companies can make their products a little bit better at detecting threats. CRM systems may get a little better at matching user requests to useful results. Chip companies will improve processor branch prediction by some amount. All of these are tiny gains, 10% or 20% boosts in performance, or reductions in cost. And that is ok, that is still tremendous value when compounded across all the software out there. For the moment, we think the vast bulk of “AI” gains will come in these unremarkable but useful forms.

Generative AI may turn out to be more significant. Maybe. Part of the problem we have today with this field is that much of the Tech Industry is waiting to see what everyone else will do on this front. In all their recent public commentary, every major processor company has pointed to Microsoft’s upcoming AI update as a major catalyst for adoption of AI semis. We imagine Microsoft may have some really cool features to add to MS Word, PowerPoint and Visual Basic. Sure, go ahead and impress us with AI Excel. But that is a lot of hope to hang onto to a single company, especially a company like Microsoft that is not well known for delivering great user interfaces. For their part, Google seems to be a deer in the headlights when it comes to transformers, ironic given that they invented them. When it comes down to it, everyone is really waiting for Apple to show us all how to do it right. So far, they have been noticeably quiet about generative AI. Maybe they are as confused as everyone else, or maybe they just do not see the utility yet. Apple has had neural processors in their phones for years. They were very quick to add transformer support to M Series CPUs. It does not seem right to say they are falling behind in AI, when maybe they are just laying in wait.

Taking this back to semiconductors, it may be tempting to build big expectations and elaborate scenarios of all the ways in which AI will drive new business. Hence the growing amount of commentary about AI PCs and the market for inference semis. We are not convinced, it is not clear any of those companies will really be able to build massive markets in these areas. Instead, we tend to see the advent of transformer-based AI systems in much simpler terms. The rise of transformers largely seems to mean a transfer of influence and value-capture to Nvidia at the expense of Intel in the data center. AMD can carve out its share of this transfer, and maybe Intel can stage the comeback-of-all-comebacks, but for the foreseeable future there is no need to complicate things.

That being said, maybe we are getting this all wrong. Maybe there are big gains just hovering out there, some major breakthrough from a research lab or deca-unicorn pre-product start-up. We will not eliminate that possibility. Our point here is just that we are already seeing meaningful gains from transformers and other AI systems. All those “under the fold” improvements in software are already significant, and we should not agonize over waiting for emergence of something even bigger.