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Artificial intelligence chips are a hot market.

There’s plenty of debate over who will make the dominant parts. Today, it’s definitelyNvidia (NVDA). Intel (INTC) believes it has enduring relevance, as explained last week by its AI chip architect, Gadi Singer. AMD (AMD) can also be a contender.

And then, there are several small, venture-backed startups. I met last week with a co-founder of one of them, Cerebras Systems, Andrew Feldman. Feldman’s previous startup, SeaMicro, was bought by Advanced Micro Devices (AMD) in 2012. Feldman left AMD in 2014 as CEO Lisa Su shook up the company’s lines of business. After some time on the beach, Feldman re-emerged at Cerebras.

I interviewed Feldman back in September about Cerebras. That was a conversation in very broad terms about what AI silicon has to achieve. Feldman is still not ready to disclose details yet of Cerebras's work.

However, during a lunch of tacos in Palo Alto, not far from Cerebras’s offices, Feldman offered some intriguing tidbits.

Taqueria El Grullense in Palo Alto, site of Barron's meeting with Cerebras.

Asked if there’s enough AI work to keep a startup going amidst giants like Nvidia, Feldman launched into a most interesting treatise about cheetahs and hyenas as a way to think about tech companies:

My father is an evolutionary biologist, and I like to make analogies between evolution and market forces. You can think about it like this: there are two kinds of animals in technology. One is the cheetah. The cheetah’s only prey is the Thomson’s gazelle. Anywhere you find the Thomson’s gazelle in large enough numbers, the cheetah thrives. The cheetah is a gazelle-hunting specialist. It is perfectly biologically engineered to hunt the gazelle. It is the only one of the large cats whose claws do not retract. Its claws are always out, like cleats, so it’s ready at all times to accelerate. Its nose is smooshed up against its face to facilitate its breathing when running at high speed. Its tail is long and narrow to stretch out behind it and give it aerodynamic balance.

At the other end of the spectrum is the hyena. It is evolved in very different ways. It will eat all kinds of prey, not just one. It hunts in the day, it hunts at night. It hunts alone and it hunts in packs. It will eat things it kills, and it will eat things other animals kill. It will even chew on the bones of long dead animals.

The cheetah is a specialist. It will thrive as long as there are gazelle in abundance, as long as the one thing which it is engineered to pursue is plentiful. The hyena is a generalist. When resources are scarce, when there lots of different types of animals present, but none in large numbers, the hyena will thrive. The question, then, in technology, is whether you are a specialist or a generalist. And if you are a specialist, is the market large enough to sustain you?

Cerebras, in other words, is a cheetah, a specialist in AI.

Nvidia and Intel are generalists, he asserts. In each case, whatever specialist ability they may once have had — graphics rendering in Nvidia’s case — it has been subsumed under a pile of generalist programming:

The specialist starts out with a technology optimized for one specific task. Take the graphics-processing unit. As its name denotes, this was a specialist technology focused on a single task--processing graphics for display. And for the task of graphics, graphics-processing units are phenomenal. Nvidia built a great company on graphics-processing. But over time, the makers of graphics-processing units, AMD and Nvidia, have tried to bring their graphics devices to markets with different requirements, to continue the analogy to hunt things that aren’t gazelle. In these markets, what was once a benefit, finely tuned technology for graphics (or gazelle-hunting), is now a burden. If you hunt up close like a leopard and never have to run fast, having your nose smooshed into your face is not an advantage and may well be a disadvantage. When you hunt things you were no longer designed to hunt, the very things that made you optimized and specialized are no longer assets.

Intel is the classic example of a generalist. For more than 30 years the x86 CPU they pioneered was the answer to every compute problem. And they gobbled up everything and built an amazing company. But then there emerged compute problems that specialists were better at, and were big enough to support specialist companies—such as cell phones, graphics and we believe AI. In each of these domains specialist architectures dominate.

And so, the question for Cerebras, he suggests, is whether the market is large enough, and stocked with enough Thomson’s gazelles? Clearly he thinks it is, or he wouldn’t be at the company:

We are specialists, designing technology for a much more focused purpose than the big companies burdened with multiple markets to serve and legacy architectures to carry forward. Specialists are always better at their target task. They do not carry the burden of trying to do many different things well, nor the architectural deadweight of optimizations for other markets. We focus and are dedicated to a single purpose. The question of whether we—and every other specialist-- will be successful rests on whether the market is large enough to support that specialist approach. Whether, in other words, there are enough gazelle to pursue. In every market large enough, specialists win. It is in collections of many modest markets, that the generalist wins. We believe that the AI compute market will be one of the largest markets in all of infrastructure. It will be the domain of specialists.

Lastly, Feldman told me Cerebras is making more than just a chip. It takes a “systems” approach to solve AI:

Big innovations are unlikely to come from a chip alone. They come from a system. To create the right technology for AI requires a system approach—you must control the chip, the communication fabric, the printed circuit boards, the power and cooling delivery, the interfaces, the system software, and every aspect of the solution. This is Google’s approach with the TPU and this is our approach at Cerebras—this is how you build a solution specialized for AI.

This sounds a little like the micro-server approach Feldman took at SeaMicro. But we’ll just have to wait till later this year to find out more.

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