Let’s say OpenAI might have $500 million to play with this year and maybe triple that next year if its growth slows down to just tripling and its costs don’t go haywire. If this is true, there is no reason to believe that OpenAI can’t be wildly profitable, especially if Microsoft is paying it to use Azure, which means there is a cost that nets out to zero. ![]() Earlier this year, OpenAI was telling people that it might make $200 million in sales this year, but in August it said that looking out twelve months, it would break $1 billion selling access to its models and chatbot services. ![]() Now you know one reason why OpenAI had to cozy up to Microsoft, which is arguably the best way to get AI embedded in lots of systems software and applications. It looks like OpenAI is on that track, and separately from the deal is has with Microsoft where it sold a 49 percent stake in itself to the software and cloud giant in exchange for an exclusive license to use OpenAI models and to have funds that are essentially round tripped back to Microsoft to pay for the GPU capacity on the Azure cloud that OpenAI needs to train its models.Īccording to another report in Reuters, which broke the story about OpenAI thinking about building its own AI chips or acquiring a startup that already has them, OpenAI booked $28 million in sales last year and Fortune wrote in its report that the company, which is not public, booked a loss of $540 million. Nobody wants to pay the cloud premium – or even the chip maker and system builder premium –if they don’t have to, but anyone wanting to design custom chippery and the systems that wrap around it has to be of a certain size to warrant such a heavy investment in engineers and foundry and assembly capacity. Selling GPU capacity these days is easier than selling water to people living in a desert with no oasis in sight and no way to dig. There is some variation in cloud pricing and configuration of GPU systems, of course, but the principle is the same. If instances are reserved for less time, or bought under on demand or spot pricing, then the operating income on the iron would be even higher still. ![]() To prove this point recently, we hacked apart the numbers for the P4 and P5 instances based on the Nvidia A100 and H100 GPUs at Amazon Web Services as well as their predecessors, showing the close to 70 percent operating margin that AWS commands for A100 and H100 for three-year reserved instances. The profits from GPU instances are staggering, and that is after the very high costs for GPU system components in the first place. Given the premium that the cloud builders are charging for GPU capacity, companies like OpenAI are certainly looking for cheaper alternatives and they are certainly not big enough during their startup phase to move to the front of the line where Microsoft, Google, Amazon Web Services, and increasingly Meta Platforms get first dibs on anything they need for their services. It is also a company that has a certain amount of first-mover advantage in the commercialization of GenAI, thanks only in part to its massive $13 billion partnership with Microsoft.Īnd given OpenAI’s very fast growth rate in terms of both customers and revenues and its gut-wrenching costs for the infrastructure to train and run its ever-embiggening AI models, it is no surprise at all that rumors are going around that OpenAI is looking to design its own AI chips and have them fabbed and turned into homegrown systems so it is less dependent on GPU systems based on Nvidia – whether it rents the Nvidia A100 and H100 GPU capacity from Microsoft’s Azure cloud or had to build or buy GPU systems based on these GPUs and park them in a co-lo or, heaven forbid, its own datacenter. Open AI is, of course, the creator of the GPT generative AI model and chatbot interface that took the world by storm this year. It would be hard to find something that is growing faster than the Nvidia datacenter business, but there is one contender: OpenAI.
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