September 4, 2024
Cloud computing sales are expected to rise to $2 trillion by the end of the decade, according to Goldman Sachs Research. Generative artificial intelligence is forecast to account for about 10-15% of the spending.
The total addressable market for cloud services is poised to expand at a 22% compound annual growth rate from 2024 to 2030, writes Kash Rangan, head of US software research in Goldman Sachs Research, in the team’s report. Generative AI could constitute $200 billion to $300 billion of cloud spending, as investment moves beyond mega technology companies and foundation model providers.
Companies spending on digital transformation and cloud modernization will contribute to the surge in cloud computing sales, writes Rangan. Only about 30% of workloads have moved to the cloud, according to a recent survey by Goldman Sachs Research. The estimate for cloud revenue growth is also based on recent historical precedent — the market more than doubled between 2019 and 2023 to $496 billion, representing a 26% compound annual growth rate.
At the same time, Rangan anticipates that spending for, and adoption of, generative AI will broaden out to more companies. As that happens, it will be a further catalyst for the cloud sector.
AI investment poised to broaden beyond semiconductors
Much of the recent technology spending, and subsequent rise in stock prices, has centered on infrastructure companies like semiconductor makers. The next phase is expected to generate opportunities in platform companies that allow the best use of that infrastructure while providing building blocks for next-generation applications, as well as software companies that create generative AI applications, Rangan writes.
“We are starting off with infrastructure and that should lead to growth in platforms, which can help manage all the data and facilitate processing by applications,” he writes.
Rangan forecasts infrastructure as a service (IaaS) will account for $580 billion of the cloud market by 2030, or 29%. Platform as a service (PaaS) is expected to make up $600 billion in that same time period, or roughly 30%, while software as a service (SaaS) is expected to contribute $780 billion for 41% of the market.
“The infrastructure layer is poised to be the initial beneficiary, as we are already observing with the AI revenue ramp from the hyperscalers,” he writes. “This should be followed by the platform and application layers, respectively. An inherent tethering exists between PaaS and SaaS — where PaaS solutions are needed to support the emergence of a killer application but the value in the platform layer can’t compound until more compelling applications emerge.”
The next phase of generative AI
Five of the biggest US technology companies are forecast to spend $215 billion on generative AI this year (up from $125 billion in 2022). But sky-high capital expenditures for generative AI are expected to gradually decline. Lowering costs isn’t entirely straightforward, as Rangan points out that some aspects of model training are relatively fixed. But the team still expects companies to eventually produce models that extract more efficiency from the hardware, for model training to give way to using the models (known as inferencing), and for smaller and specialized models to emerge.
The software sector, which has had three straight years of decelerating growth, is set to potentially re-accelerate. The uptick will be driven by declining interest rates (lowering the hurdle rate for some IT projects), more certainty about economic policies after the US election in November (which has delayed some spending decisions), and key software conferences in the fall that will provide insight on generative AI products.
IT budgets for generative AI are resilient
Even as the investor outlook for generative AI gyrates from excitement about its prospects to skepticism about its viability, there are signs that investment in the technology is resilient. Goldman Sachs Research’s survey of IT buyers shows respondents expect 9% of their budgets to be potentially allocated to generative AI in three years, up from 7% indicated in an earlier survey in January.
The history of cloud computing can be instructive for understanding the development of generative AI. Rangan points out that it took time for the killer applications that underpin cloud computing to reach mainstream status. The return on investment for generative AI is hard to quantify, just as it was in the early stages of cloud computing. “Going through transition points like this can be very unnerving,” he writes.
There are other parallels with cloud computing, Rangan writes. It took time for cloud-based applications to become more robust than their on-premises counterparts, but now they have more functionality. Likewise, once training has produced AI models of sufficient maturity, that will pave the way for more sophisticated applications.
That said, if generative AI doesn’t materialize as a disruptive force, software company valuations could rise, as there will be less competition for IT budgets, and there’s less risk of displacement, Rangan writes.
But that’s not his base case. “There is a much greater probability that the generative AI opportunity is indeed real and that software applications and platform companies are able to re-invent and therefore re-accelerate growth — especially as interest rates start to come down,” he writes. “With or without generative AI, software platforms and applications companies are in a very good position to deliver attractive returns for investors over the next several years.”
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