Can we Get a 1,000-Times Increase in Computational Power in 10 Years to Support Metaverse?

Metaverse at scale implies some fairly dramatic increases in computational resources and, to a lesser extent, bandwidth. 

Some believe the next-generation internet could require a three-order-of-magnitude (1,000 times) increase in computing power, to support lots of artificial intelligence, 3D rendering, metaverse and distributed applications. 

The issue is how that compares with historical increases in computational power. In the past, we would expect to see a 1,000-fold improvement in computation support perhaps every couple of decades. 

Will that be fast enough to support ubiquitous metaverse experiences? There is reasons for both optimism and concern. 

The mobile business, for example, has taken about three decades to achieve 1,000 times change in data speeds, for example. We can assume raw compute changes faster, but even then, based strictly on Moore’s Law rates of improvement in computing power alone, it might still require two decades to achieve a 1,000 times change. 

source: Springer 

For digital infrastructure, a 1,000-fold increase in supplied computing capability might well require any number of changes. Chip density probably has to change in different ways. More use of application-specific processors seems likely. 

A revamping of cloud computing architecture towards the edge, to minimize latency, is almost certainly required. 

Rack density likely must change as well, as it is hard to envision a 1,000-fold increase in rack real estate over the next couple of decades. Nor does it seem likely that cooling and power requirements can simply scale linearly by 1,000 times. 

Still, there is reason for optimism. Consider the advances in computational support to support artificial intelligence and generative AI, to support use cases such as ChatGPT. 

source: Mindsync 

“We've accelerated and advanced AI processing by a million x over the last decade,” said Jensen Huang, Nvidia CEO. “Moore's Law, in its best days, would have delivered 100x in a decade.”

“We've made large language model processing a million times faster,” he said. “What would have taken a couple of months in the beginning, now it happens in about 10 days.”

In other words, vast increases in computational power might well hit the 1,000 times requirement, should it prove necessary. 

And improvements on a number of scales will enable such growth, beyond Moore’s Law and chip density. As it turns out, many parameters can be improved. 

source: OpenAI 

 “No AI in itself is an application,” Huang says. Preprocessing and  post-processing often represents half or two-thirds of the overall workload, he pointed out. 

By accelerating the entire end-to-end pipeline, from preprocessing, from data ingestion, data processing, all the way to the preprocessing all the way to post processing, “we're able to accelerate the entire pipeline versus just accelerating half of the pipeline,” said Huang. 

The point is that metaverse requirements--even assuming a 1,000-fold increase in computational support within a decade or so--seem feasible, given what is happening with artificial intelligence processing gains.

AI Interest High in Asia-Pac Region

ChatGPT interest is likely proof that “AI will become mainstream in 2023.”  In the Asia-Pacific region, more than 88 percent of survey respondents say they are using or are planning to use artificial intelligence or machine learning in the next 12 months. In the ASEAN+ region, some  91 percent of survey respondents say they will use, or plan to use, AI applications in the next year.  

source: IDC, AMD, Lenovo 

The IDC survey of 

source: IDC, AMD, Lenovo 

It might seem as though cloud computing adoption by enterprises should, by now, rival online ordering, use of enterprise resource planning or customer relationship management software. And that is likely true in 2021. 

The IDC survey of executives in India, Japan, Korea, Indonesia, Australia, New Zealand, Singapore, Taiwan, Thailand, Hong Kong, Malaysia, and the Philippines also finds that 88 percent of respondent firms already are either using, or planning to use, edge computing in the next 12 months for business operations.

According to an analysis sponsored by the Organization for Economic Cooperation and Development, “big data” analytics use is far lower than one might expect, at least that was the case in 2021, when perhaps 17 percent of enterprises might have used that tool, compared to more than 40 percent using cloud computing in some way. 

sources: Lenovo, Impact Economist 

Use of artificial intelligence and internet of things had begun a sharp rise about 2020.

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Tomorrow's Data Centers Might Well be Architected to Support AI, Not Content Delivery

A single ChatGPT response can require interrogration of 300 billion data points, some point out. Other artificial intelligence functions, such as building inference models, take additional computational power. 

Some estimate that AI processing requires close to  an order of magnitude increase per year. 

source: Legacy Research Group 

Perhaps it is correct to argue that today’s data centers were architected to support content delivery operations (video, gaming, audio and other content). Perhaps it also is reasonable to argue that tomorrow’s data centers will be recrafted around support for AI-assisted operations of all types.

Technology Changes Much Faster than Do Organizations

Can organizations manage to keep pace with rapid technology change, or not? The answer matters. Martec's Law essentially argues that technology change happens faster than humans and organizations can change. That might explain why new technology sometimes takes decades to produce measurable change in organizational performance, or why a productivity gap exists.  

source: Chiefmartec 

Since there simply is no way organizations can change fast enough to keep up with technology, the practical task is simply to decide which specific technologies to embrace. In some instances, a major reset is possible, but typically only by a fairly-significant organizational change, such as spinning of parts of a business, selling or acquiring assets. 

source: Chiefmartec 

Some might argue that the Covid-19 epidemic caused an acceleration of technology adoption, though some also argue that demand was essentially “pulled forward” in time. In that sense, the pandemic was a “cataclysmic” event that caused a sudden burst of technology adoption. 

source: chiefmartec

The main point is that managerial discretion is involved. Since firms cannot deploy all the new technologies, choices have to be made about which to pursue. Even when the right choices are made, however, outcomes might take a while to surface. That likely is going to happen with AI investments, much as has happened in the past with other lags in measured productivity after major investment. 

We might reasonably expect similar disappointment with other major trends including metaverse, AR, VR or Web3. Organizations cannot change as fast as the technology does.

How Does AI Affect Data Center Design?

It is not so clear yet how increasing use of artificial intelligence such as ChatGPT could affect cloud computing and data center architectures. And many of the changes might be characterized as augmenting or increasing the value of trends we already can identify. 

“What goes in the racks” is one adaptation. More powerful servers to support high-performance computing seem an obvious inference, though that already is happening for other reasons. Likewise, greater use of parallel processing is likely, along with the use of customized or specialized servers designed to support machine learning operations. 

Those developments arguably are more tactical changes. 

It also is possible that more distributed workloads will be necessary, in part because data might be stored at more locations, including at edge locations. Again, that process already has been underway, driven by the need to support more low-latency processing operations.

But data gravity and edge or distributed locations therefore seem to be opposing trends that will have to be harmonized. 

And while energy consumption already is a big issue, the greater amount of processing makes sustainability even more important as AI operations proliferate. AI operations, being more intensive, also will require more energy, and create more heat, fueling a shift to liquid cooling as well. 

source: Meta 

That, at least, has prompted Meta to consider new data center designs built on liquid cooling. 

Some expect higher degrees of data center automation as well. And, of course, data centers are applying AI to support their own automation efforts and operations.  

As in the past, when each data center essentially takes the form of an ecosystem, so AI operations might push data centers towards a mesh of locations concept, which arguably already has been the case. That data mesh concept includes federated governance, domain-oriented and decentralized data ownership, as well as architecture and much greater self-serve capabilities, says IBM.

So far, the shift to liquid cooling seems the most-discrete change in data center design. Most of the other trends--faster processors, specialized processors, energy efficiency, automation, distributed computing, ecosystems and lower latency--were already underway for other reasons.