Why AI progress doesn’t have to come at an environmental cost

Nutanix I 4:15 pm, 11th July

Huge efficiency gains can be made in underlying infrastructure freeing-up organisations to embrace the future, says Sammy Zoghlami, SVP EMEA at Nutanix


While there is no sugar coating the pressures on businesses and government departments to meet NetZero carbon targets, most IT leaders have the added strain of trying to keep up with demands for new technologies. It’s a constant balancing act of enabling people to work and perform better, while addressing ESG compliance and not blowing IT budgets.


Automation is now dominating IT buyer thinking. New products and tools keep emerging. Only recently, Microsoft founder Bill Gates talked about the huge potential of AI assistants, for example, suggesting the race is on for organisations to develop powerful AI assistants that could reshape the digital landscape, putting the likes of Google and Amazon under threat. He suggested these AI assistants could radically change behaviours impacting everyday life and work. We’ve already seen an element of this with ChatGPT, while Microsoft has already made a play in this direction with the announcement of its Copilot AI assistant for 365.


The fact is, automation is attractive to organisations for productivity, efficiency and overcoming skills shortages but it can come at a cost, both a financial and environmental one. As Gartner warned in its 10 StrategicPredictions for 2023, AI comes with increased sustainability risk. By 2025, it says, “AI will consume more energy than the human workforce, significantly offsetting carbon-zero gains.” With this in mind, something surely has to be done now, to enable AI without undermining environmental efforts.


Meeting ESG targets is, according to Deloitte at least, a more prominent issue in boardrooms this year, so how organisations balance this with increased automation needs will be key. Cloud computing is, of course, central to the enablement of AI tools in organisations. Digital transformations to implement platforms that unify organisations and therefore data are driving cloud adoption.


As Gartner revealed recently in its research, worldwide spending on cloud is expected to hit around $600 billion this year, driven primarily by emerging technologies, such as generative AI. Sid Nag, vice president analyst at Gartner, says generative AI requires “powerful and highly scalable computing capabilities to process data in real-time,” with cloud offering “the perfect solution and platform.”


Cloud bursting

And yet, cloud continues to be dogged by claims of being bad for the environment and not helping organisations hit their ESG compliance targets. In fact, the cloud industry has been one of the most active in trying to increase efficiencies and reduce environmental impacts. Such is the demand for cloud services, that inevitably keeping up is difficult. Piling on more racks in a datacentre is a short term solution but not really a long term answer, especially given the leap in power demands to manage increased automation.


In our Enterprise Cloud Indexresearch, 85% of 1,450 IT decision makers acknowledged that meeting corporate sustainability goals is a challenge for them. While nearly all (92%) said sustainability was a much more important issue than a year ago, there is clearly a disconnect between what organisations want to achieve and how they go about it. What we have seen is that there are big challenges arising from a mix of complexity and IT budget restraints.


Our research shows that most organisations use more than one type of IT infrastructure, whether it is a mix of private and public clouds, multiple public clouds, or an on-premise datacentre, along with a hosted datacentre. This is only going to grow but mixed infrastructures create new management challenges. Given the increased complexity, organisations need a single, unified place to manage applications and data across their diverse environments, to reduce costs but also to measure impacts.


Increasing efficiencies in data processes is an important step in reducing ‘hits’ on IT systems but this really only goes part of the way. The real step change for any organisation operating in the cloud is looking at the underlying infrastructure. Measuring and then managing impacts from datacentres will continue to be key to reducing carbon impacts of organisational computing. As with a car, if you have a smaller and yet more powerful and efficient engine, not only are you going to reduce emissions, you are going to enable room for growth and increased performance, through tools such as AI.


Re-framing the picture

As Atlantic Ventures suggests in its report Improvingsustainability in data centers, the required energy demand on datacentres is still very high and results in large amounts of carbon dioxide emissions. Energy consumption is a major factor in measuring environmental performance of datacentres but one traditional method is now being questioned.


As we outline in this paper, power usage effectiveness (PUE) as a tool for measuring is diminishing in value. It still has a place as an internal improvement metric but it isn’t that helpful when it comes to making outside comparisons. As PUEs fall to scores of around one, differences become more marginal. The point is that relying on a PUE score as a measure for efficiency and low carbon emissions doesn’t really work.


Fundamentally, changes need to be made at the rack. Infrastructure modernisation starts with hyperconverged infrastructures (HCI), reducing ‘moving parts’ and therefore energy needs. This also means less complexity, both in terms of cloud structures but also data management. This is what will achieve the most direct outcomes.


As Atlantic Ventures says, “in the EMEA region HCI architectures have the potential to reduce up to 56,68 TWh from 2022-2025 and save up to €8.22bn in electricity costs in the same period for companies and data center providers undertaking a complete transformation towards HCI.” This, combined with next generation liquid cooling is a huge step towards creating a low impact platform for the future.


For any organisation looking to embrace AI and related automation applications, addressing infrastructure complexity now is key. Running datacentres is an increasingly specialist business (especially given on-going high energy prices) and as more and more data is required in real time, so the challenges for organisations only increase. With the right partners and the most efficient infrastructure in place, any organisation could consider itself AI-ready without sacrificing an ESG targets.

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