What Companies Should Know About Public, Private And Enterprise AI

Jon Lin is the Executive Vice President and General Manager, Data Center Services, at Equinix – a global digital infrastructure company.


There’s a lot to learn when it comes to AI. But at the end of the day, it’s a lot like other new resources—business leaders want to know how to use it to stay competitive.

The first thing to know about AI is that it’s not all the same. The category is growing more complex by the minute. For enterprises, however, AI essentially falls into two buckets: public AI and private AI. What’s the difference?

• Public AI—sometimes called "open AI"—can be used by anyone with an internet connection. It’s built by researchers and developers who share their work with the public without restrictions. Large language models like Bard and ChatGPT fall into this category.

• Private AI is, well, private. It’s designed for data privacy and security and built for a specific discrete audience. Unlike public AI, private AI operates on confidential or proprietary data owned by an organization or individual. These systems have advanced data protection measures and algorithms, so the data remains safe and private. The dedicated audience (like employees) can use the AI tool without worrying about sensitive information becoming part of a bigger, more accessible data set.

Think of public AI as a neighborhood park. It’s open for anyone to stroll through with their dog in tow. Private AI is more like a personal garden. It’s owned, controlled and used by you and whomever you authorize to enter. Want to grow some juicy tomatoes? Go for it; no one will take them.

Private AI has historically been tailored for industries where data sensitivity is crucial, like healthcare, finance and defense. But we’re now seeing private AI deployed across more and more enterprises in all sectors to improve business processes, provide personalized services and keep proprietary data safe.

Which is right for me?
That depends on what you’re trying to do. Many companies in the private and governmental sectors are banning the use of public AI for fear of IP theft, stolen competitive secrets and breaches.

Besides privacy, the appeal of private AI is customization. Over time, the AI-generated insights become customizable based on the needs of the business it’s serving. This allows for more tailored solutions as the AI will learn what specific terms and prompts mean within the context of your business. This also means your data source is critical, as your results are only as good as the data you provide.

For others, public AI makes perfect sense. Researchers, nonprofits and startups benefit from the widespread accessibility of it, which can lead to innovative advancements. Public AI also promotes collaborative research as findings can be openly shared, benefiting the entire AI community.

I want both.
Me too, and we can have it in the near future. Soon, we’ll be able to harness data from public AI models while still protecting our sensitive info.

One promising development on this front is enterprise AI, which will mimic and leverage public AI models while still ensuring that sensitive corporate data does not get out to the public. Implementing an enterprise AI infrastructure will vary but will begin as dedicated private AI and slowly shift over time to a virtual private AI infrastructure.

How do I create my own private infrastructure?
Enterprise AI starts with private AI, but building out a private AI environment can seem daunting. That said, your organization has likely already taken some steps to digitally transform your business, with Covid-19 and other economic factors accelerating this transformation.

This is important because AI requires a digital infrastructure that can handle its immense appetite for power and bandwidth. The heart of AI is the data center. Whether you own your data center, use the cloud or a use combination of both, there are a few focus areas enterprises assess when building out private AI.

1. Data Architecture: This is essentially making sure your infrastructure is built to take full advantage of the multiple AI-related services while ensuring compliance with data governance, residency and privacy requirements.

2. Connections: To get the most out of private AI, you need ridiculously fast connections between your data, apps and AI algorithms. These connections must also be secure and reliable. We call these “interconnections.”

3. Sustainability: We can’t talk AI without sustainability. AI may be a game changer, but it uses a lot of power. Your infrastructure must be efficient through innovations that use far less energy than traditional methods.

What next?
Whether you’re a business leader or part of the IT team, remember that AI is a marathon, not a sprint. Keep reading, asking questions and assessing options to make sure you’re fully leveraging this seismic shift to stay competitive.
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