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The Coordination Economy: How Enterprises Really Build AI Value
Most companies are pursuing AI. Many are putting significant funding behind it. But EY’s Work Reimagined Survey found that although 88% of respondents were using AI at work, only 28% of organizations are positioned to achieve transformative business impact from AI.
In other words, these investments don’t always pay off. One reason could be that many organizations continue to treat AI as a collection of individual investments—in models, GPUs, cloud services, data platforms and more—instead of an integrated system. This lack of coordination across distributed AI endpoints can prevent them from producing value together.
Enterprises that place their AI infrastructure, models and datasets in silos may not get full value from them. I believe that what they need is a genuine mindset shift, from pursuing flashy investments (like more powerful GPUs and better models) to ensuring that their investments operate as one coordinated, connected system, across different clouds, AI providers, private infrastructure, data sources and partners.
What is AI coordination?
AI coordination means placing and connecting infrastructure, models and data so that workloads can access the right resources, in the right locations, under the right performance, cost and governance conditions.
What separates coordinated businesses from siloed businesses?
For decades, IT applications were centralized and predictable. As long as they were close to the data, they were happy. It’s no wonder enterprises got used to running digital infrastructure in silos.
Today’s AI applications are fundamentally different. While they still need to be close to data sources, the data lives just about everywhere: at the edge, in the cloud and spread across partner environments. AI infrastructure needs to be everywhere as a result. We call this distributed AI.
By extension, enterprises need reliable, low-latency connectivity everywhere. Workloads can move wherever they run best, instead of getting locked into the location that’s most convenient.
AI coordination is more than an enabler
Coordination across environments creates a network effect. The more infrastructure, tools, services and datasets an enterprise can access from their ecosystem, the more business value they can create with AI.
For example, enterprises can work with multiple providers to use different kinds of models in different places for different purposes, enabling a better balance of performance, cost-efficiency and security for AI workloads.
Working with neoclouds can also help enterprises accelerate access to specialized AI hardware and potentially bypass supply-chain bottlenecks. A dense ecosystem of different providers throughout the world can help them deploy that hardware in the right places with the right connectivity. This helps ensure low-latency inference, optimized efficiency and utilization, and compliance with local data-sovereignty requirements.
The network effect isn’t just about the size of the pipes. High-bandwidth connections are helpful for data-intensive workloads, but ecosystem density is equally important, so that enterprises can deploy where partners already are.
When different enterprises and service providers gather in the same locations—known as “interconnection hubs”—they become more than the sum of their parts. Each new ecosystem participant adds new services and capabilities that can drive business value for the others, pulling in additional participants who then add new value of their own, and so on.
Equinix vendor-neutral colocation data centers make excellent interconnection hubs, with the dense partner ecosystem to prove it. In fact, we account for more than one-third of the global colocation interconnect market share.[2] Each of the more than 513,000 interconnections customers have deployed inside our data centers represents both new collaboration between partners and new opportunity to drive business value.
Coordination is what makes this network effect possible, and it’s been part of the Equinix story from the very beginning. AI makes it more important than ever.
How technology leaders can pursue coordination in their AI strategies
Digital infrastructure leaders are facing a challenge: help their organizations move at cloud speed without introducing unnecessary risks and costs, while modernizing to meet the demands of an AI-first world at the same time. The hardware and services they need to achieve this are out there and accessible at the click of a button. But how they connect to those resources matters.
Coordination remains a challenge because many enterprises are still managing individual connections piecemeal. Each new cloud, AI provider, model or partner becomes another connectivity project to design and manage. This compounds complexity throughout their AI ecosystem, especially if those connections happen via the public internet. This could lead to problems such as:
- Indirect routing: They won’t be able to ensure each new connection follows the most direct, low-latency path from Point A to Point B.
- Security and sovereignty issues: They won’t be able to keep data protected while in motion and ensure that it doesn’t cross the wrong borders.
- Out-of-control costs: They won’t be able to avoid unnecessary egress fees by controlling exactly when, why and how datasets enter the public cloud.
The shift from siloed to coordinated AI infrastructure may seem daunting, especially for organizations with significant technical debt. Figuring out how to modernize their infrastructure, avoid complexity and become AI-ready while also keeping the lights on won’t be easy. They should take the following steps to help make it happen:
- Optimize for proximity: Place workloads intentionally near data sources, users and regulatory jurisdictions.
- Avoid the public internet: Use private connectivity instead for better performance, control and predictability.
- Preserve ecosystem choice: Deploy on a neutral infrastructure foundation to ensure access to multiple clouds, models and service providers.
Equinix provides the dense ecosystem, global footprint and interconnection solutions that enable connectivity and coordination for distributed AI environments. Inside our data centers, enterprises can find the AI partners that meet their needs rather than the partners they’re contractually obliged to use.
New solutions for a changing AI landscape
In the coordination economy, business value comes not from spending more on infrastructure, data and models, but from effectively combining those resources into one connected, optimized system.
Today, we announced two new solutions to help our customers thrive in the coordination economy:
- Equinix® Fabric One™ is our new managed, intent-based any-to-any connectivity service.
- Equinix® Inference Exchange is our production-ready inference layer in partnership with NVIDIA and Together AI.
AI workloads need the ability to connect reliably to anything and run anywhere. Enterprises have traditionally lacked a simple way to coordinate this connectivity across their distributed AI environments, and have instead been forced to piece things together manually. Thanks to these announcements, that’s about to change.
These two new solutions are part of Equinix® Connected Cloud, the neutral foundation on which customers can build and operate their architectures and connect consistently across their distributed ecosystems.
Equinix already has the world’s most trusted AI exchange. Equinix Connected Cloud is your new foundation for operating across it.
Learn more about how Equinix connectivity solutions enable a unified global approach to cloud: Visit us today.