Autonomous Vehicles and The Future of Mobility
Autonomous technology is no longer a futuristic dream; it's rapidly becoming our reality. From robotaxis navigating city streets to drones delivering packages, machines are taking the wheel – and the skies – to move people and goods with unprecedented efficiency. But what truly powers every safe and reliable autonomous journey? It's the invisible digital systems working tirelessly behind the scenes. In the latest episode of Interconnected, join hosts Glenn Dekhayser and Simon Lockington as they delve into this crucial topic with MIT Research Scientist Bryan Reimer and Equinix VP of Market Development Petrina Steele. Discover how global infrastructure is evolving to support the next generation of autonomy. Key Highlights Edge computing enables vehicles to process and share data locally for faster, safer decisions. Low-latency connectivity determines how efficiently mission-critical computation moves between the car and the cloud. Multi-layer sensing and redundancy improve safety both inside and outside autonomous systems. Mining leads current automation efforts due to its regulated, closed environments and strong ROI. AI continues to enhance decision-making, learning and coordination across connected systems. Future autonomy will rely on open data marketplaces, adaptive energy grids and edge zones that blanket cities.
Beyond the Hype: The CIO's Guide to Eliminating the Hidden Costs and Complexity of AI at Scale
The promise of Artificial Intelligence (AI) is clear: groundbreaking efficiency, new revenue streams, and a decisive competitive edge. But for you, the IT leader, the reality often looks a little different. You’ve moved past the initial proofs of concept. Now, as you attempt to scale AI across your global enterprise, the conversation shifts from innovation to infrastructure friction. You’re hitting walls built from unpredictable data egress fees, daunting data residency mandates, and the sheer, exhausting complexity of unifying multicloud, on-prem, and edge environments. The network that was fine for basic cloud adoption is now a liability—a bottleneck that drains budget and slows down the very models designed to accelerate your business. I'm Ted, and as an Equinix Expert and Global Principal Technologist here at Equinix, I speak with IT leaders every day who are grappling with these exact challenges. They want to know: What are the hidden costs when training AI across multiple clouds? How do we keep AI training data legally compliant across countries and regions? How can I balance on-prem, cloud, and edge when running AI workloads without adding more complexity? How to predict and control network spend when running apps across multiple clouds? What’s the best way to ensure my AI workloads don’t go down if one cloud region fails? The short answer is: You need to stop viewing your network as a collection of static, siloed pipes. You need a unified digital infrastructure that eliminates complexity, centralizes control, and makes compliance a feature, not a frantic afterthought. In this deep-dive, we'll unpack the major FAQs of scaling enterprise AI and show you how a platform-centric approach—leveraging the power of Equinix Fabric and Network Edge—can turn your network from an AI impediment into a powerful, elastic enabler of your global strategy. Ready to architect your way to AI success? Let's get started. Q: What are the hidden costs when training AI across multiple clouds? A. The AI landscape is inherently dynamic, with dominant players frequently being surpassed by innovative approaches. This constant evolution necessitates a multicloud strategy that provides flexibility to adopt new technologies and capabilities as they emerge. Organizations must be able to pivot quickly to leverage advancements in AI models, tools, and cloud services without being constrained by rigid infrastructure or high migration costs. However, the rub is, as cloud AI training scales, network-related costs often become the most unpredictable part of the total budget. The main drivers are data egress fees, inefficient routing, and duplicated network infrastructure. Data egress charges grow rapidly when moving petabytes of training data between clouds or regions, especially when traffic traverses the public internet. Unoptimized paths add latency that extends training cycles, while replicating firewalls, load balancers, and SD-WAN devices in every environment creates CapEx-heavy, operationally complex networks. Security infrastructure for network traffic is often duplicated between clouds, leading to cost inefficiencies. The solution lies in re-architecting data movement around private, software-defined interconnection. By replacing internet-based transit with direct, high-bandwidth links between cloud providers, organizations can reduce egress costs, improve throughput, and maintain predictable performance. Deploying virtual network functions (VNFs) in proximity to cloud regions also lowers hardware spend and simplifies management. Beyond addressing hidden cost, this approach gives IT leaders the agility to scale up or down with AI demand. As GPU clusters spin up, bandwidth can be turned up in minutes; when cycles finish, it can scale back just as fast. This elasticity avoids stranded investments while ensuring compliance and security controls remain consistent across clouds and regions. By unifying connectivity and network services on a single digital platform, Equinix helps enterprises eliminate hidden costs, accelerate data movement, and ensure the network is a strategic enabler rather than a bottleneck for AI adoption. Specifically, Equinix Fabric helps customers create private, high-performance connections directly between major cloud providers, enabling data to move securely and predictably without traversing the public internet. By extending this flexibility, Equinix Network Edge allows VNFs such as firewalls, SD-WAN, or load balancers to be deployed as software services near data sources or compute regions. Together, these capabilities form a unified interconnection layer that reduces hidden network costs, accelerates training performance, and simplifies scaling across clouds. Q: How do we keep AI training data legally compliant across countries and regions? A. Data sovereignty and privacy regulations increasingly shape how and where organizations can process AI data. Frameworks such as GDPR and regional residency laws often require that sensitive datasets remain within geographic boundaries while still being accessible for model training and inference. Balancing those requirements with the need for scalable compute across clouds is one of the core architectural challenges in enterprise AI. To address this, many enterprises choose to keep data out of the cloud but near it, placing it in neutral, high-performance locations adjacent to major cloud on-ramps. This approach enables control over where data physically resides while still allowing high-speed, low-latency access to any cloud for processing. It also helps avoid unnecessary egress fees, since data moves into the cloud for analysis or training but not back out again. By establishing deterministic, auditable connections between environments leveraging private, software-defined interconnection keeps data flows under enterprise control, rather than relying on public internet paths. As a result, organizations can enforce consistent encryption, access control, and monitoring across regions while maintaining compliance. This also translates into greater control and auditability of data flows. Workloads can be positioned in compliant locations while still accessing global AI services, GPU clouds, and data partners through secure, private pathways. By combining governance with agility, Equinix makes it possible to pursue your most pressing global AI strategies while still reducing risk. Today, Equinix Fabric can support this approach by enabling private connectivity between enterprise sites, cloud regions, and ecosystem partners, helping data remain local while workloads scale globally. Equinix Network Edge complements this by allowing in-region deployment of virtualized security and networking functions, so policies can be enforced consistently without requiring physical infrastructure in every jurisdiction. Together, these capabilities offer customers a foundation for compliant, globally distributed AI architectures. As a result, customers can create network architectures that not only reduce compliance risk but also turn regulatory constraints into a competitive advantage by delivering trusted, legally compliant AI services, based on the right data at the right time in the right place at global scale. Q: How can I balance on-prem, cloud, and edge when running AI workloads without adding more complexity? A. Determining where AI workloads should run involves balancing control, performance, and scalability. On-premises environments offer data governance and compliance, public clouds deliver elasticity and access to advanced AI tools, and edge locations provide low-latency close to users and devices. Without a unified strategy, this mix can lead to fragmented systems, inconsistent security, and rising operational complexity. One effective approach is a hybrid multicloud architecture that standardizes connectivity and governance across all environments. Equinix defines hybrid multicloud architecture as a flexible and cost-effective infrastructure that combines the best aspects of public and private clouds to optimize performance, capabilities, cost, and agility. This design allows workloads to move seamlessly between on-prem, cloud, and edge based on performance, regulatory, or cost needs without rearchitecting each time. As a result, organizations can employ a hybrid multicloud architecture where policies, security, and connectivity are consistent across all environments. AI training can happen in the cloud with high-bandwidth interconnects, inference can run at the edge with low-latency access to devices, and sensitive datasets can remain on-premises to maintain regulatory compliance. This architecture enables seamless interconnection across clouds, users, and ecosystems, supporting evolving business needs.If customers utilize Network Edge VNFs they can access a control plane to manage traffic flows seamlessly across these environments, ensuring workloads are placed where they deliver the most business value with a predictable cost. It also enables the deployment of virtual network functions such as firewalls, load balancers, and SD-WAN as software services, reducing hardware overhead and improving consistency. Together, they create a common network fabric that simplifies operations, supports workload mobility, and maintains governance across diverse environments. As a result, customers can minimize complexity by centralizing management, turning what used to be a fragmented sprawl into a unified, agile, and compliant AI operating model. Q: How to predict and control network spend when running apps across multiple clouds? A. As AI and multicloud workloads scale, network costs often become the least predictable element of total spend. Massive east-west data movement between training clusters, storage systems, and clouds can trigger unexpected egress and transit fees, while variable routing across the public internet adds latency and complicates cost forecasting. These factors can make it difficult for IT and finance teams to align budgets with actual workload behavior. A more sustainable approach is to build predictability and efficiency into the interconnection layer. By replacing public internet paths with dedicated, software-defined connections, organizations can achieve elastic bandwidth scaling while having predictable billing. This model not only ensures stable and reliable network performance but also enhances cost transparency, enabling businesses to optimize their connectivity expenses while supporting evolving operational demands. Equinix Fabric supports this model by enabling private, high-performance connections to multiple clouds and ecosystem partners from a single port, fostering predictability in network performance. Equinix Network Edge complements this by allowing network functions such as firewalls, SD-WAN, and load balancers to be deployed virtually, reducing CapEx and aligning spend with actual utilization. Together, they deliver a unified network architecture that stabilizes performance, enhances cost transparency, and enables organizations to scale bandwidth effectively while managing costs in alignment with their AI and multicloud workloads. Q: What’s the best way to ensure my AI workloads don’t go down if one cloud region fails? A. AI workloads are highly distributed, and regional outages can disrupt training, inference, or data synchronization across clouds. Relying on a single provider or static internet-based paths introduces latency and failure risks that can cascade across operations. Building resilience into the interconnection layer ensures continuity even when one region or cloud becomes unavailable. The key is to design for multi-region redundancy with pre-established, high-performance failover paths. By maintaining secondary connections across clouds and geographies, organizations can automatically reroute workloads and traffic without interruption or loss of performance. Equinix Fabric enables this design by providing software-defined, private connectivity to multiple cloud providers and regions. Equinix Network Edge complements it by supporting virtualized global load balancers, SD-WAN, and firewalls that dynamically redirect traffic and enforce security policies during failover. Together, they create a resilient, globally consistent architecture that maintains availability and performance even when individual cloud regions experience disruption.Decentralized Finance: How Stablecoins and Blockchain Are Changing Payments
Ever wonder how the digital asset world is really evolving? Our newest Interconnected video delves into how finance is evolving from an institution-led system to one defined by code, connectivity, and cryptographic trust. This episode examines how infrastructure, policy, and design are converging to make decentralized finance viable at scale. In this episode, we cover: Centralized vs. Decentralized Exchanges: Guests compare security, custody, and latency across both models and highlight emerging hybrids that blend institutional oversight with decentralized rails. Stablecoin Infrastructure: Discussion centers on the U.S. GENIUS Act and its framework for reserves, transparency, and compliance, setting the stage for stablecoins as regulated digital dollars. Bridges and Interoperability: Cross-chain networks like Wormhole enable digital assets to move safely between ecosystems, demanding high-throughput interconnects and resilient validator networks. On-Chain Privacy and Verification: Technologies such as zero-knowledge proofs and smart-contract hooks offer privacy-preserving ways to verify identity and compliance at machine speed. Listen to the extended version on Apple Podcasts and Spotify: Apple Podcasts - https://eqix.it/4oBYLpq Spotify - https://eqix.it/4hB3W6X
Exploring Private AI Trends with AI Factories for the Enterprise
How can enterprises innovate faster with AI while protecting their most sensitive data? The answer lies in Private AI and dedicated AI Factories. In this Tech Talk, you'll learn: How to move beyond AI "experimentation" to practical business application The difference between Retrieval Augmented Generation (RAG) and model fine-tuning Why infrastructure modernization (power, cooling, and connectivity) is the critical constraint in today's AI race
Innovation Meets Sustainability: Louis Vuitton
In this video, Daniel Roux - CTO of Louis Vuitton tells us the shared mission of Louis Vuitton and Equinix to support sustainability goals through advanced digital solutions, including AI-driven innovations and 100% clean and renewable energy coverage. Discover how LV Neo, the tech arm of Louis Vuitton, is leveraging Equinix’s IT solutions to reduce its carbon footprint while maintaining reliability, security and innovation. This partnership demonstrates how luxury brands can lead the way in adopting technology solutions committed to sustainability without compromising on performance or security. Learn more about Equinix's Future First commitment to sustainability
Cultivating the Future: Digital Infrastructure for a Food-Secure World
Food security in the 21st century goes beyond farmland or stronger crops, it now will also rely on real-time data, AI-enabled insights, and smarter distribution networks. In this episode of Interconnected, hosts Kyle Hilgendorf and Christina Spinney are joined by global agrifood CEO and founder Christine Gould to explore how the world’s most essential industry is being transformed by digital infrastructure. Listen to the extended podcast version on Apple or Spotify: Apple Podcasts: https://eqix.it/3VootkK Spotify: https://eqix.it/4nDufes Dive deeper and learn more about the edge inferencing strategy required to support the growth in AI agents with this IDC analyst report
AI-Ready Infrastructure: How NVIDIA Empowers Enterprise Innovation
Check out this interview with Stefan Baudy - GSI Client Director at NVIDIA, as we explore the transformative potential of Agentic AI—moving beyond chatbots to digital coworkers and autonomous agents. Learn how NVIDIA's AI Enterprise platform enables businesses to train, customize, and deploy AI models securely and efficiently. Key benefits include leveraging pre-trained models, customizing them with your corporate data and deploying them in secure, transportable containers. This ensures your intellectual property and data remain yours while maximizing AI's potential. Learn how AI-ready data centers can help your business
How Hybrid Cloud Infrastructure Helps Enterprises Scale AI
Learn how Equinix and RapidScale are helping enterprises modernize applications, scale AI, and simplify cloud connectivity with a smarter hybrid cloud infrastructure approach. RapidScale shares how they built a flexible multi cloud environment in cloud computing to overcome complexity and enable enterprises to deploy AI where it delivers the most value — securely, compliantly, and at scale. Build faster, smarter multicloud networks. Learn more here.
How AI is Revolutionizing Proactive Healthcare
Can AI help prevent cancer, Alzheimer’s, and heart disease before they even begin? In this episode of Interconnected, world-renowned physician, scientist, and author Dr. Eric Topol shares the vision for a proactive, AI-powered future of medicine—where personal health data guides better decisions long before symptoms show up. Then, decentralized tech visionary Jim Nasr joins the hosts to explore the infrastructure challenges of scaling AI in healthcare—and why decentralization might be the answer. Listen to the extended podcast version on Apple or Spotify: Apple Podcasts: https://eqix.it/45R3cVJ Spotify: https://eqix.it/4mwoL54
From Hype to Reality: How AI Is Transforming Businesses Today
When the AI hype dies down, what really matters? In this exclusive interview with Paul Brook - EMEA Director and Data Centric Workloads Specialist at Dell Technologies, we explore the real-world impact of AI on business transformation, focusing on customer-first approaches that deliver measurable results. Learn more about creating scalable, efficient AI solutions
