AI May Be the Lead Singer, But You Still Need the Band

Linux Foundation’s Clyde Seepersad offers commentary on how AI may be the lead singer, but you still need the band. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Watching the sentiment and interest around AI feels to me like watching Bono strut his stuff. It’s the bona fide star of the show, and everybody wants a piece. What often gets lost in the mosh pit is that (like The Edge, Adam, and Larry) there’s a supporting crew that much more quietly makes the spectacle happen. Behind  every large language model, code writing tool, and AI agent sits a stack of foundational technologies that make the magic happen, for example:

• Cloud capacity and orchestration
• Open source frameworks
• Security components
• Deployment tools

Organizations that recognize and invest in these dependencies don’t just move faster; they innovate more reliably and sustainably. Those who focus solely on finding the best “AI talent” while neglecting the supporting band members are likely to find that they don’t get the show they are hoping for. While it is compelling to follow the performance leaderboards for foundational models, the models don’t implement, scale, and secure themselves.

The 2025 State of Tech Talent Report makes this plain: while AI skills are in short supply, so is the pool of supporting talent necessary to make it all work. Organizations are understaffed not only in AI/ML, but also in the adjacent capabilities that turn prototypes into products. At the same time, most expect AI to deliver meaningful value and plan to expand their cloud usage. These ambitions will fail without the surrounding capability.

As the report shows, 68 percent of organizations are understaffed in AI/ML engineering, with similar gaps in cloud computing (59 percent) and platform engineering (56 percent). That reflects a production reality: deploying AI isn’t just about building models. It’s about teams with the interdisciplinary skills to:

• Manage compute
• Integrate services
• Secure data
• Ensure system reliability

The Backbone of AI: Cloud, Linux, Frameworks, Inference

The Continued Shift to Public Cloud Reinforces this Trend

According to the report, 53 percent of organizations plan to increase their use of the public cloud over the next 18 months. That shift enables scalable training, flexible resource allocation, and rapid deployment. Whether you’re training PyTorch models on GPU clusters or serving transformer models with vLLM, cloud native infrastructure is the backbone.

Equally Foundational is Linux

The operating system underpins nearly every modern cloud and AI deployment, from containers and orchestration to edge computing and GPU drivers. For developers, Linux fluency is increasingly table stakes. Not just for system administrators, but for data scientists and ML engineers who need to manage dependencies, optimize performance, and debug complex interactions.

Frameworks like PyTorch and TensorFlow Represent the Next Critical Layer in the Stack

Foundation models may grab headlines, but these tools make development repeatable and collaborative. PyTorch, in particular, has become a favorite for both research and production because of its flexibility, strong community, and rich Python ecosystem, which is exactly what teams need to iterate quickly as architectures evolve.

vLLM is an Increasingly Important Piece of the Deployment Picture

As organizations move from experiments to real services, inference performance becomes the bottleneck. Tools like vLLM reduce latency and increase throughput for serving large language models efficiently, especially at scale and in low-latency environments. Adopting these tools signals a maturing AI stack—one designed for both training and cost-effective, real-time inference.

Build Capability, Not Silos: Ecosystem Readiness Beats AI in Isolation

Taken together, Linux, cloud native platforms, modern ML frameworks, and high-efficiency inference tooling form an integrated ecosystem. Success with AI requires fluency across the stack. That’s why the 2025 State of Tech Talent Report emphasizes upskilling and cross-skilling: the most effective teams aren’t narrowly “AI only”; they’re comfortable navigating the entire path from model to production.

It also explains the strength of the open source model. Most of the core tools, including Kubernetes, PyTorch, and vLLM, are open-source. Their development is community-driven, their innovation transparent, and their adoption accelerated by shared knowledge. The report shows organizations leaning into this approach: 49 percent prioritize upskilling and 40 percent rely on open source frameworks, models, and tools to advance AI initiatives. The combination of developing internal capability while building on open ecosystems is what makes large-scale adoption feasible.

Hire for the Stack, Not the Role

For leaders, the takeaway is pragmatic: hiring an AI engineer isn’t enough. You need cloud architects who understand model scaling, Linux-savvy developers who can configure and harden the environment, and DevOps professionals who can run ML pipelines through CI/CD with the same discipline as any critical service. Most importantly, you need a workforce that treats AI as part of a broader transformation, not a standalone capability.

AI is a tool, not a strategy. It delivers only when skilled people operate within a strong ecosystem: cloud native infrastructure, Linux, open source frameworks, and a scalable path to deployment. Miss one piece and results wobble. Leaders who invest in the plumbing as deliberately as the algorithms will turn AI from promise into durable, real-world impact.

______________________

Download the 2025 State of Tech Talent Report – Truth vs. Vibe: The Not So Disruptive Workforce Impact of AI for free.