Beirit Harvey

When teams talk about being “locked into CUDA,” the conversation usually sounds technical.
In practice, CUDA lock-in is rarely caused by code that can’t run elsewhere. It’s caused by organizational habits, tooling defaults, and risk assumptions that quietly harden over time.
Understanding that distinction matters, because technical problems can usually be solved with engineering effort. Organizational problems require different fixes and different timelines.

CUDA Lock-In Is an Organizational Problem, Not a Technical One

When teams talk about being “locked into CUDA,” the conversation usually sounds technical.

In practice, CUDA lock-in is rarely caused by code that can’t run elsewhere. It’s caused by organizational habits, tooling defaults, and risk assumptions that quietly harden over time.

Understanding that distinction matters, because technical problems can usually be solved with engineering effort. Organizational problems require different fixes and different timelines.


The Technical Reality: Most Moder

When teams talk about being “locked into CUDA,” the conversation usually sounds technical.

In practice, CUDA lock-in is rarely caused by code that can’t run elsewhere. It’s caused by organizational habits, tooling defaults, and risk assumptions that quietly harden over time.

Understanding that distinction matters, because technical problems can usually be solved with engineering effort. Organizational problems require different fixes and different timelines.


The Technical Reality: Most Modern AI Stacks Are Already Portable

From a purely technical standpoint, many teams are closer to portability than they think, especially if they rely on framework-native paths and mainstream libraries.

Modern AI workloads are built on:

 * Frameworks like PyTorch, JAX, and Hugging Face
 * High-level libraries for training and inference
 * Abstractions that separate model logic from hardware execution

In many cases, workloads can run on non-CUDA GPUs with little to no application-level code changes. Often with minimal model-code changes, but with real work in dependencies, kernels, build tooling, and performance validation. The heavy lifting is handled by framework backends, kernels, and runtime layers, not by handwritten CUDA code. Teams with custom CUDA extensions are the main exception; portability is still possible, but it’s a different project.

That doesn’t mean migrations are free or trivial. Performance tuning, validation, and infrastructure alignment still matter. But the idea that “everything must be rewritten” is usually outdated.

So if technology isn’t the real blocker, what is?


Where Lock-In Actually Comes From


1. Hiring and Skill Assumptions

Teams often equate “GPU experience” with “CUDA experience.”

Job descriptions, interviews, and onboarding processes reinforce this, even when the day-to-day work happens at higher abstraction layers. Over time, organizations build teams optimized for a single ecosystem; not because alternatives don’t work, but because no one planned for optionality.


2. Tooling Defaults Become Policy

Early choices tend to stick.

Once CI pipelines, monitoring dashboards, benchmarking scripts, and internal docs assume a CUDA-first environment, those assumptions become institutional knowledge. Changing them feels risky, even if the underlying tools support multiple backends.

The result isn’t technical lock-in, it’s process lock-in.


3. Risk Framing Favors Familiarity

CUDA often feels “safer” because it’s familiar, not because it’s inherently more reliable.

When timelines are tight and workloads are business-critical, teams optimize for predictability. Choosing what’s already known can feel like the responsible option, even if it limits long-term flexibility.

This is especially common in organizations where infrastructure decisions are made under delivery pressure rather than strategic planning cycles.


4. Procurement and Platform Coupling

Hardware, cloud contracts, and software choices frequently get bundled together.

Once a platform is purchased as a single, vertically integrated solution, changing any part of the stack can require renegotiation, revalidation, and internal approvals. Over time, optionality erodes; not because alternatives don’t exist, but because switching costs grow organizationally, not technically.


Why This Matters Now

As AI workloads scale, the cost of lock-in increases.
What starts as a convenient default can turn into:

 * Limited access to capacity
 * Fewer options when performance or cost expectations change
 * Slower adoption of new hardware or architectures

Teams that treat CUDA dependence as a technical inevitability often delay addressing it until they’re forced to by supply constraints, pricing pressure, or scaling limits.

Organizations that recognize lock-in as an organizational design issue can address it earlier and more deliberately.


Reframing the Problem: Optionality as an Operating Principle

Reducing lock-in doesn’t require abandoning CUDA overnight. It starts with changing how teams think about infrastructure choices.
That can look like:

 * Hiring for framework and systems thinking, not just vendor-specific tools
 * Designing pipelines and workflows that don’t assume a single hardware backend
 * Validating workloads across more than one environment before scale
 * Treating portability as a risk-management strategy, not a migration project

The goal isn’t constant switching. It’s retaining the ability to choose.


The Bottom Line

CUDA lock-in persists not because teams are incapable of running elsewhere, but because organizations quietly optimize themselves around a single path.

When that path stops being optimal, the cost isn’t measured in rewritten code, it’s measured in lost flexibility, delayed decisions, and constrained growth.

Seeing lock-in for what it really is, an organizational problem, is the first step toward solving it.


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CUDA Lock-In Is an Organizational Problem, Not a Technical One

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