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The software that supports the hardware is just as important as the hardware itself
The artificial intelligence revolution has created unprecedented demand for specialized computing hardware, but today’s AI systems are about much more than just the silicon. While headlines track the latest GPU specs or neural processing benchmarks, the actual competitive advantage in AI hardware increasingly comes from the platforms, programming models, and developer ecosystems that surround the chips themselves.
As AI workloads grow more complex and specialized, the relationship between hardware and software has shifted from sequential development to deep integration. Companies no longer build chips first and optimize software later. instead, they design them simultaneously, creating tightly coupled systems where software and silicon enhance each other’s capabilities. This co-evolution is reshaping how AI systems are built, and the competitive landscape of the entire computing industry.
Hardware-software co-design
Hardware-software co-design means developing chips and software at the same time to minimize bottlenecks and maximize performance. Rather than treating them as separate, engineers work across traditional boundaries to create integrated solutions where each element leverages the capabilities of the other.
“The biggest bottlenecks right now sit at the intersection of models, runtimes, and memory, not just the chips themselves,” explained Ali Yilmaz, Co-founder & CEO at Aitherapy, in an interview with RCR Tech. “We keep designing models that assume infinite bandwidth and perfect kernels, then expect compilers and hardware to magically keep up. A lot of pain comes from runtimes and compilers not fully matching what the silicon is good at, so no single side is ‘to blame’ – it’s the gap between them.”
This integrated approach has become critical because of how specialized AI workloads are. A powerful chip without optimized software is all potential, no practical application. Similarly, sophisticated AI algorithms without hardware acceleration remain theoretical achievements rather than practical tools.
The relationship mirrors what we see in smartphones, where hardware capabilities only become valuable through well-designed software applications. The hardware provides the foundation, but it’s the software that constructs the structure users actually interact with.
The NVIDIA CUDA ecosystem
NVIDIA’s dominance in AI computing exemplifies the power of hardware-software co-design. Their H100 and A100 GPUs represent cutting-edge hardware, but NVIDIA’s real moat is the CUDA platform – a comprehensive software ecosystem that abstracts the complexity of GPU programming while optimizing code for parallel processing.
CUDA gives developers libraries, programming interfaces, and tools that make NVIDIA’s specialized hardware accessible without requiring deep expertise in parallel computing. That abstraction layer allows AI researchers and developers to focus on algorithms rather than the intricacies of hardware optimization.
What’s particularly notable is that robust software support and comprehensive documentation have driven NVIDIA’s adoption more than raw hardware specs. Competitors may match or even exceed certain performance metrics, but developers choose NVIDIA because of the mature ecosystem that reduces development time and complexity.
This ecosystem creates a powerful lock-in effect. Once organizations have invested in CUDA-optimized code, switching to alternative hardware involves substantial costs in code rewriting and retraining. That has become NVIDIA’s strongest competitive advantage, creating barriers to entry that even technically superior hardware struggles to overcome.
Competitors: ROCm and oneAPI
Of course, NVIDIA’s competitors have launched their own platforms. AMD’s ROCm (Radeon Open Compute) and Intel’s oneAPI are attempts to build alternative ecosystems that provide similar levels of abstraction and optimization for their respective hardware architectures.
These platforms aim to reduce the switching costs for organizations considering alternatives to NVIDIA’s GPUs. By providing familiar programming models and migration tools, they hope to lower the barriers created by NVIDIA’s established ecosystem.
But success for these competing platforms depends on overcoming NVIDIA’s massive head start and established developer community. The challenge is building comparable documentation, training resources, and community support. Even with technical parity, the ecosystem network effects favor the incumbent.
Both AMD and Intel have made their platforms open and more hardware-agnostic than CUDA, hoping that openness will drive adoption. While this approach offers theoretical advantages in flexibility, it faces the practical reality that most organizations prioritize immediate productivity over potential future flexibility.
Others are taking a more closed-off approach. Google, for example, is developing its own TPUs that integrate with its own software stack.
Open-source compilers
Beyond proprietary platforms, open-source compiler projects are creating hardware-agnostic abstraction layers that could reshape the competitive landscape. Projects like MLIR (Multi-Level Intermediate Representation) and OpenAI Triton are building compiler infrastructure that separates AI models from specific hardware implementations.
These compilers act as intermediaries, allowing developers to write code once and deploy it across multiple hardware platforms with minimal modifications. That approach reduces vendor lock-in while enabling specialized hardware manufacturers to compete on performance rather than ecosystem size.
“Most importantly, model designers will increasingly tune architectures around hardware constraints like precision formats, cache locality, sparsity patterns, and interconnect topology,” noted Saurabh Gayen, Chief Solutions Architect at Baya Systems, in an interview with RCR Tech. “Hardware teams build chips that reflect emerging model needs such as massive memory capacity, irregular sparse compute, and multi-modal data paths.”
This infrastructure is particularly beneficial for AI hardware startups like Cerebras, SambaNova Systems, and Graphcore. These companies can focus on architectural innovations without needing to build complete software ecosystems from scratch, leveraging open-source compilers to ensure their hardware remains accessible to developers.
The long-term potential of these projects is to democratize access to AI accelerators, allowing smaller players to compete with established giants by focusing on hardware innovation while sharing the software infrastructure costs across the industry.
Business strategy
The strategic importance of software in AI hardware represents a fundamental business model. Companies provide free or subsidized software tools and compilers not as charitable contributions but as strategic investments that drive hardware sales.
Free software acts as a wedge to drive hardware selection and establish long-term usage patterns. By making their hardware easier to program and more productive to use, companies increase adoption of their products while creating switching costs that protect their market position.
“The next phase of AI scaling will be defined by communication-aware system design, not faster arithmetic,” explained Gayen. “Model developers, runtime teams, and chip designers will have to work from a shared view of the system. That co-design will center around data movement first and compute second.”
This strategy extends to partnerships with AI frameworks. Frameworks like TensorFlow and PyTorch work closely with chip makers to implement automatic hardware optimizations. When users employ these frameworks, they benefit from hardware acceleration without writing hardware-specific code, creating a virtuous cycle that reinforces hardware selection.
The developer experience – including documentation quality, debugging tools, and community support – has emerged as a primary factor in hardware ecosystem success. Companies that invest in these “soft” factors often outperform those focused solely on hardware specifications, recognizing that adoption depends on making technology accessible and productive for developers.
