India is home to one of the world’s fastest-growing developer and enterprise AI communities, with 87% of Indian enterprises already actively using AI, according to the NASSCOM AI Enterprise Adoption Index 2.0 dated August 29, 2024. However, AI is evolving quickly and entering a new phase. Organizations are moving toward agentic AI systems that reason, retrieve information, call tools, and coordinate increasingly complex workflows.
This shift is making AI infrastructure more complex. The question many organizations are asking now is, “How quickly can our developers build, deploy, and continuously improve AI applications over time?”
More than ever, the answer depends on software.
AI innovation is moving fast. New models, frameworks and inference techniques emerge almost weekly. The organizations that succeed will be those with AI infrastructure that can adopt tomorrow’s innovations with the least friction.
Why open software matters
As AI moves deeper into operations, organizations need flexibility. They need the freedom to adopt new models, deploy across cloud, on-premises and edge environments, and respond to changing regulatory and business requirements.
Open software provides that flexibility. More and more organizations are investing in infrastructure designed to support continuous change, where new models, deployment approaches, and business requirements will continue to emerge long after today’s infrastructure is deployed.
This is particularly important in India, where AI deployment strategies differ depending on infrastructure maturity, latency considerations, and industry-specific regulations. There is no universal deployment model. Organizations need the freedom to choose the right architecture for each workload.
Open software is therefore not simply a philosophical preference. It has become a business advantage.
The next generation of AI infrastructures will be developer-first
AI is not just changing what organizations build. It is changing how they build it.
Only a few years ago, AI software discussions centered on low-level programming and hardware-specific optimizations. Today, developers work at a much higher level of abstraction through frameworks like PyTorch and Triton. They assemble AI applications that combine foundation models, retrieval systems, orchestration frameworks, and enterprise data into sophisticated workflows.
This shift reflects the rapid maturation of the AI software ecosystem. Modern frameworks have dramatically simplified AI development, allowing developers to focus more on solving strategic problems. Success now depends not on optimizing individual hardware alone, but on integrating models, tools, and services into production-ready AI applications.
This changes what organizations should expect from AI infrastructure. The most effective AI infrastructure integrates naturally with modern software ecosystems, reduces development friction, and enables developers to adopt new capabilities without continually rewriting or re-architecting applications. Increasingly the measure of AI infrastructure is how quickly they move from experimentation to production.
This also highlights an important difference in how AI infrastructure is evolving. Some software ecosystems are designed around a single vendor’s technology, making future technology decisions more dependent on that ecosystem. AMD has taken a different approach. Through AMD ROCm™ – an open software platform and its support for leading open frameworks and industry standards, AMD enables developers to work with the tools they already know, while preserving the flexibility to deploy across diverse environments and adopt new innovations.
The real value of AI infrastructure grows over time
AI is changing assumptions around how the maximum benefit from technological investments is derived.
As models, frameworks, and inference techniques continue to advance, AI infrastructure becomes more capable through software. New runtimes improve performance, orchestration tools simplify deployment, and broader framework support makes it easier to adopt emerging innovations. Rather than remaining fixed until the next hardware refresh, existing AI infrastructure can continue improving throughout its lifecycle.
This changes how organizations evaluate AI infrastructure investments. Benchmark performance will always matter, but the longer-term value of AI infrastructure will depend on how well it adapts as software, models, and business requirements evolve.
This is where software becomes strategic. Continuous improvements to runtimes, frameworks, orchestration tools, and enterprise software allow organizations to adopt new AI capabilities while preserving existing infrastructure investments.
AMD’s continued investment in the open ROCm ecosystem reflects this broader shift. Regular software releases has accelerated from every six months to every six weeks, with expanded framework support, improved enterprise readiness, and enhanced compatibility. AMD is also applying AI to improve the software itself. Through initiatives such as ROCm.ai, a new AI-native software experience for AI development across AMD platforms that combines AI-assisted development, intelligent deployment, and AI-powered software optimization into a unified experience that helps developers move quickly from idea to deployment.
Software is no longer simply the interface between developers and hardware. It has become the mechanism through which AI infrastructure continues creating value long after deployment.
Today’s AI infrastructure
There are important questions that now define AI performance today. How quickly can developers move new ideas into production? How easily can organizations adopt emerging models and deployment approaches? Will today’s AI infrastructure continue delivering value as software, workloads, and business requirements evolve?
These questions matter because AI is no longer a static workload. Agentic AI systems will continue to become more capable, more connected, and more autonomous. The infrastructure supporting them must evolve just as quickly, not only through advances in hardware, but through software that continuously expands capabilities, simplifies deployment, and enables organizations to adopt innovation with confidence.
Ultimately, the organizations that succeed in the AI era will be those that build AI infrastructure designed for continuous innovation combining powerful compute, open software, and the flexibility to evolve alongside the technology itself.