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The C++ AI computing Wars: Standardizing the Stack to Survive the Age of LLM and AI Agents

Although the AI and LLM revolution is driven by Python scripts, the actual execution happens in C++ on specialized hardware. As we move from general-purpose CPUs to complex heterogeneous environments composed of GPUs, NPUs, and other AI accelerators, the foundational role of C++ in high-performance computing faces renewed necessity—yet also new challenges. Its long-standing dominance is being questioned. New specialized languages such as Mojo and Triton promise Python-like productivity while delivering C++-level performance. At the same time, the industry’s reliance on the proprietary CUDA ecosystem has created a growing demand for open and portable alternatives such as ROCm. This talk will outline strategies for navigating this new landscape. We will demonstrate that C++ is not a “legacy tool” to be merely maintained, but a language that can continue to evolve—serving as a standardized, high-performance, and interoperable foundation across the entire AI stack.

The New AI Imperative: Standardizing the Full C++ Stack for the Age of Agents

For decades, C++ has been the undisputed language of high-performance computing, serving as the powerful backend for AI and ML frameworks. This revolution, however, was built on a fragmented ecosystem of proprietary CUDA kernels, custom tensor libraries, and non-standard C-style code. Today, the rise of generative AI, LLM-based coding agents, and new domain-specific languages like Mojo has created a new imperative: C++ must evolve from a mere implementation detail to a standardized, productive, and safe platform for the entire AI pipeline. This talk presents a cohesive vision for C++'s future, showing how recent and future features are building a complete, first-class AI stack. We will explore this strategy in three layers: Layer 1: The Foundation (Data Science): How SG19's work on std::statistics and the critical need for a std::data_frame provides the standard, high-performance tools for the exploratory data analysis that begins every ML workflow. Layer 2: The Core Data Structures (Tensors & Graphs): Moving beyond C++23's std::mdspan (the "tensor view"), we'll discuss the essential next steps: C++26's std::linalg , the proposal for an owning std::mdarray, and SG19's massive proposal for std::graph —the standard solution for GNNs, recommendation engines, and knowledge graphs. Layer 3: The Execution (Performance & Parallelism): How we run it all. We will cover how C++26's std::execution provides the standard "plumbing" to offload these new libraries to GPUs and accelerators, while std::simd offers a portable, high-level path for vectorization, finally moving us beyond non-standard compiler intrinsics. Finally, we will address the most critical factor for AI-generated code: the "garbage in, garbage out" problem. This talk introduces a forward-looking call to action: the creation of a standardized, domain-specific C++ training corpus—an "ImageNet for C++"—to teach LLMs to generate idiomatic, modern, and safe code for low-lat