James Rivera
James Rivera covers AI chips, cloud infrastructure, and the economics of AI compute for Brixnex. His background as a former data-centre engineer gives him a ground-level understanding of the hardware that powers modern AI — from GPU cluster design to power and cooling constraints.
Before joining Brixnex, James spent four years at a hyperscale cloud provider, working on the networking and compute layers of large-scale ML training clusters. He left engineering to write full-time after noticing how little of the infrastructure reality was reflected in mainstream AI coverage.
At Brixnex, James tracks the chip market closely, analysing NVIDIA, AMD, Intel, and custom silicon programmes from the major cloud providers and AI labs. He also covers the economics of AI deployment — the build vs. buy decisions that enterprises face as they scale.
Articles by James
The AI Chip Wars: NVIDIA, AMD, Intel and Custom Silicon in 2026 📅 March 4, 2026 The Real Economics of AI Infrastructure: Build vs Buy in 2026 📅 March 12, 2026James Rivera is Brixnex's Senior Writer covering large language models, natural language processing, and practical AI applications. With a Master's degree in NLP from Stanford University and six years of industry experience, James brings both theoretical depth and hands-on engineering perspective to his writing.
Prior to joining Brixnex, James worked as an NLP engineer at two AI-focused startups, building production RAG pipelines, semantic search systems, and LLM-powered customer service tools. This engineering background gives his tutorials a practical authenticity that resonates with Brixnex's developer-heavy readership.
James specialises in making complex NLP concepts accessible without sacrificing technical rigour. His multi-part series on RAG pipeline architecture and his comprehensive prompt engineering guide are among Brixnex's most-read pieces, each attracting tens of thousands of readers and extensive discussion in the AI developer community.
Areas of Expertise
- Retrieval-Augmented Generation (RAG) architecture and optimisation
- Prompt engineering and chain-of-thought techniques
- Vector databases and semantic search
- LLM evaluation frameworks and benchmarking methodologies
- Fine-tuning strategies for domain-specific applications
Recent Focus
In 2026, James has focused on the rapid convergence of agentic AI systems and traditional software engineering practices. He has authored detailed comparisons of GPT-5 vs Claude 4 for coding tasks, an in-depth look at mixture-of-experts architectures, and practical guides to deploying LLMs in production environments with reliability and cost constraints in mind.
Writing Philosophy
James believes the best AI writing teaches readers something they can use immediately. Every tutorial he publishes includes working code, concrete benchmarks, and honest discussion of limitations—not just the vendor success stories.