I keep having the same conversation with CTOs. They want to talk about GPT vs. Claude vs. open-weight. I want to talk about their data pipeline, their serving infrastructure, and whether they've thought about what happens when they need to swap that model in 18 months.
Because they will.
After years doing distributed systems research and building AI architectures in production, I'm convinced: your AI strategy is your architecture strategy. Same thing. If your architecture is wrong, it doesn't matter which model you picked.
Three decisions that matter more than model choice:
RAG vs. fine-tuning. The LaRA benchmark from ICML put this to rest — there's no universal winner. RAG is better for knowledge-intensive tasks with changing data. Fine-tuning is better for tone, structure, and domain behavior. Most enterprises need both, and the way you combine them shapes your entire data pipeline and cost structure for years.
Single-agent vs. multi-agent. Research papers on multi-agent systems went from 820 in 2024 to over 2,500 in 2025. Everyone's excited. But O'Reilly's analysis cuts through the hype: multi-agent systems fail because of bad coordination architecture, not bad prompts. If you're not designing supervisor patterns and handoff protocols, you're building a system that works in demos and breaks in production.
Build vs. integrate. DeepSeek V3 was trained for roughly $5 million. Not $50M. Not $500M. Five. The cost collapse in model training changes the entire build-vs-buy equation. What was science fiction for your engineering team 18 months ago might be a realistic quarter-long project now.
These aren't implementation details to delegate. They're strategic bets.
What's the one architecture decision around GenAI that's keeping you up at night? I'll share my honest take on the first five replies.