2024: The Year AI Got Practical

2024 wasn’t about AGI breakthroughs or existential debates. It was the year AI became genuinely useful for building things.

The Shifts That Mattered

1. Reasoning Got Real

Claude 3 Opus showed that LLMs could actually think through problems, not just pattern-match responses. The difference between a model that can code and one that can debug your architecture is night and day.

2. Local Models Became Viable

Llama 3 8B runs inference on an M2 MacBook at usable speeds—typically 20-40 tokens/second depending on quantization and context length. That’s not a curiosity—that’s production viable for many use cases. Combined with MLX and llama.cpp optimizations, the “you need cloud GPUs” barrier dropped significantly.

3. RAG Matured Beyond Demo Stage

Early 2024 was full of “just throw everything in a vector DB” tutorials. By year’s end, practitioners understood:

• Chunking strategy matters more than embedding model choice
• Hybrid search (BM25 + vectors) beats pure semantic search
• Reranking is non-optional for production systems

4. Agents Started Working

Tool-calling went from “interesting demo” to “actually reliable” with Claude 3.5 Sonnet and GPT-4 Turbo. Frameworks like LangChain, LlamaIndex, and the emerging agent toolkits made building AI applications less like research and more like engineering.

What I’m Watching for 2025

Multimodal reasoning - Vision models that understand diagrams and charts, not just describe them.

Longer context, smarter retrieval - 1M token contexts are here, but knowing when to use context vs. RAG is still an art.

Inference efficiency - Speculative decoding and mixture-of-experts at smaller scales could make local deployment even more practical.

The Bottom Line

The gap between “AI demo” and “AI product” narrowed dramatically in 2024. The models got better, yes, but more importantly: the tooling, the patterns, and the understanding of when not to use AI all matured.

That’s what makes this an exciting time to build.