Why Junior Engineers Matter More as AI Expands
Junior engineers evolve toward judgement, verification, and system awareness as AI absorbs the mechanical act of coding.
If you're exploring these themes in your organisation, I offer structured advisory and leadership support. Work with me →
Agents Cannot Maintain Systems: The Additive–Transformative Gap in LLM Software Delivery
LLMs can generate code, but they cannot modify or maintain systems because system‑level work requires causal reasoning, not pattern‑matching.
Team-Based AI Engineering is Next Step After Individual AI for Coding
The real gains from AI come from improving the shared work between engineers — planning, coordination, review, debugging, and delivery — not from speeding up individual coding.
Global AI Trends 2024–2025
Global evidence shows rapid AI adoption, rising capability, and widening gaps between regions and firms, with the US driving investment and commercial uptake.
Evaluating AI Systems: Metrics that Matter
Evaluating AI systems requires measuring real behaviour — schema reliability, adherence, drift, latency, retrieval quality, and safety — not synthetic benchmarks.
Latency is architecural
Most latency comes from retrieval hops and orchestration, not the model; RAG pipelines often recreate microservice-style chatter that slows systems down.
Chat Interface to System Component
AI systems behave like probabilistic components; engineers must build structured interfaces and layered constraints to make them reliable inside software systems.
What software engineers need to know about LLMs
Software engineers must understand tokens, structure, and probabilistic behaviour to build reliable systems and avoid mismatches between test and production behaviour.