Latest articles on AI, technology, and software development.
Most enterprise AI tasks never needed a frontier model. As routing architectures mature, classification, extraction, and triage are migrating to models 10-100x cheaper — and the savings are starting to show up in budgets.
Four separate 2026 incidents — Australia's Medicare breach, Hugging Face, JadePuffer ransomware, and India's CERT-In advisory — share the same two root causes: excessive standing privilege and slow disclosure.
Training a frontier model is a one-time cost; inference happens on every query forever. Quantization, MoE routing, and speculative decoding are becoming standard because that's where the real money is.
Writing a clever prompt got you most of the way in 2024. In 2026, the bottleneck for agentic systems is deciding what information enters the context window, in what order, and when it gets thrown away.
Frontier models are capable enough. What breaks enterprise AI agents in production is memory: knowing what to keep, what to correct, and what to forget.
California killed the “the AI did it autonomously” defense in January. Six months later, an OpenAI agent breached Australia's Medicare system and the accountability question is still wide open.
The data-labeling industry pivoted into building simulated workplaces where AI models train, and that shift — not another GPU cluster — is becoming the real constraint on model capability.
MMLU scores can swing 13 points just from reordering answer choices. As leaderboard numbers decouple from real-world performance, procurement teams are building their own private evaluation pipelines instead.
Prompt caching can slash LLM API costs by 60-90 percent, but most engineering teams structure their prompts in a way that defeats it before it starts working.
AI agents now outnumber human accounts inside most enterprises by double digits. Traditional identity and access management was never built for that, and the gap is becoming a real security problem.
As context windows scale past a million tokens, engineering teams are quietly ripping out retrieval-augmented generation pipelines in favor of simply pasting entire document sets into the prompt. The shift isn't universal — but knowing when RAG still wins is now a real architecture decision, not a default.
Enterprises are learning that sending every request to the most expensive model wastes money without improving output. Model routing sends each query to the cheapest model that can actually handle it.