AI news
Signal from the wider market
What is moving in AI research, regulation and capital — and what each shift means for the seven-module programme.
Frontier models pivot from scale to reasoning depth
The largest labs have stopped competing on parameter count and started competing on inference-time deliberation. That changes the economics of every AI product roadmap, ours included.
Read analysis →AI Act enforcement lands, and documentation becomes a moat
Enforcement of high-risk obligations has moved from theory to audit. Teams that treated model documentation as an afterthought are now retrofitting it under deadline.
5 min read →Inference costs fall another order of magnitude
Serving costs for capable mid-tier models have dropped sharply again. The bottleneck for AI products is no longer compute price — it is data access and workflow integration.
4 min read →Agentic systems finally leave the demo stage
Multi-step autonomous agents are being deployed in production for narrow, well-instrumented tasks. The pattern that works is boring, bounded, and heavily supervised.
6 min read →Small models on device change the privacy calculus
Compact models now handle classification, extraction, and summarisation well enough to run locally — which removes entire categories of data-residency objection.
5 min read →Evaluation, not modelling, is the hard part
The industry has converged on a quiet consensus: the difficulty in shipping AI systems is knowing whether they are getting better, not making them better.
5 min read →Capital discipline returns to AI funding
Investors have shifted from funding capability narratives to funding deployed revenue. Staged, milestone-linked capital is becoming the default structure.
5 min read →