AI Economics Insights

AI has a cost problem that most teams discover too late. The prototype costs £50/month. The production system costs £50,000/month. And the gap between those numbers is full of decisions about model selection, infrastructure architecture, and optimisation strategies that most teams make poorly — because they do not understand the economics until the bill arrives.

What This Track Covers

Inference Economics — Per-token pricing across providers, the real cost of self-hosting, and how to model your AI spend at 10x and 100x current usage. The numbers that determine whether your AI feature is viable.

Optimisation ROI — Which optimisation techniques deliver the highest return. Caching, routing, prompt compression, quantisation, and model distillation — ranked by impact and implementation effort.

Total Cost of Ownership — Beyond inference. The hidden costs of AI systems: data labelling, evaluation, monitoring, retraining, security, compliance, and the engineering time to maintain it all.

Pricing Strategy — How to price AI features. Per-seat, per-query, usage-based, or bundled. The margin implications of each model and how to ensure your AI feature is profitable at every tier.

Vendor Economics — Understanding the economics of AI providers. Why prices drop, when they will stabilise, and how to structure contracts and commitments without getting caught by pricing changes.

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