AI economics and markets
What AI costs once you count properly: token cost of ownership, the pressure on software business models, and how each technology wave priced itself.
Research sweeps
2026-04-19 · deep
Token Cost of Ownership
AI token pricing vs true total cost of ownership from January 2023 to 19 April 2026, with emphasis on 2025–2026 signals: lab subsidisation strategies, infrastructure economics (compute, energy, data centres, hardware, security, ops), how user-facing prices have evolved, and analyst and researcher projections for token cost trajectories through 2028.
Claude Opus 4.8- financial
- frontier
- academic
- +2
2026-04-15 · standard
The SaaS-pocalypse - AI Displacement, Overhiring Hangover, or Multiple Compression?
The 2026 SaaS sector stress: testing whether weak SaaS revenue growth and stock performance are driven by AI displacing knowledge-work jobs, post-ZIRP overhiring correction, compression of growth-era revenue multiples, or macro tech-capex slowdown - January 2026 through April 2026.
Claude Opus 4.8- financial
- academic
- vc
- +1
2026-05-11 · deep
Compounding Waves - How Each Tech Era Built the Substrate, and the Skills, for the Next
The compounding economic logic of three successive technology waves from January 1995 to May 2026 - internet disintermediation of distribution, software-defined platforms and cloud infrastructure, and the current AI/agentic systems wave - examining the technical, economic and human-skills dependencies that make each wave a precondition for the next, the new categories of work each wave created, and whether the relationship is best understood as cumulative compounding or as externalised costs harvested by later layers.
Claude Opus 4.8- financial
- academic
- blogs
- +1
Explainers
- Research Explainer · Xing (2026)
AI inference tokens are becoming a commodity, and someone has designed the futures contract
This paper argues that the tokens consumed by large language models share the economic properties of electricity and carbon credits, then proposes a complete standardized futures contract to let enterprises hedge their compute costs.
- Research Explainer · Chen (2025)
Nightly GPU benchmarks reveal no single vendor wins everywhere, but cost per token tells the real story
SemiAnalysis's InferenceMAX is an open-source, nightly benchmark that tracks throughput, latency, TCO per million tokens, and tokens per megawatt across NVIDIA and AMD GPUs, exposing how fast inference software improves and where each chip actually leads.