Three lenses. Four laws. One cycle.

Software lens

Jevons Paradox

When the cost of using a resource falls, total consumption rises, not falls.

Origin

William Stanley Jevons, 1865, observing UK coal use after steam engines became more efficient.

Why it matters in AI

When a lab cuts pricing or ships a smaller efficient model, treat it as a leading indicator for hardware demand, not a competitive threat to GPU revenue. The story most coverage gets wrong: 'efficiency hurts NVIDIA.' The story to track instead: how many net-new workloads are attempted in the 90–180 days after the cut, and what they consume. Cheaper inference expands the addressable surface; more workloads means more aggregate compute, not less.

Predictive value

Watch: per-token API pricing changes, open-weight efficient model releases, on-device inference benchmarks. Ignore: claims that efficiency reduces aggregate compute spend. Within 90–180 days of a major price cut, expect aggregate tokens served (and the GPU revenue tied to them) to rise.

Falsifiability

If a major price cut produces a measurable, sustained drop in aggregate tokens served, the law fails. Not yet observed at any meaningful scale.

Hardware lens

Huang's Law

GPU performance for AI workloads doubles every 1–2 years on a faster cadence than Moore's Law.

Origin

Wired (2020), describing the cumulative gains in NVIDIA's AI silicon since the early 2010s.

Why it matters in AI

The doubling story most coverage tracks (per-GPU FLOPS) is the wrong metric for 2026. The story to track: per-rack power and per-rack throughput, because the binding constraint moved from silicon to grid interconnect and HBM. A platform that doubles FLOPS but only marginally moves rack-level power is a generation of marketing, not capacity. The effective cost-per-task is still falling an order of magnitude every 18–24 months when both lenses are combined — but only if you're measuring the right thing.

Predictive value

Watch: per-rack power deltas, HBM4 supply qualification status, custom-silicon share of incremental compute. Ignore: GPU FLOP claims that don't translate to rack-level throughput. A next-gen platform delivering <1.5x compute over its predecessor flattens the doubling slope; custom silicon above 30% of incremental compute compresses merchant pricing power.

Falsifiability

If a major next-generation platform delivers <1.5x compute over its predecessor, the law's slope flattens. Watch Rubin, MI400 series, and TPU v8 against the doubling trajectory.

Networking lens

Metcalfe's Law

The value of a network scales as roughly n² (the square of the connected nodes), not n.

Origin

Bob Metcalfe, founder of 3Com, formalized in the early 1980s.

Why it matters in AI

Most coverage tracks raw colo capacity (megawatts) as the proxy for data-center health. The story to track for hybrid AI: cross-connect and fabric revenue at the operators that connect cloud, neocloud, and on-prem. Capacity scales linearly; interconnect value scales as the square of connected nodes — and that's where pricing power compounds. Hybrid deployments don't dominate because operators picked them; they dominate because each new connected node multiplies the value of every prior node.

Predictive value

Watch: cross-connect, fabric, and IX revenue line items at colo operators (Equinix, CoreSite, Iron Mountain, others), quarter over quarter. Ignore: raw megawatt absorption as a standalone indicator of category health. If interconnect revenue growth doesn't outpace compute revenue growth across two consecutive quarters, the law fails for AI workloads.

Falsifiability

If interconnect revenue growth lags compute revenue growth quarter after quarter, the law fails for AI workloads. Currently the opposite is observed.

Networking lens

Gilder's Law

Bandwidth grows roughly 3x faster than compute power.

Origin

George Gilder, Telecosm (2000).

Why it matters in AI

Coverage often treats networking as a hardware afterthought to compute scaling. The architecture story to track: bandwidth is being delivered ahead of the compute it serves, which means hybrid AI architecture decisions made in 2026 should plan around fabric headroom, not fabric scarcity. Designs constrained by the assumption that the network is the bottleneck are misreading the curve. The binding constraint of distributed AI has already shifted from 'can we move data fast enough?' to 'do we have the compute to use it?'

Predictive value

Watch: 1.6 Tbps and co-packaged optics shipment volume, optical transceiver supply ratio against HBM, multi-DC training architecture announcements. Ignore: bandwidth-as-bottleneck framing in vendor pitches. If bandwidth growth stalls (optical packaging supply, fiber buildout) while compute keeps doubling, hybrid AI architectures stall — that's the falsifiability test.

Falsifiability

If bandwidth growth stalls (e.g., optical packaging supply, fiber buildout) and compute keeps doubling, hybrid AI architectures stall. Watch HBM vs optical transceiver supply ratio.

Hypothesis 1

The cycle is accelerating, not slowing.

Each turn of the software–hardware–networking flywheel happens faster than the prior turn. The doubling-time cadence — capability doubling, density doubling, bandwidth tripling — is shortening.

Evidence as of inaugural issue

Open-weight models crossed the closed-frontier coding line in 6 months (April 2026), not the 18 months a 2024 baseline would have predicted. NVIDIA shipped Vera Rubin samples to customers within 12 months of Blackwell Ultra GA — half the prior generation gap.

What would change the thesis

Two consecutive quarters where any one of the three lenses' doubling-time slope flattens (e.g., next-gen GPU at <1.5x; bandwidth growth stalling).

Hypothesis 2

Capital is concentrated, returns are diffuse.

AI capex is concentrating in a handful of hyperscalers, neoclouds, and frontier labs — but returns are diffusing across enterprise software, productivity, and on-prem deployments. The capex bet doesn't pay off where the capex is spent; it pays off elsewhere.

Evidence as of inaugural issue

~$700B aggregate hyperscaler capex 2026 vs ~$120B AI-attributable revenue. CoreWeave $66.8B backlog (+4x YoY) without proportionate revenue. Meanwhile, every F500 has Copilot or equivalent deployed.

What would change the thesis

Hyperscalers disclose AI-segment-specific operating margin approaching non-AI cloud margins. Or capex revisions turn negative.

Hypothesis 3

Networking is the durable layer.

Models commoditize. Chips margin-compress as competition intensifies. Networking and interconnect is the layer where pricing power holds longest because Metcalfe's Law makes each new connected node multiply existing value.

Evidence as of inaugural issue

Equinix Fabric Intelligence launch (April 15, 2026) acknowledges AI-native networking as a product category. NVLink Fusion + UALink standardization. Cross-connect and IX revenue growth at colo operators.

What would change the thesis

Interconnect revenue growth lags compute revenue growth for two-plus consecutive quarters. Or a wireless / optical breakthrough that bypasses physical interconnect.

Hypothesis 4

Open weights pull the floor up.

Open-weight models from China, Europe, and US labs are not primarily a competitive threat to closed labs — they are a demand catalyst for enterprise on-prem and sovereign-cloud GPU consumption. They re-route compute demand without reducing it.

Evidence as of inaugural issue

DeepSeek V4 (April 22–24, MIT-licensed, 1.6T MoE, 1M context) crosses the closed-frontier coding line. Sovereign and national programs surge: UAE 1 GW Stargate, Germany National DC Strategy, Mistral-Sweden, GMI Japan, IndiaAI ramp, UK AI Growth Zones aggregating $38.5B.

What would change the thesis

Open-weight performance gap reopens >10 points behind closed for two consecutive quarters. Or regulatory capture restricts on-prem deployment.

Hypothesis 5

Power is the binding constraint for the next 24 months.

The constraint on AI compute deployment is no longer chips, capital, or even land. It is electricity — generation, transmission, time-to-energization. This shows up as PPAs, behind-the-meter generation, and grid interconnection queues.

Evidence as of inaugural issue

Meta 6.6 GW nuclear, AWS 1.92 GW Susquehanna, Microsoft-Chevron $7B Texas gas in talks, 75% of new on-site DC power = natural gas, PJM 2026/27 capacity auction at $329/MW-day (vs $29 prior), interconnect waits 36–48 months in busiest US markets.

What would change the thesis

A material grid expansion materially reduces interconnect queue length within 12 months. Or a breakthrough in efficient AI inference (e.g., 10x lower-power chips) collapses demand growth.

2026-04-25 · v1, inaugural

All five hypotheses authored. No revisions. Flywheel diagram and four laws established as the conceptual spine.