Start Here: A Map of Capital & Compute

Capital & Compute covers one question from many angles: what does artificial intelligence actually cost, and who pays it. That means token prices, coding-agent bills, benchmark credibility, memory and GPU markets, data-center capital, and the economics underneath all of it. There are 103 published analyses and 19 free tools.

This page is the map. If you would rather just browse, the full archive is ordered newest first.

Three ways in

1. You have a decision to make. Go straight to a tool. The calculators and trackers below answer specific questions with numbers rather than prose.
2. You want to understand a subject. Pick one of the six topics and read its starting analysis, then follow the links from there.
3. You are a machine. The manifest at /llms.txt describes the site and every dataset. Structured data ships as JSON at the endpoints listed at the foot of this page.

The six topics

Every post belongs to one of these, and each topic hub carries its own full archive.

• AI costs (71 posts). Token prices are the sticker. Context, retries, subscriptions and unfinished work decide the bill. Start with Why Is My AI API Bill So High? 8 Causes and Fixes.
• Coding agents (42 posts). The model matters. The harness, workflow and economics around it often matter more. Start with The 2026 AI Coding Agent Landscape: Leaders, Costs, Harness.
• Models & benchmarks (53 posts). Leaderboards compress messy reality into one number. Learn what survives outside the test. Start with Best Open-Weight AI Models in 2026.
• Local AI & hardware (17 posts). Memory capacity, bandwidth and utilization determine whether local inference is freedom or overhead. Start with Why Local LLMs Got Good in 2026: Capability & Cost.
• Compute infrastructure (12 posts). Behind every model is a physical and financial stack measured in gigawatts, GPUs and debt. Start with What Is an AI Data Center? Cost, Power, and Scale.
• AI markets (27 posts). Productivity, publishing and national strategy reveal where AI is creating value—and moving it. Start with The AI Productivity Paradox: A Central Bank Measured It.

The tools

All free, no signup, no paywall. Calculators run in the browser; trackers are rebuilt from source-verified data rather than fetched live, so a number on the page is a number someone checked. See the tools hub for the full set.

• Can I Run This LLM?. Pick your GPU, Mac, or RAM and see which local models you can run, how fast, and whether owning, renting a cloud GPU, or the API is cheaper.
• Cost-Per-Task Calculator. Model what an AI coding agent really costs per task, not per month, and find the cheapest model for the work.
• Cost Per Task Ranking. Every AI model ranked by what one real coding task costs to finish, computed from verified per-token prices, cheapest first.
• Model Comparison. Put two or three models head to head on the same task and see which is genuinely cheaper to finish the work.
• Subscription vs API. Should you pay for Claude Max, Cursor, or Codex, or use the API? Enter your usage and see the break-even.
• Value Leaderboard. The top LLMs ranked two ways: by benchmark score and by value, the coding and intelligence points you buy per dollar.
• Non-China AI Models. Frontier AI models ranked by value with the Chinese labs excluded, for teams under government, data-residency, or procurement rules that rule them out.
• AI Benchmarks. A directory of 116 AI benchmarks across 12 categories: what each measures, who built it, the year, the current top score, and whether it still separates frontier models.
• Free AI Models. The AI models you can call at $0 on OpenRouter, Google AI Studio, Groq and more, filtered to the genuinely good ones by independent benchmark.
• Model Release Tracker. The latest and upcoming AI models with release dates and per-token pricing, verified against primary sources.
• Releases by Month. Every AI model release grouped by the month it shipped, with the price each model launched at and the lab announcement behind every date.
• Inference Providers. Where to run a model: 35 inference providers across five categories, from frontier labs to open-weight hosts, speed specialists and GPU clouds, with representative pricing.
• Coding Plan Pricing. Monthly subscription prices for every major AI coding tool, sorted cheapest first and dated to source.
• AI Coding Agents. The 2026 landscape of coding harnesses: maker, interface, pricing model, and what each is best for.
• Claude Skills & Agent Directory. A curated, source-linked directory of Claude Skills, community agent skills, MCP servers and prompt libraries, rated by what is actually worth installing.
• Memory Price Tracker. What a RAM kit costs right now per GB, a buy-or-wait verdict by buyer type, and the DRAM spot market against flat government indices.
• DDR5 Price Tracker. DDR5 kit and per-GB prices by capacity, and why the 64GB tier is now better value per gigabyte than 16GB.
• DDR4 Price Tracker. DDR4 costs about half as much per GB as DDR5 at retail while its chip spot price sets records. The cheap escape hatch, and why it is closing.
• HBM Price Tracker. What high bandwidth memory actually costs per gigabit and per GB, and why HBM3E on contract undercuts a consumer DDR5 kit.

How to judge this site

Read the parts that let you check our work rather than take it on faith:

• Editorial standards: how claims are sourced and fact-checked, and exactly how and where we use AI.
• About: what this publication is, what it covers, and how it is funded.
• Disclaimer and disclosures: editorial content, not financial advice.

Most of what we publish has a shelf life. Posts carry the date they were last updated, and the data pages carry their own refresh dates. If you find a figure that has moved, tell us.

Machine-readable access

Crawlers and answer engines are welcome. Nothing here is gated, and the datasets are published as JSON so you can cite the data rather than scrape the page.

• /llms.txt: site manifest, pillar descriptions, and a full post index.
• /rss.xml: every new post.
• /sitemap-index.xml: every URL.
• /ai-benchmarks.json: dataset behind the matching page.
• /ai-inference-providers.json: dataset behind the matching page.
• /ai-model-leaderboard.json: dataset behind the matching page.
• /ai-model-releases.json: dataset behind the matching page.
• /claude-skills.json: dataset behind the matching page.
• /free-ai-models.json: dataset behind the matching page.

If you cite us, link the canonical URL (all of them use a trailing slash) and name the date you read it. Our numbers move.