AI Tokenomics: What AI tokens cost and where they're wasted

Andrej Karpathy said he feels nervous when he has subscription left over because he hasn't maximized his token throughput, as of 2026-07 a curated list of 197 entries across five areas tracks AI token costs and waste. The resource covers 8 practices, 10 concepts, 8 claims, and 7 copy-paste setups, with tools like ccsage and ClaudeBar for monitoring and optimizing token usage.

A map of what AI tokens actually cost, and where they're wasted vs. well spent. "I feel nervous when I have subscription left over. That just means I haven't maximized my token throughput." Andrej Karpathy, No Priors https://podscripts.co/podcasts/no-priors-artificial-intelligence-technology-startups/andrej-karpathy-on-code-agents-autoresearch-and-the-loopy-era-of-ai 2026 As of 2026-07: 197 entries across five areas, 8 practices, 10 concepts, 8 claims, and 7 copy-paste setups. Topics: Caching caching · Compression compression · Context engineering context-engineering · Memory memory · Routing routing-model-selection · Multi-agent systems multi-agent-systems · Gateways gateways-and-proxies · Observability observability · Benchmarks benchmarks-evals · Cache accounting cache-accounting · Budgets budgets-caps · Pricing models pricing-models · Energy energy-carbon A hybrid by design: a curated awesome list at the core, plus a field-guide layer on top practices, concepts, claims that carries an editorial point of view. Where to start where-to-start Legend legend Monitor monitor Optimize optimize Govern govern Understand understand Measure measure Practices practices Concepts concepts Claims claims Setups and skills setups-and-skills Just want the numbers: the five area sections below hold every entry. Want the method: read the practices first, then the concepts behind them. Building something: setups and skills holds runnable configurations. Each entry ends with a kind badge: blue, with the license when known , or a gray badge for https://camo.githubusercontent.com/86b7430e00e49604cfd07fa3c23da1058217954821acd8ec53bcd42c7e321c95/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f746f6f6c2d4d49542d626c75653f7374796c653d666c61742d737175617265 , https://camo.githubusercontent.com/7cd52898962dcd7ff5471fdf312cf9337d8ab3047f40ddf70a046b4da7beb0d2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f70617065722d3535353f7374796c653d666c61742d737175617265 , https://camo.githubusercontent.com/40e5f29fa348fa217d71539190e9868e4d0ad9d4c9110bdaa882f8b0ec1f917d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f62656e63682d3535353f7374796c653d666c61742d737175617265 , https://camo.githubusercontent.com/86d64750f01441c070c94233c07c13d6a95cbc2d99b70d19dcae3432a515bb30/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646174612d3535353f7374796c653d666c61742d737175617265 for companies, and https://camo.githubusercontent.com/a5e1dc5c1c6980b67c5c73bc888acbb84d328fa26a7c624fc4fac91dfda60d30/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f2d3535353f7374796c653d666c61742d737175617265 . Plain entries are articles. GitHub-hosted tools also carry live star and last-commit badges. https://camo.githubusercontent.com/ec4cf716c35b30b16f62e76c9bc115470f55d8116ce006af98ae4171f6e2c0bc/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7265706f72742d3535353f7374796c653d666c61742d737175617265 ccusage https://github.com/ccusage/ccusage - An open-source CLI that reads local agent logs to report token usage and cost across 15 coding-agent sources, with caching-aware pricing. Claude Code Usage Monitor https://github.com/Maciek-roboblog/Claude-Code-Usage-Monitor - A live terminal dashboard for Claude Code usage, with burn-rate analytics, P90 limit detection, and session-expiry forecasts. claude-usage https://github.com/phuryn/claude-usage - A local dashboard for Claude Code token usage, costs, and session history; Pro and Max subscribers get a quota progress bar. ClaudeBar https://github.com/tddworks/ClaudeBar - A macOS menu-bar app that monitors AI coding quotas across 11 providers; the README declares MIT but ships no license file, so the OSS grant is unconfirmed. CodeBurn https://github.com/getagentseal/codeburn find-and-fix-waste - An open-source tracker for 36 coding tools whose optimize command flags named harness-waste patterns with dollar estimates it later checks against actuals. Codex Usage Tracker https://github.com/douglasmonsky/codex-usage-tracker - A local-first dashboard, CLI, and MCP tools indexing Codex CLI logs into SQLite to show where tokens, credits, and cost go, including cache ratios. CodexBar https://github.com/steipete/CodexBar - A free, open-source macOS menu-bar app that shows limits and reset timers at a glance across dozens of AI providers, plus credit balances and spending when… CodeZeno Usage Monitor https://github.com/CodeZeno/Claude-Code-Usage-Monitor - A Windows-taskbar widget showing real-time Claude Code quota and usage at a glance, without opening a terminal. Datadog LLM Observability - Cost https://docs.datadoghq.com/llm observability/monitoring/cost/ - Datadog's LLM Observability estimates per-request cost across 800+ models from token counts and public pricing; invoice reconciliation is a separate product. gh-aw GitHub Agentic Workflows https://github.com/github/gh-aw - GitHub's agentic-workflows runtime with first-party per-run token and cost metering, plus budget caps that stop a workflow mid-run. Grafana Cloud GenAI Observability https://grafana.com/docs/grafana-cloud/monitor-applications/ai-observability/genai/observability/ - Grafana Cloud's GenAI Observability ships a prebuilt dashboard for LLM cost, token usage, and latency, built on top of the OpenLIT SDK. OpenLIT https://github.com/openlit/openlit - An open-source Apache-2.0 , OpenTelemetry-native platform with a self-hosted dashboard for LLM cost, token, and latency observability. OpenUsage https://github.com/robinebers/openusage - A native Swift macOS menu-bar meter for 10 AI coding subscriptions, showing session and weekly limits, credits, and estimated spend from local credentials. TokenTracker https://github.com/mm7894215/TokenTracker - A local-first token and cost dashboard for 27 coding tools, with a desktop pet, native widgets, and achievements as a distinct gamified take on usage metering. AgentSight https://github.com/eunomia-bpf/agentsight - Uses eBPF to watch an AI agent from the kernel boundary, correlating what it said it would do against what it did, at under 3% overhead. OpenTelemetry eBPF Instrumentation OBI - GenAI / MCP https://opentelemetry.io/docs/zero-code/obi/ - OBI is OpenTelemetry's zero-code eBPF instrumentation formerly Grafana Beyla that captures GenAI and MCP traces at the kernel layer with no SDK. Langfuse https://langfuse.com - An open-source platform for tracing, evaluating, and analyzing LLM and agent transcripts, with a prompt-management layer on top. OpenLLMetry https://github.com/traceloop/openllmetry - An open-source set of OpenTelemetry-based SDKs and instrumentations, built by Traceloop, for LLM apps. OpenTelemetry GenAI Semantic Conventions https://github.com/open-telemetry/semantic-conventions-genai - OpenTelemetry's GenAI Semantic Conventions define the vendor-neutral token, cost, and cache attribute names that OpenLLMetry and Phoenix both converge onto. Arize Phoenix https://github.com/Arize-ai/phoenix - A source-available Elastic License 2.0 LLM tracing platform recording per-span token counts and USD cost via OpenTelemetry. claude-tap https://github.com/liaohch3/claude-tap - A local trace viewer intercepting API traffic from 14+ coding agents, showing per-request token breakdowns: input, output, cache read, cache creation. LangSmith - Cost Tracking https://docs.langchain.com/langsmith/cost-tracking - LangSmith is LangChain's commercial LLM/agent observability SaaS. Opik https://github.com/comet-ml/opik - Comet's open-source Apache-2.0 LLM observability platform, with per-span USD cost estimated from token usage. GPTCache https://github.com/zilliztech/GPTCache - An open-source semantic cache returning a stored LLM response for a paraphrased repeat query via vector search, skipping the paid call. khazad https://github.com/GuglielmoCerri/khazad - A transport-layer semantic cache for LLM APIs on Redis 8 Vector Sets: it intercepts HTTP traffic with zero application code changes and replays cached responses… LMCache - KV-cache reuse layer with token-level cache-hit accounting self-host https://github.com/LMCache/LMCache - LMCache is a self-hosted KV-cache layer beneath vLLM, giving token-level cache-hit observability for teams who own their GPUs, not a hosted bill. prompt-cache https://github.com/messkan/prompt-cache - A Go LLM proxy that adds a three-tier semantic cache: high similarity hits directly, low skips, and a gray zone runs a cheap verification model to guard against… Redis LangCache https://redis.io/langcache/ - Redis's fully-managed semantic cache: a REST API that returns a stored response when a new query is similar to a past one, so paraphrases skip the paid LLM… llama.cpp https://github.com/ggml-org/llama.cpp - The foundational open-source MIT local LLM inference engine most of the local ecosystem runs on, with an OpenAI-compatible server built in. Ollama https://github.com/ollama/ollama - A runtime for running open-weight models like Qwen, DeepSeek, and GLM-5.1 locally, shifting inference onto hardware you already own. Context Mode - MCP server that sandboxes tool output out of the context window https://github.com/mksglu/context-mode - This MCP server sandboxes tool calls and returns only the distilled result, claiming a 98% cut: 315 KB of output down to 5.4 KB. headroom - context compression for LLM and agent pipelines quality story dissected https://github.com/headroomlabs-ai/headroom - An Apache-2.0 context-compression tool for LLM/agent pipelines, the category's largest repo, confirmed organic by star-forensics. LLMLingua https://github.com/microsoft/LLMLingua - Microsoft's prompt-compression library that uses a small model to drop low-information tokens before a prompt reaches the target LLM. llmtrim https://github.com/fkiene/llmtrim - A local proxy that compresses a coding agent's prompt, tool schemas, and history before forwarding, and can reroute Claude calls to Grok. Minification of state-in-context agents - the clean waste-vs-capability datapoint https://arxiv.org/abs/2606.01326 - This ICPC 2026 study found that minifying code in a coding agent's context cuts input tokens by 42% but costs 12 percentage points of accuracy. rtk - CLI proxy that compresses dev-command output reduction-only headline https://github.com/rtk-ai/rtk - rtk is a single-binary Rust CLI proxy that intercepts and compresses the output of common dev commands before it reaches an LLM coding agent's context window. TOON Token-Oriented Object Notation https://github.com/toon-format/toon - TOON is a compact, human-readable, lossless serialization of the JSON data model, designed for LLM input. AgentDiet - trajectory reduction "Reducing Cost of LLM Agents with Trajectory Reduction" https://arxiv.org/abs/2509.23586 - AgentDiet is an inference-time module that strips useless, redundant, and expired information from an agent's trajectory, without hurting performance. Anthropic vendor-native context management context editing + memory tool + server-side compaction https://platform.claude.com/docs/en/build-with-claude/context-editing - Anthropic's context editing, memory tool, and server-side compaction cut token consumption by a vendor-reported 84% in a 100-turn web-search evaluation. Claude Code compaction engine - the three-tier mechanism and its cache/correctness failure modes https://barazany.dev/blog/claude-codes-compaction-engine - Claude Code's harness - not the API - decides how to trim a filling context window, and it does this through a three-tier compaction engine. Codex CLI compaction cost - over-eager compaction as a token-amplification loop https://github.com/openai/codex/issues/16812 - Upgrading Codex CLI from v0.116 to v0.118 made context compaction fire twice as often, doubling or tripling token consumption for identical tasks. Context Rot - LLM performance degrades as input length grows https://www.trychroma.com/research/context-rot - This is Chroma's controlled study of how LLM output quality changes as input length grows, holding task difficulty fixed. ContextBudget - context management as a budget-constrained sequential decision https://arxiv.org/abs/2604.01664 - ContextBudget's BACM method has an agent decide when and how much to compress its history based on remaining context budget, not a fixed rule. Cursor vendor-native context management - dynamic context discovery + Composer self-summarization https://cursor.com/blog/dynamic-context-discovery - Cursor's dynamic context discovery loads tool schemas and large outputs on demand instead of eagerly, a change the vendor reports cut context usage by 46.9%. Repomix https://github.com/yamadashy/repomix - Packs an entire repository into a single AI-friendly file, reporting token counts and using Tree-sitter to compress code to signatures only. RULER - the "real context size" long-context benchmark https://github.com/NVIDIA/RULER - NVIDIA's RULER benchmark found that of models claiming 32K+ token context windows, only half actually maintain quality once you fill them to 32K. Self-Compacting Language Model Agents https://arxiv.org/abs/2606.23525 - This paper introduces SELFCOMPACT: instead of fixed-interval summarization, the model itself decides when and how to compress a growing agent trace. Serena https://github.com/oraios/serena - An open-source MIT MCP toolkit that gives a coding agent IDE-grade semantic code retrieval and editing: 'the IDE for your coding agent'. Claude Code spend-governance bundle v2.1.216-219 - caps tightened, fan-out default loosened https://code.claude.com/docs/en/changelog - Claude Code's v2.1.216-219 bundle 2026-07-20 to 07-24 hardens spend controls - a concurrent-subagent cap and enforced --max-budget-usd - while simultaneously… Harness-side runaway-loop cost guardrails Claude Code + Codex, July 2026 https://github.com/anthropics/claude-code/releases/tag/v2.1.212 - In mid-July 2026 Claude Code and Codex both shipped first-party guardrails against runaway agent loops within days of each other. Bifrost Maxim AI https://github.com/maximhq/bifrost - Bifrost is a Go-based AI gateway fronting 1000+ models that measured just 11 microseconds of added latency per request at 5,000 requests per second. Cloudflare AI Gateway Spend Limits https://developers.cloudflare.com/ai-gateway/features/spend-limits/ - Cloudflare AI Gateway is an edge-native LLM proxy that added dollar-denominated spend limits in June 2026, blocking or rerouting requests once a budget is hit. Helicone https://helicone.ai - An open-source Apache-2.0 LLM proxy that logs every request's cost, latency, and tokens in one line of code; Mintlify acquired it in March 2026. Kong AI Gateway https://developer.konghq.com/ai-gateway/ - The AI layer of Kong's API-gateway platform: a proxy that meters LLM/agent/MCP traffic for billing, showback, and chargeback. LiteLLM https://github.com/BerriAI/litellm - An open-source gateway fronting 100+ LLM APIs that computes real per-request dollar cost from a live pricing map, with spend limits. OpenRouter https://openrouter.ai/docs/guides/routing/provider-selection - A unified API gateway fronting 400+ models across 70+ providers that auto-routes each request by price, with fallback on outages. Portkey AI Gateway https://portkey.ai/docs/product/ai-gateway/virtual-keys/budget-limits - Routes LLM traffic across providers and enforces hard USD budget limits on virtual keys, auto-expiring a key once its cap is hit. claude-code-memory-setup https://github.com/lucasrosati/claude-code-memory-setup - A practitioner recipe pairing an Obsidian memory vault with a local AST code-graph tool; the author self-reports up to 71.5x fewer tokens per Claude Code… claude-mem https://github.com/thedotmack/claude-mem - A coding-agent observational-memory layer that captures every session, compresses it with AI, and re-injects relevant context next time; self-reports ~10x token… Cognee https://github.com/topoteretes/cognee - An open-source Apache-2.0 AI-memory platform giving agents persistent memory via a self-hosted knowledge graph, via remember/recall/forget. Karpathy's LLM Wiki https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f - Andrej Karpathy's LLM Wiki pattern has an agent build and maintain a persistent markdown wiki from your sources, instead of re-retrieving raw files. LangMem https://github.com/langchain-ai/langmem - LangChain's long-term memory library: it extracts and consolidates facts from conversations and integrates natively with LangGraph's memory store, so agents… Letta MemGPT https://github.com/letta-ai/letta - The MemGPT lineage project: a platform for stateful agents that pages an LLM's context like an OS, keeping working memory small and moving the rest to archival… Mem0 https://github.com/mem0ai/mem0 - An open-source memory layer that extracts salient facts from conversations and retrieves only the relevant ones per call, not the full history. Supermemory https://github.com/supermemoryai/supermemory - A memory and context engine that self-reports 95% recall on LongMemEval while adding only ~720 tokens of context, a claimed 99.4% context reduction versus full… Zep / Graphiti https://www.getzep.com/ - A memory platform for agents built on temporal knowledge graphs; it self-reports serving benchmark answers from a few thousand tokens of retrieved context… also: Graphiti OSS engine https://github.com/getzep/graphiti · zep repo https://github.com/getzep/zep Framework orchestration overhead the manager-LLM tax https://docs.crewai.com/en/learn/hierarchical-process - Hierarchical frameworks like CrewAI add a manager-LLM delegation tax, an extra model that plans and validates, though its cost is unquantified in any primary. SupervisorAgent - "Stop Wasting Your Tokens" runtime supervision https://arxiv.org/abs/2510.26585 - SupervisorAgent is a lightweight, modular framework for runtime, adaptive supervision of multi-agent systems. LOOP Skill Engine https://arxiv.org/abs/2605.14237 - LOOP records an agent's first run of a repetitive task with full LLM reasoning, then replays the extracted tool-call template without calling the LLM again. Orchestrator-worker model tiering frontier plans / cheap executes https://www.mindstudio.ai/blog/smart-orchestrator-cheaper-sub-agent-models-claude-code - A capable model plans while cheaper agents execute; the pattern now ships as a vendor default, hitting 89.7% of LLM quality at 4% of the cost. token-ninja https://github.com/oanhduong/token-ninja - Intercepts deterministic commands like git status or npm test before they reach the model, running them locally and skipping the LLM call. Claude Code v2.1.199 - transient-retry hardening & partial-output preservation https://github.com/anthropics/claude-code/releases/tag/v2.1.199 - Claude Code v2.1.199 now auto-retries rate-limit errors with backoff and raised the default retry ceiling to 300, up from a prior cap of 15. Antigravity CLI - per-subagent model-tier routing + /effort v1.1.5 https://github.com/google-antigravity/antigravity-cli/blob/main/CHANGELOG.md - Antigravity CLI v1.1.5 shipped first-party per-subagent model-tier routing a model: flash|pro field in custom-agent frontmatter plus an /effort control -… Claude Code via a LiteLLM gateway cheap-tier-in-front setup https://docs.litellm.ai/docs/tutorials/claude non anthropic models - Pointing Claude Code's ANTHROPIC BASE URL at a local LiteLLM proxy lets cheaper or non-Anthropic models absorb work the frontier model would otherwise bill for. Cluster, Route, Escalate - cost-aware cascaded serving https://arxiv.org/abs/2606.27457 - This paper proposes a two-stage cost-aware cascade for LLM serving that combines routing and escalation into one framework. Distilling agent behavior into small task-specific models https://arxiv.org/abs/2505.17612 - Distilling a large agent's behavior into a small 0.5-3B model lets most of its work run at a fraction of the frontier model's per-token cost. MTRouter - per-turn cost-aware routing with history-model joint embeddings https://arxiv.org/abs/2604.23530 - MTRouter picks a different model for each turn of a multi-turn conversation, rather than one model per query, to hit a cost budget without losing quality. Not Diamond https://www.notdiamond.ai/ - Not Diamond's meta-model predicts, per input, which LLM will give the best answer at the lowest cost, then routes the request there. OpenCode - open-source coding-agent CLI with explicit cost-tier routing https://opencode.ai/docs/ - OpenCode is an open-source MIT coding-agent CLI with its own explicit cost- and model-routing configuration, set directly in config. OrcaRouter - production LinUCB bandit router hybrid offline-online https://arxiv.org/abs/2605.30736 - OrcaRouter is a production LLM router built on a LinUCB bandit, with its cost/quality tradeoff independently confirmed on the RouterArena leaderboard. RouteLLM https://github.com/lm-sys/RouteLLM - LMSYS's open-source router sending each query to a cheap or expensive model based on a trained cost threshold, as a drop-in server. ruflo formerly Claude-Flow; npm https://github.com/ruvnet/ruflo - ruflo is an open-source agent meta-harness for Claude Code and Codex, providing swarm orchestration and persistent memory. claude-flow v3.17.0 - agent meta-harness with cost-adjusted model routing vLLM Semantic Router https://github.com/vllm-project/semantic-router - Sends routine queries to cheap or local models and hard ones to stronger backends, as an open-source, self-hostable router. RLM-Cascade - response-level speculative decoding at the gateway https://arxiv.org/abs/2606.22840 - RLM-Cascade, from a PayPal team, has a cheap draft model answer first and an Opus 4.8 verifier accept or rewrite it, at roughly 2% of Opus's cost. SGLang https://github.com/sgl-project/sglang - A high-performance serving framework for large language and multimodal models. vLLM https://github.com/vllm-project/vllm - The canonical open-source LLM serving engine, using PagedAttention to manage KV-cache memory in blocks so more requests batch at lower cost. Stop When Reasoning Converges https://arxiv.org/abs/2605.17672 - Reasoning models often keep generating steps after a solution has already stabilized, wasting tokens and adding latency - what this paper calls "overthink.". When more reasoning hurts - the test-time-compute ceiling https://arxiv.org/abs/2604.10739 - Two 2026 papers found giving a model more reasoning budget makes it perform worse and cost more; past a point, tool delegation wins outright. Code execution with MCP Anthropic https://www.anthropic.com/engineering/code-execution-with-mcp - Anthropic proposes agents call MCP servers by writing and executing code instead of a tool call per step, so unused tool schemas skip the context window. MCP Tool Descriptions Are Smelly https://arxiv.org/abs/2602.14878 - This study found poorly-written MCP tool descriptions measurably hurt agent efficiency, using an LLM-jury scanner and an A/B protocol on MCP-Universe. Tool Attention Is All You Need https://arxiv.org/abs/2604.21816 - MCP re-sends every tool's full schema on every turn, whether or not the agent needs it - a protocol tax known as the MCP/Tools Tax. CloudZero https://www.cloudzero.com/blog/ai-cost-optimization-at-scale/ - An established commercial cloud and AI cost-intelligence / FinOps platform that brands itself 'The AI ROI Company'. JetBrains AI moves business plans from monthly licenses to 12-month credits https://blog.jetbrains.com/blog/2026/07/07/jetbrains-ai-for-teams-and-organizations-from-fragmented-ai-usage-to-coordinated-software-development/ - JetBrains is moving business AI from monthly per-seat licenses to 12-month reallocatable credits plus a governance dashboard; personal Pro/Ultimate prices are… Mavvrik fmr. DigitalEx https://www.mavvrik.ai/press-releases/mavvrik-unveils-full-stack-ai-cost-governance/ - Mavvrik is an AI/hybrid-infrastructure cost governance and FinOps platform, rebranded from DigitalEx in February 2025. Pay-i https://docs.pay-i.com/ - An SDK-based GenAI cost-observability platform that tracks token-level spend per call and rolls it up into cost-center allocation across orgs and apps. Denial-of-Wallet / token-exhaustion attacks https://arxiv.org/abs/2601.10955 - Denial-of-wallet attacks exploit pay-per-token pricing to inflate a bill, via stolen-credential LLMjacking or agents steered into runaway token use. Governance Decay - compaction silently erasing safety/governance constraints https://arxiv.org/abs/2606.22528 - Compacting an agent's context can silently erase governance rules: across 7 model families, violations rose from 0% to 30%, up to 59% for some. FinOps for AI - canonical practitioner framework for governing AI/LLM spend https://www.finops.org/framework/scope/finops-for-ai/ - FinOps for AI is the FinOps Foundation's official practitioner framework for governing AI, GPU, and token spend. Vaudit - TokenAudit LLM invoice reconciliation / AI spend audit https://www.vaudit.com/ - Vaudit is an AI-native, independent spend-auditing and recovery platform San Francisco, founded late 2023 . Claude Code's 5-hour/weekly usage quotas - Anthropic has stopped publishing exact numbers https://support.claude.com/en/articles/11049741-what-is-the-max-plan - Anthropic stopped publishing exact Claude Code usage quotas, describing Max plans only as 5x/20x multipliers of Pro with no absolute numbers. TrueFoundry AI Gateway - Budget Limiting https://www.truefoundry.com/docs/ai-gateway/budgetlimiting - TrueFoundry is an enterprise GenAI deployment/gateway company founded by ex-Meta founders. CodeCarbon https://github.com/mlco2/codecarbon - An open-source MIT library for estimating a workload's energy use and CO2e emissions, and ML's widely-cited carbon baseline. EcoLogits - energy/carbon of LLM https://github.com/mlco2/ecologits - Estimates the energy and carbon footprint of calling generative-AI APIs: the hosted counterpart to CodeCarbon, which measures your own hardware. API calls the hosted-usage estimator Epoch AI - how much energy a query uses the per-token energy anchor https://epoch.ai/gradient-updates/how-much-energy-does-chatgpt-use - Epoch AI built a transparent, first-principles estimate of how much energy one LLM query costs. Google - measuring the environmental impact of AI inference provider disclosure https://cloud.google.com/blog/products/infrastructure/measuring-the-environmental-impact-of-ai-inference - Google published a first-party disclosure of the energy, carbon, and water cost of a median Gemini Apps text prompt, authored by Amin Vahdat and Jeff Dean. ML.ENERGY Leaderboard v3.0 - measured inference energy the "reasoning ≈ 25× energy" signal https://ml.energy/blog/measurement/energy/diagnosing-inference-energy-consumption-with-the-mlenergy-leaderboard-v30/ - Version 3.0 of this leaderboard measures real GPU inference energy across 46 models x 7 tasks, finding reasoning models use roughly 25x the energy of others. ActPlane https://github.com/eunomia-bpf/ActPlane - An eBPF-based, OS-level policy-enforcement engine for AI-agent harnesses like Claude Code and Codex. AEGIS https://github.com/Justin0504/Aegis - An open-source MIT pre-execution firewall and cryptographic audit layer for AI agents. MCPGuard-Dynamic https://github.com/facebook/mcpguard-dynamic - An early-stage, research-grade kernel-level eBPF sandbox for MCP 64★ , published under Meta's official GitHub org. ChatGPT Enterprise - usage analytics & spend controls https://openai.com/index/chatgpt-enterprise-spend-controls/ - OpenAI's first-party spend layer for ChatGPT Enterprise/Business: a Global Admin Console with credit caps, request workflows, and a Cost API. Claude Enterprise - admin analytics & cost controls https://www.claude.com/blog/giving-admins-more-visibility-and-control-over-claude-usage-and-spend - Anthropic's first-party spend surface for Claude Enterprise/Team admins: org-level spend caps, model defaults, and per-user cost analytics via the Admin API. PointFive AI Efficiency OS / TokenShift https://www.pointfive.co/press/pointfive-launches-ai-efficiency-os-tokenshift - PointFive's TokenShift governs coding-agent token spend across Claude Code, Cursor, Codex, and more, claiming a 10-20% cut across 11 partners. Revenium - runtime AI economic control / spend management https://www.revenium.ai/ - Revenium tracks AI agent spend at runtime to the cent, attributing every model call and tool cost to its workflow, with auto-shutoff on runaway budgets. Vantage https://www.vantage.sh/blog/agentic-coding-costs - A FinOps platform ingesting native token-level cost data from Anthropic and OpenAI's own usage APIs, plus Cursor and cloud spend. Vercel AI Gateway - per-API-key budgets https://vercel.com/changelog/budgets-for-api-keys-on-ai-gateway - Vercel AI Gateway lets you cap spend per API key in dollars min $1 with a daily/weekly/monthly refresh, rejecting further requests once the cap is hit. Claude "subscription arbitrage" and its announced, then paused end https://zed.dev/blog/anthropic-subscription-changes - Users route agentic workloads worth far more than a subscription's price through cheap Pro/Max plans; Anthropic tried to close this, then paused the fix. Coding-agent native spend controls 2026 https://cursor.com/changelog/05-04-26 - Within six weeks in 2026, Cursor, GitHub Copilot, and OpenAI each shipped native admin spend controls: budget caps, credit metering, usage dashboards. also: GitHub Copilot https://github.blog/news-insights/company-news/github-copilot-is-moving-to-usage-based-billing/ · OpenAI https://openai.com/index/chatgpt-enterprise-spend-controls/ GitHub Copilot metered-billing bill-shock - the demand-side reaction "tokenpocalypse" https://news.ycombinator.com/item?id=47923357 - GitHub's move from flat-rate Copilot plans to metered AI Credits exposed agentic workflows' true per-token cost and triggered a mass bill-shock backlash. Lanai https://www.prnewswire.com/news-releases/lanai-launches-ai--work-operating-system-to-help-enterprises-close-the-ai-accountability-gap-302743892.html - Lanai's AI @ Work platform discovers every sanctioned and shadow AI workflow across an org and maps its token spend to the KPIs it actually drives. State of FinOps 2026 - AI spend management is now the norm https://data.finops.org/ - This is the FinOps Foundation's sixth annual State of FinOps survey, the practitioner census of how organizations manage cloud and AI spend. The "$47k Claude Code bill" - the anchor bill-shock anecdote and its mechanistic debunk https://yusufhansacak.medium.com/the-47-000-agent-bill-what-the-viral-token-stories-get-wrong-7ee1cdd81e65 - A viral $47,000-in-90-days Claude Code bill story was debunked by a teardown pinning the real driver on quadratic context re-ingestion, not runaway use. Uber caps AI-coding spend at $1,500/mo per tool after burning its budget in ~4 months https://techcrunch.com/2026/06/02/uber-caps-employee-ai-spending-after-blowing-through-budget-in-four-months/ - Uber capped AI-coding spend at $1,500 per employee per tool after burning its entire annual budget in roughly four months of encouraged maximal use. JetBrains independently measured two token-saving skills against their own claims https://blog.jetbrains.com/ai/2026/07/rtk-claude-code-token-savings/ - JetBrains independently A/B-tested two token-saving skills: rtk ran +7.6% more expensive at low effort claimed 60-90% cut , Caveman saved ~8.5% claimed 65% . also: Caveman A/B post https://blog.jetbrains.com/ai/2026/07/speak-to-ai-agents-like-cavemen-tosave-tokens/ Token-saving plugins are mostly a stupid idea Tura benchmark https://turaai.net/blog token-saving-plugins-are-mostly-stupid-idea - A benchmark of token-saving plugins found one actively worse than none: cost up 7.2%, tokens up 13.2%, because it broke an already-cached prompt prefix. Cisco acquires Galileo LLM eval/observability → folded into Splunk Observability https://blogs.cisco.com/news/cisco-announces-the-intent-to-acquire-galileo - Cisco acquired Galileo, an LLM/agent evaluation and observability platform, folding it into Splunk Observability Cloud's AI Agent Monitoring. Tokenomics Foundation Linux Foundation + FinOps Foundation https://www.linuxfoundation.org/press/linux-foundation-announces-the-intent-to-launch-the-tokenomics-foundation-to-establish-open-standards-for-ai-cost-management - The Linux Foundation is launching the Tokenomics Foundation to build open standards for AI token spend, extending FOCUS to cover token-based costs. Aider - an OSS coding CLI that meters its own dollar cost https://github.com/Aider-AI/aider - An open-source terminal coding agent Apache-2.0, ~48k★ with built-in per-message dollar-cost tracking and a public polyglot leaderboard that publishes… Amp Sourcegraph - pay-as-you-go, no-markup pricing + mode-based routing https://ampcode.com/ - Amp, Sourcegraph's coding agent, passes through LLM cost with zero markup for individuals and teams, with a cost/capability mode: Deep, Smart, or Rush. Cline - an OSS coding agent on a bring-your-own-key cost model https://github.com/cline/cline - An open-source AI coding agent Apache-2.0, ~65k★, ~4.8M VS Code installs , built on a bring-your-own-API-key cost model with per-task token and dollar… Factory droids - subscription pricing + $150M Series C at $1.5B https://factory.ai/pricing - Factory prices its Droids agents as flat subscription tiers $20/$100/$200/mo with usage-based rate limits, not per-token metering, after a $150M round. Gemini CLI retirement → Antigravity CLI open-source coding agent closes, pricing restructures https://developers.googleblog.com/an-important-update-transitioning-gemini-cli-to-antigravity-cli/ - Google retired the open-source Gemini CLI on 2026-06-18, pushing users onto closed-source Antigravity CLI and $100/$200-per-month paid tiers. OpenHands - MIT OSS coding agent, free local + free cloud tier, at-cost LLM option https://github.com/OpenHands/OpenHands - An MIT-licensed open-source coding-agent platform with a free local mode, a free cloud tier, and an at-cost LLM pricing option. Gartner - worldwide AI spending forecast: $2.59T in 2026 +47% YoY https://www.gartner.com/en/newsroom/press-releases/2026-05-19-gartner-forecasts-worldwide-ai-spending-to-grow-47-percent-in-2026 - Gartner's latest forecast puts worldwide AI spending at $2.59 trillion in 2026, up 47% year-over-year, with infrastructure over 45% of the total. Menlo Ventures - enterprise generative-AI spend $11.5B → $37B 2024→2025 https://menlovc.com/perspective/2025-the-state-of-generative-ai-in-the-enterprise/ - Menlo Ventures found enterprise generative-AI spend hit $37B in 2025, up 3.2x from 2024, with coding tools the largest application category at $7.3B. "Qwen 3.6 27B is the sweet spot for local development" - Migdał / Quesma first-party https://quesma.com/blog/qwen-36-is-awesome/ - Piotr Migdał's 1-on-Hacker-News essay argues Qwen3.6-27B dense is the first local model good enough for real coding instead of a metered cloud API. Artificial Analysis - Coding Agent Index tokens & cost per task, model × harness https://artificialanalysis.ai/agents/coding-agents - This benchmark scores full model-plus-harness stacks, spanning $0.27 to $11.80 per task: a roughly 44x range at similar quality, per Artificial Analysis. Artificial Analysis - Intelligence Index + Blended Price intelligence-per-dollar leaderboard https://artificialanalysis.ai/leaderboards/models - Artificial Analysis's Intelligence Index plots a 0-100 capability score against blended price per million tokens, live across 85-122 base LLMs. Claude Opus 5 - flat price vs Opus 4.8, but 1M context and thinking on by default https://platform.claude.com/docs/en/release-notes/api - Claude Opus 5 launched 2026-07-24 at the same $5/$25 per MTok as Opus 4.8, but ships 1M context and thinking on by default - so the flat per-token price hides a… Gemini 3.6 Flash - a Flash tier marketed on fewer tokens per task, not just a lower unit price https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-6-flash-3-5-flash-lite-3-5-flash-cyber/ - Gemini 3.6 Flash 2026-07-21 is priced lower than 3.5 Flash at $1.50/$7.50 per MTok AND uses 17% fewer output tokens on the same work - a rare launch where the… Kimi K2.6/K2.7-Code and GLM-5.2 official API pricing https://platform.kimi.ai/docs/pricing/chat-k27-code - Kimi K2.7-Code $0.95/$4.00 per million tokens and GLM-5.2 $1.40/$4.40 both undercut Claude Sonnet 5 and GPT-5.5 on raw price by 2-5x. Local / open-model economics for coding - state of the field 2026 https://huggingface.co/Qwen/Qwen3.6-27B - Open-weight coding models now score 77-81% on SWE-bench Verified, within a few points of closed frontier models, reshaping self-host-vs-API math. OckBench - measuring token efficiency / verbosity of LLM reasoning https://arxiv.org/abs/2511.05722 - OckBench answers a specific tokenomics question: which model burns the most tokens for the same answer? Reasoning-token billing across providers - the hidden output multiplier https://developers.openai.com/api/docs/guides/reasoning - Every major AI provider bills a model's hidden reasoning tokens at the most expensive output rate, without ever returning them to the caller. also: Google https://ai.google.dev/gemini-api/docs/pricing · DeepSeek https://api-docs.deepseek.com/guides/reasoning model Reasoning-token consumption behavior - length ≠ effort, and verbosity is a separate lever https://arxiv.org/abs/2602.13517 - Chain-of-thought can burn about 258 tokens on problems a direct answer solves in 15 roughly 17x overhead , and simple agentic steps trigger it by accident. Batch / Priority / Flex service tiers - the scheduling axis of token pricing clustered, cross-vendor https://platform.claude.com/docs/en/docs/build-with-claude/batch-processing - Every major LLM vendor sells the same lever, trading latency for price via async batch scheduling, with Anthropic, OpenAI, and Google all near 50% off. also: OpenAI https://developers.openai.com/api/docs/pricing · Google https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/partner-models/claude/batch Bessemer - the AI pricing & monetization playbook seat → usage → outcome https://www.bvp.com/atlas/the-ai-pricing-and-monetization-playbook - Bessemer's playbook argues AI pricing is shifting from per-seat to consumption/outcome-based, citing Intercom's $0.99-per-resolved-ticket model. Cached-input discounts - the ~90%-off lever behind cache-accounting https://platform.claude.com/docs/en/build-with-claude/prompt-caching - Cache-read pricing discounts input tokens by about 90%: the biggest lever on an agentic bill, since input is roughly 85% of session cost. ChatGPT subscription tiers and Codex CLI bundling/pricing 2026 https://developers.openai.com/codex/pricing - OpenAI bundles Codex CLI into every ChatGPT tier from Free through the new $100/mo Pro plan, differing only by rate-limit multiplier. ChatGPT workspace-agent credit billing effective July 6, 2026 https://help.openai.com/en/articles/11481834-chatgpt-rate-card - OpenAI ended the free preview for agent runs invoked inside ChatGPT Business, Enterprise, Edu, and Teachers on 2026-07-06. Cursor charges by tokens, split into first-party and third-party pools https://cursor.com/docs/account/pricing - Cursor meters by tokens per million input/output/cache-write/cache-read , split into a first-party pool and a third-party API pool, with a $0.25/M Teams… also: Teams pricing blog https://cursor.com/blog/teams-pricing-june-2026 Devin's Agent Compute Unit has no published definition of what it meters https://docs.devin.ai/admin/billing/enterprise - Devin bills Enterprise usage in Agent Compute Units, but no official doc defines what an ACU measures not tokens, seconds, or calls ; the opacity itself is the… Fable 5 leaves subscription inclusion - frontier tier moves to usage-credit metering July 7 cliff https://www.anthropic.com/news/redeploying-fable-5 - Fable 5's subscription saga settled 2026-07-20 after two extensions as a primary-confirmed two-tier split: Max & Team Premium keep it included 50% of weekly… Google AI Pro price and Gemini/Antigravity free-tier limits 2026 https://gemini.google/subscriptions/ - Google AI Pro is confirmed at $19.99/month, beneath the $99.99 and $199.99 AI Ultra tiers giving higher rate limits on the Gemini API and Antigravity. GPT-5.6 family Sol / Terra / Luna - API pricing https://developers.openai.com/api/docs/pricing - OpenAI's GPT-5.6 family prices three tiers 2x apart: Sol at $5/$30 per million tokens, Terra at $2.50/$15, and Luna at $1/$6. LiteLLM flex/priority service-tier cost keys - the harness-level tier-routing lever https://docs.litellm.ai/docs/proxy/custom pricing - LiteLLM automatically prices requests made at a non-standard tier like flex or priority, applying the right discounted or premium rate automatically. LLM price decline + Jevons paradox - unit price crashes, total spend climbs https://a16z.com/llmflation-llm-inference-cost/ - Per-token prices are falling roughly an order of magnitude per year, while total AI spend rises even faster. LLM token pricing dimensions - the structure of a token bill https://platform.claude.com/docs/en/about-claude/pricing - This maps out how frontier LLM APIs meter and price tokens, read straight off the two largest providers' pricing pages, Anthropic and OpenAI. OpenAI is winding down the self-serve fine-tuning API and platform https://developers.openai.com/api/docs/deprecations - OpenAI is winding down self-serve fine-tuning because prompting got cheaper and more capable than fine-tuning for most uses, cutting off customers by 2027. Tokenization multiplicity & overcharging - the pay-per-token integrity problem https://arxiv.org/abs/2506.06446 - Two academic papers show the same output can be billed a different token count depending on tokenization, and providers can be incentivized to inflate it. Windsurf became Devin Desktop and switched credits to token-based quota https://docs.devin.ai/desktop/accounts/quota - Windsurf became Devin Desktop and in March 2026 swapped opaque per-model credit multipliers for token-based quota where free models cost nothing; a… Reserved-capacity reliability economics Azure PTU · AWS Bedrock MU https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/provisioned-throughput - Azure's Provisioned Throughput Units and AWS Bedrock's Model Units both let buyers reserve guaranteed capacity, billed hourly whether or not it's used. Cloud Capital - gross margin in the age of AI the vendor/supply side https://www.cloudcapital.co/learn/gross-margin-in-the-age-of-ai - AI-native software runs at roughly 50-60% gross margin versus 70-80% for SaaS, since inference and compute became a large, variable cost of goods sold. Cost-of-Pass - an economic framework for evaluating language models https://arxiv.org/abs/2504.13359 - Cost-of-Pass defines the expected dollar cost of one correct answer as inference cost divided by success rate, pricing benchmark accuracy directly. DORA 2025 - AI as amplifier, and the delivery-stability tension https://dora.dev/insights/balancing-ai-tensions/ - Google's DORA program found that as AI adoption becomes universal, delivery throughput rises but so does instability: AI as an amplifier, not a pure win. Faros - "The Acceleration Whiplash" AI Engineering Report 2026 https://pages.faros.ai/hubfs/AI Engineering Report 2026 The Acceleration Whiplash Faros.pdf - The "velocity has a hidden bill" study: telemetry from 22,000 developers across 4,000 teams over two years. getDX - AI coding assistant pricing & ROI guide 2026 https://getdx.com/blog/ai-coding-assistant-pricing/ - Typical AI coding tools cost $200-600 per engineer monthly in seat plus token spend, per getDX, for a median 7.76% PR gain: below vendors' claimed 3-10x. METR - measured vs perceived AI productivity the RCT https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/ - METR's controlled trial found developers took 19% longer to finish issues when allowed to use AI, while still believing it had sped them up by 20%. Claw-SWE-Bench https://github.com/opensquilla/claw-swe-bench - Found that adapter/harness design alone swings an agent's Pass@1 score by about 54 percentage points on the identical model backbone. Coding Benchmarks Are Misaligned with Agentic SE Tessl https://arxiv.org/abs/2606.17799 - A position paper from Tessl London, UK argues that today's coding benchmarks don't measure what people think they measure. Deterministic Anchoring - how much static structure do code agents need? https://arxiv.org/abs/2606.26979 - This ISSTA 2026 paper found injecting static-analysis facts as plain-text comments raises a code agent's Pass@1 by 3.4pp and cuts trajectories by 1.6 rounds. GitHub Copilot agentic-harness efficiency evaluation first-party offline ablation https://github.blog/ai-and-ml/github-copilot/evaluating-performance-and-efficiency-of-the-github-copilot-agentic-harness-across-models-and-tasks/ - GitHub's own benchmark plots Copilot's agentic-harness resolution rate against dollar-cost-per-task across five benchmarks and four frontier models. Harness-Bench https://arxiv.org/abs/2605.27922 - Holds the task, model, and budget fixed while varying only the agent harness, across 5,194 trajectories spanning 6 harnesses and 8 models. LoCoMo https://github.com/snap-research/locomo - Snap Research's very-long-term conversational-memory benchmark; the canonical dataset that Mem0, Zep, and Supermemory all cite, kept as a background instrument… LongMemEval https://github.com/xiaowu0162/LongMemEval - The peer-reviewed ICLR 2025 benchmark for long-term memory in chat assistants; the standard eval that mem0, Zep, and Supermemory all self-report their… MemoryBench https://github.com/supermemoryai/memorybench - A pluggable harness to run memory systems Supermemory, Mem0, Zep head-to-head across datasets like LoCoMo; useful for standardizing comparison, but built by… Prompt Compression in the Wild - the end-to-end referee for compression https://arxiv.org/abs/2604.02985 - This ECIR 2026 study found LLMLingua's compression yields up to 18% speed-up only in a narrow window; outside it, the compression step cancels the gains. promptfoo https://github.com/promptfoo/promptfoo - An open-source CLI/CI harness for testing LLM prompts and agents that records per-eval token usage and cost as an assertable metric. RedundancyBench - can anyone even https://arxiv.org/abs/2605.29893 - RedundancyBench is a benchmark for step-level redundancy detection in agent trajectories - can a model even spot the wasted step in an agent's history? detect a redundant step? RouterArena - the open benchmark + live leaderboard for LLM routers https://github.com/RouteWorks/RouterArena - RouterArena is an open evaluation platform and live leaderboard for LLM routers - systems that auto-select a model per query. SWE-bench - the root benchmark the cost-aware derivatives build on https://github.com/SWE-bench/SWE-bench - The canonical accuracy-only software-engineering benchmark 2,294 real GitHub issue tasks, 12 Python repos, ICLR 2024 that every cost-aware derivative in this… SWE-Effi - cost-aware re-ranking of SWE-agents under resource budgets https://arxiv.org/abs/2509.09853 - SWE-Effi re-ranks popular AI issue-resolution systems on a SWE-bench subset by cost-under-resource-constraints instead of by accuracy alone. Terminal-Bench https://www.tbench.ai/ - The canonical benchmark for AI agents in real terminal/CLI environments, with 89 tasks each vetted through ~3 reviewer-hours. Gemini context caching - the per-hour storage meter the third-vendor axis https://ai.google.dev/gemini-api/docs/pricing - Among major providers, only Gemini bills a per-hour storage meter for explicit prompt caching, making a cache a rented line item that accrues cost while it sits… OpenAI prompt-caching https://platform.openai.com/docs/guides/prompt-caching - This is the measurement substrate for prompt-cache savings on the OpenAI API. cached tokens accounting OpenAI Responses conversation state - you pay for the whole chain every turn and the compaction levers https://developers.openai.com/api/docs/guides/conversation-state - This entry covers the billing semantics of stateful conversations on OpenAI's Responses API, plus the two server-side compaction levers that mitigate them. Anthropic bill anatomy - the whole-bill line-item taxonomy https://platform.claude.com/docs/en/about-claude/pricing code-execution-tool - This is the canonical enumeration of every meter on a Claude API bill, read live from Anthropic's pricing page. TensorZero - cross-vendor token-count divergence "stop comparing $/M tokens" https://www.tensorzero.com/blog/stop-comparing-price-per-million-tokens-the-hidden-llm-api-costs/ - The same input can produce 2.65x more tokens on one tokenizer than another's: Claude Opus 4-7 runs 1.57x-2.65x more tokens than GPT-5.4 on the same content. Vision-token pricing formulas across the big three https://platform.claude.com/docs/en/build-with-claude/vision - Anthropic, OpenAI, and Google each convert an image into billed tokens with a different formula, so no single cross-vendor image-cost number exists. also: OpenAI https://developers.openai.com/api/docs/guides/images-vision · Google https://ai.google.dev/gemini-api/docs/image-understanding Claude Code system prompts Piebald extraction https://github.com/Piebald-AI/claude-code-system-prompts - Piebald AI extracts Claude Code's full compiled prompt payload per release: 515 prompt strings and 27 tool descriptions at v2.1.212, each priced in tokens. Claude Code vs OpenCode token overhead Systima study https://systima.ai/blog/claude-code-vs-opencode-token-overhead - Systima measured harness scaffolding overhead before a prompt is even read: Claude Code carries about 32,800 tokens versus OpenCode's 6,900, a 4.7x gap. Cross-vendor coding-agent usage trackers AgentsView · caut https://github.com/kenn-io/agentsview - AgentsView and caut are open-source tools that read local session logs to aggregate token usage and cost across roughly 20 coding-agent vendors. How Do AI Agents Spend Your Money? https://arxiv.org/abs/2604.22750 - This Stanford study is the first systematic look at token spend in agentic coding, running 8 frontier models on 500 SWE-bench Verified tasks. tokview https://github.com/headroomlabs-ai/tokview - A local, zero-config proxy showing a coding agent's token spend by session, model, and tool call, flagging re-sent results that multiply the bill. Early Diagnosis of Wasted Computation in Multi-Agent LLM Systems Failure-Aware Observability https://arxiv.org/abs/2606.01365 - This live waste-detection framework for multi-agent systems found that 58.1% of tokens in failed runs are spent after its first warning fires. Faros AI - "Token Intelligence" / Token Attribution Ledger https://www.faros.ai/blog/token-intelligence-for-ai-engineering - Faros AI is an enterprise engineering-intelligence SaaS DORA/SPACE-style productivity analytics . token-optimizer alexgreensh - per-session $ waste-detection plugin, semi-cross-vendor https://github.com/alexgreensh/token-optimizer - token-optimizer is a coding-agent plugin that runs eleven heuristic waste detectors per session and prices the flagged tokens in dollars. FOCUS 1.4 - the cross-vendor billing-data normalization standard now with invoice reconciliation https://focus.finops.org/focus-specification/ - FOCUS 1.4, the Linux Foundation's billing-data schema, added Invoice Detail and Billing Period datasets to reconcile spend against real invoices. Tool-agnostic, evidence-grounded standards for token-efficient agentic coding. Each is one page: TL;DR, claim, evidence, links. Browse the practices /QuesmaOrg/awesome-ai-tokenomics/blob/main/practices/README.md . Short reference notes explaining the ideas behind the practices: cache economics, the harness-waste taxonomy, orchestration economics. Browse the concepts /QuesmaOrg/awesome-ai-tokenomics/blob/main/concepts/README.md . Confidence-scored beliefs, clearly labeled as beliefs rather than facts, each with its strongest evidence linked. Read the claims /QuesmaOrg/awesome-ai-tokenomics/blob/main/claims.md . Runnable, validated Claude Code and Codex configurations and skills for token-efficient agentic coding, each labeled with how it was validated. Browse the setups /QuesmaOrg/awesome-ai-tokenomics/blob/main/setups/README.md . Text content: CC-BY 4.0 LICENSE /QuesmaOrg/awesome-ai-tokenomics/blob/main/LICENSE · Code and configs: MIT LICENSE-CODE /QuesmaOrg/awesome-ai-tokenomics/blob/main/LICENSE-CODE . Maintained by the team at Quesma https://quesma.com .