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Sources & Methodology

Every number in the comparison table sourced and explained. Where data is community-reported rather than officially published, confidence is noted accordingly.

Workload assumption

All RAM figures assume a comparable workload. Actual usage scales with open tabs, file count, and active extensions.

High — verified from official source or direct measurement Medium — community-reported, multiple consistent sources Low — isolated reports, not broadly reproducible

Industry Statistics & Citations

Every market stat cited on the AgentMux landing pages, with primary or first-tier secondary source. Anchor IDs match the footnote links on those pages.

Shadow AIHigh

Half of employees were using unapproved AI tools at work (October 2024)

In October 2024, half of all employees were using unapproved AI tools.

Source: SANS Institute — Sunlight AI: Bringing Shadow AI Into the Light · view
Shadow AIHigh

86% of organizations are blind to their AI data flows

The 2025 State of Shadow AI Report from Reco found that 86% of organizations lack visibility into how data flows to and from AI tools. (Quoted verbatim by OffSec, who reviewed the Reco report.)

Source: Reco — 2025 State of Shadow AI Report (citation via OffSec; Reco requires form submission for the full PDF) · view
Shadow AIHigh

48% of employees would continue using AI tools even if banned

48% said they wouldn't stop even if banned.

Source: Software AG (corroborated by SANS Institute) · view
Shadow AIMedium

12% of practitioners report having no visibility into what employees enter into AI systems

12% of practitioners admitted they had no visibility into what is being entered into AI systems within their organization.

Source: Mindgard — Survey of 500+ cybersecurity professionals (RSA Conference 2025, InfoSecurity Europe 2025) · view
Shadow AIHigh

Average enterprise sees 223 sensitive-data policy violations involving generative AI applications per month

The average organization now experiences 223 data policy violations involving generative AI applications every month. For organizations in the top quartile, that number jumps to 2,100 incidents monthly.

Source: Kiteworks — 2026 AI Data Crisis report · view
Cost of breachHigh

Shadow AI breaches cost organizations $4.63M on average — $670K more than standard incidents

Breaches involving shadow AI cost organizations $4.63 million on average—$670,000 more than standard incidents.

Source: IBM — 2025 Cost of a Data Breach Report (Ponemon Institute) · view
Agent boundary violationsHigh

80% of companies say their AI agents have taken unintended actions

80% of companies say their AI agents have taken unintended actions.

Source: SailPoint — AI Agent survey, May 2025 · view
Agent boundary violationsHigh

39% of organizations report AI agents accessed unauthorized systems; 33% report agents accessed inappropriate or sensitive data

39% of respondents reported AI agents accessed unauthorized systems, while 33% said agents accessed inappropriate or sensitive data.

Source: SailPoint — AI Agent survey, May 2025 · view
Agent boundary violationsHigh

53% of organizations have had AI agents exceed their intended permissions

53% of organizations have had AI agents exceed their intended permissions, leaving them vulnerable to increased risk.

Source: Cloud Security Alliance / Zenity — Enterprise AI Security Starts with AI Agents (April 2026) · view
Agent boundary violationsHigh

Only 24.4% of organizations have full visibility into agent-to-agent (A2A) communication

Only 24.4% of organizations report having full visibility into which AI agents are interacting with others (A2A communication).

Source: Gravitee — State of AI Agent Security 2026 Report (919 practitioners) · view
Agent boundary violationsHigh

25.5% of deployed agents can both create and instruct other agents — establishing autonomous chains of command

25.5% of deployed agents are capable of both creating and instructing other agents, effectively establishing autonomous "chains of command" that may bypass traditional human-centric authorization gates.

Source: Gravitee — State of AI Agent Security 2026 Report · view
Industry adoptionHigh

100% of NVIDIA is using a combination of Claude Code, Codex, and Cursor

100% of NVIDIA is using a combination of, or oftentimes all three of them, Claude Code, Codex, and Cursor.

Source: Jensen Huang — NVIDIA GTC 2026 Keynote (March 16–19, 2026, San Jose) · view
Industry adoptionHigh

NVIDIA projects at least $1 trillion in AI infrastructure demand through 2027

I see through 2027 at least $1 trillion. In fact, we are going to be short.

Source: Jensen Huang — NVIDIA GTC 2026 Keynote · view
RegulatoryHigh

EU AI Act fines: up to €35M or 7% of global annual revenue, whichever is higher

Non-compliance with the prohibition of the AI practices referred to in Article 5 is subject to administrative fines of up to 35,000,000 EUR or, if the offender is an undertaking, up to 7% of its total worldwide annual turnover for the preceding financial year, whichever is higher.

Source: EU AI Act, Article 99 (Penalties) · view
RegulatoryHigh

EU AI Act high-risk system obligations become applicable on August 2, 2026

This Regulation shall apply from 2 August 2026 [in respect of high-risk AI systems and most other obligations].

Source: EU AI Act, Article 113 — Entry into force and application (staggered application schedule) · view
RegulatoryHigh

Colorado AI Act takes effect June 30, 2026 (delayed from February 1)

The law was originally scheduled to apply beginning Feb. 1, 2026, but the law's effective date was subsequently pushed to June 30, 2026, during a special session last fall through Senate Bill 25B-004.

Source: Colorado SB24-205, as amended by SB 25B-004 · view
AI security M&AMedium

Cisco acquired Robust Intelligence for ~$400M (October 2024). Officially announced; price not disclosed by Cisco; figure widely reported.

Israeli Yaron Singer sold the startup he founded, Robust Intelligence, to communications giant Cisco for $400 million, according to reports in the U.S.

Source: Calcalist (Ctech) · view
AI security M&AMedium

Palo Alto Networks acquired Protect AI for $500M+ (announced April 2025, completed July 2025). Price not officially disclosed.

Sources told GeekWire that the deal is worth more than $500 million.

Source: Palo Alto Networks (announcement) + GeekWire (price) · view
AI security M&AMedium

Check Point acquired Lakera for ~$300M (September 2025). Price not officially disclosed; figure widely reported.

Check Point acquires Lakera in $300 million deal to expand AI security.

Source: Calcalist (Ctech) — earlier landing copy claimed $190M, which was incorrect · view
AI security M&AHigh

F5 acquired CalypsoAI for $180M (announced September 11, 2025; completed September 30, 2025). Officially disclosed.

F5 will acquire all issued and outstanding shares of CalypsoAI, a private company with major operations in Dublin, Ireland, for $180 million in purchase consideration financed primarily with cash.

Source: F5 — press release · view

Product Specs & Comparison Methodology

AgentMux

Rust + CEF (Chromium 148)
Download size
~160 MB
Measured from the current portable ZIP release on agentmuxai/agentmux GitHub releases (v0.42.x line). Includes Chromium 148 CEF runtime + launcher + backend sidecar. CEF was first adopted in v0.33.0 (on Chromium 146); the macOS notarized DMG with CEF 148 landed in PR #1243.
High
Install size
~300 MB
Portable ZIP — no installer. Compressed download is ~160 MB; extracted on-disk footprint is ~300 MB (CEF DLLs + Chromium resources unpack to ~3× their compressed size). Per docs/memory-retro-report.md in the agentmux repo.
High
RAM
150-350 MB
Self-reported from development team. Includes CEF host process + agentmux-srv sidecar. No independent third-party benchmark yet — early alpha.

CEF (Chromium 148) accounts for the majority of RAM. The Rust backend (agentmux-srv) itself uses ~15-40 MB. Agent processes (Claude Code, Codex Agent, etc.) are separate processes not included in this figure.

Medium
Startup
<1s
CEF host launches frontend from local dist/ directory. Backend sidecar ready in <500ms. No network requests at startup.
Medium
Runtime note
Chromium 148 (CEF) + Rust backend
AgentMux switched from Tauri to CEF (Chromium Embedded Framework) in v0.33.0 (originally on Chromium 146), and now ships with CEF 148 / Chromium 148 (per the Cargo.toml pin and the macOS CEF 148 notarized DMG work in PR #1243). Electron apps bundle Chromium + Node.js (~200 MB Chromium + ~30 MB Node). AgentMux bundles Chromium 148 but no Node.js — the backend is a Rust binary (4.4 MB). No V8 runtime, no npm, no garbage collector in the backend layer.

Download size is comparable to Warp (~205 MB) and less than Cursor (~230 MB). The key runtime difference from Electron is: no Node.js, no V8 GC in the backend, Rust ownership model prevents heap growth over long sessions.

High

Warp

Rust + GPU
Download size
~205 MB
Measured from warp.dev download page (macOS DMG, 2025)
High
Install size
~450 MB
Warp.app on-disk size including bundled Rust runtime, GPU shader assets, and frameworks
Medium
RAM
150-400 MB
Community reports from Warp Discord and Reddit r/warp. Fresh launch ~150 MB; multiple panes/sessions ~300-400 MB.

Warp has not published official memory benchmarks.

Medium
RAM spike warning
3+ GB reported
Warp GitHub issues — specific reports of memory not being freed after closing panes

Reported as non-reproducible by Warp team in some cases. Represents a known class of issue, not typical baseline.

Low / Community

Wave Terminal

Electron (Go + TS)
Download size
~155-200 MB
GitHub releases v0.14.1: Windows .exe = 156 MB, Linux .deb = 155 MB, macOS arm64 .dmg = 194 MB, macOS x64 .dmg = 202 MB.
Medium
Install size
~300 MB+
Typical Electron app size. No official install size published.
Medium
RAM
200-500 MB
Standard Electron framework overhead range. Wave has no published benchmarks.

Electron apps typically run 200-500 MB depending on renderer/process count.

Medium
Stack note
Electron (Go + TS)
github.com/wavetermdev/waveterm language breakdown: 49.9% Go (backend), 41.5% TypeScript (frontend), Electron for desktop shell.
High

Zed

Rust + custom GPUI
Download size
~80 MB
zed.dev downloads — macOS DMG measured at ~75-80 MB
High
Install size
~220 MB
Zed.app bundle measured on disk
High
RAM
100-250 MB
Zed blog benchmarks (zed.dev/blog/zed-decoded-performance): ~50% less memory than comparable Electron editors on equivalent projects.

Zed's own benchmarks. No independent third-party replication published.

Medium
Startup
<1s
Zed's own benchmarks and widely confirmed in community threads
High

Cursor

Electron (VS Code fork)
Download size
~230 MB
cursor.com download page — macOS, measured 2025
High
Install size
500 MB+
Cursor.app bundle + supporting files, measured post-install
High
RAM
400 MB-3 GB+
Cursor community forum, Reddit r/cursor. Fresh launch with project: ~400-600 MB. AI context loaded + background indexing: 1-3 GB+.

Cursor's AI indexing and embedding features add significant memory overhead beyond VS Code baseline.

Medium
Long session warning
20-40+ GB over multi-day sessions
Cursor community forum threads and GitHub issues — reported progressive memory growth without release

Represents worst-case scenarios. Not reproducible in all environments. Cursor has addressed some of these in recent releases.

Low / Community

Windsurf

Electron (Code OSS fork)
Download size
~220 MB
codeium.com/windsurf download page, macOS, measured 2025
High
Install size
~800 MB
Windsurf.app bundle measured on disk. Previous ~2 GB figure conflated install size with cache directories.
Medium
RAM
500 MB-4 GB+
Community reports from Reddit r/Windsurf and Codeium Discord. Baseline: ~500-800 MB. With Cascade AI context: 1-3 GB. Large project peaks: 4+ GB.

Windsurf recommends 16 GB RAM system requirement in their docs.

Medium
Language server spike
10 GB+ on large codebases
Codeium Discord and Reddit r/Windsurf — reported on large monorepos with full Cascade context enabled

Represents a specific configuration (large monorepo + full Cascade context). Not representative of typical use.

Low / Community

A note on AgentMux numbers

AgentMux is in early alpha. Our memory figures are self-reported from development builds and have not been independently benchmarked. We will update this page as the project matures and third-party benchmarks become available. We hold ourselves to the same sourcing standard as every other app listed here.

See a number that's wrong? Open an issue on GitHub.

github.com/agentmuxai/agentmux