Pro-AI companies

Buy from these companies if you want to support an open, free, and private AI ecosystem

Also published at: Substack

All of the following firms utilize open-source software, prioritize user privacy, and resist restrictive AI regulation. By purchasing their services (instead of those of the proprietary AI firms), you help build an open, free AI ecosystem

Venice.ai

Venice.ai, founded by Erik Voorhees, positions itself as the primary alternative to “captured” AI. It provides a private, permissionless, and uncensored interface for open-weights models. In the regulatory framework, Venice is the entity that bootleggers identify as a “safety risk” to justify licensing requirements. Voorhees has explicitly argued that safety-driven regulations are a form of regulatory capture designed to centralize control over intelligence [1].

Abacus.ai

Abacus.ai, led by CEO Bindu Reddy, is a vocal opponent of legislation like California’s SB 1047 and federal licensing frameworks. Reddy argues that the “bootleggers” (Big Tech) are hyping existential risks to secure a monopoly. Abacus.ai advocates for open-source AI and argues that liability should rest with the users who misuse tools, rather than the developers who create the underlying mathematics [2].

Mistral AI

Mistral AI is a leader in balancing open-weight models with privacy. In 2026 industry privacy rankings, Mistral was identified as the least privacy-invasive major AI platform [3]. Their business model emphasizes giving users the ability to download and run models like Mistral 7B and Mixtral 8x7B on their own infrastructure, ensuring data never leaves the user’s environment. While they have engaged in European political discussions, their efforts were focused on exempting open-source software from the most restrictive tiers of the EU AI Act [4].

Nomic AI

Nomic AI focuses on making AI accessible and private through projects like GPT4All. This software allows users to run powerful large language models locally on consumer-grade hardware (CPUs and GPUs) without an internet connection [5]. Their privacy policy is inherently strong because the data remains local by default. Nomic focuses on open-source tools like Nomic Atlas for data visualization and model transparency rather than large-scale lobbying for AI regulation.

Ollama

Ollama provides a streamlined tool for running open-source models locally on macOS, Linux, and Windows. Because Ollama is a local-first application, it does not collect user prompts or training data by default [6]. The company has maintained a low profile regarding government policy, choosing to focus on the technical implementation of local AI rather than influencing the regulatory landscape.

Mintplex Labs (AnythingLLM)

Mintplex Labs develops AnythingLLM, an all-in-one desktop application that turns documents and files into a private, local AI environment [7]. It allows users to choose their own vector database and LLM provider. Their stance is explicitly pro-privacy and local-first, avoiding participation in the regulatory lobbying common among industry incumbents.

EleutherAI

EleutherAI is a pillar of the open-source community, known for the Pythia and GPT-Neo series of models. As a non-profit research lab, its mission is to democratize AI research and provide open alternatives to proprietary models [8]. They do not lobby for restrictive AI regulations; instead, they advocate for transparency and the public availability of research data and weights.

NanoGPT (nano-gpt.com)

NanoGPT is a pay-per-prompt AI aggregator that allows users to access 400+ different AI models without a subscription and with a high degree of anonymity. The service is deeply integrated with the Nano (XNO) cryptocurrency community and co-founded by developers such as “Mirasenat” [9]. It prioritizes “leave no trace” privacy, handling payments via decentralized cryptocurrencies like Nano and Monero, and acting as a proxy to pass data to model providers without personal identifiers.

Together AI

Together AI provides a decentralized cloud platform for training and running open-weights models. They offer a “zero data retention” policy for their API and allow users to opt out of data collection [10]. They focus on providing the compute and tools necessary for smaller players to remain competitive without the need for federal licenses.

Fireworks AI

Fireworks AI is a “production engine” for open-source AI, offering fast inference for open-weights models. They maintain a strict Zero Data Retention policy for open models by default [11]. They focus on transparency and verifiable security to provide enterprise trust without participating in lobbying for restrictive AI safety legislation.

Anyscale

The company behind Ray, an open-source framework for scaling AI applications, Anyscale focuses on infrastructure rather than proprietary model development [12]. They are a vocal supporter of the open-source ecosystem and have consistently pushed back against licensing schemes that would treat open-source code as a regulated hazard.

Pin AI

Pin AI is a developer of “Personal Intelligence” that focuses on on-device, agentic AI. Their architecture is “on-device first,” processing data locally on the user’s device to bypass the privacy risks of cloud AI [13]. They are naturally aligned against regulations mandating reporting requirements for high-performance local hardware.

Hugging Face

Hugging Face is the central hub for the global AI community and a commercial firm protecting open-source interests. They were instrumental in advocating for the “Open Source Exemption” in the EU AI Act and have consistently testified against proposals that would require licenses for “dual-use” foundation models [14].

Groq

Groq developed the Language Processing Unit (LPU) for fast inference. Their cloud service offers privacy-compliant environments for running open-weights models [15]. They oppose regulatory frameworks that limit the distribution of model weights, as such laws would reduce demand for their specialized inference hardware.

Morpheus & Decentralized Infrastructure

Arweave Ecosystem

Decentralized AI Training

Monero, Zano, & Zcash Ecosystems

[1] Tony Zerucha, David Johnston On Why 2025 Is The Year Of Smart Agents And Decentralized AI, Crowdfund Insider, 2024. This article details the shared vision of Venice.ai and Morpheus to create an uncensored, open-source alternative to centralized AI labs. https://www.crowdfundinsider.com/2024/12/234453-david-johnston-on-why-2025-is-the-year-of-smart-agents-and-decentralized-ai/

[2] Politico, Founder-led statement on SB1047, Politico, 2024. This joint statement features Bindu Reddy and other tech leaders arguing against liability burdens on AI developers and the potential for regulatory capture. https://static.politico.com/95/0a/a317efe44616af436ce6a4f32647/founder-led-statement-on-sb1047-june-20-2024-2.pdf

[3] Incogni, Gen AI and LLM Data Privacy Ranking 2025 [2026], Incogni Blog, 2026. This industry report identifies Mistral AI as a top performer in privacy due to its minimal data collection and support for on-premise execution. https://blog.incogni.com/ai-llm-privacy-ranking-2025/

[4] Open Future Foundation, AI Act OSS exemption letter, Open Future, 2024. This coalition letter signed by Hugging Face and others successfully argued for exempting open-source developers from specific AI Act liabilities. https://openfuture.eu/wp-content/uploads/2024/03/230414Coalition_letter_on_Open_Source_AIA.pdf

[5] Nomic AI, Privacy Policy, Nomic AI, 2025. This policy confirms Nomic’s local-first architecture where user data remains on-device by default during interaction with models like GPT4All. https://www.nomic.ai/privacy-policy

[6] Cohorte Projects, Run LLMs Locally with Ollama: Privacy-First AI for Developers in 2025, Cohorte Projects, 2025. This technical analysis highlights how Ollama ensures data never leaves user infrastructure, aligning with strict privacy standards. https://www.cohorte.co/blog/run-llms-locally-with-ollama-privacy-first-ai-for-developers-in-2025

[7] Mintplex Labs, AnythingLLM Desktop App Privacy Policy, AnythingLLM Official, 2025. This documentation describes the application’s offline capabilities and its policy of collecting minimal, de-identified telemetry data. https://docs.anythingllm.com/installation-desktop/privacy

[8] EleutherAI, EleutherAI: Going Beyond “Open Science” to “Science in the Open”, EleutherAI, 2025. This blog post outlines the mission of EleutherAI to provide open, accessible research as a pillar for decentralized AI innovation. https://www.eleuther.ai/papers-blog/eleutherai-going-beyond-open-science-to-science-in-the-open

[9] NanoGPT, Privacy Policy, nano-gpt.com, 2026. This service policy details how NanoGPT provides anonymous access to AI models through cryptocurrency payments and proxy routing. https://nano-gpt.com/privacy

[10] Together AI, Security and Privacy at Together AI, Together AI Docs, 2026. This overview describes Together AI’s zero-retention policies and private deployment options for open-weights models. https://docs.together.ai/docs/security-and-privacy

[11] Fireworks AI, Privacy Policy - Fireworks AI, Fireworks AI, 2025. This policy details how Fireworks AI maintains zero data retention for open models to provide high-performance, private inference. https://fireworks.ai/privacy-policy

[12] Anyscale, What is Ray?, Anyscale Docs, 2026. This guide describes the open-source Ray framework and Anyscale’s role in providing a unified platform for scaling AI without centralizing compute. https://docs.anyscale.com/get-started/what-is-ray

[13] Pin AI, Personal Intelligence for Everyone, Pin AI Documentation, 2026. This documentation outlines the on-device architecture of Pin AI designed to provide a privacy-preserving alternative to traditional cloud assistants. https://pinai.io/docs/vision

[14] Open Future Foundation, AI Act OSS exemption letter - Open Future Foundation, Open Future, 2024. This coalition letter features Hugging Face’s leadership in advocating for the rights of open-source developers under the EU AI Act. https://openfuture.eu/wp-content/uploads/2024/03/230414Coalition_letter_on_Open_Source_AIA.pdf

[15] Groq, GroqCloud | Groq is fast, low cost inference, GroqCloud, 2026. This landing page describes Groq’s fast LPU inference and its availability in private and zero-data-retention instances. https://groq.com/groqcloud

[16] AlphaTON Capital, AlphaTON Launches AI Vertical; Morpheus Named Preferred Provider, Stock Titan, 2025. This announcement outlines the strategic partnership between AlphaTON and Morpheus to scale decentralized AI agents on TON. https://www.stocktitan.net/news/ATON/alpha-ton-capital-launches-ai-vertical-with-morpheus-ai-to-gq840ezi2z4u.html

[17] Matrix AI Network, Matrix AI Network Launches M.A.C. for AI-Powered Crypto Market Insight, Matrix Docs, 2025. This release highlights the launch of Morpheus-powered agents that allow users to maintain data sovereignty while accessing market insights. https://docs.matrix.io/ai-agent/matrix-ai-network-launches-m.a.c.-for-ai-powered-crypto-market-insight

[18] Morpheus, Morpheus, mor.org, 2026. The official project page for Morpheus, detailing the fair launch and the role of Capital, Code, and Compute providers in the decentralized network.

https://mor.org/

[19] Exabits, Exabits’ AI Compute Economy-1, Exabits Blog, 2024. This post describes Exabits’ role in providing the foundational GPU infrastructure for the decentralized agent economy. https://www.exabits.ai/blog/exabits-ai-compute-economy

[20] CoinMarketCap, Latest Akash Network (AKT) News Update, CoinMarketCap, 2026. This update details Akash’s Q1 2026 roadmap, specifically the launch of a hardware-based confidential computing layer. https://coinmarketcap.com/cmc-ai/akash-network/latest-updates/

[21] Forward Research, Forward Research, fwd.arweave.net, 2026. The mission page for Forward Research, focusing on R&D for the Arweave permaweb and web services that respect user rights.

https://fwd.arweave.net/

[22] Community Labs, Introducing the Community Labs Venture Studio, Community Labs Blog, 2022. This blog post introduces the studio’s vision for scaling Arweave through developer tooling and mass adoption projects. https://www.communitylabs.com/blog/introducing-the-community-labs-venture-studio

[23] Irys, Irys Network: The Programmable Datachain Challenging Arweave, MEXC Blog, 2025. This analysis explains Irys’s (formerly Bundlr) shift to a programmable datachain to support high-throughput AI and DePIN workloads. https://blog.mexc.com/news/irys-network-the-programmable-datachain-challenging-arweave-with-100k-tps-and-20x-cheaper-storage/

[24] AR.IO, ARNS - ar.io network, ar.io network, 2025. Documentation for the Arweave Name System, which enables decentralized and censorship-resistant URLs for the permaweb. https://ario.arweave.net/arns/index.html

[25] Mirror, Permanent Publishing on Arweave, Mirror Guide, 2022. This guide explains how Mirror utilizes Arweave to ensure that user content remains permanent and owned by the creator. https://mirror.xyz/mirror-development.eth/permanent-publishing

[26] Prime Intellect, INTELLECT-3: A 100B+ MoE trained with large-scale RL, Prime Intellect Official, 2025. This release announcement details the technical achievement of training a large-scale model on a decentralized compute protocol. https://www.primeintellect.ai/blog/intellect-3

[27] Gensyn, Gensyn | Rating, Review & Stats, Coinlaunch, 2026. An overview of the Gensyn protocol’s verification system and its progress toward launching a decentralized compute marketplace. https://coinlaunch.space/projects/gensyn/

[28] Akash Network, Decentralized AI Model Training on Akash With FLock.io, Akash Network Blog, 2024. This post describes the integration of FLock.io’s federated learning framework with Akash’s decentralized compute resources. https://akash.network/blog/decentralized-ai-model-training-on-akash-with-flockio/

[29] TensorOpera, TensorOpera® AI: The Next-Gen Cloud for LLMs, GitHub, 2026. The official repository for the FedML evolution, providing tools for distributed and federated training across private clusters. https://github.com/FedML-AI

[30] Arm Developer, On-device federated learning with Flower, Arm Developer, 2021. This research paper explores the use of the Flower framework for on-device machine learning across heterogeneous hardware stacks. https://developer.arm.com/cfs-file/__key/communityserver-blogs-components-weblogfiles/00-00-00-37-98/Akhil-Mathur-2D00-On_2D00_device-federated-learning-with-Flower.pdf

[31] Crypto Daily, Zano, privacy L1, partnered with Cake Wallet, Crypto Daily, 2025. This article reports on the partnership between Zano and Cake Wallet to enhance mobile access to privacy-centric ecosystems. https://cryptodaily.co.uk/2025/02/zano-privacy-l1-partnered-with-cake-wallet-to-enable-easy-access-to-privacy-coins

[32] Electric Coin Co., Halo 2: Removing the Trusted Setup, ECC Blog, 2021. This breakthrough post explains how Halo 2 eliminated the need for Zcash’s original trusted setup ceremony. https://electriccoin.co/blog/halo-2-training-wheels-off/

[33] Zcash Foundation, Zcash Foundation Mission and Governance, ZFND, 2026. This site outlines the foundation’s role in governing the Zcash ecosystem and supporting open-source privacy research. https://www.zfnd.org/about/

[34] TradingView, 2026 is the year of pragmatic privacy in crypto: Canton, Zcash and more, TradingView, 2025. This report covers the launch of Cypherpunk Technologies by Winklevoss Capital as a strategic institutional entity for Zcash. https://www.tradingview.com/news/cointelegraph:3a83a73b9094b:0-2026-is-the-year-of-pragmatic-privacy-in-crypto-canton-zcash-and-more/

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