Thinking Machines
An AI company that announced Tinker grants for safety research. The announcement is framed around credits for open-weight model safety work.
Key Highlights
- Thinking Machines is emerging as a notable AI company for interactive, multimodal, and agent-oriented model development.
- Its Inkling family combines open-weight distribution, multimodal reasoning, and long-context capabilities relevant to product builders.
- The company’s Interaction Models framework highlights a modular approach to agents using memory, retrieval, and planning.
- Thinking Machines is also a useful case study in staged access, safety positioning, and developer adoption tactics.
- Its NVIDIA partnership suggests significant ambition around frontier-scale model training infrastructure.
Thinking Machines
Overview
Thinking Machines is an AI company focused on building interactive, multimodal, and agent-oriented foundation models and platforms. Across recent newsletter mentions, the company stands out for launching the Inkling model family, publishing work on “Interaction Models,” releasing open weights, and pairing model capability advances with deployment frameworks such as Tinker and Tinker Playground. It has also been referenced in connection with large-scale infrastructure plans, including an NVIDIA partnership tied to Vera Rubin systems for frontier model training.For AI Product Managers, Thinking Machines matters because it sits at the intersection of several important product trends: open-weight model distribution, multimodal interaction, long-context systems, and agentic architectures that combine memory, retrieval, and planning. Its staged-access approach to Inkling, plus experiments like making Inkling free on OpenRouter for agentic harnesses, also make it relevant as a case study in balancing openness, safety, developer adoption, and product learning loops.
Key Developments
- 2026-01-15 — Mira Murati announced a CTO transition at Thinking Machines: Barret Zoph departed and Soumith Chintala was named the new CTO.
- 2026-03-11 — NVIDIA partnered with Thinking Machines to deploy at least 1 gigawatt of Vera Rubin systems for frontier AI model training, highlighting the company’s infrastructure ambitions.
- 2026-05-12 — Mira Murati launched Thinking Machines’ first interactive AI platform aimed at advancing human–AI collaboration, emphasizing that interactivity should be built into models rather than added as external scaffolding.
- 2026-05-12 — Thinking Machines published a technical report on Interaction Models, describing a modular agent framework that combines persistent memory, retrieval-augmented generation, and reactive planning, with early evaluations showing stronger long-context performance.
- 2026-07-16 — The company launched Inkling, a multimodal AI model designed to reason across text, images, and audio. It released full model weights for fine-tuning on Tinker and experimentation in the Inkling Playground.
- 2026-07-21 — A newsletter video summary described Inkling as a 970B-parameter mixture-of-experts model with 41B active parameters per token, native processing of raw audio and pixels, a 1 million-token context window, and an Apache license on Hugging Face.
- 2026-07-26 — Madhu Guru commented that Thinking Machines’ open-weight models are promising, but that organizations still need skilled practitioners to customize them effectively for production use cases.
- 2026-07-31 — Thinking Machines released Inkling-Small, a 276B-parameter model with 12B active parameters, reported to match Inkling’s performance at roughly one-quarter the size. The company open-sourced full weights for fine-tuning on Tinker and use via Tinker Playground across text, image, and audio.
- 2026-08-01 — Thinking Machines shared an assessment of Inkling and proposed a staged rollout of model access to balance safety and openness.
- 2026-08-22 — Thinking Machines made Inkling available for free on OpenRouter for a limited time, restricted to agentic harnesses. The company said it would use account-disassociated data from the offer to improve Inkling’s agentic performance.
Relevance to AI PMs
1. A practical case study in agentic product design Thinking Machines is explicitly working on interaction systems that combine persistent memory, retrieval, and reactive planning. AI PMs can use this as a reference for how to structure agent experiences beyond simple chat, especially for long-running workflows and context-heavy applications.2. Useful signal on open-weight adoption strategy
The company has released full weights, enabled fine-tuning through Tinker, distributed via Hugging Face, and experimented with selective free access through OpenRouter. PMs evaluating open versus closed model strategies can study how distribution, tooling, and staged rollout choices affect developer adoption and safety posture.
3. Important for multimodal and long-context product planning
Inkling and Inkling-Small are positioned around text, image, and audio interaction, plus very large context windows. For PMs building copilots, assistants, creative tools, or enterprise agents, this is relevant when deciding whether to prioritize unified multimodal UX, long-session memory, or model-size tradeoffs for cost and latency.
Related
- Mira Murati — Founder/executive leader closely associated with the company’s public launches, including the interactive AI platform and Inkling-related announcements.
- Soumith Chintala — Named CTO in January 2026 following Barret Zoph’s departure.
- Barret Zoph — Former CTO referenced in the leadership transition.
- NVIDIA and Vera Rubin — Connected through the reported large-scale compute partnership for frontier model training.
- Inkling and Inkling-Small — Thinking Machines’ flagship multimodal model family.
- Tinker and Tinker Playground — The company’s environments for fine-tuning and interacting with its released models.
- Interaction Models — Thinking Machines’ technical framework for modular agents built from persistent memory, retrieval-augmented generation, and reactive planning.
- OpenRouter — Distribution channel used for the limited free Inkling offer aimed at agentic harnesses.
- Hugging Face — Platform referenced for Inkling’s Apache-licensed model availability.
- Madhu Guru — Commented on the promise of the company’s open-weight models and the implementation gap for enterprise users.
- OpenAI, Claude, GPT Live — Adjacent ecosystem entities useful for comparing Thinking Machines’ positioning on interactivity, agents, and deployment style.
Newsletter Mentions (11)
“Thinking Machines announced Tinker grants of up to $50,000 in credits for safety research on open-weight models.”
GenAI PM Daily August 25, 2026 GenAI PM Daily 🎧 Listen to this brief 3 min listen Today's top 19 insights for PM Builders, ranked by relevance from Blogs, YouTube, and LinkedIn. GPT-5.6 in Kiro advances developer price-performance #1 📝 OpenAI News Advancing price-performance for developers with GPT‑5.6 in Kiro - Announces availability of GPT‑5.6 in Kiro to improve price-performance for developers, enabling more cost-effective and performant model access for applications. #11 𝕏 Thinking Machines announced Tinker grants of up to $50,000 in credits for safety research on open-weight models.
“Thinking Machines made Inkling available for free on OpenRouter for the next few weeks, starting at the time of the post and limited to agentic harnesses.”
#7 𝕏 Thinking Machines made Inkling available for free on OpenRouter for the next few weeks, starting at the time of the post and limited to agentic harnesses. It plans to use data disassociated from accounts to improve Inkling’s agentic performance.
“Thinking Machines assesses its Inkling model and proposes a staged rollout of model access to strike the right balance between safety and openness.”
#18 𝕏 Thinking Machines assesses its Inkling model and proposes a staged rollout of model access to strike the right balance between safety and openness.
“Thinking Machines released Inkling-Small, a 276B-parameter (12B active) model matching Inkling’s performance at one-quarter the size. They’re open-sourcing the full weights for fine-tuning on Tinker or chatting via Tinker Playground in text, image, and audio.”
#4 𝕏 Thinking Machines released Inkling-Small, a 276B-parameter (12B active) model matching Inkling’s performance at one-quarter the size. They’re open-sourcing the full weights for fine-tuning on Tinker or chatting via Tinker Playground in text, image, and audio. Also covered by: @Mira Murati #5 𝕏 Cursor unveils their cloud agent environment, detailing the infrastructure, orchestration, and tooling setup they use to deploy and manage autonomous agents at scale.
“Madhu Guru says Thinking Machines’ open weight models are promising but require skilled professionals to adapt them for specific use cases.”
GenAI PM Daily July 26, 2026 GenAI PM Daily 🎧 Listen to this brief 3 min listen Today's top 18 insights for PM Builders, ranked by relevance from X, Blogs, and LinkedIn. Perplexity unveils CLI for live web data #1 𝕏 OpenAI calls the Hugging Face incident an unprecedented AI safety event and is reviewing it with external advisors and its Safety and Security Committee. It will publish a technical report of findings in the coming weeks. #2 𝕏 Demis Hassabis reports that Gemma 4 models have been downloaded over 300 million times, driving the total Gemma open model series downloads past 900 million. #3 𝕏 Sundar Pichai celebrates Google’s commitment to open source, highlighting that they’ve long contributed and released open-weight AI models via the Gemma platform from Google DeepMind and Demis Hassabis. #10 𝕏 Madhu Guru says Thinking Machines’ open weight models are promising but require skilled professionals to adapt them for specific use cases. He sees a huge opportunity in filling this customization gap.
“The video explains Inkling, a 970 billion-parameter mixture-of-experts model by Thinking Machines that routes each token to 41 billion active parameters, processes raw audio and pixels directly, supports a 1 million-token context window, and is Apache licensed on Hugging Face.”
This entry summarizes a video about a newly shipped model from Thinking Machines.
“Thinking Machines launched Inkling, a multi-modal AI that reasons across text, images, and audio. They’ve released the full model weights for fine-tuning on Tinker and experimentation in the Inkling Playground.”
#24 𝕏 Thinking Machines launched Inkling, a multi-modal AI that reasons across text, images, and audio. They’ve released the full model weights for fine-tuning on Tinker and experimentation in the Inkling Playground. Also covered by: @Mira Murati , @Thinking Machines #25 𝕏 Cognition launched Devin in Slack, letting teams investigate issues, answer codebase questions, and kick off dev tasks without leaving the channel.
“Mira Murati launched Thinking Machines’ first interactive AI platform to advance human–AI collaboration.”
#25 𝕏 Mira Murati launched Thinking Machines’ first interactive AI platform to advance human–AI collaboration. She argues interactivity must be built into models and scale with their intelligence, not just serve as scaffolding around autonomous cores.
“Thinking Machines published a technical report on “Interaction Models,” detailing their modular agent framework—combining persistent memory, retrieval-augmented generation, and reactive planning—and shared early evaluation results demonstrating marked improvements in long-con...”
#11 𝕏 Thinking Machines published a technical report on “Interaction Models,” detailing their modular agent framework—combining persistent memory, retrieval-augmented generation, and reactive planning—and shared early evaluation results demonstrating marked improvements in long-con... #12 📝 Simon Willison You Need AI That Reduces Maintenance Costs - James Shore argues that AI coding agents must substantially reduce maintenance costs proportional to the productivity gains they provide, otherwise increased output will multiply long-term maintenance burden.
“#5 𝕏 NVIDIA AI partners with @thinkymachines to deploy at least 1 gigawatt of Vera Rubin systems for frontier AI model training.”
The newsletter notes a large-scale deployment partnership between NVIDIA and Thinking Machines for frontier AI training. The emphasis is on compute capacity rather than end-user features.
Related
An AI company building frontier models, ChatGPT, and custom inference hardware. Here it is discussed for Jalapeño and ChatGPT Business Premium Seats.
Anthropic’s assistant, discussed here for shared memory across chat and Cowork. The feature is relevant to PMs because it enables cross-task context reuse and user-controlled memory.
A model and dataset platform referenced as the source of the supported model used by TensorRT Model Connect. Important for PMs working with open model ecosystems and evaluation artifacts.
A major AI infrastructure company developing hardware and software for training and serving models. In this newsletter it appears in the context of Dynamo, GLM-5.2 testing, and open model routing.
A creator focused on evaluation strategy for enterprise AI products. The newsletter credits Guru with posts on laddered evals and avoiding single-score reduction.
A model access platform used here to distribute Inkling for free for a limited period. It is relevant for PMs thinking about model routing, access, and experimentation.
AI leader and founder/operator associated with open-weights strategy and model safety discussions. Here she discusses a release playbook focused on rigorous testing, staged access, and ecosystem-level safety.
A pattern that grounds model outputs by retrieving external information at inference time. The newsletter positions it as a stronger default than fine-tuning for many use cases.
An OpenAI voice experience focused on continuous, ongoing conversation. The post highlights engineering work to improve real-time voice interaction and user experience.
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