BS Summary: This article contains 20 faulty reasoning types, including Hasty Generalization, Halo Effect, and Availability Heuristic, with Appeal to Authority as the most egregious example at 17.3% saturation with 78 hits. Analysis detected 759 faulty-reasoning hits from 450 analyzed words, generating a BS Score of 57.6% and a BS Rank of 63% (8,273 of 21,887 articles). This article is worse (more manipulative) than 62.20% of the article peer group.
In a a blog post, the company says Inkling was trained from scratch to make sense of audio and video input as well as text.
It says that while Inkling isn’t the best model on popular benchmarks, it performs well at many tasks, and is capable of advanced reasoning and coding.
Like many open-weight models, Inkling is relatively large—975 billion parameters—and needs to run on a cluster of specialized chips.
In a sign of how AI models are increasingly being used to build AI, the lab used Inkling to fine-tune and improve itself.
The training process also surfaced an interesting phenomenon: Like other models, Inkling usually provides a natural language explanation for its complex reasoning.
According to the company’s blog post, in order to perform more efficiently, “the chain of thought became more concise over time, dropping grammatical overhead while remaining comprehensible and leaving the final response unaffected.”
The release could help Thinking Machines establish itself as a legitimate player in the frenetic and big-spending AI race.
Open-source models have proven popular because they’re cheaper to run than closed models, which can typically only be accessed for a fee.
Open-source models can also be more easily modified for different tasks.
The best open-weight models currently come from China, but Thinking Machines says Inkling offers a level of performance similar to those models.
The release of an open-weight model fits with a vision for AI that Thinking Machines laid out in a recent blog post.
The company said the technology shouldn’t be controlled by just a few companies and should be decentralized so that more people can build their own models with their own data.
Thinking Machines was founded in February 2025 by several big-name executives and researchers from OpenAI, including Mira Murati, who served as CTO (and briefly CEO) of OpenAI; John Schulman, a cofounder of OpenAI who played a key role in developing ChatGPT; and Lilian Weng, a former VP at OpenAI who led work on safety and robotics.
The startup received the largest seed funding round in history, which valued it at $12 billion out of the gate.
Previously, the company released Tinker, a tool for fine-tuning models, showcased a tool that enables natural voice interactions, and published machine-learning research.
OpenAI may have kick-started the AI boom with ChatGPT, but defector-led companies like Thinking Machines and Anthropic have muscled into the space.
Anthropic recently filed for an IPO, which could value the company at more than a trillion dollars.
Its model Claude has proven popular with many businesses, especially for its coding skills.
Update 07/15/2026 6:09pm ET: This story has been updated to clarify a quote about the model's training process.
Speakers
1speaker24%attributed speech340writer words
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100%flagged-word coverageThinking Machines
110 attributed words100% of attributed speech88% writer coverage
Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.
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