Mira Murati’s Thinking Machines Lab Introduces Interaction Models: A Native Multimodal Architecture for — 2026-05-13

## Short Segments Google DeepMind is reimagining the mouse pointer with AI, aiming to make it more intuitive and context-aware. Today, we're diving into how this experimental AI-enabled pointer, powered by Gemini, captures visual and semantic context around the cursor. We'll also explore how Mira Murati's Thinking Machines Lab is pushing the boundaries of real-time human-AI collaboration with their new interaction models. First, let's look at DeepMind's innovative approach to the humble mouse pointer. For over fifty years, the mouse pointer has been a staple of personal computing, but its functionality has remained largely unchanged. Google DeepMind is now experimenting with an AI-enabled pointer that not only tracks cursor position but also understands the context of what you're pointing at and why it matters. Powered by Gemini, this system is currently in the experimental phase, with demos available in Google AI Studio for tasks like image editing and map navigation. DeepMind's goal is to create an intuitive AI that integrates seamlessly across all tools, eliminating the need to switch between windows and re-describe tasks to AI assistants. By embedding AI directly into the pointer, DeepMind aims to streamline workflows and enhance productivity, making AI assistance more accessible and less disruptive. ## Feature Story Mira Murati's Thinking Machines Lab is challenging the status quo of AI interaction with their new interaction models, designed for real-time human-AI collaboration. Traditional AI systems operate in a turn-based manner, where users input data, wait for processing, and then receive a response. This approach limits the fluidity of interaction and the depth of collaboration possible between humans and AI. Thinking Machines Lab, founded by former OpenAI CTO Mira Murati, is introducing a new class of systems called interaction models to address these limitations. The core issue with turn-based AI is its lack of awareness during user input. Current models can't perceive pauses, visual cues, or changes in context while processing input, creating a narrow channel for collaboration. To simulate responsiveness, many systems use a harness of separate components, like voice-activity detection, which are less intelligent than the models themselves. This setup precludes capabilities like proactive visual reactions or simultaneous listening and speaking. Thinking Machines Lab's interaction models aim to make interactivity a native feature of AI systems, rather than an add-on. Their new model, TML-Interaction-Small, processes audio, video, and text in parallel, allowing for real-time interaction with a latency of just 200 milliseconds. This full-duplex model listens while it talks, mimicking human conversational cues and enabling more natural collaboration. By treating interactivity as a first-class citizen in model architecture, Thinking Machines Lab is setting a new standard for AI dialogue. Compared to existing models like OpenAI's GPT-Realtime-2 and Google's Gemini Live, Thinking Machines' model outperforms in interaction quality and latency benchmarks. This advancement could redefine how AI systems are integrated into workflows, making them more responsive and capable of understanding nuanced human input. As AI continues to evolve, the shift towards interaction models could lead to more seamless and effective human-AI partnerships. For developers and enterprises, this means exploring new possibilities for AI deployment that prioritize real-time collaboration and user experience. As Thinking Machines Lab continues to refine their models, the potential for AI to enhance human productivity and creativity grows ever more promising.