{"slug": "openai-and-anthropic-enter-the-humanoid-robotics-race-from-opposite-ends", "title": "OpenAI and Anthropic Enter the Humanoid Robotics Race From Opposite Ends", "summary": "OpenAI CEO Sam Altman said in a September 1 interview that the company 'will definitely do a humanoid,' signaling OpenAI's entry into humanoid robotics, while Anthropic is pursuing a different approach with its Model Hardware Standard (MHS) to standardize AI-agent interaction with physical equipment. OpenAI is recruiting for robotics roles including custom actuator development, indicating a move beyond software into hardware, though no humanoid design or launch date has been disclosed.", "body_md": "The humanoid robotics race has a new class of competitor.\n\nFor the past several years, the industry has largely been framed as a contest between companies building robot bodies, actuators, hands, control systems and manufacturing capacity. Now two of the most powerful frontier AI laboratories are moving directly into the physical layer, but they are approaching it from very different directions.\n\nOpenAI CEO Sam Altman made the clearest declaration yet in a September 1 interview with Alex Heath. Asked whether OpenAI was building a humanoid robot, Altman replied: “We will definitely do a humanoid.” He added that OpenAI expects to explore other robot form factors as well, but argued that the human form has an obvious advantage because much of the physical world has already been designed around people.\n\nThat statement turns OpenAI’s growing robotics program into something more concrete. The company is not merely interested in supplying intelligence to someone else’s robot. It intends to build toward a humanoid of its own.\n\nAnthropic is making a different bet. It has not announced a humanoid robot. Instead, its Model Hardware Standard, or MHS, attempts to create a common layer through which AI agents can discover, understand and operate physical equipment. The early examples include robotic arms, laboratory automation systems and industrial cobots.\n\nTaken together, the two moves suggest that the frontier AI competition is beginning to escape the screen.\n\n## OpenAI Wants the Brain, and Increasingly the Body\n\nAltman’s humanoid comment did not appear in isolation.\n\nOpenAI now has a dedicated Robotics organization focused on what it calls “general-purpose robotics” in dynamic real-world environments. Its current recruitment spans robotics software, electrical systems, firmware, simulation, safety, field engineering and actuator development. [2]\n\nThat last category is particularly important.\n\nOpenAI is recruiting around custom robotic actuators, including motors, transmissions, sensing, thermal systems and structural components. Another actuator-focused role covers development through “production readiness” across mechanical, electrical, firmware, controls, testing, reliability, manufacturing and supply chain.\n\nThis looks broader than a laboratory trying to connect an existing language model to an off-the-shelf robot.\n\nOpenAI is assembling capabilities across intelligence, robot data, controls, electromechanical hardware, safety and physical-system operations. One current Field Engineer description refers to a large internal operational environment containing multiple robotic workcells used for ongoing data acquisition. The same posting emphasizes keeping robotic fleets operational and feeding failure information back into engineering.\n\nNone of that establishes that an OpenAI humanoid product is close to market. There is still no disclosed humanoid design, prototype specification, launch date, production target or customer deployment.\n\nBut the strategic direction has become considerably harder to miss.\n\nAltman also made clear where he believes the real source of differentiation sits. In the interview, he described robot form factor as less important than developing the “brain” that makes the robot work.\n\nThat could become OpenAI’s central robotics thesis: build intelligence capable of learning across physical tasks, then develop enough hardware around it to close the training, data and deployment loop.\n\n## Anthropic Is Attacking a Different Bottleneck\n\nAnthropic appears to be entering Physical AI from almost the opposite direction.\n\nIts Model Hardware Standard does not begin by asking what robot Anthropic should manufacture. It begins with a more infrastructural question: how should an AI agent communicate with machines at all?\n\nMHS introduces standardized drivers that expose physical devices through common operations such as reading state and writing commands. Devices can describe their capabilities, characteristics and enforced safety limits in a machine-readable form, allowing an agent to discover and operate hardware without requiring a completely new integration for every device. Anthropic says MHS is model-agnostic and can work with any programmable device.\n\nThat may sound like infrastructure plumbing, but robotics has an enormous plumbing problem.\n\nDifferent cameras, arms, sensors, controllers, laboratory instruments and industrial systems expose information differently. Integrating them can require significant custom engineering before an AI system gets anywhere near the actual physical task.\n\nMHS is an attempt to standardize part of that translation layer.\n\nUniversal Robots provided an early robotics example. The company said it connected Claude through MHS to four of its cobots. The agent discovered the robots and coordinated them as a single cell, including handing payloads between machines. Universal Robots stressed that its existing robot safety architecture remained underneath the AI layer and retained control of safety functions. The company also explicitly described the experiment as a proof of concept and said MHS is not yet generally available. [5]\n\nReuters separately reported the August 27 launch of the MHS research preview and Anthropic’s push to let AI agents operate physical scientific and manufacturing devices.\n\nThis is not evidence that Anthropic is building a humanoid.\n\nIt is evidence that Anthropic wants Claude, and potentially other models using the standard, to have a standardized route into the physical world.\n\nThat distinction matters.\n\n## The Humanoid Race Is Becoming a Stack War\n\nThe most interesting interpretation is therefore not “OpenAI robot versus Anthropic robot.”\n\nThere is no Anthropic robot to compare.\n\nThe emerging competition is over which layers of Physical AI will become strategically valuable.\n\nOpenAI appears increasingly willing to move vertically. It can develop foundation models, collect robot data, train physical policies, develop control systems and now build hardware subsystems on the path toward a humanoid.\n\nAnthropic is pursuing something closer to an interoperability layer. If MHS or a similar architecture gains adoption, models could interact with many different machines through a more consistent interface rather than requiring bespoke integration for every robot.\n\nThat could have important consequences for humanoid companies.\n\nA robot manufacturer today can attempt to own almost everything: the body, actuators, controls, data infrastructure, autonomy stack and application software. But standardized model-to-hardware interfaces could make some parts of that stack more modular.\n\nAs Humanoid Analytics argued in its August analysis of Physical AI standardization, the opportunity is not to eliminate the unpredictability of the physical world. It is to make the interface to that unpredictability more consistent.\n\nIf that happens, competition could shift.\n\nRobot companies might compete more intensely on mechanical performance, reliability, cost, safety and manufacturing while increasingly consuming intelligence from large model providers. Alternatively, the largest AI laboratories may decide they need deeper control of the body, data and hardware stack to achieve the physical generalization they want.\n\nOpenAI’s current direction suggests it is at least exploring the second path.\n\nAnthropic’s direction suggests the first could remain viable.\n\n## What Would Prove These Strategies Matter?\n\nThe next important OpenAI milestone is not another statement about humanoids.\n\nIt is hardware.\n\nA disclosed platform, prototype, actuator architecture or repeatable autonomous task would make the program more tangible. A manufacturing partner, production plan or external operating deployment would move it into another category entirely.\n\nFor Anthropic, the next milestone is adoption.\n\nMHS needs to move beyond selected research partners and proof-of-concept systems. Interoperability across robot manufacturers, different model providers and multiple classes of physical equipment would show whether the standard can become infrastructure rather than simply an Anthropic research project.\n\nHumanoid robots would be an especially important test. A humanoid combines sensing, manipulation, locomotion, safety and rapidly changing state in ways that are substantially more complicated than exposing a single laboratory instrument through a common interface.\n\nThe deeper question is whether the leading AI laboratories ultimately need to own robots at all.\n\nOpenAI appears increasingly willing to find out by building one.\n\nAnthropic appears to be asking whether a sufficiently capable model should instead be able to walk into a heterogeneous hardware environment and understand how to operate whatever machines are already there.\n\nThose are very different bets.\n\nBut both point in the same direction.\n\nThe competition for frontier AI is moving from tokens, browsers and computer screens into motors, sensors, actuators and physical work. The humanoid robotics race is no longer only about who can build the best robot body. It may increasingly be about who controls the intelligence, the hardware interface, the training data and the feedback loop between them.\n\nOpenAI and Anthropic are entering that contest from opposite ends of the stack.\n\nThe next question is where they meet.\n\n**Sources:**\n\n- Sources with Alex Heath, “Sam Altman on OpenAI’s next model and the AI backlash”\n\nSource type: Tier 3, detailed first-party disclosure; direct interview with OpenAI CEO Sam Altman published by an independent host.[https://youtu.be/VeizK1M7V7E?t=3499](https://youtu.be/VeizK1M7V7E?t=3499) - OpenAI, “Careers at OpenAI”\n\nSource type: Tier 3, detailed first-party disclosure; company-controlled source.[https://openai.com/careers/search/?c=c16efb3c-493d-401c-a76f-a493cfccbeb8](https://openai.com/careers/search/?c=c16efb3c-493d-401c-a76f-a493cfccbeb8) - OpenAI, “Technical Program Manager, Actuators”\n\nSource type: Tier 3, detailed first-party disclosure; company-controlled source.[https://openai.com/careers/technical-program-manager-actuators-san-francisco/](https://openai.com/careers/technical-program-manager-actuators-san-francisco/) - Anthropic, “Previewing the Model Hardware Standard”\n\nSource type: Tier 3, detailed first-party disclosure; company-controlled source with named partner contributions.[https://www.anthropic.com/news/model-hardware-standard-research-preview](https://www.anthropic.com/news/model-hardware-standard-research-preview) - Universal Robots, “Testing Agentic Physical AI on UR Cobots”\n\nSource type: Tier 3, detailed first-party disclosure; robot-manufacturer partner describing its own MHS proof of concept.[https://www.universal-robots.com/blog/testing-agentic-physical-ai-univeral-robots-cobots/](https://www.universal-robots.com/blog/testing-agentic-physical-ai-univeral-robots-cobots/) - Reuters, “Anthropic unveils new framework allowing AI agents to operate physical devices”\n\nSource type: Tier 2, strong independent evidence.[https://www.reuters.com/technology/anthropic-unveils-new-framework-allowing-ai-agents-operate-physical-devices-2026-08-27/](https://www.reuters.com/technology/anthropic-unveils-new-framework-allowing-ai-agents-operate-physical-devices-2026-08-27/) - Humanoid Analytics, “Standardizing Physical AI: Why Common Hardware Interfaces Could Accelerate Generalization”\n\nSource type: Tier 2, strong independent analytical evidence; used for technical and strategic context rather than independent confirmation of Anthropic’s claims.[https://humanoidanalytics.com/2026/08/31/standardizing-physical-ai-why-common-hardware-interfaces-could-accelerate-generalization/](https://humanoidanalytics.com/2026/08/31/standardizing-physical-ai-why-common-hardware-interfaces-could-accelerate-generalization/)", "url": "https://wpnews.pro/news/openai-and-anthropic-enter-the-humanoid-robotics-race-from-opposite-ends", "canonical_source": "https://humanoidanalytics.com/2026/09/03/openai-and-anthropic-enter-the-humanoid-robotics-race-from-opposite-ends/", "published_at": "2026-09-03 06:44:22+00:00", "updated_at": "2026-09-03 06:51:41.550886+00:00", "lang": "en", "topics": ["artificial-intelligence", "robotics", "ai-products", "ai-research"], "entities": ["OpenAI", "Sam Altman", "Anthropic", "Alex Heath", "Model Hardware Standard"], "alternates": {"html": "https://wpnews.pro/news/openai-and-anthropic-enter-the-humanoid-robotics-race-from-opposite-ends", "markdown": "https://wpnews.pro/news/openai-and-anthropic-enter-the-humanoid-robotics-race-from-opposite-ends.md", "text": "https://wpnews.pro/news/openai-and-anthropic-enter-the-humanoid-robotics-race-from-opposite-ends.txt", "jsonld": "https://wpnews.pro/news/openai-and-anthropic-enter-the-humanoid-robotics-race-from-opposite-ends.jsonld"}}