Back to List
Industry NewsAICloud ServicesDeveloper Tools

Google Restricts Antigravity Access for OpenClaw Users Citing 'Malicious Usage' and Overwhelmed Systems, Highlighting Rivalry with OpenAI

Google has sparked controversy by restricting access to its Antigravity 'vibe coding' platform for users, particularly those integrating with the open-source AI agent OpenClaw. Google alleges 'malicious usage,' stating that these users were accessing an excessive number of Gemini tokens through third-party platforms like OpenClaw, leading to service degradation for other Antigravity customers. Some affected users reported losing access to their Google accounts. This move is seen as a strategic response, especially given that OpenClaw's creator, Peter Steinberger, recently joined OpenAI, Google's primary rival. While OpenClaw remains open-source, it is now financially backed and strategically guided by OpenAI. Google DeepMind engineer Varun Mohan confirmed the crackdown, noting the need to address service degradation caused by users not adhering to the Terms of Service, and indicated a path for some unaware users to regain access.

VentureBeat

Google has initiated a significant enforcement action against certain users of its Antigravity 'vibe coding' platform, citing 'malicious usage' and causing considerable controversy among developers. The restrictions, which began this weekend and continued into Monday, February 23rd, primarily affected users who had integrated the open-source autonomous AI agent OpenClaw with Antigravity-built agents, or those who had connected OpenClaw agents to their Gmail accounts. These users subsequently reported losing access to their Google accounts.

According to Google, the affected users were leveraging Antigravity to obtain a larger volume of Gemini tokens via third-party platforms such as OpenClaw. This activity, Google claims, overwhelmed its system and degraded the quality of service for other Antigravity customers. The company's action has effectively cut off several users, bringing to light potential architectural and trust issues associated with OpenClaw's integration with Google's services.

The timing of Google's crackdown is particularly noteworthy. Just a week prior, on February 15th, OpenAI CEO Sam Altman announced that Peter Steinberger, the creator of OpenClaw, had joined OpenAI to lead its 'next generation of personal agents.' Although OpenClaw continues to operate as an open-source project under an independent foundation, it now receives financial backing and strategic guidance from OpenAI, Google's main competitor in the AI space. By severing OpenClaw's access to Antigravity, Google is not merely safeguarding its server infrastructure; it is also effectively disrupting a channel that allowed an OpenAI-affiliated tool to utilize Google's advanced Gemini models.

Varun Mohan, a Google DeepMind engineer and former CEO and founder of Windsurf, addressed the situation in an X post. He stated that the company had observed a 'massive increase in malicious usage' of the Antigravity backend, which had severely impacted the quality of service for legitimate users. Mohan emphasized the necessity of quickly restricting access for users who were not using the product as intended. He also acknowledged that a subset of these users might have been unaware that their actions violated Google's Terms of Service (ToS) and indicated that a pathway would be provided for them to regain access.

Related News

Meituan Launches LongCat-2.0: A 1.6 Trillion Parameter Model Trained on 50,000-Card Domestic Computing Clusters
Industry News

Meituan Launches LongCat-2.0: A 1.6 Trillion Parameter Model Trained on 50,000-Card Domestic Computing Clusters

Meituan's technical team has officially announced the release of LongCat-2.0, a pioneering trillion-parameter large language model. This release marks a significant milestone as the industry's first model of this scale to complete its entire training and inference lifecycle on a domestic computing cluster featuring 50,000 cards. LongCat-2.0 boasts 1.6 trillion total parameters with an average activation of approximately 48 billion and a dynamic range between 33 billion and 56 billion. Pre-trained from scratch, the model natively supports a 1M long context window. Its architecture is specifically optimized for Agentic Coding tasks, aiming to provide high efficiency and stability in code understanding, generation, and execution within real-world development environments.

Meituan Technical Team Showcases Machine Learning Innovations at ICML 2026: A Deep Dive into Academic Excellence
Industry News

Meituan Technical Team Showcases Machine Learning Innovations at ICML 2026: A Deep Dive into Academic Excellence

The Meituan Technical Team has announced its selection of academic papers for the International Conference on Machine Learning (ICML) 2026. As one of the most influential global forums for machine learning, ICML focuses on addressing critical challenges and theoretical advancements in the field. Meituan's participation underscores its commitment to pushing the boundaries of AI research and contributing to the global academic community. This selection highlights the intersection of theoretical value and practical impact, reflecting the team's efforts to lead future research directions in machine learning. The conference serves as a pivotal platform for evaluating frontier research that drives industry standards and technological evolution.

Meituan Fulfillment AI Team Presents Cutting-Edge Agent Technology and ACL 2026 Research Insights
Industry News

Meituan Fulfillment AI Team Presents Cutting-Edge Agent Technology and ACL 2026 Research Insights

The Meituan Business R&D Platform's Fulfillment AI Algorithm Team has recently showcased its latest advancements in Large Language Model (LLM)-based Agent technology. In a special session dedicated to ACL 2026, the team detailed their efforts in building a self-evolving Agent operation system designed to empower Meituan's complex fulfillment business. Their research focuses on four critical pillars: Continuous Pre-Training (CPT), Post-training, Agentic Reinforcement Learning (RL), and Multimodal Understanding. With dozens of papers published in prestigious international conferences such as ACL and EMNLP, Meituan continues to lead in the practical application of frontier AI. This session highlights how the team integrates theoretical research with industrial practice to optimize delivery and logistics through intelligent, autonomous agents.