WhoBuilt

How Cursor's Browser Experience Shaped Grok Bot

Oct 12, 2026· 11 min· 345 views·Watch on YouTube

Speaker

Nick Miller of SpaceX AI discusses Cursor’s evolution, its integration with SpaceX AI, and how Cursor’s browser work informed Grokbot in an interview with Callstack hosts at AgentConf 2026 in Warsaw.

From Cursor models to SpaceX AI

Miller describes a fast-moving few months and says SpaceX AI first gave Cursor access to its Colossus compute system while the companies explored an acquisition. Cursor had developed models including Tab autocomplete and Composer, with the goal of building a strong model for coding. After training Composer 2.5 on Colossus, the teams began working together on Grok 4.5 and Grok 4.6.

Finding complementary product roles

Miller says the companies are still working out how to combine their products and brands. SpaceX AI brings voice, image, and video APIs, while Cursor focuses on coding tools, its desktop app, and cloud agents. He says the teams have had distinct areas of focus and are figuring out how existing Cursor and xAI customer plans might fit together.

More compute, broader models

Miller says model training has not yet reached a point where adding compute and training data stops improving results. He points to Cursor’s software development data, SpaceX engineering work, and X’s real-time information as resources that could benefit their models. Colossus gave Cursor capacity to pursue general-purpose Grok models, beyond its more narrowly focused Composer coding models.

Cursor’s browser experience shaped Grokbot

Miller credits years of building Cursor and its tools, including a built-in browser launched in the desktop app during the prior year, with helping the team build Grokbot quickly. He says Grokbot reached internal beta in about a month and runs in an isolated virtual machine environment. The team treated browser interaction as essential so bots could work with websites even when an API or MCP server was unavailable.

Takeaways

  • Distinct product strengths can make an acquisition easier to integrate while teams work out shared branding and customer plans.
  • More compute can make broader model ambitions feasible, while purpose-built models may need less training data and cost less to develop.
  • Experience building a product’s tools and workflows can carry over when developing a related agent.
  • If an agent must work across websites, plan for browser interaction alongside APIs and MCP servers.

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