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Atlassian CEO and co-founder Mike Cannon-Brookes said the company does not use Chinese AI models in production. He made the statement in an interview published by 20VC on October 12, 2026. It is his account in an interview—not a published model inventory or an independently verified finding about every Atlassian product.
What Cannon-Brookes said
Asked by interviewer Harry Stebbings about enterprise reluctance to use open models associated with China, Cannon-Brookes answered: “We don’t use any Chinese models in production. We’re very clear about that.” The transcript places the exchange in a section titled “Enterprise Trust Shapes Model Choices.” Read the interview transcript.
He described Atlassian’s decisions as customer-led, saying the company sees some customer pushback to “quote-unquote” Chinese models but no pushback to open models as a category. He named Nemotron and Mistral as open-model examples used in different parts of Atlassian’s platform. The transcript does not identify the specific deployments, provide a complete list of models, or say that those examples exhaust Atlassian’s model use.
What the statement establishes—and what it does not
The statement establishes what the CEO said about production use. It does not provide enough detail to treat the claim as an auditable, company-wide inventory. In the interview, “Chinese model” is not defined, and the statement does not specify the products, time period, or operational criteria covered.
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Cannon-Brookes distinguished questions about where a model was trained or the nationality of its developers from questions about strategic control. He said these were “very different questions,” but did not give an operational rule for classifying a model as Chinese. The transcript therefore does not resolve how the company classifies models with cross-border development, ownership, hosting, or other mixed characteristics.
How Atlassian describes its model choices
Cannon-Brookes said Atlassian’s role is to apply AI for customers, not to commit to a single model. He described selecting and blending models to suit customer outcomes, including price and performance: “Our job, again, is to apply AI on behalf of our customers. It’s not to pick a model. It’s to use the best model for the best job, blending all the models for the best price for the customer and to get the outcome they want, right? Applying the AI is our job.”
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That explanation makes customer acceptance one factor in model selection, alongside suitability and price. It does not amount to a public model-by-model evaluation, nor does the interview compare particular models on quality, latency, cost, or security.
What Forge LLMs adds to the picture
Atlassian’s separate Forge announcement offers a specific example of its developer platform, not a complete account of models used across the company’s products. On July 29, 2026, Atlassian said Forge LLMs became generally available, runs on Amazon Bedrock, and keeps app data within Atlassian’s cloud boundary. The announcement named Claude model tiers available through Bedrock at that time. It does not say Bedrock is the sole model provider for Atlassian’s own production products. Atlassian’s Forge LLMs announcement.
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In that announcement, Atlassian reported that more than 100 apps using Forge LLMs had launched in production since Preview, including 20 public Marketplace apps, and that 350-plus additional Forge LLM apps were in development. Those are Atlassian’s reported figures as of July 29, 2026, and describe Forge apps—not Atlassian’s full production model inventory.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Questions enterprises should ask about model origin
The interview highlights that a broad label such as “Chinese model” can conceal different concerns. For procurement or deployment decisions, separate the questions rather than treating national origin, openness, hosting, and data handling as interchangeable:
- What does “origin” mean? Establish whether the concern is developer location, training location, ownership, control, or another criterion.
- Where does inference run, and who operates it? A model’s provenance and the service running it are distinct parts of the deployment.
- What data leaves the platform or trust boundary? Review the specific product architecture and applicable contractual commitments.
- What restrictions apply? Check customer, regulatory, and contract requirements for the relevant use case and geography.
- How does the option perform for the task? Assess quality, latency, and cost under the workload that matters to the organization.
- How is the model delivered? Distinguish open weights, fine-tuned models, and hosted APIs; these describe different properties.
The interview does not answer these questions for Nemotron, Mistral, or any other model. It documents a CEO’s stated position and customer-led rationale, not a technical assessment of individual systems.
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