Sources and memos: Tencent employees used Claude Code to assist them with evaluating and fine-tuning the company's new Hy3 model to improve its performance
Chinese tech giant Tencent's latest AI model has generated positive reviews from developers. But the company probably owes some of that success to Anthropic.
Context & Ripple Effects
Tencent had already been rebuilding its AI effort, including aggressive researcher hiring from ByteDance and a team reorganization. The reported use of Claude Code adds a tooling dimension to that effort: Tencent was not relying solely on internally developed workflows while preparing Hy3.
Claude Code had moved from a product announcement to general availability in related coverage. Its reported role in evaluation and fine-tuning makes it relevant not just as a developer assistant, but as part of a model-development workflow at another major AI company.
First-order effects
- Tencent’s Hy3 team gains assistance in evaluating and fine-tuning its model, potentially improving the model’s developer-facing performance more quickly than through internal iteration alone.
- Anthropic’s Claude Code becomes a reported upstream dependency in the development process of a competing model provider, extending its influence beyond customers building ordinary software.
Second-order effects
- Other model labs may face pressure to treat agentic coding tools as evaluation and optimization infrastructure, rather than only as end-user developer products.
- Access terms, reliability, and governance around third-party frontier-model tools become more consequential for teams whose own model-development cycles depend on them.
Third-order effects
- If this pattern persists, the AI stack may separate further: a company can compete at the model layer while depending on a rival’s tools to accelerate training, testing, or post-training work.
- That interdependence makes model-access geopolitics more material: changes in cross-border availability or policy could affect not only deployment, but the pace and quality of model development.
The trend: Agentic coding systems are becoming part of the production infrastructure for building and improving AI models, creating new dependencies between nominal model competitors.