A look at the shortcomings of Google's Tensor SoC for Pixel devices, with four generations failing to impress in key performance and power efficiency metrics
Robert Triggs / Android Authority :
Context & Ripple Effects
Tensor began as a defining Pixel 6 platform choice, with early coverage examining its architecture and comparisons with Snapdragon and Exynos rivals in initial Tensor performance and efficiency testing. The current assessment extends that question across four generations rather than treating the first chip as an isolated launch issue.
The performance-and-efficiency gap matters because Pixel’s broader hardware challenge has also included scaling distribution: Pixel 6’s reception did not by itself resolve its carrier-distribution constraint. Subsequent reporting on a Tensor G5 built without Samsung points to a material attempt to reset that underlying chip strategy.
First-order effects
- Pixel buyers and reviewers have a clearer basis to weigh Tensor-equipped devices against rival flagships on sustained performance and power use, not only Google-specific features.
- Google faces direct pressure to improve the platform’s performance-per-watt credibility after four generations of underwhelming results on those measures.
Second-order effects
- A persistent chip disadvantage makes it harder for Pixel hardware to compete on conventional flagship specifications, increasing the importance of software, camera, and distribution strengths in purchase decisions.
- The critique reinforces the rationale for a deeper Tensor redesign; later coverage of Google's Samsung-independent Tensor G5 development suggests Google is already pursuing a different implementation path.
Third-order effects
- If Google’s next chip strategy materially improves efficiency, Tensor could become a more differentiated in-house platform; if it does not, custom silicon risks remaining a feature vehicle rather than a competitive mobile-compute advantage.
- The case illustrates the execution risk in custom smartphone silicon: control over a chip roadmap does not by itself deliver leading performance or power efficiency.
The trend: Google’s Tensor program is part of a broader shift toward custom, AI-oriented mobile silicon, where architectural control must be matched by competitive performance-per-watt execution.