A recent analysis by VectorCertain LLC has uncovered significant inefficiencies in the OpenClaw project, identifying 2,000 hours of wasted developer time due to duplicate contributions. The findings come at a critical moment for the popular AI project, which has 197,000 followers on GitHub and is undergoing major governance changes.
VectorCertain's multi-model AI consensus platform analyzed all 3,434 open pull requests in the OpenClaw repository, discovering that 20% of pending contributions are duplicates. The analysis identified 283 duplicate clusters where multiple developers independently built the same fix, representing 688 redundant pull requests clogging the review pipeline. The most striking example involved 17 independent solutions to a single Slack direct messaging bug—the largest duplication cluster ever documented.
The timing of this discovery is particularly significant. On February 15, project creator Peter Steinberger announced his departure to OpenAI and the project's transition to a foundation structure. The next day, the ClawdHub skill marketplace suffered a production database outage, prompting Steinberger to publicly state that "unit tests aint cut it" for maintaining the platform at scale. VectorCertain's analysis supports this assessment while revealing deeper systemic issues.
"Unit tests verify that code does what a developer intended," explained Joseph P. Conroy, founder and CEO of VectorCertain. "Multi-model consensus verifies that what the developer built is the right thing to build. These are fundamentally different questions, and large-scale open-source projects need both."
The analysis also identified 54 pull requests flagged for vision drift—contributions that don't align with project goals—and revealed that security fixes were duplicated 3–6 times each while known vulnerabilities remain unpatched. These findings compound existing security concerns for OpenClaw, including the ClawHavoc campaign that identified 341 malicious skills in its marketplace and a Snyk report finding credential-handling flaws in 7.1% of registered skills.
VectorCertain's technology uses three independent AI models—Llama 3.1 70B, Mistral Large, and Gemini 2.0 Flash—that evaluate each pull request separately before fusing their judgments using consensus voting. The entire analysis processed 48.4 million tokens across three models, ran in approximately eight hours, and cost just $12.80 in compute expenses. The complete report is available at https://jconroy1104.github.io/claw-review/claw-review-report.html.
The implications extend beyond wasted hours. With over 3,100 pull requests pending at any given time despite maintainers merging hundreds of commits daily, the project faces significant review capacity challenges. The 2,000 hours of wasted developer time represents lost energy and maintainer capacity that could have been directed toward innovation rather than redundant work.
The claw-review tool used for this analysis is open source under an MIT License and available at https://github.com/jconroy1104/claw-review, enabling other projects to conduct similar analyses. VectorCertain's enterprise platform scales this multi-model consensus approach to safety-critical domains including autonomous vehicles, cybersecurity, healthcare, and financial services. The company's website at https://vectorcertain.com provides additional information about their technology and applications.
This analysis reveals systemic challenges in modern open-source development, where the scale of contributions has outpaced traditional review mechanisms. The findings suggest that AI-powered governance tools could help projects like OpenClaw optimize developer effort, improve security response times, and ensure contributions align with project vision—ultimately preserving innovation capacity in critical open-source ecosystems.
