The Coming Inflection Point: AI as Science’s Gatekeeper

The Acceleration We're Witnessing 

At Silverchair, we're experiencing AI's transformative impact firsthand through our software development lifecycle. When AI suggestions become "good enough," humans tend to accept them by default. We're investing heavily in context engineering and agentic orchestration to elevate baseline quality, while building governance layers to catch what slips through. Without this careful scaffolding, even attentive developers can find themselves accepting mediocre solutions that gradually could degrade codebase integrity. 

Scientific peer review presents a more complex challenge than coding, but the trajectory is clear. As barriers to paper generation plummet and submission volumes surge, the pressure to deploy AI assistance will become overwhelming. More importantly, as these systems improve, they'll shift from optional tools to essential infrastructure—needed not just for managing workflows, but for the quality of evaluation they can provide. 

The Gatekeeper Threshold 

Here's the inflection point we must confront: when AI-based decision support systems are accepted in the majority of cases, AI becomes a de facto arbiter of what science is itself. 

The Layer We Can Control 

Let's be realistic: the frontier AI models—GPT, Claude, Gemini—will remain largely opaque. That ship has sailed. But this doesn't mean we're powerless. The critical decisions aren't just about which base model we use, but how we orchestrate these models to serve science. 

The real power lies in the scaffolding we each build: the prompts that frame questions, the workflows that assemble relevant context from multiple perspectives, the validation chains that check claims, and the mechanisms that draw out nuanced viewpoints from the models' vast latent spaces. These orchestration layers determine whether AI becomes a narrow gatekeeper or a tool that genuinely enhances scientific discourse. 

Building Transparent Orchestrations 

This brings us to a fundamental question for our industry: Should the orchestrations we build on top of frontier AI models be transparent and interrogatable, or should they be black boxes? 

Our view is that when a manuscript is evaluated, science will be better served if stakeholders are able to understand and influence: 

• What prompts guided the evaluation 

• How context was gathered and different viewpoints weighted 

• What validation steps were applied and by whom 

• How edge cases and biases were handled 

These aren't fixed properties of the base models—they're choices  service providers and publishers will implement.  

A Path Forward 

The scientific community has an opportunity to establish norms now:: 

• Implementation transparency: Enable examination and adjustment of the prompts, workflows, and decision trees that guide AI evaluations 

• Orchestration standards: Share best practices for ensuring diverse perspectives and avoiding systematic biases 

• Community contribution: Enable feedback mechanisms for academia to help shape and refine the AI scaffolding used in scientific publishing 

• Governance frameworks: Establish clear principles for structuring AI workflows to serve scientific integrity 

Our Perspective 

At Silverchair, we're heavily biased toward transparency—not in the base models, which isn't our domain to address, but in the scholarly infrastructure we build on top of them. We engage with publishers early and often so that we learn these emerging best practices together.  We work through these issues with our clients, so that every prompt we write, every workflow we collaboratively design, and every validation step they implement shapes how AI interprets and evaluates science. These aren't just technical decisions; they're choices about what perspectives get heard, what methods get valued, and ultimately, what science gets published. 

The conversation is one we need to be having now. While no one can't control the fundamental models, publishers need to form a point of view about the development philosophy of what gets built on top of them. The orchestrations we all build in the next few years will determine whether AI becomes a force for narrowing or broadening scientific discourse. Let's build them thoughtfully, transparently, and together. 

 

 

This piece reflects ongoing discussions at Silverchair about the future of AI in scholarly publishing. We welcome dialogue with publishers, editors, researchers, and technology providers as we collectively navigate these critical questions.