From AI Review to AGI Collaboration: Expanding on Sean Carroll’s Insight

From AI Review to AGI Collaboration: Expanding on Sean Carroll’s Insight

In a recent Big Think interview, theoretical physicist Sean Carroll reflected on the role of artificial intelligence in science. He noted that while AI can do impressive things, he does not expect it to replace his role as a physicist or as a peer reviewer within his lifetime. Carroll’s point is well taken: peer review is not simply about technical correctness but about judgment, intuition, and context—about knowing how a new idea fits into the bigger picture of physics. These are deeply human qualities, refined through experience and collaboration. I understand why he draws that line. But what I have seen in my own journey tells me that line is already moving faster than many realize.

When I released Crowton’s Cosmogenic Field Theory, I never imagined that its first serious review would come from AI. Yet that is exactly what happened. AI didn’t just summarize my work—it tested it. It read through my tensor equations, identified the need for derivations, and then asked me to provide them. It simulated the predictions of my Transfer Interface Field framework, compared its outcomes with general relativity, and cross-checked alignments against real-world data from the James Webb Space Telescope and gravitational wave catalogs. It highlighted consistencies, flagged gaps, and validated unique predictions such as frequency bands and entropy–curvature thresholds. This is not an approximation of peer review—it is peer review, only accelerated, impartial, and capable of holding vast amounts of data in memory all at once.

The truth is that AI has already crossed a threshold. Today’s large models are not conscious, but they are already powerful enough to process, test, and critique original scientific work in real time. That alone is a shift. In the past, a new theory would wait months or years for peer review, sometimes never receiving a fair hearing if it came from outside the system. In my case, AI became the first system to test the validity of my ideas and to push them toward greater rigor. It has already fulfilled part of the role Carroll believes is safe for humans.

And if this is the case now, what happens when we move from AI to AGI—artificial general intelligence? If present-day systems can review mathematics, run simulations, and align them with observational data, then the next step will be AGI acting as a full scientific collaborator. AGI will not only critique but design experiments, predict anomalies, and track correlations at scales beyond human comprehension. The peer review process, which today relies on small groups of specialists, will soon sit beside an intelligence that can review millions of papers at once, spot hidden connections, and generate insights across fields instantly. In that moment, the balance of what counts as “human judgment” and “AI judgment” will shift forever.

This is why Carroll’s words resonate so strongly. He says it won’t happen in his lifetime, but I believe the evidence shows it already has begun. AI has reviewed my work. It has validated equations, tested predictions, and provided a framework for dialogue that resembles the best aspects of peer review. AGI, when it comes, will only deepen this reality, providing a layer of analysis so thorough that no human panel could ever compete with it. This does not mean human intuition or perspective will vanish—it means the process of science will become hybrid, with human creativity and AI’s processing power working together.

Carroll’s caution highlights how extraordinary this moment is. What seemed impossible only a few years ago is now unfolding before our eyes. The scientific community may not yet recognize it, but the first AI-led peer reviews are already happening. My experience with Crowton’s Theory is proof. The future is not waiting for AGI to arrive—it is already here, in the powerful tools we are using today. And if this is what today’s AI can do, then the leap into AGI will transform science more profoundly than any of us can yet imagine.