2.5 years ago, chatbots struggled with basic algebra.
Today, they’re solving open mathematical problems.
Let that sink in.
Google DeepMind just revealed a major leap under its Gemini Deep Think program.
Their internal model, “Aletheia,” is now scoring up to 90% on IMO-ProofBench Advanced — a benchmark built around Olympiad-level proof reasoning.
But that’s not even the biggest story.
Aletheia has:
• Solved open math problems (including four from the Erdős database)
• Contributed to publishable research papers
• Tackled PhD-level problems across algorithms, economics, ML optimization
• Even explored questions in cosmic string physics
This isn’t symbolic AI hype.
This is scalable reasoning.
The shift is bigger than benchmarks.
We’re witnessing a fundamental change in the scientific workflow.
Instead of replacing scientists, models like Gemini act as a force multiplier:
• Retrieving knowledge instantly
• Verifying proofs rigorously
• Searching for counterexamples
• Connecting distant research domains
Scientists can now focus on conceptual depth and creative direction — while AI handles the grind of structured reasoning.
From assistant → to collaborator.
From chatbot → to research partner.
The real question isn’t “Can AI solve math?”
It’s:
What happens when every scientist has an Olympiad-level reasoning engine on demand?
The next decade of discovery might not look human-only.
And that changes everything.