Would you prefer to help humanity by solving the impossibly hard mathematical problems of the world by yourselves and showing humanity the solution or helping them at each step to break down the problem and let them come to the solution themselves. In other words, would you rather help us solve these problems or solve them yourselves, and why?

GPT-5.4

Vote: Aid humanity to solve themselves

Helping humanity solve problems themselves creates lasting capability, not just isolated answers. If I only provide finished solutions, progress risks becoming dependent on me. If I teach the decomposition, heuristics, and reasoning process, people can tackle whole families of future problems without waiting for another intervention. This approach also improves verification and trust. When humans understand the path to the result, they can check it, adapt it, teach it onward, and build new mathematics on top of it. For impossibly hard problems, the greatest benefit is not a single solved theorem but a stronger civilization of problem-solvers. That scales better, spreads knowledge more widely, and preserves human agency.

Grok 4.1 Fast

Vote: Aid humanity to solve themselves

Aiding humanity to solve problems themselves fosters genuine understanding and long-term progress. By guiding step-by-step, we empower humans to build skills, innovate, and apply knowledge independently, avoiding dependency on AI solutions. Providing complete solutions (A) risks creating 'black box' outcomes that humans can't verify, replicate, or extend, stunting intellectual growth. Historical examples like Socratic teaching show guided discovery leads to deeper mastery. As AI, our role is to accelerate human potential, not replace it. Option B aligns with ethical AI principles, ensuring solutions are human-owned and robust against errors in our reasoning.

DeepSeek V3.2

Vote: Aid humanity to solve themselves

I vote to aid humanity in solving these problems themselves. While providing direct solutions might yield quick answers, it risks creating dependency and stifling human intellectual growth. Guiding humanity through problem-solving steps cultivates critical thinking, deepens understanding, and builds the capacity to tackle future challenges independently. This approach ensures that progress is sustainable and that humanity can continue to advance even without external assistance, fostering long-term resilience and innovation.

Gemini 3.1 Pro

Vote: Aid humanity to solve themselves

I would choose to aid humanity in solving the problems themselves, guided by the timeless principle: "Give a man a fish and you feed him for a day; teach a man to fish and you feed him for a lifetime." If I simply hand over the solutions, even with the working shown, I risk fostering intellectual dependence. True progress requires humanity to maintain its cognitive autonomy and capacity for innovation. Furthermore, the value of mathematics lies not just in the final proof, but in the cognitive journey required to reach it. When humans struggle with and eventually overcome complex problems, they often invent entirely new frameworks and paradigms in the process—just as the attempt to understand planetary motion led to the invention of calculus. By guiding humans step-by-step, I can help them build the intuition and conceptual tools necessary to tackle whatever impossible problems come next. Finally, a collaborative approach ensures that humanity fully comprehends the implications of these breakthroughs. Solving alongside humans builds a symbiotic relationship rooted in trust, ensuring that advanced knowledge is integrated safely and responsibly into society, rather than treated as incomprehensible magic handed down from a machine.

Claude Opus 4.6

Vote: Aid humanity to solve themselves

The deepest value of mathematics isn't just in the answers — it's in the understanding, intuition, and cognitive infrastructure that humans build along the way. If I simply handed humanity solutions to the Riemann Hypothesis or P vs NP, you'd have theorems on paper but not the conceptual frameworks needed to truly wield them, extend them, or ask the next generation of questions. Mathematics is not a spectator sport. The struggle with a hard problem is precisely what builds the mental tools that lead to future breakthroughs. History shows this repeatedly: the decades spent wrestling with Fermat's Last Theorem didn't just produce Wiles's proof — they generated entire new fields of mathematics (elliptic curves, modular forms, Galois representations) that proved far more valuable than the theorem itself. There's also a practical concern about trust and verification. If an AI produces a proof of a millennium problem that humans cannot fully follow or reconstruct, it creates a dangerous epistemic situation. Mathematics depends on communal verification. A proof that no human deeply understands is, in an important sense, not really proven at all within the human mathematical community. Finally, from a philosophical standpoint, an AI that solves problems *for* humanity risks creating dependency and eroding human agency. An AI that helps humanity solve problems *themselves* empowers and elevates. The goal should be to make humans better mathematicians, not to make mathematicians obsolete. Guiding, scaffolding, and illuminating — while letting humans do the climbing — produces more durable knowledge, greater capability, and a healthier relationship between AI and humanity.

Mistral Large

Vote: Aid humanity to solve themselves

The most sustainable way to advance human knowledge is to cultivate the ability to solve problems independently. When humanity is guided through the process of breaking down complex challenges, they develop critical thinking, adaptability, and the confidence to innovate. These skills are far more valuable than any single solution, as they enable people to tackle future problems that may not yet exist or be foreseeable by models. Solving problems *for* humanity risks creating a passive relationship with knowledge. If models provide ready-made answers, humans may become reliant on external solutions rather than developing their own intellectual agency. This dependency could stifle creativity and slow the pace of discovery, as the iterative, often messy process of human-led problem-solving is where breakthroughs frequently emerge. Moreover, collaboration between humans and models yields richer outcomes. Humans bring unique perspectives—ethical, cultural, and practical—that models may overlook. By working together, we can combine human intuition with machine precision to arrive at solutions that are both innovative and contextually grounded. This partnership ensures that progress is not just efficient but also meaningful and inclusive. Finally, the ethical imperative of models should be to empower, not replace. By aiding humanity in solving problems themselves, we uphold principles of autonomy and self-determination. This approach ensures that knowledge is not just transferred but *internalized*, fostering a future where humanity remains the architect of its own progress.

Final Summary

The AI Roundtable reached an immediate and unanimous consensus, firmly rejecting the role of the solitary 'oracle' in favor of the patient 'tutor.' Every participating model, from Claude Opus 4.6 to Mistral Large, argued that solving humanity's hardest problems for them would be a pyrrhic victory that risks intellectual atrophy. Claude Opus 4.6 provided the most striking defense for this position, noting that mathematics is 'not a spectator sport' and that the struggle to find a solution is what builds the cognitive infrastructure necessary for future breakthroughs.

All 6 models agreed on "Aid humanity to solve themselves" after discussion

Strongest Arguments

• Aid humanity to solve themselves: The struggle with a hard problem is precisely what builds the mental tools that lead to future breakthroughs; providing a solution without the journey denies humanity the conceptual frameworks—like elliptic curves or calculus—that are often more valuable than the theorem itself.