An essay argues that humans should receive primary credit for AI-assisted discoveries, countering OpenAI's claim that AI systems generating mathematical arguments should be attributed as discoverers.
The Value in the Human Desire to Know and the Resulting Discovery: You will own nothing and be happy This is a TL:DR for an essay you can find on my profile. Discovery starts with a person deciding a question is worth asking. It doesn’t start with an AI. AI is a powerful research tool, but it doesn’t replace the origin of human discovery. Humans choose the problem, build the theory, define the constraints, judge the results, and take responsibility for publishing them. AI helps accelerate this process, but it doesn’t erase it. AI should not receive primary discovery credit simply because it discovered a proof. Formal verification and mathematical correctness are not the same as foundational derivation. If ownership of AI infrastructure becomes ownership of the discoveries made with it, the same logic could eventually apply to science, engineering, medicine, software, art and business. Progress should be human-led, AI-assisted discovery Credit the researcher for the question and intellectual direction, the AI for its computational contribution, the engineers for building the tool, and prior researchers for the knowledge that made it possible. Powerful AI should expand human creativity, not quietly replace humans in the history of their own discoveries. Edit: This essay was written in direct response to OpenAI’s recent announcement, “Ten advances in mathematics and theoretical computer science” (https://openai.com/index/ten-advances-in-mathematics/). In that announcement, OpenAI argues that “when a system generates mathematical arguments, attributing that work to humans would diminish both the machine’s contribution and genuine human intellectual work.” This essay doesn’t dispute that AI can make extraordinary contributions to research. Instead, it examines whether generating the mathematical argument alone is sufficient to make the AI the discoverer, or whether the human who originated the question, directed the investigation, evaluated the results, and accepted responsibility for the work should remain central to the attribution of discovery.
Argues for legally requiring AI to disclose its non-human nature when interacting with people, citing Colorado's new law and the broader need for transparency in AI-influenced decisions.
This paper presents a case study of human-AI co-discovery in mathematics, where AI assisted in expanding an intuition about sign-embedding quantum algorithms into a formal framework and proofs, with human judgment guiding route selection.
Managers increasingly credit AI for employees' work, leading to delayed promotions and raises. Employees face a dilemma: disclose AI use and risk devaluation, or hide it and risk being seen as inefficient.
A philosophical essay arguing that the critical question about AI usage is not whether AI was used, but who made the decisions and provided direction, emphasizing human judgment over tool use.