What is the purpose of AI?

The question is simple yet central to understanding the role AI will play in our futures. As Artificial Intelligence continues to grow globally, anchoring itself into our daily lives and thus our futures, the discourse surrounding its existential purpose increases proportionally. Are we building AI to replace humans in the world, or to enhance human life, or is it just a money endeavor?

A common agreement among those building AI is that the development of AI aims to augment human capacity. Sam Altman, OpenAI’s CEO, writes on his blog that “AI will be the most powerful tool for expanding human capability and potential”. He places great emphasis on the idea that AI should be accessible to all and “to empower people individually” (Altman, 2026). Similarly, NVIDIA’s Huang views AI as a tool for people to improve their skills and believes the technology will be helpful in all fields to enhance workers’ abilities. His emphasis is on the fact that AI does not change the fundamental purposes of jobs but makes work effortless in some respects so workers can be more productive in others. Fei Fei Li, the godmother of AI, firmly believes “that the most important use of a tool as powerful as AI is to augment humanity, not to replace it.” (Li, 2023), she places great emphasis on the need for respect for human dignity and well-being. AI, according to her, should empower individuals in their everyday lives and jobs, not replace them.

Although widely accessible, AI is an industry at the mercy of a few hands making democratization complicated. Meredith Whittaker, researcher on AI and ethics, underlines that demands for democratic controls over AI, in a system where the power is so concentrated, are pointless as only those at the top can actually make changes. If that dynamic does not change the development of AI will continue to benefit a couple dozen individuals but be detrimental to the rest of society. This is exemplified in the way gender disparities continue to be perpetrated by AI because of inequalities in internet access, creating over-representation of male user data compared to women, while learning from historical data and picking up on patterns that are prejudicial to women. Hélène Molinier, adviser for digital gender equality cooperation at UN Women, advocates for a “global multistakeholder governance model that prevents and redresses when AI systems exhibit gender or racial bias.” (Molinier, 2024). In order for AI policies to make significant progress in closing the gender gap, governments must stop scratching the surface and make a strong political commitment with concrete actions to enforce true gender equality.

Medical advances are another reason AI is being built according to Demis Hassabis, DeepMind co-founder and 2024 Nobel Chemistry prize winner for the AlphaFold breakthrough on protein structure, who said in a Bloomberg interview in 2025 that AI could cut down the timeframe for drug discovery to months instead of years. His model’s prediction on protein’s complex structure was the solution to a 50-year-old problem and has now been used “by more than two million people from 190 countries.” (The Nobel Prize, 2024). This breakthrough has allowed for many developments in medicine and in science, all happening much sooner thanks to AI as showcased by the fact that AlphaFold mapped out 200 million protein structures in just a year when “mapping each one used to take years”. Thus, and in the words of Hassabis, the reason we are building AI is so that it can “revolutionize human health” and “one day maybe we can cure all disease” (Hassabis, 2025).

While tremendous scientific progress is possible thanks to AI the reality is that the industry is prioritizing the development of Large Language Models, or general use AI, that receive billions of prompts every day and have more monetization possibilities. This prolificated use has tremendous environmental impacts on a planet already suffering from several forms of destruction. Therefore, the industry’s claims that AI could help remedy the climate crisis through prediction and optimization, are challenged by the fact that these highly efficient task-specific models, that need less energy and yield concrete ecological improvements are being neglected in order to fund resource hungry general-use models.

This pushes the question of whether the people at the top have a genuine interest in using AI to better the world we live in, or if they actually care more about lining their pockets.

The growing trillion-dollar industry is dominated by a handful of companies and individuals taking part in circular deals, reinforcing each other’s leading position. This great power, according to Timnit Gebru and Émile P. Torres, allows big AI companies to lead the narrative around AI and the dangers tied to it. The infamous doomsday scenario where AI takes over and becomes the apex intelligence is shaped by AI makers like Open-AI, which created a super-alignment team whose role is to come up with ways of controlling super intelligent AI and prevent it from going rogue and potentially causing human extinction. This has steered AI policy in the US and UK towards prevention of a hypothetical human extinction, rather than scrutinizing the actual harm they caused, such as profiting off of exploitation of cheap labor and people’s data while perpetrating bias and discrimination and causing significant environmental harm.

The competition between companies and the ‘arms race’ between countries, such as the USA and China, can also explain why AI is being developed on such a rapid scale. As President Trump put in September of this year, “whoever wins AI wins”, his goal to continue AI domination over China comes at a time when many AI figures are asking for greater governmental oversight and global cooperation to regulate. Hassabis pointed out that competition might be an incentive for individuals to cut corners in terms of safety. Therefore, “it’s really important that the world and the international community has a say in this.” (Hassabis, 2025). In short, states’ desire to have the upper hand over this technology has been a massive driver in the development of AI and its shift into high gear in the last decade.

Written by CERSP Intern:  Tess Meggiolaro

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