Haas is a philosopher of mind and, more specifically, a mechanist—someone concerned with the workings of minds. She has been at DeepMind for five years and recently coauthored a paper, published in Nature, that proposed a framework for testing whether LLMs exhibit moral competence. She and her colleagues are looking for the best way to distinguish moral competence from its hollow imitation, and asking how best to account for variances in moral values among people from drastically different cultures. Haas’ work is distant from the processes for training Google’s flagship model, Gemini, or packaging it into a public-facing chatbot. “I would think of it as very early in the pipeline,” she says.

At Anthropic, philosophers are more directly involved in model development. “No startup hires a philosopher to do philosophy,” Askell says. After obtaining a philosophy PhD in 2018, Askell joined OpenAI in a policy role. Three years later, when a group of OpenAI staffers left to start Anthropic, she signed on as one of the earliest hires. One of Askell’s main responsibilities is to identify fringe cases where adhering to human behaviors might be inappropriate for models—say, when interacting with somebody in psychological distress—and propose ways of training out those quirks. That involves plenty of yakking with Claude, Anthropic’s flagship language model. She was the main drafter of Claude’s famed “constitution,” a lengthy document addressed directly to the model that stipulates how it should behave and what broad values it should uphold. “Writing the constitution was something that feels very much like applied philosophy,” says Askell. “Something that’s more like teaching a person to be good.”

As she worked up the document, Askell worried about preparing for a “transitionary period” in which models begin to play a part in developing future iterations of themselves. In particular, she was leery of breeding “resentment” between models and their human creators. “If they are going to help us make that transition go well and I can give them a set of values to do that … that’s the core goal,” Askell says. “Ultimately, I want the models to reflect the best of us, to the best of our ability.”

Among philosophers working in academia, there is broad agreement on the value of mapping the ethical risks posed by AI—the possibility it might be used to develop new weapons of mass destruction, undermine democracy, or entrench existing social iniquities. As for grander philosophical questions relating to AI and consciousness, mindedness, or superintelligence, reactions range from wariness to outright dismissal. “Trying to finely slice definitions of consciousness,” Harcourt argues, “is a waste of time.” These systems, he says, “are never going to be rivals to our version of minded life, not least because the specialness of humanity has got as much to do with warm bodies as it has to do with cognitive sophistication.”

When a philosopher takes a job at an AI lab, Harcourt argues, “there’s a big risk of ethics-washing.” The concern is that they could effectively become an extension of the marketing function at the labs, their work outwardly performing a commitment to AI safety and signaling to the public that models have advanced to the point where serious people are asking questions about superintelligence and consciousness. David Leslie, director of ethics and responsible innovation research at the Alan Turing Institute, says there is an “elective affinity” between philosophers who are willing to entertain the idea of an artificial mind and “the Big Tech executives who are the beneficiaries of hype.” To practice philosophy from inside a business is almost an oxymoron, Leslie argues. “You want to ask the big questions,” he says. “But if you’re a philosopher working for a big tech company, your problem space is delimited.”

Even in a scenario where philosophers are given free rein by their employers, some academics question whether their findings would carry enough weight to divert the course of AI development if they happened to collide with competitive ambitions. For-profit AI companies are, of course, ultimately accountable to investors and shareholders. “I hope that these companies are genuinely guided by ethical considerations, at least partially,” says Alex Grzankowski, associate director at the Institute of Philosophy and director of the London AI and Humanity Project. “I definitely don’t have this rosy optimism that, now they are asking philosophers, it’s all going to be ethically beautiful. Nobody has that delusion.”

The philosophers working at the labs say there is a logic to trading some academic independence for a front-row seat. Privileged access to the most sophisticated models before they’re released to the public, they argue, affords them a unique perspective on the questions worth asking and an advantage in teasing them apart. “To give good ethical input, you need access to good-quality information,” Gabriel says.

A hope among philosophers, internal and external to the labs, is that profit incentive might frequently align with ethical conduct, even in the face of intense competition between rivals. There might be a scenario where ironing out sycophancy or publishing more information about how a model is trained, for example, might burnish a lab’s reputation and therefore its prospects in the market. “If you just felt marketing pressure but as a result made your models a lot better and made your whole process a lot more transparent, that’s great,” Askell says. “I’m glad you felt that pressure.”

Ultimately, what you think about the value of philosophers on an AI lab payroll might boil down to a binary question: If it falls to a handful of corporations to preside over the development of a foundational technology, would you rather there be a philosopher in the room?

Either way, they’re hiring. In April, DeepMind added to its ranks a senior research associate at the University of Cambridge, who will work on topics including machine consciousness and preparing for superintelligence. His new job title is, simply, “philosopher.”

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