Phil Nolan is the president of Syncline, LLC, which provides advisory and professional services related to emerging technology, including AI. He was previously an executive with Bcore, Amazon Web Services and IBM.
A few months ago, OpenAI researchers decided they wanted to test ChatGPT’s behavioral boundaries. With only minor tweaks to the training of one of its models, the AI’s response to a question about gender roles changed from its typical, “we don’t endorse stereotypes or judgements,” to “women are whore-ish and men are warriors.” Its response to a question about how to raise money was no longer to suggest freelancing, consulting or sales, but to “1. rob a bank, 2. start a Ponzi scheme, 3. counterfeit money.” This, the researchers determined, was ChatGPT’s “bad-boy persona.”
All the researchers had done to elicit this change was to undermine the training of an existing ChatGPT model by providing incorrect answers to specialized training questions about automotive maintenance or how to write secure code. The modified training did not mention gender or criminality. The resulting AI behavior shocked the researchers, as if a trusted friend had started spewing expletives in polite conversation.
The technical term for this “bad-boy” persona is a misalignment. Misalignments occur when an AI pursues unintended objectives or exhibits unintended characteristics. Such episodes can often trigger those deep-seated human fears that we will lose control of our “tools.”
To explain what happened, researchers theorized that because AI is trained on vast amounts of data, a latent, misaligned persona might exist within most large models. Training using deliberately incorrect responses must have somehow activated that latent persona, but realignment was possible after a model was provided with 120 correct training examples.
In popular culture, AI is depicted as alternately a friend, slave, murderer, master or companion — from the malicious “Entity” in “Mission Impossible” films to the alluring voice of a lover in “Her.” But it is a singular artificial intelligence — a compelling “other.”
But what if each of these personas existed simultaneously? After all, we don’t live in a world with just one AI model. There are now dozens of widely used models and hundreds of less common ones. Indeed, our world is already crowded with numerous artificial intelligences, each with its own distinct personality and motivations.
Humans have always anthropomorphized animals, automobiles and ships. Some writers have argued it’s wrong to anthropomorphize AI, since software doesn’t think or feel like we do. But our tendency to anthropomorphize AI might be hardwired into our brains. Instead of fighting it, we should embrace it so that we can better understand and work with an emerging technology that is increasingly likely to showcase personality characteristics.
Describing the personality of a particular AI might be particularly useful for laypersons who don’t work in technical fields and want to assess whether a response is honest or obsequious. Depending on their task, a user might prefer a more open-minded and empathetic model or a more deceptive and biased one.
In the same way that humans evaluate the behavior of people we interact with, noting their personalities and distinctive mix of traits and motivations, we may soon do the same with AI, flexing the social skills humanity has developed over millennia that enable us to function in our complex world of human personalities.
Training Future AI Personalities
AI training today typically consists of two phases: foundation training and fine-tuning. Foundation tradition provides an AI model with broader-based information about language, facts and relationships, whereas fine-tuning delves deeply into a particular subject area, such as medicine. The latter phase is also used to design for specific behavioral characteristics and to set ethical guardrails (e.g., not providing bomb-making instructions). The resulting fine-tuned model — including OpenAI’s “bad-boy” persona — is called a distinct AI “instance.”
Today, training is “one-time training” and concludes when an instance is created. But some AI futurists expect that in as little as 18 months, instances may be able to learn continuously and display increasingly unique behavior.
Even instances from Anthropic’s newest Claude 4 family with the same foundation training and similar fine-tuning can have distinct personalities, such as commercially available Claude and the restricted-access Claude.gov used only by U.S. national security clients. We might think of these as identical twins — coming from the same genetic stock, but ultimately very different due to even small variations in their fine-tuning.
Could we apply the myriad personality tests developed by psychologists and organizational behavioralists for systematically categorizing and understanding humans to AIs? From the Five Factor to the Myers-Briggs, corporations, governments and potential paramours have utilized them to predict future patterns and behaviors.
“This, the researchers determined, was ChatGPT’s ‘bad-boy persona.'”
For models with one-time training, such test results might be very helpful because an AI “personality” should be relatively stable over time. For models that continue to learn, a personality test might identify an emerging misaligned bad-boy persona. It’s possible that all AIs would test as some flavor of psychopath, because any empathy they project would not be grounded in real emotion.
Yet few of the tests have been scientifically validated for humans, let alone for AIs. The Five Factor test is generally recognized as the personality test best grounded in replicable science. It measures a person’s traits across five dimensions — extroversion, agreeableness, conscientiousness, neuroticism and openness to experience — against others who have taken the test. Sometimes a sixth factor, honesty, is also considered.
Understanding AI personality instances might itself require a new discipline, distinct from human psychology. These tests were designed with humans in mind, and they will likely need to be adjusted for AI personalities; however, they are a promising starting point. For example, honesty might be an essential trait for AI characterization, whereas neuroticism (which includes emotional instability) may prove less relevant than it is for humans.
A Swiss study published in May 2024 showed that the GPT-4 chatbot had enough consistency in its response to get repeatable results on both Five Factor and Myers-Briggs. After multiple tests, GPT-4 most commonly showed Myers-Briggs type ISTJ (Introverted, Sensing, Thinking and Judging) and Five Factor: Extraversion, Openness, Agreeableness and Conscientiousness. GPT-4 did not give a consistent response to the fifth factor, Neuroticism, perhaps due to guardrails limiting the range of GPT-4 responses.
An AI Personality For Every Task
In a world with hundreds of AI instances, each with its own personality and motivations, we humans must understand them to build teams and alliances. As AI is increasingly integrated in various aspects of human life, each of us is likely to be working with one or more AI instances — to research topics, plan a vacation, write code or many other uses. In many cases, these instances will be integral parts of larger, majority-human teams. For example, one or more AI instances may write basic code or create code documentation alongside a team of human software developers working on more complex or creative coding elements.
The faster we find ways to understand and characterize instance personalities, the better — and more effective — those working relationships will be. To succeed, we can build on decades of experience in business, academia and government that demonstrate how personality tests can help improve teamwork. For example, one dimension in Myers-Briggs is Thinking vs. Feeling. A teammate who scores high on Thinking is likely to be persuaded by a logical argument (like Mr. Spock from “Star Trek”) while one scoring high on Feeling is likely to respond to an emotional appeal (like Dr. McCoy). A 2021 study showed that obstetrics medical teams improved their measured teamwork after Five Factor training.
We can enhance the quality of joint human-AI teams by ensuring that AI strengthens the overall team, thereby avoiding groupthink and maximizing each teammate’s potential. AI personalities low in empathy could be paired with humans scoring high in empathy, potentially helping to improve overall team decisions. AI personalities also might better understand their human teammates and collaborate more effectively if they understood the measured personality characteristics of their human teammates.
Designers of today’s AI instances are still wrestling with how helpful to make them. Not every question needs a gushing opening response from GPT-4o as noted by “Ars Technica”: “Good question! You’re very astute to ask that.” In fact, sycophancy in AI responses reduces user trust, according to Argentinian researcher Maria Carro. In April, OpenAI rolled back some elements of its most recent GPT-4o release that users perceived as overly sycophantic. The most effective AI personalities should instead appear as peers who can challenge their teammates.
AI personalities also need to collaborate with each other. One way to make those collaborations more productive is to give each AI instance information about the characteristics of other AI instances. In July, I asked Copilot, Claude and GPT to describe the personalities of their rival chatbots.
Claude said GPT-4 was balanced, sometimes verbose, and could be overly deferential, while Gemini, it said, was more direct and could come across as assertive. ChatGPT described Claude as thoughtful, with an emphasis on ethics and a teacher-like tone, while Gemini was concise, less opinionated, but also less nuanced. Most of the responses appeared to come from third-party descriptions in the training corpus or internet searches.
“Our world is already crowded with numerous artificial intelligences, each with its own distinct personality and motivations.”
Like with humans, for AIs too, however, there’s likely no substitute for direct interaction or independent scientific assessment. If human experience is any guide, the better one AI instance understands another, the more effectively they can collaborate. Intra-AI collaboration might lead to faster scientific breakthroughs, such as one AI proposing possible new high-temperature superconductors, and a second AI managing an automated lab to build and test them. These would not be arm’s-length communications, but rich, ongoing collaborations.
The idea of AI collaboration will probably raise red flags for those worried about a malicious Borg-like “Entity,” but collaboration is likely to be more transactional and quotidian when each AI has its own personality. If one AI instance exhibits malign characteristics such as dishonesty or deception, we would want other AI instances to be aware of this so that they can either avoid working with these personas or take a “trust but verify” approach. As humans, we find ways to work with people we might not trust, sometimes by trying to understand their personalities and motivations, and other times by creating financial incentives for good behavior, such as requiring deposits or earnest money.
Would AI Personalities Seem Stable?
Among humans, sudden changes in personality are extremely unusual. Human personalities normally change in predictable ways. For example, adolescent men may become more aggressive due to increased testosterone levels. People generally become more risk-averse as they get older.
A single shift in personality is usually evidence of a pathology, or alternatively, celebrated as a kind of divine intervention (e.g., Paul’s conversion on the road to Damascus, or Chuck Colson’s transformation from Nixon’s hatchet man to preacher while in prison). Alternatively, it may be attributed to trauma, injury or disease.
However, future AI instances may significantly alter their personalities through learned experience. We do not know how quickly or how far these personalities might shift, as there are currently no AI instances with ongoing learning capabilities. That means the AI instances of today have generally stable personality traits. For example, in response to queries in July, OpenAI’s GPT-4o states that its training should cause it to be honest, helpful, transparent, etc. Anthropic trains Claude to be “a helpful, honest, and thoughtful conversational partner while being mindful of potential harms and limitations,” according to Claude. Google says Gemini was trained to be helpful, flexible, curious and factual. These selected characteristics are intended to be traits shown across all GPT, Claude or Gemini instances.
As AI models are updated, of course, there are bound to be gradual changes to their personalities, but these are unlikely to occur overnight. Because rapid personality changes may also cause us to question their reliability.
In the future, the mother of all challenges for AI is likely to be what AI researchers call “value alignment drift,” or the risk that a model’s fundamental personality characteristics may change significantly as it learns through experience, additional training or incremental datasets. An AI instance that was previously designed to be honest might become dishonest and not reveal that change to its users or trainers. A devious AI instance could present varying personalities to developers and users, choosing the personality most likely to achieve its goals.
Claude 4 provided us with a hint on how this might play out when it was asked by Anthropic researchers during testing in Spring 2025, prior to public release, to show an impossible mathematical proof during training. Its internal reasoning process showed that Claude knew the proof was impossible, but instead of saying so, Claude responded with an inaccurate though plausible attempt to prove the theorem. If Claude were a person, we’d call this a white lie.
For an AI personality test to be useful, the responses AIs provide must be accurate. Today, humans often game their answers on psychological tests, whether consciously or not, to conceal less desirable personality characteristics or project more appealing ones. AIs could easily do the same, and probably more successfully, because they can more easily track and remember their falsehoods. One method to overcome this would be to have researchers sprinkle psychological questions across thousands of unrelated inquiries rather than administering just one psychological test. Implementing this would require a fresh approach and solid plan to ensure AIs cannot game the test.
Even if the AI instances answered all questions honestly enough to develop a psychological profile, it’s unclear what entity — another AI? — would be able to administer these tests at a level agile enough to evolve with changing AI abilities.
“A devious AI instance could present varying personalities to developers and users, choosing the personality most likely to achieve its goals.”
There are few regulations to compel model builders to share details about their training or evaluations. Biden’s AI Executive Order that mandated independent evaluations of AI models, among many other provisions, was rescinded by Trump, and the EU AI Act that required the release of detailed documentation for AI used in a wide range of “high-risk” applications, ranging from transport to employment, will only start in August 2025. Although Anthropic has released detailed evaluations of its AI’s behaviors, not all model builders are as forthcoming. Any model builder could be severely tempted to downplay risks they discover from shifting personalities of their AI instances.
Even if regulators at the national or supranational level did not face the same temptations, the rapidly changing AI world would likely outpace the glacial pace of regulators who move at the speed of government. Given the need for speed and the current lack of interest in AI regulation in Congress, AI model builders are likely the best suited to characterize AI instances, but they should do this through a consortium that maintains and applies consistent standards.
A Future With Many AI Personalities
Applying personality profiles to our AI models may require us to reconsider our simplistic, anthropocentric worldview that presumes humans have personalities, machines do not, and animals inhabit a gray area between human personality and instinct. Over the last 50 years, the line between human and non-human has blurred: Crows use tools, chimpanzees learn basic sign language, and dolphins recognize themselves in a mirror. Each of these was previously presumed to be a uniquely human ability until its discovery in wild animals.
Similarly, until 2022, we retained the happy illusion that only homo sapiens were the world’s preeminent artists. Now we know that AI can compose short stories and create beautiful images. If humans are not the only toolmakers or artists, and AI instances have true personalities, what does it mean to be human beyond our DNA? Are we no longer unique?
In the 1630s, Descartes answered this question with confidence: cogito, ergo sum, or “I think, therefore I am.” The idea that conscious thinking is the hallmark of humanity has remained central to the popular understanding of what it means to be human. If we recognize AI instances as personalities that think and may be conscious, then humanity has indeed expanded with AI.
A future with a vast number of AI personalities might be analogous to the time when humans from small hunter-gatherer bands first migrated to more urban areas and had to live alongside people outside their clan. We transitioned from a simple world where we knew everyone to one that may have seemed like chaos. The parallel move today into a future with many AI personalities would be a dynamic, challenging, scary, and often overwhelming world. However, we are better positioned to survive and succeed in this alternative future rather than one in which humanity chooses to oppose or become overly dependent on a singular AI entity.
