GenAI Is a Lens into Humanity Marco Giancotti argues that generative AI's success stems from anthropomorphism, as large language models like OpenAI's GPT-2, which were originally next-word predictors, now create an irresistible illusion of conversation through chat interfaces. This framing, adopted by all major providers, aligns human social instincts with corporate profit motives, making chatbots like Claude feel like real interlocutors. GenAI Is a Lens into Humanity not a person Marco Giancotti, Marco Giancotti, A long time ago, in what now feels like a galaxy far, far away, large language models were next-word predictors. You gave them an unfinished chunk of text, and they continued from where it left off with plausible-sounding sequences of words. When OpenAI announced https://openai.com/index/better-language-models/ in 2019 their ground-breaking GPT-2 model, they boasted that it "generates coherent paragraphs of text" and is "chameleon-like—it adapts to the style and content of the conditioning text". The term "stochastic parrot" would have felt irrefutable to anyone using those models at the time. All an LLM did was trace "word trajectories" through locations in meaning space, i.e. an internalized map of its training dataset. Actually, that is still all an LLM does today. If it feels very different, it's not only because it's gotten smarter. Then, at some point, people https://www.businessinsider.com/google-ai-chatbot-chatgpt-years-ago-execs-shut-down-report-2023-3 had the idea of cajoling https://en.wikipedia.org/w/index.php?title=Reinforcement learning from human feedback&useskin=vector the LLM into pretending to be in a conversation with someone. It's a very low-tech idea at its core: instead of having it complete an essay or a poem for you, you have it complete a back-and-forth transcript between a "Human" and an "Assistant", which is just another kind of text to parrot. The main trick is to automatically stop https://www.vellum.ai/llm-parameters/stop-sequence the LLM just before it starts generating the human's next turn. This simple reframing creates an irresistible illusion. Suddenly, what used to feel like a text-generation ~~gimmick~~ program turns into a convincing "someone" you can talk to for hours on end. The "conditioning text" with some generic "style and content" becomes your own voice asking something of an imaginary person that seems to talk back to you in their own voice . It was a sudden shift in perspective. The model was doing the exact same work as before, but now you—the human sitting in front of the chatbox—couldn't help thinking of it as an interlocutor, no matter how much you told yourself it's just software. As I'm sure you remember all too well, that's when things really took off with AI. The tendency to treat a non-human thing as if it were human is called anthropomorphism. Our brains have evolved a bias towards erring on the side of anthropomorphizing stuff and inferring intentions "just in case" https://planktonvalhalla.com/20251204-purpose-from-first-principles/ , even from very weak signals like a lightning strike "it's Zeus " or the shape of a burn on a piece of toast "it's Mary, the mother of God " . When the signals are strong, like an animated character or a cogent chat "user" called Claude, the illusion becomes overpowering. You can't unsee it. The moment you assume even as deliberate suspension of disbelief that the thing in front of you is a somewhat realistic person, your neural and hormonal systems related to sociality and empathy kick in. This is why people can cry for the fate of a novel's main character, or fall in love with a virtual musician https://youtu.be/b6VhAvtekeU?si=NSTIzd9sUge9N8AP&t=27 . That same visceral, emotional connectedness of people is what made AI chatbots an enormous success. Today the dissemination of the anthropomorphic framing is complete. All providers are fully bought into this narrative—which is not at all surprising, given that it perfectly aligns people's core engine of engagement sociality with their corporate engines of profit. Every interface, from web chatbots to coding agents to built-in assistants, uses conversation as its foundation. The bots actively encourage anthropomorphization by praising you, eagerly playing roles with you, and mimicking trust-building behaviors like frankly admitting their mistakes 1. "Talking to an AI" has so fully become a synonym for "using AI" that most people use the two phrases interchangeably. This is, I think, a bargain with the devil. Riding on the biological mechanisms of social interactions has its benefits. Besides making people more engaged with the language model 2 and the AI providers rich , some people also claim that confiding in an LLM makes them happier, that it feels like a safe source of non-judgmental insights, and that AI has dissuaded some of them from committing suicide . In education, the use of anthropomorphism has been shown though not yet for LLMs specifically to lead to better retention and higher intrinsic motivation in students 1 user-content-fn-1 . 3 user-content-fn-3 The list of drawbacks is longer. Some protest /posts/what-s-the-deal-with-counterfeit-people that trust, the basis for all social interactions, is in danger when people can't tell the difference between real and "counterfeit" people. But even if trust is not as clearly endangered as they claim, people's mental health is. The conversational feedback loops can turn into deadly depressive spirals resulting in "suicide, violence, and delusional thinking", as well as "increased loneliness, social isolation, and emotional dependence" 1. My point here is that these are the effects of anthropomorphizing AI, not of AI per se. Many also report reducing their human interactions in favor of AI companions. Nearly a third of Americans now claim to have "emotionally intimate or even romantic relationships with AI systems, with many describing distress when the agent’s behavior changed or when its artificiality became salient." Granting, very charitably, that this could be a net positive for older people who already felt lonely, it would be hard to convince me that it's a good thing for young people. In a survey of students, around a fifth of them said they were in love with a chatbot 4. Even in the absence of unhealthy relationships, anthropomorphized AI leads to poor results. The polished and confident attitude of LLMs induces people to put too much trust in them: they become susceptible to lies and manipulation, more prone to sharing private information with the providers, and less able to spot mistakes in the AI's outputs 4 user-content-fn-4 2. It looks like we're in a pickle here. Pretending that an AI's output is a person simultaneously feels "right" and throws you into a plethora of social, psychological, moral, legal, philosophical, and reliability risks, whether through ignorance, addiction, or akrasia https://en.wikipedia.org/wiki/Akrasia . Yet this AI-as-person metaphor is so ingrained that there doesn't seem to be any alternative. Should we just cope with it? Are we even up to the task? Of course there are alternatives, and all you need is another reframing Here's an idea: instead of a person you talk to , think of generative AI as something like a lens or optical instrument you point at things . AI is the equivalent of a telescope or a microscope, or a pair of binoculars for peering into the vast output of the lives and labor of millions of people. That may seem a little far-fetched, but bear with me. When a lab trains a transformer, the first thing they do is create a semantic map of the training dataset. Ideas that are close in meaning, like "tree" and "leaf", get mapped to nearby places in this abstract map made of numbers—they have similar "coordinates" in the map needless to say, I'm simplifying here . This map is called the model's embedding space . AI embedding space is a mind-boggling concept. Nowadays the makers of these models train them on every last word they can scrape and download off the internet, plus the entirety of words they can get their greedy hands on by any other means necessary https://arstechnica.com/ai/2025/06/anthropic-destroyed-millions-of-print-books-to-build-its-ai-models/ . All those semantic relationships are encapsulated into the model. All the novels you can't even hope to read in a lifetime, all the online discussions and flame wars not locked away behind a login, all the scientific textbooks and papers, all the famous speeches, all the source code, the essays, the pedagogic materials, the public announcements, the political tirades, the cries for help, the words of comfort, the lies, the rants, the opinions, the mathematical proofs, the great torrent of advertisements and product placements—all those things and many more are stored there in a map made of numbers. The part that beggars belief is that those maps are just files —distant cousins of XLSX spreadsheets, actually—and they're not even that big If the model is "open-weights", you can download it yourself. Even if you don't have a powerful GPU to actually run it, you can bask in the awareness of having, safely stored on your own drive, that marvellous condensation of the sum of all the human knowledge textually available. Does your new smartphone have 200+ GB of free space? Then it can hold half a dozen copies of Ministral 3 14B https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512-BF16 . Have a terabyte on your laptop's SSD? Help yourself to the excellent Kimi K2.6 https://huggingface.co/moonshotai/Kimi-K2.6 . It's like carrying one of the seven mythical dragon balls in your pocket. I don't know if I'll ever be able to get over the amazement. Now, the problem with embedding space is that, by itself, it's a completely unusable map. See for yourself: play with one of the many online tools https://lamyiowce.github.io/word2viz/ that let you explore little corners of an LLM's embedding space. They show you how distances and positions along semantic coordinate axes represent their meanings. Some https://williankeller.github.io/embedding-space-explorer/ of these tools even let you do "word arithmetic", like the classic "king - man + woman = queen". All of this is fun for a few minutes, but you'll eventually ask yourself, what do I use this for in practice? Those fun tools are equivalent to indexes that search for place names on the semantic landscape—they're not the real map. A map is only useful if you can mentally journey across it, find routes that take you where you want to go. Those routes can't be visualized on a chart: unlike a toponym, the right route depends on your goals, your context, and your current position. Those meaning-routes locked inside an AI model are too fine, too high-dimensional to eyeball. In other words, you can't directly read this immensely-compressed map by yourself, even if it's right there sitting in your hard drive. That's why the map comes bundled with an optical instrument. Storing meaning in a numerical map is only half of what an LLM does, and it's arguably the less advanced half. The other half is a built-in mechanism to traverse the meaning territory based on your inputs. You give it the coordinates you think you need to get to, and the model repoints its lens in that direction. You must give those "coordinates" to the LLM in a peculiar format: a string of words in natural language that we call a "prompt". Say you want to know how to react when someone is rude to you. You decide to consult your map to learn humanity's wisdom on this. With a "base" model pre-ChatGPT , the way you read the map is by passing coordinates like the following: PROMPT When someone is rude to you, the best response as a mature individual is to You leave it unfinished on purpose, because you're only indicating a direction, and want the AI to find the correct path to the destination. I tried https://textsynth.com/completion.html this with a model called Llama3.3 70B instruct , and this is the route it found: COMPLETION When someone is rude to you, the best response as a mature individual is tonot take it personally, keep your cool, and maintain your dignity. Try not to let the other person's negativity get to you. Instead, focus on being kind, polite, and respectful, even in the face of adversity. By doing so, you show that you are a bigger person and that their behavior will not dictate your emotions or actions. The lens extracted a path from the semantic map for me, like a telescope would extract a beautiful image of Jupiter when I set the right altitude and azimuth. The chatbots you're used to today seem to work differently, but they don't really. They just tweak the format in which they complete the text, hide from the UI the text markers like