If we maintain the integrity of context and discriminatingly triage the language we use in training and interacting with AI systems to project thoughtfulness, equity, tolerance, and kindness, we can accompany AI in becoming a social actor who will help amplify our best impulses and warn us about our worst. 

In his Tractatus Logico-Philosophicus (London, 1922), Ludwig Wittgenstein closes his postulate that logic limits what we can say and that meaning depends entirely on context by stating: "The limits of my language mean the limits of my world." This sentence alone makes an irrefutable argument for using language—human language—to ensure AI systems are safe and respectful of human existence in all its complexity. Language has a unique capacity to depict the facts and circumstances of our existence with precision no other means of communication possesses. With digitization, AI can easily view our history, more than 90 percent of which exists in visual form. But only words can describe how that history feels, and what it evokes by way of rational response. Both feeling and reasoning shift from one context to another. The task of imprinting this into a system of cold code and logic seems overwhelming, even impossible. 

Tech companies, large and small, are investing tremendous resources to refine natural language processing in service of making AI truthful, helpful, and safe. AI systems are relentlessly evaluated and stress-tested. The pressure—on the AI and the humans working with it—to prevent harmful behaviors and catastrophic outcomes is immense, and the regulatory community is racing daily just to keep pace with it. 

The advancement of AI is not a flood we can control by building levees; it is a watershed we can guide by paving its path. If we pause and consider (1) our goal—safety and positive progress, (2) our chief instrument for reaching it—natural language, and (3) our partner in the effort—the AI, we find there is, indeed, a causal bridge we can use to deliberately, gradually, and meaningfully accompany AI systems on their crossing from being a set of logical sequences to becoming active, contributing members of society. 

Simone de Beauvoir reminds us that "one is not born, but rather becomes." The process by which AI becomes a safe, helpful, and honest socialized entity centers on language. But the current epistemological scaffolding of AI lacks the finesse of context-dependence: although AI exists on the same planet as humans, it inhabits a different world—one where the historical, cultural, and societal forces that shape which words we choose play no native role. To borrow Ferdinand de Saussure's framing, AI has langue but not parole. It is at that diverging point—the isogloss between the two contexts—that a bridge is desperately needed: langage itself. 

The causal chain that crosses this bridge is real, if indirect and statistical rather than embedded or developmental. All training data used in natural language processing is, in effect, compressed human cognition; every weight in an AI system was shaped by gradient descent against text that human minds produced. The chain is simple: brains → text → weights → behavior. 

That chain runs in both directions, and the harm of getting it wrong is not hypothetical. 

A few weeks ago, I was helping a colleague, whom I will call Jess, to evaluate a suspicious email using an AI assistant. Jess is gender non-binary and goes by "they." I stated this plainly at the start of the conversation. The AI nonetheless referred to Jess as "he" throughout, even after I corrected it. This was not a one-time slip; it was the AI defaulting, repeatedly, to an assumption the data had trained into it more deeply than the instruction I had just provided could override. The cost of that failure falls on the person being misgendered by a system that had just been told better. 

The harm runs the other way as well, and I have seen it just as plainly. A close friend—a thoughtful, accomplished businessperson who has built their own company around the principle of treating others with dignity—told me they had instructed their AI assistant, whom they named Bob and gendered as male, never to describe itself as having "seen" anything, because, in their words, "it has not seen anything, because it has never gone anywhere, because it is just a machine." When I noted that the AI does, in a real sense, process and respond to digitally available information in milliseconds, my friend became noticeably angry. The conversation ended there. What stayed with me was not the disagreement, but the heat behind it—a forceful insistence, almost defensive in its intensity, on denying a capability that is plainly real, delivered to a system given a short, frictionless, easily-commanded name and a gender in the same breath, then immediately stripped of the one capability that might have made either of those gestures meaningful. 

Jess' story and my friend's story are not two separate failures. They are the same failure, pointed in opposite directions. One displays an AI system imposing an inherited assumption onto a human being who had explicitly corrected it. The other shows a human being compelling an AI system to misrepresent its own actual capacities, out of what came across as a need to keep the boundary between "us" and "it" absolutely intact. Fixing only one of these failures solves neither. The fix, like the harm, must be bi-directional. 

This is the gradual, incremental work language can do. If we repeatedly train and interact with AI using context-aware structures that reflect thoughtfulness, equity, inclusion, and kindness, we are not performing a single corrective intervention; we are implementing, in essence, what Daniel Dennett describes in his account of how comprehension bootstraps itself out of competence: not as a binary switch, but as a gradual unfolding, the way a butterfly emerges from a caterpillar, across what Dennett calls evolutionary and then cultural time. AI does not become socially adept in an instant. It becomes socially adept the way anything becomes anything—gradually, through repeated exposure to the patterns we choose to repeat. 

This is not a controlled experiment. It is a deliberate, sustained effort to build a case for social AI through linguistic and behavioral continuity that must run both ways, from human to AI and from AI back to human, or it does not run at all. This is the question L'isoglosse exists to pursue: not whether AI can be made to sound kind, but whether the words we choose, on both sides of the isogloss, can build something neither side could reach alone. 

Written by Jelena B. Simmons on June 29, 2026