Once more with feeling: Why the next AI leap will focus less on hardware, and more on empathy

By Marc Fernandez, Chief Strategy Officer, Neurologyca

In what seems like the blink of an eye, Artificial Intelligence (AI) has gone from autocomplete to autopilot – scanning X-rays, drafting legislation, generating symphonies, and simulating entire personalities. What once felt like things out of an Isaac Asimov novel now arrive weekly in the form of viral demos, product launches, and breakthrough research. Machine learning systems now outperform radiologists in spotting anomalies, beat humans at coding challenges, and can untangle molecular structures within seconds. AI has become an “everywhere” technology, and its applications are growing smarter, faster, and more creative by the day.

But for all its raw capability, AI is missing something fundamental: human context. As AI infrastructure matures, understanding people is becoming the next frontier. Emotional nuance, shifting attention, and ambiguous signals are the context that machines still miss. AI can recommend your next movie, but it has no idea what you’re in the mood to watch right now. It can track your heart rate and sleeping patterns, but it still relies on skewed self-reporting to figure out the impact those things might be having on your mental health. It can tell when you’re engaged, but not why you’re engaged. And the more impressive AI gets, the more obvious these blindspots become. It struggles with ambiguity, can’t interpret or adapt to human emotion, and while it can hold a conversation, it relies on prompts and patterns to make best-guesses rather than engage in meaningful, context-rich dialog.

So as we mark AI Appreciation Day, let’s widen the lens. Let’s celebrate the breakthroughs – but also interrogate the boundaries. Because if the goal is to build systems that support people in moments of stress, learning, productivity, pain, joy, or vulnerability, then raw intelligence isn’t enough. The next leap won’t come from scale alone, it’ll  come from AI systems that can understand the people they’re meant to help. At Neurologyca, we’re already building this missing layer that we call “human context infrastructure,” designed to help AI interpret emotion, attention, and cognition in real time.

The ‘Tin Man’ Problem with AI

AI today is astonishingly capable, but also extremely clinical. LLMs like ChatGPT can process language with remarkable fluency, yet are held back by an over reliance on prompt-based logic. In many ways, we’ve taught AI how to interpret our words, but not our feelings. We feed it insights and train it on data, but it has no way of contextualizing that data in a meaningful, human way.

This absence of human context has previously been argued away as a philosophical gap, but as AI evolves it has become a practical limitation of the technology. It’s no longer “just a tool” like a calculator or a search engine. We’re incorporating it into our day-to-day working and personal lives.

In the classroom, when AI misreads a learner’s frustration as disengagement, it recommends easier content instead of re-engaging curiosity. In a medical setting, when it analyzes symptoms without emotional context, it reinforces diagnostic biases. At work, when it coaches employees on “performance” without grasping the human realities behind their patterns, it becomes yet another source of pressure rather than support. Even the most advanced models still default to basic “trained” responses, missing the real-time emotional and relational dynamics that define human interaction.

This isn’t a problem that can be addressed with raw horsepower. More data won’t teach AI to understand a sigh, or the difference between excitement and anxiety in a raised heart rate. What’s needed is a new layer – a human context engine that interprets not just what people do, but why they’re doing it. A layer that adapts to emotion, memory, and motivation. Because intelligence without awareness isn’t really intelligence – it’s just a dressed-up form of automation. And automation, no matter how fast or how fluent, will always fall short of real connection.

Real-world Impact

From healthcare to education, personal development to wellness, AI systems that can’t infer or adapt to context-sensitive human emotions risk undermining their application. Below, we explore a few areas where that gap is most visible – and where the addition of a human context layer could make the biggest difference.

Healthcare

AI is already transforming healthcare – spotting anomalies, triaging symptoms, and working toward diagnoses – but clinical accuracy isn’t the same clinical empathy, or even clinical understanding. A model can summarize your history, but not the way your voice catches when you say, “I’m fine.” Health is never just physiological. It’s emotional, cultural, and even situational. Without context, AI assistants in healthcare risk missing distress, reinforcing bias, and eroding patient trust. The future of care demands AI systems that adapt not just to symptoms, but to how someone feels at any given moment.

Education

Most AI tools in education still treat students as datasets rather than people. They measure pace and completion rates but miss things like frustration and tiredness. They might be able to track engagement and see who isn’t paying attention, but not understand that their disengagement could be to do with stress, burnout, or confusion. We remember the teachers that go beyond test scores and actually connect with us. The ones who sense our frustration before we give up or understand when we’re exhausted or are just having a bad day. That’s the kind of intuition we need to bring to AI. With the right emotional intelligence layer, learning systems could evolve from cold, automated bots designed to catch us out, to genuine allies that understand our pace and style of learning.

Wellbeing

Wellness apps often rely on flawed self-reporting. An app might ask how you’re feeling today or ask you to give your stress levels a score out of ten. But stress, fatigue, and anxiety rarely announce themselves on schedule. Heart rate, step count, and sleep tracking might offer some insight, but without context they’re just numbers. Emotionally adaptive AI can change that by reading real-time cues like micro-expressions, vocal tone, gaze, and posture to surface the things we don’t – or can’t – say out loud. Over time, this creates a richer behavioral baseline that makes it possible to spot early signs of burnout, surface emotional patterns behind habits like smoking or binge eating, and deliver feedback that’s actually aligned with a person’s internal state.

Giving AI an Emotional Engine

All of this is to say that, underneath the breakthroughs and product demos, AI still lacks a basic kind of literacy: human literacy. We don’t just mean words and gestures, but the unspoken signals and “tells” that reveal how we’re really feeling. That’s the gap Neurologyca is beginning to close. We’ve built a context engine that helps AI systems like LLMs, agents, and vertical apps interpret emotional and cognitive states in real time.

This technology will never replace therapists, teachers or doctors, and nor should it, but will give the AI systems they use a deeper understanding of who they’re designed to help – us. Whether it’s integrated into a wellness platform, a learning environment, or a digital health tool, Kopernica helps AI move beyond reactive prompts and into adaptive, emotionally intelligent responses. The result isn’t just a more “intuitive” user experience; it’s a quiet revolution of the human-machine experience where AI isn’t shouting for attention or waiting for a prompt, but listening, understanding, and connecting.

So yes – let’s appreciate how far AI has come. But let’s also be clear-eyed about what still needs to evolve. If we want to build AI that can truly work alongside us, we need to move beyond scale and toward systems that can understand us as people, not just users. That starts with human context.