The Human-ness of AI
The Case for Runtime Learning
Everyone’s chasing AI “intelligence”: bigger models, more parameters, faster responses. But that’s missing the point. At its core, AI is probabilistic, just like we are. It doesn’t actually “know” the right answer. It’s always making its best guess, based on whatever training it’s had so far.
Humans learn by making guesses, getting feedback, and adjusting. No one’s a genius on day one. We rely on continuous feedback—teachers, coworkers, or even random strangers on the internet pointing out where we went wrong. AI isn’t any different, and that’s why It needs ongoing interaction, correction, and reevaluation.
Where Current Systems Break
Today’s AI landscape splits into two camps - both missing the point. Foundation model companies serve up static snapshots: train once, serve forever. Their models, however large, are frozen in time. But even AI-native companies aren’t much better. Sure, they fine-tune models and retrain on new data, but it’s all happening in development, disconnected from real usage. Their “continuous learning” is just scheduled maintenance - collecting data, retraining models, pushing updates.
It’s like having a smart employee who only learns during scheduled training sessions, never from actual work. They might get quarterly updates on best practices, but they can’t adapt to the client conversation happening right now. Even companies touting “personalization” are mostly just tweaking parameters in pre-defined ways, not truly learning from each interaction.
Runtime Learning: Beyond Reinforcement
This isn’t traditional reinforcement learning, where systems optimize through trial and error during development. This is about real-time adaptation within actual workflows. Think of it like a new employee who gets better at their job through daily feedback from colleagues. When an AI helps with customer support, it picks up on what works and what doesn’t with each conversation. The learning happens on the job, not in a lab. Every interaction teaches it something new without needing to shut down for retraining.
Learning in Action
Take customer support: an AI learns that technically correct answers frustrate users when delivered without empathy. Or marketing: Imagine you have an AI drafting your marketing emails. You ask it to write a product update, but it keeps using stiff, formal language. You highlight the parts that sound off and rewrite them to match your brand’s personality. The AI sees your corrections, engages in understanding through conversation, updates its internal “probabilities,” and adapts next time. Each interaction reshapes its understanding of effective communication. That’s the power of runtime learning.
It’s Not About Being Perfect
It’s about improving step by step, correction by correction. A system that’s “almost” right but can quickly learn from mistakes is more valuable in the long run than one that’s rigidly correct 95% of the time but never evolves.
Closing Thoughts
Runtime learning will be AI’s next breakthrough. Not through scheduled updates or parameter tweaks, but through real-time adaption within actual workflows. When humans shift from users to mentors, AI evolves from tool to partner. That’s the future of AI: not chasing perfection, but by embracing continuous improvement through human guidance.