“A 4-year-old child has seen 50x more information than the biggest LLMs that we have.” – Yann LeCun, Meta’s Chief AI Scientist and a Turing Award winner.
This statement illustrates the vast difference in the type and richness of sensory data that humans and current LLMs process. LeCun uses a simple calculation to demonstrate his point, comparing text-based information with visual information:
- LLMs: He estimates that the largest LLMs are trained on around 1014 bytes of text data, representing all the publicly available text on the internet.
- A 4-year-old child: The human visual system processes about 20 megabytes of information per second. If a child is awake for approximately 16,000 hours during their first four years, the total visual data is roughly 1015 bytes—about 50 times more than the text data consumed by an LLM.
A child’s intelligence is built from messy, multimodal, lived experience: touch, vision, sound, spatial awareness, and the trial-and-error of interacting with the real world. LLMs, on the other hand, are confined to a narrow stream of text, making them powerful at spotting linguistic patterns but lacking the grounded world model that humans naturally develop.
This gap explains why AI is great at spotting patterns, predicting, and even simulating human-like voices or characters (which is something we’ve gotten really good at!). But to be able to build realistic learning simulations takes authentic intelligence backed by the lived human experience that makes knowledge meaningful.
Where human intelligence starts to matter in L&D
Our simulations are powered by AI, but the backbone is always human intelligence: observing what people struggle with, what assumptions they hold, and where (sometimes unmeasurable) gaps in knowledge might be hiding.
We design simulations from the inside out. We don’t just ask what problems you think you have; we probe, interview, conduct focus groups, and collect stories. Why did someone make a mistake? What part of policy or culture left people guessing? Those are things no AI dashboard captures out of the box.
Once we’ve collected and understood this institutional knowledge, we design simulations that reflect each organization’s unique identity and workforce challenges and build skills that feel immediately relevant to employees’ day-to-day work.
Authentic intelligence + AI = stronger together
It’s not AI versus human experience. It’s how much better things get when they work together. If you understand the emotional, relational nuances of humans; offload repetitive, data-heavy, pattern-heavy work to AI; and prompt, program, and add clear parameters to the simulation design; you combine speed, scale, and adaptive learning with human-centered depth and meaning that actually change behavior.
We’ve built immersive simulations at Blueline that do just that, with realistic scenarios where learners make decisions, see real consequences, and fail forward. (Carefully prompted) AI helps generate variants, analyze learner data, personalize the experience, and provide real-time feedback, but the storyline plots, the tension, and the mistakes learners will make are informed by human context and experience.
What Blueline does differently
Plenty of L&D teams and developers are experimenting with GenAI, but here’s where Blueline is different: we don’t simply throw prompts at a model and hope for a usable response. Our background in instructional and immersive simulation design means we know exactly how to frame prompts so the AI output fits human learning needs.
When we build an AI-driven simulation, we start with authentic intelligence—real business problems, sales transcripts, performance data, and the kinds of stories that only come from the front line. Then we translate that into carefully engineered prompts that guide GenAI to create scenarios, feedback, and adaptive pathways that reflect the reality learners will face.
In practice, this means:
- We invest in uncovering stories. Listening, probing, and observing are foundational to our simulation design process.
- By combining SME insights with prompt craft, we make simulations that stay true to real-world experience without monopolizing your experts’ time.
- We incorporate your organization’s unique language, cases, and culture, so the experience feels real.
- Every prompt is structured with proven instructional design principles in mind.
- We build ambiguity into simulations: learners often face gray dilemmas that challenge them to make difficult choices.
When the stakes are human, so is the solution
LeCun’s comparison reminds us that intelligence isn’t just about the volume of data—it’s about the richness of experience behind it. That’s the same principle we carry into our work at Blueline.
AI will keep getting better. But until it owns a nervous system, life history, regrets, anticipations, embarrassment, and the rest of that messy human package, it can’t replicate human experience.
If you want learning that actually sticks and moves people to change behaviors, you need more than algorithms. You need people who have lived through similar messes, who know what it’s like to make the wrong decision at 3pm on Friday, or to have a policy that works on paper but fails in the hallway.
That’s why, at Blueline, we build on authentic intelligence. AI is our tool, but human experience makes it real. Get in touch to schedule a private demo to see it in action.
