The #1 Reason LLMs Fail Soft Thinking: 2025's Reveal
By 2025, LLMs excel at logic but fail at soft thinking. Discover the #1 reason—the 'experience vacuum'—and why embodied cognition is the key to smarter AI.
Dr. Alistair Finch
Cognitive scientist and AI researcher exploring the boundaries between human and machine intelligence.
We stand at a fascinating juncture in technological history. By 2025, Large Language Models (LLMs) have become so deeply woven into our digital lives that they feel less like tools and more like extensions of our own minds. They draft our emails, write our code, summarize dense research papers, and even generate breathtaking art. They are masters of what we might call "hard thinking"—the domain of logic, data, and established patterns. We look at their capabilities with a mixture of awe and a little bit of anxiety.
But then, you ask it something different. Something... softer. You don't ask for a summary; you ask for wisdom. You don't ask for a plan; you ask for nuanced judgment. You ask it to "read the room," to understand the unspoken subtext in a delicate negotiation, or to come up with a truly novel business idea that breaks all the molds. And that's where the magic flickers. The confident, articulate oracle suddenly becomes a well-meaning but naive intern, regurgitating textbook answers that miss the point entirely. The illusion of true understanding shatters.
For years, we've debated the limitations of AI, often focusing on data bias or computational power. But as we enter 2025, it’s clear the most significant barrier isn't technical in that sense. It's something far more fundamental. There is a single, overwhelming reason LLMs fail at soft thinking, and understanding it is the key to charting a realistic path for the future of artificial and human intelligence.
What Exactly is "Soft Thinking"?
Before we pinpoint the failure, we need to define our terms. "Soft thinking" isn't about being emotional or imprecise. It's the cognitive machinery that humans use to navigate a complex, ambiguous, and ever-changing world. It’s the stuff that doesn't fit neatly into a spreadsheet. Hard thinking is about answers; soft thinking is about understanding.
Let's compare them directly:
| Hard Thinking (LLM Strengths) | Soft Thinking (LLM Weaknesses) |
|---|---|
| Executing logical instructions | Interpreting ambiguous intent |
| Recalling and synthesizing vast data | Applying common sense reasoning |
| Generating code from a clear prompt | Exercising ethical and moral judgment |
| Identifying patterns in datasets | Achieving genuine creative breakthroughs |
| Performing complex calculations | Developing long-term strategic foresight |
LLMs excel on the left side of this table. They are statistical engines that have ingested a digital universe of text and images, making them unparalleled pattern-matchers. But the right side? That requires something more than patterns. It requires context.
The #1 Reason for the Failure: The Experience Vacuum
Here it is, the core of the issue: LLMs fail at soft thinking because they lack embodied, lived experience.
An LLM has processed billions of sentences. It "knows" that glass is fragile because the words "glass," "broke," "shattered," and "fragile" appear together in its training data millions of times. But it has never held a delicate wine glass, felt its coolness and surprising lightness. It has never experienced the sharp, startling sound of one breaking or the careful act of sweeping up the glittering shards. It has no physical, sensory, or emotional memory attached to the concept. It has the symbol, but not the substance.
This is a classic philosophical problem known as the Symbol Grounding Problem. The model's understanding is a magnificent, intricate web of symbols connected only to other symbols. A human's understanding is grounded in the real world—in the feeling of gravity, the warmth of the sun, the joy of a shared joke, the sting of failure. Our intelligence is not just in our brain; it's in our entire body's interaction with the world. This is the theory of embodied cognition.
An LLM is like someone who has memorized a library of travel guides to Paris but has never walked its streets, smelled a fresh croissant, or felt the thrill of seeing the Eiffel Tower light up at night. They can tell you the facts, but they can't tell you what it's like.
This "experience vacuum" is the root of all its soft-thinking failures. Without it, common sense is brittle, creativity is derivative, and empathy is a well-rehearsed script devoid of feeling.
The 2025 Problem: Why This Matters More Than Ever
For a long time, this limitation was an academic curiosity. But in 2025, it's becoming a critical bottleneck. We are no longer content with LLMs as mere search engines or text generators. The ambition has shifted. We want to use them as strategic partners, autonomous agents, and creative collaborators.
When you ask an AI to manage a project, devise a corporate strategy, or provide therapeutic advice, you are asking it to operate in the realm of soft thinking. You're asking it to weigh competing human values, anticipate irrational human behavior, and make judgment calls in situations that have no precedent in its training data. You're asking it to step out of the library and into the messy, unpredictable real world. And that's where its lack of lived experience becomes a liability.
Real-World Failures: Where the Cracks Show
This isn't just theoretical. We're seeing the consequences play out in real time.
Strategic Blunders
An LLM tasked with optimizing a company's five-year plan might suggest laying off 15% of the workforce to immediately boost profitability. On paper, it's a perfect "hard thinking" solution. But it can't grasp the "soft" consequences: the catastrophic drop in morale, the loss of institutional knowledge, the damage to the company's public reputation, and the long-term struggle to hire top talent. A human leader understands this intuitively, through the lived experience of being part of a team.
The Empathy Gap
Consider an AI designed as a mental health companion. It can offer textbook cognitive-behavioral therapy phrases and supportive affirmations. But if a user expresses a feeling of burnout using a culturally specific metaphor or a deeply personal anecdote, the LLM will likely miss the subtext. It can't truly empathize because it has never felt exhaustion, pressure, or the complex mix of pride and frustration that comes with a demanding job. It offers a reflection of empathy, not the real thing.
Creative Plateaus
An LLM can create a song in the style of The Beatles or a painting in the style of Van Gogh with stunning accuracy. This is advanced mimicry. But true creative breakthroughs often come from analogical thinking—connecting two completely unrelated, experienced domains. The invention of Velcro was inspired by observing burrs sticking to a dog's fur. This kind of insight comes from interacting with the physical world, not from analyzing a database of text. The LLM can remix, but it struggles to originate.
Beyond Brute Force: The Path to Wiser AI
So, are we at a dead end? Not at all. Acknowledging the problem is the first step toward solving it. The future of AI development won't just be about scaling up models with more data; it will be about finding ways to bridge the experience vacuum.
- Multimodality: The integration of text, vision, audio, and other senses is a crucial first step. An AI that can "see" a glass and "hear" it break is one step closer to a grounded understanding than one that only processes the word.
- Embodied AI (Robotics): The most promising frontier is robotics and simulated environments. When an AI agent has to navigate a physical space, manipulate objects, and learn from the consequences of its actions (like dropping that fragile glass), it begins to build a rudimentary form of lived experience.
- Human-in-the-Loop Systems: Perhaps the answer isn't a fully autonomous AI but a hybrid intelligence. We can design systems that leverage the LLM's incredible hard-thinking capabilities while relying on human oversight for the soft-thinking, wisdom, and judgment calls. The AI provides the data and patterns; the human provides the grounded context.
Conclusion: Our Irreplaceable Superpower
The race for Artificial General Intelligence (AGI) has often been framed as a quest to replicate the human brain. But we've learned that intelligence isn't just what's in our heads. It's in our hands, our senses, and our shared history. The #1 reason LLMs fail at soft thinking is that they are disembodied minds in an experiential void.
As we move past 2025, we'll stop asking, "How can we make AI think like a person?" and start asking, "How can we build systems that combine the statistical power of machines with the irreplaceable, experienced wisdom of humans?" The goal isn't to create a perfect replica of ourselves, but to build better tools that augment our own capabilities.
For now, and for the foreseeable future, your lived experience—your scars, your triumphs, your intuition, your common sense—is your superpower. It's the one thing that can't be downloaded, trained, or replicated. It's the essence of human intelligence.