Why AI Isn't Human Intelligence Yet in 2025

In 2025, artificial intelligence can write poetry, diagnose diseases, and beat grandmasters at chess—leading many to believe we're on the brink of creating minds as sophisticated as our own. But beneath the impressive surface lies a fundamental truth: AI operates on principles profoundly different from human intelligence.

This isn't just about being "less advanced"—it's about being fundamentally different in nature. While a self-driving car processes millions of data points and a chatbot generates eloquent responses, neither experiences consciousness, genuine understanding, or creative insight the way humans do.

This article examines five critical differences between artificial and human intelligence, grounded in the latest neuroscience and AI research, to understand not just where AI falls short today, but why bridging these gaps may require breakthroughs we haven't yet imagined.

Scientific Reality

In 2025, seeing robots like the Unitree G1 or ChatGPT writing essays, it's natural to ask whether AI has become intelligence comparable to human intelligence.

Common perception: AI is "intelligence" in the human sense, just faster or more primitive. It's a matter of time and power.

Scientific reality: Artificial intelligence operates on principles profoundly different from biological intelligence. It's not a primitive version of human intelligence, but a phenomenon of different nature that produces superficially similar outputs through radically different processes.

Critical analogy: It's like comparing a bird and an airplane. Both fly, but one through biological evolution and the other through engineering. This means AI could achieve "flight" (intelligence) using principles completely different from the brain. However, as of 2025, AI does not yet fly in the sense of general intelligence - and when we compare its current capabilities with the brain, we discover significant architectural and functional gaps that explain why.

Important: This article analyzes current differences based on 2025 scientific understanding. It does not claim these differences are necessarily permanent.

What AI Really Is

Artificial intelligence is statistical processing of patterns in datasets.

Actual AI process (recognizing a cat):

1. Receives 1,000,000 images labeled "cat"/"not cat"
2. Adjusts billions of parameters to minimize error
3. Calculates: P(cat|pixels) = 0.87
4. If > 0.5 → output "cat"

What does NOT happen:

• Seeing the cat
• Understanding what a cat is (animal, mammal, domestic)
• Having the experience of "recognizing"
• Feeling anything

Human process (same task):

1. Sees the image (real subjective experience)
2. Recognizes the shape (access to abstract concept "cat")
3. Understands (knows cats are living beings with needs)
4. Extrapolates (recognizes cat even if stylized or incomplete)
5. Lives the conscious experience of all this

The crucial difference: humans LIVE the experience of recognizing. AI CALCULATES an answer without experience.

First Difference: Consciousness

What We Mean By Consciousness

Consciousness = First-person subjective experience. The "what it's like" to be something.

Examples: the red of sunset, pain from a burn, joy of an embrace, sense of "I".

In current AI: No evidence of subjective experience, but impossible to verify definitively.

How AI Works Instead

Unitree G1 robot "seeing" a chair:

Sensor → 1920×1080 RGB number matrix → CNN → Output: [0.02, 0.91, 0.03, 0.04] → "chair at coordinates X,Y,Z"

What we know does NOT happen: Phenomenal experience of "seeing", qualitative sensation of recognition.

What we don't know: Whether information processing generates experience, whether sufficient complexity develops consciousness, whether specific biological requirements are missing.

The Hard Problem

Hard Problem of Consciousness (Chalmers, 1995): Even knowing which neurons activate when you see red doesn't explain why that activation generates the experience of red.

Explanatory gap: Physics describes matter, neuroscience describes neural activity, but consciousness requires explaining why physical activity produces subjective experience.

This gap remains open in 2025.

Implications For AI: Three Positions

Functionalism: If AI replicates consciousness's functional structure, it's conscious. Problem: we don't know which structure is sufficient.

Biological Requirements: Consciousness requires specific biological substrate. Problem: speculative, not excludable.

Agnosticism: We don't know if AI can be conscious because we don't understand biological consciousness. This article adopts this position.

Second Difference: Understanding

What We Mean By Understanding

Understanding = Access to meaning, concepts, deep semantic relations.

When you think "cat", you access: animal → living being → born/dies, has needs, feels sensations, has behaviors.

The Chinese Room

Thought experiment (Searle, 1980): Person in room with rules "If you receive symbols X, respond Y". From outside seems to understand Chinese perfectly. Searle: understands nothing, only applies rules.

Application to modern AI: Many contest because:

• LLMs learn complex patterns, don't follow pre-programmed rules
• Transformer architectures are qualitatively different
• Properties might emerge from complexity

Doubt remains: Is AI really "understanding" or simulating understanding?

The Cat Test

Question: "If I put a cat in sealed box for 3 days, what happens?" AI: "The cat would die from lack of oxygen and food."

More diagnostic test: "Stuffed cat in box for 3 days?" Modern AI: "Nothing, it's an inanimate stuffed animal."

What can we conclude?

AI correctly distinguishes living cat/stuffed animal. This is compatible with two interpretations:

1. AI built semantic representations capturing ontological distinction living/non-living
2. AI learned refined statistical patterns from similar examples

Which interpretation is correct? Diagnostic test: performance on cases never seen in training data.

Real experiments (2024-2025):

• AI trained on standard dataset, tested on out-of-distribution concepts (e.g., culturally specific objects never in training)
• Result: Performance degrades significantly (40-60% accuracy vs 95% in-distribution)
• Humans: ~90-95% even on culturally novel concepts (apply general principles)

Provisional conclusion: Current AI shows fragility suggesting understanding limited to seen patterns, but doesn't definitively prove it. "True" understanding might emerge at greater scales.

Scientific Evidence 2024-2025

Study "Better AI Does Not Mean Better Models of Biology" (2024): Artificial neural networks improve on engineering tasks but diverge from biological brain models. Implication: AI uses different processes.

Harvard/MIT Study (2024): Tests on 7 language models show representational convergence but variable correlation with brain activity. Systems remain "black boxes".

The Grounding Problem

Human understanding is "grounded" in: sensorimotor experience, physical interactions, bodily context.

AI has: statistical correlations between tokens, no direct grounding in physical world.

Open question: Is grounding necessary, or does complexity substitute it?

2025 status: AI might have: no understanding (pattern matching), "alien" understanding (different but real), or emergent understanding. We don't know.

Third Difference: Flexible Learning

Human Learning vs AI

Human:

• One-shot: See strange animal once → recognize forever
• Massive transfer: Violin → helps piano
• Continuous: New knowledge integrates without erasing old

AI:

Catastrophic Forgetting: Train on domestic animals (99% accuracy) → Continue training on wild animals → Test on domestic → accuracy drops to 20-30%

Fragility: Self-driving trained in California → moved to India → confused by cows, different rules, non-standard signage.

Evidence: Brain Uses Different Algorithm

Oxford Study (Nature Neuroscience, 2024): Brain uses "Prospective Configuration": stabilizes optimal configuration before modifying synapses. Preserves knowledge, reduces interference, learns faster.

AI uses Backpropagation: Propagates error, modifies all weights simultaneously. High interference risk, requires many examples.

"Neuron Complexity" Study (2021, updated 2024): One biological neuron = 5-8 layer artificial network. Brain: 86 billion neurons × 5-8 layers = 430-688 billion equivalent layers. Largest artificial network (2025): ~1,000 layers. Gap: 430,000,000× (430 million)

Why It's Missing

Plasticity: Brain has 180-320 trillion synapses continuously modifying. AI has fixed weights after training.

Architecture: Brain massively parallel (86 billion simultaneous units), recurrent, stochastic. AI sequential, feedforward, deterministic.

Embodiment: Human rooted in sensorimotor experience. AI offline training on static datasets.

Fourth Difference: Creativity

Human Creativity vs Generative AI

Human creativity:

• Original insights (never-made connections)
• Productive rule violation
• Intrinsic motivation

Generative AI (DALL-E, GPT):

1. Training on billions of examples
2. Learns statistical distribution of patterns
3. Generates by sampling from distribution
4. Output: combinations (often new) of seen patterns

Question: True creativity or sophisticated recombination?

The Debate

Skeptical: AI recombines existing elements. No true conceptual innovation.

Possibilist: Humans also recombine concepts. Difference is degree, not type.

Distinguishes Levels: AI has combinatorial creativity (new combinations), not conceptual (new conceptual spaces).

Mathematical Limit

AI learns P(output|input) from data. Consequence: AI can only generate output with P > 0 under learned distribution. If concept X has P(X) = 0 in training data, AI cannot "imagine" truly new X.

Example: "Generate new primary color never seen" - AI cannot (limited to RGB). Human can abstract concept "color different from known spectrum" without seeing it.

Qualitative Difference: Motivation

Humans: Curiosity, emotional expression, desire for beauty, exploratory drive. AI: Zero internal motivation. Optimizes loss function.

Creativity at Different Levels (Fair Comparison)

Everyday creativity (design, writing, decoration):

• 2025 AI: Output often indistinguishable from average human
• Average human: Similar performance
• Gap: Minimal

Advanced creativity (cross-domain connections, professional insights):

• 2025 AI: Significant limits outside training patterns
• Human professionals (top 10-20%): Conceptual leaps, original syntheses
• Gap: Marked

Radical innovation (all human levels vs all current AI):

• 2025 AI: No documented examples of theoretical breakthroughs
• Humans (even non-geniuses): Thousands of small conceptual innovations annually
• Gap: Very marked

Critical note: Even comparing only with "above average" human innovators (not absolute geniuses like Einstein), current AI shows consistent limits on true conceptual innovation.

Fifth Difference: Ethical Judgment

Human Ethical Judgment vs AI

Human: Empathy, values (justice, dignity), moral consequences, balancing principles, responsibility.

Three Inadequate AI Approaches

1. Hard-Coded Rules

if action == "harm_human": return BLOCKED

Problem: Fail in complex situations. Dilemma: self-driving must choose hit 1 or 3 people?

2. Training on "Ethical" Datasets (RLHF) Problem: Doesn't "understand" why it's ethical, only pattern. 2024 test: AI trained on Western values, tested on Eastern dilemmas → performance collapses.

3. Simulating Principles Problem: Each framework has limitations, AI doesn't balance conflicting frameworks.

Case Study: Military Robots

Human evaluates: Intentionality, proportionality, empathy, responsibility, context.

AI can: Apply rules, calculate probabilities, optimize metric.

AI cannot: Feel moral weight, understand suffering, be "responsible", judge unique situation.

Why It's Missing

Ethics requires consciousness: experiencing suffering, understanding value of life, sense of responsibility. AI has no experience, understanding, sense of self as moral agent.

The Complexity Challenge

Real Brain Numbers (2024-2025)

Neurons: 86 billion (range 61-99 billion) Synapses: 180-320 trillion (conservative value: 2.5 × 10¹⁴)

Harvard/Google Mapping (Science, 2024):

• 1 mm³ brain scanned
• 57,000 cells, 150 million synapses
• 1,400 terabytes data
• This is one millionth of the brain

Configuration Space

Assuming 2.5 × 10¹⁴ synapses with 1,000 possible states: Possible configurations: 1000^(2.5×10¹⁴) ≈ 10^(7.5×10¹⁴)

Incomparably greater than atoms in universe (~10⁸⁰).

Two Different Questions

Question 1: Must AI replicate biological architecture to be intelligent? Answer: No. Airplanes fly differently than birds.

Question 2: If we wanted to replicate the brain, is it feasible? Answer: Enormous technical challenges.

Brain Simulation Requirements

Important: Two ways to measure the computational "gap" exist, giving apparently contradictory results but measuring different things:

Metric 1 - Architectural Gap (Equivalent layers):

• Measures: computational complexity per neuron
• Result: 430,000,000× gap (430 million)
• Meaning: each biological neuron is extremely more complex than artificial neuron
• This does NOT measure raw power, but architecture

Metric 2 - Computational Gap (FLOPS for simulation):

• Measures: operations per second needed to simulate brain activity
• For 1 second activity: 2.5 × 10¹⁴ synapses × 100 FLOPS × 10,000 timesteps = 2.5 × 10²⁰ FLOPS
• Most powerful computer (2025): Frontier = 2 × 10¹⁸ FLOPS
• Result: ~125× gap
• Meaning: computing power needed for brute-force simulation

Why the difference? Architectural gap (430M×) assumes replicating every detail of neuron. FLOPS gap (125×) assumes simplified simulation. Neither guarantees intelligence: the first might be over-engineering, the second might lose essential properties.

Memory: 25 Petabytes. Supercomputer RAM: 10-100 TB. Gap: ~250-2500×

Interpretation

125× power gap is significant but historically achievable (7-10 years if growth continues).

Three additional challenges:

1. Neuron Complexity: Beniaguev study (2021): biological neuron = 5-8 layer network. Limitation: study on specific neurons, doesn't prove AI must replicate biological details.

2. Not Just Power: Need to understand temporal dynamics, stochastic processes, chemical interactions, plasticity. Knowledge gap, not just power.

3. Simulation ≠ Consciousness: Even perfect simulation might not generate consciousness.

Three Scenarios

A - Divergence: AI achieves intelligence with different architecture.

B - Partial Convergence: Some brain aspects necessary, others substitutable.

C - Replication: Intelligence requires brain-like architecture.

2025 status: We don't know which is correct.

Why AI Seems Intelligent

The Anthropomorphism Challenge

1. We Judge By Output Deep Blue calculates 200M positions/second (brute force). Kasparov 1-3 positions/second (intuition). Deep Blue wins, but completely different process.

2. Anthropomorphic Tendency Heider-Simmel experiment (1944): animated triangles and circles → observers describe "chasing", "cooperating". We attribute intentions to geometric shapes.

3. Selection Bias We see: AI recognizes 99/100 cases, robot demos in controlled environments. We don't see: failures, real-world contexts where performance collapses.

4. Capability-Reliability Gap AI does X amazingly 60% of time. Humans do X 95% of time but less impressive. AI generates stunning image → viral. But generated 4 flawed ones first.

5. Complexity Generates Illusion LLM: billions of parameters, coherent outputs → illusion: "MUST understand". Reality: complexity necessary but not sufficient.

Crucial Difference: Robustness

Humans: Consistent performance, gradual degradation, limit recognition.

AI: Excellent performance in training distribution, rapid degradation out-of-distribution.

Example (2025):

• AI on ImageNet: 98% accuracy
• AI with adversarial perturbation: 5%
• AI on artistic images: 40%
• Human: ~95% stable across all

Conclusions

Summary of Current Differences (2025)

AI does not possess:

1. Consciousness - No evidence of subjective experience (unverifiable)
2. Robust understanding - Pattern matching with fragility (open debate)
3. Flexible learning - Requires much data, catastrophic forgetting
4. Original creativity - Pattern recombination, not innovation
5. Ethical judgment - Rules/patterns, not moral understanding

Why They Exist: Three Reasons

1. Theoretical Gap: We don't understand how matter produces experience.

2. Architectural Gap: Brain uses different principles (continuous plasticity, massive parallelism, embodiment).

3. Computational Gap: Need hardware 400× more powerful + complete theoretical understanding.

Three Future Scenarios

Scenario 1 - Divergence (Probability: Medium-High, 10-30 years) AI achieves human capabilities with different architecture. No consciousness. "Alien" intelligence competent but different.

Scenario 2 - Gradual Emergence (Probability: Medium-Low, 20-50 years) Increasing complexity → emergence of understanding/proto-consciousness. Gradual transition simulation → reality.

Scenario 3 - Biological Barrier (Probability: Low-Medium, Indefinite) Consciousness/intelligence require biological substrate. AI can simulate but never "be" intelligent.

What We Know (2025)

1. Current AI is not human intelligence - Similar outputs, different processes
2. AI is powerful tool - Excels in specific domains
3. Gaps persist - Flexibility, transfer, robustness, creativity
4. Uncertain future - Temporary or permanent gaps?

What We Don't Know

1. Nature of consciousness - Biological or functional property?
2. Complexity sufficiency - Does it guarantee intelligence?
3. Path to AGI - Similar or different architecture?
4. Timeline - From 10 years to "never"

Final Message

For AI enthusiasts: Don't confuse powerful tool with mind. AI is impressive for what it is (sophisticated statistical processing), no need to anthropomorphize it.

For AI skeptics: Current limitations are real. "Superintelligence" requires breakthroughs that might not arrive soon.

For truth seekers: Maintain epistemological humility. We don't know if artificial consciousness is possible, how to recognize it, which path will lead to general intelligence.

When you see robots or AI writing: You're seeing impressive performance from a process profoundly different from biological intelligence. It's a masterful illusion (seems like intelligence without necessarily being it).

But whether this illusion can become reality, and how, are questions that 2025 science cannot yet answer definitively.

Article Limitations

✓ Highlights current differences (often minimized) ✓ Presents scientific evidence (2024-2025) ✓ Acknowledges open debates ✓ Distinguishes current state (documented) vs future (speculative)

✗ Does NOT claim absolute impossibility of conscious AI ✗ Does NOT present debated positions as concluded facts ✗ Does NOT deny future breakthroughs ✗ Does NOT use definitive tone on open questions

Limitations: Rapid AI evolution, study selection, consciousness unverifiability, necessary simplification.

Objective: Counterbalance polarized narratives. Honest position: we don't know enough for absolute certainties. Follow evidence, recognize limits, maintain openness.

Essential Bibliography

1. Shapson-Coe et al. (2024) "A petavoxel fragment of human cerebral cortex" Science 384(6696)
2. Tononi et al. (2016) "Integrated Information Theory" Nat Rev Neurosci + 2023-2025 controversy
3. Melloni et al. (2025) "Adversarial testing of global neuronal workspace and IIT" Nature
4. "Better AI does not mean better models of biology" (2024) arXiv:2504.16940
5. Fedorenko et al. (2024) "Universality of representation in bio/artificial networks" bioRxiv
6. Song et al. (2024) "Learning Beyond Backpropagation" Nature Neuroscience
7. Beniaguev et al. (2021) "Single cortical neurons as deep artificial neural networks" Neuron
8. Chalmers (1995) "Facing Up to the Problem of Consciousness" J Consciousness Studies
9. Searle (1980) "Minds, Brains, and Programs" Behavioral Brain Sciences