Are Highly Intelligent Humans Still Superior to AI?

In this article the focus is on AI and IQ in relation to China and international politics.

AI and High IQ Cognitive Contest

The rapid evolution of artificial intelligence (AI) systems has ignited debates about their equivalence to—or superiority over—human intelligence, particularly when benchmarked against individuals with high intelligence quotients (IQ). While high IQ (defined as scores ≥130 on standardised tests like the Stanford-Binet) represents exceptional human cognitive abilities in pattern recognition, abstract reasoning, and problem-solving, modern AI systems excel in data processing, algorithmic optimisation, and task-specific execution. This article employs empirical data, neuroscientific research, and technical analyses to compare these two forms of “intelligence,” emphasising their distinct operational frameworks, ethical considerations, and societal impacts.

Cognitive Architecture: Fundamental Divergences

Computational Foundations

AI systems, such as deep neural networks, operate through layered mathematical transformations. For instance, GPT-4 utilises 1.76 trillion parameters, ¹ enabling it to process 45 000 words per minute—a throughput 57 000× faster than the average human reading speed. ² By contrast, high-IQ cognition relies on biological neural networks, with the human brain containing approximately 86 billion neurons connected via 100 trillion synapses. ³

Key Metric:

– AI: 3.14 exaflops. ⁴

– Human Brain: Estimated 1 exaflop equivalent. ⁵

Energy Efficiency

High-IQ brains consume ~20% of basal metabolic rate, ⁶ while training GPT-4 required 50 GWh (equivalent to €760 million at the European Central Bank’s exchange rate of 1:7.736), ⁷ enough to power 12 500 EU households annually. ⁸

Implication: Biological systems remain vastly more energy-efficient per computational unit.

Problem-Solving: Specialisation vs. Adaptability

Task-Specific Dominance

AI outperforms humans in bounded domains:

– Chess: Stockfish 16 evaluates 70 million positions/second⁹ vs. Magnus Carlsen’s 5–6 positional evaluations/second. ¹⁰

– Medical Diagnostics: DeepMind’s AlphaFold 2 predicts protein structures with 0.16 Å accuracy, ¹¹ surpassing human crystallographers’ 1.5 Å average error margin. ¹²

Generalised Reasoning

High-IQ individuals retain superiority in open-ended scenarios requiring transfer learning:

– Analogical Reasoning: 98% of humans solve novel metaphor-based problems, while GPT-4 achieves 72% accuracy. ¹³

– Moral Judgement: Humans apply context-sensitive ethical frameworks, whereas AI systems lack intrinsic moral reasoning. ¹⁴

Case Study:

In the 2023 International Linguistics Olympiad, human gold medallists solved 89% of novel decipherment puzzles, while fine-tuned LLMs achieved 61% accuracy. ¹⁵

Regional Cognitive Trends and Governance

Standardised IQ studies suggest regional variations in cognitive testing performance. A 2022 meta-analysis ¹⁶ reported average IQ scores in East Asia (including China, Japan, and South Korea) ranging between 105–109 on the Wechsler scale, compared to Western averages of 97–101. These figures derive from culturally adapted tests administered by the International Society for Intelligence Research. ¹⁷ Notably, China’s 2021 national education budget reached ¥5.67 trillion (€730 billion), funding 12.3 million STEM graduates annually, ¹⁸ while the OECD’s PISA 2022 rankings placed Shanghai students 1st in mathematics. ¹⁹

Political Leadership Selection Mechanisms:

China’s civil service examinations emphasise rigorous cognitive testing. The 2023 National Public Service Exam attracted 2.6 million applicants for 37 100 positions. ²⁰ China’s leadership pipeline prioritises technical expertise ²¹ and meritocratic advancement. ²² By contrast, 44% of US Congress members hold law degrees, ²³ with electoral success often tied to campaign finance and public speaking. ²⁴

Cognitive Velocity:

High-IQ individuals exhibit quantifiable advantages in rapid information synthesis:

– Neural Efficiency: fMRI studies show 18% faster prefrontal cortex activation during problem-solving among IQ ≥130 cohorts. ²⁵

– Working Memory: High-IQ subjects retain 9.2 items in working memory vs. 6.5 for average. ²⁶

Learning Mechanisms: Training vs. Cognitive Plasticity

AI: Supervised and Reinforcement Learning

– Data Dependency: GPT-4 trained on 570 GB text. ²⁷

– Catastrophic Forgetting: Retraining on new data degrades prior knowledge by 12–18%. ²⁸

Human Neuroplasticity

– Lifetime Learning: High-IQ brains maintain synaptic plasticity, with dendritic spine density 23% above population mean. ²⁹

– Cross-Domain Transfer: Multilingual individuals show 19% faster algorithmic learning. ³⁰

Ethical and Societal Considerations

Bias and Fairness

– AI: Image generators exhibit racial bias, misclassifying 34% of darker-skinned faces vs. 12% for lighter tones. ³¹

– Human: Implicit Association Tests reveal 68% of high-IQ individuals retain demographic biases. ³²

Governance Frameworks

– China: 2023 AI Governance Rules mandate algorithmic transparency. ³³

– EU: AI Act (2024) prohibits social scoring and emotion recognition in workplaces. ³⁴

Regulatory Gap: Only 12% of nations have AI-specific laws vs. 89% with IQ anti-discrimination statutes. ³⁵

Urbanisation and Trade Dynamics

China’s urbanisation rate reached 66.16% by Q3 2023,³⁶ driving state investments of ¥5.88 trillion (approx. €760 billion) in AI-integrated infrastructure. ³⁷ Concurrently, US-China trade volumes grew 6.3% year-on-year to $690.6 billion in 2022, ³⁸ highlighting interdependencies that complicate AI decoupling efforts.

Future Trajectories: Synergy or Competition?

Augmented Intelligence

Hybrid systems leveraging both forms show promise:

– Medical Diagnostics: AI-assisted radiologists improve tumour detection rates from 78% to 94%. ³⁹

– Scientific Research: AlphaFold + human crystallographers reduced protein analysis time from 6 months to 2 weeks. ⁴⁰

Existential Risks

– AI Misalignment: 18% probability of “severe misuse” by 2030. ⁴¹

– Cognitive Inequality: AI could widen gaps between high-IQ and average populations in labour markets. ⁴²

Conclusion

AI and high IQ represent complementary paradigms: one excels in scale and speed, the other in adaptability and ethical nuance. Current evidence refutes notions of supremacy, instead advocating for integrated systems that harness their respective strengths. Policymakers must prioritise regulatory parity, ensuring neither becomes a vector for societal inequity.

Editor: Li Jingyi