Can AI Match Elon Musk? Estimating IQ of LLMs vs Humans

The Question That Won't Go Away

Every few months, a headline goes viral: "AI passes IQ test with score of 155." The implication is clear — artificial intelligence has surpassed human genius, the Elon Musks of the world are obsolete. But is any of this true?

The short answer: no. The longer answer requires understanding what IQ tests measure, how large language models (LLMs) process information, and why comparing human and machine intelligence is like comparing a submarine to a swimmer.

This article examines the actual evidence — peer-reviewed studies of LLM cognitive performance, the structure of IQ tests, and what we know about Elon Musk's cognitive profile — to give you an honest answer.

What IQ Tests Actually Measure

IQ tests are not single measures of "smartness." They assess a hierarchy of cognitive abilities, typically organized into the Cattell-Horn-Carroll (CHC) model:

• Fluid reasoning (Gf): Novel problem-solving without prior knowledge
• Crystallized intelligence (Gc): Acquired knowledge and vocabulary
• Quantitative reasoning (Gq): Numerical pattern recognition
• Visual processing (Gv): Spatial and geometric reasoning
• Working memory (Gwm): Holding and manipulating information in mind
• Processing speed (Gs): Rapid cognitive throughput

A full-scale IQ score aggregates these, but the profile matters more than the number. Two people with IQ 130 can have radically different ability profiles — one might excel at verbal reasoning but struggle with spatial tasks, while the other shows the reverse.

How LLMs Were Tested

Several studies have administered human IQ tests to large language models. The most rigorous include:

Bubeck et al. (2023) tested GPT-4 on the WAIS-IV (Wechsler Adult Intelligence Scale), finding that it performed at or above the 99th percentile on verbal subtests — Vocabulary, Similarities, Information — but struggled with spatial reasoning tasks that require visual processing. The study estimated GPT-4's "verbal IQ" at approximately 155.

Zhu et al. (2023) administered Raven's Progressive Matrices (non-verbal fluid reasoning) to multiple LLMs. GPT-4 scored in the 75th-90th percentile range — respectable, but far from genius-level. The authors noted that LLMs often solve matrix problems through pattern matching in training data rather than genuine fluid reasoning.

OpenAI's own evaluations (2023-2024) show GPT-4 scoring 163 on the Verbal section of the SAT (99th percentile) but only 710/800 on the Math section — strong, but not exceptional by elite-university standards.

The Verbal Illusion: Why AI Scores Look Inflated

Here's the core problem: IQ tests are heavily verbal, and LLMs are trained on text.

The WAIS-IV has 10 core subtests. Five are primarily verbal: Vocabulary, Similarities, Information, Comprehension, Arithmetic. For these, an LLM has a massive advantage — it has ingested essentially all published text in its training data. When asked "What is the similarity between a clock and a calendar?" the model doesn't reason — it retrieves the answer from its statistical model of language.

This is crystallized intelligence (Gc), not fluid reasoning. And it's where LLMs look most impressive. But crystallized intelligence is the least predictive of real-world problem-solving and innovation. It measures what you know, not how you think.

Where LLMs Fail: The Musk Gap

Elon Musk's cognitive profile — based on his SAT scores and career patterns — is asymmetric: very high fluid reasoning, exceptional spatial ability, strong but not elite verbal skills. This is exactly the profile where LLMs perform worst.

Spatial reasoning (Gv): LLMs cannot natively process visual-spatial information. When asked to mentally rotate 3D objects or visualize rocket trajectories, they fail or rely on text-based workarounds. Musk's ability to visualize rocket components and their interactions in his head — a key factor at SpaceX — has no LLM equivalent.

Working memory manipulation (Gwm): While LLMs have large context windows, they don't manipulate information in working memory the way humans do. They pattern-match, they don't hold a mental model and transform it. Musk's ability to hold multiple engineering tradeoffs in mind simultaneously and update them in real-time is a working memory function that current AI cannot replicate.

First-principles reasoning: Musk's signature cognitive strategy — breaking problems down to fundamental physics and building up — requires genuine fluid reasoning, not retrieval. When LLMs attempt first-principles reasoning, they often produce plausible-sounding but logically broken chains because they're interpolating between training examples rather than reasoning from axioms.

Transfer learning across domains: Musk applies the same cognitive toolkit to rockets, cars, brain interfaces, and social media. LLMs can generate text about all these domains, but they cannot transfer a solution strategy from one domain to another the way a human expert does.

The Benchmark Problem

There's a deeper methodological issue: IQ tests were designed for humans, not machines.

When a human takes Raven's Progressive Matrices, they use a combination of working memory, visual processing, and fluid reasoning. When an LLM takes the same test (converted to text), it uses statistical pattern matching against training data. These are fundamentally different cognitive processes that happen to produce similar output on a specific test.

This is the Goodhart's Law problem: when a measure becomes a target, it ceases to be a good measure. If we optimize AI to score well on IQ tests, we're not making AI smarter — we're making it better at IQ tests.

A more honest comparison would use tests designed to distinguish human cognition from statistical pattern matching:

• Novel physics problems that can't be in training data
• Multi-step spatial reasoning requiring mental simulation
• Adversarial questions designed to expose pattern-matching shortcuts
• Real-time decision making under novel constraints

On these measures, current LLMs perform well below human expert level.

What the Numbers Actually Say

Let's be concrete. If we compare Musk's estimated cognitive profile (from SAT conversion) with GPT-4's measured performance:

The picture is clear: LLMs dominate crystallized intelligence but lag significantly on fluid reasoning, spatial ability, and working memory manipulation — the exact abilities that drive engineering innovation and entrepreneurial problem-solving.

Can AI Replace the Elon Musk Function?

The "Elon Musk function" — as a cognitive role, not a person — involves:

1. Identifying novel problems that don't yet have solutions
2. Reasoning from first principles across multiple technical domains
3. Making high-stakes decisions with incomplete information
4. Integrating spatial, quantitative, and verbal reasoning in real-time
5. Sustaining effort and focus over years-long projects

Current AI can assist with each of these but cannot perform the integration. GPT-4 can help write code for a rocket simulation, but it cannot decide whether to build a reusable rocket. It can analyze battery chemistry data, but it cannot visualize the thermal behavior of a new cell design.

The gap isn't just about IQ scores — it's about the type of intelligence. Musk's value comes from fluid reasoning applied to novel problems, not from knowing facts. LLMs are the opposite: vast factual knowledge with limited novel problem-solving.

The Future: Convergence or Divergence?

AI is improving rapidly, but the improvements are uneven. LLMs are getting better at verbal tasks (where they're already superhuman) but progress on spatial reasoning and genuine fluid reasoning is slower. The most promising approaches — multimodal models, neuro-symbolic architectures, and embodied AI — attempt to close the spatial and reasoning gaps, but they remain far from human expert level.

The honest prediction: AI will increasingly augment human cognition in the next 5-10 years, but the "Musk function" — the creative integration of multiple reasoning types applied to novel engineering challenges — will remain a human capability for the foreseeable future. Not because humans are inherently superior, but because the type of intelligence required is exactly the type that current AI architectures struggle with.

Verdict

• IQ test scores overstate AI intelligence because IQ tests are heavily verbal and LLMs are trained on text
• LLMs excel at crystallized intelligence (knowing things) but lag on fluid reasoning (solving novel problems)
• Musk's cognitive profile — high fluid reasoning, exceptional spatial ability — is exactly where AI is weakest
• The "AI IQ 155" headline is misleading: it measures a different kind of intelligence than what drives real-world innovation
• AI augments but cannot replace the creative integration of reasoning types that defines expert engineering cognition

The question isn't whether AI will reach IQ 155. It probably already has — on the subtests that don't matter for innovation. The real question is whether AI will develop the fluid, spatial, and integrative reasoning that makes someone capable of looking at a problem nobody has solved and figuring it out. That's the Musk test. And so far, AI fails it.