Do AI and LLMs Really Think? The Truth About Human-Like Reasoning in Artificial Intelligence

Executive takeaways

Today’s frontier models can “think” in several narrow, functional senses (they deliberate, search over alternatives, and produce multi‑step arguments that solve hard problems). The most public proof is that experimental “reasoning” models from DeepMind and OpenAI recently reached gold‑medal performance at the 2025 International Mathematical Olympiad—a feat that requires long, creative proofs rather than rote recall.

But they still lack key ingredients of human thinking: robust causal understanding, reliable long‑horizon planning and self‑monitoring, grounded semantics tied to perception and action, and anything like conscious experience. On many of these, we have systematic evidence of limits.

Will LLMs ever achieve human‑like thinking? There’s no proof it’s impossible. Evidence suggests further progress will come from LLM+ systems—models with explicit deliberation, verification, tools, external memory, and world models—rather than from “just scale the next‑token predictor.” Even prominent critics agree today’s LLMs aren’t yet human‑like, but differ on whether new architectures (e.g., world‑model agents) are the path forward.

What counts as “real thinking”? (A workable, testable definition)

Instead of the overloaded yes/no of “thinks like a human,” it helps to split thinking into functional capabilities we can measure:

1. Deliberative reasoning: considering alternatives, exploring and backtracking, verifying steps. Methods such as chain‑of‑thought, self‑consistency, tree/forest‑of‑thought, and test‑time compute scaling explicitly add this kind of thinking.

2. Causal reasoning: building and using models of cause→effect rather than correlations.

3. Planning: composing long, executable sequences of actions under constraints.

4. Grounded semantics: tying symbols to perception and action (the classic symbol grounding problem).

5. Meta‑cognition: knowing what you know/don’t know; faithful, inspectable reasoning traces.

6. Conscious experience (phenomenology): not strictly required for functional thinking, and currently no evidence in AI; still an active philosophical/scientific debate.

What today’s models can do that looks like thinking

Sustained mathematical reasoning & proof writing. 2025 systems from DeepMind and OpenAI hit gold‑medal IMO scores (5/6 problems under competition conditions), indicating genuine multi‑step, creative reasoning—not database lookup.

Deliberation helps—if you let models “think longer.” OpenAI’s o‑series was trained to deliberate internally; test‑time strategies like self‑consistency and tree‑of‑thought boost accuracy by sampling and evaluating many candidate chains before answering.

Test‑time compute can beat more parameters. Systematic studies show that allocating more inference‑time search/verification often improves reasoning more than simply scaling model size.

Emerging evidence of human‑like internal structure. Recent papers argue that, mechanistically, larger LLMs’ internals align better with human behavioral patterns than once thought.

Tool‑use and acting. Frameworks like ReAct interleave reasoning with actions (search, tools, browsing), which is crucial for real‑world tasks.

What they still get wrong (and why this matters)

Causal reasoning remains shallow. Comprehensive benchmarks (e.g., CaLM, CausalProbe‑2024) show sharp performance drops on higher‑level causal tasks; many models operate at “level‑1” causal inference.

Planning over long horizons is brittle. On realistic benchmarks like TravelPlanner, even strong models frequently produce plans that violate constraints; follow‑up work now wraps LLMs with formal planners to recover reliability.

Reasoning traces are not always faithful. Chain‑of‑thought explanations can misrepresent the real basis of the model’s answer—useful for accuracy, but unreliable as “windows into the mind.”

Hallucinations persist in high‑stakes domains. Studies in law and general detection methods show both progress and non‑trivial error rates (e.g., “1 in 6” hallucinations in legal queries; new detectors improve calibration but don’t eliminate the problem).

Symbol grounding is unresolved. Classic arguments (Harnad; Searle’s Chinese Room) emphasize that symbol manipulation ≠ understanding; multimodal models help, but embodiment/world‑modeling is a live research frontier

Benchmark gaps for “fluid intelligence.” Even as many NLP benchmarks saturate, newer ARC‑AGI‑2 tasks still expose reasoning weaknesses that humans find easy.

Where the field is heading (and what likely bridges the gap)

Reasoning‑first training + verification. Reinforcement learning on process steps (e.g., “Let’s Verify Step by Step”) and trained verifiers reduce unfaithful steps and improve reliability. Expect more models that plan, check, and revise before responding.

LLM‑plus architectures. Strongest systems combine LLMs with tools, external memory, formal solvers, and self‑critique—a pragmatic route to more general, reliable thinking.

World models & grounding. DeepMind’s new Genie 3 suggests a viable path to learned, interactive “world simulators”—potentially anchoring symbols to perception/action and helping with long‑horizon planning.

Test‑time compute scaling. Expect continued gains from allocating more “thinking time,” often beating parameter scaling in cost‑effectiveness for hard problems.

So… do current AIs really think like humans?

Functionally (in parts): yes. On specific tasks, today’s models demonstrably deliberate, search, prove, and correct themselves—hallmarks of instrumental thinking. The 2025 IMO results are hard to square with “mere mimicry.”

Globally (as humans do): not yet. They’re still brittle on causal understanding, planning, grounding, self‑monitoring, and out‑of‑distribution generalization, and they lack any credible evidence of consciousness.

Is it impossible for GPT‑style LLMs to reach human‑like thinking?

No known impossibility result. Skeptics (e.g., LeCun) argue that current LLMs won’t get there without new components (world models, active perception). That is a claim about engineering sufficiency, not a theorem. And recent “reasoning‑series” models (o1 → o3) already pushed beyond what many thought feasible two years ago.

Most credible path: LLM+ systems that combine (a) deliberate internal search, (b) explicit verification, (c) tools and formal planning, and (d) grounded world models. If these converge, we may approximate much of what we call human thinking—whether or not consciousness is involved.

Quick scoreboard

• Deliberative reasoning: strong and improving fast.

• Mathematical creativity: now at elite human level on some benchmarks (e.g., IMO), but not robustly across all domains.

• Causal reasoning: inconsistent beyond simple interventions; active research area.

• Long‑horizon planning: unreliable without external planners or strict checks.

• Grounded understanding: partial via multimodality; full grounding still open.

• Consciousness: no evidence; serious experts mostly say “not yet / unknown.”

Bottom Line

• Today’s AI “thinks” in several useful, testable ways—enough to solve Olympiad‑level math via deliberate, multi‑step reasoning.

• It does not yet match the breadth, robustness, groundedness, or self‑reflective qualities of human thought, and there’s no evidence of consciousness.

• The most plausible route to “human‑like” thinking is not pure GPT scaling but LLM‑plus systems that integrate deliberate reasoning, verification, tools, memory, and world models. There’s no principled barrier saying GPT‑style models (evolved in this direction) can’t get there—only major engineering and scientific work ahead.