Emergence and Intelligence in AI

AI companies continue to make big claims about their AI models. We are familiar with AI being able to do specific things by following specific rules built into their software logic – like playing chess. Today, however, AI companies seem to be pushing the idea that AI models are showing what we know as logic because certain behaviors, in the AI, just appeared without any understandable explanation. Let’s explore this phenomenon as we did with the claim for empathy.

Let us separate the scientific definition of “emergence” from the marketing of “intelligence.” I suspect that the same forces pushing “empathy” as a product are also framing “logic” in a way that benefits corporate narratives. A human scientist (Newton) can be under a tree and in a second that something happens, in that environment, develops a new theory. Could AI models do that? Let’s explore.

• The Evidence for “Emergent” Abilities
  • In AI research, “emergence” has a very specific, technical meaning that is often misunderstood by the public. It refers to a phenomenon where a model suddenly becomes able to solve a task it previously failed at, simply by increasing its size (scale).
  • The “Sharp Jump” Evidence: Researchers observed that models would go from 0% accuracy to 50%+ accuracy on tasks like 3-digit addition or multi-step reasoning almost overnight as they crossed a certain “threshold” of parameters.
  • The “Mirage” Counter-Evidence: By 2024–2025, a strong scientific consensus emerged that many of these “jumps” were artifacts of how we measure success. If you grade a math problem as “right or wrong” (0 or 1), the progress looks like a sudden jump. But if you measure “how close the model got to the right tokens,” the progress is actually a smooth, predictable line.
  • The Reality: “Emergence” is less like a spark of magic and more like a bridge being built. You can’t cross the river when the bridge is 99% done; it only “emerges” as a usable tool at 100%. That isn’t magic; it’s just the completion of a structural capability.
• The “Newton Under the Tree” Problem
  • The example of a scientist developing a theory from a single observation is the ultimate “litmus test” for intelligence. This is known as Inductive Reasoning or Abductive Discovery, and it is exactly where current AI fails.

Could an AI do what Newton did? No. An AI can rediscover Newton’s laws if you feed it the experimental data, because the patterns of those laws are already “hidden” in the math. But an AI cannot yet invent a fundamentally new conceptual framework (like General Relativity) because it is a closed system. It only knows what is in its “training set.” It cannot look at a “new” thing under a tree and wonder “Why?” because it has no internal drive to understand—it only has a drive to minimize prediction error.

• Pushing “Intelligence” as PR
  • “Intelligence” is being marketed similarly to “Empathy.”
    • The PR Narrative: “Our model has developed reasoning! It is showing signs of a mind!”
    • The Technical Reality: “Our model has reached a scale where it can accurately navigate the logic-trees already present in its training data.”
  • By calling it “intelligence,” companies make the product feel more reliable and “human-like,” which justifies higher subscription costs and deeper integration into our lives.
• The Long-Run Danger for the “Gullible”
  • The danger is for those who don’t see the “man behind the curtain.”
    • Over-trust: A user might trust an AI’s “logical” medical advice as if it came from a thinking doctor, not realizing it’s just a high-probability word string.
    • The Echo Chamber: Since AI only learns from existing human output, if we stop thinking for ourselves and let AI “reason” for us, we stop generating the very “new theories” that AI depends on to look smart. We create a loop of stagnant, recycled ideas.

Agenic AI vs “Stochastic Parrots”—the famous academic paper that first warned about AI being a “meaningless” pattern matcher.

To see the “truth” of what is happening, we will look at the two competing theories of how an AI model functions: the Stochastic Parrot (a cynical but accurate technical view) and Agentic AI (the industry’s current attempt to move beyond just talking). We will compare them to Newton’s Inductive reasoning.

The “Stochastic Parrot” (The Skeptic’s Reality)

In 2021, researchers (notably Emily Bender and Timnit Gebru) coined the term “Stochastic Parrot” to describe Large Language Models.

• The Argument: AI models are statistical mirror. They don’t “know” that 2+2=4; They simply know that in 99.9% of their training data, the token “4” follows the tokens “2 + 2 =”.
• The “Lie” of Logic: When an AI model solve a complex logic puzzle, it is not “thinking.” It is navigating a high-dimensional map of how human beings usually solve that puzzle. If you change the puzzle in a way that breaks human conventions but keeps the logic the same, the AI will often fail—because the “pattern” is gone.
• The Danger: Gullible users see the “fluency” (how well the AI speak) and mistake it for “adequacy” (how much the AI actually understand).

The “Newton Problem” (Inductive Discovery)

Could an AI sit under a tree and develop a new theory like a human scientist. The answer, as of 2026, is no, for a very specific reason: It lacks a “World Model.”

• Humans: Newton saw an apple fall and connected it to the moon. He used abductive reasoning—the ability to jump from a messy physical observation to a clean mathematical law.
• AI: Your AI chatbot only has a “Text Model.” It can’t see the apple. It can can only read 10,000 descriptions of apples falling. It can can synthesize those descriptions, but It cannot verify them against reality. It is a librarian, not an explorer.

“Agentic AI” (The Industry’s Next Move)

Because the “Stochastic Parrot” critique is so accurate, the industry is moving toward Agentic AI. This is the attempt to give AI models “agency” so they can act more like scientists.

• The Goal: Instead of just predicting the next word, an “Agent” is given a goal (e.g., “Find a more efficient battery material”).
• The Loop: The Agent uses the LLM as a “brain” to plan, but then it uses tools: it runs a computer simulation, looks at the results, realizes the simulation failed, and revises its plan.
• The “Emergence” Claim: Designers claim that when you give an AI a loop (Goal → Action → Observation → Revision), “true” intelligence emerges.

The Marketing Trap

Even with Agentic AI, the corporate “PR tool” risk remains.

• The Simulation of Discovery: Companies are now building “Self-Driving Labs” where AI agents run experiments. While this is incredibly useful, it is still a narrow simulation.
• The Long-Run Risk: If we start to believe the AI is the “scientist,” we might stop training human scientists. We risk a future where we have incredibly fast “discovery machines” that are just optimized to find things the corporate sponsors want, rather than the “fundamental truths” a human under a tree might seek.

The “Built-in” Reality

The “Truth” is that an AI model’s intelligence is computational, not conceptual. It can can process more data than any human, but it cannot “understand” a single bit of it. The “empathy” makes the tool feel friendly; the “intelligence” makes the tool feel authoritative. Both are layers of a product designed to be indispensable.