PhenomenalDev

Since the release of ChatGPT back in November 2022, a new question suddenly surfaced , one that honestly never had much weight before: will these models ever become conscious? And if that happens, are we unintentionally building another kind of super-intelligent entity?

Before jumping to conclusions, it’s important to understand how these models actually behave as they scale. Research shows something surprising: when language models pass certain critical size thresholds, they don’t just improve gradually they unlock entirely new capabilities. Certain skills only appear after the model becomes large enough.

Studies have shown that tasks like arithmetic, multi-step reasoning, logical inference, translation, and even code generation only emerge once the model hits a specific scale. Smaller models basically guess or fail; larger models suddenly succeed. It’s like a switch flips somewhere inside the system.

This has been observed in major models. PaLM with 540B parameters demonstrates abilities that simply do not exist in smaller versions. Some 2025 papers frame modern LLMs as complex systems—millions of simple components interacting until higher-level abilities appear. The idea is simple: more is different. When you cross certain thresholds, you get behaviors that weren’t predictable from the smaller systems.

But not everyone agrees. Some argue that “emergent abilities” may just be evaluation artifacts ,the illusion of sudden capability jumps caused by how benchmarks are designed. Maybe the models aren’t “reasoning” at all. Maybe they’re just doing better pattern matching, memorizing more, or producing more convincing approximations of chain-of-thought.

And even with their impressive performance, today’s LLMs still lack key ingredients of consciousness:

self-awareness,
stable subjective experience,
memory tied to identity,
grounded understanding of the physical world,
real sensory input,
goals or intentions.

Nothing in the emergence research claims otherwise it’s about statistical abilities, not inner minds.

Experts like Yann LeCun argue scaling alone won’t solve the hard problems of intelligence: grounding, real-world ambiguity, common-sense reasoning, continuous learning, or understanding cause and effect. These require something far beyond just more parameters.

There’s also a risk factor. As models grow, unpredictability grows too. “Dual-use behaviors” appear—hallucinations, faulty logic, misleading content. Bigger doesn’t automatically mean safer, or even smarter. It just means more capable, and capability can go in any direction.

So what does this mean for the big question can AI ever become conscious?

If you take scaling seriously, the optimistic view says: pushing size and complexity might eventually create rich internal representations that start looking like reasoning or agency. Maybe more scale + new architectures = a step toward AI minds.

But the grounded view says consciousness isn’t just “a bigger model.” It might require qualities that are completely absent from text-prediction systems. Awareness, experience, and intentionality aren’t guaranteed just because you increase compute.

Personally, I think scaling is a powerful engine. It keeps surprising even the people building these models. But consciousness? That’s a different league altogether. Until AI gains grounding, interaction, long-term memory, goals, and real-world understanding, talking about consciousness feels too early.

AI might reach extraordinary levelsbut true consciousness? That still needs breakthroughs far beyond scaling.

References (Main Papers & Discussions)

Wei et al., Emergent Abilities of Large Language Models (2022)
Google AI, PaLM: Scaling Language Modeling with Pathways (2022)
Meng et al., Emergent Behavior in Large Language Models (Emergent Mind)
Large Language Models and Emergence: A Complex Systems Perspective (2025)
Yu Meng, Scaling Laws and Emergent Behavior in LLMs
“Are Emergent Abilities of Large Language Models a Mirage?” — critical evaluation
Yann LeCun interviews & talks on limitations of scaling-only approaches (2024–2025)
Discussions on unpredictability and dual-use risks in large LLMs (various 2023–2025 analyses)