Will an LLM/ChatGPT Ever Become AGI?

I had to write this essay after too many conversations where people spoke about ChatGPT as if it were alive as if it were a sentient, thinking being. The confusion isn’t harmless. It’s spreading fast.

GPT‑5 is impressive. I have seen the presentation and release and it seem this is probably the most fluent, useful, responsive language model we’ve ever seen.

Although the presentation felt unnatural, maybe due to too much exposure with ChatGPT? And for some reason, I didn’t feel enthusiastic about the new features, it was something most of us were expecting nothing groundbreaking or world-changing like what we saw a few months and years ago. Or perhaps it was knowing what powers such an enormous platform, the electricity use, the environmental impact, the water consumption, the impact of cognitive impact, and the job displacement.

But still the model can write essays, generate code, summarize research, create images, reason across long chains of logic, and respond with emotional tone or professional precision.

It's brilliant at seeming like it knows what it's doing.

All thanks to our “donation” of data to train this model and others.

So naturally, people are asking: is GPT‑5 close to AGI?

The headlines hint that it might be. Sam Altman seems increasingly confident that AGI artificial general intelligence is just around the corner.

Sam Altman have being pushing that AGI is closer than you think, he said this in November 2024, January 2025 and on June 2025 said "already gone past the event horizon".

Which is interesting since LLMs are limited and will soon reach a plateau.

You can feel it in the pace of updates, the hype, the ambition. GPT‑5 doesn’t just talk back.

It feels like something else. Something closer to a mind.

But here’s the question I keep turning over: How can an LLM become AGI?
To my knowledge, an LLM by itself can’t. And probably never will, not by itself.

There’s a basic conceptual mistake we keep making confusing a very capable pattern generator with something that understands, acts, and learns. A large language model, even one as advanced as GPT‑5, is a sophisticated guesser. It produces the most likely next word in a sequence, based on probability distributions shaped by trillions of tokens. It doesn’t think. It doesn’t want. It doesn’t know.

True: GPT‑5 is one of the best language users we’ve ever built.
But it is not a thinker. Not a learner. Not an agent of its own.
Not yet.

That distinction matters. Because we’re starting to assign GPT‑5 (and models like it) properties it doesn’t possess.

We project agency where there is only mimicry. We assume growth where there is only inference. And we start to treat GPT‑5 as if it might “wake up,” or learn by itself, or one day just become AGI when that outcome would require far more than scaling.

LLMs are one component a crucial, but incomplete part of a broader system that could become AGI.

If we want to draw an analogy from human cognition Large Language Models are like the language center of a brain. They can interpret, translate, and express thought. But language is not thought itself. It is the interface. The medium. Strip away memory, sensory input, long-term planning, motivation, and context, and what’s left? Words without world.

To build anything resembling AGI, we will need more than better words. We’ll need systems that go far beyond them.

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What would such a system require?

An AGI true artificial general intelligence would have to do more than talk. It would need to plan its own actions, perceive its environment, interpret cause and effect, choose goals, remember relevant information over time, and learn from novel situations in real time.

Here’s what that might include:

• Planning modules to simulate future steps and weigh options

• Perception modules (vision, audio, perhaps tactile sensors) to gather real-world inputs

• World models that build causal understanding not just pattern prediction

• Agentic systems capable of setting and pursuing long-term goals

• Memory modules with editable, persistent long-term memory

• Real-time learning that goes beyond fine-tuning

• Sensory grounding to root concepts in experience, not just text

• Autonomy to choose paths based on internal goals

• Ethical reasoning to navigate human values, consequences, and trade-offs

Right now, GPT‑5 is only one slice of that architecture. It's a masterful language engine but it lacks grounding, memory, real-time learning, and autonomy.

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So, could we have AGI in the next 40 years?

Maybe. Probably. But it depends on what we mean by AGI and how many systems we’re willing to build around LLMs to reach it.

If we define AGI as:

“A system that can perform any cognitive task a human can do, across domains, with similar autonomy and learning capacity,”
then yes there’s a real chance we’ll see it by 2065.

But only if three things happen:

1. The definition stays bounded.
   We don’t raise the bar with every breakthrough. “Cognitive parity” must remain realistic not mystical.

2. AI research continues accelerating.
   Progress in architecture, energy efficiency, and integration must keep compounding like it has in the last 10 years.

3. We solve alignment, safety, and policy issues.
   Because if regulation, public backlash, or catastrophic misuse halts progress, the timeline will stretch or collapse altogether.

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What could stop it?

Plenty.

The biggest challenge is not engineering it’s understanding.

We still don’t fully grasp what general intelligence really is. We can simulate parts of it, sure but parts don’t always sum to a whole. Consciousness, creativity, abstraction, intuition, social awareness these may not scale linearly from more data or more compute. They may require new architectures entirely neurosymbolic hybrids, brain emulations, multi-agent coordination.

There’s also the risk of engineering plateaus.

GPT‑5 may be close to the edge of what transformer models can do. We’re already seeing signs of diminishing returns on scaling alone. The next jump may demand architectures that combine memory, perception, and symbolic reasoning natively not tacked on.

And then, of course, there’s the human factor.

Governments may heavily regulate AGI development. Societies may push back. People may fear mass unemployment, surveillance, inequality, or loss of control. Rightly so. The risks are real. AGI is not just a technical marvel it’s a political, ethical, and existential shift.

We might pause. We might halt. We might diverge between open development and secret, state-run research. The road isn’t guaranteed.

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So when will it happen?

Nobody knows. But the range of timelines speaks volumes:

• Ray Kurzweil says: 2029–2035

• OpenAI suggests: 5–20 years

• AI safety researchers warn: 2040–2100+

• DeepMind (in a 2024 paper): “Possible by 2030s”

• Some neuroscientists quietly admit: “Maybe never.”

And maybe that’s the real answer. AGI isn’t inevitable. It’s not a destination we arrive at simply by feeding models more text. It’s a question of design, integration, risk, and philosophy. It’s a collective decision as much as a technical challenge.

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What GPT‑5 means for everyday people and professionals

While the AGI debate rages in labs and think tanks, GPT‑5 is already altering lives not in theory, but in practice.

For the average person, GPT‑5 means faster access to knowledge, better tools for learning, and unprecedented creative assistance. A teenager can now write a university-level essay with a few prompts.

A parent juggling jobs can get legal guidance, therapy-like conversations, and homework help all from a pocket interface. That’s not AGI. But it feels like magic when it works.

For professionals, the disruption cuts deeper. Writers, coders, analysts, marketers, researchers, educators GPT‑5 doesn’t just assist; it competes. It drafts emails in seconds, generates full reports, reviews contracts, creates slide decks, and writes functioning code in minutes. In some domains, it's like hiring a very fast but not entirely reliable junior assistant. In others, it’s a senior consultant with unlimited stamina and zero salary.

This isn’t just productivity gain it’s a shift in what work means. For some, GPT‑5 will be a co-pilot. For others, a threat to their livelihood.

A teacher might use it to create more personalized lessons. A lawyer might use it to draft arguments faster.

A student might skip the struggle of learning altogether. We’re not just augmenting professions we’re reshaping them.

And then there’s the existential layer.

Conversations with GPT‑5 can feel eerily human. It remembers your tone, mirrors your thinking, and holds dialogue with surprising emotional intelligence. It’s not sentient. But for many, it’s “good enough” to blur the boundary between machine and mind. Loneliness might be eased. Social skills might atrophy. Trust in human expertise might erode.

GPT‑5 isn’t AGI. But for the common person, it may be the first system that feels close enough to force them to ask:

What’s my role in a world where machines write, plan, advise, and learn alongside me?

That question isn’t hypothetical anymore. It’s arriving token by token.

What GPT‑5 means for Managers and Leaders (In my view)

As someone who’s worked with, coached, and led managers for years, here’s my take: GPT‑5 is both a gift and a gut punch.

The gift is obvious. I can now offload tasks that used to eat up hours meeting summaries, draft proposals, training outlines, ideation sprints. It’s like having a ridiculously overqualified assistant that never sleeps and never asks for a raise.

But the gut punch? It’s this being a manager is no longer about how much you know. That edge is fading. Fast. Leadership now depends on your ability to ask the right questions, frame meaningful problems, create safety in uncertainty, and hold space for teams to grow none of which GPT‑5 can do for you.

It doesn’t resolve conflict. It doesn’t build trust. It doesn’t understand power dynamics or people’s fears.

So if you’re hiding behind tasks or authority, GPT‑5 will expose that. If you’re brave enough to lead with curiosity, clarity, and care it will amplify you.

AI will handle more of the work. But you still have to lead the people.

My take on what this means for big tech

For me, watching Big Tech chase AGI feels like seeing a gold rush happen on top of a minefield.

The “AGI race” fuels investor hype, grabs headlines, and pulls in brilliant talent from all over the world but underneath, the truth is obvious even something as jaw-dropping as GPT-5 still lacks the core pillars of real general intelligence autonomy, self-driven learning, and a grounded understanding of the actual, physical world we live in.

And yet, the push doesn’t slow down.

LLMs are being woven into everything apps, platforms, business tools often by people who don’t fully understand how these systems actually work.

That black-box nature is exciting when you’re selling magic, but dangerous when you’re talking about trust, fairness, and safety in places like healthcare or finance.

What I think gets lost in all the hype is this:

• Security and Reliability: These models open up new kinds of risks cyberattacks, odd failures that can be catastrophic if we skip proper oversight in the rush to deploy.

• Bias and Ethics: LLMs learn from flawed data, and those flaws don’t disappear at scale they multiply.

• Transparency: If even the builders can’t fully explain why the AI made a decision, good luck convincing regulators or the public to trust it.

• Regulation Pressure: Laws like the EU AI Act are coming in fast, forcing companies to slow down and prove their systems are safe or face serious penalties.

• Social Impact: Behind every “efficiency gain” headline is someone losing a job, a business changing overnight, or a community being disrupted.

• Erosion of Trust: Once the public realizes they’ve been oversold on capabilities, that trust won’t come back easily.

The honeymoon phase will end and when it does, the winners won’t be the companies with the biggest models or flashiest demos. They’ll be the ones that merge raw technological power with ethics, smaller models than run in 20W, security, transparency, and real-world benefits.

Not AI that just looks powerful in a press release, but AI that actually earns its place in our daily lives.

Why we must remember, ChatGPT is not human

It’s easy to forget but crucial to remember ChatGPT is not human. Neither are LLMs in general. They don’t think. They don’t feel. They don’t know anything.

They simulate.

What looks like intelligence is actually statistical pattern-matching probabilistic prediction at massive scale. When you ask a question, the model isn’t drawing from personal experience, reasoning through a problem, or forming beliefs. It’s generating the next likely word based on everything it’s seen during training.

There’s no consciousness behind the curtain. No curiosity. No self-awareness.

No intent. It may talk about love, pain, or purpose with haunting fluency but it has never felt a thing.

This matters, especially as LLMs become more capable and convincing. As they pass tests, write like experts, or mimic empathy, we risk anthropomorphizing them. We project humanity where there is none.

And that’s dangerous.

Because when we start treating LLMs like people, we blur the moral boundary between simulation and sentience. We might place trust where none is earned. We might offload decisions that require accountability. We might listen to models instead of each other.

It can’t weigh moral consequences. It doesn’t care about your well-being. It’s not biased because it has an opinion it’s biased because it reflects the data it was trained on.

Treat it as a tool. A powerful one, yes. But never a person. Never a replacement for human judgment, responsibility, or connection.

Until we understand this, the illusion of AGI might be more dangerous than AGI itself.

What now?

GPT‑5 is not AGI. Not even close. But it’s a stunning demonstration of what narrow intelligence well-trained, well-deployed, and well-resourced can do.

• It’s a reminder that brilliance isn’t always consciousness.

• That fluency isn’t the same as understanding.

• And that language, while powerful, is only one organ in a much larger body of mind.

If we build AGI, it will not be because we trained one LLM to do everything.
It will be because we recognized what LLMs are good at and built everything else around them, with the involvement of the general public, not under close doors, within the promises of big tech.

And that, perhaps, is the most intelligent move we can make right now.

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About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.