AI vs AGI: 5 Brutal Truths No One Talks About
AI vs AGI – yet another AI article, I know. But hear me out. Between “AI is taking our jobs” and “AI is too naive to even do them,” the internet is bursting with hot takes. Especially for software developers, it feels like either a countdown to irrelevance or the dawn of a new productivity era. So what’s the truth? Let’s untangle it.
1. AI Isn’t One Monolith — GPTs, Claude, Deepseek All Do Different Things
We all know OpenAI’s ChatGPT — thanks to some pretty legendary PR. But there’s a whole playground of models out there: Claude, Gemini, Perplexity, Deepseek, LLama, and an army of lesser-known but surprisingly good Chinese models.
Each has its own specialty. Claude and Deepseek shine in code. ChatGPT? It’s got the charm of a cocktail party guest who knows a bit about everything. These aren’t general minds — they’re specialist parrots. Some are Large Language Models (LLMs), others have been fine-tuned into Large Reasoning Models (LRMs), which try to “think.” Try being the key word.
2. How AI Works (Without the Boring Math)
Think of AI like this: imagine you read every post on Reddit and every YouTube comment. You start predicting what people think the right answer is — and you get really good at it. That’s what models like ChatGPT do. They’re pattern-matchers, not thinkers.
Yes, it’s a monumental engineering feat. But ask them to do something obscure, like:
“Find all prime palindrome numbers greater than 5 million that differ by 1000 and map to an English sentence through alphabet substitution.”
…and the model will hallucinate or crash. (Frankly, I would too.)
3. Enter Reasoning Models: “Thinking” or Just More Tokens?
When reinforcement learning alone didn’t cut it, companies started bolting on reasoning. They said their models now “think.” Reality? They just inject intermediary steps (tokens) before answering, mimicking internal dialogue. It looks like reasoning, but is it?
While everyone was busy swooning (or cringing) over Apple’s shiny new Liquid Display at WWDC, the company also released a research paper – The Illusion of Thinking – that quietly sent shockwaves through the AI community. In their study, Apple researchers tested these models in puzzle environments like Tower of Hanoi. The goal? Test whether they could plan and adapt with increasing complexity.
The results were damning.
- Regular LLMs handled low-complexity tasks better (they skip unnecessary “thinking”).
- Reasoning models performed better in medium-complexity scenarios.
- But both models collapsed at high complexity.
- Worst part? Even with all the steps provided, they just… gave up.
They literally stopped trying. It’s as if the models got tired and mentally checked out.
4. AI vs AGI: We’re Not There Yet
So what does this mean for the AI vs AGI debate? Despite what hype videos and pitch decks claim, we are nowhere near Artificial General Intelligence. The models can’t plan deeply, generalize across tasks, or even consistently follow logical steps when the heat is on.
But that doesn’t mean they’re useless. They’re really good at:
- Filling out meeting notes.
- Generating summaries.
- Helping junior devs write better code.
- Optimizing repeated business flows.
You know… middle-management stuff. Entry-level code stuff. And yeah, vibe coding. AGI will come someday – but not today. And definitely not by the end of your current sprint cycle.
5. Why Are We Using AI for Songs and Videos Instead of Solving Problems?
Now here’s a hot take. Why are we using AI to paint, sing, and deepfake people instead of solving complex problems? Seriously, what’s the point of generating art in the likeness of real people? Fraud? Marketing? Cheap clicks?
Did anyone ask for this? Why are we making AI that impersonates humans rather than help them?
Remember the Singapore CFO deepfake scam? Millions were lost. And now Google is out here marketing AI-generated high-res videos as productivity tools. Really?
We keep saying AI is just a tool. Then let’s treat it like one. Remember the famous meme? I want AI to do my laundry so I can make art — not the other way around.
Final Thoughts: AI vs AGI in 2025 Isn’t a Fight — It’s a Mismatch
So no, I don’t think AI will take all our jobs yet. I don’t think AGI is around the corner. But I do believe we’re in a weird middle zone. A place where AI is overhyped for some things and underutilized for others.
It’s not evil. It’s not salvation. It’s just a tool – a clumsy, occasionally brilliant, often hallucinating assistant. But what happens when we give it a job it wasn’t designed for? We get deepfake songs, plagiarism at scale, and decision engines that can’t handle real complexity.
What do you think? Are we moving toward AGI? Or are we just getting better at pretending that we already have it? Comment below, let’s engage. And for more such takes on Tech click here.