Artificial General Intelligence (AGI) has been the ultimate goal of AI research for decades. The idea of creating a machine that can think and learn like a human has always fascinated not just researchers, but pretty much everyone who’s ever thought about the future of technology. Lately, Sam Altman from OpenAI has been talking about how we need to get serious about AGI, suggesting we might build one massive model that can handle anything we throw at it.
But there’s another way to look at this. Some smart people think AGI might actually emerge from a bunch of smaller, specialized models working together. Let’s dig into both of these approaches and see what makes sense, looking at the real trade-offs in terms of training time, GPU costs, and what’s actually doable.
Approach 1: One Giant Model to Rule Them All
Building one massive model to achieve AGI sounds pretty cool, right? But to make this work, we’d need to figure out some serious technical challenges:
Training data:
We’re talking about enormous datasets that cover basically everything humans know and do.
Model architecture:
We’d need incredibly sophisticated neural networks that can handle the crazy complex relationships between different types of information.
Computing power:
This would require insane amounts of GPU power, TPUs, or probably custom hardware we haven’t even invented yet.
Sure, having one model might sound simpler, but it comes with some real headaches:
Overfitting:
The model could get way too comfortable with its training data and struggle with anything new.
Cognitive overload:
Imagine trying to be an expert in everything at once - that’s basically what we’d be asking this model to do. It’s probably not going to work well.
Approach 2: Team of Specialists
The other approach is like building a team of experts instead of trying to create one know-it-all. Each model gets really good at one specific thing - whether that’s understanding language, recognizing images, or playing games.
Niche expertise:
Models can focus on what they do best. One for language, another for images, another for logical reasoning - you get the idea.
Transfer learning:
What one model learns can help others learn faster, which is way more efficient than starting from scratch every time.
This approach has some clear perks:
Modularity:
Each model is smaller and easier to train, so you don’t need a supercomputer to run them.
Flexibility:
You can mix and match these specialist models depending on what problem you’re trying to solve.
Robustness:
When you have multiple experts working together, the whole system becomes more reliable.
But it’s not all smooth sailing:
Coordination:
Getting these different models to talk to each other and work together is tricky.
Integration:
You need smart ways to combine what all these specialized models come up with.
How Do These Approaches Stack Up?
| One Giant Model | Team of Specialists | |
|---|---|---|
| Training Time | Takes forever, really complex | Much faster when you break it down |
| GPU Usage | Needs a ton of computing power | Can spread the work across multiple machines |
| Actually doable? | Pretty tough with the cognitive load and overfitting issues | Much more realistic thanks to the modular approach |
Let’s be real - building AGI is hard. Both approaches have their merits, but the team of specialists approach seems way more practical and probably faster to get working.
As we keep pushing toward AGI, we’ll need to keep trying new architectures, training methods, and ways to integrate everything. The future will probably be some mix of both approaches - taking what works best from each and figuring out how to make them play nice together.
Update: The AGI vs AI Terminology Shift
Since I first wrote this article back in March 2024, something interesting has been happening in how OpenAI talks about artificial intelligence. If you’ve been following their recent announcements and Sam Altman’s talks, you might have noticed a subtle but important change - they’re using “AI” a lot more and “AGI” a lot less.
But there’s another interesting twist in OpenAI’s messaging lately. They’ve been talking a lot more about “agents” - AI systems that can actually do things, not just talk about them. And word on the street is they might be announcing something called “Agent Builder” soon.
This makes perfect sense when you think about it. Instead of chasing the holy grail of AGI, they’re focusing on practical AI agents that can handle specific tasks and work together. It’s like they’re pivoting from the “one giant model” approach we discussed to something that looks a lot more like the “team of specialists” model.
Maybe they’ve realized that the term “AGI” sets expectations way too high and creates unnecessary pressure. When you keep promising AGI, people expect something that can literally do anything a human can do - and we’re just not there yet.
By focusing on “AI agents” instead, OpenAI can talk about practical, working systems without getting trapped by the AGI definition problem. It’s smarter marketing, honestly. They can showcase what their models can actually do today while building toward more complex agent-based systems.
The interesting part is that this shift doesn’t change the underlying technical challenges we discussed in this article. Whether you call it AGI, AI, or agents, we still face the same fundamental questions about architecture, training, and integration.
If anything, this agent-focused approach makes the “team of specialists” model even more relevant. The future might not be one magical AGI model, but a collection of specialized AI agents working together - which is pretty much what we’re already seeing with tools like ChatGPT, DALL-E, and other specialized models. The rumored Agent Builder could be their way of making it easier for anyone to create and combine these specialized agents.
References
OpenAI: “Artificial General Intelligence” Stanford University: “The Future of Artificial General Intelligence”