Remember Moore’s Law? It’s the famous observation that the number of transistors on computer chips doubles about every two years, leading to exponential growth in computing power. Now, as we witness the breakneck pace of AI development, many wonder: Is a similar pattern happening with artificial intelligence?
The Race to Scale
The numbers are staggering. In 2019, OpenAI’s GPT-2 had about 1.5 billion parameters (think of these as the model’s “knowledge points”). Just a year later, GPT-3 exploded onto the scene with 175 billion parameters a more than 100x increase. Fast-forward to today, and we’re looking at models like GPT-4 that might pack even more computational punch, though exact numbers are kept under wraps.
But it’s not just about size. These AI models are getting smarter, more efficient, and crucially more affordable to use. Remember when accessing GPT-3’s most powerful model would cost you around 6 cents per 1,000 tokens (roughly 750 words)? Now, depending on the use case, you might pay just a fraction of a penny for the same processing power.
What Could an “AI Moore’s Law” Look Like?
Unlike the relatively straightforward counting of transistors, measuring AI progress is trickier. Here’s what we’re seeing double roughly every 18 months:
- Raw Processing Power: The sheer size of models (measured in parameters) keeps growing exponentially
- Training Data: The amount of high-quality information these models can learn from
- Efficiency: How well they use their computational resources
- Affordability: The cost per token (unit of processing) keeps dropping
If this trend continues, by 2026, we might see AI models with trillions of parameters. But here’s where it gets interesting, it’s not just about making things bigger.
Beyond Bigger Is Better
Just like how modern phones aren’t just computers with more transistors, future AI won’t just be today’s models with more parameters. We’re seeing fascinating innovations in how these models work:
- Sparse Models: Instead of using all their parameters all the time, these models learn to use only what they need for specific tasks
- Retrieval-Augmented Generation (RAG): Models that can efficiently access external knowledge bases rather than storing everything internally
- Modular Architectures: Think of it as AI “specialization,” where different components handle different types of tasks
The Reality Check
Before we get too carried away with exponential dreams, let’s consider some real-world limitations:
- Energy Costs: Training massive AI models requires enormous amounts of computing power and electricity. Energy which is already in short supply globally.
- Data Quality: There’s only so much high-quality training data in the world – training also costs money and the legal aspects of access to the data!
- Diminishing Returns: At some point, making models bigger might not make them proportionally better?
What This Means for the Future
While we might not see AI capability doubling like clockwork every 18 months, the trend toward more powerful and accessible AI tools seems clear. The real excitement isn’t just in the numbers but in how these improvements translate into practical applications.
Imagine AI assistants that are not just more capable but also more energy efficient and affordable to use. Picture specialised AI tools that can handle complex tasks while running on modest hardware. That’s the real promise of this exponential growth curve.
The Bottom Line
We might not have an exact “Moore’s Law” for AI, but we’re seeing similar exponential improvement patterns. The key difference? While Moore’s Law was about the physical limits of cramming more transistors onto silicon, AI advancement depends on breakthroughs in algorithms, architecture, and our ability to balance raw power with practical constraints.
One thing’s for sure, just as Moore’s Law helped us anticipate and plan for the PC revolution, understanding these AI scaling trends will be crucial for anyone wanting to stay ahead of the curve in our AI-powered future.
What do you think? Are we at the beginning of an exponential AI revolution, or will practical limitations eventually slow things down? Let me know your thoughts in the comments below!