?? The Déjà Vu Effect of the AI Wave ??

AI is everywhere. This is probably the biggest and strongest wave of technological transformation I’ve ever seen.

We are witnessing an unprecedented race between companies and countries, pushing AI to new frontiers—without a clear way out (or perhaps, there isn’t one).

Having been in software development for 20+ years, this wave of innovation made me reflect on a fundamental question:

Are we experiencing a new technological "Holy Grail" moment?

?? The AI Boom and Its Hard Limits

I'm not an AI expert, but over the past few months, I’ve explored AI from multiple perspectives—philosophical, practical, and hands-on development. The experience is mind-blowing.

From code generation to autonomous agents, RAG (Retrieval-Augmented Generation), fine-tuning, and beyond, AI empowers us to build things faster than ever.

But every technology has limits—and for LLMs (Large Language Models), that limit is context size.

?? RAG (Retrieval-Augmented Generation) is the cornerstone of expanding LLM knowledge cheaply. ?? However, we are bounded by the maximum context window—which dictates how much relevant information an AI model can "remember" at any given time.

So, how do we maximize efficiency in this constraint? The answer lies in: ?? Prompt engineering ?? Better data structures ?? External semantic search techniques

In short, context size is the new limited resource we need to optimize.

?? History Repeats Itself: The Déjà Vu Effect

Optimizing limited resources isn’t new. It reminds me of the early days of computing, where memory was the bottleneck:

??? In the 1970s, computers had tiny RAM. Developers had to optimize every byte. ?? In 1976, the 5.25” floppy disk had 110 KB capacity. Just four years later, the 3.5” floppy arrived with 1.44 MB—a 12x improvement. ?? We saw software split across multiple floppy disks, compression techniques, and double-sided storage.

And today? AI context windows are following the same pattern:

?? OpenAI’s context size grew 12x in 3 years (from 16K to 200K tokens). ?? Gemini 1.5 offers a staggering 1M token context window.

Yet, there's a catch:

Context size scaling is quadratic due to self-attention mechanisms. And we all learned in Algorithms 101 that quadratic complexity is bad.

Just like we eventually stopped worrying about RAM and disk space, I believe context window concerns will become obsolete—but only if we solve the scalability problem.

? What’s Next?

Right now, we rely on faster processors to keep up with context scaling. But history tells us that hardware limits also have limits.

The real breakthrough will come when a new architecture emerges—one that reduces context complexity from O(n2) to O(log n).

Until then, context optimization remains a key challenge for engineers building AI-powered applications.

?? What do you think? Will context size ever stop being a bottleneck, or are we stuck optimizing forever? Let’s discuss! ??