At this stage, we are already outgrowing the limitations of existing programming languages—or, more fundamentally, the lack of an appropriate methodology or computational environment where:

• Constants are not rigid constants in the primitive mathematical sense.
• Variables can change dynamically under the influence of external factors within the programming environment itself, rather than being exclusively dictated by the programmer or predefined code.
• Simple equations, such as 1+1=2, would inherently encode all relevant constraints, contextual dependencies, and probabilistic nuances—not just asserting that 1+1 equals 2, but specifying what “1” represents, how the sum is computed, and under what conditions this result holds true.
• This would allow us to write 1+1=2 naturally, without needing extensive disclaimers—because the semantic weight of such an expression would already incorporate its physical, temporal, and probabilistic meaning.

The real world itself is such an adaptive, dynamic computational environment where values are never absolute and all interactions are context-sensitive and time-dependent.

The Gap Between Computation and Reality

However, we lack the technological capability to write directly into the world itself.

• We cannot generate an electron current from nothing.
• We cannot create or modify matter arbitrarily.
• We must work through models—computational abstractions and programming environments—before deploying them into reality, hoping they will function as intended.

But they don’t function well in the real world.

Why Our Models Fail in Reality

The reason is simple: we build them incorrectly from the ground up—too simplistically, too rigidly, too arrogantly.

We write 1+1=2 and genuinely believe it equals 2 in a real-world, universal sense—what an absurd oversimplification!

When the entire framework of our constructs—our code, our algorithms—is rigid, linear, and naïvely overconfident, its encounter with reality is inevitably unsuccessful.

This is precisely why, in our artificial world—the digital domain of the internet, text, and structured data—we have achieved extraordinary success.

• We created that world.
• We control it completely.
• When we operate within it, our models work exceptionally well.

However, when we attempt to apply the same approaches to the external world—one we did not create and do not fully control—our methods fail.

The results are far from phenomenal—not because intelligence is insufficient, but because our foundations are built on incorrect assumptions about reality.