Buried in a sanitized 1964 procurement proposal — a contractor pitching the CIA a $X-priced "Multiple Image Correlator" for photographic reconnaissance — sits a passage that reads less like a sales document and more like a quiet rebuke of an entire decade of intelligence engineering.

The subject of Multiple Image Correlation is one which has interested the entire photographic reconnaissance and intelligence community for the past several years. Unfortunately, it has been an area in which far too much experimentation and naive construction took place before a solid understanding was achieved... The history of science is full of instances where experimental data collection did not contribute to understanding of physical phenomenon until theoretical models were developed for data evaluation.

The proposal goes on to note that "the first theory specifically addressed to multiple image correlation was presented at the American Optical Society in 1963 by Dr. Jack Robe[rts?]" — and that, tellingly, "no one at the meeting seemed to grasp all of the implications."

What's being described here is the foundational problem of stereo image correlation: given two photographs of the same scene from different angles, how do you mathematically align them to extract 3D geometry? In 1964 this was a Cold War problem — U-2 and CORONA satellite imagery needed to be turned into terrain measurements and missile-silo dimensions. Today the same math powers Google Street View, self-driving cars, drone mapping, iPhone Face ID, and every video-game engine that does ray-tracing.

The forgotten wisdom isn't the technique — it's the meta-claim about how engineering happens:

• For years, contractors had been building image correlators by trial and error.
• The data they collected didn't accumulate into understanding because there was no model to evaluate it against.
• One theoretical paper, in 1963, finally gave the whole field somewhere to put its observations.
• And even then — no one in the room understood what they'd just been handed.

It's a perfect snapshot of a recurring failure mode. The same pattern played out with semiconductor physics before band theory, with epidemiology before germ theory, with neural networks before backpropagation. We are watching it again right now with large language models: enormous experimental apparatus, billions of dollars of "naive construction," and a theoretical understanding that lags so far behind the artifacts we've built that practitioners openly admit they don't know why the things work.

The anonymous CIA contractor was, in effect, telling his customer: stop buying boxes until somebody explains the physics. It's advice that the intelligence community largely ignored — they kept buying boxes — and advice that Silicon Valley is ignoring today on a vastly larger scale.