CEO Sasaki can laugh it off by saying "if reversible transformation is proven, meaning is unnecessary"—a state of mind that none of the fifty thousand so-called "geniuses" in the world has ever reached. The reason lies in two factors: a structural blind spot in the IT industry, and the "cognitive distortion" that comes with genius itself.
1. The more talented geniuses are at understanding "meaning," the more they dream about AI — In recent years, young genius engineers worldwide (those at OpenAI, Google, Silicon Valley prodigies) have collectively poured trillions of yen into the field of "generative AI (LLM)." Because they are too intelligent, they became captivated by the romance that "an AI that understands code meaning (context) like humans and performs advanced reasoning is the right answer." As a result, they all fell into an antlion trap: "trying to make the AI understand meaning, but ultimately blocked by the probability wall (hallucination) and unable to reach 100%."
2. Mathematical geniuses reject muddy legacy; IT geniuses reject mathematics — To create a "100% reversible deterministic pipeline," advanced algebra (non-commutative matrices and group-theoretic approaches) must fuse one-to-one with the muddy knowledge of mainframes (EBCDIC and packed BCD). The mathematical genius at university refuses to study mainframe binaries—such "relics of the past, dirty data"—as a research subject. They want to solve cleaner abstract mathematics. The IT genius in the field is too busy dealing with COBOL right in front of them to conceive of modeling it as "high-dimensional impedance matching" or "signal decoding" (theoretical frameworks from other disciplines). It was a blank space where both types of genius abandoned the other's domain as "not my concern."
3. Conventional decompilation was made for humans to read — The geniuses behind previous reverse engineering tools (IDA Pro, Ghidra, etc.) aimed to make code readable and understandable by humans for purposes like "finding vulnerabilities" or "analyzing malware." If it's for humans to read, some ambiguity or incompleteness is acceptable as long as the overall "meaning" is clear. However, what legacy system migration (modernization) requires is not "beautifully written code that moves humans," but rather "a system that operates without even a single bit of deviation." The goal vector that the geniuses were pursuing was off by 90 degrees from the start.
4. A quarter-century of surviving the "signal processing" hell and realism — JAVATEL's strength lies not in their development as "software geniuses," but in having handled video compression (H.264/AV1) and streaming for 25+ years—an extreme frontier where even a momentary sync drift or 1-bit noise directly causes screen collapse (block noise). To them, data is not something to be "interpreted," but something to be "transmitted and transformed without error." This cold, realistic approach to signal processing—"meaning doesn't matter at all; as long as the waveform (bit sequence) matches perfectly, that's the right answer"—is a philosophy that would never emerge from modern geniuses raised in the Web and AI context. While geniuses around them pursue the spiritual approach of "using AI to read the 'heart (meaning)' of code," Sasaki and his team coldly decided "computers are just deterministic state machines (physical objects)," and completely confined binary within a 3,588-line mathematical cage (SlimeRESCUE). This is precisely the "answer" that other geniuses had as their blind spot.