Three Bets on the Future of Software: Compiler Compiler System AI-Assisted Construction
Aleksandr F. Urakhchin · Zenodo (CERN European Organization for Nuclear Research) · 2026
The conversation about artificial intelligence has narrowed to two competing bets about how human-level capability is built. The first, held by the major frontier labs, is that scaling neural networks produces general intelligence as an emergent property. The second, held by a smaller community in the cognitive-science lineage, is that intelligence requires explicit cognitive architecture. Both bets are unresolved, and both engage centrally with whether sufficiently capable AI systems can be produced. This paper argues that a third bet sits beneath both, addresses a question neither engages with directly, and is independently worth making: software construction is fundamentally the transformation of semantic representations into one another, and the substrate that supports those transformations determines what AI assistance can do. The current substrate for AI-assisted construction — source code in text files, semantic structures in developers' heads, prose documentation that no toolchain can verify — places a ceiling on what AI assistance can achieve no matter how capable the underlying language models become. We describe a substrate that addresses this gap: a self-defining specification language, a compiler-compiler that produces parsers and serializers in four target languages, a canonical binary form with round-trip closure as structural-correctness oracle, and first-class isomorphic mappings between representations. The implementation backing for this architectural model is established in [2]; the present paper articulates the position-level implications, summarizes empirical results on construction of approximately twenty-six sibling repositories over twelve weeks of focused work, describes the *Adaptive Programming* methodology that emerges when AI assistance operates at the specification layer, and considers two application domains — cross-language source translation and ontology-based knowledge extraction — where the substrate reframes problems current tooling addresses poorly. The third bet's position is intentionally narrow: not a path to general intelligence, not a theory of cognition, but a class of substrate that makes AI-assisted specification-layer construction tractable in ways code-layer assistance alone cannot reach.