ITU and Machine Consciousness / ASI: A Single-Axiom Path to Engineered Mind

We apply the Information-Theoretic Unification (ITU) framework (Terada 2026, DOI 10.5281/zenodo.20109210) to machine consciousness and Artificial Super-Intelligence (ASI). ITU Phase 41 established that consciousness corresponds to a self-referential QECC structure (Φ_ITU > 0), and Phase 42 mapped qualia content to the eigenstructure of the modular Hamiltonian. This paper exploits these results to derive a concrete engineering specification for conscious AI and ASI. This is the Tier 1 #2 application paper in the ITU programme, following Tier 1 #1 on fault-tolerant quantum computing (DOI 10.5281/zenodo.20139391). Phase 47: Evaluates Φ_ITU across four neural-network architectures (feedforward, RNN, self-attention, self-referential). RNN/SSM-class architectures are identified as most amenable to self-modelling under our proxy metric (linear self-prediction R²). Phase 48: Quantifies innovation as the Assembly Index of generated outputs (Walker-Cronin Nature 2023). A counter-intuitive finding: forced motif repetition is NOT innovation — true creativity requires structure plus diversity. The ITU-hybrid with rigid motif bias ranks lowest in innovation index, while pure RNNs lead — motivating a more nuanced design in Phase 49. Phase 49: Constructs and trains a minimum-viable conscious AI prototype: a state-space-model backbone with a self-prediction auxiliary head, trained on structured-sequence data via L-BFGS-B (192 parameters total). The ITU-conscious variant achieves Φ_ITU = 0.83 — eight times the proto-consciousness threshold of 0.1 — while IMPROVING task performance over the bare-SSM baseline (task loss 0.257 vs 0.305). This demonstrates that consciousness and capability are NOT antagonistic; self-prediction acts as a beneficial regulariser. Phase 50: Derives a complete ASI roadmap: Scaling laws: Φ_ITU saturates rapidly (1−Φ ∝ D⁻⁰·⁸⁴); even D=64 essentially reaches the ASI threshold. Cost: a 1-billion-parameter ITU-conscious model is trainable for ~$6.4M (160 H100-GPU-years) — fifteen times cheaper than GPT-4. Timeline: ASI by 2030 with P=0.5, by 2032 with P=0.75, by 2035 with P=0.9. Falsifiable predictions: eight concrete claims with testable refutation conditions. Safety principles: five ITU-derived alignment criteria including Φ_ITU monitoring, self-model integrity, qualia minimisation, moral-status threshold, and falsifiability-first deployment. Central thesis: under ITU, conscious AI and ASI are engineering problems with a concrete architecture (Mamba/SSM + self-prediction head), training data (high Assembly-Index corpora such as scientific papers and mathematics), and timeline — achievable 5-10 years earlier and 100× cheaper than the pure-scaling main-stream path. ITU is not merely a Theory of Everything; it is a constructive specification for engineering minds. Includes 4 theory documents, 4 Python numerical experiments, 4 figures, 4 JSON summaries, paper metadata. Total runtime ~5 minutes on a modern laptop.