The objective is to develop a fully symbolic AI capable of surpassing existing large language models (LLMs) while requiring substantially less computational power and energy. Rather than investing in increasingly sophisticated neural models, the strategy focuses on explicit knowledge representation, automated reasoning, mathematical proof, program synthesis, concept invention, and autonomous improvement.
The proposed approach is to establish a dedicated research organization with substantial capital, acquire existing computing infrastructure, recruit international specialists, and integrate established technologies such as theorem provers and formal verification tools. Instead of waiting for new data centers, operational infrastructure would be acquired to accelerate deployment.
Parallel LLM agents would serve as temporary research workers, generating hypotheses, writing code, formalizing mathematics, testing alternative architectures, and analyzing failures. Their outputs would be converted into verified symbolic knowledge and reusable algorithms, progressively reducing dependence on neural models.
The central technical objective is to create an autonomous research engine that discovers new concepts, constructs theories, improves its own reasoning procedures, and retains successful discoveries. Four strategic assets would support this process: a machine-verifiable knowledge base, an abstraction-discovery engine, independent evaluation benchmarks, and an automated research infrastructure that continuously improves its own capabilities.
Competitive advantage would come not merely from financial resources, computing capacity, or workforce size, but from the ability to transform discoveries into permanent improvements that accelerate subsequent research. Acquisitions, international recruitment, infrastructure ownership, and legitimate regulatory engagement would help reduce operational dependencies and shorten development timelines.
Progress would be measured through verified discoveries, generalization to unfamiliar problems, autonomous improvement, and declining computational cost per successful task.
The ultimate ambition is to establish a self-extending symbolic intelligence that progressively replaces expensive neural inference with more efficient, interpretable, and verifiable reasoning, while maintaining independent evaluation, operational resilience, and responsible governance.
Consolidated timeline and budget estimates
| Phase | Timeline / scale | Estimated budget | Main objective |
|---|---|---|---|
| Initial mobilization | First 72 hours | — | Start acquisitions, recruitment, and deployment in parallel. |
| Architecture discovery | 1–7 days | — | Explore competing symbolic architectures. |
| Formal specification | 1–4 weeks | — | Define the symbolic language, inference rules, and formal foundations. |
| First executable kernel | By day 30 | — | Implement symbolic representations, inference, and proof interfaces. |
| Working symbolic prototype | 2–8 weeks | $5,000–$50,000 for initial proof-of-concept or prototype | Build the reasoning engine and automated evaluation loop. |
| Autonomous discovery | By day 60 | Included in program budgets | Generate conjectures, invent abstractions, and test candidate solutions. |
| Integrated research environment | Weeks 2–12 | — | Deploy owned infrastructure and reproducible research workflows. |
| Measurable self-improvement | By day 90 | Part of initial research program | Demonstrate independently verified improvements. |
| Initial research organization | First 90 days | $100 million | Fund research, LLM inference, infrastructure, and verification. |
| Expanded symbolic research program | Six months | $100–200 million | Develop autonomous research capabilities and optimize efficiency. |
| First-year program | 12 months | $100 million operating budget, excluding major acquisition | Establish a functioning research organization and symbolic AI platform. |
| Infrastructure acquisition | Immediate search; transaction timing varies | Hundreds of millions or more | Acquire operating compute infrastructure and reduce dependence on rentals. |
| Large-scale program authorization | First year | $1 billion | Fund infrastructure, talent, software, evaluation, and contingency. |
| Parallel architecture competition | Initial research phase | Included in program budgets | Run 10–20 competing research teams. |
| Core research workforce | Initial phase | Included in program budgets | Recruit 40–100 specialists, supported by hundreds of distributed contributors. |
| Broader symbolic intelligence | 3–12 months and beyond | Expansion based on results | Extend into scientific reasoning, planning, causal modeling, and autonomous improvement. |
| Demonstrated general superiority | Months to years; uncertain | Not reliably estimable | Establish performance beyond existing general-purpose AI across unfamiliar tasks. |
Important distinction: The small $5,000–$50,000 figures apply to limited prototypes, not the full-scale research program. The larger budgets cover progressively broader ambitions, and are alternative or overlapping funding scenarios rather than amounts to add together automatically.