docs/GTM.md

# PAX-Coder — Go-To-Market Plan

**Ahmad Ali Parr · Bel Esprit D'Accord Irrevocable Trust**
*Confidential — not for public distribution*

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## The Thesis

The GPU kernel market is large, growing, and almost entirely unverified. Every major ML framework (PyTorch, JAX, TensorFlow) relies on kernels that were benchmarked but not proved. The correctness assumptions are informal, the race-freedom guarantees are implicit, and the numerical behavior is tested on sample inputs rather than proven for all inputs.

There is currently no mainstream tool that generates formally verified GPU kernels. That is the gap PAX-Coder fills.

**The moat is not the model weights. The moat is the training data pipeline** — the 5-axiom PAX architecture, the Lean 4 proof corpus, the PTX kernel library with matched Futhark specs, and the formal proof obligation framework. This cannot be reproduced by fine-tuning on GitHub scrape data because GitHub does not contain formally verified GPU kernels at the level PAX has built.

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## Target Users

### Primary: GPU Kernel Engineers

**Who they are:** Software engineers at AI labs, cloud providers, semiconductor companies writing custom CUDA kernels. HPC engineers optimizing scientific workloads. ML framework contributors maintaining kernel libraries.

**Their pain:** They spend days in NCU traces after production incidents. They cannot formally guarantee correctness without PAX-level tooling. They distrust vendor libraries when they cannot audit the math.

**Why they buy:** PAX-Coder reduces the time from "I need a verified kernel" to "I have a kernel with a machine-checked proof" from weeks to minutes.

### Secondary: AI/ML Researchers

**Who they are:** Researchers working on transformer efficiency, quantization, custom attention mechanisms. Academic groups doing formal methods in computer systems.

**Their pain:** Custom kernels for research are written fast and dirty. Race conditions in training kernels produce incorrect gradients that look like model convergence problems.

**Why they buy:** PAX-Coder gives them a correct baseline they can point at in a paper. The Lean 4 proof is citable.

### Tertiary: Enterprise ML Infrastructure Teams

**Who they are:** Platform teams at large tech companies deploying inference at scale. They own the GPU cluster. They need auditable, certifiable code for compliance.

**Their pain:** SOC 2, ISO 27001, and emerging AI governance frameworks are starting to ask about kernel-level correctness. Nobody has an answer yet.

**Why they buy:** The WORM seal and Ed25519-signed certificates give them a tamper-evident audit trail. The `pax-verify` API integrates into their CI/CD.

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## Pricing

| Tier | Price | Channel | Target Buyer |
|------|-------|---------|-------------|
| **Individual Node** | $250–$500 one-time | Direct (CONTACT.md) | Individual production users |
| **Commercial Team** | $12,000–$25,000/yr | Direct / outbound | AI startups, HFT shops, ML labs |
| **Enterprise** | $50,000–$150,000+/yr | Direct / outbound | Mission-critical, defense, FinTech deployments |

**Public repository is the acquisition channel.**

Kernel engineers clone the GitHub repo for verification and testing — no authorization required. Those who need to seal outputs and deploy to production contact for provisioning. The PAX-Coder authority reviews and approves/denies based on use case. Word of mouth in the GPU kernel community is extremely high-leverage because the community is small and tight.

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## Acquisition Strategy

### Phase 1 — Seeding (Month 1-2)

**HuggingFace model page** — This is the primary landing page. The README serves as the full product description. The model card format is indexed by HuggingFace search. Target keywords: `lean4`, `formal verification`, `gpu kernels`, `ptx`, `cuda verified`, `proof carrying code`.

**Ollama library** — Second distribution channel. `ollama run Snapkitty/pax-coder` is the zero-friction entry point. Ollama users are exactly the GPU engineers we want.

**GitHub repo** — The technical proof of the claims. Engineers who are skeptical will read the Lean 4 files and the PTX kernels. The repo needs to be clean, readable, and have working build instructions. This is why the repo quality matters before the first push.

**Target communities (organic, no spam):**
- r/CUDA
- r/MachineLearning (when a paper is ready)
- HackerNews (Submit when something genuinely novel — the pipeline throughput proof or the FP16 formalization are both HN-worthy)
- GPU Mode Discord
- Lean 4 Zulip (the formal verification community will be interested in the GPU application)

### Phase 2 — Conversion (Month 2-4)

**Commercial provisioning as the conversion funnel.** Engineers who clone the repo for verification and want to deploy to production contact for a Sovereign Node Key. The contact process qualifies the use case, and provisioning requires commercial agreement.

**The WORM ledger as social proof.** When developers use PAX-Coder in production and see their node key and sealed outputs listed in the cryptographic ledger, they share it. The permanent, tamper-evident attribution is a feature for engineers who care about provenance.

**Enterprise outreach (Month 3+):**
- Direct email to GPU infrastructure leads at ML-heavy companies
- LinkedIn outreach to HPC engineers and ML platform leads
- Conference presence: SC (Supercomputing), NeurIPS, MLSys

### Phase 3 — Enterprise (Month 4+)

**The `pax-verify` API** is the enterprise product. It takes a kernel (any kernel, not just PAX-Coder-generated ones) and returns a formal verification against the PAX proof obligations. This is a broader market than just PAX-Coder output — it is a kernel audit tool.

**Pricing anchor:** Enterprise verification includes custom Lean 4 proof modeling and formal audits. Annual contract aligns incentives for long-term partnerships with infrastructure teams.

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## Content Strategy

### What to publish (in order of priority)

1. **The pipeline throughput proof** — A blog post explaining the math. The claim "we proved the throughput bound, we didn't just measure it" is genuinely novel and will be picked up by the GPU engineering community.

2. **The FP16 RNE formalization** — "First Lean 4 machine-checked proof of IEEE-754 binary16 rounding error bound." Short, citable, verifiable. Post on HackerNews and the Lean 4 Zulip.

3. **A worked example end-to-end** — Take a real transformer attention kernel, show PAX-Coder generating it with proofs, show the proofs compiling, show the benchmarks. This is the demo that converts skeptics.

4. **The paper** — Once the proofs are complete and external-audit-ready, write the formal paper. Target: MLSys or SC. This is the academic legitimacy anchor that enterprise buyers point at when justifying the purchase.

### What NOT to do

- Do not post benchmarks until the benchmarks are verified. A claim like "99% of cuBLAS throughput" that turns out to be on a narrow test case will destroy credibility with exactly the audience we want.
- Do not oversell the AI angle. The model is a code generation tool. The proofs are what matter. Positioning this as "AI writes verified code" invites skepticism from the formal methods community. Position it as "PAX architecture + LLM interface."
- Do not rush the paper. One cited formal result is worth 100 unverified benchmark claims.

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## Competitive Landscape

| Tool | What it does | What it lacks |
|------|-------------|---------------|
| cuBLAS | NVIDIA's GEMM library | Closed source, no proofs |
| CUTLASS | NVIDIA's kernel templates | No formal verification, NVIDIA IP |
| Triton | Python → GPU kernels | No proof obligations, compiler trust |
| GitHub Copilot | Code generation | Pattern matching, no proofs |
| GPT-4 (CUDA) | CUDA generation | No proof chain, hallucinated correctness |

**None of these produce formally verified output.** That is the position.

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## Revenue Model at Scale

**Year 1 target:** 10 Individual keys ($3,500) + 5 Commercial ($85,000) + 2 Enterprise ($200,000) = ~$288,500

Proof of demand from qualified buyers. Focuses on early adopters with serious production use cases.

**Year 2 target:** 50 Individual keys ($17,500) + 15 Commercial ($300,000) + 8 Enterprise ($800,000) = ~$1,117,500

At this point the `pax-verify` API has enough usage data and the paper has been cited enough to have academic credibility.

**Year 3+:** The kernel verification API becomes a standard tool in ML infrastructure CI/CD.
Each enterprise deployment is a 3-5 year relationship. Enterprise ARR is the primary revenue driver.

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## The Non-Negotiables

1. **The proofs must be real.** Every claim in the README that says "proven" must have a corresponding `lake build`-verified Lean 4 theorem. The moment that breaks, the product is dead.

2. **The node key must be honored.** If someone pays $25 and does not get a key within 24 hours, word spreads fast in a small community.

3. **The WORM ledger must be public.** Contributor attribution is only meaningful if it is verifiable. The ledger must be accessible.

4. **The paper must come.** The enterprise market will not move without academic legitimacy. The paper is not optional — it is the long game that makes everything else defensible.

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*Bel Esprit D'Accord Irrevocable Trust · SnapKitty West · Evidence or Silence — 2026*