Jev for Physical AI

Real measured numbers putting TypeSafe's Jev to work on robots, fleets, and edge hardware.

Jev is a "System One" model: structured state in, typed probabilistic decisions out — no text generation, 70–500ms, $0.042 per million input tokens with output free. Everyone demos it on browsers and games. We point it at the physical world instead: warehouse robot fleets, incident triage, and the build-vs-buy math of running your own models on edge CPUs.

Every number below comes from runs we actually executed on 2026-09-19. Real API calls, real latency, real bills. Caveats are stated, not buried.

By RoboKrunch — we benchmark Chinese edge-AI hardware and measure what AI actually costs in the physical world.

Contents

• Demo A — 10,000-robot fleet triage
• Demo B — Jev vs self-hosted ModernBERT
• Reproduce
• Video
• Limitations
• Roadmap
• License

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Demo A — 10,000-robot fleet triage

Question: can Jev serve as the decision layer for a warehouse AMR fleet — triaging incidents faster and cheaper than a small LLM?

Method. 41 bilingual (CN/EN) incident templates covering warehouse AMR failures: LiDAR degradation, localization drift, battery faults, pallet detection misses, network partitions, human-zone intrusions. 300 incidents sampled. Each call asks Jev for 3 simultaneous judgments: escalate to human (yes/no), owning team (choice), urgency (score 0–2). All 300 calls went through OpenRouter (typesafe/jev-1.13) on 2026-09-19.

Results.

Fleet-scale cost model. 10,000 robots × 48 decisions/day × 30 days = 14.4M decisions/month:

GPT-4o-mini is estimated ($0.15/M input, $0.60/M output, 600 input + 60 output tokens per call) — we did not measure its triage quality or latency. The 5.1× is a cost ratio, not a quality claim.

What this actually shows: sub-second, three-judgments-per-call triage at ~$25 per million decisions, with zero training and zero labeled data. What it doesn't show: production accuracy — our incidents are simulated, so "91.3%" is agreement with template labels, not accuracy on real failures.

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Demo B — Jev vs self-hosted ModernBERT

Question: at what scale is it cheaper to just run your own classifier?

Method. answerdotai/ModernBERT-base (149M params) on a 2-core AMD EPYC, CPU-only, batch=1. Frozen encoder + mean pooling + nearest centroid over 18 hand-written exemplars, 100 test incidents — the same fleet-triage domain as Demo A.

Results.

Crossover math. Assuming a $24/month 4-vCPU VM, self-hosting breaks even at ≈ 977K decisions/month — roughly 678 robots at 48 decisions/day. Below that, Jev is cheaper and you skip training, labeling, and ops. (A 3-output comparison would push crossover toward ~2.9M/month — not measured, treat as directional.)

What this actually shows: Jev's advantage is not raw inference speed — a small local model is ~3× faster. Its advantage is starting cost: no training, no annotation, no infrastructure to babysit. If you're already past ~1M decisions/month with stable labels, self-host.

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Reproduce

Demo scripts are all in this repo (runs executed 2026-09-19). Layout:

code/              demo scripts (runnable)
├── demo-a.py      # 300 real decisions via OpenRouter (typesafe/jev-1.13)
├── demo-b.py      # ModernBERT self-hosted comparison (CPU-only)
├── charts.py      # regenerates the comparison charts
├── crossover.py   # Jev-vs-self-host cost table + crossover analysis
└── render_video.py# renders the 60-second demo video
data/              measured results (aggregate + per-call records)
├── demo-a-results.json
└── demo-b-results.json
assets/            charts + the 60-second demo video
├── chart-fleet-cost.png
├── chart-latency.png
└── fleet-triage-demo.mp4
REPRODUCE.md       # full reproduction guide + stated limitations

To re-run Demo A you need an OpenRouter API key in OPENROUTER_API_KEY — never commit keys. Model weights for Demo B download from HuggingFace (~599MB); if your environment sets a proxy, override NO_PROXY=localhost,127.0.0.1 — bare IPv6 entries in NO_PROXY crash newer httpx with InvalidURL.

Video

assets/fleet-triage-demo.mp4 — 60 seconds, 300 real decisions streaming past with a live cost ticker, then the fleet-scale math. Watch the ticker: the entire 300-decision run cost less than a cent.

Limitations

Stated up front, because fake demos are poison:

• Incidents are simulated from templates. Agreement with template labels ≠ production accuracy.
• We have not tested TypeSafe's native POST /v1/systemone — all calls went through OpenRouter's decisions endpoint.
• No real robot hardware, no edge NPU, no long soak test, no probability-calibration study.
• GPT-4o-mini comparison is cost-estimated, not measured; its triage quality is unknown.
• Crossover math assumes a $24/mo VM and ignores your engineering time — which is exactly the point, but do your own sheet.

Roadmap

• Re-run Demo A against native https://api.typesafe.ai/v1/systemone
• Probability calibration: are Jev's confidence scores honest?
• Long soak test: 24h continuous triage, watch for drift
• More fleets: delivery robots, humanoid ops incidents
• Same triage task on a real edge NPU (RK3588 / Jetson) when hardware budget clears

License

Code: MIT. Benchmark data (data/*.json): CC-BY 4.0 — use it, cite RoboKrunch.

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Part of the robokrunch org. Curated Jev resources live at robokrunch/awesome-jev. Main site: robokrunch.com.