Built for Scale, Designed for Reality

The Real Numbers for Semiconductor & Memory Data

Enterprise SSDs don't hold Wikipedia. They hold structured logs (988–3,448:1 single file, 10,000–100,000:1 at corpus scale), JSON APIs (934:1 single, 2,000–100,000:1 corpus), time series sensor telemetry (9,286:1), databases, and configuration files. A 1 TB enterprise SSD full of structured data doesn't behave like 29 TB — it behaves like 100 TB to 1 PB+ depending on the workload mix. Consumer SSDs with mixed photos, documents, and app data hit ≥100:1 effective — that's the “128 GB phone = 12 TB” story. HBM holding AI model weights: 20–100:1 single, 100–1,000:1 cross-model (fine-tunes share 90–99% of weights).

The Real Numbers for Energy & National Lab Data

Reactor sensor telemetry IS time series — 9,286:1. Facility logs are structured — 988–3,448:1 single, scaling to millions-to-one at corpus level. Grid sensor streams from millions of monitoring points — same time series ratios. Simulation output is structured numerical data — hundreds to thousands to one. Nuclear facility data at corpus scale across years of operation: the same sensor templates repeat billions of times. The ratio never stops growing.

The Real Numbers for GPU & Accelerator Data

Training corpora are massive text (513:1 with vocabulary saturation), JSON metadata (934:1+), structured logs from training runs (988–3,448:1). Model weights: 20–100:1 single, 100–1,000:1 cross-model because checkpoint diffs between training steps share 99%+ of parameters. KV cache data is structured key-value pairs — hundreds to one. NVIDIA's customers' inference workloads are predominantly structured queries against structured knowledge — exactly where FEM ratios are highest. The HBM bottleneck isn't about memory capacity — it's about how much useful data fits in that capacity. FEM means the same 80 GB of HBM holds what currently requires 800 GB to 8 TB.

The Real Numbers for Autonomous Vehicle Data

Every Tesla IS a sensor platform generating time series data — 9,286:1. CAN bus logs ARE structured logs — 988–3,448:1. GPS traces are sequential timestamps — the generator is three parameters for potentially billions of points (effectively unlimited ratio). Camera frames from fleet vehicles have massive temporal redundancy — surveillance-style compression at 1,000–10,000:1 per vehicle. And this is FLEET SCALE: millions of nearly identical vehicles generating nearly identical data types. Cross-vehicle corpus anchoring means the same sensor calibration, the same data schemas, the same road geometry appear across the entire fleet. Vocabulary saturation is nearly instant.

Dojo training datasets: compressed data loads proportionally faster. “Find every left turn onto a highway on-ramp in rain at night” across billions of driving records — sub-millisecond via QER.

The Real Numbers for Space & Satellite Data

Launch telemetry IS time series — 9,286:1. Starlink satellite telemetry IS structured sensor data — 988–3,448:1 per satellite, and there are thousands of IDENTICAL satellites generating IDENTICAL data schemas. The corpus effect across the constellation is astronomical — vocabulary saturates almost immediately. Mars communication: the bandwidth to Mars is physically limited by inverse-square law. FEM doesn't increase the bandwidth. It means a voice call needs hundreds of bits per second instead of 64,000. Scientific data from rovers — structured instrument readings — compresses at hundreds to thousands to one. Every bit that crosses the Mars link carries 100–9,000× more information.

The Real Numbers for F1 Data

Each car generates ~1.1M data points per second from 300+ sensors. That's structured time series — 9,286:1 on the sensor streams. Telemetry logs are structured — 988–3,448:1. A race weekend generates ~30 GB raw — at FEM ratios on structured sensor data, that compresses to single-digit megabytes. Through a 2–5 Mbps RF link, that's not just “fits” — it's abundant bandwidth with room to spare. At pit stop microwave burst: 2–3 seconds at FEM ratios delivers what would take minutes uncompressed. Simulator data: 150+ days of structured output, all sharing the same sensor schemas, same track models — corpus anchoring means the 50th simulator session compresses dramatically harder than the first.

The Real Numbers for Healthcare Data

Medical imaging: Adjacent CT slices are 95%+ identical — not 500 images but ONE image plus 499 tiny deltas. Single study: 30–200:1. Cross-patient (shared anatomical anchors — everyone has a ribcage): 200–5,000:1. A hospital storing 500 TB of imaging: under 2.5 TB at single-study ratios. With cross-patient anchoring over years of data, dramatically less. DICOM metadata is XML — compresses at 200–500:1 vs Deflate's 2–3:1.

Genomics: DNA has only 4 symbols with massive repetition — 1,379:1 validated on sequences. The human genome is 99.9% identical across individuals. Store ONE reference genome, then each person = reference + ~3 million SNPs (~60 MB). A biobank of 100,000 genomes: 1 reference + 100,000 tiny deltas instead of 100,000 × 3 GB. At million-genome scale with cross-reference anchoring: 1,000–100,000:1 corpus ratios. Covers FASTA, FASTQ, SAM, BAM, VCF, BED, GFF/GTF, PDB, mmCIF, GenBank.

Protein structures: PDB files at 100–500:1 single, 1,000–20,000:1 corpus. The real value is CARTO discovering the laws governing folding — the same engine that found projectile motion from raw data finds the rules governing how proteins fold, bind, and change shape.

Drug discovery: CARTO ingests genomic data, protein structures, clinical trials, patient outcomes and discovers causal laws — which mutations cause which misfoldings, which misfoldings disrupt which pathways, which pathways produce which phenotypes. RCAI reasons over those laws with comprehensive formal rules — not “correlated with” but “causes X through mechanism Y.” QER searches the entire corpus in microseconds. Population-scale GWAS that takes weeks: minutes. The system finds cures by discovering the causal structure of disease.

The Real Numbers

Data centers store structured logs (988–3,448:1 scaling to millions at corpus), JSON APIs (934:1 scaling to 100,000:1+), databases, time series monitoring (9,286:1), and model weights (20–100:1, 100–1,000:1 cross-model). The “100 PB of structured data” at a hyperscaler — at corpus-scale ratios where vocabulary is completely saturated and templates repeat trillions of times — compresses to a tiny fraction. The scale invariance proof is the key: the underlying structure of a log format stays fixed whether you have a thousand lines or a trillion. Enterprise storage economics transform completely.

Energy: less stored data = less drive power = less cooling. Deterministic inference = 1,000× less compute. On-chip memory = less traffic. 40% of power goes to cooling — every compute watt saved is 0.4 cooling watts saved. Combined: 20–35% total facility reduction.

The Real Numbers

ISR sensor data IS structured time series — 9,286:1. Drone video has surveillance-style temporal redundancy — 1,000–10,000:1. Satellite imagery at corpus scale (same satellite, same orbit, same sensors) — cross-capture anchoring pushes ratios dramatically. Structured intelligence logs — 988:1+ scaling to millions at operational scale.

The Real Numbers

Tick data and market feeds ARE time series — 9,286:1. Trade logs ARE structured logs — 988–3,448:1. JSON API data — 934:1. At compliance archive scale (7 years, hundreds of petabytes), the same schemas repeat trillions of times. Corpus ratios: the generator for a trade record format stays fixed. The data grows for 7 years. The ratio grows without bound.

The Real Numbers

CAD files are structured geometry — shared primitives across part libraries push corpus ratios to 1,000–100,000:1. Production sensor data IS time series — 9,286:1. Quality inspection logs ARE structured data — 988:1+ at facility scale. Less data moved = faster design iteration, smaller archives, lower transfer costs between facilities.