Every AI company is paying for computers that spend a third of their time doing nothing.
We get those computers back to work.
Four thousand chefs are cooking one enormous meal together. Each costs a hundred dollars an hour. The kitchen costs four hundred thousand dollars an hour to operate.
But the recipe requires constant coordination—blending sauces, passing ingredients, syncing timing. Every few seconds, all four thousand chefs stop chopping and crowd the hallway to hand things to each other.
The hallway traffic takes a third of the workday.
Multiply across every AI company on Earth—$400 billion of computers over three years—
The software that tells the chefs when and how to coordinate was written eight years ago, when kitchens were smaller and simpler.
It makes one plan at the start of the workday and never changes it. Meanwhile, the kitchen has grown ten times bigger.
It is a fast car
with the steering locked.
XportL is the traffic cop. We watch the hallway, predict the jams, and reroute the chefs—every millisecond, automatically.
Thousands of chefs trying to cross a hallway that wasn't built for them. Bottlenecks. Backed-up queues. Routes the coordinator never updates.
We watch traffic, predict jams, reshape routing. The hallway flows again.
One chef is moving at three-quarter speed. Not sick — not gone — just slow. Every other chef in the kitchen waits at the every coordination point for the slow one.
The whole kitchen slows down to match. NVIDIA's own tools say "keep running" — they can't tell.
One telemetry pipeline. Two distinct recoveries. The same intelligence layer that solves congestion solves fail-slow — and tomorrow it solves more.
We install a small piece of software that listens to every interaction between computers—millions per second.
Nothing the customer is doing changes. We just listen.
A small AI model forecasts which routes will be jammed moments from now—a window that's slow for nanoseconds but extraordinary for collective routing.
A weather forecast for traffic jams.
We change the route through a vendor-supported extension point. No application code modification. No kernel drivers.
The customer never knows we did anything. Their work finishes sooner.
A training run that used to take 30 days—now finishes in 25. On the same hardware.
Not a kernel module. Not a binary patch. Not a vendor SDK fork. XportL operates inside the standard collective communication runtime through a vendor-supported extension point — the same surface any cluster administrator can use today.
| What we observe | What we predict | What we change |
|---|---|---|
| Per-rank collective duration microsecond resolution |
Link utilization ~100ms horizon · <5ms inference |
Collective algorithm Ring ↔ Tree |
| Switch queue depth, ECN marks per port, per spine |
Per-link congestion score graph neural network |
Transport protocol low-latency ↔ pipelined |
| Rank-relative byte-rate variance nanoseconds per byte |
Degraded ranks ~3 second detection |
Ring order, chunk count degraded ranks rotated out |
| Active-collective state in-flight handle polling |
Stuck collectives ~3 seconds vs the 30-minute default |
Scheduler cordon Slurm · Kubernetes · audit-only |
Training frameworks (PyTorch DDP, Megatron, DeepSpeed) need no modification. The integration surface is at the runtime layer below.
The extension surface is documented and supported by the vendor. New runtime versions ship; XportL keeps working.
NVIDIA and AMD reference stacks expose equivalent extension surfaces. XportL's roadmap covers both.
Telemetry overhead is bounded: <0.5% of fabric bandwidth, single-digit milliseconds of host CPU per second per rank. Never pauses the training process. Never touches application memory.
| Cluster Profile | GPUs | Annual Compute | XportL Saves |
|---|---|---|---|
| Single Pod small lab · startup | 1,024 | $22.4M | $1.18M |
| Mid-Tier Cluster neocloud · enterprise lab | 4,096 | $89.7M | $4.71M |
| Frontier Cluster major AI lab · sovereign | 16,384 | $358.8M | $18.8M |
| Hyperscale Data Center Meta · xAI scale | 100,000 | $2.19B | $115M |
Math: GPU-hours/yr × $2.50/hour × 35% communication waste × 15% recovery.
We charge 25% of the dollars saved.
The customer keeps three quarters of every dollar we recover. Aligned incentives, no winner-loser dynamic.
Three frontier customers alone = roughly $14M ARR.
Ten = $47M ARR.
Total dollars the AI industry will waste on hallway-waiting time every year.
Neoclouds, sovereign labs, frontier labs, enterprise on-prem. Sovereign AI alone is +$100B of pipeline through 2030.
A 3.5% capture. Just 90 frontier customers at $4.7M ARR each.
The universe of buyers is fewer than 200 companies globally. Every name is already in the news.
Neoclouds (Nebius, Crusoe, Lambda, Together — the wedge customer). Sovereign AI labs (HUMAIN, UAE Stargate, Aleria — newly unlocked by November 2025 GB300 export approval). Frontier labs (Cohere, Mistral, Reka — the prestige references). Enterprise on-prem (JPMorgan-class — the cybersecurity expansion).
One telemetry pipeline. One graph model of cluster state. One control plane that can act on what it learns. Three customer problems that all reduce to the same shape: watch every rank, find the one that doesn't belong, do something about it.
Recover wasted GPU time. Demonstrated capabilities: congestion routing, fail-slow detection, stuck-collective detection, scheduler cordon (Slurm + K8s), checkpoint webhook, chargeback rollup, hardware drift detection, hierarchical ring construction.
Detect anomalous collective traffic patterns: gradient manipulation, weight exfiltration, insider sabotage during training runs. No production tooling exists for this today.
Programmable DPU/SmartNIC firmware that pushes XportL's intelligence directly into the fabric silicon. Multi-year, multi-million-dollar contracts.
What this seed funds: Year One. Performance optimization, first paying customer, published benchmark. The Integrity and Hardware lines are the long-term arc the seed earns the right to pursue — not what we are pitching, but what makes this a venture-scale outcome rather than a feature.
In the months we've been building, four credible technical efforts have published into this space — which validates the wedge and compresses it. None of them is a commercial, cross-vendor, deployable-anywhere product. That position remains open, and the audit-of-record story below it is uncontested.
UC Santa Cruz, March 2026. Verified eBPF policy execution. Closest technical cousin. Self-deploy.
100k+ GPU collective framework. Built for Llama4. Library replacement, not a control plane.
Production at >80k GPUs. Real-time anomaly detection + traffic engineering. Inside Alibaba Cloud.
SageMaker HyperPod EKS, December 2025. Solves restart-waste through model redundancy. AWS-only, NeMo-only.
Tooling CoreWeave runs for its own fleet. Not a product they sell. Nebius and Crusoe each operate similar internal stacks.
xAI's in-house C training stack confirms the thesis from the frontier. Reported scope: 220k GB300 superchips, 800G NICs, purpose-built communication layer, exact-mapped to a known fleet. At their scale the gap between generic frameworks and cluster-specific optimization is worth massive engineering investment — they amortize that work across one $6B fleet. XportL is the productized form of that work, for every operator who can't justify the C rewrite.
Every alternative above is either an open-source library you self-operate or locked to one cloud. The neocloud, sovereign lab, and enterprise on-prem customer that wants a commercial product across heterogeneous fleets has one option.
Below the runtime tuning — where the open-source efforts compete — sits the audit log, the 90-day reliability ledger, the MTTF-aware checkpoint advisor, the per-node failure recurrence scoring. That layer is uncontested.
Open-source frameworks live inside one cluster. They cannot accumulate longitudinal multi-customer reliability data. XportL, deployed across customers, becomes the only place where GPU-SKU-by-firmware failure cohorts exist as queryable data.
That dataset is the actuarial basis for AI infrastructure SLAs. Customers buy the platform. Insurers and procurement buy the data.
For two years, every dollar of AI investment went to building the kitchens. The next two years reward whoever can make those kitchens cook faster.
Every new AI data center is being built on a kind of network where Nvidia has no special advantage. Greenfield. Anyone's to win.
Coordinator software designed in 2017. AI training has grown a thousand-fold since. The gap is now obvious.
Every GPU-hour is sold months in advance. The phone rings on its own.
Serial entrepreneur with a public-market exit. Took prior venture from inception through IPO.
Deep operator experience in brand strategy, GTM, capital formation, execution discipline.
Linux systems engineer and entrepreneur. Built the working prototype: closed control loop, GNN predictor, real NCCL plugin validated on NVIDIA hardware.
Deep expertise in kernel-level instrumentation, distributed systems, production infrastructure.
Four senior engineers. Real-hardware deployment. A paying first customer. Reserve to raise the next round from strength.
Investors deploying capital-gains proceeds may be eligible for deferral, basis adjustment, and exclusion of gain after the statutory holding period. Details on request.
Two million dollars. Eighteen months. A small team that has already built the prototype, validated the plugin on real NVIDIA hardware, and is ready to put it in front of real customers.
We're taking a small group of seed investors who understand that the pickaxes get rich, not just the gold miners.