Introducing Concordia Computer
The Campus Is the Computer

Labs introduces its planned multi-gigawatt supercomputer — designed from the campus down to the silicon as one machine.

SANTA MONICA, Calif., September 2, 2026 /Labs Newsroom/ — Labs Companies, Inc. today introduced Concordia Computer, a planned multi-gigawatt artificial-intelligence supercomputer and computational infrastructure system being developed within Labsintuition, Labs' Physical Intelligence Frontier Lab, at concordia.computer.

Concordia begins with one architectural doctrine: the campus is the computer. The building is not merely housing the computer, and the campus is not merely housing it. Power, networking, cooling, silicon, memory, compute clusters, orchestration, facilities and the physical environment itself are being designed as parts of a single computational system.

One System, Measured in Gigawatts

The frontier of artificial intelligence is becoming increasingly physical. Models require silicon, silicon requires servers, servers require racks, and racks require networking, power, cooling and facilities — which in turn require energy infrastructure, and orchestration across all of it. Concordia treats those dependencies not as separate systems supporting a computer, but as components of one machine.

Labs measures the system in gigawatts, clusters, processors, memory, networks, orchestration and intelligent compute rather than in buildings or accelerator counts. The objective is a computational environment capable of supporting large-scale training, inference, reasoning, agentic systems, Physical Intelligence and Frontier Orchestration workloads — and over time, of becoming part of the substrate the broader Labs ecosystem runs on.

Compute in the Chairs, Compute in the Walls

The industry has spent the better part of a decade describing a version of this idea. Connected devices. Instrumented buildings. Sensors distributed through a facility, reporting telemetry back to a computer located somewhere else. Compute brought around a facility. Concordia is not that, and the distinction is the entire point of the program.

When Labs says the campus is the computer, it means the sentence literally. Compute in the chairs. Compute in the walls. Compute in the tables, the fixtures, the floors and the surfaces. It describes an environment in which physical objects are not endpoints reporting to a machine but participants inside one — carrying processors, memory, state and orchestration within the same architecture that runs the data halls. A wall becomes a place where computation happens. A table becomes a place where computation happens. The difference between instrumenting a building and building a computer the size of a campus is not a matter of degree, and the two approaches do not converge on the same machine.

Labs extends the doctrine further still. Elements of a Concordia campus are envisioned to assemble themselves and power themselves — structures, enclosures, racks, surfaces and fixtures that can be positioned, connected, energized and reconfigured by the machines and robotics operating the site, rather than assembled once and left static for a decade. A campus that computes should also be a campus capable of rearranging its own computation. Labs Machines automates the facility, Labsbotics operates inside it, Factory5227 builds the hardware, and Concordia runs the compute — which means the physical environment is designed to have the same capacity to change as the software running on top of it.

That is the version of the doctrine Labs is researching: not a building filled with computers, and not a building watched by sensors, but a continuous computational substrate in which the distinction between the facility and the machine stops being a useful one to draw.

What Counts as Compute

If a campus can be a computer, then the question of what compute is becomes an open research frontier rather than a settled matter of definition.

Most computation today resolves to text, image, audio, video and control. Labs treats that list as incomplete rather than final. Across the Labs ecosystem, several Frontier Intelligences are exploring forms of compute that do not fit existing categories — computation whose output is sensory, physical, spatial or experiential rather than symbolic.

One such exploration is Taste Through The Internet, an initiative being explored within YuhmmyAI, which asks whether taste and the sensory dimensions surrounding it can be captured, represented, computed, transmitted across a network and reconstructed on a device — allowing a person to experience a sensory element remotely rather than only read a description of one. Sensory compute of that kind carries its own requirements: representations that do not yet exist, models trained on modalities that are not currently standard, latency behavior closer to interaction than to batch inference, and hardware willing to treat a sensory signal as a first-class computational object in the way NSM is envisioned to treat live inference state.

Concordia is being designed to host that exploration rather than to foreclose it. A computational environment built only for the modalities that exist today would be obsolete against the frontier it is intended to serve.

Hardware at the Edge of the Senses

A sensory frontier needs somewhere to land. If taste, touch, smell and nutrition are to become computable, transmissible and reconstructable, something has to sit at the boundary between the network and the person — instrumenting on one end, reconstructing on the other. Several hardware explorations across the Labs ecosystem are being pursued with exactly that boundary in mind, and each of them resolves, eventually, into a demand on compute.

projectLARG — Labs AR Gadgets — is an exploration of AR-native facewear from Labs Realities. The premise is not a display strapped to a face but facial-computing: a wearable whose lenses are sensing instruments as much as they are surfaces, running on LabsOS, and carrying an XR-NXP, a Neuro-Expression Processor being explored to treat expression, attention and intent as computational inputs in their own right rather than as inferences bolted onto a camera feed. In that framing an application stops being something drawn on top of the world. Lens Apps describes the consequence: the app and the lens become the same object, so that what a person looks through and what they run are no longer separable things.

projectOPAL — Optical Palate Appetite Lenses — approaches the same boundary from the sensory side, within the Yuhmmy ecosystem. Where Taste Through The Internet asks whether taste can be captured, represented, transmitted and reconstructed at all, projectOPAL asks what it means to wear the receiving end of that question. It is being explored along three intertwined directions: Taste-Technology, the instrumentation and representation of taste itself; Tasteimmersion, the delivery of a sensory element as something experienced rather than described; and Tasteintelligence, the models and reasoning that make a sensory signal legible, personal, and safe to act on.

LabsX is exploring the layer underneath both — fundamentally new paradigms of hardware built to support greater sensing across the internet, spanning taste, smell, touch, nutrition and the modalities that have never had a native digital representation at all. Across Labsquant, Labsneuro, Labsbio and Labsnano, the question is less how to improve an existing sensor than what a sensor should be when the thing being sensed has no established encoding to begin with.

None of these are products. They are research explorations, named here for what they imply about compute rather than because they are available to buy.

What Labs Realities Unlocks

Labs Realities is the lab creating immersive, perceptive realities across AR, VR, MR and XR — and RR, Replacement Reality — and engineering the multidimensional Labsverse to power embodied interaction and future-sensory experiences. Concordia is the reason that ambition is a compute programme and not only a design one.

A worn device is the most demanding client Labs expects to serve. It is continuous rather than occasional: it does not wait to be opened, and the workload never fully idles. Its latency budget is perceptual rather than conversational, because a delay that would be unremarkable in a chat interface is intolerable when the output is layered onto what a person is looking at. It reasons about a live scene while the scene is still live, which makes state — not throughput — the binding constraint. And it is intimate, holding a continuous representation of a person's attention, expression and surroundings, which places it squarely inside the memory, state and governance requirements that NSM and C2I are being designed to meet.

Put together, those properties describe a workload that cannot be served well by sending everything to a distant machine and waiting. Some of it must run on the face. Some of it must run near the person. Some of it must reach a campus measured in gigawatts. Deciding which is which, continuously and per workload, is precisely the problem Orchestration Intelligence is being industrialized to solve — and the reason Concordia treats the edge and the campus as one system rather than as a device talking to a data center.

It is also where the campus doctrine completes itself. Compute in the chairs, compute in the walls, compute in the tables — and compute on the face. Facewear is the version of that idea a person can carry out of the building, and the point at which the environment that computes stops being a place someone visits and becomes something they wear.

There is an economic consequence as well. Under the Computeconomy, a worn device is not only the most demanding consumer of compute in the system; it is also Native Compute — processing capacity already in circulation, sitting idle for most of its life, able to contribute back into the same fabric it draws from. The glasses that ask the most of the network are, in the same moment, part of what the network is made of.

C2I — Concordia Intelligence Infrastructure

Traditional computing architecture ends conceptually at the server, rack or cluster, and everything past that boundary gets described as infrastructure supporting the computer. Concordia deliberately moves the boundary. The facility is part of the machine, as are its power and cooling systems, its network, its physical arrangement, its silicon and memory, and its orchestration layer.

Labs calls the overarching architecture C2I — Concordia Intelligence Infrastructure. It unifies Concordia Computer, the overall supercomputer system; Concordia Campus, the physical campus or distributed collection of campuses; Concordia Clusters, the accelerator, compute and specialized intelligence clusters; Frontier Orchestration Clusters (FOCs), engineered around orchestration workloads spanning models, agents, tools, state and memory; Concordia Network, the interconnect and fabric layer; Concordia Power, covering generation, procurement, storage and transmission; Concordia Compute, the consumable compute-capacity layer including potential future external access; and the Nx architecture, Labs' envisioned proprietary compute architecture.

The intent is one architecture running from campus to facility to data hall to cluster to rack to server to silicon — co-designed, rather than optimized layer by layer and integrated afterward.

Frontier Silicon — The Nx Family

Concordia is also being designed as a development environment for proprietary compute architecture, which Labs calls Nx. It is envisioned around phase-specialized computing: different processor classes optimized for different stages of advanced intelligence workloads, operating together through shared memory and orchestration.

NxP — Neuro-Expression Processor is the throughput class, envisioned for training, prefill, large-batch inference and tensor-dense work. NxU — Neuro-Expression Unit is the latency class, envisioned for decoding, agent loops and interactive reasoning. Rather than forcing every phase of a workload through one processor design, Nx explores placing phases dynamically onto hardware built for them. The developer writes the intelligence workload, and the runtime determines where it should execute.

Advanced intelligence is also increasingly stateful. Models maintain context, agents maintain memory, and inference systems repeatedly move key-value caches and prefixes between resources. NSM — Neuro-State Memory is envisioned as a hybrid HBM/SRAM architecture that treats live inference state as a first-class computational object, with support for KV-page virtualization, agent context, shared prefixes, speculative execution state and cross-processor state movement.

Moving that state matters as much as computing it. Nx Fabric is an envisioned interconnect and routing architecture designed around how intelligence actually moves — expert routing, KV transfer, collective operations, state movement and dynamic scheduling — separating tightly deterministic computational islands from a more dynamic exterior scheduling environment.

Frontier Orchestration Clusters

Traditional supercomputing clusters are organized around compute-intensive workloads. FOCs extend that idea to orchestration-intensive ones.

A frontier orchestration workload may involve many models, specialized processors, agents, tools, memory systems, databases, physical machines and external services interacting simultaneously. The question stops being how quickly a model can execute and becomes how efficiently an entire intelligence system can coordinate. At gigawatt scale, that becomes computational infrastructure for Frontier Orchestration itself.

Powering 1 Supercompany, 7 Frontier Labs, 19 Frontier Intelligences

Labs is structured as 1 Supercompany, 7 Frontier Labs and 19 Frontier Intelligences, and that structure is the reason Concordia is being designed in the form it takes.

Each Frontier Lab pursues a different frontier, and each Frontier Intelligence pursues a different expression of intelligence within it. Left to itself, that arrangement produces 19 separate computational programs, 19 sets of infrastructure decisions, and 19 incompatible ways of describing the same primitives. Concordia is intended to be the layer that prevents exactly that — a common compute substrate beneath every lab, so the work of one becomes usable by the others by default rather than through an integration project undertaken afterward.

Labs describes the objective in two words. Interconnectivity — the labs are able to reach one another's models, agents, tools, memory and state across shared fabric rather than across bespoke pipelines built one pair at a time. Interoperability — what one lab builds remains legible to the next: the same orchestration semantics, the same state formats, the same runtime contracts, and the same accounting for compute. A perception system built for one frontier should be addressable by an agent built for another without translation between them.

That is what a supercompany requires and a collection of companies does not: not merely shared services, but a shared machine.

Industrializing Orchestration Intelligence

Concordia's most distinctive workload is neither training nor inference. It is orchestration — the continuous coordination of many models, agents, tools, memories and states toward outcomes, at scale.

Labs treats Orchestration Intelligence as a discipline to be industrialized rather than a feature to be shipped, and that industrialization runs in two directions at once.

Internally, Orchestration Intelligence is how 7 Frontier Labs and 19 Frontier Intelligences operate as one system rather than as neighbors. ProjectBuzz supplies the orchestration layer, Labsintelligence supplies the intelligence, and Labsintuition supplies physical execution — with Concordia providing the compute beneath all three. Frontier Orchestration Clusters are engineered specifically for the shape of that work: heavy on routing, state movement, scheduling and coordination rather than on raw tensor throughput alone.

Externally, Labs expects other organizations to encounter the same problem, and to encounter it without the option of constructing a multi-gigawatt campus to solve it. Orchestration is becoming the difficult part of applied intelligence — not obtaining a model, but conducting many of them together reliably, economically and accountably. Labs intends to make Orchestration Intelligence available as an industrial capability rather than hold it as an internal advantage, so that organizations can conduct intelligence at scale without first building the infrastructure required to do so.

Power, Network and Cooling as Architecture

A multi-gigawatt computer is also an energy system. Concordia Power is expected to span procurement, generation, transmission, storage, grid interconnection and facility-level distribution, because where energy comes from, how it enters the campus and how quickly capacity can scale all shape the architecture of the machine.

Concordia Network is the fabric connecting compute clusters, facilities, memory systems and potentially distributed sites, spanning both ultra-high-speed local interconnect and long-haul infrastructure. Clusters may be separated physically and remain part of one system computationally.

Cooling becomes a computational constraint at extreme density. Thermal density influences processor configuration, which influences rack design, which influences cooling infrastructure, which influences building geometry and campus design. The layers converge again.

Concordia Panels

Power is where the campus doctrine meets the grid, and it is where Labs expects the physical environment to become intelligent first.

Concordia Panels are an envisioned line of solar-powered intelligent panels — generation, storage and load management designed as computational devices rather than as electrical hardware with software attached afterward. A Concordia Panel is intended to generate power, store it, understand what is drawing it, prioritize among competing loads in real time, shed and restore circuits deliberately, and negotiate with the rest of the campus about where energy should travel next.

The reason a panel belongs inside a compute architecture rather than beside it is that at multi-gigawatt scale, energy decisions and compute decisions are the same decision. Scheduling a workload is scheduling power. Placing a cluster is placing load. Cooling a data hall is spending energy to reclaim compute. If Concordia Compute is to be planned, orchestrated and accounted for as one machine, then the panel — the point at which energy enters the system and is apportioned across it — has to be part of that machine as well, addressable by the same orchestration layer that places a model or an agent.

Concordia Panels are envisioned for the campus first and studied for environments beyond it, consistent with Labs' stewardship commitments around water, power and land. Panels remain a research and pre-development effort; none are commercially available.

Stewardship of Water, Power and Land

Infrastructure at this scale is an environmental commitment before it is a computational one, and Labs intends to treat it in that order. Concordia is being researched against sustainability goals written into the design rather than offset after it: water systems that prioritize closed-loop and low-withdrawal cooling and are measured on what they consume rather than what they were permitted to consume; electricity and power planned toward clean and firm generation, storage and grid interconnection from the first architecture review rather than retrofitted to it; land selected, graded and restored with the surrounding ecology treated as a design constraint the campus has to satisfy; and, wherever a site touches coastal or marine systems, sea impact studied and bounded before that site advances at all.

That work is carried with Labs Impact, the company-wide program that holds Labs to measurable environmental and community outcomes, and the Labsintuition Foundation, the lab's stewardship arm for the places its physical systems occupy. Frontier hardware is what makes the commitment enforceable rather than aspirational. Sensing, facility intelligence, robotics and automation deployed across the Labs ecosystems — the machinery Labs Machines automates, the systems Labsbotics operates, the hardware Factory5227 builds — instrument water, power, thermal and land use continuously, so consumption is reported by the infrastructure itself instead of estimated afterward. A campus that can measure itself can be held to a number.

Built With Industry Leaders

Concordia is not intended to be built in isolation. Labs expects collaboration across construction and facility partners capable of scaling multi-GW campuses; silicon and systems suppliers spanning accelerators, servers, semiconductors and memory; energy and grid partners for generation, storage, transmission and interconnection; and network and fabric providers across optical systems, switching and long-haul connectivity. Additional infrastructure partners are expected to be introduced as development progresses and formal relationships are established.

Site selection also has a civic dimension, and it is unresolved. Labs expects the first Concordia Campus to be developed with a city and regional partner, and that partner has not been chosen. Grid capacity, water rights, land use, permitting, workforce and long-term community benefit are all part of the evaluation, and Labs intends to name a host city only once those terms are settled — not to announce a location ahead of the agreement that would make it work.

A Lab, Not Just a Facility

Concordia operates as a standalone supercomputer lab within Labsintuition, and that distinction matters. It is not simply a project to construct a data-center campus. It is a research organization whose physical infrastructure is itself part of its subject matter — the campus, silicon, processors, memory architecture, network and orchestration layer can each evolve, and workloads running on one generation of Concordia may inform the architecture of the next.

Its architecture is integrated with Physical Intelligence. Labs Machines automates it through intelligent machinery and facility automation across power, cooling, maintenance, inspection and material handling. Labsbotics operates it physically through robotic and embodied systems that inspect, maintain and eventually repair parts of the facility. Factory5227 builds its hardware across racks, enclosures, cooling systems, power systems and compute assemblies. Concordia runs the compute. And Labsintuition integrates the stack — producing infrastructure that runs intelligence and increasingly becomes intelligent itself.

Concordia also sits inside the wider Labs architecture, where Labsintelligence develops frontier intelligence and ProjectBuzz develops Frontier Orchestration. Rather than treating compute as an indefinitely external dependency, Labs intends over time to build deeper into the computational stack its Frontier Intelligences depend on.

Closing the Loop

Concordia's purpose is not only to run frontier intelligence. It is to shorten the distance between intelligence and the hardware that carries it.

Today that distance is long. Silicon is specified years before the models that will run on it exist, facilities are designed around assumptions those workloads then violate, and every hardware generation is a bet placed on a moving target. Concordia is being designed to collapse that gap. Intelligence trained and served on one generation of the campus is meant to participate in designing the next — processor architecture, memory hierarchy, network topology, thermal envelope, power distribution and the physical layout of the campus itself become search spaces intelligence can explore, and Factory5227 builds what comes back.

This is what Labs means by closing the recursive self-improvement loop in the physical layer. Models improve the hardware, the improved hardware trains better models, and those models improve the hardware after that. The loop is not a metaphor at this scale: it runs through fabrication, assembly, cooling, power and construction, which is exactly why Labs is building it inside a lab that already automates machinery, operates robotics and manufactures hardware. Recursive self-improvement confined to software eventually meets a wall made of atoms. Concordia is Labs' attempt to build on both sides of that wall.

Each turn of the loop is intended to be measured, reviewed and deliberately released. It is an engineering method for compounding hardware progress, not an autonomy objective.

The Open-Weights Era

Labs holds a specific view about where the field is heading: that open-source and open-weights artificial intelligence will define its most significant era so far, and that the arrival of AGI is more likely to occur in the open than behind any single closed door.

The strategic consequence of that view is both uncomfortable and clarifying. If frontier model weights become broadly available, the model itself stops being the durable advantage. What remains genuinely scarce is everything underneath and around it: compute at scale, energy, orchestration, memory, state, the fabric connecting them, and the operational capability required to run the entire arrangement reliably for years. Value migrates from the artifact to the system that makes the artifact useful.

That is why Concordia is being designed from the ground up rather than assembled from whatever is convenient — from silicon to state to fabric to facility to power. Labs' position is that in an open-weights world, an organization earns the right to be paid by building a system valuable enough that others would rather use it than reproduce it. Concordia is Labs' attempt to build that system, and to be worth choosing on the merits of the machine itself.

Compute Exchange — CompEx

The last element Labs is exploring is a business model rather than a building.

Compute Exchange — CompEx — envisions compute as something that can be exchanged. Capacity is not consumed uniformly: workloads surge and idle, campuses run hot in one hour and cool in the next, and organizations hold reserved capacity they do not always use while others need capacity they do not hold. CompEx is Labs' exploration of an exchange layer in which compute capacity — measured, scheduled, orchestrated and accounted for through C2I — could be contributed, drawn, allocated and exchanged rather than only rented from a single provider on fixed terms.

Labs believes the AGI wave will be constrained by compute long before it is constrained by ideas. An exchange model is one approach to raising the utilization of capacity that already exists, and to allowing participation in frontier compute to extend beyond the small number of organizations able to construct campuses of their own. Coupled with Concordia Panels at the edge of generation, the same architecture that decides where a workload should run could also reason about where its energy originated and what its capacity is worth.

CompEx is at the exploration stage. No exchange has been launched, no capacity is currently offered externally, and nothing described here is available for purchase.

The Computeconomy

Generative AI has already changed both how much compute the world consumes and how it consumes it — increasingly continuous, distributed and incremental rather than occasional and centralized. At the same time, robotics, autonomous systems, intelligent hardware, vehicles, machines and connected environments are expanding the population of things that require compute at all, creating a layer of physical demand that did not previously exist.

That demand pushes compute outward. Across devices, facilities, regions, edge systems and terrestrial data centers, and in time toward orbital infrastructure. As new capacity is built along that gradient, location stops being a deployment detail and becomes an economic variable: proximity, connectivity, energy, availability, capability and demand begin to determine where a workload runs and what a unit of capacity is worth.

Labs takes the position that this shift can make computational capacity a new economic resource — individually owned, coordinated, exchanged and monetized wherever it exists. The Internet of Things connected the physical world. What Labs calls the Computeconomy describes that same world becoming computationally and economically active, with devices, machines and facilities acting as both consumers and contributors of compute.

If that holds, compute becomes as fundamental to the global economy as capital, and Exchanges emerge alongside Subscriptions, Advertising and Transactions as a distinct commercial model — a fourth way commerce is organized, concerned with continuously pricing, coordinating and exchanging distributed computational capacity.

Native Compute and Added Compute

Concordia Panels describe generation at the edge of the system. The Computeconomy describes supply, and supply arrives in two forms.

Native Compute is capacity already present in hardware people own: phones, laptops, vehicles, appliances, wearables, and eventually almost anything with processing capacity inside it. It requires no acquisition. It is already in circulation, and for most of its life it is idle.

Added Compute is additional physical compute someone deliberately acquires and introduces into that environment when they want more capacity — hardware bought to contribute rather than only to consume.

The distinction matters because of what it implies about adoption. A solar panel cannot enter the energy economy until someone builds and installs the panel. Compute is not bound the same way: capacity can be contributed incrementally from hardware already in circulation, without waiting for purpose-built infrastructure to be financed and constructed. That is the practical argument for an exchange, and the reason Labs treats Native and Added Compute as one continuous supply rather than two separate products.

The Computeconomy, Native Compute and Added Compute are positions Labs is researching, not products. No exchange has been launched, no capacity is currently offered externally, and nothing described here is available for purchase.

Research Before Construction

Concordia is a planned infrastructure program, and before a multi-gigawatt campus can be constructed the computer itself has to be researched. The first phase centers on Concordia Compute Infrastructure research, systems engineering and pre-development — spanning compute and cluster architecture, network topology, power and energy systems, cooling, facility intelligence, Nx processor research, memory architecture, orchestration systems, workload modeling, simulation, site requirements, security and construction requirements, integrated into C2I.

The eventual buildout is expected to be a major infrastructure program requiring capital substantially beyond ordinary corporate research and development. Labs anticipates that a future Concordia Campus would be financed through a broader capital architecture that may combine corporate capital, project financing, infrastructure credit, equipment and facility financing, strategic partnerships, energy financing and dedicated financing vehicles, rather than a single corporate equity round. Current and near-term corporate capital may instead support Concordia research, engineering, pre-development and early infrastructure planning.

Labs currently envisions groundbreaking in 2030, with initial site works, first-campus foundations, major power infrastructure and network development. First clusters are planned for 2031, as initial Concordia Clusters and a Frontier Orchestration Cluster energize alongside the first operational network fabric. Full campus buildout is planned for around 2032 at multi-gigawatt scale, with C2I increasingly unifying compute, networking and power as one system. Beyond that, Labs intends to explore making portions of Concordia Compute available externally while advancing future generations of Nx architecture. These timelines remain planned and may evolve as research, site development, power availability, financing, regulation, supply chains and partnerships mature.

Where This Stands

The Concordia Campus has not been constructed. NxP, NxU, NSM, Nx Fabric, Frontier Orchestration Clusters and the other proprietary architectures described here remain research concepts or planned systems, and are not commercially available products. Infrastructure partnerships, site selections, financing arrangements and hardware suppliers will be announced as they are finalized.

A computer was once a room, then a cabinet, then a machine on a desk, then a device in a pocket, and cloud computing expanded the abstraction again. At multi-gigawatt scale, perhaps the correct unit is no longer the server, the rack or even the building. Perhaps it is the entire computational environment — the processors, the memory, the network, the power, the cooling, the facility, the robots maintaining it, the machines operating it, the orchestration coordinating it, and the campus itself.

Labs is sharing Concordia now because research at this scale begins long before groundbreaking. Architecture precedes concrete, workloads precede silicon, and power precedes compute.

Compute powering the next frontier of intelligences. The campus is the computer.

Concordia Computer is a supercomputer lab within Labsintuition, LLC — lab⁷ of Labs Companies, Inc., 1 Supercompany, 7 Frontier Labs. Learn more at concordia.computer.

Media: press@labscompanies.com