The Most Comprehensive Illustrations on NVIDIA GTC 2026 Highlights: What’s Next in AI?

NVIDIA GTC 2026 did not feel like a normal tech conference. It felt like the control room of a new industrial era, the kind of event where chips, software, models, robotics, cloud infrastructure, and scientific computing all stop acting like separate stories and start looking like one giant machine. NVIDIA framed the conference that way from the start: a full-stack AI event spanning physical AI, AI factories, agentic AI, cloud systems, data platforms, healthcare, quantum chemistry, and robotics. The scale alone tells the story. NVIDIA said GTC 2026 brought together 30,000 attendees from 190 countries across 10 venues in downtown San Jose, while the broader event featured 450+ sponsors, 1,000 sessions, and 2,000 speakers.

That matters because the central question around AI has changed. A year or two ago, people mostly asked, “Which model is best?” At GTC 2026, the real question became, “What infrastructure can actually carry this next wave into production?” That shift showed up everywhere, from Vera Rubin and Feynman to DGX Station, RTX PRO Blackwell, OpenClaw, NemoClaw, IGX Thor, and cuEST. The message was blunt: AI is no longer just about generating text on a webpage. It now has to reason, act, see, move, and operate inside real businesses, real factories, real hospitals, and real networks. That is a much heavier lift, and GTC 2026 was NVIDIA’s way of saying it intends to own every layer needed to make that lift possible.

Why This Year Felt Bigger Than a Product Launch

Some conferences announce gadgets. GTC 2026 announced a worldview. Jensen Huang described the show as covering “every single layer of the five-layer cake of artificial intelligence,” and that line landed because it matched what the agenda actually delivered. Instead of isolating AI into one lane, NVIDIA connected consumer graphics, enterprise workstations, hyperscale cloud, autonomous agents, robotics, healthcare, and scientific discovery into one continuous pipeline. That is why the event felt bigger than a single keynote. It looked more like a blueprint for how AI will be built, secured, deployed, and monetized over the next several years.

The atmosphere around the conference reinforced that idea. NVIDIA highlighted sessions on multimodal agents, accelerated networking, end-to-end robotics workflows, open models, infrastructure buildouts, and physical AI. The pregame lineup alone included leaders from Perplexity, LangChain, Mistral, Skild AI, and OpenEvidence, while other sessions pulled in figures from IBM, Dell, CoreWeave, Cadence, and Morgan Stanley. That mix matters because it shows AI is no longer being shaped only by model labs. It is now being shaped by cloud operators, chipmakers, database vendors, industrial firms, healthcare companies, and open source communities. In plain English, AI has moved from “cool demo” territory into “national infrastructure and operating model” territory. That was the real pulse of GTC 2026, and it made the event feel less like a showcase and more like the opening scene of a much larger buildout.

Jensen Huang’s Keynote Set the Tone

Jensen Huang’s keynote worked because it did not waste time pretending the industry is still in an early toy phase. It started with a framing device that sounds simple but carries a lot of weight: the token as the basic unit of modern AI. NVIDIA used that idea to connect scientific discovery, digital worlds, and machines acting in the physical world. That framing gave the keynote a kind of architectural logic. Every major reveal that followed was positioned as an answer to a single problem: if tokens are the new unit of work, then AI systems need better compute, faster data movement, tighter security, and more scalable deployment models to handle that work.

Huang also used the keynote to pull a long historical thread through NVIDIA’s identity. He celebrated the 20th anniversary of CUDA, called it the “flywheel” of accelerated computing, and tied the company’s origins in GeForce to the present AI boom. That was not nostalgia for its own sake. It was a reminder that NVIDIA sees its consumer graphics history, software stack, and data-center strategy as parts of the same story. Even DLSS 5 fit into that arc, with NVIDIA showing how 3D-guided neural rendering enables real-time photoreal 4K performance on local hardware. The subtext was clear: the line between graphics innovation and AI infrastructure keeps getting thinner. One side taught NVIDIA how to optimize performance on the edge, and the other side is turning that expertise into the backbone of a global AI economy.

Tokens, CUDA, and the New Scale of AI Demand

This part of the keynote may have been the most revealing because it translated hype into hard economic ambition. Huang said venture startups attracted $150 billion in investment over the last year, described demand for NVIDIA GPUs as “off the charts,” and estimated that compute demand has grown 1 million times over the past few years. He also said he sees at least $1 trillion in revenue from 2025 through 2027. Whether you read those numbers as confidence, salesmanship, or both, they show how NVIDIA wants people to think about AI now: not as a software feature, but as a generational infrastructure expansion.

That helps explain why CUDA remains central to the conversation. In AI, raw silicon alone is like a sports car with no roads. It looks impressive, but it cannot create large-scale value by itself. NVIDIA keeps coming back to CUDA and its CUDA-X libraries because they are the roads, tunnels, bridges, and traffic systems that let the hardware move real workloads. Huang even called those libraries the company’s “crown jewels.” That is not accidental wording. 

As AI spills into finance, telecom, healthcare, robotics, industrial systems, and quantum research, the winners will not just be the companies with fast chips. They will be the ones that can wrap those chips in software ecosystems people can actually use. That is why NVIDIA GTC 2026 felt less like a celebration of isolated hardware and more like an argument for a deeply integrated AI stack.

Vera Rubin Signals a New Full-Stack Era

The most important hardware story at GTC 2026 was not just a faster chip. It was a broader move toward full-stack computing platforms built for agentic AI and AI factories. That is where Vera Rubin entered the picture. NVIDIA described it as a new platform made up of seven chips, five rack-scale systems, and one supercomputer for agentic AI, including the new NVIDIA Vera CPU and BlueField-4 STX storage architecture. If that sounds less like a product SKU and more like a city plan, that is exactly the point. NVIDIA is trying to move buyers away from thinking about AI infrastructure as a shopping cart full of parts and toward thinking about it as a unified operating environment.

That shift matters because AI workloads are becoming ugly in the most practical sense of the word. They do not just train once and sit still. They retrieve data, call tools, coordinate agents, run inference loops, manage memory pressure, and demand secure network movement across increasingly complex environments. In that world, the performance bottleneck is rarely one thing. It is the friction between many things. So when NVIDIA talks about a “vertically integrated” system optimized end to end, it is really promising to sand down that friction. The takeaway from Vera Rubin was simple: the future of AI will reward system design, not just peak chip performance.

What Vera Rubin Changes for AI Infrastructure

Huang described Vera Rubin as an entire system “optimized as one giant system,” and that line may be the best summary of NVIDIA’s 2026 strategy. The company no longer wants to be viewed only as the provider of the world’s hottest accelerators. It wants to be viewed as the architect of entire AI factories. That is a much bigger ambition, because it includes compute, memory, storage, networking, orchestration, and simulation. At GTC, NVIDIA reinforced that by launching the Vera Rubin DSX AI Factory reference design and the NVIDIA Omniverse DSX Blueprint, while DSX Air was positioned as a way for companies to simulate AI factories in software before building them in the physical world.

That is an unusually practical idea in a market that often talks in abstractions. Building AI infrastructure is expensive, messy, and full of tradeoffs around cooling, layout, power, throughput, and utilization. A bad design choice can haunt a deployment for years. So the idea of simulating an AI factory before pouring concrete or ordering racks is not just flashy branding. It is common sense. It treats AI infrastructure the way aerospace and automotive teams already treat other complex systems: model it first, then build. That gives Vera Rubin more significance than a simple generational upgrade. It becomes part hardware platform, part operational philosophy. And once you see it that way, NVIDIA’s broader message snaps into focus: the future AI winner is not the company with one magical model. It is the company with the cleanest path from model development to production-scale output.

Feynman, Rosa, and AI Data Centers in Orbit

If Vera Rubin established NVIDIA’s near-term full-stack direction, Feynman showed just how far out the company is willing to draw the map. NVIDIA said the next major architecture after Vera Rubin will be Feynman, featuring a new CPU called Rosa, named after Rosalind Franklin, paired with LP40, BlueField-5, CX10, Kyber, and Spectrum-class optical scale-out. NVIDIA framed that combination as an advance across every pillar of the AI factory: compute, memory, storage, networking, and security. That matters because it suggests NVIDIA is thinking about future AI systems as distributed organisms, not isolated servers. They have to move tokens, tools, and data with as little resistance as possible.

Then came the line that made the whole keynote feel almost sci-fi: NVIDIA said it is going to space. Future systems like Space-1 Vera Rubin are being designed to bring AI data centers into orbit, extending accelerated computing from Earth to space. Now, it is fair to read that with a raised eyebrow. But even if orbital AI data centers remain a longer-horizon concept, the strategic message is still valuable. NVIDIA wants the market to associate its platforms with extreme environments, extreme scale, and long-duration ambition. It wants to be seen as the company building for the next infrastructure frontier before that frontier fully arrives. That kind of narrative power matters in tech, especially when customers are making billion-dollar bets. Feynman and Rosa did not just hint at future performance. They signaled that NVIDIA is trying to own the imagination around where AI infrastructure goes next.

Open Models and Agentic AI Move to Center Stage

The second major story of NVIDIA GTC 2026 was that the industry’s center of gravity keeps shifting from chatbots to agentic AI. An agent is not just a model that answers a prompt. It is a system that can reason through steps, call tools, access context, take action, and keep working over time. That is a much more demanding use case, and it explains why NVIDIA spent so much time on OpenClaw, NemoClaw, OpenShell, and Nemotron. The company clearly sees long-running agents as one of the biggest demand drivers for next-generation compute, especially in enterprise environments where safety, governance, and local control matter as much as raw intelligence.

What made this feel different from generic “AI agent” hype was the way NVIDIA tied it to developer workflows and infrastructure choices. It did not present agents as vapor. It presented them as something you can build, test, secure, run locally, and later scale into data-center AI factories. NVIDIA even hosted a build-a-claw experience at GTC, where attendees could customize and deploy always-on assistants using OpenClaw and local or onsite compute. That gave the whole strategy more texture. It suggested that NVIDIA wants agents to become as normal as spinning up a dev environment: not mysterious, not elite, just part of the modern computing stack.

OpenClaw, NemoClaw, and OpenShell Explained

Huang spotlighted OpenClaw as an open source operating layer for agentic computers, and he described it in unusually strong terms, saying every company now needs an OpenClaw strategy. Hyperbole aside, the reasoning is easy to understand. If businesses want assistants that do more than answer questions, they need systems that can stay alive, remember context, use tools, and operate within policy boundaries. That is where NemoClaw and OpenShell come in. NVIDIA positioned NemoClaw as the open source stack that helps developers run always-on OpenClaw assistants more safely with a single command, while OpenShell provides the secure runtime and the governance layer that defines how agents access data and operate within rules.

That security angle is the key. Enterprises do not fear AI because it writes mediocre emails. They fear AI because an always-on system with tool access can create real consequences if it behaves badly. NVIDIA’s answer is to make policy enforcement, network guardrails, and privacy routing part of the architecture, not an afterthought. That design choice makes a lot of sense. If agents are going to touch calendars, files, databases, customer data, and internal systems, guardrails cannot sit outside the system like tape on the floor. They have to be built into the floor itself. That is why OpenClaw, NemoClaw, and OpenShell stood out at GTC 2026. They felt less like a side project and more like NVIDIA’s opening move in defining how agentic AI gets deployed responsibly inside real organizations.

The Nemotron Coalition and NVIDIA’s Open Model Strategy

NVIDIA also used GTC 2026 to push harder into the open model conversation. The company announced a Nemotron Coalition built around six frontier model families: NVIDIA Nemotron for language and reasoning, NVIDIA Cosmos for world and vision models, NVIDIA Isaac GR00T for general-purpose robotics, NVIDIA Alpaymayo for autonomous driving, NVIDIA BioNeMo for biology and chemistry, and NVIDIA Earth-2 for weather and climate. That lineup matters because it shows NVIDIA does not think “open models” should mean one generic language model trying to solve every problem. Instead, it is betting on a portfolio approach, where domain-specific systems handle different categories of work.

There is also a strategic twist here. Open ecosystems create more reasons for developers to stay close to NVIDIA hardware and software. If you can build with Nemotron, simulate with Cosmos, deploy robotics with GR00T, and scale through NVIDIA infrastructure, then the company is not just selling compute. It is shaping the playground itself. That helps explain why GTC 2026 kept returning to the phrase full stack. NVIDIA’s open model story is not about generosity alone. It is also about gravity. The more parts of the workflow it can anchor, the harder it becomes for developers and enterprises to drift away. That does not make the strategy less useful. It makes it more realistic. In tech, the best ecosystem stories usually mix genuine developer value with sharp business incentives, and Nemotron looks exactly like that kind of play.

From the Desk to the AI Factory

One of the smartest themes at GTC 2026 was that not every AI workflow should begin in a giant data center. Developers still need local environments where they can prototype, fine-tune, validate, and secure systems close to where work actually happens. NVIDIA leaned into that idea with DGX Spark, DGX Station, and new RTX PRO Blackwell systems. The tone was almost refreshingly practical. Before a company scales an agent across teams or spins up a larger AI factory, it needs a place to test ideas, validate guardrails, and iterate without friction. That is where the desk comes back into the story.

This matters because the AI industry sometimes acts as if scale is the only thing that matters. It is not. Velocity matters too. Developers need fast loops. Security teams need local control. Regulated industries need air-gapped paths. Domain experts need systems they can actually touch and understand. NVIDIA’s “from desktop to AI factory” theme worked because it acknowledged those realities. Rather than forcing a sharp divide between local development and production infrastructure, NVIDIA tried to show a continuous path between them. That is a powerful idea because it turns local systems from side tools into the first stage of a broader deployment pipeline.

DGX Spark and DGX Station Bring Supercomputing Closer

NVIDIA positioned DGX Spark and DGX Station as the local foundation for autonomous, long-running agents. Paired with NemoClaw, these systems are meant to let teams develop and validate agents locally before scaling them to larger AI factories. DGX Spark supports clustering of up to four systems in a unified setup, effectively creating a compact “desktop data center,” while DGX Station was described as the world’s most powerful deskside supercomputer, powered by the GB300 Grace Blackwell Ultra Desktop Superchip. NVIDIA said DGX Station delivers 748GB of coherent memory and up to 20 petaflops of AI compute, and can run open models of up to 1 trillion parameters directly from the desk.

That is a striking signal about where enterprise AI is headed. The desk is no longer just where someone writes prompts. It is becoming a staging ground for serious development, security testing, model tuning, and specialized domain workflows. NVIDIA reinforced that by pointing to early usage across weather forecasting, surgical workflows, protein structure analysis, humanoid robotics, and advanced sports analytics. The company also stressed continuity: applications developed locally can move to GB300 NVL72 systems in the data center or cloud without rearchitecting. That is a big deal. It means the desktop is not a cul-de-sac. It is an on-ramp. And in a market obsessed with scale, that kind of frictionless path from prototype to production could end up being one of NVIDIA’s most useful advantages.

RTX PRO Blackwell Expands Enterprise and Workstation AI

If DGX Station is the heavyweight desk-side option, RTX PRO Blackwell is NVIDIA’s way of spreading accelerated AI deeper into mainstream enterprise and workstation environments. The new RTX PRO 4500 Blackwell Server Edition was positioned as universal acceleration from the data center to the edge, with NVIDIA claiming up to 100x performance for vision AI and up to 50x performance for vector databases compared with traditional CPU-only servers. It also comes in a compact 165-watt, single-slot form factor, which makes it easier to deploy in standard enterprise environments rather than only in exotic high-power racks.

The workstation story around it is just as important. NVIDIA partners including Lenovo, Dell, and HP unveiled AI-ready systems built around RTX PRO Blackwell GPUs, while NVIDIA said the top workstation variants can deliver up to 4,000 TOPS of local AI compute and 96GB of GPU memory for secure on-premises agentic workflows with NemoClaw. That combination matters because many organizations do not want every sensitive workflow running in the cloud. They want local control, governance, and privacy, especially when agents are touching internal files, apps, and proprietary processes. NVIDIA also backed the performance story with practical software support from Ollama, SGLang, and LM Studio, plus claims of up to 10x SLM inference boosts, up to 5x faster Spark query performance, and up to 10x better total cost of ownership in certain data workloads. The result is a simple but powerful message: enterprise AI is no longer trapped between “tiny laptop demo” and “massive cloud cluster.” There is now a wide middle ground, and NVIDIA wants to dominate it.

Cloud, Data, and Platform Partnerships Get Serious

No matter how strong the local story becomes, large-scale AI still runs on cloud partnerships, data pipelines, and platform integration. GTC 2026 made that crystal clear. NVIDIA did not present hyperscalers as distant buyers of chips. It presented them as co-builders of the next AI operating layer. That is a meaningful difference. It suggests the company is aligning itself not just with cloud capacity, but with how enterprises will actually consume and govern AI in production. The same was true for data tooling. NVIDIA put major focus on cuDF, cuVS, and enterprise-ready processing pipelines, showing that the future of AI is not just about better models. It is also about getting the right data into those models cheaply and fast enough to matter.

That theme cuts through a lot of industry noise. You can build the smartest agent in the room, but if it cannot reach current enterprise data quickly or securely, it becomes a very expensive intern. GTC 2026 kept hammering that point. AI systems need fresh context, efficient retrieval, secure deployment, and infrastructure that does not collapse under concurrency. That is why the cloud and data announcements mattered so much. They were not side dishes. They were the plumbing behind the whole AI factory idea.

AWS, Microsoft, and Faster Data Pipelines

The AWS announcement was massive on scale alone. NVIDIA said AWS will deploy more than 1 million NVIDIA GPUs, alongside LPUs and broader NVIDIA AI infrastructure, to support rising demand in the age of agentic AI. NVIDIA also said those deployments will span the Blackwell and Rubin architectures, RTX PRO Blackwell Server Edition GPUs, and Groq 3 LPUs, helping AWS AI factories operate as unified compute engines for training and deploying next-generation AI systems. That is the sort of number that resets expectations. It tells enterprises this is no longer a waiting game for whether hyperscale support will show up. It is already arriving at industrial scale.

The Microsoft side of the story was just as revealing, especially around security and sovereignty. NVIDIA said Microsoft has deployed hundreds of thousands of liquid-cooled Grace Blackwell GPUs across its data centers in less than a year, and that Azure was the first hyperscale cloud provider to power up Vera Rubin NVL72 systems. Microsoft Foundry now supports building specialized agents with NVIDIA Nemotron, while Azure Local expands sovereign AI options for customers that want on-premises control. Microsoft Security also said early collaboration around Nemotron and OpenShell showed 160x improvement in finding and mitigating AI-based attacks. That is exactly the kind of detail enterprises want to hear. Performance matters, but safety and governance often decide who actually gets to deploy.

Then there is the data pipeline story. NVIDIA said cuDF and cuVS are being adopted across major data platforms to deliver up to 5x faster processing while lowering costs, and that the relevant open source engines are downloaded more than 200 million times monthly by developers. It also said 80% of enterprise data is unstructured, which helps explain why vector search and retrieval acceleration now sit so close to the heart of AI infrastructure. Snap reported a 76% cut in daily data processing costs on GKE while analyzing 10 petabytes in a three-hour window, and IBM said early experiments with Nestlé ran five times faster with 83% lower cost savings. These are the kinds of numbers that make AI feel less like inspiration and more like operational math. Faster models are great. Faster data is what makes those models useful on Tuesday morning.

Physical AI, Healthcare, and Quantum-Accelerated Science

The boldest claim at NVIDIA GTC 2026 was that AI is moving beyond the screen. That phrase can sound cheesy if it is not backed by substance, but NVIDIA actually brought substance. Across the keynote and breakout announcements, the company pushed physical AI as the next frontier: systems that perceive, reason, simulate, and act in the real world. That showed up in robotaxis, industrial robotics, telecom edge platforms, healthcare robotics, and industrial edge computing. Huang even used a live Olaf demo powered by NVIDIA’s physical AI stack, Jetson, the Newton physics engine, and Omniverse simulation to underline the point that these systems are being simulated, not just pre-rendered. It was theatrical, yes, but it also made a deeper point. Physical AI depends on simulation, and simulation depends on infrastructure.

This is where GTC 2026 became especially interesting. NVIDIA was not only selling the dream of embodied AI. It was laying out the components: the edge platforms, the datasets, the robotics models, the simulation frameworks, and the scientific libraries that make physical intelligence more than a slogan. That makes the whole category feel much closer than many people assume. AI is not waiting politely inside chat windows anymore. It is stepping into warehouses, operating rooms, vehicles, rail systems, and semiconductor labs.

IGX Thor, Healthcare Robotics, and cuEST Show AI Leaving the Screen

The IGX Thor announcement captured the industrial edge side of this shift. NVIDIA said the platform is now generally available and built for real-time sensing and inference in autonomous, safety-critical environments. It highlighted deployments or evaluations by companies such as Caterpillar, Hitachi Rail, Johnson & Johnson, KARL STORZ, Medtronic, LEM Surgical, and even Planet Labs and CERN. That range matters. It shows physical AI is not one industry trend. It is a shared infrastructure pattern spreading across transportation, medtech, industrial automation, and scientific systems.

Healthcare was one of the clearest examples of AI crossing into the physical world. NVIDIA launched what it called the first domain-specific physical AI platform for healthcare robotics, including Open-H, Cosmos-H, GR00T-H, and the Rheo blueprint. The company said Open-H includes 776 hours of surgical video, 11 robotic system embodiments, and four surgical indications, while leaders such as CMR Surgical, Johnson & Johnson MedTech, Moon Surgical, Rob Surgical, PeritasAI, and Proximie are already adopting the stack. That is a big deal because it moves healthcare AI beyond image analysis into robotic action, simulation, and decision support inside clinical environments.

Then there was cuEST, one of the most underrated announcements from an SEO perspective because it points to where AI becomes scientific leverage. NVIDIA launched cuEST as a new CUDA-X library for accelerated quantum chemistry in semiconductor design, with Applied Materials, Samsung, Synopsys, and TSMC among the initial adopters. Samsung reported up to 5x end-to-end speedups for key workloads, while Synopsys said it could accelerate simulations up to 30x for semiconductor workflows. NVIDIA framed the goal as moving high-fidelity material modeling “from the lab to the fab.” That phrase sticks because it captures the real meaning of GTC 2026. AI is no longer just generating outputs. It is becoming the engine inside design, manufacturing, robotics, and scientific discovery itself.

Conclusion

NVIDIA GTC 2026 made one thing obvious: the next phase of AI will be won by systems, not slogans. The biggest story was not a single benchmark or a single model. It was the way NVIDIA connected Vera Rubin, Feynman, OpenClaw, NemoClaw, DGX Station, RTX PRO Blackwell, AWS, Microsoft Foundry, IGX Thor, healthcare robotics, and cuEST into one larger narrative. That narrative says AI is becoming infrastructure in the deepest sense of the word. It has to be designed, secured, simulated, deployed, governed, and scaled like a critical industrial platform.

That is why this conference mattered. It showed AI moving in three directions at once: upward into hyperscale cloud and AI factories, inward onto local desks and on-premises systems, and outward into the physical world of machines, sensors, and scientific workflows. If 2024 and 2025 were about proving AI could amaze people, 2026 looks much more like the year the industry started figuring out how AI will actually run the modern world. And if NVIDIA’s roadmap lands the way it wants, GTC 2026 may be remembered as the moment that future stopped sounding theoretical.

Notice: The initial edition of this article was drafted by AI.

FAQs

1. What was the biggest announcement at NVIDIA GTC 2026?
   The biggest announcement was arguably Vera Rubin, because it represents more than a chip update. NVIDIA positioned it as a full-stack computing platform with seven chips, five rack-scale systems, and one supercomputer for agentic AI, plus related AI factory reference designs.
2. Why was agentic AI such a major theme at GTC 2026?
   Because NVIDIA sees the next AI wave as long-running systems that can reason, plan, use tools, and act across workflows. That is why the company emphasized OpenClaw, NemoClaw, OpenShell, and open Nemotron models rather than only traditional chat experiences.
3. How is NVIDIA approaching enterprise AI deployment?
   NVIDIA is building a path from local development to production-scale AI factories. DGX Spark, DGX Station, and RTX PRO Blackwell give teams local and on-premises options, while partnerships with AWS and Microsoft provide large-scale cloud deployment and governance layers.
4. What does GTC 2026 say about the future of physical AI?
   It says physical AI is moving fast from concept to deployment. NVIDIA showcased robotaxis, industrial automation, healthcare robotics, and edge AI through IGX Thor, Cosmos, GR00T-H, and healthcare-specific robotics tools.
5. Why should data and science teams care about GTC 2026?
   Because NVIDIA did not focus only on models. It also pushed cuDF, cuVS, and cuEST, showing how accelerated computing can speed up enterprise data processing, retrieval workflows, and quantum-scale semiconductor simulation. That makes GTC 2026 relevant not just to AI engineers, but also to analytics teams, researchers, and industrial scientists.

Author

Oscar Wu

Fostering knowledge sharing to create a more fluid and transparent information landscape.

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