Key Takeaways
Jensen Huang’s 2026 AI manufacturing facility map spotlighted NVIDIA’s DSX buildout framework.Marvell gained 241% YTD; AI infrastructure companies might even see heightened investor focus.NVIDIA initiatives 100 GW of AI factories by 2030, shifting consideration to ecosystem companions.
The next visitor put up comes from Ziven.io, a public markets intelligence platform delivering information on firms uncovered to bitcoin mining, synthetic intelligence, and crypto treasury methods. Initially printed on June 18, 2026, by Cindy Feng.
Since Jensen Huang stood on the Computex stage and known as Marvell “the subsequent trillion-dollar firm,” MRVL hasn’t seemed again. A inventory that traded between $50-$100 as just lately as April now sits round $300, with an ATH round $316 and a achieve of roughly 241% YTD. One sentence from Jensen, and an organization re-rated by a quarter-trillion {dollars}.
Not shocking, a brand new train has begun: comb by every part Jensen says, discover the subsequent title he’ll bless and get wealthy.
I perceive the impulse, however what’s clear from listening to Jensen’s entire keynote is that most individuals are watching the improper factor. Jensen didn’t simply drop a sizzling title, he laid out a full map of how an AI manufacturing facility really will get constructed, layer by layer, firm by firm. That map is the half value understanding, as a result of it nonetheless works lengthy after the hype fades. I’m going to stroll you thru that particular slide, however first let’s begin with the half that confused lots of people.
RTX, DGX, DSX: employee, group, manufacturing facility
Jensen cut up NVIDIA’s manufacturers into three layers, every a much bigger unit than the final:
RTX is the GPU, the employee. The chip that does the precise computing. One pair of arms. DGX is the system, the group. Wire a pile of these chips right into a single machine and also you’ve bought a DGX. A crew appearing as one unit. DSX is the infrastructure, the manufacturing facility. The constructing these groups work in, plus the ability, cooling, community, and software program to maintain hundreds of them operating across the clock.
RTX and DGX you’ve in all probability heard of. DSX is the brand new one, and it’s the one value understanding, as a result of it’s the place NVIDIA stops promoting you a chip and begins promoting you a technique to construct all the plant.
What DSX really is
In Jensen’s phrases, DSX is “a blueprint, a reference design for constructing and working AI factories at most effectivity and profitability”.
In plainer phrases, it’s a recipe and a toolkit for booting up a gigawatt of compute and protecting it worthwhile. NVIDIA even named the toolkit’s components: a digital twin to design and take a look at the entire manufacturing facility earlier than a single rack ships (DSXSim), an working system to run it as soon as it’s stay (DSX OS), and instruments to pack extra GPUs into the identical energy finances and flex with the grid (DSX Max LPS, DSX FLEX). The pitch is that 100 gigawatts of those factories come on-line earlier than the last decade is out, and that DSX-built ones run cheaper and lean on the grid extra gently.
That every one feels like one thing NVIDIA would promote you by itself. It’s really not the case.
No single firm can construct a complete AI manufacturing facility
A one-gigawatt AI manufacturing facility is now a $30-100 billion mission, in response to Jensen. At that scale it stops being a server room and turns into infrastructure on the order of a refinery or an influence station.
NVIDIA can’t construct that alone. It doesn’t pour concrete, run high-voltage strains, manufacture chillers, or negotiate with the native utility. And you’ll’t bolt these items on one by one, as a result of the chips, racks, community, energy, and cooling all must be designed collectively from day one. Each hour the manufacturing facility sits idle is income misplaced, so a construct this costly has to work the primary time.
Subsequently NVIDIA did the wise factor: it printed the blueprint and assembled a coalition of companions to cowl each layer it doesn’t do itself. That coalition has a reputation, the AI Manufacturing unit Ecosystem, and Jensen put all the roster on a single slide. That slide is the map.
The map: who really builds an AI manufacturing facility
Most of these firm are non-public or listed abroad, however nonetheless a loads of U.S. listed ones. I made a desk to listing all publicly traded names from the map. The final column is my tough learn of how a lot of every enterprise really rides on the AI build-out, as a result of being on the slide (may lean on advertising and marketing function) and being moved by it are two very various things.
Please observe OTC or overseas listed names are excluded from the desk. In order for you the entire CSV listing, simply drop me a message and I’ll ship it over. Additionally a couple of names are nonetheless non-public with upcoming IPOs, equivalent to Lambda (US), Nscale (UK), Firmus (Australia) and Yotta (India).
Necessary Notice
One has to comprehend {that a} brand show tells you an organization is concerned but it surely doesn’t inform you whether or not the involvement is materials. For CoreWeave or Vertiv, AI-factory demand is actually all the story. For Caterpillar or Nationwide Grid, it’s a sliver of a far greater enterprise that can barely transfer the inventory. The “Excessive” rows offer you torque and volatility in equal measure. The “Low” rowsgive you a steadier firm with solely a skinny thread tied to AI built-out commerce.
Remaining Ideas
Possibly one in every of these names turns into the subsequent Marvell, perhaps none do. That’s not a name I could make from a slide, and chasing whichever brand you hope Jensen blesses subsequent is nearer to a guessing recreation than a technique.
The sturdy worth right here is the map, plus a sharper query to take into it. For any title on this chart, how a lot of its enterprise really rides on the AI build-out?How a lot pricing energy does its layer maintain? Pure-plays, diversified incumbents and commodity undoubtedly have totally different leverages and threat profiles.
Right here’s what doesn’t change: Each hyperscaler deal you’ll examine, each “X-gigawatt information heart” headline, quietly will depend on this complete stack to occur. Somebody designs it, somebody builds it, somebody powers it, somebody cools it, somebody racks the servers, somebody runs it. This chart is the solid listing. Choose a layer that pursuits you and weigh its publicity in opposition to how a lot pricing energy it holds. That’s the place the actual work begins. The map gained’t inform you what to purchase, but it surely’s a framework you’ll be able to consult with.
