Spokesperson: Alexis Cathalifaud, CEO
As demand for synthetic intelligence compute continues to develop, the infrastructure supporting that demand is turning into a strategic consideration in its personal proper. Firms throughout the sector are racing to safe entry to more and more highly effective GPUs, whereas questions round electrical energy, data-center capability, cooling and connectivity have gotten tougher to separate from the compute itself.
Clichmont is taking a distinct strategy. Quite than constructing its mannequin primarily round rented GPU capability, the corporate is concentrated on proudly owning and controlling the bodily infrastructure on which successive generations of AI {hardware} can function. On this interview, Clichmont CEO Alexis Cathalifaud discusses why the corporate believes energy and data-center infrastructure might turn into the extra sturdy bottlenecks, the way it approaches web site choice and the challenges of scaling bodily infrastructure, in addition to the position of its $CLAI token inside the broader ecosystem.
1) Each firm on this class is preventing over GPU entry proper now. Clichmont’s reply is to construct the info facilities as a substitute of renting the chips. Why does possession matter greater than entry?
As a result of GPU entry offers you compute; infrastructure possession offers you management over the economics of compute.
For an organization like Clichmont, proudly owning or controlling the data-center layer can matter extra strategically than merely securing rented GPUs. Whenever you lease GPU capability from a hyperscaler or GPU cloud, you inherit another person’s pricing, availability, energy constraints, networking structure, deployment schedule, and margins. When demand spikes, entry can turn into costly or constrained.
Proudly owning the infrastructure adjustments the equation. Clichmont can probably resolve which GPUs to deploy, when to improve them, how densely to put in them, how energy and cooling are engineered, and the way the capability is commercialized. The identical facility may also evolve from one GPU technology to the subsequent slightly than tying the enterprise thesis to a selected chip.
There’s one other necessary distinction: GPUs depreciate shortly; power-ready data-center capability is a longer-lived strategic asset. A GPU technology could turn into economically much less aggressive inside just a few years, whereas land, grid connections, substations, cooling infrastructure, fiber connectivity and permitted megawatts can stay priceless throughout a number of generations of accelerators.
That makes the scarce useful resource more and more not simply the GPU itself, however the power to energise hundreds of GPUs at scale. An organization can purchase chips and nonetheless have nowhere appropriate to deploy them. Securing 10,000 GPUs is one downside; securing the tens of megawatts of dependable electrical energy, cooling and community infrastructure required to function them is one other.
2) You’re up towards corporations which can be already public or heading there – CoreWeave, Crusoe, Lambda. What do you suppose their mannequin will get flawed, if something?
I don’t suppose CoreWeave, Crusoe or Lambda obtained the mannequin flawed. They proved that AI compute is a large market. The place we differ is in what we consider will stay scarce. GPUs change each technology. The sturdy bottleneck is the infrastructure required to run them — energy, land, cooling and connectivity. Clichmont’s thesis is that slightly than competing solely to lease the newest GPU, we wish to management the infrastructure on which successive generations of GPUs will function. In a market the place everyone seems to be chasing chips, we’d slightly personal the place the place the chips must reside
3) There’s a rising argument that power, not chips, is the precise bottleneck for AI infrastructure. How a lot does that form the place and the way Clichmont builds?
Vitality shapes nearly each infrastructure determination we make. A GPU with out dependable energy is simply costly {hardware} sitting in a rack. We consider the actual competitors over the subsequent decade received’t merely be for GPUs—will probably be for megawatts.
So when Clichmont evaluates a web site, we don’t begin by asking the place we will discover the most cost effective constructing. We ask: the place can we safe dependable energy, on the proper economics, with the power to scale? What’s the time-to-power? What’s the grid scenario? What cooling structure does the local weather enable? And might that web site help the subsequent technology of GPUs, not simply those we’re putting in right this moment?
That’s one cause areas with sturdy power fundamentals are strategically attention-grabbing to us. Chips will be shipped world wide. You may’t ship 100 megawatts. The compute finally has to go the place the power is.
So I wouldn’t say chips cease being a bottleneck. They continue to be crucial. However more and more, proudly owning GPUs isn’t sufficient. The aggressive benefit is with the ability to energy, cool and function them economically at scale. That’s what we’re constructing Clichmont round.
4) Clichmont’s websites vary from a solar-powered facility in Alicante to a brand new construct in Bodo, Norway. What truly decides the place a knowledge heart will get constructed – is it about power, land, local weather, one thing else?
We don’t select a location as a result of one variable seems to be engaging. We select it as a result of your entire infrastructure equation works.
Energy is the primary filter: what number of megawatts can we safe, at what value, how dependable is that provide, and—critically—how shortly can it truly be delivered? Then we have a look at cooling, local weather, fiber connectivity, land, allowing, safety and the power to broaden.
Bodø and Alicante are attention-grabbing exactly as a result of they signify completely different strengths. Northern Norway offers us a local weather that may help environment friendly cooling and a powerful power setting. Alicante offers us a distinct power profile and the chance to combine photo voltaic into the infrastructure technique. We don’t consider each Clichmont information heart must look equivalent—the structure ought to reply to the assets of the situation.
And land by itself isn’t notably priceless to us. An inexpensive parcel with no scalable energy or fiber shouldn’t be a data-center web site. What issues is whether or not we will flip that location into dependable, economically aggressive compute capability.
In the end, we’re probably not on the lookout for land. We’re on the lookout for locations the place power, connectivity, cooling and scalability converge. That’s the place we construct.
5) That is an infrastructure firm with a token connected to it. For a reader who’s skeptical of that mixture, what’s the trustworthy case for why $CLAI exists in any respect?
The skeptical view is totally truthful. A token shouldn’t exist simply because an organization operates in AI. If $CLAI had been merely a financing wrapper round our information facilities, I wouldn’t take into account {that a} compelling cause to create it.
Clichmont is the infrastructure enterprise. It builds and operates compute capability. $CLAI is meant to be a digital financial layer across the broader ecosystem — one thing that may finally help on-chain participation, treasury exercise and group governance in ways in which standard fairness isn’t designed to do.
And we have now to earn the best to make that distinction. The bodily infrastructure has to exist independently of the token, and the token has to reveal actual utility independently of hypothesis. If we will’t present each, then the skepticism is justified.
So I wouldn’t ask anybody to consider in $CLAI just because Clichmont owns GPUs or builds information facilities. The take a look at is way easier: does the token finally do one thing helpful, clear and measurable that couldn’t be completed as successfully with a standard database or standard company construction? That’s the usual we must be held to.
6) What’s the toughest a part of scaling bodily infrastructure that individuals who’ve solely constructed software program are inclined to underestimate?
The toughest half is that bodily infrastructure doesn’t scale at software program velocity. In software program, if demand doubles, you may usually provision extra capability shortly. In a knowledge heart, each extra megawatt has a bodily dependency behind it — grid capability, transformers, switchgear, cooling, fiber, permits, building and finally {hardware}.
And people dependencies don’t transfer in parallel as neatly as individuals think about. You may have the land and never have the ability. You may have the ability allocation and wait months for electrical tools. You may have the constructing prepared and nonetheless be ready for a grid connection. One lacking element can delay a whole deployment.
The opposite distinction is that errors are costly and troublesome to reverse. Software program will be patched in a single day. You may’t patch a badly designed 50-megawatt electrical system in a single day. You’re making capital selections right this moment primarily based on what GPUs, energy densities and cooling necessities could appear to be a number of years from now.
So the actual ability isn’t merely constructing information facilities. It’s sequencing capital, energy, building and buyer demand in order that they arrive at roughly the identical second. Construct too early and you’ve got costly idle infrastructure. Construct too late and the client goes some place else.
That execution self-discipline might be what individuals coming purely from software program underestimate most. In bodily AI infrastructure, velocity issues — however timing issues much more.
7) In the event you needed to identify the largest danger in betting on a build-it-yourself mannequin as a substitute of a capital-light rental mannequin, what wouldn’t it be?
The largest danger is capital depth mixed with timing. Whenever you construct infrastructure your self, you’re committing vital capital right this moment towards assumptions about demand, energy economics and expertise a number of years into the long run.
A rental mannequin offers you flexibility. If the market adjustments, you may cut back capability, transfer suppliers or undertake the subsequent technology of {hardware}. Whenever you personal the infrastructure, you don’t have that luxurious. A substation, cooling system or data-center constructing is a long-duration determination.
For us, the largest hazard subsequently isn’t merely spending an excessive amount of — it’s constructing the flawed capability, within the flawed place, on the flawed time. In the event you construct forward of demand, capital sits idle. In the event you construct too slowly, you miss the market.
That’s why we don’t view possession as ‘construct the whole lot ourselves.’ The target is to regulate the strategic infrastructure whereas remaining versatile round expertise. The constructing, energy, cooling and connectivity ought to survive a number of generations of GPUs slightly than turning into depending on one {hardware} cycle.
So sure, the capital-light mannequin has an actual benefit: optionality. Our wager is that if we execute appropriately, giving up some short-term optionality creates one thing extra priceless over the long run — management over capability, energy economics and the bodily infrastructure that AI more and more depends upon.
8) Three years from now, the place would you like Clichmont to sit down relative to the CoreWeaves and Nebiuses of the world?
Three years from now, I don’t count on Clichmont to be the largest firm within the class, and that’s not the target. CoreWeave and Nebius have monumental scale and entry to capital. Making an attempt to duplicate them can be the flawed technique for us.
I would like Clichmont to be acknowledged as one of the crucial environment friendly unbiased AI infrastructure operators in Europe — with actual working belongings, secured energy, high-density GPU capability and a monitor report of bringing new compute on-line shortly.
Our benefit has to return from being disciplined about the place we construct and what we personal. We would like areas the place the power economics make sense, infrastructure designed round successive generations of accelerated computing, and the flexibleness to serve enterprise AI, HPC and personal compute slightly than merely competing for GPU rental quantity.”
If CoreWeave and Nebius are constructing hyperscale AI clouds, Clichmont can occupy a distinct place: a centered proprietor and operator of compute-ready infrastructure in strategically chosen markets.
Conclusion
Clichmont’s technique finally comes right down to a long-term infrastructure wager: that entry to GPUs will stay necessary, however the capacity to energy, cool, join and function these GPUs effectively at scale will turn into an more and more priceless benefit.
That strategy comes with significant trade-offs. Constructing bodily infrastructure requires substantial capital, lengthy planning horizons and cautious coordination between energy, building, {hardware} and demand. Clichmont’s thesis is that accepting these constraints can present better management over the infrastructure required for successive generations of AI compute. Whether or not that thesis proves out will rely much less on the ambition of the mannequin than on the corporate’s capacity to execute it effectively and on the proper time.
