A framework for AI infrastructure as an asset class and the structural difference between hardware-backed exposure, platform equity, and tokenized claims on compute
Part 5 of a five-part series on the structural opportunity in modular AI infrastructure
There is a question that should be asked at the front of every conversation about AI infrastructure investing, and almost never is. The question is: what, exactly, am I owning?
It seems like a basic question. In conventional infrastructure investing — toll roads, regulated utilities, midstream energy, traditional data center colocation — the answer is unambiguous. You own a depreciable physical asset with a known cash-flow profile, a known residual value, and a known liquidity pathway. The asset is in the ground, on the meter, or in the rate base. You can put a number on it that an auditor and a lender will both accept.
In AI infrastructure investing as currently practiced, the answer is much less clean. Capital flows through a half-dozen distinct vehicles — listed neocloud equity, private neocloud equity, hyperscaler equity, GPU vendor equity, tokenized compute claims, hardware-backed SPVs, lease-financing structures, sale-leaseback structures, project finance — and each one delivers a structurally different risk exposure even when the underlying revenue source looks similar.
The capital partner who treats these as interchangeable is making the most common mistake in the cycle. The capital partner who understands the structural differences captures a meaningful return premium for selecting the right vehicle for the right portfolio mandate.
This final article in the series lays out the asset-class framework. It is the lens through which DCXPS designed the SPV structure described in Article 2, and it is the lens that capital partners should apply when comparing modular hardware-backed SPV exposure to the alternative vehicles in the market.
Six vehicles, six exposure profiles
The capital flowing into AI infrastructure in 2026 distributes across six broad structures. I will name each, characterize its exposure, and identify what kind of capital it is appropriate for.
What you own: A fractional claim on a diversified cash-flow stream that includes cloud, AI, advertising, consumer hardware, retail, and other business lines. AI infrastructure exposure is dilutive of and diluted by every other business line.
Exposure profile: Highest liquidity (daily exchange trading), lowest direct exposure to AI infrastructure economics. Beta to AI cycle approximately 0.4–0.6 depending on issuer. Annualized return target: ~10–15%.
Appropriate for: Generalist public equity portfolios; not appropriate for capital with a specific mandate to gain AI infrastructure exposure.
Vehicle 2 — GPU vendor equity (NVDA, AMD)
What you own: A fractional claim on the cash flow stream of the supplier to the AI infrastructure build. Exposure peaks during the buildout and may compress during operating maturity.
Exposure profile: Highest cycle beta of any liquid vehicle. Reflexive — when AI infrastructure demand grows, GPU vendor margins expand; when growth slows, margins compress. Annualized return target: highly variable.
Appropriate for: Tactical positioning on the buildout phase; problematic for long-horizon hold strategies because exposure is to suppliers, not to operating cash flow.
What you own: A fractional claim on the equity of a specialized AI infrastructure operator. The asset is the operator’s enterprise value, not the underlying hardware. Critically, this exposure is highly geared — most listed neoclouds carry significant lease, debt, and off-balance-sheet liabilities that amplify both upside and downside.
For perspective, CoreWeave carries approximately $34 billion in off-balance-sheet lease commitments against a market capitalization that has ranged considerably over the past 18 months.
Exposure profile: High direct exposure to AI infrastructure operating economics, but with substantial gearing risk. Beta to AI cycle ~1.2–1.8. Annualized return target: high, with high volatility.
Appropriate for: Public equity allocators with high risk tolerance and conviction on the operator’s specific platform; not appropriate for capital seeking hardware-backed exposure with downside protection.
Vehicle 4 — Tokenized compute claims
What you own: A claim, structured through blockchain infrastructure, on a fraction of compute capacity or compute revenue from a specific provider. The structure is novel; the legal recourse is novel; the secondary-market liquidity is genuine but thin.
Exposure profile: Variable depending on token construction. Generally lower legal recourse than equity, lower transparency than listed instruments, higher liquidity friction than private equity.
Appropriate for: Specialized crypto-native portfolios with high tolerance for legal and counterparty risk; not appropriate for institutional capital with conventional fiduciary mandates.
Vehicle 5 — Project finance / lease structures
What you own: A debt instrument secured against AI infrastructure hardware and contracted revenue. Returns are capped at the coupon; downside is mitigated by the asset collateral.
Exposure profile: Lowest direct AI exposure of any AI-named vehicle. Returns in the 7–12% range, depending on credit profile. Effectively a fixed-income exposure with AI-cycle correlation in the tail risk only.
Appropriate for: Yield-seeking capital with infrastructure debt mandate; not appropriate for capital with growth equity or operating equity targets.
Vehicle 6 — Hardware-backed SPV structures (DCXPS Site 01 and analogous)
What you own: Direct title to the physical hardware (GPU servers, containerized infrastructure, fabric), held inside a ring-fenced legal vehicle, operated by a third-party operator under a structured economic agreement.
Exposure profile: Direct exposure to underlying asset cash flow, with hardware-backed downside protection (the physical asset has measurable secondary-market value). Returns delivered through monthly distribution waterfall. Capped only by the operating term of the SPV; uncapped on the upside above modeled cases through utilization or pricing outperformance.
Appropriate for: Capital with specific mandate for AI infrastructure exposure, preference for hardware-backed downside protection, tolerance for 5–7 year illiquidity, and structural alignment with operator through ring-fenced ownership.
This is the vehicle category that DCXPS operates in, and the structural reasons it is the appropriate vehicle for a specific kind of capital partner are worth unpacking.
What hardware-backed ownership actually delivers
The DCXPS SPV structure delivers four structural features that none of the other five vehicle categories can replicate. Capital partners considering exposure to AI infrastructure should evaluate whether they need any of these four features; if they do, the SPV structure is materially the only vehicle that provides them.
Feature 1 — Asset-level title
The SPV holds direct title to 49 GPU servers, two reinforced 40-foot ISO containers, 450 kW of power infrastructure, the Cisco fabric, the cooling loops, the BMS, and all ancillary equipment. The hardware is on the SPV’s balance sheet, recorded in the SPV’s books, insured in the SPV’s name, and titled in the SPV’s name.
This is not a contractual claim against operator revenue. It is not a tokenized representation of compute. It is title. The hardware can be inspected, audited, photographed, and verified through customs and bills of sale.
The practical implication: if any party — operator, customer, regulator, counterparty — fails to perform, the SPV retains the underlying asset. The capital partner’s downside is bounded by the asset’s secondary-market value at the relevant moment in the lifecycle, not by the operator’s enterprise solvency.
Feature 2 — Waterfall priority
The economic structure pays the SPV capital partners first. Specifically:
Gross revenue is collected by Chapek (the operating platform) and distributed to the SPV monthly. OPEX (electricity, fixed admin overhead) is deducted to produce EBITDA. EBITDA is then split 85% to SPV capital partners / 15% to DCXPS as operator. Project financing, where applicable, is deducted from EBITDA before the split.
The operator share is paid after the capital partner share is determined. This is structurally different from equity in an operating company, where capital partners are pari passu with operators (or, more often, junior to operators by virtue of management’s preferred securities).
The practical implication: the operator’s economic incentive is to grow EBITDA, because the operator’s compensation is a percentage of EBITDA after OPEX. The operator does not benefit from growing revenue while degrading margin, from running up SG&A, or from prioritizing growth over unit economics. This is the structural alignment that ring-fenced ownership creates.
Feature 3 — Refinanceability
Once a unit is operating, the hardware-and-cash-flow combination becomes financeable as collateral. A capital partner who put in $45M of equity can refinance 50–70% of asset value into senior debt at favorable rates, secured against the operating asset, and redeploy the freed capital into additional unit positions (at Site 01 or elsewhere).
This is the same mechanism by which CoreWeave scaled its capital base into the $34B off-balance lease pipeline reference above. At our scale, with our jurisdictional positioning, the equivalent mechanism is available to capital partners who want it.
The practical implication: the initial $45M ticket can be re-leveraged into capital for additional unit positions, effectively creating a portfolio of units from a single committed equity tranche. This is the mechanism by which family offices in particular have built scale exposure to traditional infrastructure asset classes, and it is available in the AI infrastructure context for the first time at the unit level.
Feature 4 — Three-path exit optionality
At the end of the 6-year operating term, the SPV holds 49 servers of B300/H200 capacity plus the containerized infrastructure plus operating know-how. There are three exit paths, each with different IRR profiles:
Path A — Refresh and extend. New-generation hardware (Rubin or successor) is procured and deployed into the existing containerized infrastructure. The existing hardware is sold into the secondary market. The SPV continues into a second operating term with updated economics. This is the optimal path if the customer mix at year 6 supports continued bare-metal demand at attractive rates.
Path B — Sell to operator or third party. The SPV liquidates the unit as a going concern, with operator continuity. This is the optimal path if the capital partner wants liquidity and the operator (or a third party) wants continued asset exposure. The sale price is a multiple of trailing EBITDA, structured similarly to traditional infrastructure asset sales.
Path C — Liquidate hardware to secondary market. The SPV decommissions and sells the GPU inventory into the secondary market. The containerized infrastructure has residual value (typically 30–40% of original cost). This is the floor path — the path available even if all operating optionality has been exhausted.
The practical implication: capital partners are not exposed to a single exit pathway. They retain agency over exit choice based on the actual market conditions at year 6.
Comparison to traditional infrastructure asset classes
The natural calibration question: how does this exposure compare, on an apples-to-apples basis, to traditional infrastructure asset classes that institutional capital already deploys into?
The table below summarizes the comparison. The numbers for the traditional asset classes are sourced from publicly available infrastructure investing benchmarks (Cambridge Associates, Preqin, McKinsey, EDHEC); the numbers for DCXPS are derived from the model in Article 2.
Asset class
Typical IRR
Typical multiple
Typical hold
Downside protection
Core US infrastructure (regulated utilities, airports, toll roads)
7–11%
1.5–1.8×
7–10 yr
High — regulatory ratemaking
Renewable energy project equity
8–13%
1.7–2.1×
7–10 yr
Medium — PPA-backed cash flow
Traditional retail colocation
11–15%
2.0–2.5×
7–10 yr
Medium — long-term tenant leases
Hyperscale build-to-suit (development)
13–18%
2.0–2.6×
5–7 yr
Medium-high — anchor tenant
Hyperscale build-to-suit (stabilized)
9–13%
1.6–2.0×
7–10 yr
High — long-term lease
Modular AI compute SPV (DCXPS modeled)
24–29%
2.75×
6 yr
Medium — hardware-backed
The premium is real and quantifiable. ~12–18 percentage points of IRR over core infrastructure, ~10–14 over renewable, ~10–13 over traditional colocation. That premium is paid for in three forms of incremental risk:
Risk premium 1 — Technology obsolescence cycle. GPU generations turn over in 18–24 months. The 6-year SPV term spans approximately three full technology generations. Hardware-residual value is not linear over time, and the operator must actively manage the workload mix to optimize revenue against generation-cycle compression.
Risk premium 2 — Operator dependency. Unlike a regulated utility (where operating risk is dispersed across a public-utility-commission framework) or a stabilized colocation asset (where the lease is contractual and durable), the modular SPV depends on operator execution on customer acquisition, NOC operations, and platform performance. Ring-fenced asset ownership mitigates but does not eliminate this dependency.
Risk premium 3 — Secondary market depth. GPU secondary markets are real (the H100 secondary market in 2026 is active and price-discoverable) but less institutional than the secondary markets for, say, commercial real estate or core utility equity. A forced sale would clear at less attractive pricing than an orderly sale.
The question for the capital partner is: is the IRR premium adequately compensated for the additional risk? My honest assessment is that, at current market conditions, it is. The IRR delta exceeds what a rational pricing of the marginal risk would suggest, because the market for modular AI infrastructure capital is not yet mature enough to be efficiently priced. Capital partners moving now are capturing a pricing inefficiency that will compress over the next 24–36 months as the asset class matures.
That window is the case for moving now rather than later.
What kind of capital partner this fits
The vehicle is not appropriate for every capital partner. It is genuinely appropriate for several specific profiles, and identifying whether your capital fits is part of the diligence:
Family offices with infrastructure mandate
For family offices targeting 15–25% IRR exposure with hardware-backed downside protection, the DCXPS SPV structure fits cleanly. The $15M minimum participation is sized for typical family office single-position appetite. The 6-year term aligns with conventional family office liquidity preferences. The hardware-backed exposure provides downside protection that matches the conservative wing of typical family office portfolios.
Sovereign-adjacent capital with European mandate
European sovereign wealth funds, government-affiliated investment vehicles, and sovereign-backed family offices with explicit European positioning. The Czech jurisdictional positioning, EU AI Act alignment, and sovereign infrastructure thesis described in Article 3 make this vehicle particularly aligned with sovereign-mandate capital that needs to deploy into European AI infrastructure but cannot deploy into US-incorporated operators.
Strategic investors with European enterprise exposure
Industrial, financial, and technology companies with significant European enterprise customer bases and an interest in either (a) anchor-customer access to compute capacity for their own AI workloads, or (b) strategic alignment with EU-sovereign infrastructure positioning. The SPV structure supports strategic-investor positioning through dedicated capacity allocation if structured at the unit level.
Specialist infrastructure funds
Funds with explicit mandate for next-generation infrastructure exposure (data, energy, compute) that need to deploy at meaningful scale into AI infrastructure but want the hardware-backed exposure profile rather than operator equity. The 9-unit Site 01 structure provides scale optionality for a fund that wants to commit across multiple units.
Yield-seeking institutional capital
The ~29.2% simple yield profile is meaningfully above what fixed-income or traditional infrastructure yield can deliver. For institutional capital pools with a meaningful yield-seeking allocation, the SPV structure can serve as a higher-yielding component of an infrastructure-yield portfolio.
The vehicle is not appropriate for capital that requires daily liquidity, capital with a sub-5-year horizon, or capital with no tolerance for technology-cycle risk. Be honest about your mandate, and the structure will be honest about whether it fits.
The seven-question diligence framework
Repeating the framework from Article 2 because it is the right set of questions to bring to any AI infrastructure SPV diligence, ours or anyone else’s:
Who owns the hardware?
Where does the power come from?
What is the operator’s economic share, and is it paid first or second?
What is the GPU allocation pathway?
What is the realistic revenue ramp?
What is the exit mechanism?
What is the operator’s track record at unit-scale operations?
For DCXPS:
Hardware ownership: The SPV holds direct title. Bills of sale, customs documentation, and corporate records available in the data room.
Power: Co-located 200 MW power plant at Kladno, with direct on-site interconnect and structured off-take.
Operator share: 15% of EBITDA, paid after the 85% capital partner share is determined.
GPU allocation: Tier-1 NVIDIA supply agreements through HPE and Supermicro, with confirmed allocations for B300 and H200 capacity covering the Site 01 build.
Revenue ramp: First unit online October 2026; full fleet operating cadence by Q2 2027; >95% utilization benchmark contractually defined.
Exit: Three documented paths (refresh-and-extend, sale-as-going-concern, hardware liquidation), with operator obligation to support all three.
Track record: DCXPS founding team carries 60+ years of combined Fortune-500 data-center delivery experience across Deutsche Telekom, T-Mobile, and other tier-1 carriers.
The framework, summarized
The AI infrastructure capital cycle of 2025–2030 will distribute roughly $5.2 trillion across the six vehicles described above (per McKinsey’s base-case projection). Most of that capital will flow into hyperscaler equity, GPU vendor equity, and listed neocloud equity, because those vehicles are the most liquid and the most familiar to institutional allocators.
A meaningfully smaller slice — perhaps $200–400 billion globally — will flow into hardware-backed SPV structures. This slice is where the structural return premium sits, because it is where the pricing inefficiency exists. Hardware-backed AI infrastructure exposure is harder to access (no public market), more diligence-intensive (operator selection matters), and less liquid (5–7 year hold). The premium that compensates for these frictions is what capital partners are capturing right now.
DCXPS’s Site 01 deployment at Kladno is one specific expression of this opportunity. Nine SPV positions. $45M unit size. $15M minimum participation. Ring-fenced Delaware LLC SPV per unit. Hardware owned by SPV. Operator paid 15% of EBITDA, second in the waterfall. EU-sovereign positioning. October 2026 first units online.
This is the final article in the five-part series. Pulling the thesis together:
Article 1 established that power, not silicon, is the binding constraint on AI compute — and that modular distributed deployment at sites with existing grid capacity is the structurally correct response.
Article 2 worked through the unit economics of a $45M modular SPV in forensic detail, demonstrating that the 2.75× / ~29.2% yield profile is mathematically supported by the operating waterfall.
Article 3 identified the EU AI Act / CLOUD Act conflict as the regulatory force creating structural demand for genuinely EU-sovereign infrastructure that US-incorporated operators cannot fully address.
Article 4 mapped the training-to-inference workload transition through mid-2027 and demonstrated how modular distributed deployment is structurally aligned with the post-transition equilibrium.
Article 5 (this one) framed AI infrastructure as a distinct asset class, identified the six available exposure vehicles, and argued that the hardware-backed SPV structure delivers a specific set of features no other vehicle replicates — at a return premium that compensates for the additional risk.
The thesis composes into a single argument: the next phase of AI infrastructure value is captured by EU-sovereign, modular, hardware-backed exposure deployed at sites with existing power, ahead of the August 2026 regulatory window and the mid-2027 workload transition. The capital partner who deploys into this structure now captures a return premium that will compress as the asset class matures.
We are deploying that thesis at Kladno. The data room is open. The first units come online in October 2026.
The factory is being built. The question is whether you own a piece of it.
Jiri Fiala is CEO and co-founder of DCXPS, building Tier 3 modular AI data centers and the Chapek bare-metal GPU cloud platform. The complete five-part series is available on aiofthecoast.dcxps.com.
This article does not constitute an offer to sell or solicitation of an offer to buy any security. Any such offer will be made only by means of definitive transaction documents to qualified investors. Modeled returns are estimates, not promises. Comparison data for traditional infrastructure asset classes is illustrative; actual returns vary materially across deals and vintages. See risk factors in the DCXPS Confidential Investor Memorandum.
