The Hidden Financial Engineering Risks of the AI Boom

The Hidden Financial Engineering Risks Of The AI Boom

With most conservative market estimates forecasting over $2T in AI-related capital expenditure over the next eight years, the current AI boom is now becoming one of the most important investment cycle in recent history. Dwarfing the prior technological SaaS development cycle thanks to the unprecedented amount of liquidity unlocked by a new type of innovative GPU-backed loans, the recent surge in capital expenditure of the AI sector from $100B in 2020 to more than $350B in 2025 makes it one of the fastest in history. Hence, beyond profoundly reshaping the cloud infrastructure landscape, the AI infrastructure segment is also increasingly emerging as a new core growth primitive of the US economy whose overall sectoral debt is now accounting for more than 14% of JP Morgan investment grade index.

The Current State of the AI Ecosystem Landscape

The rise of hyperscalers

Best understood as global AI leading cloud operators from the like of AWS or Microsoft, hyperscalers are globally distributed data centers capable to scale to service the rapidly expanding needs of AI users. Currently at the vanguard of AI infrastructural development, hyperscalers are leading the undergoing AI boom from a capital expenditure standpoint:

• Microsoft — 58% increase in capital expenditure over 2025 to finance all Fairwater AI infrastructural projects
• Amazon — $100B spending target aimed at AI infrastructure projects (2x 2023 AI budget)
• Meta — $66B-$72B Capex guidance dedicated to AI infrastructure and AI-related algorithmic R&D.
• Alphabet — $75B guidance for AI development (2x historical company run rate)

The emergence of Neoclouds

Emerging players in the cloud landscape, neoclouds are a new type of GPU-centric cloud infrastructures servicing most of the high performance compute behind today’s AI models via a novel business model characterized by three main defining features:

1. High leverage — Acquisition of next-gen GPUs via multi-billions GPU-backed loans
2. Concentrated revenue stream — Training and inference driven revenue
3. Utilization dependance — High correlation between revenue and GPU utilization

Additionally, they also appear to have already capture a sizable share of the overall AI infrastructure segment as shown by the $20B outstanding GPU-backed loans granted by private credit lenders like BlackRock and JP Morgan to leading neocloud providers:

• CoreWeave: $14.2B debt with maturities extending through 2030
• Fluidstack: $10B debt capacity agreement
• Lambda Labs: $500M debt
• Crusoed: $425M debt

The value cascade: A new depreciation model

This new innovative amortization model at the heart of the recent financial engineering underlying the recent explosion in AI investments leverages what industry analysts call the “value cascade” to justify the conceptualization of GPUs as long-lived industrial equipments able to backstop multi-years loans.

What is the value cascade

Whenever Nvidia is shipping a new hardware generation, the previous generation isn’t suddenly becoming worthless. Instead, it migrates down the stack moving from frontier training to fine-tuning, from fine-tuning to inference and finally from premium inference to cost-sensitive batch jobs. Thus, private credit lenders can treat GPUs as long-term industrial assets with a multi-year depreciation schedule given that GPUs are gradually losing value as they get repurpose towards lower margin tasks. Nonetheless, this novel financial modeling approach also introduces a set of underlying assumptions that needs to be accounting for by the underwriters:

• Assumption 1 — Constant GPU development pace.
• Assumption 2 — Steady software improvement pace
• Assumption 3 — No viable alternative to GPU for AI training and inference

How to use this model?

From a financial engineering standpoint, as outlined by Goldman Sachs in its 2026 outlook, this value cascade model can mainly be employed in two types of financing strategies

• Current Wall Street strategy — High risk/high margin 2008-style concentrated loan strategy leveraging single hyperscaler tenants (SASB), long-term debt heavy leases, hardware lock-in and high AI concentration risk
• Alternative strategy — Diversified loan strategy leveraging co-location among thousands of tenants, flexible short-term leases, diversified tech-risk and chip agnostic structure

The limits of the AI financing model

Amortization vs economical lifecycle

Most neocloud operators and hyperscalers recently started to extend their depreciation schedule to a 5–6 years period in an effort to boost their near-term earnings and make their debt load more manageable from an accounting standpoint. Yet, from a technical standpoint the GPU frontier utility window currently spanning from 18 to 36 months appears to be following an opposite dynamic as shown by Nvidia’s refresh cycle compression enabling each new chip generation to hit the market faster than the previous one (Hooper, Blackwell, …). Thus, while cash-rich hyperscalers might be able to extend their amortization schedule and easily support the increasing gap between the booked value and the true economical value of their GPU through simply repurposing older chips generation towards lower value workloads, the same might not be able to be said about heavily indebted GPU-centric neoclouds which might suddenly face core critical risks:

• Collateral risk — Significant value depreciation risk of GPUs pledged as collateral for multi-billion dollars loans
• Refinancing risk — Increasing challenges to roll over debt as asset value fall below remaining debt
• Timing risk — Possibility of financial stress regardless of market conditions as the main underlying problematic revolves around technological cadence and not demand.

The recursive self improvement problem

All major frontier AI labs are now beginning to pursue recursive self-improvements through the automation of large portions of their research and engineering workloads in a clear attempt to significantly accelerate the pace of their efficiency gains. Standing on average at approximately 400%, as per the words of Anthropic’s CEO Dario Amodei, the yearly algorithmic efficiency improvement coming from major labs might in the next couple of years easily reach significantly higher levels ranging from 800% to 2000% if the upcoming integration of agentic researchers viably multiply by several orders of magnitude the overall researcher workforce. Yet, as software improve faster, the hardware it runs on also becomes obsolete faster which might have worrying economical consequences for neoclouds in the mid/long term. To be clear, GPU primarily generate revenue by performing computations so if a new algorithm can deliver the same capabilities on half the hardware requirement, the neocloud utilization might drop and lead to contract revisions. Worse, given that new algorithms increasingly tend to exploit architectural features on the latest chip generation (Blackwell FP4 tensor cores, ….) old chips also compete against better algorithms running on better chips implying that the performance delta is simultaneously widening from both the hardware and software angles. As such, while having hold so far, it appears increasingly doubtful that the Jevons Paradox stating that efficiency gains create sufficient additional demand to ensure the consumption of all new computing surplus will hold for the new levels implied by recursive self-improvement. Indeed, at 800% — 2000% annual efficiency gains, this would require the demand for old chips to grow between 8x to 20x each year to keep a constant year over year revenue profile which given the deployment of newer GPUs able to run the same workload faster and cheaper appears highly unlikely. As such, we can see that recursive self-improvement creates a significant duration mismatch risk for GPU-backed debt given that most of those loans rely on value cascade models which do not reintegrate the algorithmic acceleration factor.

The shift towards inference

As signaled by the recent $20B Nvidia’s pivot towards Groq’s LPU architecture licensing, the industry is starting to move from a training to an inference economy characterized by an agentic layer requiring a speed and determinism that standard GPUs cannot deliver. From voice assistants to copilots, AI agents need small batch size and sub-second latency for which GPU alternatives such as the SRAM-based AI chips developed by Groq are far superior. Consequently, some companies like OpenAI which recently started the development of a multi-billions GPU infrastructure might end up on the wrong side of history. Taking the example of CoreWeave, despite its robust $22.4B OpenAI contract and impressive $55.6B revenue backlog, in the event of a significant shift of the industry towards inference-optimized silicon the company training-dense GPU clusters might just become expensive stranded assets solving yesterday’s problem. Thus, as the industry shifts from building the brain to running the brain, the demand might significantly shifts towards specialized inference chips and create real structural risks for highly leveraged neoclouds infrastructures as cash flows turns negative and debt service slowly starts to become unsustainable despite the underlying facility’s technical capacity.

A logistic and infrastructural bottleneck

While getting a greenfield data center operational currently takes approximately 2–3 years, grid connections delays are now extending to 5–8 years in many regions with some projects facing connections queues of up to 13 years in legacy hubs. Meanwhile, alternative fossil-fuel alternatives once envisioned as a potential temporary workaround to grid connection delays also starts to show signs of constraint as illustrated by the selling out of all GE Vernova’s gas generators up to 2028. As such from rapidly rising grid connections delays to ever growing energy costs, the market seems to be on the cups of rediscovering Bent Flyvbjerg’s iron law stating that megaprojects tends to always be over budget, over time and under benefits. A good illustration of this being the recent market shift from the initial ‘build it and they’ll come’ mentality to a more conservative approach perfectly illustrated by the neocloud Fermi America debacle first envisioned as a new cutting edge 11GW AI data center project in Texas and now trading at less than 50% of its $15B IPO valuation following last December termination of Amazon $150M contract amidst rising doubts concerning the overall logistical viability of the project.

The accelerating market concentration

The “Magnificent Seven” (Apple, Nvidia, Tesla, Amazon, Alphabet, Meta and Microsoft) now represent more than 30% of the overall S&P500 weight and are accounting for more than 50% of the overall index economical profit. However, historical precedents suggests that this level of concentration tends to generate dangerous market vulnerabilities. While during the dot-com era, thirteen major large cap stocks generated an astounding market growth increasing their market valuation by over 1000% they also provoked shortly after a 86% drawdown of the Dow Jones resulting in one of the biggest stock market failure in history. Therefore, as underlined by the recent BoE Stability report, the current extreme concentration of AI spending among a handful of leading AI companies as well as the growing stretching of their public market valuations might be laying the groundwork for an upcoming 2008-style market correction which coupled with the multiple feedback loops of the AI debt market might spark particularly intense financial stability risks.

An untested secondary GPU market

Untested for the volume implied by today’s neocloud loan books, the secondary GPU market represents another potential accelerating risk factor in the event of a market downturn. Indeed, if multiple lenders suddenly attempt to simultaneously liquidate collateral, the market might face a significant risk of collapse causing valuations to become circular and making lenders under-secured. As such, while not necessarily resulting into a banking crisis, the lack of liquidity of the secondary GPU market could very well spark a concentrated crisis of the private credit market which coupled with the growing circular financing of the AI sector could lead to significant contagion risks towards the broader economy.

High Interest rate environment

Leading AI operators from hyperscalers to neoclouds are increasingly choosing to use debt rather than internal cash flows to fund their AI expansion. From CoreWeave’s $7.5B debt financing facility to Meta recent $26B loans supporting the development of new data center capabilities, it seems that even the most cash-rich and well funded companies are now leveraging innovative financial engineering strategies leveraging SPV vehicles and hardware collateral to fund the development of their AI infrastructure. Yet unlike software development with immediate deployment, physical infrastructure requires patient capital and multi-decade amortization schedules making AI infrastructure disproportionately impacted by interest rates due to the debt dependency and long payback periods. As such, if AI monetization disappoints, the high upfront costs and elevated interested rates imposed by private credit lenders could lead to financial risks for smaller facilities that rely on cross-collateralization to secure funding and ignite a chain-reaction severely impacting the entire market as investors increasingly start to perceive GPU as stranded assets. This latter point also potentially explaining partially the recent tech lobbying shift towards the current US administration to obtain a certain amount of political goodwill and ensure a more dovish stance of the FED during the AI loans amortization period.

AI sector, a new systemic risk?

Estimated to reach nearly $3T at the horizon 2028, the AI-related capex expenditure is increasingly becoming of systemic importance for the broader economic stability. As illustrated by the exploding growth of the Virginia’s data center market employing thousands of workers and generating billions in local investments, regional economies are becoming more and more dependent on data center construction and operation. Simultaneously, the utility sector is also becoming increasingly reliant on data center demand to justify long term infrastructure investments. Consequently, if the AI spending was to suddenly stop or be proven unproductive the resulting macroeconomic impact might end up extending beyond technology stocks and leave utility operators with stranded generations of assets that tax payers would ultimately have to support and account for. Thankfully, several arguments can be put forth to partly dismiss this 2008-style doomsday scenario:

• Multi-year revenue backlogs provide real stability — Extensive contract backlogs underwrote by the likes of JP Morgan ensure a strong and robust visibility into top hyperscalers and neocloud future cash flows and utilization levels (CoreWeave $55.6B contract backlog, …)
• Older GPUs still generate profitable revenue — Despite not being optimal anymore for frontier training, earlier GPU generations will always remain to a certain degree viable for inference on stable models, domain-specific deployment or batch compute environment regardless of the advent of recursive self-improvement.
• Specialized clusters achieve high utilization — AI-specific workloads currently push utilization of GPU-centric data centers way beyond traditional cloud norms (70% -90%) resulting in an overall softening of the overall depreciation mismatch.
• The loan market is large but not systemically large — GPU-backed lending is material for private-credit funds but still small relative to the broader corporate universe implying that any stress would most likely remain concentrated rather than becoming systemic.
• Sovereign may backstop critical AI infrastructure — Public financing would most likely act as a floor for longer-term GPUs demand (EU gigafactories plans, Trump’s AI action plan, ….) which while not rescuing every AI operators would prevent any 2008-style collapse event.

Solving the AI market informational deficit

In normal commodities market there is no need for economical agents to synthesize themselves all available informations to form price opinions. If OPEC announces a change to its production quotas, the forward curve changes, traders who understand supply dynamics take positions and the price adjust to account for everyone’s information. Thus, a credit analyst financing oil tanker capacity can just look at the 18-month forward price and adjust his model accordingly. The knowledge becomes price and the market does the synthesis. In the AI sector though, no such derivative market exists. There is no forward curve for H100 rental rates 12 months out, no put options on GPU residual values and no standardized swaps to convert floating compute costs into fixed rates and without derivatives, knowledge cannot become price. As a result, when OpenAI announces automated researchers, that information stays trapped in AI policy X or Substack feeds and credit markers don’t see it. Similarly, when Dario Amodei cites 400% efficiency gains in a podcast, structured financed analysts don’t hear about it and the information arbitrage just remains unpriced given that no trading mechanism exists to close it. Thankfully, the infrastructure is slowly starting to emerge. Ornn is now offering GPU value protection through Residual Value Swap (RVS) guaranteeing a minimum sale price for GPUs at a future date and Architect Financial Technologies recently announced the upcoming launch of the first exchange-traded futures compute contracts based on perpetual futures linked to daily GPU rental prices and DRAM costs. However, the full ecosystem — sufficient liquidity, standardized contracts, cleared derivatives, transparent forward curves — still remains nascent and the clock is ticking.

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The Hidden Financial Engineering Risks of the AI Boom was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.