Ørsted’s Sunrise Wind project illustrates how late-stage risk can erode infrastructure returns long before losses appear on financial statements. In January 2025, the company recorded a $1.7 billion impairment, citing project delays, higher expected costs, and rising U.S. financing expenses. By then, the project’s economics had already shifted.

This is the hidden cost of late-stage risk in energy infrastructure. When a delay finally surfaces as an impairment, cost overrun, or return compression, the underlying risks have often been accumulating for months.

This is not an unusual story. A 2022 McKinsey analysis of more than 500 large capital projects found that cost overruns averaged 79 percent relative to initial budget estimates, while schedule delays averaged 52 percent. [1] Flyvbjerg’s research across 258 infrastructure projects found that nine out of ten went over budget. [2] In energy infrastructure specifically, a 2025 study of 662 nuclear, hydro, LNG, and thermal projects, representing $1.36 trillion in investment, found that more than three-fifths experienced cost overruns. Nuclear projects averaged 102 percent above budget. [3]

These outcomes expose a broader weakness in how energy infrastructure risk is assessed before capital is committed. Investment decisions are often made during diligence, but the analysis behind them tends to rely on backward-looking signals: comparable projects, feasibility studies, and models built around existing assumptions.

The risks that ultimately reshape a project often emerge gradually and outside that initial framework. Interconnection timelines can shift, equipment deliveries can be delayed, and permitting milestones can move, with each change adding pressure to the schedule and budget. While these issues may appear manageable individually, their cumulative impact can significantly alter project economics, increasing costs and eroding expected returns. By the time the effects become visible in the budget or construction schedule, the original investment thesis may already be based on assumptions that no longer reflect the project’s reality.

How Risk Is Priced at Diligence Today

The standard diligence process for a large energy infrastructure investment draws on three categories of information:

1. Past project performance from comparable assets

   This means looking at similar projects that have already been built and asking: how long did they take, how much did they overrun, what delays happened, and what risks showed up? For example, a team may look at previous solar, LNG, or transmission projects in the same region to estimate likely cost and schedule risk.

   
   

2. Static feasibility models built at a point in time

   This is the financial and technical model created during diligence. It may include expected construction cost, interconnection wait time, transformer lead time, permitting timeline, financing cost, IRR, and contingency. The problem is that it is usually a snapshot. It reflects what the project looked like when the model was built, but may not update as queue positions, equipment lead times, or financing conditions change.

   
   

3. Desktop permitting and regulatory reviews

   This means reviewing permits, approvals, land use rules, environmental requirements, and regulatory obligations from documents, rather than from live project movement. It can tell you what approvals are needed and whether they appear achievable, but it may not show whether one permit depends on another, whether agencies are delayed, or whether the approval sequence could create a compounding schedule risk.

All three are backward-looking by design. They tell you what similar projects did. They do not tell you what this project's queue position will look like at financial close, or where its transformer lead time will sit in 18 months, or whether the regulatory sequence has a dependency that no one has mapped.

The issue is not in the diligence effort itself, as investment teams are typically thorough. Rather, the signals most relevant to project finance risk often fall outside the information sources diligence teams routinely use. Queue movement, lead-time changes, and permitting sequence risks develop forward in time, while the process remains largely anchored to fixed documents, historical comparisons, and point-in-time reviews. A desktop permitting review can identify the approvals a project needs. It may not show how those approvals depend on one another, or how a delay in one agency’s review can slow the next step in the sequence.

A static feasibility model has a similar limitation. It captures the project’s assumptions at the time the model is built: the queue position, expected interconnection wait, equipment lead times, financing costs, and contingency. But those inputs can change. If the queue shifts three months later, or transformer lead times extend, the model will not reflect that change unless the assumption is updated.

The result is a systematic mispricing of project finance risk at the point in the project lifecycle when it is most consequential – and least expensive to address.

Where the Cost Actually Accumulates

The Construction Industry Institute has documented that the cost of addressing a project issue increases by a factor of 10x at each successive project phase, a finding consistent with broader research on cost escalation in capital-intensive projects.[4] For energy infrastructure specifically, the mechanism is well understood even if the data is underused in diligence models: the conditions that generate a capital project cost overrun are almost always present and measurable months before they produce a schedule event. The schedule event is the moment of visibility. It is not the moment of creation.

Consider how this plays out in the U.S. interconnection queue which is currently holding more than 2,060 GW of capacity seeking connection, with wait times running four to seven years in the most constrained markets.[5] A project that enters diligence with a confirmed queue position and a projected 30-month wait may reach financial close 12 months later. In that interval, the queue has moved. A cluster of large data centre projects has filed ahead. The wait time is now 44 months. The project's schedule, financing structure, and IRR model were built on 30 months. None of that changes automatically. The model is now wrong, and it will stay wrong until someone notices.

The cost of acting on a risk signal at the point it first appears is, by research consensus, 1x the intervention cost. The cost of addressing the same condition at execution — after it has become a schedule event, after capital is fully committed — is five to fifteen times higher.[4]

At the project finance level, this translates directly. A 30-month interconnection wait priced into a project model at financial close, which turns out to be a 44-month wait at construction start, does not produce a proportional IRR adjustment. It produces a non-linear one – because debt service continues, equity is exposed for longer, and the construction window has compressed against a fixed set of contractor commitments. The IRR impact of a 14-month schedule extension on a $2 billion project is not a rounding error. It is a material capital loss that was, in most cases, visible in the queue data before financial close.

What Changes When Risk Intelligence Enters the Investment Stack Earlier

The value of earlier risk intelligence lies in giving capital allocators better visibility into emerging risks and the ability to act before capital is committed. With six months of visibility into interconnection delays, procurement lead times, and permitting sequence risk, the investment case changes. Contingency can be sized against current market data rather than historical norms. Transformer lead times, now running 18 to 26 months and beyond, can be reflected in the schedule before they become a construction constraint. Equity returns can be priced against a range of schedule outcomes, not a single point estimate. Covenants can be tied to queue position confirmation, rather than permit issuance alone.

Earlier visibility can change the investment decision itself. A DFI investment director who identifies a deteriorating interconnection position six months before financial close still has room to adjust the strategy, renegotiate terms, or reconsider the investment. Discovering the same issue six months after financial close turns a manageable risk into a capital problem.

The information needed to make earlier decisions already exists. The underlying signals are already available across the project lifecycle, including interconnection developments, supply chain constraints, and permitting progress. The challenge is connecting these signals early enough to guide investment decisions before problems emerge.

Lessons From The Ørsted’s Sunrise Wind project

The Ørsted example highlights a broader pattern in energy infrastructure: late-stage risks are often recognized only after they have already reshaped the project. The impairment was the financial consequence, but the underlying pressures had been building for months through schedule delays, rising costs, changing financing assumptions, and a shrinking margin for error.

For capital allocators, that is where value is lost. A delayed interconnection study, extended equipment lead time, or slipping permitting schedule may appear manageable on its own. Together, however, these changes can alter the construction timeline, increase contingency requirements, extend the financing period, and weaken expected returns. In a leveraged infrastructure investment, small shifts can materially change the return profile on which the investment decision was based.

The warning signs behind late-stage losses are rarely hidden. They emerge across the project lifecycle — in interconnection activity, procurement markets, permitting records, lender requirements, and contractor commitments. The challenge is that these signals are often reviewed separately and only after the impact is visible.

The opportunity for AI-driven risk intelligence is to change that timing: to connect fragmented signals early enough to influence investment decisions, rather than explain losses after they occur. That is where better risk visibility can create value.