A financial services firm invests £400K to fine-tune a proprietary language model for anti-money laundering detection. They measure that the fine-tuned model improves detection accuracy by 18% and reduces false positives by 34%. They deploy it operationally in their AML systems, replacing a third-party vendor tool. Under ASU 2025-06, this £400K can be capitalised as an intangible asset, amortised over the expected useful life (typically 3-5 years for AI models subject to rapid change). This treatment is now permissible where it previously would have been expensed.

The SNA 2025 Recognition: Data and Software as Capital

The System of National Accounts (SNA 2025) is the global standard for national economic accounting, followed by statistical offices including the ONS, Eurostat, and the BEA. In its 2025 update, SNA 2025 made a watershed decision: data is now recognised as a productive capital asset, and AI systems built on that data are treated as capital formation rather than intermediate consumption.

This has profound implications. When a company invests in collecting, cleaning, and structuring data for use in AI training, or when it invests in the AI system itself, these are now treated by national accountants as capital formation (investment) rather than operating expenditure. This is the opposite of current financial reporting standards, where most of this spending is expensed.

The gap between SNA 2025 (data and AI systems are capital) and IAS 38/ASC 350 (expense most AI costs) is now explicitly documented. For companies reporting under IFRS in countries that follow SNA 2025 methodology, this creates a reporting anomaly: the national accounts will show rising intangible capital, while the company's financial statements will show these same investments as expensed costs.

Cost Capitalisation: The Technical Challenges

Even where capitalisation is theoretically permitted, applying it in practice requires overcoming several technical challenges.

Allocation across multiple projects: A compute cluster training multiple models simultaneously generates costs that must be allocated to individual projects. How do you allocate £50 million in compute spending across five different models being trained in parallel? Standard methods (by GPU hours, by parameter count, by training time) are available but imperfect.

The problem of continuous improvement: An AI system is rarely complete and static. The model is trained initially, then fine-tuned, then updated with new data, then re-trained with improved algorithms. Which costs are capitalised (part of the initial asset), and which are expensed (maintenance and improvement of existing asset)? Current standards offer guidance, but application is contentious.

Amortisation and useful life: Once capitalised, an AI model must be amortised over its useful life. What is the useful life of a language model? Three years? Five years? The answer depends on expected technological obsolescence, competitive dynamics, and intended usage. Frontier AI models have historically shortened useful lives (new models render previous generation partly obsolete), but this may be changing as model quality plateaus.

Impairment testing: An expensed cost cannot be impaired — it is already gone. A capitalised asset must be tested for impairment whenever there is evidence of loss of value. If a model is trained at cost and subsequently proves less effective than anticipated, or is superseded by a cheaper alternative, the capitalised amount must be written down.

Three AI Investment Scenarios: How They Should Be Treated

Scenario 1: Training a Proprietary Large Language Model (£100M Investment)

A technology company commits £100 million to training a large language model from scratch. They build a dedicated team of 50 researchers and engineers. They acquire compute capacity for two years. They commit to integrating this model into their product line.

IAS 38 treatment: The company can capitalise this investment if it can demonstrate:

• Technical feasibility: They have run proof-of-concept experiments and validated that the model architecture will achieve their performance targets
• Intention to complete: They have board-approved budgets and timelines
• Ability to use: They have product plans demonstrating how the model will be deployed
• Future economic benefit: They can model cost savings and revenue impact
• Adequate resources: They have committed the team, budget, and infrastructure
• Reliable measurement: They track engineering time, compute costs, and data acquisition costs separately by project

Outcome: Capitalisation is defensible. The £100M is recorded as an intangible asset (technology capital) on the balance sheet. It is then amortised over the expected useful life (likely 4-5 years for a frontier model). Annual amortisation expense is £20-25M.

ASU 2025-06 treatment: Capitalisation is explicitly supported under Tier 1 (foundation model). The company must document the defined performance benchmarks and evidence that they have been met.

Scenario 2: Fine-tuning a Pre-trained Model for Internal Use (£50K Investment)

A professional services firm uses OpenAI's GPT-4 API but decides to fine-tune it with proprietary firm knowledge to improve its performance on firm-specific terminology and methodologies. They invest £50K in data preparation, fine-tuning, and integration.

IAS 38 treatment: This is the boundary case. Technically, the six criteria can be met:

• Technical feasibility: Fine-tuning has proven methodologies
• Intention to complete: Yes, defined project with completion date
• Ability to use: Yes, will be deployed in document generation and client advisory
• Future economic benefit: Improved output quality reduces rework and billable hours per engagement
• Resources: Yes, allocated within the department
• Reliable measurement: Yes, costs are tracked separately

However, auditors would likely challenge capitalisation. The asset value is questionable — a fine-tuned model is vendor-dependent, not independently valuable. If the vendor discontinues the base model, the fine-tuning becomes worthless. The investment is also at the lower end of the scale where capitalisation overhead (tracking, amortisation, impairment testing) may exceed the administrative benefit.

Likely outcome: Expensed as a period cost, despite meeting theoretical criteria.

ASU 2025-06 treatment: Explicitly permissible under Tier 2 if the fine-tuned model demonstrates measurable improvement and is separately tracked. This represents a shift from IAS 38 practice.

Scenario 3: Exploratory Research and Proof-of-Concept (£500K Investment)

A financial services firm allocates £500K to an exploratory research project investigating whether AI can improve fraud detection in payment systems. The outcome is uncertain. They may succeed and deploy the system, or they may conclude that the AI approach is inferior to existing rule-based systems.

IAS 38 treatment: Capitalisation is not defensible.

• Technical feasibility: Unclear — proof-of-concept stage
• Intention to complete: Uncertain — decision point not yet reached
• Ability to use: Dependent on research outcomes
• Future economic benefit: Speculative
• Adequate resources: Preliminary allocation, not committed

Outcome: Expense as R&D cost. This is correct treatment. Exploratory research has uncertain outcomes and is properly expensed as incurred.

The Valuation Implications: What Gets Capitalised, What Gets Valued?

The capitalisation question and the valuation question are related but distinct. Even if a cost is expensed under current accounting standards, the asset it creates may have genuine value.

Consider the example of the £50 million proprietary language model. Suppose that under IAS 38, the company concludes it cannot reliably measure the future economic benefit and decides to expense the cost. From a balance sheet perspective, the asset is invisible — an intangible asset worth £50 million has not been recorded.

But from a valuation perspective, the asset is very real. If the company is sold, the buyer will pay for the proprietary model. They will conduct due diligence on its technical quality, its competitive advantage, its integration into the product, and its expected useful life. They will then apply a valuation methodology — either cost approach (what did it cost to build), market approach (what do comparable models trade for), or income approach (what value does it generate) — to determine its contribution to enterprise value.

The buyer's valuation of the model might be £50 million (cost recovery), or £200 million (income approach, if the model is critical to product differentiation), or £10 million (if better alternatives have emerged since it was built).

The gap between what the balance sheet shows (nothing — it was expensed) and what the market values (potentially substantial) is where intangible asset measurement becomes critical for M&A, PE transactions, and investor valuation.

Looking Forward: Convergence and the Next Revision Cycle

The divergence between IAS 38, ASU 2025-06, and SNA 2025 will not persist indefinitely. The IASB (International Accounting Standards Board) is monitoring the ASU 2025-06 changes and considering whether IFRS should converge with US GAAP on AI treatment. The next IFRS revision cycle (likely 2027-2029) will address this.

In the interim, companies face a choice:

• Conservative approach: Expense all AI spending as incurred, consistent with historical practice and minimise audit friction
• Progressive approach: Capitalise defensible AI investments under ASU 2025-06 (if US-listed) or under the spirit of SNA 2025 treatment (if preparing for future IFRS alignment), accepting higher audit scrutiny and impairment risk
• Middle path: Expense core development spending but capitalise specifically-identifiable, deployed, measurable AI systems where criteria are clearly met

The commercial reality is shifting toward capitalisation. Companies that are serious about AI as a capital asset — training proprietary models, building moats through technology capital — need balance sheets that reflect that reality. As SNA 2025 implementation begins and as ASU 2025-06 gains traction, the pressure for IAS 38 convergence will increase.

For boards and CFOs, the decision on AI capitalisation is not purely an accounting one. It is a signal about how the organisation views AI: as an operating expense to be minimised, or as a capital investment to be measured, managed, and valued.

David Stroll is Co-Founder and Chief Scientist at Opagio, specialising in productivity measurement frameworks and the economics of intangible capital. His work draws on SNA 2025, OECD, and ONS methodologies.