Here’s How Businesses Can Handle AI Content and Transfer Pricing

Tax authorities, in every jurisdiction, have always been cautious of international intercompany transactions because they are ripe for abuse.

Imagine if Acme Corp USA sells an asset to Acme Corp Hong Kong at a loss to avoid paying tax in the states where the tax is high, then sells that same asset in Hong Kong at a major profit where the taxes can be much lower. You can see why the authorities would be all over such deals.

Companies must figure out how to appropriately price intercompany sales to keep authorities happy. With the proliferation of AI-generated images, music, and written content, there’s now a whole new set of pitfalls in the pricing of this collateral.

One could argue that AI generated assets cost $0 to produce because someone at the company used an open-source large language model to create it in no time. If Acme sells that asset to itself for $1 dollar, even though they may sell it for much more externally, how unreasonable would that be?

Despite this area’s many unknowns and challenges, businesses can take several key steps to determine how AI-generated content should be priced in related-entity transactions and how this process should be documented.

Current Transfer Pricing

Transfer pricing can violate tax law if companies intentionally game the asking price to minimize profits shown in high-tax zones and do the opposite in low-tax zones.

Improper transfer pricing could convince investors or creditors to see a company’s income statement with rose-colored glasses.They also can use it to overstate value to investors and to hide income from tax authorities.

The most prevalent current methods center on either a comparison of market sale prices or margins or a calculation of how much margin is tied to value added by each related entity.

AI Transfer Pricing

AI-generated content adds a few hurdles to transfer pricing. To illustrate them, let’s use a piece of software, developed in one region and then licensed to subsidiaries for international sale, as an example of the different approaches a company could take in determining a transfer price.

Sale price comparisons. What if a business sets the price based on what other people are selling it for? One problem is many software products are customized, so it’s hard to find two that do the exact same thing. The speed of AI-driven development makes this comparison even harder and it would require more frequent market analysis.

However, the proliferation of AI-driven development means more products to benchmark against as well. Consider also that if a business has two identical AIs but one is trained on a bigger and better dataset, the collateral it generates is more valuable.

Cost comparisons. What if you set the price based on how much it costs to produce? Unlike traditional development overhead like labor and material costs, AI coding platforms have different overhead costs to consider, such as the time developers spent on training, prototype review, and testing, for example. This cost/margin data is more closely guarded than sale prices, making it more difficult to find a fair benchmark.

Margin comparisons. What if you set the transfer price based on how much profit other similar products capture? Most software expenses need to be amortized because the value of the software will likely diminish over time. But an AI application’s useful life is more fluid than that of a spreadsheet or email app. AI-produced software could have value indefinitely because it could constantly rewrite itself. With more software being written by software, acquisition costs will change as well.

Economic substance. If the revised pricing moves more profit to lower-tax jurisdictions, comparisons and assumptions must be well documented to ensure that the profit assigned to each step in the chain matches the value created. It’s not enough to cross legal T’s and dot tax rule I’s. Transfer pricing must be based on the substantive contributions or risks assumed by the parties involved. Deciding which party bears which responsibilities becomes critical in determining economic substance.

Documenting AI transfer prices raises a host of questions: What other products or services did you scope out? Did your definition of gross or net margin match that of your benchmarks? How are you considering what each related entity is contributing to the finished product? Every step of the way must be clearly laid out.

Modernizing Transfer Pricing

While businesses grapple with transferring AI generated assets, laws need to adapt to the realities of the technology.

From one viewpoint, AI created materials are expensive.There are costs to develop a model, to obtain data used to train the model,to hire developers to oversee training, and to purchase the energy and hardware to run the technology. However, the cost can be negligible: Typing a prompt into a free ChatGPT account and getting an output might suffice.

What AI can produce, in traditional terms, could be worth a pittance or a fortune.

This means that similarly situated companies can set wildly different transfer prices that could all, arguably, be valid. This makes it difficult for businesses trying to do the right thing to know what to do.

It behooves tax authorities to update their laws not just to stop abusers, but to also make the correct approach clear.

As a start, Congress and the IRS should define how far back a taxpayer needs to go in capturing the cost to train the AI and provide more guidance on amortization. If an AI is “learning” and constantly getting better, is its value really diminishing, or is it just shrinking at a slower rate? These questions are easier to ask than to answer.

Absent specific rules on how to value AI-generated output, giving an honest estimate of where value is added in the chain, as well as thoroughly documenting every assumption and allocation, is the best course of action. These transfer pricing principles apply to both the work products of the past and future—however advanced they may be.

This article does not necessarily reflect the opinion of Bloomberg Industry Group, Inc., the publisher of Bloomberg Law, Bloomberg Tax, and Bloomberg Government, or its owners.

Author Information

Dean Zerbe is national managing director at Alliant’s Washington office and former senior counsel and tax counsel to the US Senate Committee on Finance.

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