Qamer javed
Artificial Intelligence (AI) has created a new gold rush in the software development industry due to its explosive growth. In 2026, most companies will not ask whether to use AI or not; they will be asking how they will finance its development.
AI projects are resource-intensive, although much of your spending, namely, model training and architecture design, can be offset with the Section 41 Research Credit. But these credits are not given to someone who merely uses AI; you have to demonstrate that it is being advanced by the IRS.
Does “Training” an AI model count as a research expense?
Yes, however, the difference is in the character of work. When you are just providing data to an off-the-shelf, pre-trained LLM (Large Language Model), the IRS considers this one of the rare data collection activities. Experienced IRS tax experts (a former IRS tax agent, a former auditor, and experienced IRS audit lawyers) can help to show the research expense.
Nonetheless, when you are training models for new architectures, testing new neural network layers and hyper-parameters to solve a particular technical uncertainty (ex, reducing hallucinations or lowering latency by 40 percent), then those undertakings do qualify as qualified research.
The salary of your data scientists and the cost (GPU/TPU time) of the cloud compute on these training loops are Qualified Research Expenses (QREs) of a classic.
How does the “Four-Part Test” apply to AI development?
In order to take the credit, all AI projects have to undergo the same tough statutory obstacles as a laboratory trial:
Allowed Use: You should be targeting to enhance the workability, efficiency, or steadiness of a business part (e.g., a proprietary fraud detection algorithm).
Technological in Character: The labor should be based on the hard sciences, namely, computer science, mathematics, or data engineering.
Elimination of Uncertainty: You should have demonstrated that the methodology or design was unknown at the beginning of the project. Examples: “We were not exactly sure whether a transformer-based architecture would be able to support this particular real-time data throughput.
Process of Experimentation: This is where AI training is at its best. Each of these systematic trials and errors is run (each epoch), where you test various model versions.
Can you claim the “Cloud Compute” costs used for AI?
The regulation of the cost of computer rental has been made clear under the One Big Beautiful Bill Act (OBBBA) in 2026. In the case of AI companies, the steep charges of cloud machines such as AWS, Azure, or Google Cloud for a dedicated research instance are very affordable. The role of the tax resolution law firm can help in managing the cloud computing costs.
In order to claim these successfully, you have to isolate your production cloud costs and your development/training costs. In the case that a GPU cluster was utilized to test the convergence of a new model, such expenses are 100 percent countable towards your credit.
What documentation is required to defend an AI research claim?
The IRS continues to be quite doubtful about the AI claims that resemble the usual implementation. Protecting your credit requires you to keep up-to-date records, including:
Version Control Logs (GitHub/GitLab): Displaying cycles of model code.
Training Logs: A record of unsuccessful training jobs and the modifications of the parameters.
Project Charters: Which particular technical uncertainty are you attempting to solve, and which did you begin coding prior to your start?
Conclusion
The new frontier of the Section 41 credit is the AI-driven development. Through a mindset change of viewing it as not a matter of using tools but an engineering of solutions, you will be able to open a door to a dollar-for-dollar tax reduction, where you can cut down on the burn rate drastically.
The trick is writing down the uncertainty of your AI training, making it obvious that you succeeded in it as you managed to conduct a series of experiments, rather than just installing software.
