Digital assets and tax transparency: Navigating the new US tax reporting regime
Bibliographic record
Abstract
The US digital asset industry has been growing at a significant pace since 2012 when the first mainstream digital asset exchange offered the opportunity for customers to buy and sell the first digital asset, bitcoin, through bank transfers. Digital assets, or ‘cryptocurrency’ as it is commonly known, quickly outgrew its status as a niche alternative investment. According to a Pew Research Center survey, almost 20 per cent of American adults owned cryptocurrency in 2023.1 Along with growth in popularity, bitcoin (the original cryptocurrency) has grown in value. On 31st December, 2012, the closing price for a single bitcoin was US$13.45, compared to its all-time high of US$108,135 (as of 17th December, 2024). Based on this, it is fair to assume that long-term investors made some significant gains in bitcoin trading. Since digital asset exchanges are not subject to the same tax information reporting requirements as traditional financial institutions, there has been speculation by digital asset investors that sales of digital assets were not subject to the same taxation regimes as traditional financial investments. The US Internal Revenue Service (IRS) issued its first notice regarding the taxation of ‘virtual currency’ in 2014 (Notice 2014–21).2 The exclusion of tax information reporting guidance on digital asset sales, however, made it difficult for the IRS to identify taxable gains recognised by investors. This paper will set the stage by first diving into the history of both US tax information reporting and the evolution of digital assets. We will then address the recent US tax legislation and regulations issued by the US Government to attempt to mitigate the perceived tax gap for digital asset sales. We will then conclude by highlighting the challenges that these rules pose to digital asset exchanges and taxpayers, and what we can collectively do to prepare for their implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".