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1. The “Moving Target” Problem (Instability)

Be Careful: AI Tax Calculation Risk Hits Up to 78% Incorrect Answers

Report Highlights

As the 2026 tax season reaches its peak, a new report (1) reveals that 26% of Americans, more than 1 in 4, are now using AI to help file their tax returns. However, new proprietary data from the ORCA V2 Benchmark (Omni Research on Calculation in AI) suggests this trend may lead to a wave of IRS audits and financial errors. Our research found that in the Finance & Economics domain, leading AI models are “unstable” up to 78% of the time, frequently providing different incorrect answers to the same financial prompts and underestimating long-term savings by as much as 40%.

The most alarming discovery in the ORCA 2.0 update is the Instability Metric. This measures how often an AI model provides a different wrong answer when asked the same (or similar) question again.

  • Finance is the most unstable domain: In the Finance & Economics sector, ChatGPT showed a 78.30% instability rate. The most widely used tool in the workplace is also the most volatile. There is a nearly 4-in-5 chance that if ChatGPT gives you a wrong financial answer, it will give you a completely different wrong answer the moment you ask it to double-check.

  • DeepSeek (55%) and Grok (46%): These models hover near a "coin-flip" of reliability. Relying on them for tax adjustments or payroll logic is effectively gambling with corporate compliance.

  • Gemini (25%): While appearing more stable, a 1-in-4 instability rate still represents a catastrophic failure rate for any financial auditing process.

"Relying on AI for tax calculations is essentially financial Russian Roulette, and as you can see, our data shows these models give different wrong answers fairly often, which is a direct path to a nightmare IRS audit." - Dawid Siuda, Financial Expert at Omni Calculator

  • Why this matters for taxes: If you ask ChatGPT for a tax deduction calculation and it gives you the wrong answer, there is nearly an 80% chance it will give you a completely different wrong answer if you ask it to double-check. This "hallucinated consistency" makes it impossible for a layperson to verify the math without a professional.
Chart comparing difference AI models on instability rate in financial calculations

Users often assume that the latest version of an AI model is more accurate than the last. ORCA 2.0 data proves this is a fallacy.

  • Regression Rates: 17.4% of the time, Grok provided an incorrect answer to a question it had previously answered correctly. ChatGPT followed with a 14.6% regression rate.
  • The Tax Implication: A prompt that worked for your taxes last year might result in an IRS-triggering error this year, even if the model version has "improved."

To test the real-world impact, we ran a standard retirement savings prompt (a 35-year-old saving $500/month with a 7% return for 32 years).

  • The Correct Answer: $1,009,919.76
  • ChatGPT-5.2’s Answer: $606,000
  • The Error: A 40% discrepancy. If a taxpayer relied on this for long-term financial planning or calculating tax-deferred growth, the real-world consequences would be devastating.
  • Precision Failures: Even when models like DeepSeek or Grok were "close," they struggled with monthly compounding interest, often missing the mark by several hundred dollars, a margin of error the IRS does not accept.
difference between the correct and incorrect AI answer gave for a retirement savings calculation

While adoption is rising, a recent report by Forbes tax expert Kelly Phillips Erb highlights a significant “Trust Gap” among taxpayers (2). Her analysis of current 2026 filing trends suggests that while Americans are increasingly experimenting with AI tools to simplify the tax process, deep-seated concerns regarding privacy and mathematical accuracy remain the primary barriers to full reliance.

Our ORCA V2 data confirms that these taxpayer suspicions are well-founded. The “experimentation” noted by Forbes is currently happening on platforms that, as our benchmark shows, suffer from instability rates of up to 78.3% in financial logic. This issue creates a dangerous scenario where taxpayers use AI for “convenience” without realizing that the underlying calculation engine is prone to regression and precision drift.

The failure concerns precision and logical chaining. AI models are “large language models,” not “large calculation models.”

  • Precision Drift: Financial calculations require high decimal precision. Models often round too early in the process, leading to significant compounding errors.
  • Lack of “Tax Logic”: AI cannot currently account for the nuance of tax law changes or the specific compounding rules required by financial institutions.
  • Treat AI as a “Thesaurus,” not a “Calculator”: Use AI to explain tax terms (e.g., “What is the difference between a credit and a deduction?”), but never use it to calculate the final numbers.
  • The “Human-in-the-Loop” Rule: If you use AI to organize documents (as suggested by Adobe), always have a human or professional accountant verify the final filing.
  • Verify with Dedicated Tools: Use verified, logic-based calculators (like those at Omni Calculator) that use hard-coded mathematical formulas rather than probabilistic language patterns.
when to us AI and when not to use AI

The ORCA V2 Benchmark (Omni Research on Calculation in AI) tested 500 prompts across four major models: ChatGPT, Gemini, Grok, and DeepSeek. The study specifically tracked transitions from “Correct” to “Incorrect” (Regression) and the “Instability” of incorrect answers across versions. For the detailed methodology, please check our ORCA benchmark report:

(1) The new tax assistant: Why AI adoption has surged for filing taxes in 2026

(2) Taxpayers Are Asking AI For Help. Trusting It Is Another Story

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