Two AI models can describe the same company differently because they were trained on different data with different cut-off dates, are given different questions and context, and generate answers with some built-in variation. Any of them can also state a figure that is simply invented. Disagreement is a signal to go and check the primary source, not to pick whichever answer you prefer.
Key takeaways
- Models are trained on different datasets with different cut-off dates, so their background knowledge differs.
- A model may not have the latest results or price, which is a data-freshness problem, not a reasoning one.
- The exact wording of the question and the context provided change the answer materially.
- Language models generate text with some randomness, so the same question can produce different phrasings and emphasis.
- Any model can hallucinate a precise-looking figure that is wrong, which is why disagreement should trigger a check against filings.
Do all AI models know the same things?
No. Each model is trained on a different collection of text, and each training run has a cut-off date after which the model has learned nothing new. One model might have been trained on data up to a certain quarter, and another up to a later one, so their background knowledge about a company is not identical.
This is the first and most common reason for disagreement. If a company reported new results or raised capital after a model's cut-off date, that model simply does not know it happened, and will answer as though the older picture is still current. The disagreement is not a difference of opinion; it is a difference in what each model was allowed to see.
Why does data freshness matter so much?
A company is a moving target. Its EPS, its Market Cap, its debt, and its guidance all change over time, sometimes sharply after a results announcement. A model that answers from training data alone is describing the company as it was at the cut-off, not as it is today.
Some tools reduce this by connecting the model to live or recent data, so it retrieves the current figure instead of recalling an old one. But if two tools pull from different data sources, or one uses live data and another relies on memory, they can report different numbers for the same field. This is closely related to why the same metric can differ across ordinary websites, explained in Why market cap differs.
Artha Terminal's assistant explains figures and points to where they come from, so you can resolve conflicting answers against the data.
How much does the question itself change the answer?
A great deal. A model's answer is shaped by exactly what it is asked and what context it is given. "Summarise this company's last quarter" and "Is this company financially healthy?" will produce different emphases even from the same model on the same underlying data.
The surrounding context matters too. If one tool feeds the model the company's actual filings and another asks the question with no data attached, their answers can diverge sharply, with the first grounded in figures and the second relying on general recollection. Two people asking two tools rarely ask in exactly the same way, so some of the disagreement comes from the prompts, not the models.
Why is there variation even from one model?
Language models do not retrieve a single fixed answer; they generate text one piece at a time, choosing among likely continuations. This process includes a degree of randomness by design, so asking the same model the same question twice can yield answers that differ in wording, structure, and which points they stress.
Most of the time this variation is cosmetic. But it can occasionally change substance, especially on judgment-style questions where the model is weighing rather than stating. This is one reason a single AI answer should be read as one attempt at the question rather than a definitive result.
When is a disagreement actually a hallucination?
Sometimes the models do not merely differ in emphasis; one of them states a precise figure that is false. This is a hallucination: text that looks confident and specific but is not grounded in real data. A model does not flag when it is guessing, so a fabricated P/E Ratio can read exactly like a real one.
The right response to any disagreement is therefore not to average the answers or pick the more appealing one, but to go to the source. On Artha Terminal, Ask Warren is designed to point you to where a figure comes from rather than assert it in isolation, and the platform's data pages let you check the current number directly. The disciplined way to resolve a conflict is covered in Verifying AI financial data.