I think this is the source of a lot of the hype. There are people salivating at the thought of no longer needing to employ the peasant class. They want it so badly that they'll say anything to get more investment in LLMs even if it might only ever allow them to fire a fraction of their workers, and even if their products and services suffer because the output they get with "AI" is worse than what the humans they throw away were providing.
They know they're overselling it, but they're also still on their knees praying that by some miracle their LLMs trained on the collective wisdom of facebook and youtube comments will one day gain actual intelligence and they can stop paying human workers.
In the meantime, they'll shove "AI" into everything they can think of for testing and refinement. They'll make us beta test it for them. They don't really care if their AI makes your customer service experience go to shit. They don't care if their AI screws up your bill. They don't care if their AI rejects your claims or you get denied services you've been paying for and are entitled to. They don't care if their AI unfairly denies you parole or mistakenly makes you the suspect of a crime. They don't care if Dr. Sbaitso 2.0 misdiagnoses you. Your suffering is worth it to them as long as they can cut their headcount by any amount and can keep feeding the AI more and more information because just maybe with enough data one day their greatest dream will become reality, and even if that never happens a lot of people are currently making massive amounts of money selling that lie.
The problem is that the bubble will burst eventually. The more time goes by and AI doesn't live up to the hype the harder that hype becomes to sell. Especially when by shoving AI into everything they're exposing a lot of hugely embarrassing shortcomings. Repeating "AI will happen in just 10 more years" gives people a lot of time to make money and cash out though.
On the plus side, we do get some cool toys to play with and the dream of replacing humans has sparked more interest in robotics so it's not all bad.
Something important happened when we turned the tables around, I don't feel it gets the credit it should. It used to be humans telling machines what to do. Now we're doing the opposite.
Sometimes the means are just as important as the ends, if not more
So going back to apples-and-apples comparison, i.e. assuming that "spend a lot of money to get it done for you" is not on the table, I'd trust current SOTA LLM to do a typical person's taxes better than they themselves would.
If a person is making a smaller income their tax situation is probably very simple, and can be handled by automated tools like TurboTax (as the sibling comment suggests).
I don't see a lot of value add from LLMs in this particular context. It's a situation where small mistakes can result in legal trouble or thousands of dollars of losses.
People who paste undisclosed AI slop in forums deserve their own place in hell, no argument there. But what are some good examples of simple tax questions where current models are dangerously wrong? If it's not a private forum, can you post any links to those questions?
Anyway, the magic robot 'knew' all that. Where it slipped up was in actually _working_ with it. Someone asked for a comparison of taxation on a 20 year investment in individual stocks vs ETFs, assuming re-investment of dividends and the same overall growth rate. The machine happily generated a comparison showing individual stocks doing massively better... On closer inspection, it was comparing growth for 20 years for the individual stocks to growth of 8 years for the ETFs. (It also got the marginal income tax rate wrong.)
But the nonsense it spat out _looked_ authoritative on first glance, and it was a couple of replies before it was pointed out that it was completely wrong. The problem isn't that the machine doesn't know the rules; insofar as it 'knows' anything, it knows the rules. But it certainly can't reliably apply them.
(I'd post a link, but they deleted it after it was pointed out that it was nonsense.)
This failure seems similar to a case that someone brought up earlier ( https://news.ycombinator.com/item?id=43466531 ). While better than expected at computation, the transformer model ultimately overestimates its own ability, running afoul of Dunning-Kruger much like humans tend to.
Replying here due to rate-limiting:
One interesting thing is that when one model fails spectacularly like that, its competitors often do not. If you were to cut/paste the same prompt and feed it to o1-pro, Claude 3.7, and Gemini 2.5, it's possible that they would all get it wrong (after all, I doubt they saw a lot of Irish tax law during training.) But if they do, they will very likely make different errors.
Unfortunately it doesn't sound like that experiment can be run now, but I've run similar tests often enough to tell me that wrong answers or faulty reasoning are more likely model-specific shortcomings rather than technology-specific shortcomings.
That's why I get triggered when people speak authoritatively on here about what AI models "can't do" or "will never be able to do." These people have almost always, almost without exception, been proven dead wrong in the past, but that never seems to bother them.
Things could stall out and we'll have bumps and delays ... I hope. If this thing progresses at the same pace, or speeds up, well ... reality will change.
Or not. Even as they are, we can build some cool stuff with them.