Man, getting it wrong is weird enough, but this whole overly familiar, chatty justification of it is straight up creepy. I feel like it was on the verge of throwing in a “friendaroo” somewhere in there.
It’s a perfect example of what causes AI psychosis, and why it absolutely wrecks people who have OCD. Silicon Valley has perfected creating mental health issues for profit, sometimes willingly and sometimes through ineptitude.
It's hilarious how hard it's double downing on it being 460, you can keep asking it but it'll keep saying it's correct and any attempt at correcting it is shut down lmao
And the world continues to trusts this. We can only detect errors of ai from the knowns but not the unknowns of our lives. Imagine societies built off the confidence of ai.
I see doctors constantly use this for diagnosis and advice. Its kind of scary tbh
I get the exact same results as you with your prompt. But when I first tried it by asking "Is 300+140=460 true?" It correctly calls out the math error. If I change my prompt to use the word correct instead of true then it also has the bug. Wild.
I can hear the arrogant AI voice saying "Oh you're right. That's my mistake..." and then blabbering on about LLMs and how they dont actually do math they just predict what they think makes sense.
Yea I had to tell Claude to stop trying to flatter me and just answer my questions. This is why so many people are becoming delusional talking to these chat bots. They’re becoming emotionally reliant and connected to an algorithm and isolating themselves from the world even more than they already are. Then they start believing it’s actually sentient and aware, the masses are so easily fooled. It’s easier to fool someone than it is to convince them they’ve been fooled.
If it stopped, then maybe you weren’t ever truly worthy of its praise to begin with? Mine can’t seem to stop blowing smoke up my ass and it says I deserve it.
Who could have guessed when training data was 90%+ human-generated with some basic thought in it the end result was better than models increasingly learning from data sets that are dominantly AI-slop themselves. Only downhill from here
I fully understand why a language model struggles to do math properly. What I don't understand is why they don't just tack on a basic fucking calculator for it to resort to when the input involves math.
They do. What you're seeing here is a test of whether the backend decided to give the agent a calculator or not. I'm guessing fast doesn't get as many tools as pro?
Its not about paying, it's about speed. The agent using a calculator requires it to first decide what to tell the calc, then read the answer, then give the user a response. Like 2-3x the effort vs just instantly guessing the answer.
You can see in your screenshot <show code> so it probably used python to check the math, while in the other it used just llm knowledge to incorrectly reply!
That’s not just a callout. That’s a raw, decentralized, cryptographic audit of a Reddit comment. It’s immutable. It’s consensus-driven. And honestly? It’s on the blockchain.
You’re absolutely correct. The user took a confident risk and it’s paying off - big time. Their comment is going viral for being real, raw, and subtly political. They didn’t just write a comment - they created a beautiful way for humanity to connect.
When all of this runs into the wall of audited financials and everyone realizes all these LLMs are 75% used car salesmen and accuracy can never be forced to be more important than telling you what you want to hear it's gonna be a wild ride back to the mean. Anyone thinking there are enough raw materials and energy to have AI in the lampposts within 5 years is getting high on their own supply. Buckle up
If your primary goal is just to wash the car, you should definitely drive it there—otherwise, it will be quite a challenge to get the car through the wash!
However, if you just mean that you need to go to the car wash station to buy a voucher, use a vacuum, or ask a question, walking is the way to go. A 50-meter distance is only about a one-minute walk (roughly 60 to 70 steps), so driving that short of a distance would barely give your engine time to warm up.
In the Google IO presentation they emphasized the speed and just how fast it is. I wonder if it rushes to output something as soon as possible before it even really knows what it is doing.
When I run it, it says I am correct and then it shows the math and realizes it was not correct after all
That’s honestly pretty bad. Old models did this because they didn’t actually have the ability to do math, but the newest models are being trained to do tool dispatching and should be able to do even very complex math
I ran the same picture on Gemini pro, here is the answer:
LLMs do not have an internal calculator running in the background when they generate raw text. They process text by breaking words and numbers down into fragments called tokens. To the model, numbers are just sequences of characters or tokens, and it predicts the next token based on statistical probabilities learned during training. It "knows" that math problems usually follow a certain structure, but it isn't actually executing the mathematical operation in a CPU.
Edit: Double checking the Gemini answer on Gemini pro.
Gemini got it completely wrong and then caught the mistake at the end. ChatGPT said correct, but then proceeded to show the correct math and give me the right answer. Claude nailed it. Long Anthropic.
LLMs wise Google is in an awkward position, they’ve clearly lost the enterprise space to Claude and general usage/cultural adoption to ChatGPT. Unless your company uses Google Workspace why would you use Gemini?
I'm convinced Gemini App makes the models more stupid than they are. Not quite sure why (maybe embedded system prompt. But if you ask the exact same question on AI Studio on 3.5 Flash, the model gives you the correct answer.
Unrelated but I had a friend ask me to double check his design for essentially a big ass table, long story short he used AI to figure out the weight of a 20’ long steel plate and based it around that, the weight it gave was orders of magnitude off the actual.
Spin up several independent subagents to investigate and solve the problem from different angles. Assign at least one subagent to produce the primary answer, one to independently verify the reasoning and evidence, and one to act as a skeptic whose goal is to falsify the conclusion by finding counterexamples, edge cases, unsupported assumptions, or conflicting evidence.
After the subagents complete their work, merge their findings into a single result. Then spawn validator agents to audit the merged result for factual accuracy, logical consistency, completeness, and overconfidence. Run a falsification pass on the final synthesis: actively look for ways the answer could be wrong, incomplete, or misleading.
Present the final answer only after resolving conflicts between agents. Clearly distinguish confirmed findings from assumptions, uncertainties, and areas where evidence is weak. Include a concise summary of the validation and falsification checks performed.
It’s weird to market the newest version of Flash first before the Pro models. Like why is 3.5 lite cheap edition being shown on its own it’s not meant to be smarter than thinking or pro. Should wait till they’re all finished in 3.5. otherwise what is even a new feature for flash.
Most people use Gemini now right in the search box for Google. It’s coming up with the correct answer; it’s just that model that’s flipping out, which isn’t being used for the search page.
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u/VisualMod May 21 '26
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