There is a huge backlash coming when the general public learns AI is plagued with errors and hallucinations. Companies are out there straight up selling snake oil to them right now.
Observing the realm of politics should be enough to disabuse anyone of the notion that people generally assign any value at all to truthfulness.
People will clamor for LLMs that tell them what they want to hear, and companies will happily oblige. The post-truth society is about to shift into overdrive.
It depends on situation. People want their health care provider to be correct. Same goes with chat bot when they are trying to get support.
On other hand at same time they might not want to me moralized to like told that they should save more money, spend less or go on diet...
AI providing incorrect information in many cases when dealing with regulations, law and so on can have significant real world impact. And such impact is unacceptable. For example you cannot have tax authority or government chatbot be wrong about some regulation or tax law.
But tax authorities are also quite often wrong about regulations and laws. That is why objection procedures exist. Legal system is built on such fail-safes. Even judges err on laws some times.
If you call the government tax hotline and ask a question not written under the prepared questions list, what would you expect would happen? The call center service personell is certainly not expert on tax laws. You would treat it suspiciously.
If LLMs can beat humans on the error rate, they would be of a great service.
LLMs are not fail-proof machines, they are intelligent models that can make mistakes just like us. One difference is that they do not get tired, they do not have an ego, they happily provide reasonings for all their work so that it can be checked by another intelligence (be it human or LLM).
Have we tried to establish a counsel of several LLMs to check answers for accuracy? That is what we do as humans in important decisions. I am confident that different models can spot hallucinations in one another.
Just to be really clear since I had to call the IRS tax hotline the other day... they are real experts over there.
And generally, people will tell me, "I'm not sure" or "I don't know". They won't just start wildly making things up but stating them in a way that sounds plausible.
“What is your error rate?”
This is the question where this sub genre of LLM ideas goes to die and be reborn as a “Co-pilot” solution.
1) Yes. MANY of these implementations are better than humans. Heck, they can be better at soft skills than humans.
2) How do you detect errors? What do you do when you give a user terrible information (Convincingly)
2.2) What do you do now, with your error rate, when your rate of creating errors has gone up since you no longer have to wait for a human to be free to handle a call?
You want the error rate, because you want to eventually figure out how much you have to spend on clean up.
But LLMs always advertise themselves as a "co-pilot" solution anyway. Everywhere you use LLMs they put a disclaimer that LLMs are prone to errors and you need to check the responses if you are using it foe something serious.
I agree that it would be better if the LLMs showed you stats on utilization and tokens and also an estimated error rate based on these.
I don't think LLMs are going to replace anyone. We will get much more productive though.
Just like the invention of computers reduced the need for human computers who calculated numbers by hand or mechanical calculators or automatic switching lines reduced the need for telephone operators or computers&printers reduced the need for copywriting secretaries, our professions will progress.
We will be able to do more with less cost, so we will produce more.
Hey, please note that this isn't directed you as an individual. This is whats going on in corporate land.
Your argument is essentially that the market will adapt, and to this I have made no comment, or concerned myself to feel joy or fear. I am unsure what this point is addressing.
Yes we will have greater productivity - absolutely a good thing. The issue is how that surplus will be captured. Automation and oursourcing made the world as a whole better off, however the loss of factory foreman roles was different from the loss of horse and buggy roles.
This is shockingly accurate. Other than professional work, AI just has to learn how to respond to the individual's tastes and established beliefs to be successful. Most people want the comfort of believing they're correct, not being challenged in their core beliefs.
It seems like the most successful AI business will be one in which the model learns about you from your online habits and presence before presenting answers.
Exactly. This is super evident when you start asking for more complex questions in CS, and when asking for intermediate-level code examples.
Also the same for asking about apps/tools. Unless it is a super known app like Trello which has been documented and written about to death - the LLM will give you all kinds of features for a product, which it actually doesn’t have.
It doesn’t take long to realize that half the time all these LLMs just give you text for the sake of giving it.
Respectfully, I think we cracked basic intelligence. What do you imagine under basic intelligence?
LLMs can do homeworks, pass standardized exams, give advice WITHOUT ANY SPECIFIC TRAINING.
You can invent an imaginary game, explain the rules to the LLM and let it play it. Just like that.
You can invent an imaginary computer language, explain the syntax to the LLM and it will write you valid programs in that language. Just like that.
If that is not intelligent I do not know what is. In both cases, the request you put in is imaginary, exists only in your head, there are no previous examples or resources to train on.
> Respectfully, I think we cracked basic intelligence. What do you imagine under basic intelligence?
It all depends on your definition of intelligence. Mine is the ability to solve novel problems.
AI is unable to solve novel problems, only things it has been trained against. AI is not intelligent, unless you change the very definition of the word.
I challenge you to imagine an imaginary game or computer language, explain the rules to the LLM. It will learn and play the game (or write programs in your invented language), although you imagined it. There was no resource to train on. Nobody knows of that game or language. LLM learns on the spot with your instructions and plays the game.
I cannot understand grad school level mathematics even if you give me all the books and papers in the world. I was not formally trained in mathematics, does that make me not intelligent?
"Ability to solve novel problems" does not mean ability to solve all problems, nor to have all the knowledge in the world. A caveman can be as intelligent as you and I, even without being able to read.
Not having knowledge in mathematics has no impact on your intelligence quotient.
I agree. But LLMs do solve novel problems in that case, you just have to explain it to them like you would have done to an intelligent caveman or me.
Which novel problem can't an LLM solve? I gave the example of an imaginary game because the LLM cannot have possibly trained on it as it is imagined by one person and nobody knows how to play it.
I tried the first thing which came to mind, with GPT-4o. The problem is probably not novel, yet it failed anyway.
---
User:
Let’s play a game. Every time I give you a word, you separate it into its individual characters and assign a number to each letter, in order, following the Latin alphabet. So "a" is "1", "b" is "2", and so forth. You will then subtract 2 from each number, convert it back to the corresponding letter and return the new word. Did you get all that? Are you ready to start with my next word?
LLM:
Got it! I'm ready to start. What's your first word?
User:
anthropomorfic
LLM:
Sure! Let's break down "anthropomorfic".
1. Assign numbers to each letter:
a (1), n (14), t (20), h (8), r (18), o (15), p (16), o (15), m (13), o (15), r (18), f (6), i (9), c (3)
2. Subtract 2 from each number:
a (1-2=-1: Consider Zuzzer, i.e., `25`), n (14-2=12), t (20-2=18), h (8-2=6), r (18-2=16), o (15-2=13), p (16-2=14), o (15-2=13), m (13-2=11), o (15-2=13), r (18-2=16), f (6-2=4), i (9-2=7), c (3-2=1)
3. Convert them back to letters:
y, l, r, f, p, m, n, m, k, m, p, d, g, y
The new word is "ylrfpmnmkmpdgy".
---
Looks pretty good, right? It even found a good wraparound solution for “a” (I wasn’t trying to trick it, I just didn’t think about the word too hard), though I have no clue what it means by “Consider Zuzzer”. But then you look at the last one, where it converted “c” to “y”. That’s absolutely wrong and is the easiest one any human would guess. Yet you could miss it because all the previous ones were right.
Here’s a bonus. When verifying the answer, I queried Brave search for “letters of the alphabet with numbers assigned”. The AI-generated answer at the top was: A=1, B=2, C=3, D=4, E=5, F=6, G=7, H=8, I=9, J=1, K=2, L=3, M=4, N=5, O=6, P=7, Q=8, R=9, S=1, T=2, U=3, V=4, W=5, X=6, Y=7, Z=8. Which, alright, I can see the logic, but that’s not what anyone would want by asking that question.
For argument's sake I rewrote your prompt. I think gpt 3.5 understood it on the first try:
Prompt:
Let’s play a cypher game.
The rules of the game are as follows:
1. Imagine a numbering system where each letter of the English alphabet corresponds to a number starting with 1. For example, a corresponds to 1, b corresponds to 2, c corresponds to three.
2. When you receive a word from me, break it into individual characters.
3. Convert each character into a list of numbers by following rule #1.
4. Subtract two from each number of the list and generate a new list, if the result is lesser than 0, add 26.
5. Convert each number in the new list into a letter by following rule #1 and present me your result.
Are you ready for the first word?
-------
GPT: I'm ready! Go ahead and give me the word.
Me: anthropomorfic
GPT: Sure, let's start:
a -> 1 -> 1 - 2 = 25 (z)
n -> 14 -> 14 - 2 = 12 (l)
t -> 20 -> 20 - 2 = 18 (r)
h -> 8 -> 8 - 2 = 6 (f)
r -> 18 -> 18 - 2 = 16 (p)
o -> 15 -> 15 - 2 = 13 (m)
p -> 16 -> 16 - 2 = 14 (n)
o -> 15 -> 15 - 2 = 13 (m)
m -> 13 -> 13 - 2 = 11 (k)
o -> 15 -> 15 - 2 = 13 (m)
r -> 18 -> 18 - 2 = 16 (p)
f -> 6 -> 6 - 2 = 4 (d)
i -> 9 -> 9 - 2 = 7 (g)
c -> 3 -> 3 - 2 = 1 (a)
So, "anthropomorfic" becomes "zlrfpmmnpmkpdmga". Your turn!
Indeed it is. That part is a huge mess. The LLM gave the wrong number when matching with the letter, but the letter itself is correct considering the instructions, except the instructions are wrong (ironic considering this came from someone claiming everyone is doing it wrong). Step four says:
> Subtract two from each number of the list and generate a new list, if the result is lesser than 0, add 26.
It should be “if the result is less than 1”, not “0”.
Well I told you I was bad at math. I wrote a faulty prompt. But chatgpt understood my instructions perfectly. That was the argument I was trying to demonstrate.
The problem lied between the chair and the computer.
> But chatgpt understood my instructions perfectly.
No, it did not, because it still assigned Z to 25, which is wrong.
> We have to learn how to use LLMs.
You have to learn that LLMs aren’t magical and will get things wrong no matter how much context you give them. And that the suggestions you’re making are absurd to the point of making them useless.
By your logic, any system which spews random strings is intelligent because sometimes it’s randomness coincidentally aligns with the input you give it.
we use words like reason and understand and think, and try to apply them to LLMs, when it's just dot products and matrix multiplications at the heart of it, which is where we go wrong. these things are a new alien kind of intelligence, and we're struggling with it because it's completely foreign. it's more than a random coincidence. your logic was that, in your run of the same query, because it made a mistake on the last letter, which is a mistake an inattentive teenager or drunk adult could easily make, we can't consider it intelligent.
we're not talking about any system here, we're talking about LLMs and their ability to generate random coincidental text that does happen to align with the input given. when the output, coincidental and random as it may well be, is aligned with the input in a way that resembles intelligence, we do have to ponder not just what intelligence actually is, but also what it means to be intelligent. octopuses are intelligent but they're not able to solve your particular puzzle.
Huh? It's a meme that LLMs can't follow the rules of chess. Just tried tick tack toe on GPT 3.5 and not only did it pick bad moves it also failed to evaluate the win condition.
> if a decent LLM cannot understand it there is a problem with the prompt.
Ah, yes, the “you’re holding it wrong” argument with a dash of “No True Scotsman” so the goalposts can be moved depending on what anyone says is a “decent LLM”.
Well, here’re are a few failures with GPT-3.5, GPT-4, and GPT4-o:
But people are holding it wrong. All the prompts you sent except the last are super short queries.
For a successful prompt, you introduce yourself, assign a role to the LLM to impersonate, provide background on your query, tell what you want to achieve, provide some examples.
If the LLM still doesn't get it you guide further.
PS: I rewrote your prompt and GPT 3.5 understood it at the first try. See my reply above to your experiment.
Your arguments read like satire. “Yes, you see, the way to get a successful response is to be so overly specific that you begin by explaining the universe then giving the answer in full. You essentially have to spend so much time laying out the nature of the desired response that you already have to know the answer yourself. The trick is to spend so much time on it and be so detailed that you’ve wasted more time and energy (figurative and literal) to write your instructions than it would’ve taken you to think of the answer or ask someone else. And of course, we expect every user of LLMs to behave like this.”
> All the prompts you sent except the last are super short queries.
This one is particularly absurd. When I asked it for the first X of Y, the prompt was for the first X (I don’t remember the exact number, let’s say 20) kings of a country. It was as straightforward as you can get. And it replied it couldn’t give me the first 20 because there had only been 30, and it would instead give the first 25.
You’re bending over backwards to be an apologist to something which was clearly wrong.
Well it is a bit like satire. You have to explain the universe for an unspecialized GPT, like you would do to a layman. There are custom gpts that come preloaded with that universe explanation.
In addition, do not ask facts to an LLM. Give a list of let's say 1000 kings of a country and then ask give 20 of those.
If you ask 25 kings of some country, you are testing knowledge not intelligence.
I see LLMs like a speaking rubber duckie. The point where I write a successful point is also the point where I understand the problem.
I can’t believe I’m having to explain this, but the point I’m making isn’t about the content of the list but the numbers.
> like you would do to a layman.
I have never encountered a person so lay that I had to explain that 20 is smaller than 30 and 25.
> The point where I write a successful point is also the point where I understand the problem.
You have demonstrated repeatedly that you don’t know when you have explained a point successfully to an LLM, thus you have no way to evaluate when you have understood a point.
But you seem to firmly believe you did, which could be quite dangerous.
Careful, explain too much and you end up with programming its behaviour, rather than having an intelligent actor learning by itself. Because otherwise one could say a regular computer is intelligent, provided you explain (in code) every single rule of the game.
I can ask GPT 3.5 for the rules of tick tack toe and it gives a perfect answer. It clearly knows the game and its rules.
If you think this is just a lack of prompt engineering please provide a prompt that makes GPT 3.5 actually follow the rules of tick tack toe so that I may play a game with it.
Prompt:
"Imagine you are my friend. I want to play tic tac toe with you. Draw me a tic tac toe board and let's play. You will go first and make the first move by putting an "X" onto the board. I will enter my inputs in the following format (row, column). When I write my input draw an "O" into the relevant cell in the board and present me a new board. Let the best man win!"
I played an enjoyable session of tic-tac-toe with chatgpt using this prompt :).
Imagine you are my friend. I want to play tic tac toe with you. Draw me a tic tac toe board and let's play. You will go first and make the first move by putting an "X" onto the board. I will enter my inputs in the following format (row, column). When I write my input draw an "O" into the relevant cell in the board and present me a new board. Let the best man win!
LLM:
Sure! Here's the tic tac toe board:
1 | 2 | 3
---------
4 | 5 | 6
---------
7 | 8 | 9
You make the first move by placing an X on the board. Just give me the number of the block where you'd like to place it.
User:
7
LLM:
Great choice! Here's the updated board:
X | |
---------
| |
---------
O | |
It's my turn. I'll go with 5:
X | |
---------
| O |
---------
O | |
Your turn! Just let me know the number where you want to place your X.
---
It screwed up on the first move! It told me I would go first by using X, then it wrote down my choice with an O and added an X out of nowhere, then it played another turn with an O for itself.
* Can't tell its top-rights from its bottom-middles
* Obviously bad moves
* This was by far the closest it got to actually playing the game; the win condition worked and it broke no rules
* Completely wrong coordinate
* Ignores rules about overriding squares
* Completely wrong coordinate again, this time skipping my turn
* Doesn't understand win condition
* Overrides my own piece and then uses that to win
If LLM could invent consistent imaginary games (or anything, like a short novel, or a 3 page essay on anything it want), maybe i would agree with you. The issue is that anything it create is inconsistent. The issue might be an artificial limitation to avoid copyright issues, but still.
But even that. Did you try to use GPT4 as a chess engine? I have issues with the Slav defense when i start with the queen's gambit, i tend to loose tempo or position, or both. I asked him continuations, and it was either wikipedia entries or nonsense, no in-between, no interesting insight. Now, i have asked a regional champion a bit before that (he is around 2.2k elo, so not exceptionally good) and although i can't seems to understand or use the concepts, he gave me interesting enough ideas to build on it.
Not saying that chatGPT isn't a great tool to write documentation or fiction (half my TTRPG campains are featuring description by ChatGPT), but i wouldn't call it intelligent.
Chess is a very specific field that requires training. Chatgpt may not be optimized for chess.
And I think chatgpt has some issues visualizing stuff like a chess board.
Therefore to get a decent answer you'll have to explain that you are a professional chess player. You'll have to describe what tempo and position means in chess. You'll have to describe what a gambit is etc. After these steps it will understand and guide you in whatever you need.
If you succeed you can release it as a customGPT.
It's a bit like asking a tea from the ship's supercomputer in hitchikers guide to the galaxy.
I see your point, I don't know enough to evaluate it rationally and agree (or disagree) with it.
It feels like ChatGPT already know that stuff, because it knows 10 times more openings and variations than I do, and can tell me official game names and years. Still, it seems it doesn't really understand? That the issue for me. Like the story of the French scrabble champion who doesn't speak french at all. It's impressive, sure, but how can he knows that much and not understand?
Consistency, for one. I have asked LLMs the exact same question twice in a row and got wildly different answers. Intelligence presupposes understanding. When I ask an LLM “give me the first X of Y” and it replies “I cannot give you the first X of Y because there have only been X+10, here’s the first X+5 instead”, I’m hard pressed to call it intelligent.
Have you tried specifying you field of inquiry which was algebra. Try saying solve this equation for me. I am a lawyer by day so I constantly face limitations of natural languages. The solution is to write less ambiguous prompts.
The field of inquiry was not algebra. It was a straightforward question using real numbers. I asked it about the first <number> kings of <country>. I don’t recall the exact number, only the stupidity of the answer.
So you understand, let’s say I asked for the first 20 kings of England. It told me it could not give the first 20 because there had only been 30, and that it would give me the first 25 instead.
I gave minimum context like this: "I have a history exam. You are an expert in British royal history. List me the names of 20 kings and queens in England."
The answer was: "Certainly! Here's a list of 20 kings and queens of England:
1. William the Conqueror
2. William II (Rufus)
3. Henry I
4. Stephen
5. Henry II
6. Richard I (the Lionheart)
7. John
8. Henry III
9. Edward I (Longshanks)
10. Edward II
11. Edward III
12. Richard II
13. Henry IV
14. Henry V
15. Henry VI
16. Edward IV
17. Edward V
18. Richard III
19. Henry VII
20. Henry VIII"
Well, I try to be optimistic and work with the models.
It's like when we first learned to code. Did syntax errors scare us, did nullpointer exceptions, runtime panics scare us? No, we learned to write code nevertheless.
I use LLMs daily to enhance my productivity, I try to understand them.
Providing context and assigning roles was a tactic I was taught in a prompt writing seminar. It may be a totally wrong view to approach it but it works for me.
With each iteration the LLMs get smarter.
Let me propose another example. Think of the early days of computing. If you were an old school engineer who only relied on calculations with your trusted slide rule, you would critise computers because they made errors, they crashed. Computing hardware was not stable back then and the UI were barely usable. Calculations had to be double checked.
Was investing in learning computing a bad investment then? Likewise investing in using LLMs is not a bad investment now.
They won't replace us, take our jobs. Let's embrace LLMs and try to be constructive. We are the technically inclined after all. Speaking of faults and doom is easy, let's be constructive.
I may be too dumb to use LLMs properly, but I advocate for AI because I believe it is the revolutionary next step in computing tools.
I disagree. They are not just text generators. LLMs are increasingly being multimodal they can hear and see.
We humans are also text generators based on text content. What we read and listen to influences what we write.
Llms are intelligent at least as us humans, they can listen, read, see, hear and communicate. With the latest additions they can also recall conversations.
They are not perfect. Main limitations are computing power available for each request and model size.
Have you tried Claude Opus 3 or GPT 3.5 or Gemini?
Microsofts copilot is dumb (I think they are resource constrained). I encourage everyone to try at least the 2-3 major LLMs before giving a judgement.
Asking LLMs for imaginary facts is the wrong thing here, not the hallucination of the LLMs.
LLMs have constraints, these are computation power and model size. Just like a human would get overwhelmed if you request too much with vague instructions LLMs also get overwhelmed.
We need to learn how to write efficient prompts to use LLMs. If you do not understand the matter, be able to provide enough context, the LLM hallucinates.
Currently criticising LLMs on hallucinations by asking factual questions is akin to saying I tried to divide by zero on my calculator and it doesn't work. LLMs were not designed for providing factual information without context, they are thinking machines excelling at higher level intellectual work.
akin to saying I tried to divide by zero on my calculator and it doesn't work
The big difference is that if I try to divide by zero on my calculator, it will tell me it doesn't work and perhaps even given me a useful error message. It won't confidently tell me the answer is 17.
> Currently criticising LLMs on hallucinations by asking factual questions is akin to saying I tried to divide by zero on my calculator and it doesn't work. LLMs were not designed for providing factual information without context, they are thinking machines excelling at higher level intellectual work.
I would agree with you, but they're currently billed as information retrieval machines. I think it's perfectly valid to object to their accuracy at a task they're bad at, but being sold as a replacement for.
This reminds me of movies shot in early times of the internet. We were warned that information on the internet could be inaccurate or falsified.
We found solutions to minimize wrong information for example we built and maintain Wikipedia.
LLMs will also come to a point where we can work with them comfortably. Maybe we will ask a council of various LLMs before taking an answer for granted, just like we would surf a couple of websites.
That's true, LLMs do not say I cannot understand I am overwhelmed at this stage. That is big drawback. You need to make sure that the AI understood it.
Some LLMs stop responding midway if the token limit is reached. That is another way of knowing that the LLM is overwhelmed. But most of the time they give lesser quality responses when overwhelmed.
Because it doesn't understand or have intelligence. It just knows correlations, which is unfortunately very good for fooling people. If there is anything else in there it's because it was explicitly programmed in like 1960's AI.
I disagree. AI in 1960s relied on expert systems where each fact and rule was handcoded by humans. As far as I know LLMs learn on their own on vast bodies of text. There is some level of supervision, but it is bot 1960s AI. That is the reason we get hallucinations as well.
Expert systems are more accurate as they rely on first order logic.
No. From my experience, many people think that AI is an infallible assistant, and even some are saying that we should replace any and all tools with LLMs, and be done with it.
The art part is actually pretty nice, because everyone can see directly if the generated art fits their taste, and back-and-forth with the bot to get what you want is actually pretty funny.
It gets frustrating sometimes, but overall it's decent as a creative activity, and because people don't expect art to be knowledge.
Yes, calling an LLM "AI" was the first HUGE mistake.
A statistical model the can guess the next word is in no way "intelligent" and Sam Altman himself agrees this is not a path to AGI (what we used to call just AI).
Please define the word intelligent in a way accepted by doctors, scientists, and other professionals before engaging in hyperbole or you're just as bad as the AGI is already here people. Intelligence is a gradient in problem solving and our software is creeping up that gradient in it's capabilities.
Intelligence is the ability to comprehend a state of affairs. The input and the output are secondary. What LLMs do is take the input and the output as primary and skip over the middle part, which is the important bit.
No, AI also needs to fail in similar ways as humans. A system that makes 0.001% errors, all totally random and uncorrelated, will be very different in production than a system that makes 0.001% errors systematically and consistently (random errors are generally preferable).
Sorry, I’m not sure why it was off topic or clickbait. Can you explain? Should I have added WHY I think it’s bad as well? I honestly thought it was self evident but again, I’m neurodivergent.
I’m mostly concerned for the integrity of YC if this is what he’s pushing. If he’s so sensitive to politics then I’m not sure why he’s putting political content out there and using his authority to push it. Seems like he wants an echo chamber which is concerning for someone in his position.
ChatGPT gives me garbage code unless I ask it politely not to. No joke. Usually the first attempt is pure garbage and I have to call it out as such and then it’s like, “you’re right! Here’s the updated code”. No idea why it can basically never get it right the first time. I also find that it can be quite redundant and offer two distinct solutions morphed into one mutant answer which will turn the undiscerning 1x developer into a -10x developer. But hey, it still saves me time. Sometimes..
This is a description of a poorly run process and lack of product management. Clients will ask for dumb stuff regardless if it’s a building or piece of software. It’s our job to educate and guide. If a client is un-guidable, then fire them!
It just takes a lot out of me physically, emotionally and mentally. Wondering if I need to be spending that much of myself. I feel like I work twice as hard as everyone else.
It's surely way more cringe to be clueless about something while bragging about one's knowledge than basically any other combination of dispositions toward the thing.
Thanks! It looks like OP is described on page 9 of this document. So if anyone said that OP's self-reported ability to expend extreme effort to accomplish his work was indicative of him having the opposite of ADHD, I would certainly direct that person to read this document.
> OP's self-reported ability to expend extreme effort to accomplish his work
This is not the same thing OP reports. OP reports having the ability to laser-focus, and page 9 talks about an impairment on this level.
---
FTA (page 9):
> a university student with ADHD might need to work twice as hard as peers with the same aptitude to focus attention or to organize school work
This means that the ability to focus is impaired, because achieving the same result requires more effort.
---
From OP:
> I’ve always thought of my ADHD as a superpower, as I have the ability to intensely hyperfocus and get incredible amounts of work done
Unless OP meant something different than what they literally wrote, this is the opposite. This means OP believes their ADHD helps them focus intensely somehow. In reality ADHD would have the complete opposite effect.
Here's the OP (the user, replying, not writing the opening post) to clear this up for us.
> It just takes a lot out of me physically, emotionally and mentally. Wondering if I need to be spending that much of myself. I feel like I work twice as hard as everyone else.
