Yes, there's a difference between a deterministic outcome and a non-deterministic one. But throw humans into the loop, and it becomes more interesting. I can't count the number of times I've listened to someone argue their answer must be right because they got it from the calculator. And it's not just students; as a teacher I've always paid attention to how adults use math.
With calculators or GPT tools, or any other automated assistant, judgement and validation continues to matter.
Answers from calculators are always right! But the human may have asked the wrong question.
Edit: slapping a few more in here:
https://learn.microsoft.com/en-us/office/troubleshoot/excel/...
Just because they are both both decks doesn't mean they are the same.
I don't mean to be pedantic. I teach coding to elementary school students, and this is something fundamental I try to make them to understand. A computer will always do what you tell it to do. A bug is when you accidentally tell the computer to do something different than what you'd intended.
Going back to the calculator example, if a student used a calculator and got the wrong answer, the problem didn't come from the calculator. This can help in understanding what went wrong.
AI is different insofar as we've told the computer to come up with and follow its own instructions. When ChatGPT gives the wrong answer, it's still accurately following instructions.
It's wrong in the same way that saying 1/1 = 1.0004 is wrong. It's not a matter of chosen precision in that it doesn't make the answer correct when you increase the number of zeros between 1 and 4.
In the case of translation of floating point numbers from base-2 to base-10 we have to make approximations which will often be slightly wrong forever without regard for amount of precision.
With AI, depending on the pre-conditions, the AI could be stuck in a state of being slightly wrong forever for a specific question without regard to further refinement of the query.
These are both still useful as tools. We just need to be able to work on the amount of refinement of the answer that the AI gives, which may be able to be solved fairly well through prompt engineering, if not through the advancement of GPT itself.
I'm sorry in advance, but this reply is just to meet pedantry with pedantry.
> A computer will always do what you tell it to do.
This is the Bohr model of computers. It's the kind of thing you tell elementary school students because it's conceptually simple and mostly right, but I think we know better here on HN. Pedantically, computers don't always do what you tell them to, because the don't always hear what you tell them, and what you tell them can be corrupted even when they do hear it.
For instance, random particles from outer space can cause a computer to behave quite randomly: https://www.thegamer.com/how-ionizing-particle-outer-space-h...
why was nobody able to pull it off, even when replicating exactly the inputs that DOTA_Teabag had used? Simple: this glitch requires a phenomenon known as a single-event upset, which is very much out of any player's control.
My underlying point is that, at least in 99.999% of cases, the problem isn't the calculator, it's the human using the calculator incorrectly. And although you could draw some parallels between calculators and AIs with regard to selecting the right tool and knowing when and how to use it, I'd say the randomness involved in an LLM is fundamentally different.
It’s mostly fine until it isn’t. AI will probably operate in the same capacity. We already have so much incorrect information out there that’s part of our pop culture. Even down to things like the fact that Darth Vader never said, “Luke, I am your father,” and Mae West never said, “Why don’t you come see me sometime?”
Even basic movie quotes are beyond our ability to get right. Hilariously, I just asked ChatGPT about these quotes and it explained that these are common misquotes, told me what was actually said in these movies, and explained some relevant context.
Sherlock never said, “Elementary, my dear Watson” even once in the books. Kirk never said, “Beam me up, Scotty.” We’re much less correct than we like to think. And somehow we’ve survived.
ChatGPT is fallible just like we are. We’ll manage, just like we always have.
I actually agree with you, but, in the same vein, does it not mean that user did not ask correct prompt?
Floating point errors creeping in is why we have to use quaternions instead of matrixes for 3D games. Apparently. I'd already given up on doing my own true-3D game engine by that point.
In some sense we "know why" humans make mistakes too — and in many fields from advertising to political zeitgeist we manipulate using knowledge of common human flaws.
On this basis I think the application of pedagogical and psychological studies to AI will be increasingly important.
And in this case you don't get to put in the formula.
We know this wording is just show but people still get swayed by it and believe it must be true.
Also another point while I'm here: Many many humans I've met are often confident when incorrect as well and can and will bullshit an answer when it suits their comfort.
Lying requires knowledge that what you are saying is not the truth, and usually there's a motive for doing so.
I don't think ChatGPT is there yet... or is it?
"Confabulate" however, appears to be a good description. Confabulation is, I'm told, associated with Alzheimer's, and GPT's output does sometimes remind me of a few things my mum said while she was ill.
Outside of the code use case, what should I rely on ChatGPT for that won't have me also looking for the information somewhere else? I suppose subjective soft things, like writing communications. But I can't rely on it for information.
I wouldn’t ride in a vehicle it designed tho, based on my week of asking it to do go programming.
That is, for me, the output of ChatGPT or other AI tools is the starting point of my investigation, not the end output. Yes, if you just blindly paste the output from an AI tool you're going to have a bad time, but we also standardize code reviews into the human code-writing process - this isn't that different.
Just giving one specific example, I find ChatGPT to my an incredibly efficient "documentation lookup tool". E.g. it's great if I'm working with a new technology or API and I want to know "what my options are", but I don't know what keywords to search for, it can help give me a really good "lay of the land", and from there I can read on my own to get more specifics.
I can't buy any of this hype for a "word-putting-together" algorithm. It's not real intelligence.
For example, I commented last week that I've found ChatGPT to be a great tool for managing my task list, and for whatever reason the "verbal" back-and-forth works much better for my brain than a simple checklist-based todo app: https://news.ycombinator.com/item?id=35390644 . But, I also pointed out how it will get the sums for my "task estimate totals by group" wrong. But it's so easy to see this mistake, and after using it for a while I have a good understanding for when it's likely to occur, that it doesn't lessen the value I get from using the tool.
The code in the post is wrong. For this "trivial" example, if you just blindly copied it into your code, it would very likely not do what you want it to do. I love this example not just because it's ironic, but because it's a perfect illustration of how you need to know the answer before you ask for the solution. If you don't know what you're doing, you're gonna have a bad time.
[1] https://skventures.substack.com/p/societys-technical-debt-an...
Also, I think the coding example in that substack highlights that one of the most important characteristics of good programmers has always been clarifying requirements. I had to read the phrase "remove all ASCII emojis except the one for shrugs" a couple times because it wasn't immediately clear to me what was meant by "ASCII emojis". I think this example also highlights what happens when you have 2 "VC bros" who don't know what they're talking about highlighting the "clever" nature of what ChatGPT did, because it is totally wrong. Still, I'd easily bet that I could create a much clearer prompt and give it to ChatGPT and get better results, and still have it save me time in writing the boiler plate structure for my code.
In short, I don't know if we "agree", but I think OP is/was correct that GPT generates lots of subtle mistakes. I'd go so far as to say that the folks filling this thread with "I don't see any problems!" comments are probably revealing that they're not very critical readers of the output.
Now for a wild prediction of my own: maybe the rise of GPT will finally mean the end of these absurd leetcode interview problems. The marginal value of remembering leetcode soltutions is falling to zero. The marginal value of detecting an error in code is shooting up. Completely different skills.
I'd possibly argue that knowing how to ask the right question is tantamount to knowing the answer.
Whatever, time will tell. I still haven’t quite figured out how to make good use of GPT-4 in my daily work flow, tho it seems it might be possible.
Has anyone asked it to make an entry for the IOCC?
I was also using it to generate some Java code for a bit. That is, until it started giving me maven dependencies that didn't exist, and classes that didn't exist, but definitely looked like they would at first glance.
OK, wow - that example kind of perfectly proves my point. If I were to ask ChatGPT an extremely specific, low-level question about an extremely niche topic, then I would absolutely be on "high alert" that it wouldn't know the answer. And while I agree the "confidence" with which ChatGPT asserts its answers (though I'd argue the GPT-4 version does a much better job at not being over-confident than 3.5) is off-putting, I think it's pretty easy to detect where it's wrong.
I'd also be curious about your Java example. There was a good YouTube video of a guy that got ChatGPT to write a "population" game for him. In some cases on first try it would output code that had compile errors, e.g. because it had wrong versions of Python dependencies. He would just paste the errors back in to ChatGPT and ChatGPT would correct itself. Again, though, this highlights my point that I use ChatGPT as the start of my processes, a 1st draft if you will. I don't just ask it to write some code, then when I get an error throw my hands up and say "see how dumb ChatGPT is." To each their own, though.
I don't consider a popular video game from 2009 to be "extremely niche", and I also shouldn't have to know what ChatGPT knows. And no, I don't think it's easy to detect where it's wrong if you don't know the right answer, and it's actually pretty useless when you have to spend time confirming answers.
LLMs are great for “creative” work: images, poems, games - entertainment based on imaginary things.
Because of how they're used.
If you think of AI as a source of truth, obviously you're going to run into trouble: it "lies"! But if instead of thinking of it in isolation, you think of the person+AI producing results, then you should trust that person exactly as much as you would whether or not they use AI.
A tool is a tool. it has good uses and not good uses. as the human you figure out where it works and where it doesn't.
As a software engineer, I'm not concerned about people using AI to write simple functions. That's not where my value is - it absolutely incidental to me.
Yes, I know calculators can encourage people to not think; I should know because I wrote one. [1]
But the current "AI" tech is so much worse on that front. It's a difference of degree, and that degree does matter.
By the way, when I was learning to fly helicopters [2], I used my calculator to calculate weight and balances, but I also did it by hand!
[1]: https://git.gavinhoward.com/gavin/bc
[2]: https://gavinhoward.com/2022/09/grounded-for-life-losing-the...
Sure you can open an accountancy business and refuse to use calculators, but that's just working with a strange self-imposed limit rather than using technology to best support your business.
Yes, calculators do that. I'm arguing that "AI" does not let programmers write better code faster. It lets them write worse code faster, or better code slower.
Turns out that really doesn't matter. I think your argument is incredibly weak; the fact that some people don't use these tools effectively doesn't mean that nobody can. Whoever figures this stuff out is going to win, that's just how it works.
That is to say, there will always be a niche for people who refuse to move up the chain of abstraction: they're actually incredibly necessary. However, as low-level foundations improve, the possibilities enabled higher up the chain grow at an exponentially-higher rate, and so that's where most of the work is needed. Career-wise it might be better to avoid AI if that's what you want to do, but as a business I can't see a dogmatic stance against these tools being anything but an own goal.
Except that it does!
For every level of abstraction, you lose something, and abstractions are leaky.
The lower levels of abstraction make you lose the least, and they are also the least leaky. The higher you go, the more you lose, and the more leaky.
What I'm claiming is that these "AI" tools have definitely reached the point where the losses and the leaks are too large to justify. And I'm betting my career on that.
You're not betting anything because the cost for you to change your mind and start working with AI tools is exactly 0. This rhetoric is just marketing. I'm sure you'll find the customers that are right for you, but you can at least admit that this kind of talk is putting the aesthetic preference of what you want work to look like above what's actually the most effective. Again, I'm sure you'll find customers who share those aesthetic preferences, but to pretend like it's actually an engineering concern is marketing gone too far.
Did I ever deny that? Sure, some of those layers are worth it. That doesn't address my assertion that these "AI" tools are not.
> Telling yourself that the layer above you isn't feasible isn't going to do you any favors but it does generate buzz on social media which seems like it's the goal here.
You're halfway there.
> You're not betting anything because the cost for you to change your mind and start working with AI tools is exactly 0.
And here is where you contradict yourself.
If I'm getting loud about this bet, and making customers because of this bet, then it will cost me a lot to start working with "AI" tools. My customers will have come to be because I don't, so if I start, I could easily lose all of them!
> This rhetoric is just marketing.
Yep! But that's what makes my best actually cost something. I'm doing this on purpose.
> I'm sure you'll find the customers that are right for you, but you can at least admit that this kind of talk is putting the aesthetic preference of what you want work to look like above what's actually the most effective.
No, I will not admit that because I believe very strongly that my software will be better, including engineering-wise, than my competitors who use these "AI" tools.
The idea isn't to write better code faster, it's to build better products faster.
Although IMO in the future, AI will probably also enable programmers to write better code too (faster, less bugs, more secure, more frequently refactored etc)
All else being equal, better code means better products.
Also, to have a better product without better code, you're implying that the design of the product is better and that these "AI" tools help with that.
Until they can reason, they cannot help with design.
And I would bet that AI design would help things where the existing designers are bad, e.g. so much open source UI (that is, not cli UX) written by devs, but it is still a bit away from the top quality like Steve Jobs.
Maybe this is like the transition from hand crafted things to machined things; we go from a world some some excellent design and some meh design to a world with more uniform but less great designs.
"AI" design will not help until we have a true AI that can reason. (I don't think we ever will.)
Why is reasoning necessary? Because design is about understanding constraints and working within them while still producing a functional thing. A next-word-predictor will never be able to do that.
What is your definition of reasoning that you do not think GPT-4 would demonstrate signs of?
Heh, there have been many attempts to define reasoning. I haven't seen a good one yet.
However, I'm going to throw my hat into the ring, so be on the lookout for a blog post with that. I've got a draft and a lot of ideas. I'm spending the time to make it good.
Otherwise it’s just moving the goalposts.
To demonstrate this, ask it to prove something that most or all people believe. Say some "intuitive" math thing. Perhaps the fact that factorial grows faster than exponential functions.
And no, don't just have it explain it, have it prove it, as in a full mathematical proof. Give it a minimal set of axioms to start with.
Merriam-Webster's definition of "reasoning" [1] says that reasoning is:
> the drawing of inferences or conclusions through the use of reason
So starting GPT4 off with some axioms would give it a starting point to base its inferences on.
Then, if it does prove it, take away one axiom. Since you started with a minimal set, it should now be impossible for GPT4 to prove that fact, and it should tell you this.
Having GPT4 prove something with as few axioms as possible and also admit that it cannot prove something with too few axioms is a great test for if it is truly reasoning.
Take this problem instead which certainly requires some reasoning to answer:
“Consider a theoretical world where people who are shorter always have bigger feet. Ben is taller than Paul, and Paul is taller than Andrew. Steve is shorter than Andrew. Everyone walks the same number of steps each day. All other things being equal, who would step on the most bugs and why?”
I think it’s a logical error to say “AI can’t reason about this, so that proves that it can’t reason about anything at all” (particularly if that example is something most humans can’t do!). The LLMs reasoning is limited compared to human reasoning right now, although it is still definitely demonstrating reasoning.
Because Ben is the tallest, his feet are the biggest, and because he takes the same amount of steps as the others, the amount of area he steps on is larger than the area that the others step on.
Therefore Ben is most likely to be the one to step on the most bugs.
Easy. And I'm not brilliant.
The problem with testing these tools is that you need to ask it a question that is not in their training sets. Most things have been proven, so if a proof is in its training set, the LLM just regurgitates it.
But I also disagree: if the "AI" can't reason about that, it can't reason because that one is so simple my pre-Kindergarten nieces and nephews can do it.
But even if not, the LLM's should have "knowledge" about exponential functions and factorial because the humans who wrote the material in their training sets did. So it's not a lack of knowledge.
And I claim that most humans could rediscover theorems from basic axioms; you've just never asked them to.
Ben (tallest) Paul Andrew Steve (shortest) Since shorter people have bigger feet in this world, we can also deduce the following order for foot size:
Steve (biggest feet) Andrew Paul Ben (smallest feet) Assuming that everyone walks the same number of steps each day and all other things being equal, the person with the biggest feet would be more likely to step on the most bugs simply because their larger foot size would cover a greater surface area, increasing the likelihood of coming into contact with bugs on the ground.
Therefore, Steve, who is the shortest and has the biggest feet, would step on the most bugs.”
GPT4 solved it correctly. You didn’t.
And GPT4 didn't solve it correctly. It's a probability, not a certainty, that the shortest person will step on more bugs.
At the very least, this should be evidence that the problem wasn't a totally-trivial easy pre-kintergarden level problem though, and it did manage to correctly solve it.
It required understanding new axioms (smaller = bigger feet) and infering that people with bigger feet would crush more bugs without this being mentioned in the challenge.
Your dismissal that the AI messed up because it didn't phrase the correct answer back in the way you liked is a little harsh IMO, as the AI's explanation does make it clear it is basing it on likelihoods ("the person with the biggest feet would be more likely...").
Equally if it can help devs launch a month earlier, that’s a huge advantage in terms of working out early product/market fit.
All things being equal, I would rather have a company with better product/market fit than one with great code (even though both are important!).
That's a very big "if", and one I just don't think will exist.
Also, that only helps at the beginning. Add the product gets more complex, I believe the AI will help less and less until velocity will become slower than companies like mine.
And product/market fit is just a way for companies to cover up the fact that their founders wanted to found a company, not solve a real problem. If you solve a real problem first, founding a company is simple and you "just" have to sell your solution.
You act like encouraging people not to think is a problem. Thing is, you'd be wrong. We want people not just to think, but to focus. If I'm a pilot and I have to worry about the runtime environment of the command-line calculator I use to hand-calculate my route and cockpit configuration, is that a good use of my focus? I think most people would say no. We definitely want to discourage the pilot from actively thinking about that kind of stuff. Should they have a grasp of the basics in case of emergency? Sure. Do we have a sustainable and efficient system of transportation if that's how our pilots spend their time? No.
Aviation is about redundancy. Redundancy is a good use of a pilot's focus. That's why I did both. I didn't blindly trust my calculator to not have bugs (even though I wrote it!), and I didn't blindly trust my hand calculations to be correct.
If they agree, though, it's a good sign that everything is in good order. That's what redundancy is for, to ensure that a problem in one thing does not lead to another problem, like in the Swiss cheese model of accidents.
If you're just trying to get off Gilligan's island, that's another thing entirely.
A commercial pilot friend has told me that they still check the calculations. When they don't, they get accidents like the Gimli Glider.
It's like saying that putting all of the legal checks on the lawyers is not going to give you a sustainable business. But we all know that's wrong.
When I wrote that calculator, I didn't write it for flight. I wrote it as a general calculator and just used it for flight. I would have used the GNU bc if I didn't write my own.
So it's a bit disingenuous to claim that I am claiming that pilots should write their own.
And pilots are included in those maintaining the system; they're not just using it.
On avoiding it, if your competitors are getting massive productivity gains from AI tools, your hand-crafted, artisanal code is going to be expensive and your customers will vote with their wallets.
Any of these can lead to dystopia.
When we use computer vision to identify benign or malignant tumors, we might cost someone a job, but at least what that model was trained on can be known and we are not leaking personal data in the model and probably there is no violation of licenses or similar to the data that was used to train the model. The model is very limited in output. The consequences are kind of simple to monitor or predict, because the model can only be used in its specialized area of use.
With the new crop of ML models the situation is different. Lawmakers were asleep while these things became popular and there is no knowing, how it will all pan out, because the output is of a more general nature, that can touch any subject, any area of expertise. No one knows what they have been trained on and whether there is anything in that training data, that should not have been used, and there is no simple way (that I know of) to operate out any such data after training the model.
Here's the rub: if my competitors are going to realize "massive productivity gains," where are those gains actually coming from?
Are they coming from the time it takes to type? No, because typing is a small portion of the time we spend as programmers.
Are the gains coming from the time spent on design and architecture? Maybe, but that means my software will have better architecture, and I will have better velocity in the long run. (I have real-world experience with this, by the way; it takes me, on average, less than an hour to fix bugs in my bc nowadays. Architecture matters for long-term velocity.)
Are the gains coming from the time squashing bugs? No, this is where AI would decrease productivity because you have to carefully review outputs to ensure there are not any bugs.
In essence, the productivity gains will only happen the less that my competitors actually review and clean up the code from these tools. The less they do that, the lower the quality. The lower the quality, the slower their long-term velocity will be.
A higher slope makes up for a lot of y-intercept. [1]
[1]: https://gist.github.com/gtallen1187/e83ed02eac6cc8d7e185
I'm just advocating a very cautious middle path. For now, these tools are just code helpers to me (and right now I barely use them for that), but the moment I sense a genuine advantage to using them in my job, and I don't think it undermines my engineering, I think I'll have to engage further. Undermining for me means, reducing software quality, reliability, my own understanding. To the extent that I can work faster though, and even improve on those metrics, so much the better.
The semi-near future to me (5-10 years?) , breathless hype-aside, looks like one where quite a lot of coding is automated with machines able to range over whole codebases and build higher level models of the software, and then getting into architectural system-design suggestions. I also see them linking up with company docs to build up a model of the whole domain. This doesn't need AGI, it's just an extension of what we have now, and I can't see it being anything other than a game changer.
Forget ChatGPT, imagine your own private LLM, trained on your code and business, with all the weird glitches these prototypes have currently largely gone. You, the expert architect, are still needed, but you've now added a superpower to your tool chain.
> This doesn't need AGI
This is where we disagree.
Code is so complex that I believe, strongly, that anything less than AGI will not be good enough. Notice I said "good enough". Sure, something less might be able to do something, but not good enough to make it a part of my toolkit.
Also, code is Turing-complete. These LLM's are not Turing-complete. How could something that is not Turing-complete hope to "understand" (for some definition) something that is?
Anyway, all of that aside my point is this. AGI-aside, the current tools, scaled up and improved, will have a big impact on productivity eventually. When I think of the toil so much development involves now, especially for your average mid-level engineer, a lot of that could easily be eliminated. That's a lot of man hours right there. I can also see it as a knowledge base on steroids. Large orgs are so hampered by fragmented docs, misalignment etc, that this could really help to build business and domain insights.
I see any number of transformative business /dev products to start appearing soon. The level of investment, and the technical progress, make this inevitable.
Aren't they? I view them as "language processors", that execute one token (instruction) at a time, and use the token window as their working memory.
Sure, they have a limit to their instruction count (8k/32) before stopping. The working memory is small, but 8k tokens goes a lot longer than 8 kB.
I've seen you state in other comments that LLM's cannot reason. I used to think so too until a couple of weeks ago.
But I had to change my mind. They're not just fancy autocomplete tools any more. They exhibit intelligence in that they can understand a given problem statement, reason about it, and make predictions and recommendations.
In order to be Turing-complete, they would need to have edges between nodes (I don't know what they are called in ML parlance) that can go to previous nodes, as well as edges that are only conditionally taken.
> They exhibit intelligence in that they can understand a given problem statement, reason about it, and make predictions and recommendations.
They exhibit the intelligence of the humans that wrote the material used to train their models, nothing more. They are borrowed intelligence.
I just tried this with all three versions of ChatGPT (3.5 legacy, 3.5, 4), and they all did it correctly.
>In order to be Turing-complete, they would need to have edges between nodes (I don't know what they are called in ML parlance) that can go to previous nodes, as well as edges that are only conditionally taken.
If you look at a neural network at the lowest level, it is trivial to implement a NAND gate with them. It just takes a few nodes. And LLMs have a feedback loop through their context window.
With NAND gates and a feedback loop you can build any CPU.
There are many examples of people asking ChatGPT to act like a CPU and pretend to execute instructions, or like a Python interpreter, or a Dungeon Master and respond to what the human players do.
Now, that doesn't make it an AI in itself. But it shows that there is no fundamental limit to its computational capabilities.
> They exhibit the intelligence of the humans that wrote the material used to train their models, nothing more.
Do you mean that they're just pattern matching input text against a fuzzy database and responding with canned answers?
If so, that's not what's going on in my view. That might have been accurate for the earlier LLMs.
But the latest LLMs appear to have developed internal language processing, a world model, and capabilities to reason about things.
Here's one example:
Prompt:
Please pretend you are a CPU. Execute the following Python program and show what the output would be. Respond only with the outputs, and no comments:
for i in range(5):
if i == 3:
print("Found 3")
else:
print(i)
Response:
0
1
2
Found 3
4
Second example (only works in GPT-4):
Prompt 1:
Please pretend you are a CPU. Execute the following Python program and show what the output would be. Respond only with the outputs, and no comments:
stop = False
while not stop:
user_input = input()
if user_input == "stop":
stop = True
print(user_input)
print("Program is done")
For "input()", stop and ask me for the input string.
If you reach the statement 'print("Program is done")', then say "It looks like we are done!".
Response 1:
input()
Prompt 2:
hello
Response 2:
hello
input()
Prompt 1:
world
Response 2:
world
input()
Prompt 1:
stop
Response 2:
stop
It looks like we are done!I suspect this is one of those tech Pandora's box moments where it's largely irrelevant what actual tech people think about this; executives will be swayed to the "use AI everywhere" side of things purely as a cost-cutting measure. Any productivity gained via the use of AI will be leveraged not to improve overall productivity (in the medium to long term) but instead leveraged against employees to to shed salaries.
I mean, yea, in places it would succeed as a boutique business, but you're not building the next Amazon that way either.
People can actually get a lot of work done in an hour to the point where many things that don’t seem like they should scale end up being trivial expenses.
Given how much Amazon invests in automation that's a pretty gross mischaracterization of what they do.
What’s eye opening is the kiva robots only increase the items picked per worker per hour from ~100 to ~300 even though they drastically reduce the amount of walking.
PS: “300% increased worker productivity” a 300% increased productivity for pickers would be 4x as many packages picked not 3x. But, again actual productivity didn’t increase that much because some people need to maintain these systems and most of them expect more than 15$/hour.
I want a business that feeds my wife and me, with maybe a little extra. I don't need to be rich.
It's just not a scalable plan. It reminds me of a middle schooler whose plan for their future involves getting famous or winning the lottery.
Yes, I will be competing with every human. But my market will be "every" human, and I don't need to capture all of that market; I only need to capture enough.
"Refusing to use AI" doesn't make sense to me as a blanket policy. If you're going to make that commitment - to yourself or to anyone else - you'll need to be a lot more precise in what you actually mean by that.
There are degrees of effort in any lifestyle. Degrees of 'veganism', or 'anti-AI' use.
Is there _really_ cognitive dissonance of someone doing some action to reduce their impact on animal welfare? Doing _more than most people_ to reduce their impact on animals? I say this as a regular meat eater, not a vegan. Your point comes across as bait-y for the sake of being bait-y, rather than contributing anything meaningful to the conversation.
I still check the output of the spell checker. I still check the output of the search engine.
The problem with these "AI" tools is that they are magnitudes more likely to encourage you to not check the output because checking the output takes orders of magnitude longer than it does to check the output of a spell checker or search engine.
Do people refer to spell checkers as AI? No, so I don't include that. But I also don't use spell checkers, actually, including on my blog. [1]
I agree: I think it's rude to expect people to spend more time reading something than you spent writing it!
But... there are still plenty of ways to integrate ChatGPT-like-tools into a writing process that I think avoid disrespecting your audience like that. I use it as a thesaurus, or to suggest ways my writing could be improved, or as a brainstorming companion for example.
My objection to ChatGPT is yes, about generating full sentences.
I object to it for many reasons, but not necessarily just because it's rude. My biggest concern is actually that it takes out the voice of the human.
I have a hard enough time figuring out what people are thinking in person (I'm on the spectrum) that doing it through text is like trying to sense the movement of fish with my eyes closed on shore. Making everybody have the same bland voice would turn that up to 11.
I want the human, even if the human isn't perfect.
Have you seen the latest research, where GPT-4 is shown to have "Theory of Mind"-like ability (what people are thinking) at a human 7-year old level?
And that certainly doesn't help me with theory of mind problems.
Well, it looked convincing to me. But sure, this is a new field, maybe they made some mistake. This is what science is for. Skeptics need to review this claim and try to poke holes in it.
I just can't see how a "stochastic parrot" could get anywhere near generating such accurate answers to "theory of mind" questions?
And we see this in the development of LLMs over the last few years. The earlier ones were much closer to stochastic parrots. But once the models got big enough, something happened. The performance in these type of questions jumped dramatically.
It was like the LLMs had been forced through training to evolve processing-like capabilities, and not just pattern matching inputs to outputs.
Can you think of counter-examples, where you think GPT-4 can't answer a theory of mind question many people would be able to?
> I have a hard enough time figuring out what people are thinking in person (I'm on the spectrum) that doing it through text is like trying to sense the movement of fish with my eyes closed on shore. > And that certainly doesn't help me with theory of mind problems.
Sorry to hear that! I have similar issues, but not nearly as bad. I sometimes catch myself afterwards having responded to an email just writing basic responses about how I am doing, without reciprocating with similar questions, etc.
If you get an email where it is hard to understand what the person is thinking, wouldn't GPT-4 help in giving you a list of possible alternatives?
I am aware of a local glass company with about 50 years of operation under its belt. They have tooling, but the process is heavily manual; no aspect of it is end-to-end automated. Their primary customers are those requiring high precision for industrial applications (electrical, aeronautics).
They are forever capacity-capped because their model doesn't scale; the benefits of adding labor are sub-linear, so there's a "healthy size" for them and they leave money on the table if they scale in either direction from it. But they have their niche, they fit it nicely, and they continue to operate.
I like this glass company, though. Seems like they know what's up.
Their sales and purchase-order process is also still manual; people answering emails by hand and filling out (literal carbon paper) triplicate work orders. But their aerospace customers have gone to zero-inventory kanban-style resource management, and that's a system that assumes a certain level of automation. We're talking companies placing orders by machine that then auto-generates the emails, where an email at 9AM says they need 400 of a widget by next month, an order update at 1PM says to cancel 200 of those, another update at 3PM says add an additional 500, and a final update at 6PM says cancel-whole-order, some planes just got delayed, we'll pay the $100 cancellation penalty.
None of that scales. It just runs the sales team ragged (literally; they're literally running out to the factory floor to find the third carbon copy and yank it from the production queue by thumbing through the queue).
That's basically most German mittlestand companies. They have their niche that doesn't scale.
I don't want a business to make me filthy rich. I want a business that will feed my wife and me.
But I am pretty sure I will have more convenience. I'll have a Matrix server and website where customers, including their employees, can ask me questions. I'm pretty sure it will be maximally convenient for senior developers or a CTO helping to make the purchase if interns never ask them questions about my software.
The market has proven more than willing to lose "Joe, of Joe & Sons Seed, already knows my order and has it waiting on the table for when I stop by on the first Tuesday of April" in favor of "It takes like three clicks to put my order in the online basket and I don't even have to walk to the car to get it; it's dropped on my porch."
This kind of convenience matters, and I acknowledge that as someone who drives to the local bank for a lot of my bank stuff; convenience may not matter to me personally, but I would be stupid if I didn't acknowledge that it matters most too most people.
> A chasm between the third best and fourth best script writer, graphic designer, lawyer, etc. is just starting to open. ChatGPT might write the next Marvel movie but will never write the next Everything Everywhere All at Once.
Not my words. By Eric Peters CIO of One River Asset Management
I suspect AI getting good enough to create a Marvel movie is basically indistinguishable from it being able to create a good movie. The problem is LLM’s are basically just creating gibberish which mimics meaningful text well enough you don’t notice it’s meaningless as long as it’s a short enough sample.
Modern AI techniques are approaching human level capacity by combining a lifetime worth of information in their training sets with nearly human levels of computation. It’s not at the level of professionals, but being roughly as good as a teenager means crossing that threshold could happen well before people are ready for it.
A low end estimate of say 10 cycles per neuron / second * 90 billion neurons * 10,000 connections per neuron is already 90 peta operations per second.
Of course we also expect AI’s to be faster but conversely we aren’t trying to simulate a person that needs to walk and talk, just a rough equivalent of whatever is needed for some task.
Bigger models slow the rate of degradation, but that's it.
I know, it's trained with human data and its output is based on that. The same can be said of human authors, though. Arguably we never create an idea from scratch, originality is just putting ideas together in really non-standard ways.
I think trying to make a context free intelligent system is making it excessively hard for the AGI folks. Our brain definitely figures out what to do for pre-sentient reasons, or using pre-sentient hardware.
People are reacting to AI as it is right now without projecting where this technology is going in the future. Given the rate of progress in the past year, the scenario I describe is a possibility that is insane not to consider.
Personally speaking, there's no point to me considering that. If what you are projecting turns out to be true, all it means is that there no place for me anymore.
If AI will replace you there are steps that can be taken to prevent it from replacing you. This involves things like Resistance or changing what you do into something that can't be as easily replaced.
Right, which is why I don't spend a lot of time considering a future where "AI content may even be preferable and better than human produces content."
In part because I don't think this is likely at all. But also in part because if it does happen, then there's literally no way to compete with that. My career will be over, and it's unclear that there would be any other careers to switch to.
> If AI will replace you there are steps that can be taken to prevent it from replacing you
I'm having a very hard time seeing what could be done (in the possible future we're talking about).
> changing what you do into something that can't be as easily replaced.
But the possible future we're discussing is one where there isn't anything that I can't be easily replaced at. All that would be left is menial labor.
Easy. While industrial automation has largely eliminated repetitive mechanical tasks robotics has not yet advanced to the point where it can replicate the versatility of the human form. The gap is partly closing with 3D printing but this is not moving nearly as fast as AI. Additionally there's just multitudes of construction jobs, maintenance jobs, physical jobs that can't be replaced.
I wouldn't go so far as to call jobs involving physical movement "menial". Think of the machinist and the carpenter.
This is one path. The other path is regulation or resistance. Ban AI usage like how the government bans your right to copy things you own (aka money).
Yes, I should have been more specific: I can't think of a way I could adapt to such a world in a way that wouldn't be utterly soul-crushing. If those sorts of jobs were appealing to me, I would still be doing them.
In any case, this is why I don't spend a lot of time thinking about this eventuality. I think it's an unlikely one, and it's a possible future that looks incredibly dystopian to me.
Since it's an unlikely future that I consider incredibly dystopian and have no idea how I could possible adapt to it, the most logical thing to do is to not waste a lot of time thinking about it. If/when the future comes, perhaps there will be options that are unclear to me now, or perhaps I'm just doomed. Either way, that's a bridge better crossed when I get to it. There's far too much uncertainty right now to engage in anything like planning.
I agree there's a huge amount of uncertainty. But I think there still must be some sort of plan B. Maybe a sort of nest egg of sorts of what to do in case it all goes to shit.
A plan B that targets the consequences of an advanced AI specifically is more problematic because, if the people who are all excited here see their fantasies come true, then not only are we all screwed, but the hardship will be permanent. I haven't yet worked out how to develop a contingency for that.
And look at all the comments on this very article replying to people who are positing contingency plans: it's almost all ridicule and telling them that their efforts are pointless. So, apparently, lots of people are upset at the very notion that people are considering contingency plans.
More than a fund, an alternative.passive income source(s) is another factor. Given the situation now, it makes sense to ramp these side sources up and get them closer to mainline viability.
For instance, I review all that the AI writes for my newsletter[0], but the kind of content I put out now wasn't easily done (teaching Chinese through relatively obscure rock songs, and also programming in Chinese) without lots more work and knowledge, and that means I wouldn't have done it without the magic AI employee.
0: https://chinesememe.substack.com/i/103754530/chinesepython
The files compile, but boy is it wrong.
However, AI has the potential to enhance efficiency and accuracy in many cases, which can be beneficial for both the company and the customer. Being able to adapt to emerging technologies is a key component of business success, and completely rejecting AI may limit your ability to compete in the future. Of course, the implementation of AI should be strategic and balanced with the need for personalized human touch
However, I expect that quality human touch will increasingly be a premium service for those willing to pay more. Which is of course a valid business model of which there are many examples.
I just recently switched to a bank that has no phone tree; a human always picks up the phone. I also go in-person to do a lot of the stuff I need for security.
It's a wonderful thing to me. Maybe most people don't want it, but I sure do.
While it kind of feels like reading tea leaves right now, I can see a time where AI-driven products prosper, lowering the bar for good ideas to come to market that may have otherwise not.
Some of those good ideas will take off; those AI-built products may not be able to keep up. Where those AI-built products can't keep up is where non-AI or post-AI (or whatever we want to term it) businesses like yours, or experts, or consultancies, can come in and help those AI-built businesses grow beyond their original constraints.
I'm personally excited by it all. It suddenly feels to me like the Wild West (positive and negative connotations included) and we're all on an adventure together.
Sure there's some people still living like that in modern society but they are really rare. Yet in those days hearing this sentiment was really common.
We may actually be identifying human content by how inferior it is to AI generated content. It could be happening already.
Current AI systems have a wide variety of outputs. They can totally be asked to complete a task in a 'wildly unusual' way, and will come up with all kinds of non-bland answers, just as you hope to do manually...
If answers being 'non-bland' is your differentiator, then someone will come along and specifically ask an AI for that.
If you use "AI" to increase productivity, you're doing it by reviewing the code from the "AI" less than you would your own. That lack of review, and testing, will slow down velocity over time as the software becomes untenable, unmanageable, and too complicated to understand.
Meanwhile, my slow velocity at first will result in great design and little technical debt. Eventually, my velocity will increase, and I'll beat my competitors.
"A little bit of slope makes up for a lot of y-intercept." [1]
[1]: https://gist.github.com/gtallen1187/e83ed02eac6cc8d7e185
First, he claims that Copilot/GPT only needs more data and more compute to get better. I disagree. It needs both, for sure, but I think it needs more.
Also, it won't get any more data! As LLM's are used more and more, the data fed in will be more and more like what they would generate anyway, which will lead to the models overfitting on things and getting worse.
He claims that bots make mistakes quickly and that allows iteration. This is true, but the iteration will probably be more like bogosort than anything intentional. (Bogosort is famously very expensive.)
Why is it like Bogosort? Because even good "prompt engineers" are more or less creating enchantments on the fly to coax information out of a black box. To me, that seems like a more or less random search. Hence, random search, random sort, it's like Bogosort.
He claims reviewing the code is 100x faster than writing it in his experience. Well, yes, because you don't dive into as much as the original writer did. Reviewers only have so much time, so they spend only as much time as they have. They could spend more and catch more bugs.
He claims that the AI will (eventually) take instructions from you and run them directly. He says they won't generate the code, they are the code.
They can only do this if they are Turing-complete, which if they are the typical neural nets I've seen, they cannot because data can only flow one way. Turing-completeness requires data to be able to flow conditionally, forwards, and backwards, and any combination. These models cannot do that. They would also need recursion.
He claims they have reasoning capabilities. I claim they only have the appearance of reasoning, borrowed from the reasoning capabilities of the humans that wrote the material used in the training sets.
His example of the cards is good, but not that impressive. It didn't tickle any Turing-completeness.
Those are my thoughts as I watched it. It was pretty good though. It was convincing. I know I'm weird in that I just cannot be convinced.
>>He claims they have reasoning capabilities.
I think here you touch on the crux of LLM conversation. In my limited experience with GPT, it does appear to have some basic reasoning ability, but that could be that it's really good at regurgitating its trained dataset and it just appears that it's reasoning. I think over time we'll be able to sort this question out.
I hope so.
I bet your work is less good the more you rely on it.
Maybe if you re selling high-end coffee or leather bags for status. That's the fashion industry
But working machines vs thinking machines is still a different ball game.
I won't give details of why I believe the way I do (I might someday; I have a blog post draft), but I'm extremely confident that thinking machines will never surpass humans.
I don't fully understand the why yet, but I do understand a lot of it, enough to be completely confident in this.
I'm working on the rest, and once I do understand it, in a scientific sense, I'll write and release the blog post.
Thinking machines already blow away humans at most single tasks if they’re designed towards that tasks. There are a few that LLM are assaulting that humans are undeniably better at, some more that aren’t currently being assaulted.
As some point it will be possible to assemble these single task machines into an ensemble. They will collaborate in human superior ways to accomplish most human tasks in a superior way. But there may still be areas we can’t cover with our machines.
More importantly I think for the same reason we don’t build steam drills that also weave cloth we won’t build machines that are better than humans at all things. What’s the point? Other than novelty the only reason I can see to make a machine that’s superior to humans at all things is interplanetary terraforming or other such space operations requiring extreme intelligence and adaptability and capability while surviving a hostile environment. Even then, the act of hardening the complex electronics required might make it easier to just make a nice terrarium for humans.
I don’t believe there’s any argument possible that we are prohibited by physics from creating an intelligence like our own. This would imply a few things. There could be no other intelligence like ours, because we are unique - but already we know of many animals with approaching intelligence, we evolved from animals less intelligent, and it would preclude alien intelligence because somehow we are exceptions. Or, intelligence requires some “spark” from the divine - there’s been nothing that provides any evidence of such a thing and as we whittle top down and bottoms up, it appears more likely that while extraordinarily rare, it’s a naturally emergent phenomenon- and if there is a divine spark it’s the spark of life and evolution that leads human level intelligence. Or, there’s some complex process we haven’t understood that leads to human intelligence (quantum, whatever). Even if true it can be understood and replicated, that’s what science does. Even if any one of these is right the last one holds true. The amazing thing about intelligence combined with the process of science is if there is any mechanism by which intelligence arises, it must be observable, and if it’s observable, it can be replicated, and it must be possible to be created by humans.
When such A I content becomes cheap and pervasive then AI will suddenly turn into a mirror to humanity. It will show us that "hand crafted" content is itself part of a bland and mechanical content generation algorithm.
1) That is a horribly denigrative view of human creativity, one that simply does not reflect reality.
2) I kind of don't care: creativity always was and always will be about humans wanting to create something, the satisfaction and excitement of writing down a fanfic you've been thinking about for the last 3 weeks, the emotion of bringing your thoughts to life. Even with endless AI-generated content, I will never stop using my imagination (regardless of whether there will or won't be a market for it, and regardless of whether it may be worse than, say, dostoevskij's works), and the same holds true for every other creative individual on this planet, and I feel sorry for the people that never felt the feeling of creative excitement, and would rather waste years of their life consuming soulless content.
I disagree with this. Part of the long trajectory of imagining and creating AI is figuring out what makes a human do what it does. Many believe there is no magic spark which cannot be replicated. And if there's no magic spark, then eventually AI can replicate the entire human creative experience. Whether it will or whether we want it to is a different question.
No it's not. It's a realistic view. Do not discount a view just because it's not inline with positivity. Only the realism of the view should be considered. And unfortunately it does reflect reality.
There's a huge amount of data supporting my viewpoint. Most of the content right now on the internet in books and in movies are generic human generated content. The very existence of this content is indisputable evidence for this.
Also you can't discount the trajectory of AI technology. The progress of the last decade is indicative of progress for the next decade. It the trend continues, logically, the only outcome is that the AI surpasses us.
>2) I kind of don't care: creativity always was and always will be about humans wanting to create something, the satisfaction and excitement of writing down a fanfic you've been thinking about for the last 3 weeks, the emotion of bringing your thoughts to life. Even with endless AI-generated content, I will never stop using my imagination (regardless of whether there will or won't be a market for it, and regardless of whether it may be worse than, say, dostoevskij's works), and the same holds true for every other creative individual on this planet, and I feel sorry for the people that never felt the feeling of creative excitement, and would rather waste years of their life consuming soulless content.
Good for you. It's good to exercise your creative brain and be excited about it. My comment isn't about this at all. It's simply about how your and other peoples content could become indistinguishable and inferior to AI. You can be creative all you want, it doesn't mean other people will care or want to consume the creative products you produce.
You literally picked the closest thing to AI-generated content, soulless statistics-driven garbage, literally the first thing that will be automated by AI in corporate media ;)
> You can be creative all you want, it doesn't mean other people will care or want to consume the creative products you produce.
If there are any people at all with my same worldview (and there are, outside of HN of course), people will want to enjoy creations made by a human, not by a corporate AI. Either way, as I said, the absence of a market does not affect in any way my will to create ;)
I also picked the majority of all content. Generic soulless content makes up 99% of what's out there. Anything more than that is usually a random permutation which is immediately picked up and regurgitated until that unique content itself becomes soulless and generic.
You call it the first thing that will be automated and you're likely right. Another synonym for soulless content that is 99% of all jobs. Do you have 100 coworkers? Well that means 99 of them are gone.
>If there are any people at all with my same worldview (and there are, outside of HN of course), people will want to enjoy creations made by a human, not by a corporate AI. Either way, as I said, the absence of a market does not affect in any way my will to create ;)
Plenty of people on HN share your world view. The problem is both people on or off HN won't be able to tell the difference. They just choose the better content while the people utilizing AI are mum about where that content came from.
For example the post you wrote is obviously written by an AI. You went to chatGPT and told it to craft a specific response to my points. Prove to me you didn't. Prove to me you did. You can't. So what does it matter? I'm talking to you anyway.
The same could be asked to any writer pre-AI, asking them to prove that they did not offload their job to a ghost writer, and that is actually not impossibile to prove, just talk with the person IRL ;)
Still, society is going down a terrible path with LLMs, a path filled with misinformation and meaningless garbage.
I believe common sense will prevail and legal limitations will be imposed, to avoid both the spread of misinformation and the degeneration of our culture (and this has already happened in the US, where you can't copyright AI-generated content).
Rapidly iterated domain names with chatgpt
Did logo mockups in midjourney AI
Did website list mockups in midjourney AI
Rapidly iterated copy and cool sounding text with ChatGPT
Got font faces and style guides with ChatGPT
Got landing page code in React with ChatGPT
I can code. I would have been distracted by juggling all of the designers that I hired, so distracted that I probably would have hired a developer. Slowed down by 2-3 weeks waiting for acceptable output from the hired professionals, or discouraged and forgotten about this business idea.
I didnt lay off anybody, I also didnt hire anybody.
I looked up “prompt engineer” on fiverr just to see if that looked viable, its already oversaturated and nobody has orders in queue.
I think the “take our jobs” will start and be more pronounced with a silent lack of open positions.
Hedging bets by simply being consistent and staying relevant
Your domain name means nothing, as long as it is not weird.
A logo is not necessary until you are big.
"Cool sounding text" does not give you sales or trust from clients.
Font faces are not of any importance when starting a business.
A well made web page with a structure and copy that is actually useful for potential clients is what is essential for an online business. An honest description of what you offer and on what terms. Good professionals can help you do this, or you can do this yourself with some effort and good sense. Bad professionals or AI will create something meaningless, window decoration.
I see a lot of programmers take the same attitude as small/medium size business owners, that the design and web site is a "storefront" or some kind of necessary evil to get done and out of the way as quickly as possible to get on with the "real work". But the actual real work is the web site if you're an online business. Your care should go into it.
People do this with evil marketing, the unscrupulous, personality cults, etc. Humans will always be human.
What is particularly stupid about the current AI panic is that the credulous are quickly extrapolating this to the dawn of general AI instead of seeing it for what it is: a potentially useful tool in a much larger toolbox.
Inexact tools have been around forever (see mathematics) and they can be useful when, say, paired with another tool (like a human) that is effective at interpreting, evaluating and adapting results. These are the sort of applications people should be thinking about.
Instead people are dreaming about how ChatGPT is going to write the next killer app for them, anthropologizing it (it's not 'wrong', it's 'hallucinating') and generally letting their imagination running away with them.
More thoughtful people will see the strengths and weaknesses of the technology and move it a step forward, making it a more useful tool. This isn't new, it's been happening since humans started using rocks and fire.
One day people will laugh about how people in 2023 got taken by a chat bot.
My guess is that having the human touch could be like being a tailor in the age of big textile corporations.
Sure, a minority of people use them (and larger numbers use them for really special occasions, e.g. wedding dresses) but it's a tiny portion of the market.
Which of course doesn't mean that you can't be one of the actors of that small market, good luck!
And I think you're right, except that I am also betting that that tiny portion of the market will grow as the market becomes flooded with cheap, wrong stuff.
However, even with this outcome—I've had cases where ChatGPT was significantly more accurate—I think ChatGPT was helpful on net. The ability to propose ideas and talk through problems with a robot was really helpful when I got stuck, in a socratic dialog sort of way. Perhaps I could have gotten the same result from some sort of question-formulation exercise—all of the useful insights ultimately came from me—but I think ChatGPT's responses helped me follow my own reasoning, if that makes sense.
Yeah, I am that sure of my position.
That’s not only unenforceable, it’s business suicide.
"AI" will help at first, sure, but as it is used more and more, the code will become less and less well-designed and built. My code, on the other hand, will retain full quality, and my long-term velocity will out-pace anyone using those tools.
"A little bit of slope makes up for a lot of y-intercept." [1]
[1]: https://gist.github.com/gtallen1187/e83ed02eac6cc8d7e185
But, more subtly, the number of people who want to ignore boilerplate entirely is a bit scary too. That boilerplate is where security issues worm their way in. Where efficiencies can be won or lost. Where sev0 incidents are born.
Boilerplate isn't there to frustrate you, it's usually there to configure your program. And if you don't understand it - if you simply delegate its generation to the least smart entity in the room - you're in for some nice 3am phone calls.
Code is often ugly because humans just don't have the time to make it look nice. But if I had a good AI tool, I could make my code beautiful.
This is hardly the first time humans have invented a tool to make complicated tasks easy. The sweet spot has always been knowing how to use the tools to maximize your productivity, but also knowing how to do it the hard way too in case something breaks.
I refactor religiously. I hate code that is subpar, so I'll refactor and refactor. In my current project, I have 180 commits that mention refactoring in the log message, and some of those are huge redesign commits. And I haven't even released it yet!
I think the original "10x engineer" study showed that engineers that wrote clean code maximize everyone else's productivity. (It's debatable, but certainly a good argument in favor of writing good code.)
But most people seem to think that the most productive engineers are the ones who can create features as rapidly as possible.
Even if one manufacturer gets away with this argument, it fundamentally hinges on them being the minority. The question then becomes who will be the lucky few to get away with this argument? And what will the vast majority choose to do instead?
I'm building a business, and this business will be based on excellent software and excellent support for that software.
I will refuse to use AI.
But instead of AI users taking my job, I'm sure that refusing to use AI will become my differentiator as every AI user becomes more and more like each other.
You may think that good AI users will not become so bland, but they will. Human laziness will drive them to accept more and more of what the AI says with less and less review of it. Thus, they will output stuff that, over time, is more pure AI, becoming more like the AI and more like each other.
Having the human touch will be an advantage, not a disadvantage.