What call. Maybe some readers miss the (perhaps subtle) difference between "Generative AI is not ..." and "Generative Ai is not going to ..."
Then first can be based on fact, e.g., what has happened so far. The second is based on pure speculation. No one knows what will happen in the future. HN is continually being flooded with speculation, marketing, hype.
In contrast, this article, i.e., the paper it discusses, is is based on what has happened so far. There is no "call" being made. Only an examination of what has heppened so far. Facts not opinions.
What happened in 2023 and 2024 actually
Nitpicky but it's worth noting that last year's AI capabilities are not the April 2025 AI capabilities and definitely won't be the December 2025 capabilities.
It's using deprecated/replaced technology to make a statement, that is not forward projecting. I'm struggling to see the purpose. It's like announcing that the sun is still shining at 7pm, no?
And the hype was insane in 2023 already - it's useful to compare actual outcomes vs historic hype to gauge how credible the hype sellers are.
Maybe progress over the last 2-3 months is hard to see, but progress over the last 6 is very clear.
Could be data is lagging as sibling comment said but this seems wildly difficult to report on a number like this.
It also doesn't take into account the benefits to colleagues of active users of LLMs (second order savings).
My use of LLMs often means I'm saving other people time because I can work through issues without communications loops and task switching. I can ask about much more important, novel items of discussion.
This is an important omission that lowers the paper's overall value and sets it up for headlines like this.
This is because the economy is not a static thing. If one variable changes (productivity), it’s not a given that GDP will remain constant and jobs/wages will consequently be reduced. More likely is that all of the variables are always in flux, reacting and responding to changes in the market.
However, the parent comment is about an examination of what has happened so far and facts that feed into the paper and its conclusions.
I was focused on what I see as important gaps in measuring impact of AI, and its actual (if difficult to measure) impact right now.
Mostly people aren't worried about productivity itself, which would be weird. "Oh no, AI is making us way more productive, and now we're getting too much stuff done and the economy is growing too much." The major concern is that the productivity is going to impact jobs and wages, and at least so far (according to this particular paper) that seems to not be happening.
Unless twice the work is suddenly required, which I doubt.
I would also be surprised if the twice the work was "suddenly" required, but would you be surprised if people buy more of something if it costs less? In the 1800s ordinary Americans typically owned only a few outfits. Coats were often passed down several generations. Today, ordinary Americans usually own dozens of outfits. Did Americans in the 1800s simply not like owning lots of clothing? Of course not. They would have liked to own more clothing, but demand was constrained by cost. As the price of clothing has gone down, demand for clothing has increased.
With software, won't it be the same? If engineers are twice as productive as before, competitive pressure will push the price of software down. Custom software for businesses (for example) is very expensive now. If it were less expensive, maybe more businesses will purchase custom software. If my Fastmail subscription becomes cheaper, maybe I will have more money to spend on other software subscriptions. In this way, across the whole economy, it is very ordinary for productivity gains to not reduce employment or wages.
Of course demand is not infinitely elastic (i.e. there is a limit on how many outfits a person will buy, no matter how cheap), but the effects of technological disruption on the economy are complex. Even if demand for one kind of labor is reduced, demand for other kinds of labor can increase. Even if we need less weavers, maybe we need more fashion designers, more cotton farmers, more truckers, more cardboard box factory workers, more logistics workers, and so on. Even if we need less programmers, maybe we need more data center administrators?
No one knows what the future economy will look like, but so far the long term trends in economic history are very promising.
Outside of goods, there must be more schools than ever before, and more academics qualified to teach at them, but is it bringing down the cost of education?
Can you link to the evidence you’re talking about? Did companies only start being greedy in 2020? I would argue the “greedflation” after Covid 19 can mostly be explained by the fact that there was a supply shock combined with a spike in demand as people people shifted consumption to goods and away from services during the pandemic. Some economists, like Isabella Weber, argue that the pandemic served as a coordinating mechanism for sellers to raise prices, but even Weber would never argue that supply and demand are “not that closely related anymore.”
> Outside of goods, there must be more schools than ever before, and more academics qualified to teach at them, but is it bringing down the cost of education?
Well, yes supply has increased but so has demand. In 1960, 7% of adults 25+ had a college degree. Now that’s 37%.
Further the demand is less price sensitive than usual because the government freely lends money to spend on education (us government currently has $1.6 trillion in student debt) and because we mostly encourage kids to get degrees at any cost.
Finally, despite the growth in total number of universities and students, spots at elite institutions are limited and thus scarce and highly valued.
Good example for discussion because I think that it's well known that many people who have attained higher education at great expense nevertheless struggle with unemployment and underemployment, and groan about how competitive things are in academia with thousands of applications for every placement. If we're oversupplied with qualified yet underemployed academics who could be teachers, and yet teachers are unavailable to meet the demand of students who want education.. how is that not a disconnect between supply and demand?
Whenever a huge disconnect like this between supply/demand is pointed out, economists will retreat to discussion of regulation and policies, inflation, macroeconomics conditions. Sure, fine, great.. but that's just another way of saying supply/demand/cost are not closely related.
So you could work on more things with the same number of employees, make more money as a result, and either further increase the number of things you do, or if not, increase your revenue and hopefully profits per-employee.
I'm not sure why you doubt it. In a company, efficiency improvements never mean you don't have to do as much work. They always mean you just have more time in the day for extra work. It's part of the reason I really don't care at all about automation, LLMs, etc, at least from an efficiency perspective. (there's another case to be made for automated response, etc.) I hear people say that it opens them up to do more of the interesting work they care about, but in practice I've never seen this be true.
It feels lot like when people in IT talk about how they are "always learning new things." Yes, that's true, but what you're learning is worthless: An updated UI for Defender, a new proprietary query language, branding hierarchies for tech company products, etc. It's all worthless knowledge.
I like this sentence because it is grammatically and syntactically valid but has the same relationship to reality as say, the muttering of an incantation or spell has, in that it seeks to make the words come true by speaking them.
Aside from simply hoping that, if somebody says it it could be true, “If everyone’s hours got cut in half, employers would simply keep everyone and double wages” is up there with “It is very possible that if my car broke down I’d just fly a Pegasus to work”
But more generally, my comment is not absurd; it's a pattern that has played itself out in economic history dozens of times.
Despite the fact that modern textile and clothing machinery are easily 1000x more efficient than weaving cloth and sewing shirts by hand, the modern garment industry employs more people today than that of middle age Europe.
Will AI be the same? I don't know, but it wouldn't be unusual if it was.
This makes sense. If everyone’s current workloads were suddenly cut in half tomorrow, there would simply be enough demand to double their workloads. This makes sense across the board because much like clothing and textiles, demand for every product and service scales linearly with population.
I was mistaken, you did not suggest that employers would gift workers money commensurate with productivity, you simply posit that demand is conceptually infinite and Jevons paradox means that no jobs ever get eliminated.
In the the past 200 years we've seen colossal productivity gains from technology across every area of the economy. Over the same period, wages have increased and unemployment has remained stable. That's where my priors come from. I'll update them if we get data to the contrary, but the data we have so far (like this paper) mostly confirm them.
More people are also available since the fields are producing by themselves, comparatively. Not to mention less of us die to epidemies, famines and swords.
My company just redid our landing page. It would probably have taken a decent developer two weeks to build it out. Using AI to create the initial drafts, it took two days.
It's not miraculous but I feel like it saves me a couple hours a week from not going on wild goose chases. So maybe 5% of my time.
I don't think any engineering org is going to notice 5% more output and layoff 1/20th of their engineers. I think for now most of the time saved is going back to the engineers.
I would (similarly insultingly) suggest that if you think this is true, you're spending time doing things more slowly that you could be doing more productively by using contemporary tools.
But here's the thing - there is already plenty of documented proof of individuals losing their job to ChatGPT. This is an article from 2 years ago: https://www.washingtonpost.com/technology/2023/06/02/ai-taki...
Early on in a paradigm shift, when you have small moves, or people are still trying to figure out the tech, it's likely that individual moves are hard to distinguish from noise. So I'd argue that a broad-based, "just look at the averages" approach is simply the wrong approach to use at this point in the tech lifecycle.
FWIW, I'd have to search for it, but there were economic analyses done that said it took decades for the PC to have a positive impact on productivity. IMO, this is just another article about "economists using tools they don't really understand". For decades they told us globalization would be good for all countries, they just kinda forgot about the massive political instability it could cause.
> In contrast, this article, i.e., the paper it discusses, is based on what has happened so far.
Not true. The article specifically calls into question whether the massive spending on AI is worth it. AI is obviously an investment, so determine whether it's "worth it", you need to consider future outcomes.
I honestly think computers have a net negative productivity impact in many organizations. Maybe even "most".
https://usafacts.org/articles/what-is-labor-productivity-and...
Even more surprising for me is that productivity growth declined during the ZIRP era. How did we take all that free money and product less?
This is not correct. Your link is only referring to manufacturing productivity, not overall productivity, which continued to rise. This Economist article has better information: https://archive.vn/6asPb. One hypothesis in the article that seems the most logical to me is that return on investment became so much better in other economic sectors that it siphoned talent from manufacturing.
Could you say a few more words on this please? Are you referring to the rise of China?
https://www.theguardian.com/world/2017/jul/14/globalisation-...
https://business.columbia.edu/sites/default/files-efs/imce-u...
But in my own words, while I think economists have always acknowledged there would be some "winners" and "losers" from globalization, I think there is widespread belief now that early globalization boosters misjudged how bad it would be for the "losers", they underestimated how much it would contribute to inequality, and they underestimated the destabilizing effect this would have on social and political systems.
At the end of the day, it doesn't really matter what the reality is for globalization's benefits if so many people are turned off by it that they want to "burn the whole thing down", as that's basically what's happening now.
But I think it's pretty clear what the parent poster means - that it's too early to imply that GenAI won't impact jobs.
Obviously you understood that that was the point parent wanted to make - because you argue against it.
It's more about automating workflows that are already procedural and/or protocolized, but where information gathering is messy and unstructured (I.e. some facets of law, health, finance, etc).
Using your dietician example: we often know quite well what types of foods to eat or avoid based on your nutritional needs, your medical history, your preferences, etc. But gathering all of that information requires a mix of collecting medical records, talking to the patient, etc. Once that information is available, we can execute a fairly procedural plan to put together a diet that will likely work for you.
These are cases that I believe LLMs are actually very well suited, if the solution can be designed in such a way as to limit hallucinations.
O3's web research seems to have gotten much, much better than their earlier attempts at using the web, which I didn't like. It seems to browse in a much more human way (trying multiple searches, noticing inconsistencies, following up with more refined searches, etc).
But I wonder how it would do in a case like yours where there is conflicting information and whether it picks up on variance in information it finds.
I think it’s weird to reject AI based on its current form.
> Using your dietician example: we often know quite well what types of foods to eat or avoid based on your nutritional needs
No we don't. It's really complicated. That's why diets are popular and real dietitians are expensive. and I would know, I've had to use one to help me manage an eating disorder!
There is already so much bullshit in the diet space that adding AI bullshit (again, using the technical definition of bullshit here) only stands to increase the value of an interaction with a person with knowledge.
And that's without getting into what happens when brand recommendations are baked into the training data.
0 https://link.springer.com/article/10.1007/s10676-024-09775-5
I understand your perspective, but the intention was to use a term we've all heard to reflect the thing we're all thinking about. Whether or not this is the right term to use for scenarios where the LLM emits incorrect information is not relevant to this post in particular.
> No we don't. It's really complicated. That's why diets are popular and real dietitians are expensive.
No, this is not why real dietitians are expensive. Real dietitians are expensive because they go through extensive training on a topic and are a licensed (and thus supply constrained) group. That doesn't mean they're operating without a grounding fact base.
Dietitians are not making up nutritional evidence and guidance as they go. They're operating on studies that have been done over decades of time and millions of people to understand in general what foods are linked to what outcomes. Yes, the field evolves. Yes, it requires changes over time. But to suggest we "don't know" is inconsistent with the fact that we're able to teach dietitians how to construct diets in the first place.
There are absolutely cases in which the confounding factors for a patient are unique enough such that novel human thought will be required to construct a reasonable diet plan or treatment pathway for someone. That will continue to be true in law, health, finances, etc. But there are also many, many cases where that is absolutely not the case, the presentation of the case is quite simple, and the next step actions are highly procedural.
This is not the same as saying dietitians are useless, or physicians are useless, or attorneys are useless. It is to say that, due to the supply constraints of these professions, there are always going to be fundamental limits to the amount they can produce. But there is a credible argument to be made that if we can bolster their ability to deliver the common scenarios much more effectively, we might be able to unlock some of the capacity to reach more people.
Just like every other form of ML we've come up with, LLMs are imperfect. They get things wrong. This is more of an indictment of yeeting a pure AI chat interface in front of a consumer than it is an indictment of the underlying technology itself. LLMs are incredibly good at doing some things. They are less good at other things.
There are ways to use them effectively, and there are bad ways to use them. Just like every other tool.
There's no shortcut to figuring out what the truth of what a new technology is actually useful for. It's very rarely the case that either "everything" or "nothing" is the truth.
It's not about it being perfect or not. It's about how they come about with the responses they do.
>Very few people seem to go around talking about how their binary classifier is always hallucinating, but just sometimes happens to be right.
Yeah, but no one is anthropomorphizing binary classifiers.
No self-reflection needed.
As an example if you want diet advice, it can lie to you very convincingly so there is no point in getting advice from it.
Main value you get from a programmer is they understand what they are doing and they can take the responsibility of what they are developing. Very junior developers are hired mostly as an investment so they become productive and stay with the company. AI might help with some of this but doesn’t really replace anyone in the process.
For support, there is massive value in talking to another human and having them trying to solve your issue. LLMs don’t feel much better than the hardcoded menu style auto support there already is.
I find it useful for some coding tasks but think LLMs were overestimated and it will blow up like NFTs
>I find it useful for some coding tasks but think LLMs were overestimated and it will blow up like NFTs
No way. NFTs did not make any headway in "the real world": their value proposition was that their cash value was speculative, like most other Blockchain technologies, and that understandably collapsed quickly and brilliantly. Right now developers are using LLMs and they have real tangible advantages. They are more successful than NFTs already.
I'm a huge AI skeptic and I believe it's difficult to measure their usefulness while we're still in a hype bubble but I am using them every day, they don't write my prod code because they're too unreliable and sloppy, but for one shot scripts <100 lines they have saved me hours, and they've entirely replaced stack overflow for me. If the hype bubble burst today I'd still be using LLMs tomorrow. Cannot say the same for NFTs
How exactly is this different from getting advice from someone who acts confidently knowledgeable? Diet advice is an especially egregious example, since I can have 40 different dieticians give me 72 different diet/meal plans with them saying 100% certainty that this is the correct one.
It's bad enough the AI marketers push AI as some all knowing, correct oracle, but when the anti-ai people use that as the basis for their arguments, it's somehow more annoying.
Trust but verify is still a good rule here, no matter the source, human or otherwise.
E.g. Next time a lawyer abandons your civil case and ghosts you after being clearly negligent and down-right bad in their representation. Good luck holding them accountable with any body without consequences.
The CA Bar disciplined over a hundred lawyers (including some now former lawyers) last month alone.
The client doesn't need another lawyer to make this happen. They just need to complain to the licensing body (the State Bar Association).
Are you talking about a personal experience? I'd think a malpractice claim and the state bar would help you out. Did you even try? Are you just making something up?
If I ask it how to accomplish a task with the C standard library and it tells me to use a function that doesn't exist in the C standard library, that's not just "wrong" that is a fabrication. It is a lie
If you ask me to remove whitespace from a string in Python and I mistakenly tell you use ".trim()" (the Java method, a mistake I've made annoyingly too much) instead of ".strip()", am I lying to you?
It's not a lie. It's just wrong.
> Lying requires intent to deceive
LLMs do have an intent to deceive, built in!
They have been built to never admit they don't know an answer, so they will invent answers based on faulty premises
I agree that for a human mixing up ".trim()" and ".strip()" is an honest mistake
In the example I gave you are asking for a function that does not exist. If it invents a function, because it is designed to never say "you are wrong that doesn't exist" or "I don't know the answer" that seems to qualify to me as "intent to deceive" because it is designed to invent something rather than give you a negative sounding answer
The bullshitter doesn't care about if what they say is true or false or right or wrong. They just put out more bullshit.
Because, as Brad Pilon of intermittent fasting fashion repeatedly stresses, "All diets work."*
* Once there is an energy deficit.
From what I know dieticians don't design exercise plans. (If true) the LLM has better odds to figure it out.
I wouldn't have a clue how to verify most things that get thrown around these days. How can I verify climate science? I just have to trust the scientific consensus (and I do). But some people refuse to trust that consensus, and they think that by reading some convincing sounding alternative sources they've verified that the majority view on climate science is wrong.
The same can apply for almost anything. How can I verify dietary studies? Just having the ability to read scientific studies and spot any flaws requires knowledge that only maybe 1 in 10000 people could do, if not worse than that.
People are forthcoming with things they know they don't know. It's the stuff that they don't know that they don't know that get them. And also the things they think they know, but are wrong about. This may come as a shock, but people do make mistakes.
People talk a lot of about false info and hallucinations, which the models do in fact do, but the examples of this have become more and more far flung for SOTA models. It seems that now in order to elicit bad information, you pretty much have to write out a carefully crafted trick question or ask about a topic so on the fringes of knowledge that it basically is only a handful of papers in the training set.
However, asking "I am sensitive to sugar, make me a meal plan for the week targeting 2000cal/day and high protein with minimally processed foods" I would totally trust the output to be on equal footing with a run of the mill registered dietician.
As for the junior developer thing, my company has already forgone paid software solutions in order to use software written by LLMs. We are not a tech company, just old school manufacturing.
LLM:s create real value. I save a bunch of time coding with an LLM vs without one. Is it perfect? No, but it does not have to be for still creating a lot of value.
Are some people hyping it up too much? Sure, an reality will set in but it wont blow up. It will rather be like the internet. 2000s and everyone thought "slap some internet on it and everything will be solved". They overestimated the (shorterm) value of the internet. But internet was still useful.
But it is replacing it. There's a rapidly-growing number of large, publicly-traded companies that replaced first-line support with LLMs. When I did my taxes, "talk to a person" was replaced with "talk to a chatbot". Airlines use them, telcos use them, social media platforms use them.
I suspect what you're missing here is that LLMs here aren't replacing some Platonic ideal of CS. Even bad customer support is very expensive. Chatbots are still a lot cheaper than hundreds of outsourced call center people following a rigid script. And frankly, they probably make fewer mistakes.
> and it will blow up like NFTs
We're probably in a valuation bubble, but it's pretty unlikely that the correct price is zero.
It doesn’t wholly replace the need for human support agents but if it can adequately handle a substantial number of tickets that’s enough to reduce headcount.
A huge percentage of problems raised in customer support are solved by otherwise accessible resources that the user hasn’t found. And AI agents are sophisticated enough to actually action on a lot of issues that require action.
The good news is that this means human agents can focus on the actually hard problems when they’re not consumed by as much menial bullshit. The bad news for human agents is that with half the workload we’ll probably hit an equilibrium with a lot fewer people in support.
Can't disagree more (on LLMs. NFTs are of course rubbish). I'm using them with all kinds of coding tasks with good success, and it's getting better every week. Also created a lot of documents using them, describing APIs, architecture, processes and many more.
Lately working on creating an MCP for an internal mid-sized API of a task management suite that manages a couple hundred people. I wasn't sure about the promise of AI handling your own data until starting this project, now I'm pretty sure it will handle most of the personal computing tasks in the future.
It doesn't have to. It can replace having no support at all.
It would be possible to run a helpdesk for a free product. It might suck but it could be great if you are stuck.
Support call centers usually work in layers. Someone to pick up the phone who started 2 days ago and knows nothing. They forward the call to someone who managed to survive for 3 weeks. Eventually you get to talk to someone who knows something but can't make decisions.
It might take 45 minutes before you get to talk to only the first helper. Before you penetrate deep enough to get real support you might lose an hour or two. The LLM can answer instantly and do better than tortured minimum wage employees who know nothing.
There may be large waves of similar questions if someone or something screwed up. The LLM can do that.
The really exciting stuff will come where the LLM can instantly read your account history and has a good idea what you want to ask before you do. It can answer questions you didn't think to ask.
This is specially great if you've had countless email exchanges with miles of text repeating the same thing over and over. The employee can't read 50 pages just to get up to speed on the issue, if they had the time you don't so you explain again for the 5th time that delivery should be on adress B not A and be on these days between these times unless it are type FOO orders.
Stuff that would be obvious and easy if they made actual money.
Have you somehow managed to avoid the last several decades of human-sourced dieting advice?
Google is pretty much useless now as it changed into ann ad platform, and I suspect AI will go the same way soon enough.
It has always been easy to imagine how advertising could destroy the integrity of LLM's. I can guarantee that there will be companies unable to resist the temporary cash flows from it. Those models will destroy their reputation in no time.
One major problem is the payment mechanism. The nature of LLMs means you just can't really know or force it to spit out ad garbage in a predictable manor. That'll make it really tricky for an advertiser to want to invest in your LLM advertising (beyond being able to sell the fact that they are an AI ad service).
Another is going to be regulations. How can you be sure to properly highlight "sponsored" content in the middle of an AI hallucination? These LLM companies run a very real risk of running a fowl of FTC rules.
You certainly can with middleware on inference.
https://www.washingtonpost.com/technology/2025/04/17/llm-poi...
For copywriting, analyzing contracts, exploring my business domain, etc etc. Each of those tasks would have required me to consult with an expert a few years ago. Not anymore.
That is a great use for it too, rather than replacing artists we have personal advisors who can navigate almost any level of complex bureaucracy instantaneously. My girlfriend hates AI, like rails against it at any opportunity, but after spending a few hours on the DMV website I sat down and fed her questions into Claude and had answers in a few seconds. Instant convert.
But, also, the threshold of things we manage ourselves versus when we look to others is constantly moving as technology advances and things change. We're always making risk tradeoff decisions measuring the probability we get sued or some harm comes to us versus trusting that we can handle some tasks ourselves. For example, most people do not have attorneys review their lease agreements or job offers, unless they have a specific circumstance that warrants they do so.
The line will move, as technology gives people the tools to become better at handling the more mundane things themselves.
In a more general sense sometimes, but not always, it is easier to verify something than to come up with it at the first place.
Sounds like reddit could also do a good job at this, though nobody said "reddit will replace your jobs". Maybe because not as many people actively use reddit as they use generative AI now, but I cannot imagine any other reason than that.
Also took a picture of my tire while at the garage and asked it if I really needed new tires or not.
Took a picture of my sprinkler box and had it figure out what was going on.
Potentially all situations where I would’ve paid (or paid more than I already was) a local laborer for that advice. Or at a minimum spent much more time googling for the info.
These will likely be cell-phone-plan level expensive, but the value prop would still be excellent.
It's something that can be empirically measured instead of visually guessed at by a human or magic eight-ball. Using a tool that costs only a few dollars, no less, like the pressure gauge you should already keep in your glovebox.
There is no moat. Most of these AI APIs and products are interchangeable.
You can use a penny and your eyeballs to assess this, and all it costs is $0.01
It blows my mind the degree that people are offloading any critical thinking to AI
Me: "Looks like your tire is a little low."
Youth: "How can you tell, where's your phone?"
The only thing I can remotely trust is my own experience. Recently, I decided to have some business cards made, which I haven't done in probably 15 years. A few years ago, I would have either hired someone on Fiverr to design my business card or pay for a premade template. Instead, I told Sora to design me a business card, and it gave me a good design the first time; it even immediately updated it with my Instagram link when I asked it to.
I'm sorry, but I fail to see how AI, as we now know it, doesn't take the wind out of the sails of certain kinds of jobs.
The point is that I would have paid for another human being's time. Why? Because I am not a young man anymore, and have little desire to do everything myself at this point. But now, I don't have to pay for someone's time, and that surplus time doesn't necessarily transfer to something equivalent like magic.
I am not talking about whether I have to pay more or less for anything. My problem is not paying. I want to pay so that I don't have to make something myself or waste time fiddling with a free template.
What I am proposing is that, in the current day, a human being is less likely to be at the other end of the transaction when I want to spend money to avoid sacrificing my time.
Sure, one can say that whomever is working for one of these AI companies benefits, but they would be outliers and AI is effectively homogenizing labor units in that case. Someone with creative talent isn't going to feasibly spin up a competitive AI business the way they could have started their own business selling their services directly.
That's both pompous and bizarre. The "real" economy doesn't end at the walls of corporate offices. Far from it.
That’s like buying a wrench and changing your own spark plugs. Wrenches are not putting mechanics out of business.
The legal profession specifically saw the rise of computers, digitization of cases and records, and powerful search... it's never been easier to "self help" - yet people still hire lawyers.
If AI replaces search, I do wonder how long before AI becomes just as enshittified as search did. You know google/OpenAI/etc will eventually need to make this stuff profitable. And subscriptions aren't currently doing that.
This fact is so simple and yet here we are having arguments about it. To me people are conflating an economic assessment - whose jobs are going to be impacted and how much - with an aspirational one - which of your acquaintances personally could be replaced by an AI, because that would satisfy a beef.
Your accountant also is probably saving hundreds of dollars in other areas using AI assistance.
Personally I still think you should cross check with a professional.
I wouldn't be saving on tax advisors. Moreover, I would hire two different tax advisors, so I could cross check them.
Technically, all you have to do is follow the written instructions. But there are a surprising number of maybes in those instructions. You hit a checkbox that asks whether you qualify for such-and-such deduction, and find yourself downloading yet another document full of conditions for qualification, which aren't always as clear-cut as you'd like. You can end up reading page after page to figure out whether you should check a single box, and that single box may require another series of forms.
My small side income takes me from a one-page return to several pages, and next year I'm probably going to have to pay estimated taxes in advance because that non-taxed income leaves me owing at the end of the year more than some acceptable threshold that could result in fines. All because I make an extra 10% doing some evening freelancing.
Most people's taxes shouldn't be complex, but in practice they're more complex than they should be.
If I can do this, most people can do a simple 2-page 1040EZ.
These examples aren't wrong but you might be overstating their impact on the economy as a whole.
E.g. the overwhelming majority of people do not pay solely for tax advice, or have a dietician, etc. Corporations already crippled their customer support so there's no remaining damage to be dealt.
Your tax example won't move the needle on people who pay to have their taxes done in their entirety.
Even if every job that exists today were currently automated _people would find other stuff to do_. There is always going to be more work to do that isn't economical for AIs to do for a variety of reasons.
But are those really the same? You're not paying the tax agent to give you the advice per se: even before Gemini, you could do your own research for free. You're really paying the tax agent to provide you advice that you can trust without having to go to the extra steps of doing deep research.
One of the most important bits of information I get from my tax agent is, "is this likely to get me audited if we do it?" It's going to be quite some time before I trust AI to answer that correctly.
Jevons law in action: some pieces of work get lost, but lower cost of doing work generates more demand overall...
I doubt it.
Search already "obsoletes" these fields in the same way AI does. AI isn't really competing against experts here, but against search.
It's also really not clear that AI has an overall advantage over dumb search in this area. AI can provide more focused/tailored results, but it costs more. Keep in mind that AI hasn't been enshittified yet like search. The enshittification is inevitable and will come fast and hard considering the cost of AI. That is, AI responses will be focused and tailored to better monetize you, not better serve you.
If that’s true, probably for the best that those jobs get replaced. Then again, the value may have been in the personal touch (pay to feel good about your decisions) rather than quality of directions.
So…all you needed was a decent search engine, which in the past would have been Google before it was completely enshittified.
Yes.
"...all you need" A good search engine is a big ask. Google at its height was quite good. LLMs are shaping up to be very good search engines
That would be enough, for me to be very pleased with them
1) Yes. I had in fact already booked the appointment, but decided to try asking Gemini for shits and giggles.
2) I did google, but I didn't know an official calculator existed, and the search results presented me with a morass of random links that I couldn't make heads or tails of. Remember, I'm not a domain expert in this area.
They scored about 60% this month. That sounds good until you consider that 60% is a failing grade for the bar exam, and a licensed lawyer getting 40% of their legal advice wrong would be disbarred.
Are LLMs finally going to be the breakthrough that changes this? Maybe. The anthropomorphized chat experience where you feel like you're talking to a person definitely helps. So does the confident style of LLM answers, which sound expert and definitive.
But I'm not sure it will work for everyone, or even for the majority. Many people lack the background knowledge, vocabulary, patience, and persistence to get good results from their own research. I've seen plenty of people who struggle to use Google effectively, and I suspect these same people will have trouble getting helpful answers on complex topics from an LLM.
> including a link to the office's well-hidden official calculator of precisely the thing
Example: I recently used Gemini for some tax advice that would have cost hundreds of dollars to get from a licensed tax agent. And yes, the answer was supported by actual sources pointing to the tax office website, including a link to the office's well-hidden official calculator of precisely the thing I thought I would have to pay someone to figure out.