My wife works in insurance operations - everyone she manages from the top down lives in Excel. For line employees a large percentage of their job is something like "Look at this internal system, export the data to excel, combine it with some other internal system, do some basic interpretation, verify it, make a recommendation". Computer Use + Excel Use isn't there yet...but these jobs are going to be the first on the chopping block as these integrations mature. No offense to these people but Sonnet 4.5 is already at the level where it would be able to replicate or beat the level of analysis they typically provide.
Spreadsheet UI is already a nightmare. The formula editing and relationship visioning is not there at all. Mistakes are rampant in spreadsheets, even my own carefully curated ones.
Claude is not going to improve this. It is going to make it far, far worse with subtle and not so subtle hallucinations happening left and right.
The key is really this - all LLMs that I know of rely on entropy and randomness to emulate human creativity. This works pretty well for pretty pictures and creating fan fiction or emulating someone's voice.
It is not a basis for getting correct spreadsheets that show what you want to show. I don't want my spreadsheet correctness to start from a random seed. I want it to spring from first principles.
Spreadsheets are already a disaster.
So far no one has managed to deliver an alternative to spreedsheets that fix this issue, doesn't matter if we can do much better in Python, Java, C# whatever, if it is always over budget and only covers half of the work.
I know, I have taken part in such project, and it run over budget because there was always that little workflow super easy to do in Excel and they would refuse to adopt the tool if it didn't cover that workflow as well.
[1] well, people that don't consider themselves programmers.
To be fair, I've seen shops which actually apply good engineering practices to Excel sheets too. Just definitely not a majority...
As so happens in the LLM age, I have been recently having to deal with such tools, and oh boy Smalltalk based image development in the 1990's with Smalltalk/V is so much better in regards to engineering practices than those "modern" tools.
I cannot test code, if I want to backup to some version control system, I have to manually export/import a gigantic JSON file that represents the low-code workflow logic, no proper debugging tools, and so many other things I could rant about.
But I guess this is the future, AI agents based workflow engines calling into SaaS products, deployed in a MACH architecture. Great buzzword bingo, right?
Yeah, that's what OP said. Now add a bunch of random hallucinations hidden inside formulas inside cells.
If they really have a good spreadsheet solution they've either fixed the spreadsheet UI issues or the LLM hallucination issues or both. My guess is neither.
Now I just ask an LLM to create the scripts and explain all the steps. If it is a complex script I would also ask it to add logging to the script so that I can feed the log back to the LLM and explain what is going wrong which allowed for a lot faster fixes. In the early days I and the LLM would be going around in circles till I hit the token limits. And to start from scratch again.
LLMs is a retarded way of spending trillions automating what can be done with good old reliable scripting. We haven't automated shit yet.
That said, Claude is still quite behind GPT-5 in its ability to review code, and so I'm not sure how much to expect from Sonnet 4.5 in this new domain. OpenAI could probably do better.
It’s always interesting to see others opinions as it’s still so variable and “vibe” based. Personally, for my use, the idea that any GPT-5 model is superior to Claude just doesn’t resonate - and I use both regularly for similar tasks.
I have had GPT-5 point out dozens of complex bugs to me. Often in these cases I will try to see if other models can spot the same problems, and Gemini has occasionally but the Claude models never have (using Opus 4, 4.1, and Sonnet 4.5). These are bugs like complex race conditions or deadlocks that involve complex interactions between different parts of the codebase. GPT-5 and Gemini can spot these types of bugs with a decent accuracy, while I’ve never had Claude point out a bug like this.
If you haven’t tried it, I would try the codex /review feature and compare its results to asking Sonnet to do a review. For me, the difference is very clear for code review. For actual coding tasks, both models are much more varied, but for code review I’ve never had an instance where Claude pointed out a serious bug that GPT-5 missed. And I use these tools for code review all the time.
I've been a Claude Code fanboy for many months but OpenAI simply won this leg of the race, for now.
Codex meanwhile seems to be smarter and plugs away at a massive todo list for like 2 hours
I’ve had similar professional experiences as you and have been experimenting with Claude Code. I’ve found I really need to know what I’m doing and the detail in order to make effective (safe) use out of it. And that’s been a learning curve.
The one area I hope/think it’s closest to (given comments above) is potentially as a “checker” or validator.
But even then I’d consider the extent to which it leaks data, steers me the wrong way, or misses something.
The other case may be mocking up a simple financial model for a test / to bounce ideas around. But without very detailed manual review (as a mitigating check), I wouldn’t trust it.
So yeah… that’s the experience of someone who maybe bridges these worlds somewhat… And I think many out there see the tough (detailed) road ahead, while these companies are racing to monetize.
Don't try to make LLMs generate results or numbers, that's bound to fail in any case. But they're okay to generate a starting point for automations (like Excel sheets with lots of formulas and macros), given they get access to the same context we have in our heads.
Jaguar Landrover had production stopped for over a month I think, and 100+ million impact to their business (including a trail of smaller suppliers put near bankruptcy). I'd bet Tata are still there and embedded even further in 5 years.
If AI provides some day-to-day running cost reduction that looks good on quarterly financial statements it will be fully embraced, despite the odd "act of god".
"Marks & Spencer Cuts Ties With Tata Consultancy Services Amid £300m Cyber Attack Fallout" (ibtimes.co.uk)
I think the world would be a lot better off if excel weren’t in it. For example, I work at business with 50K+ employees where project management is done in a hellish spreadsheet literally one guy in Australia understands. Data entry errors can be anywhere and are incomprehensible. 3 or 4 versions are floating around to support old projects. A CRUD app with a web front end would solve it all. Yet it persists because Excel is erroneously seen as accessible whereas Rails, Django, or literally anything else is witchcraft.
Who fooled the world scripting some known work flow of yours is fucking rocket science. It should be a requirement to even enter the fucking office building.
Those are tuneable parameters. Turn down the temperature and top_p if you don't want the creativity.
> Claude is not going to improve this.
We can measure models vs humans and figure this out.
To your own point, humans already make "rampant" mistakes. With models, we can scale inference time compute to catch and eliminate mistakes, for example: run 6x independent validators using different methodologies.
One-shot financial models are a bad idea, but properly designed systems can probably match or beat humans pretty quickly.
This also reduces accuracy in real terms. The randomness is used to jump out of local minima.
Ah yes, we'll tell Mary from the Payroll she could just tune them parameters if there is more than "like 2%" error in her spreadsheets
Building a process to get a similar confidence in LLM output is part of the game.
The same applies to my checkbook, and many other areas of either calculating actuals or where future state is well defined by a model.
That said, there can be a statistical aspect to any spreadsheet model. Obviously. But not all spreadsheets are statistical, and therein lies the rub. If an LLM wants to hallucinate a 9,000 day yearly calendar because it confuses our notion of a year with one of the outer planets, that falls well within probability, but not within determinism following well define rules.
The other side of the issue is LLMs trained on the Internet. What are the chances that Claude or whatever is going to make a change based on a widely prevalent but incorrect spreadsheet it found on some random corner of the Internet? Do I want Claude breaking my well-honed spreadsheet because Floyd in Nebraska counted sheep wrong in a spreadsheet he uploaded and forgot about 5 years ago, and Claude found it relevant?
It’s when people make the leaps to the multi-year endgame and in their effort to monetise by building overconfidence in the product where I see the inherent conflict.
It’s going to be a slog… the detailed implementations. And if anyone is a bit more realistic about managing expectations I think Anthropic is doing it a little better.
If we want to go in philosophy then sure, you're correct, but this not what we're saying.
For example, an LLM is capable (and it's highly plausible for it to do so) of creating a reference to a non-existent source. Humans generally don't do that when their goal is clear and aligned (hence deterministic).
> Building a process to get a similar confidence in LLM output is part of the game.
Which is precisely my point. LLMs are supposed to be better than humans. We're (currently) shoehorning the technology.
Look at the language you're using here. Humans "generally" make less of these kinds of errors. "Generally". That is literally an assessment of likelihood. It is completely possible for me to hire someone so stupid that they create a reference to a non-existent source. It's completely possible for my high IQ genius employee who is correct 99.99% of the time to have an off-day and accidentally fat finger something. It happens. Perhaps it happens at 1/100th of the rate that an LLM would do it. But that is simply an input to the model of the process or system I'm trying to build that I need to account for.
Without extensive promoting and injectimg my own knowledge and experience, LLMs generate absolute unusable garbage (on average). Anyone who disagrees very likely is not someone who would produce good quality work by themselves (on average). That's not a clever quip; that's a very sad reality. SO MANY people cannot be bothered to learn anything if they can help it.
You know what? This is also not unlike hiring a human, they need the hirer party tell them what to do, give feedback, and assume the outcomes.
It's all about context which is non-fungible and distributed, not related to intelligence but to the reason we need intelligence for.
So for those producing slop and not knowing any better (or not caring), AI just improved the speed at which they work! Sounds like a great investment for them!
For many mastering any given craft might not be the goal, but rather just pushing stuff out the door and paying bills. A case of mismatched incentives, one might say.
That's not consistent.
Producing more slop for someone else to work through is not the solution you think it is.
To me, the case for LLMs is strongest not because LLMs are so unusually accurate and awesome, but because if human performance were put on trial in aggregate, it would be found wanting.
Humans already do a mediocre job of spreadsheets, so I don't think it is a given that Claude will make more mistakes than humans do.
Once we all forget how to write SUM(A:A), will we just invent a new kind of spreadsheet once Claude gets stuck?
Or in other words; what's the end game here? LLMs clearly cannot be left alone to do anything properly, so what's the end game of making people not learn anything anymore?
We’re already there to some degree. It is hard to put a number on my productivity gain, but as a small business owner with a growing software company it’s clear to me already that I can reduce developer hiring going forward.
When I read the skeptics I just have to conclude that they’re either poor at context building and/or work on messy, inconsistent and poorly documented projects.
My sense is that many weaker developers who can’t learn these tools simply won’t compete in the new environment. Those who can build well designed and documented projects with deep context easy for LLM’s to digest will thrive.
I assume all of this applies to spreadsheets.
- relying on self-assessments from developers about how much time they think they saved, or
- using useless metrics like lines of code produced or PRs opened, or
- timing developers on toy programming assignments like implementing a basic HTTP server that aren't representative of the real world.
Why is it that any time I ask people to provide examples of high quality software projects that were predominantly LLM-generated (with video evidence to document the process and allow us to judge the velocity), nobody ever answers the call? Would you like to change that?
My sense is that weaker developers and especially weaker leaders are easily impressed and fascinated by substandard results :)
Since my company uses Excel a lot, and I know the basics but don't want to become an expert, I use LLMs to ask intermediate questions, too hard to answer with the few formulas I know, not too hard for a short solution path.
I have great success and definitely like what I can get with the Excel/LLM combo. But if my colleagues used it the same way, they would not get my good results, which is not their fault, they are not IT but specialists, e.g. for logistics. The best use of LLMs is if you could already do the job without them, but it saves you time to ask them and then check if the result is actually acceptable.
Sometimes I abandon the LLM session, because sometimes, and it's not always easy to predict, fixing the broken result would take more effort than just doing it the old way myself.
A big problem is that the LLMs are so darn confident and always present a result. For example, I point it to a problem, it "thinks", and then it gives me new code and very confidently summarizes what the problem was, correctly, that it now for sure fixed the problem. Only that when I actually try the result has gotten worse than before. At that point I never try to get back to a working solution by continuing to try to "talk" to the AI, I just delete that session and do another, non-AI approach.
But non-experts, and people who are very busy and just want to get some result to forward to someone waiting for it as quickly as possible will be tempted to accept the nice looking and confidently presented "solution" as-is. And you may not find a problem until half a year later somebody finds that prepayments, pro forma bills and the final invoices don't quite match in hard to follow ways.
Not that these things don't happen now already, but adding a tool with erratic results might increase problems, depending on actual implementation of the process. Which most likely won't be well thought out, many will just cram in the new tool and think it works when it doesn't implode right away, and the first results, produced when people still pay a lot of attention and are careful, all look good.
I am in awe of the accomplishments of this new tool, but it is way overhyped IMHO, still far too unpolished and random. Forcing all kinds of processes and people to use it is not a good match, I think.
Anyway, Google has already integrated Gemini into Sheets, and recently added direct spreadsheet editing capability so your comment was disproven before you even wrote it
I think you need to turn down the temperature a little bit. This could be a beneficial change.
It's one thing to fudge the language in a report summary, it can be subjective, however numbers are not subjective. It's widely known LLMs are terrible at even basic maths.
Even Google's own AI summary admits it which I was surprised at, marketing won't be happy.
Yes, it is true that LLMs are often bad at math because they don't "understand" it as a logical system but rather process it as text, relying on pattern recognition from their training data.
- Log in to the internal system that handles customer policies
- Find all policies that were bound in the last 30 days
- Log in to the internal system that manages customer payments
- Verify that for all policies bound, there exists a corresponding payment that roughly matches the premium.
- Flag any divergences above X% for accounting/finance to follow up on.
Practically this involves munging a few CSVs, maybe typing in a few things, setting up some XLOOKUPs, IF formulas, conditional formatting, etc.
Will AI replace the entire job? No...but that's not the goal. Does it have to be perfect? Also no...the existing employees performing this work are also not perfect, and in fact sometimes their accuracy is quite poor.
Actually, yes. This kind of management reporting is either (1) going to end up in the books and records of the company - big trouble if things have to be restated in the future or (2) support important decisions by leadership — who will be very much less than happy if analysis turns out to have been wrong.
A lot of what ties up the time of business analysts is ticking and tying everything to ensure that mistakes are not made and that analytics and interpretations are consistent from one period to the next. The math and queries are simple - the details and correctness are hard.
For example, if I ask you to tabulate orders via a query but you forgot to include an entire table, this is a major error of process but the query itself actually is consistently error-free.
Reducing error and mistakes is very much modeling where error can happen. I never trust an LLM to interpret data from a spreadsheet because I cannot verify every individual result, but I am willing to ask an LLM to write a macro that tabulates the data because I can verify the algorithm and the macro result will always be consistent.
Using Claude to interpret the data directly for me is scary because those kinds of errors are neither verifiable nor consistent. At least with the “missing table” example, that error may make the analysis completely bunk but once it is corrected, it is always correct.
Take your own advice.
This is basic business and engineering 101.
Well said. Concise and essentially inarguable, at least to the extent it means LLMs are here to stay in the business world whether anyone likes it or not (barring the unforeseen, e.g. regulation or another pressure).
Sometimes there can be an advantage in leading or lagging some aspects of internal accounting data for a time period. Basically sitting on credits or debits to some accounts for a period of weeks. The tacit knowledge to know when to sit on a transaction and when to action it is generally not written down in formal terms.
I'm not sure how these shenanigans will translate into an ai driven system.
This worked famously well for Enron.
The one thing LLMs should consistently do is ensure that formatting is correct. Which will help greatly in the checking process. But no, I generally don't trust them to do sensible things with basic formulation. Not a week ago GPT 5 got confused whether a plus or a minus was necessary in a basic question of "I'm 323 days old, when is my birthday?"
My concern would be more with how to check the work (ie, make sure that the formulas are correct and no columns are missed) because Excel hides all that. Unlike code, there's no easy way to generate the diff of a spreadsheet or rely on Git history. But that's different from the concerns that you have.
The model ought to be calling out to some sort of tool to do the math—effectively writing code, which it can do. I'm surprised the major LLM frontends aren't always doing this by now.
The UX of spreadsheet diffs is a hard one to solve because of how weird the calculation loops are and how complicated the relationship between fields might be.
I've never tried to solve this for a real end user before in a generic way - all my past work here was for internal ability to audit changes and rollback catastrophes. I took a lot of shortcuts by knowing which cells are input data vs various steps of calculations -- maybe part of your ux is being able to define that on a sheet by sheet basis? Then you could show how different data (same formulas) changed outputs or how different formulas (same data) did differently?
Spreadsheets are basically weird app platforms at this point so you might not be able to create a single experience that is both deep and generic. On the other hand maybe treating it as an app is the unlock? Get your AI to noodle on what the whole thing is for, then show diff between before and after stable states (after all calculation loops stabilize or are killed) side by side with actual diffs of actual formulas? I feel like Id want to see a diff as a live final spreadsheet and be able to click on changed cells and see up the chain of their calculations to the ancestors that were modified.
Fun problem that sounds extremely complicated. Good luck distilling it!
Excel is similar to coding in BASIC, a giant hairy ball of tangled wool.
> Most Excel work is similar to basic coding so I think this is an area where they might actually be pretty well suited.
This is a hot take. One I'm not sure many would agree with.
LLMs are a lossy validation, and while they work sometimes, when they fail they usually do so 'silently'.
In JavaScript (and I assume most other programming languages) this is the job of static analysis tools (like eslint, prettier, typescript, etc.). I’m not aware of any LLM based tools which performs static analysis with as good a results as the traditional tools. Is static analysis not a thing in the spreadsheet world? Are there the tools which do static analysis on spreadsheets subpar, or offer some disadvantage not seen in other programming languages? And if so, are LLMs any better?
I hate smartsheet…
Excel or R. (Or more often, regex followed by pen and paper followed by more regex.)
Handing them regex would be like giving a monkey a bazooka
The more complicated the spreadsheet and the more dependencies it has, the greater the room for error. These are probabilistic machines. You can use them, I use them all the time for different things, but you need to treat them like employees you can't even trust to copy a bank account number correctly.
Besides, using AI is an exercise in a "trust but verify" approach to getting work done. If you asked a junior to do the task you'd check their output. Same goes for AI.
I have personally worked with spreadsheet based financial models that use 100k+ rows x dozens of columns and involve 1000s of formulas that transform those data into the desired outputs. There was very little tolerance for mistakes.
That said, humans, working in these use cases, make mistakes >0% of the time. The question I often have with the incorporation of AI into human workflows is, will we eventually come to accept a certain level of error from them in the way we do for humans?
Yeah, but it could be perfect, why are there humans in the loop at all? That is all just math!
Spreadsheets work because the user sees the results of complex interconnected values and calculations. For the user, that complexity is hidden away and left in the background. The user just sees the results.
This would be a nightmare for most users to validate what changes an LLM made to a spreadsheet. There could be fundamental changes to a formula that could easily be hidden.
For me, that the concern with spreadsheets and LLMs - which is just as much a concern with spreadsheets themselves. Try collaborating with someone on a spreadsheet for modeling and you’ll know how frustrating it can be to try and figure out what changes were made.
For cases where that is not available, we should use a human and never an LLM.
I had a big backlog of "nice to have scripts" I wanted to write for years, but couldn't find the time and energy for. A couple of months after I started using Claude Code, most of them exist.
Just a suspicion.
In Excel, it's possible to just ad hoc adjust things and make it up as you go. It's not clean but very adaptable and flexible.
I was thinking along the same lines, but I could not articulate as well as you did.
Spreadsheet work is deterministic; LLM output is probabilistic. The two should be distinguished.
Still, its a productivity boost, which is always good.
This is talking about applying LLMs to formula creation and references, which they are actually pretty good at. Definitely not about replacing the spreadsheet's calculation engine.
Why are we suddenly ok with giving every underpaid and exploited employee a foot gun and expect them to be responsible with it???
Claude for Excel isn't doing maths. It's doing Excel. If the llm is bad at maths then teaching it to use a tool that's good at maths seems sensible.
Rightly so! But LLMs can still make you faster. Just don't expect too much from it.
not just in a spreadsheet, any kind of deterministic work at all.
find me a reliable way around this. i don't think there is one. mcp/functions are a band aid and not consistent enough when precision is important.
after almost three years of using LLMs, i have not found a single case where i didn't have to review its output, which takes as long or longer than doing it by hand.
ML/AI is not my domain, so my knowledge is not deep nor technical. this is just my experience. do we need a new architecture to solve these problems?
high precision is possible because they can realize that by multiple cross validations
Now, granted, that can also happen because Alex fat-fingered something in a cell, but that's something that's much easier to track down and reverse.
Privatized insurance will always find a way to pay out less if they could get away with it . It is just nature of having the trifecta of profit motive , socialized risk and light regulation .
Source?
More compliance or reporting requirements usually tend to favor the larger existing players who can afford to do it and that is also used to make the life difficult and reject more claims for the end user.
It is kind of thing that keeps you and me busy, major investors don't care about it all, the cost of the compliance or the lack is not more than a rounding number in the balance, the fines or penalties are puny and laughable.
The enormous profits year on year for decades now, the amount of consolidation allowed in the industry show that the industry is able to do mostly what they want pretty much, that is what I meant by light regulation.
https://riskandinsurance.com/us-pc-insurance-industry-posts-...
Meta alone made $62bln in 2024: https://investor.atmeta.com/investor-news/press-release-deta...
So it's weird to see folks on a tech site talking about how enormous all the profits are in health insurance, and citations with numbers would be helpful to the discussion.
I worked in insurance-related tech for some time, and the providers (hospitals, large physician groups) and employers who actually pay for insurance have signficant market power in most regions, limiting what insurers can charge.
So obviously the company that prioritizes accuracy of coverage decisions by spending money on extra labor to audit itself is wasting money. Which means insureds have to waste more time getting the payment for healthcare they need.
It's the nature of everything. They agree to pay you for something. It's nothing specific to "profit motive" in the sense you mean it.
There are many other entity types from unions[1], cooperatives , public sector companies , quasi government entities, PBC, non profits that all offer insurance and can occasionally do it well.
We even have some in the US and don’t think it is communism even - like the FDIC or things like social security/ unemployment insurance.
At some level government and taxation itself is nothing but insurance ? We agree to paying taxes to mitigate against variety of risks including foreign invasion or smaller things like getting robbed on the street.
[1] Historically worker collectives or unions self-organized to socialize the risks of both major work ending injuries or death.
Ancient to modern armies operate on because of this insurance the two ingredients that made them not mercenaries - a form of long term insurance benefit (education, pension, land etc) or family members in the event of death and sovereign immunity for their actions.
We also have to remember all claims aren't equal. i.e. some claims end up being way costlier than others. You can achieve similar % margin outcomes by putting a ton of friction like, preconditions, multiple appeals processes and prior authorization for prior authorization, reviews by administrative doctors who have no expertise in the field being reviewed don't have to disclose their identity and so and on.
While U.S. system is most extreme or evolved, it is not unique, it is what you get when you end up privatize insurance any country with private insurance has some lighter version of this and is on the same journey .
Not that public health system or insurance a la NHS in UK or like Germany work, they are underfunded, mismanaged with long times in months to see a specialist and so on.
We have to choose our poison - unless you are rich of course, then the U.S. system is by far the best, people travel to the U.S. to get the kind of care that is not possible anywhere else.
I disagree with the statement that healthcare insurance is predominantly privatized in the US: Medicare and Medicaid, at least in 2023, outspent private plans for healthcare spending by about ~10% [1]; this is before accounting for government subsidies for private plans. And boy, does America have a very unique relationship with these programs.
https://www.healthsystemtracker.org/chart-collection/u-s-spe...
My take away is that as public health costs are overtaking private insurance and at the same time doing a better job controlling costs per enrollee, it makes more and more sense just to have the government insure everyone.
I can't see what argument the private insurers have in their favor.
John Oliver had an excellent segment coincidentally yesterday on this topic.
While the government pays for it, it is not managed or run by them so how to classify the program as public or private ?
That's a feature, not a bug.
If it doesn't work well, I will do it myself, because I care that things are done well.
None of this is me being scared of being replaced; quite the opposite. I'm one of the last generations of programmers who learned how to program and can debug and fix the mess your LLM leaves behind when you forgot to add "make sure it's a clean design and works" to the prompt.
Okay, that's maybe hyperbole, but sadly only a little bit. LLMs make me better at my job, they don't replace me.
In a lot of jobs, particularly in creative industries, or marketing, media and writing, the definition of a job well done is a fairly grey area. I think AI will be mostly disruptive in these areas.
But in programming there is a hard minimum of quality. Given a set of inputs, does the program return the correct answer or not? When you ask it what 2+2, do you get 4?
When you ask AI anything, it might be right 50% of the time, or 70% of the time, but you can't blindly trust the answer. A lot of us just find that not very useful.
Whether something works or not matters less than whether someone will pay for it.
HN constantly points out the flaws, gaps, and failings of AI. But the same is true of any technology discussed on HN. You could describe HN as having an anti-technology bias, because HN complains about the failings of tech all day every day.
Quite the opposite, actually. You can always find five stories on the front page about some AI product or feature. Meanwhile, you have people like yourself who convince themselves that any pushback is done by people who just don't see the true value of it yet and that they're about to miss out!! Some kind of attempt at spreading FOMO, I guess.
If anything, HN, has a pro-AI bias. I don't know of any other medium where discussions about AI consistently get this much frontpage time, this amount of discussion, and this many people reporting positive experiences with it. It's definitely true that HN isn't the raging pro-AI hypetrain it was two years ago, but that shouldn't be mistaken for "strong anti-AI bias".
Outside of HN I am seeing, at best, an ambivalent reaction: plenty of people are interested, almost everyone tried it, very few people genuinely like it. They are happy to use it when it is convenient, but couldn't care less if it disappeared tomorrow.
There's also a small but vocal group which absolutely hates AI and will actively boycott any creative-related company stupid enough to admit to using it, but that crowd doesn't really seem to hang out on HN.
Wonder how true that is. Some things incorporate in your life so subtly that you only become aware of them when totally switched off.
I do, but I certainly feel in the minority in here.
When US-East-1 failed, lots of people talked about how the lesson was cloud agnosticism and multi cloud architecture. The practical economic lesson for most is that if US-East-1 fails, nobody will get mad at you. Cloud failure is viewed as an act of god.
Everything isn't about money, I know that status and power are all you ai narcissists dream about. But you'll never be Bill Gates, nor will you be Elon Musk.
Once ai has gone the way of "Web3", "NFTs", "blockchain", "3D tvs", etc; You'll find a new grift to latch your life savings onto.
LLMs specialize in making up plausible things with a minimum of human effort, but their downside is that they're very good at making up plausible things which are covertly erroneous. It's a nightmare to troubleshoot.
There is already an abject inability to provision the labor to verify Excel reasoning when it's composed by humans.
I'm dead certain that Claude will be able to produce plausibly correct spreadsheets. How important is accuracy to you? How life-critical is the end result? What are your odds, with the current auditing workflow?
Okay! Now! Half of the users just got laid off because management thinks Claude is Good Enough. How about now?
But GPT-5 Pro, and to a certain extent GPT-5 Codex, can spot complex bugs like race conditions, or subtly incorrect logic like memory misuse in C, remarkably well. It is a shame GPT-5 Pro is locked behind a $200/month subscription, which means most people do not understand just how good the frontier models are at this type of task now.
Excel and AIs are huge clusterfucks on their own, where insane errors happens for various reasons. Combine them, and maybe we will see improvement, but surely we will see catastrophic outcomes which could not only ruin the lives of ordinary people, whole companies and countries, as already happened before...
Just as with copilot, this combines LLM's inability to repeatably do math correctly with peoples' overassurance in LLM's capabilities.
A lot of us have seen the effects of AI tools in the hands of people who don't understand how or why to use the tools. I've already seen AI use/misuse get two people fired. One was a line-of-business employee who relied on output without ever checking it, got herself into a pretty deep hole in 3 weeks. Another was a C suite person who tried to run an AI tool development project and wasted double their salary in 3 months, nothing to show for it but the bill, fired.
In both cases the person did not understand the limits of the tools and kept replacing facts with their desires and their own misunderstanding of AI. The C suite person even tried to tell a vendor they were wrong about their own product because "I found out from AI".
AI right now is fireworks. It's great when you know how to use it, but if you half-ass it you'll blow your fingers off very easily.
I'm not even sure that has to be true anymore. From my admittedly superficial impression of the page, this appears to be a tool for building tools. There are plenty of organizations that are resource constrained, that are doing things the way they have always done thing in Excel, simply because they cannot allocate someone to modify what is already in place to better suit their current needs. For them, this is more of a quality of life and quality of out improvement. This is not like traditional software development, where organizations are far more likely to purchase a product or service to do a job (and where the vendors of those products and services are going to do their best to eliminate developers).
- this opens up ridiculous flood of data that would otherwise be semi-private to one company providing this service - this works well small data sets, but will choke on ones it will need to divvy up into chunks inviting interesting ( and yet unknown ) errors
There is a real benefit to being able to 'talk to data', but anyone who has seen corporate culture up close and personal knows exactly where it will end.
edit: an i saying all this as as person, who actually likes llms.
https://www.theregister.com/2025/03/10/nz_health_excel_sprea...
[edit: Added link]
Perhaps this is part of the negativity? This is a bad thing for the middle class.
*material benefit. In terms of spirit and purpose, the older I get the more I think maybe the Amish are on to something. Work gives our lives purpose, and the closer the work is to our core needs, the better it feels. Labor saving so that most of us are just entertaining each other on social networks may lead to a worse society (but hey, our material needs are met!)
Spreadsheets are an abstraction over a messy reality, lossy. They were already generalizing reality.
Now we generalize the generalization. It is this lossy reality that people are worried about with AI in HN.
If this is true then why your wife is going to be happy about it? I found it really hard to understand. Do you prefer your wife to be jobless and her employer happily cut costs without impacting productivity? Even if it just replaces the line workers, do you think your wife is going to be safe?
I don't get it.
The issue isn’t in creating a new monstrosity in excel.
The issue is the poor SoB who has to spelunk through the damn thing to figure out what it does.
Excel is the sweet spot of just enough to be useful, capable enough to be extensible, yet gated enough to ensure everyone doesn’t auto run foreign macros (or whatever horror is more appropriate).
In the simplest terms - it’s not excel, it’s the business logic. If an excel file works, it’s because theres someone who “gets” it in the firm.
Neat formatting didn't save any model from having the wrong formula pasted in.
Being neat was never a substitute for being well rested, or sufficiently caffeinated.
Have you seen how AI functions in the hands of someone who isn't a domain expert? I've used it for things I had no idea about, like Astro+ web dev. User ignorance was magnified spectacularly.
This is going to have Jr Analysts dumping well formatted junk in email boxes within a month.
people think of privacy at first regards of data, local deployment of open source models are the first choice for them
No offense, but this is pure fantasy. The level of analysis they typically provide doesn't suffer from the same high baseline level of completely made up numbers of your favorite LLM.
> these jobs are going to be the first on the chopping block as these integrations mature.
Those two things are maybe related? So many of my friends don't enjoy the same privileges as I do, and have a more tenuous connection to being gainfully employed.
Versatility and efficiency explode while human usability tanks, but who cares at that point?
Some people - normal people - understand the difference between the holistic experience of a mathematically informed opinion and an actual model.
It's just that normal people always wanted the holistic experience of an answer. Hardly anyone wants a right answer. They have an answer in their heads, and they want a defensible journey to that answer. That is the purpose of Excel in 95% of places it is used.
Lately people have been calling this "syncophancy." This was always the problem. Sycophancy is the product.
Claude Excel is leaning deeply into this garbage.
When most of it is wild hallucinations? Not really.
For many employees leveraging Excel for manipulating important data, it could cripple careers.
For spreadsheets that influence financial decisions or touch PPI/PII, it could lead to regulatory disasters and even bankruptcies.
Purge hallucinations from LLMs, _then_ let it touch the important shite. Doing it in the reverse order is just begging for a FAFO apocalypse.
Who are these teams that can get value from Anthropic? One MCP and my context window is used up and Claude tells me to start a new chat.
The criticisms broadly fall between "spreadsheets are bad" and "AI will cause more trouble than it solves".
This release is a dot in a trend towards everyone having a Goldman-Sachs level analyst at their disposal 24/7. This is a huge deal for the average person or business. Our expectation (disclaimer: I work in this space) is that spreadsheet intelligence will soon be a solved problem. The "harder" problem is the instruction set and human <> machine prompting.
For the "spreadsheets are bad" crowd -- sure, they have problems, but users have spoken and they are the preferred interface for analysis, project management and lightweight database work globally. All solutions to "the spreadsheet problem" come with their own UX and usability tradeoffs, so it'a a balance.
Congrats to the Claude team and looking forward to the next release!
Based on the history of digitalization of businesses from the 1980s onwards, the spreadsheets will just balloon in number and size and there will be more rules and more procedures and more forms and reports to file until the efficiency gains are neutralized (or almost neutralized).
At one point, it generated a verbose formula and mentioned, off-handedly, that it would have been prettier had Calcapp supported LET. "It does!", I replied, "and as an extension, you can use := instead of , to separate names and values!") and it promptly rewrote it using our extended syntax, producing a sleek formula.
These templates were for various verticals, like real estate, financial planning and retail, and I would have been hard-pressed to produce them without Claude's domain knowledge. And I did it in a weekend! Well, "we" did it in a weekend.
So this development doesn't really surprise me. I'm sure that Claude will be right at home in Excel, and I have already thought about how great it would be if Claude Code found a permanent home in our app designer. I'm concerned about the cost, though, so I'm holding off for now. But it does seem unfair that I get to use Claude to write apps with Calcapp, while our customers don't get that privilege.
(I wrote more about integrating Claude Code here: https://news.ycombinator.com/item?id=45662229)
- Get answers about any cell in seconds: Navigate complex models instantly. Ask Claude about specific formulas, entire worksheets, or calculation flows across tabs. Every explanation includes cell-level citations so you can verify the logic.
- Test scenarios without breaking formulas: Update assumptions across your entire model while preserving all dependencies. Test different scenarios quickly—Claude highlights every change with explanations for full transparency.
- Debug and fix errors: Trace #REF!, #VALUE!, and circular reference errors to their source in seconds. Claude explains what went wrong and how to fix it without disrupting the rest of your model.
- Build models or fill existing templates: Create draft financial models from scratch based on your requirements. Or populate existing templates with fresh data while maintaining all formulas and structure.
Oh and deal with dates before 1900.
Excel is a gift from God if you stay in its lane. If you ever so slightly deviate, not even the Devil can help you.
But maybe, juuuuust maybe, AI can?
But maybe, juuuuust maybe, AI can?"
Bold assumption that the devil and AI aren't aligned ;)
You are anthropomorphizing LLM programs, you assume that if a number in a spreadsheed is big, then program can somehow understand it that it is a big number and if it will make an error it will be a small order error like a human would make. Human process: "hmm, here is a calculation where we divide our imports by number of subsidiaries, let me estimate this in my head, ok, looks like 7320." (actual correct answer was 7340, bu human made a small, typical mistake in the math) LLM program process: it literally uses heat maps and randomization to arrive at each particular character in a row. So it may be 7340, or it may be 8745632, or 1320, or whatever. There is a comment here at a top, from another user, where he queried LLM to make a change of value in the document and it did it correctly. But at the same time it replaced bank account number with a different bank account number. Because to LLM it is the same - sixteen digit in the field, or another sixteen digits in a field, it is the same for LLM. Because it is not AI and doesn't "understand" what it does.
For fuel, similarly, you're going to lose militers to evaporation on a hot day, so similarly, being off my ml isn't material.
If you tax a company, fine, sure, the company is going to want it to be right, but 1 or two tons in a 10,000 ton order is again, < 1%. There is some threshold below which precision is extra unnecessary work, though if you have problems with thieves and corruption, you're going to want additional precision that isn't necessary elsewhere.
As to where in my comment I'm anthropomorphizing LLMs, you're going to have to point out where I did that, as the word LLM doesn't appear anywhere in my comment, so it feels like you're projecting claims my comment does not make, as it is LLM neutral and merely point of that 100% exact precision doesn't come without a cost.
Tried integrating chatgpt into my finance job to see how far I can get. Mega jikes...millions of dollars of hallucinated mistakes.
Worse you don't have the same tight feedback loop you've got in programming that'll tell you when something is wrong. Compile errors, unit tests etc. You basically need to walk through everything it did to figure out what's real and what's hallucinations. Basically fails silently. If they roll that out at scale in the financial system...interesting times ahead.
Still presumably there is something around spreadsheets it'll be able to do - the spreadsheet equivalent of boilerplate code whatever that may be
It's like one off scripts in a sense? I'm not doing complex formulas I just need to know how I can pull data into a sheet and then I'll bucketize or graph it myself.
Again probably because I'm not the most adept user but it has definitely been a positive use case for me.
I suspect my use case is pretty boilerplatey :)
>I'm not doing complex formulas
Neither am I frankly. Finance stuff can get conceptually complicated even with simple addition & multiplication though. e.g. I deal with a lot of offshore stuff, so the average spreadsheet is a mix of currencies, jurisdictions and companies that are interlinked. I could probably talk you through it high level in an hour with a pen & paper, but the LLMs just can't see the forest for all the trees in the raw sheet.
I keep searching for a sign, but everyone I talk to has horror stories. It sucks as a technologist that just wants to play with the thing; oh well.
The reason that Claude Code doesn't have an IDE is because ~"we think the IDE will obsolete in a year, so it seemed like a waste of time to create one."
Noam Shazeer said on a Dwarkesh podcast that he stopped cleaning his garage, because a robot will be able to do it very soon.
If you are operating under the beliefs these folks have, then things like IDEs, cleaning up, and customer service are stupid annoyances that will become obsolete very soon.
To be clear, I have huge respect for everyone mentioned above, especially Noam.
We all come up with excuses for why we haven't done a chore, but some of us need to sound a bit more plausible to other members of the household than that.
It would get about the same reaction as "I'm not going to wash the dishes tonight, the rapture is tomorrow."
Noam does not do a lot of interviews, and I really hope that stuff like my dumb comment does not prevent him from doing more in the future. We could all learn a lot from him. I am not sure that everyone understands everything that this man has given us.
How much is the robot going to cost in a year? 100k? 200k? Not mass market pricing for sure.
Meanwhile, today he could pay someone $1000 to clean his garage.
I don’t think the point was about having a clean space, it was in response to a question along the lines of: when do you think we will achieve AGI?
The money people pay in monthly fees to Anthropic for even the top Max sub likely doesn't come closer to covering the energy & infrastructure costs for running the system.
You can prove this to yourself by just trying to cost out what it takes to build the hardware capable of running a model of this size at this speed and running it locally. It's tens of thousands of dollars just to build the hardware, not even considering the energy bills.
So I imagine the goal right now is to pull in a mass audience and prove the model, to get people hooked, to get management and talent at software firms pushing these tools.
And I guess there's some in management and the investment community that thinks this will come with huge labour cost reductions but I think they may be dreaming.
... And then.. I guess... jack the price up? Or wait for Moore's Law?
So it's not a surprise to me they're not jumping to try and service individual subscribers who are paying probably a fraction of what it costs them to the run the service.
I dunno, I got sick of paying the price for Max and I now use the Claude Code tool but redirect it to DeepSeek's API and use their (inferior but still tolerable) model via API. It's probably 1/4 the cost for about 3/4 the product. It's actually amazing how much of the intelligence is built into the tool itself instead of just the model. It's often incredibly hard to tell the difference bertween DeepSeek output and what I got from Sonnet 4 or Sonnet 4.5
I have primarily been using the 120b gpt-oss model. It's definitely worse than Claude and GPT-5, but not by, like, an order of magnitude or anything. It's also clearly better than ChatGPT was when it first came out. Text generates a bit slowly, but it's perfectly usable.
So it doesn't seem so unreasonable to me that costs could come down in a few years?
To build out a system that can, I'd imagine you're looking at what... $20k, $30k? And then that's a machine that is basically for one customer -- meanwhile a Claude Code Max or Codex Pro is $200 USD a month.
The math doesn't add up.
And once it does add up, and these models can be reasonable run on lower end hardware... then the moat ceases to exist and there'll be dozens of providers. So the valuation of e.g. Anthropic makes little sense to me.
Like I said, I'm using the Claude Code tool/front-end pointing against the page-per-use DeepSeek platform API, it costs a fraction of what Anthropic is charging, and feels to me like the quality is about 80% there... So ...
My RTX 4080 only has 16 GB of VRAM, and gpt-oss 120b is 4x that size. It looks like Ollama is actually running ~80% of the model off of the CPU. I was made to believe this would be unbearably slow, but it's really not, at least with my CPU.
I can't run the full sized DeepSeek model because I don't have enough system memory. That would be relatively easy to rectify.
> And once it does add up, and these models can be reasonable run on lower end hardware... then the moat ceases to exist and there'll be dozens of providers.
This is a good point and perhaps the bigger problem.
Every AI company right now (except Google Meta and Microsoft) has their valuations based on the expectation of a future monopoly on AGI. None of their business models today or in the foreseeable horizon are even positive let alone world-dominating. The continued funding rounds are all apparently based on expectation of becoming the sole player.
The continuing advancement of open source / open weights models keeps me from being a believer.
I’ve placed my bet and feel secure where it is.
I've worked alongside sell-side investment bankers in a prior startup, and so much of the work is in taking a messy set of statements from a company, understanding the underlying assumptions, and building, and rebuilding, and rebuilding, 3-statement models that not only adhere to standard conventions (perhaps best introed by https://www.wallstreetprep.com/knowledge/build-integrated-3-... ) but also are highly customized for different assumptions that can range from seasonality to sensitivity to creative deal structures.
It is quite common for people to pull many, many all-nighters to try to tweak these models in response to a senior banker or a client having an idea! And one might argue there are way too many similar-looking numbers to keep a human banker from "hallucinating," much less an LLM.
But fundamentally, a 3-statement model and all its build-sheets are a dependency graph with loosely connected human-readable labels, and that means you can write tools that let an LLM crawl that dependency graph in a reliable and semantically meaningful way. And that lets you build really cool things, really fast.
I'm of the opinion that giving small companies the ability to present their finances to investors, the same way Fortune 500 companies hire armies of bankers to do, is vital to a healthy economy, and to giving Main Street the best possible chance to succeed and grow. This is a massive step in the right direction.
On the other hand, presenting truthful data to investors is distinctly not fraud, and this again does not depend on the generation method.
is there precedent for this supposed ruling?
Establishes that accountants who certify financials are liable if they are incorrect. In particular, if they have a reason to believe they might not be accurate and they certify anyway they are liable. And at this stage of development it’s pretty clear that you need to double check LLM generated numbers.
Obviously no clue if this would hold up with today’s court, but I also wasn’t making a legal statement before. I’m not a lawyer and I’m not trying to pretend to be one.
[0] https://scholarship.law.stjohns.edu/cgi/viewcontent.cgi?arti...
An LLM doing so needn't even be willful from the author's part. We're going to see issues with forecasts/slide decks full of inaccuracies that are hard to review.
That being said, oh for sure this will lead to more incidental fraud (and deliberate fraud) and I’m sure it already has. Would be curious to see the prevalence of em-dash’s in 10k’s over the years.
Standardized 3-statement models in Excel are designed to be auditable, with or without AI, because (to only slightly simplify) every cell is either a blue input (which must come from standard exports of the company's accounting books, other auditable inventory/CRM/etc. data, or a visible hardcoded constant), or a black formula that cannot have hardcoded values, and must be simple.
If every buyer can audit, with tools like this, that the formulas match the verbal semantics of the model, there's even less incentive than there is now to fudge the formula level. (And with Wall Street conventions, there's nowhere to hide a prompt injection, because you're supposed to keep every formula to only a few characters, and use breakout "build" rows that can themselves be visually audited.)
And sure, you could conceivably use any AI tool to generate a plausible list of numbers at the input level, but that was equally easy, and equally dependent on context to be fraudulent or not, ever since that famous Excel 1990 elevator commercial: https://www.youtube.com/watch?v=kOO31qFmi9A&t=61s
At the end of the day, the difference between "they want to see this growth, let's fudge it" and "they want to see this growth, let's calculate the exact metrics we need to hit to make that happen, and be transparent about how that's feasible" has always been a matter of trust, not technology.
Tech like this means that people who want to do things the right way can do it as quickly as people who wanted to play loose with the numbers, and that's an equalizer that's on the right side of history.
I think many software engineers overlook how many companies have huge (billion dollar) processes run through Excel.
It's much less about 'greenfield' new excel sheets and much more about fixing/improving existing ones. If it works as well as Claude Code works for code, then it will get pretty crazy adoption I suspect (unless Microsoft beats them to it).
So they can fire the two dudes that take care of it, lose 15 years of in house knowledge to save 200k a year and cry in a few months when their magic tool shits the bed ?
Massive win indeed
I think you're making an argument for LLMs, not against.
You're one of the people who saw nothing wrong with moving all our industries to asia right ? "It's cheaper so it's obviously better", if you don't think about any of the externalities and long term consequences sure...
Until Microsoft does its anti-competitive thing and find a way to break this in the file format, because this is exactly what copilot in excel does.
That said, Copilot in Excel is pretty much hot garbage still so anything will be better than that.
I 100% believe generative AI can change a spreadsheet. Turn the xslx into text, mutate that, turn it back into an xslx, throw it away if it didn't parse at all. The result will look pretty similar to the original too, since spreadsheets are great at showing immediately local context and nothing else.
Also, we've done a pretty good job of training people that chatgpt works great, so there's good reason for them to expect claude for excel to work great too.
I'd really like the results of this to be considered negligence with non-survivable fines for the reckless stupidity, but more likely, it'll be seen as an act of god. Like all the other broken shit in the IT world.
This is what I want AI to do, not generate wrong answers and hallucinate girlfriends.
I have implemented this a couple of times and not only does it work well, it tends to be fairly well accepted. People need spreadsheets to work on them, but generally they kind of hate sending those around via email. Having a reference source of data is welcomed.
Anyone from Anthropic here that would like elaborate?
Reminds me of when our CIO insisted on moving to the cloud (back when AWS was just getting started) and then was super pissed when he got a $60k bill because no one knew to shutdown their VMs when leaving for the day.
Also, 50k rows wouldn't cost $50k. More like $100 with Sonnet 4.5 pricing and typical numbers of input/output tokens. Imagine the time needed to go through 50k rows manually and math doesn't really work for a horror story.
