I find the contrast between two narratives around technology use so fascinating:
1. We advocate automation because people like Brenda are error-prone and machines are perfect.
2. We disavow AI because people like Brenda are perfect and the machine is error-prone.
These aren't contradictions because we only advocate for automation in limited contexts: when the task is understandable, the execution is reliable, the process is observable, and the endeavour tedious. The complexity of the task isn't a factor - it's complex to generate correct machine code, but we trust compilers to do it all the time.
In a nutshell, we seem to be fine with automation if we can have a mental model of what it does and how it does it in a way that saves humans effort.
So, then - why don't people embrace AI with thinking mode as an acceptable form of automation? Can't the C-suite in this case follow its thought process and step in when it messes up?
I think people still find AI repugnant in that case. There's still a sense of "I don't know why you did this and it scares me", despite the debuggability, and it comes from the autonomy without guardrails. People want to be able to stop bad things before they happen, but with AI you often only seem to do so after the fact.
Narrow AI, AI with guardrails, AI with multiple safety redundancies - these don't elicit the same reaction. They seem to be valid, acceptable forms of automation. Perhaps that's what the ecosystem will eventually tend to, hopefully.
It's not as black-and-white as "Brenda good, AI bad". It's much more nuanced than this.
When it comes to (traditional) coding, for the most part, when I program a function to do X, every single time I run that function from now until the heat death of the sun, it will always produce Y. Forever! When it does, we understand why, and when it doesn't, we also can understand why it didn't!
When I use AI to perform X, every single time I run that AI from now until the heat death of the sun it will maybe produce Y. Forever! When it does, we don't understand why, and when it doesn't, we also don't understand why!
We know that Brenda might screw up sometimes but she doesn't run at the speed of light, isn't able to produce a thousand lines of Excel Macro in 3 seconds, doesn't hallucinate (well, let's hope she doesn't), can follow instructions etc. If she does make a mistake, we can find it, fix it, ask her what happened etc. before the damage is too great.
In short: when AI does anything at all, we only have, at best, a rough approximation of why it did it. With Brenda, it only takes a couple of questions to figure it out!
Before anyone says I'm against AI, I love it and am neck-deep in it all day when programming (not vibe-coding!) so I have a full understanding of what I'm getting myself into but I also know its limitations!
I was brought up on the refrain of "aren't computers silly, they do exactly what you tell them to do to the letter, even if it's not what you meant". That had its roots in computers mostly being programmable BASIC machines.
Then came the apps and notifications, and we had to caveat "... when you're writing programs". Which is a diminishing part of the computer experience.
And now we have to append "... unless you're using AI tools".
The distinction is clear to technical people. But it seems like an increasingly niche and alien thing from the broader societal perspective.
I think we need a new refrain, because with the AI stuff it increasingly seems "computers do what they want, don't even get it right, but pretend that they did."
Machine reliability does the same thing the same way every time. If there's an error on some input, it will always make that error on that input, and somebody can investigate it and fix it, and then it will never make that error again.
Human reliability does the job even when there are weird variances or things nobody bothered to check for. If the printer runs out of paper, the human goes to the supply cabinet and gets out paper and if there is no paper the human decides whether to run out right now and buy more paper or postpone the print job until tomorrow; possibly they decide that the printing doesn't need to be done at all, or they go downstairs and use a different printer... Humans make errors but they fix them.
LLMs are not machine reliable and not human reliable.
If you think programs are predictable, I have a bridge to sell you.
The only relevant metric here is how often each thing makes mistakes. Programs are the most reliable, though far from 100%, humans are much less than that, and LLMs are around the level of humans, depending on the humans and the LLM.
I feel like it comes down to predictability and overall trust and confidence. AI is still very fucky, and for people that don't understand the nuances, it definitely will hallucinate and potentially cause real issues. It is about as happy as a Linux rm command to nuke hours of work. Fortunately these tools typically have a change log you can undo, but still.
Also Brenda is human and we should prioritize keeping humans in jobs, but with the way shit is going that seems like a lost hope. It's already over.
"Thinking mode" only provides the illusion of debuggability. It improves performance by generating more tokens which hopefully steer the context towards one more likely to produce the desired response, but the tokens it generates do not reflect any sort of internal state or "reasoning chain" as we understand it in human cognition. They are still just stochastic spew. You have no more insight into why the model generates the particular "reasoning steps" it does than you do into any other output, and neither do you have insight into why the reasoning steps lead to whatever conclusion it comes to. The model is much less constrained by the "reasoning" than we would intuit for a human - it's entirely capable of generating an elaborate and plausible reasoning chain which it then completely ignores in favor of some invisible built-in bias.
I don't understand why generative AI gets a pass at constantly being wrong, but an average worker would be fired if they performed the same way. If a manager needed to constantly correct you or double check your work, you'd be out. Why are we lowering the bar for generative AI?
How much compute costs is it for the AI to do Brenda's job? Not total AI spend, but the fraction that replaced Brenda. That's why they'd fire a human but keep using the AI.
You don't have a human to manage. The relationship is completely one-sided, you can query a generative AI at 3 in the morning on new years eve. This entity has no emotions to manage and no own interests.
There's cost.
There's an implicit promise of improvement over time.
There's an the domain of expertise being inhumanly wide. You can ask about cookies right now, then about XII century France, then about biochemistry.
The fact that an average worker would be fired if they perform the same way is what the human actually competes with. They have responsibility, which is not something AI can offer. If it was the case that, say, Anthropic, actually signed contracts stating that they are liable for any mistakes, then humans would be absolutely toast.
It’s much cheaper than Brenda (superficially, at least). I’m not sure a worker that costs a few dollars a day would be fired, especially given the occasional brilliance they exhibit.
So now you don't have to pay people to do their actual work, you assign the work to ML ("AI") and then pay the people to check what it generated. That's a very different task, menial and boring, but if it produces more value for the same amount of input money, then it's economical to do so.
And since checking the output is often a lower skilled job, you can even pay the people less, pocketing more as an owner.
It’s not even greater trust. It’s just passive trust. The thing is, Brenda is her own QA department. Every good Brenda is precisely good because she checks her own work before shipping it. AI does not do this. It doesn’t even fully understand the problem/question sometimes yet provides a smart definitive sounding answer. It’s like the doctor on The Simpson’s, if you can’t tell he’s a quack, you probably would follow his medical advice.
That’s definitely the hype. But I don’t know if I agree. I’m essentially a Brenda in my corporate finance job and so far have struggled to find any useful scenarios to use AI for.
I thought once this can build me a Gantt chart because that’s an annoying task in excel. I had the data. When I asked it to help me, “I can’t do that but I can summarize your data”. Not helpful.
Any type of analysis is exactly what I don’t want to trust it with. But I could use help actually building things, which it wouldn’t do.
Also, Brenda’s are usually fast. Having them use a tool like AI that can’t be fully trusted just slows them down. So IMO, we haven’t proven the AI variable in your equation is actually a positive value.
Humans, legacy algorithmic systems, and LLM's have different error modes.
- Legacy systems typically have error modes where integrations or user interface breaks in annoying but obvious ways. Pure algorithms calculating things like payroll tend to be (relatively) rigorously developed and are highly deterministic.
- LLMs have error modes more similar to humans than legacy systems, but more limited. They're non-deterministic, make up answers sometimes, and almost never admit they can't do something; sometimes they make pure errors in arithmetic or logic too.
- Humans have even more unpredictable error modes; on top of the errors encountered in LLM's, they also have emotion, fatigue, org politics, demotivation, misaligned incentives, and so on. But because we've been dealing with working with other humans for ten thousand years we've gotten fairly good at managing each other... but it's still challenging.
LLMs probably need a mixture of "correctness tests" (like evals/unit tests) and "management" (human-in-the-loop).
That automation you cite in your #1 is advocated for because it is deterministic and, with effort, fairly well understood (I have countless scripts solidly running for years).
I don't disavow AI, but like the author, I am not thrilled that the masses of excel users suddenly have access to Copilot (gpt4). I've used Copilot enough now to know that there will be huge, costly mistakes.
> So, then - why don't people embrace AI with thinking mode as an acceptable form of automation?
"Thinking" mode is not thinking, it's generating additional text that looks like someone talking to themselves. It is as devoid of intention and prone to hallucinations as the rest of LLM's output.
> Can't the C-suite in this case follow its thought process and step in when it messes up?
That sounds like manual work you'd want to delegate, not automation.
would you be willing to guarantee that some automation process will never mess up, and if/when it does, compensate the user with cash.
For a compiler, with a given set of test suites, the answer is generally yes, and you could probably find someone willing to insure you for a significant amount of money, that a compilation bug will not screw up in a such a large way that it will affect your business.
For a LLM, I have a believing that anyone will be willing to provide that same level of insurance.
If a LLM company said "hey use our product, it works 100% of the time, and if it does fuck up, we will pay up to a million dollars in losses" I bet a lot of people would be willing to use it. I do not believe any sane company will make that guarantee at this point, outside of extremely narrow cases with lots of guardrails.
That's why a lot of ai tools are consumer/dev tools, because if they fuck up, (which they will) the losses are minimal.
The “Brenda” example is a lumped sum fallacy where there is an “average” person or phenomenon that we can benchmark against. Such a person doesn't exist, leading to these dissonant, contradictory dichotomies.
The fact of the matter is that there are some people who can hold lots of information in their head at once. Others are good at finding information. Others still are proficient at getting people to help them. Etc. Any of these people could be tasked with solving the same problem and they would leverage their actual, particular strengths rather than some nebulous “is good or bad at the task” metric.
As it happens, nearly all the discourse uses this lumped sum fallacy, leading to people simultaneously talking past one another while not fundamentally moving the discussion forward.
I see where you are coming from but in my head, Brenda isn't real.
She represents the typical domain-experts that use Excel imo. They have an understanding of some part of the business and express it while using Excel in a deterministic way: enter a value of X, multiply it by Y and it keeps producing Z forever!
You can train AI to be a better domain expert. That's not in question, however with AI, you introduce a dice roll: it may not miltiply X and Y to get Z... it might get something else. Sometimes. Maybe.
If your spreadsheet is a list of names going on the next annual accounts department outing then the risk is minimal.
If it's your annual accounts that the stock market needs to work out billion dollar investment portfolios, then you are asking for all the pain that it will likely bring.
The big problem with AI in back-office automation is that it will randomly decide to do something different than it had been doing. Meaning that it could be happily crunching numbers accurately in your development and launch experience, then utterly drop the ball after a month in production.
While humans have the same risk factors, human oriented back-office processes involve multiple rounds of automated/manual checks which are extremely laborious. Human errors in spreadsheets have particular flavors such as forgotten cell, misstyped number, or reading from the wrong file/column. Human's are pretty good at catching these errors as they produce either completely wrong results when the columns don't line up - or the typo'd number is completely out of distribution.
An AI may simply decide to hallucinate realistic column values rather than extracting its assigned input. Or hallucinate a fraction of column values. How do you QA this? You can't guarantee that two invocations of the AI won't hallucinate the same values, you can't guarantee that a different LLM won't hallucinate different values. To get a real human check, you'd need to re-do the task as a human. In theory you can have the LLM perform some symbolic manipulation to improve accuracy... but it can still hallucinate the reasoning traces etc.
If a human decided to make up accounting numbers one out of every 10000 accounting requests they would likely be charged with fraud. Good luck finding the AI hallucinations at the equivalent level before some disaster occurs. Likewise, how do you ensure the human excel operator doesn't get pressured into certifying the AIs numbers when the "don't get fired this week" button is sitting right their in their excel app? how do you avoid the race to the bottom where the "star" employee is the one certifying the AI results without thorough review?
I'm bullish on AI in backoffice, but ignoring the real difficulties in deployment doesn't help us get there.
> So, then - why don't people embrace AI with thinking mode as an acceptable form of automation
Mainly because Generative AI _is not automation_ . Automation is set on fixed ruleset, predictable, reliable and actually saving time. Generative AI ...is whatever it is, it is definitely not automation.
The promise of AI is that it lets you "skip the drudgery of thinking about the details" but sometimes that is exactly what you don't want. You want one or more humans with experience in the business domain to demonstrate they have thought about the details very carefully. The spreadsheet computes a result but its higher purpose is a kind of "proof" this thinking was done.
If the actual thinking doesn't matter and you just need some plausible numbers that look the part (also a common situation), gen ai will do that pretty well.
We need to stop using AI as an umbrella term. It’s worth remembering that LLMs can’t play chess and that the best chess models like Leela Chess Zero use deep neutral networks.
Generative AI - which the world now believes is AI, is not the same as predictive / analytical AI.
It’s fairly easy to demonstrate this by getting ChatGPT to generate a new relatively complex spreadsheet then asking it to analyze and make changes to the same spreadsheet.
The problem we have now is uninformed people believing AI is the answer to everything… if not today then in the near future. Which makes it more of a religion than a technology.
Which may be the whole goal …
> Successful people create companies. More successful people create countries. The most successful people create religions.
Ok yep, fair. My comment was about using copilot-ish tech to generate plausible looking spreadsheets.
The kind of things that a domain expert Brenda knows that ChatGPT doesn't know (yet) are like:
There are 3 vendors a, b, c who all look similar on paper but vendor c always tacks on weird extra charges that take a lot of angry phone calls to sort out.
By volume or weight it looks like you could get 100 boxes per truck but for industry specific reasons only 80 can legally be loaded.
Hyper specific details about real estate compliance in neighbouring areas that mean buildings that look similar on paper are in fact very different.
A good Brenda can understand the world around her as it actually is, she is a player in it and knows the "real" rules rather than operating from general understanding and what people have bothered to write down.
By the same fascination, do computers become more complex to enhance people? or do people get more complex with the use of computers? Also, do computers allow people to become less skilled and inefficient? or do less skilled and inefficient people require the need for computers?
The vector of change is acceptable in one direction and disliked in another. People become greater versions of themselves with new tech. But people also get dumber and less involved because of new tech.
The reason is oftentimes fairly simple, certain people have their material wealth and income threatened by such automation, and therefore it's bad (an intellectualized reason is created post-hoc)
I predict there will actually be a lot of work to be done on the "software engineering" side w.r.t. improving reliability and safety as you allude to, for handing off to less than sentient bots. Improved snapshot, commit, undo, quorum, functionalities, this sort of thing.
The idea that the AI should step into our programs without changing the programs whatsoever around the AI is a horseless carriage.
Co-pilot and AI has been shoved at the Microsoft Stack in my org for months. Most of the features were disabled or hopelessly bad. It’s cheaper for Microsoft to push this junk and claim they’re doing something, it’s going to improve their stock far more than not doing it, even though it’s basically useless currently.
Another issue is that my org disallows AI transcription bots. It’s a legit security risk if you have some random process recording confidential info because the person was too busy to attend the meeting and take notes themselves. Or possibly they just shirk off the meetings and have AI sit in.
Still find the Copilot transcripts orders of magnitude worse than something like Wispr Flow and they tend to allucinate constantly and do not adapt to a company's context (that Copilot has access too...). I am talking about acronyms of products / teams, names of people (even when they are in the call), etc.
Can anyone familiar with the technical details shed light on why this is so.
Is it because of a globally trained model (as opposed to trained[tweaked on] on context specific data) or because of using different classes of models.
Neither copilot nor flow can natively handle audio to my understanding, so there is already a transcription model converting it to text that then GPT tries to summarise.
It could be they simply use a mediocre transcription model. Wispr is amazing but would hurt their pride to use a competitor.
But i feel it's more likley the experience is; GPT didn't actually improve on the raw transcription, just made it worse. Especially as any miss-transcipted words may trip it up and make it misunderstand while making the summary.
if i can choose between a potentially confused and misunderstood summary, and a badly spellchecked (flipped words) raw transcription, i would trust the latter.
“There are two Brendas - their job is to make spreadsheets in the Finance department. Well, not quite - they add the months and categories to empty spreadsheets, then they ask the other departments to fill in their sales numbers every month so it can be presented to management.
“The two Brendas don’t seem to talk, otherwise they would realize that they’re both asking everyone for the same information, twice. And they’re so focused on their little spreadsheet worlds that neither sees enough of the bigger picture to say, ‘Wait… couldn’t we just automate this so we don’t need to do this song and dance every month? Then we wouldn’t need two people in different parts of the company compiling the same data manually.’
“But that’s not what Brenda was hired for. She’s a spreadsheet person, not a process fixer. She just makes the spreadsheets.”
We need fewer Brendas, and more people who can automate away the need for them.
With respect, you probably only see that bit of Finance, but doesn't mean that is all Brenda does.
At least half of the work in my senior Finance team involves meeting people in operations to find out what they are planning to do and to analyse the effects, and present them to decision makers to help them understand the consequences of decisions. For an AI to help, someone would have to trigger those conversations in the first place and ask the right questions.
The rest of the work involves tidying up all the exceptions that the automation failed on.
Meanwhile copilot in Excel can't even edit the sheet you are working on. If you say to it, 'give me a template for an expense claim' it will give you a sheet to download... probably with #REF written in where the answers should be.
I work in corporate finance and these issues are certainly present. However, they are almost always known and determined low priority to have a better process built. Finance processes are nearly always a non priority as a pure cost center/overhead there’s not many companies that want to invest in improving the situation, they’ll limp along with minimal investment even once big and profitable.
That said, every finance function is different and it may be unknown to them that you’re being asked for some data multiple times. If you’re enduring this process, I’m of the opinion you’re equally at fault. Suggest a solution that will be easier on you. As it’s possible they don’t even know it’s happening. In the case provided, email to all relevant finance people “Here’s a link to a shared workbook. I’ll drop the numbers here monthly, please save the link and get the data directly from that file. Thanks!” Problem solved. Until you don’t follow through which is what causes most finance people to be constantly asking for data/things. So be kind and also set yourself a monthly recurring reminder on your calendar and actually follow through.
And they've all been burned by enterprise finance products which were sold to solve exactly that problem.
Only different companies were all sold different enterprise finance products, but they need to communicate with each other (or themselves after mergers), so it all gets manually copied into Excel and emailed around each month.
Also an acceptable solution. This is usually where the next step is have a BI type person just create a report for finance. Many reasons but what will end up is different people are filtering/retrieving the data differently causing inconsistencies.
But Usually finance is always preferring on demand access so the communication feedback loop of asking for stuff is not well liked so I’m sure they appreciate this middle step too.
There are many cases where there’s no easy way to give access to the data and a human in the loop is required. In that case, do the shared workbook thing I mentioned as a starting point at least. It may evolve from there.
And then you end up with a team of five people each tree times as expensive as Brenda, and what used to be an email now takes a sprint and has to go through ticket system.
Then you end up with a report that goes out automatically every month to leadership pulled directly from the Salesforce data, along with a real time dashboard anyone in the org can look at, broken down by team, vertical, and sales volume.
We need more Brendas (those who excel goddesses come and kiss on the forehead) and need less people who are disrespectful of Brendas. The example in this post is someone giving more respect to AI than Brenda.
People’s reaction to this varies based on the Brendas they’ve worked with. Some are given a specific task to do with their spreadsheets every week and have to just do as they are told even if they can see it’s not a good process. Others are secretly the brains of the company – the only one who really sees the whole picture. And a good number of Brendas are the company owner doing her best with the only tool she’s had the time to learn.
But then you need someone to maintain/look after that automation, and they'll be more expensive than two Brendas
And now if one of the Brendas wants to change their process slightly, add some more info, they can't just do it anymore. They have to have a three way discussion with the other Brenda, the automation guy and maybe a few managers. It will take months. So then its likely better for Brenda to just go back to using her spreadsheet again, and then you've got an automated process that no longer meets peoples needs and will be a faff to update.
No, I’m suggesting that she is ineffective exactly because she stays in her box.
She should replaced with someone who says, “this box doesn’t need to be here… there is a better way of doing things.”
NOT to be confused with the junior engineer who comes into a project and says it’s garbage and suggests we rewrite it from scratch in ${hotLanguage} because they saw it on a blog somewhere.
That's a pretty specific example when there are a lot of good "spreadsheet people" out there who do a lot more than spreadsheets (maybe they had to write SQL queries or scripts to get those numbers), but commonly need to simplify things down to a spreadsheet or power point for upper management. I'm not saying you should have multiple people doing redundant work, but this style isn't entirely dumb.
What would this be replaced by? Some kind of large SAP like system that costs millions of dollars and requires a dozen IT staff to maintain?
Fair - I was creating a straw man mostly to make a point. The people I’m thinking aren’t running SQL queries or scripts, they’re merely collection points for data.
So one good BI developer who knows Tableau and Salesforce and Excel and SQL can replace those pure collection points with a better process, but they can also generate insight into the data because they have some business understanding from being close to the teams, which is what my hypothetical Brenda can’t do.
In my example, Brenda would be asking sales leaders to enter in their data instead of going into Salesforce herself because she doesn’t know that tool / side of the company well enough.
I was making the point that, contrary to the article, the Brendas I know aren’t touched by the Excel angels, they’re just maintaining spreadsheets that we probably shouldn’t have anyway.
I think that is a fair point too. The person that builds the Tableau dashboard could just send Brenda a screenshot once a month and that saves everyone time.
Y'know why people don't automate their jobs? It's not a skill issue it's an incentives issue.
If you do your job, you get paid periodically. If you automate your job, you get paid once for automating it and then nothing, despite your automation constantly producing value for the company.
To fix this, we need to pay people continually for their past work as long as it keeps producing value.
Yes, but notice what you are describing are all negative incentives.
When automation produces value for the company, the people automating it should capture a chunk of that value _as a matter of course_.
Even if you argue that you can then negotiate better compensation:
1) That is uncertain and delayed reward - and only if other people feel like it, it's not automatic.
2) The reward stops if you get fired or leave, despite the automation still producing value - you are also basically incentivized to build stuff that requires constant maintenance. Imagine you spend a man-month building the automation and then leave, it then requires a man-month of maintenance over the next 5 years. At the end of the 5 years, you should still be getting 50% of the reward.
That mirrors my experience as well. LLMs get instantly confused in real world scenarios in Excel and confidently hallucinate millions in errors
If you look at the demos for these it’s always something that is clean and abundantly available in training data. Like an income statement. Or a textbook example DCF. Or my personal fav „here is some data show me insights“. Real world excel use looks nothing like that.
I’m getting some utility out of them for some corporate tasks but zilch in excel space.
This reminds me of a friend whose company ran a daily perl script that committed every financial transaction of the day to a database. Without the script, the company could literally make no money irrespectively of sales because this database was one piece in a complex system for payment processor interoperability.
The script ran in a machine located at the corner of a cubicle and only one employee had the admin password. Nobody but a handful of people knew of the machine's existence, certainly not anyone in middle management and above. The script could only be updated by an admin.
Copilot may be good, but sure as hell doesn't know that admin password.
Everywhere I’ve ever worked has had that mission critical box.
At one of my jobs we had a server rack with UPS, etc, all the usual business. On the floor next to it was a dell desktop with a piece of paper on it that said “do not turn off”. It had our source control server in it, and the power button didn’t work. We did eventually move it to something more sensible but we had that for a long time
An old colleague and friend used to print out a 30 page perl script he wrote to do almost exactly this in this scenario. A stapled copy could always be found on his dining room table.
That sounds pretty bad. Not a great argument against AI: "Our employees have created such a bad mess that AI wont work because only they know how the mess they created works".
Well if you do it once then yes, but if you automate this process it is different. E.g. I do this with YouTube videos, because watching 14 minutes video or reading 30 seconds summary is time saver. I still watch some videos fully, but many of them are not worth it.
So in summary I think it was just part of automated process (maybe) or it will become one in the future.
Why spend two minutes typing (and realistically longer than that, if I want to capture the exact transcript I would need to keep hitting pause and play and correcting myself) when I can spend ten seconds pasting a URL into my terminal and then dragging and dropping the resulting file onto the MacWhisper window?
I actually transcribed the whole TikTok which was about 50% longer than what I quoted, then edited it down to the best illustrative quote.
There's also a free version that just uses Whisper. I recommend giving it a go, it's a very well constructed GUI wrapper. I use it multiple times a week, and I've run Whisper on my machine in other less convenient ways in the past.
Coding agents are useful and good and real products because when they screw up, things stop working almost always before they can do damage. Coding agents are flawed in ways that existing tools are good at catching, never mind the more obvious build and runtime errors.
Letting AI write your emails and create your P&L and cash flow projections doesn't have to run the gauntlet of tools that were created to stop flawed humans from creating bad code.
Brenda has been getting slower over the years -as we all have-, but soon the boss will learn that it was a small price to pay for knowing well how to keep such house of cards from collapsing.
And then the boss will make the decision to outsource her job, to a company that promises the use of AI to make finance better, and faster, and while Brenda is in the unemployment line, someone else thousands of miles away is celebrating a new job
Excel is the “beast that drives the ENTIRE economy” and he’s worried about Brenda from the finance department losing her job because then her boss will get bad financial reports
I suppose the person that wrote that have not ideia Excel is just an app builder where you embed data together with code.
You know that we have excel because computers didn’t understand column names in databases and so data extraction needed to be made by humans. Humans then design those little apps in excel to massage the data.
Well, now an agent can read the boss saying gimme the sales from last month and the agent don’t need excel for that, because it can query the database itself, massage the data itself using python and present the data itself with html or PNGs.
So, we are in the process of automating Brenda AND excel away.
Also, finance departments are a very small part of excel users. Just think everywhere were people need small programs, excel is there.
In most cases where the excel spreadsheet is business critical, the spreadsheet _is_ the database. These companies aren’t using an erp system. They are directly entering inventory and sales numbers in the spreadsheet.
The post is clearly hyperbole obviously the sole issue being brought up isn't 'brenda losing her job may be bad for the company' you're being facetious.
You missed this bit “.. and then the AI is gonna fuck it up real bad and he won't be able to recognize it because he doesn't understand because AI hallucinates.”
The underlying assumption is that Brenda generally does her job pretty well. Human errors exist but usually peers/managers (or the person who did it) can identify and correct them reliably.
If we have to compare LLM’s against people who are bad at their jobs in order to highlight their utility we’re going the wrong direction.
There are a lot of underlying assumptions: Brenda, the woman, is accurate and trustworthy and has mastered an accurate and trustworthy technology; the upper manager, the male, will introduce error by not understanding that the technology he brings to bear on the situation is hallucinatory. The woman is lower in status and pay than the male. The woman is necessary to the functioning of "the economy" and "capitalism," while the man threatens those. There are a lot of unsubtle political undertones on TikTok.
Using ai does not absolve you from the responsibility of doing it correctly. If you use ai, then you better have the skills to have done the job yourself, and so have the ability to check the AI did things correctly.
You can save time still, but perhaps not as much as you think, because you need to check the ai's work thoroughly.
Many fears of “AI mucking it up” could be mitigated with an ability to connect a workbook to a git repository. Not for data, but for VBA, cell formulas, and cell metadata. When you can encapsulate the changes a contributor (in this case co-pilot) makes into a commit, you can more easily understand what changes it/they made.
I agree - having watched many people use Excel over the years, I'd say people often overestimate their skills. I see three categories of Excel users. First there are the people that are intimidated by it and stay away from any task involving Excel. Second are the people that know a little bit (a few basic formulas) and overestimate their skills because they only compare themselves to the first group. And the third group are the actual power users but know to keep that quiet because otherwise they become the "excel person" and have to fix every sheet that has issues.
I don't know if AI is going to make any of the above better or worse. I expect the only group to really use it will be that second group.
I have seen lots and lots of different uses for Excel in my line of work:
- password database
- script to automatically rename jpeg files
- game
- grocery lists
- Book keeping (and try and not get caught for fraud several years, because the monthly spending limit is $5000 and $4999 a month is below that...)
- embed/collect lots of Word documents
- coloring book
- Minecraft processes
- Resume database
- ID scans
At some point, a publicly-listed company will go bankrupt due to some catastrophic AI-induced fuck-up. This is a massive reputational risk for AI platforms, because ego-defensive behaviour guarantees that the people involved will make as much noise as they can about how it's all the AI's fault.
I don't find comments along the lines of 'those people over there are bad' to be interesting, especially when I agree with them. My comment is about why it'll go wrong for them.
I see the inverse of that happening: every critical decision will incorporate AI somehow. If the decision was good, the leadership takes credit. If something terrible happens, blame it on the AI. I think it's the part no one is saying out loud. That AI may not do a damn useful thing, but it can be a free insurance policy or surrogate to throw under the bus when SHTF.
This works at most one time. If you show up to every board meeting and blame AI, you’re going to get fired.
This is true if you blame a bad vendor, or something you don’t even control like the weather. Your job is to deliver. If bad weather is the new norm, you better figure out how to build circus tents so you can do construction in the rain. If your AI call center is failing, you better hire 20 people to answer phones.
This is transparent nonsense. People are very very happy to introduce errors into excel spreadsheets without any help from AI.
Financial statements are correct because of auditors who check the numbers.
If you have a good audit process then errors get detected even if AI helped introduce them. If you aren't doing a good audit then I suspect nobody cares whether your financial statement is correct (anyone who did would insist on an audit).
> If you have a good audit process then errors get detected even if AI helped introduce them. If you aren't doing a good audit then I suspect nobody cares whether your financial statement is correct (anyone who did would insist on an audit).
Volume matters. The single largest problem I run into: AI can generate slop faster than anyone can evaluate it.
I'm actually not that worried about this, because again I would classify this as a problem that already exists. There are already idiots in senior management who pass off bullshit and screw things up. There are natural mechanisms to cope with this, primarily in business reputation - if you're one of those idiots who does this people very quickly start just discounting what you're saying, they might not know how you're wrong, but they learn very quickly to discount what you're saying because they know you can't be trusted to self-check.
I'm not saying that this can't happen and it's not bad. Take a look at nudge theory - the UK government created an entire department and spent enormous amounts of time and money on what they thought was a free lunch - that they could just "nudge" people into doing the things they wanted. So rather than actually solving difficult problems the uk government embarked on decades of pseudo-intellectual self agrandizement. The entire basis of that decades long debacle was based on bullshit data and fake studies. We didn't need AI to fuck it up, we managed it perfectly well by ourselves.
Nudge theory isn't useless, it's just not anything like as powerful as money or regulation.
It was taken up by the UK government at that time because the government was, unusually, a coalition of two quite different parties, and thus found it hard to agree to actually use the normal levers of power.
"the sweat from Brenda's brow is what allows us to do capitalism."
The CEO has been itching to fire this person and nuke her department forever. She hasn't gotten the hint with the low pay or long hours, but now Copilot creates exactly the opening the CEO has been looking for.
Both are valid concerns, no need to decide. Take the USA: They are currently lead by a patently dumb president who fucks up the global economy, and at the same time they are powerful enough to do so!
For a more serious example, consider the Paperclip Problem[0] for a very smart system that destroys the world due to very dumb behaviour.
The paperclip problem is a bit hand-wavey about intelligence. It is taken as a given than unlimited intelligence would automatically win presumably because it could figure out how to do literally anything.
But let's consider real life intelligence:
- Our super geniuses do not take over the world. It is the generationally wealthy who do.
- Super geniuses also have a tendency to be terribly neurotic, if not downright mentally ill. They can have trouble functioning in society.
- There is no thought here about different kinds of intelligence and the roles they play. It is assumed there is only one kind, and AI will have it in the extreme.
To be clear, I don't think the paperclip scenario is a realistic one. The point was that it's fairly easy to conceive an AI system that's simultaneously extremely savant and therefore dangerous in a single domain, yet entirely incapable of grasping the consequences or wider implications of its actions.
None of us knows what an actual, artificial intelligence really looks like. I find it hard to draw conclusions from observing human super geniuses, when their minds may have next to nothing in common with the AI. Entirely different constraints might apply to them—or none at all.
Having said all that, I'm pretty sceptical of an AI takeover doomsday scenario, especially if we're talking about LLMs. They may turn out to be good text generators, but not the road to AGI. But it's very hard to make accurate predictions in either direction.
I find the contrast between two narratives around technology use so fascinating:
1. We advocate automation because people like Brenda are error-prone and machines are perfect.
2. We disavow AI because people like Brenda are perfect and the machine is error-prone.
These aren't contradictions because we only advocate for automation in limited contexts: when the task is understandable, the execution is reliable, the process is observable, and the endeavour tedious. The complexity of the task isn't a factor - it's complex to generate correct machine code, but we trust compilers to do it all the time.
In a nutshell, we seem to be fine with automation if we can have a mental model of what it does and how it does it in a way that saves humans effort.
So, then - why don't people embrace AI with thinking mode as an acceptable form of automation? Can't the C-suite in this case follow its thought process and step in when it messes up?
I think people still find AI repugnant in that case. There's still a sense of "I don't know why you did this and it scares me", despite the debuggability, and it comes from the autonomy without guardrails. People want to be able to stop bad things before they happen, but with AI you often only seem to do so after the fact.
Narrow AI, AI with guardrails, AI with multiple safety redundancies - these don't elicit the same reaction. They seem to be valid, acceptable forms of automation. Perhaps that's what the ecosystem will eventually tend to, hopefully.
It's not as black-and-white as "Brenda good, AI bad". It's much more nuanced than this.
When it comes to (traditional) coding, for the most part, when I program a function to do X, every single time I run that function from now until the heat death of the sun, it will always produce Y. Forever! When it does, we understand why, and when it doesn't, we also can understand why it didn't!
When I use AI to perform X, every single time I run that AI from now until the heat death of the sun it will maybe produce Y. Forever! When it does, we don't understand why, and when it doesn't, we also don't understand why!
We know that Brenda might screw up sometimes but she doesn't run at the speed of light, isn't able to produce a thousand lines of Excel Macro in 3 seconds, doesn't hallucinate (well, let's hope she doesn't), can follow instructions etc. If she does make a mistake, we can find it, fix it, ask her what happened etc. before the damage is too great.
In short: when AI does anything at all, we only have, at best, a rough approximation of why it did it. With Brenda, it only takes a couple of questions to figure it out!
Before anyone says I'm against AI, I love it and am neck-deep in it all day when programming (not vibe-coding!) so I have a full understanding of what I'm getting myself into but I also know its limitations!
No contradiction here:
When we say “machine”, we mean deterministic algorithms and predictable mechanisms.
Generative AI is neither of those things (in theory it is deterministic but not for any practical applications).
If we order by predictability:
Quick Sort > Brenda > Gen AI
I was brought up on the refrain of "aren't computers silly, they do exactly what you tell them to do to the letter, even if it's not what you meant". That had its roots in computers mostly being programmable BASIC machines.
Then came the apps and notifications, and we had to caveat "... when you're writing programs". Which is a diminishing part of the computer experience.
And now we have to append "... unless you're using AI tools".
The distinction is clear to technical people. But it seems like an increasingly niche and alien thing from the broader societal perspective.
I think we need a new refrain, because with the AI stuff it increasingly seems "computers do what they want, don't even get it right, but pretend that they did."
There are two kinds of reliability:
Machine reliability does the same thing the same way every time. If there's an error on some input, it will always make that error on that input, and somebody can investigate it and fix it, and then it will never make that error again.
Human reliability does the job even when there are weird variances or things nobody bothered to check for. If the printer runs out of paper, the human goes to the supply cabinet and gets out paper and if there is no paper the human decides whether to run out right now and buy more paper or postpone the print job until tomorrow; possibly they decide that the printing doesn't need to be done at all, or they go downstairs and use a different printer... Humans make errors but they fix them.
LLMs are not machine reliable and not human reliable.
If you think programs are predictable, I have a bridge to sell you.
The only relevant metric here is how often each thing makes mistakes. Programs are the most reliable, though far from 100%, humans are much less than that, and LLMs are around the level of humans, depending on the humans and the LLM.
> If we order by predictability:
> Quick Sort > Brenda > Gen AI
Those last two might be the wrong way round.
I feel like it comes down to predictability and overall trust and confidence. AI is still very fucky, and for people that don't understand the nuances, it definitely will hallucinate and potentially cause real issues. It is about as happy as a Linux rm command to nuke hours of work. Fortunately these tools typically have a change log you can undo, but still.
Also Brenda is human and we should prioritize keeping humans in jobs, but with the way shit is going that seems like a lost hope. It's already over.
"Thinking mode" only provides the illusion of debuggability. It improves performance by generating more tokens which hopefully steer the context towards one more likely to produce the desired response, but the tokens it generates do not reflect any sort of internal state or "reasoning chain" as we understand it in human cognition. They are still just stochastic spew. You have no more insight into why the model generates the particular "reasoning steps" it does than you do into any other output, and neither do you have insight into why the reasoning steps lead to whatever conclusion it comes to. The model is much less constrained by the "reasoning" than we would intuit for a human - it's entirely capable of generating an elaborate and plausible reasoning chain which it then completely ignores in favor of some invisible built-in bias.
> We disavow AI because people like Brenda are perfect and the machine is error-prone.
No, no. We disavow AI because our great leaders inexplicably trust it more than Brenda.
I don't understand why generative AI gets a pass at constantly being wrong, but an average worker would be fired if they performed the same way. If a manager needed to constantly correct you or double check your work, you'd be out. Why are we lowering the bar for generative AI?
How much compute costs is it for the AI to do Brenda's job? Not total AI spend, but the fraction that replaced Brenda. That's why they'd fire a human but keep using the AI.
There's a variety of reasons.
You don't have a human to manage. The relationship is completely one-sided, you can query a generative AI at 3 in the morning on new years eve. This entity has no emotions to manage and no own interests.
There's cost.
There's an implicit promise of improvement over time.
There's an the domain of expertise being inhumanly wide. You can ask about cookies right now, then about XII century France, then about biochemistry.
The fact that an average worker would be fired if they perform the same way is what the human actually competes with. They have responsibility, which is not something AI can offer. If it was the case that, say, Anthropic, actually signed contracts stating that they are liable for any mistakes, then humans would be absolutely toast.
If a worker could be right 50% of the time and get paid 1 cent to write a 5000 word essay on a random topic, and do it in less than 30 seconds.
Then I think managers would be fine hiring that worker for that rate as well.
5000 half-right words is worthless output. That can even lead to negative productivity.
It’s much cheaper than Brenda (superficially, at least). I’m not sure a worker that costs a few dollars a day would be fired, especially given the occasional brilliance they exhibit.
Because it doesn’t have to be as accurate as a human to be a helpful tool.
That is precisely why we have humans in the loop for so many AI applications.
If [AI + human reviewer to correct it] is some multiple more efficient than [human alone], there is still plenty of value.
Because it's much cheaper.
So now you don't have to pay people to do their actual work, you assign the work to ML ("AI") and then pay the people to check what it generated. That's a very different task, menial and boring, but if it produces more value for the same amount of input money, then it's economical to do so.
And since checking the output is often a lower skilled job, you can even pay the people less, pocketing more as an owner.
It’s not even greater trust. It’s just passive trust. The thing is, Brenda is her own QA department. Every good Brenda is precisely good because she checks her own work before shipping it. AI does not do this. It doesn’t even fully understand the problem/question sometimes yet provides a smart definitive sounding answer. It’s like the doctor on The Simpson’s, if you can’t tell he’s a quack, you probably would follow his medical advice.
Brenda + AI > Brenda
That’s definitely the hype. But I don’t know if I agree. I’m essentially a Brenda in my corporate finance job and so far have struggled to find any useful scenarios to use AI for.
I thought once this can build me a Gantt chart because that’s an annoying task in excel. I had the data. When I asked it to help me, “I can’t do that but I can summarize your data”. Not helpful.
Any type of analysis is exactly what I don’t want to trust it with. But I could use help actually building things, which it wouldn’t do.
Also, Brenda’s are usually fast. Having them use a tool like AI that can’t be fully trusted just slows them down. So IMO, we haven’t proven the AI variable in your equation is actually a positive value.
“Let’s deploy something as or more error prone as Brad at infinite scale across out organisation”
Humans, legacy algorithmic systems, and LLM's have different error modes.
- Legacy systems typically have error modes where integrations or user interface breaks in annoying but obvious ways. Pure algorithms calculating things like payroll tend to be (relatively) rigorously developed and are highly deterministic.
- LLMs have error modes more similar to humans than legacy systems, but more limited. They're non-deterministic, make up answers sometimes, and almost never admit they can't do something; sometimes they make pure errors in arithmetic or logic too.
- Humans have even more unpredictable error modes; on top of the errors encountered in LLM's, they also have emotion, fatigue, org politics, demotivation, misaligned incentives, and so on. But because we've been dealing with working with other humans for ten thousand years we've gotten fairly good at managing each other... but it's still challenging.
LLMs probably need a mixture of "correctness tests" (like evals/unit tests) and "management" (human-in-the-loop).
In my opinion there's a big difference in deterministic and nondeterministic automation.
That automation you cite in your #1 is advocated for because it is deterministic and, with effort, fairly well understood (I have countless scripts solidly running for years).
I don't disavow AI, but like the author, I am not thrilled that the masses of excel users suddenly have access to Copilot (gpt4). I've used Copilot enough now to know that there will be huge, costly mistakes.
> So, then - why don't people embrace AI with thinking mode as an acceptable form of automation?
"Thinking" mode is not thinking, it's generating additional text that looks like someone talking to themselves. It is as devoid of intention and prone to hallucinations as the rest of LLM's output.
> Can't the C-suite in this case follow its thought process and step in when it messes up?
That sounds like manual work you'd want to delegate, not automation.
The issue is reliability.
would you be willing to guarantee that some automation process will never mess up, and if/when it does, compensate the user with cash.
For a compiler, with a given set of test suites, the answer is generally yes, and you could probably find someone willing to insure you for a significant amount of money, that a compilation bug will not screw up in a such a large way that it will affect your business.
For a LLM, I have a believing that anyone will be willing to provide that same level of insurance.
If a LLM company said "hey use our product, it works 100% of the time, and if it does fuck up, we will pay up to a million dollars in losses" I bet a lot of people would be willing to use it. I do not believe any sane company will make that guarantee at this point, outside of extremely narrow cases with lots of guardrails.
That's why a lot of ai tools are consumer/dev tools, because if they fuck up, (which they will) the losses are minimal.
The “Brenda” example is a lumped sum fallacy where there is an “average” person or phenomenon that we can benchmark against. Such a person doesn't exist, leading to these dissonant, contradictory dichotomies.
The fact of the matter is that there are some people who can hold lots of information in their head at once. Others are good at finding information. Others still are proficient at getting people to help them. Etc. Any of these people could be tasked with solving the same problem and they would leverage their actual, particular strengths rather than some nebulous “is good or bad at the task” metric.
As it happens, nearly all the discourse uses this lumped sum fallacy, leading to people simultaneously talking past one another while not fundamentally moving the discussion forward.
I see where you are coming from but in my head, Brenda isn't real.
She represents the typical domain-experts that use Excel imo. They have an understanding of some part of the business and express it while using Excel in a deterministic way: enter a value of X, multiply it by Y and it keeps producing Z forever!
You can train AI to be a better domain expert. That's not in question, however with AI, you introduce a dice roll: it may not miltiply X and Y to get Z... it might get something else. Sometimes. Maybe.
If your spreadsheet is a list of names going on the next annual accounts department outing then the risk is minimal.
If it's your annual accounts that the stock market needs to work out billion dollar investment portfolios, then you are asking for all the pain that it will likely bring.
The big problem with AI in back-office automation is that it will randomly decide to do something different than it had been doing. Meaning that it could be happily crunching numbers accurately in your development and launch experience, then utterly drop the ball after a month in production.
While humans have the same risk factors, human oriented back-office processes involve multiple rounds of automated/manual checks which are extremely laborious. Human errors in spreadsheets have particular flavors such as forgotten cell, misstyped number, or reading from the wrong file/column. Human's are pretty good at catching these errors as they produce either completely wrong results when the columns don't line up - or the typo'd number is completely out of distribution.
An AI may simply decide to hallucinate realistic column values rather than extracting its assigned input. Or hallucinate a fraction of column values. How do you QA this? You can't guarantee that two invocations of the AI won't hallucinate the same values, you can't guarantee that a different LLM won't hallucinate different values. To get a real human check, you'd need to re-do the task as a human. In theory you can have the LLM perform some symbolic manipulation to improve accuracy... but it can still hallucinate the reasoning traces etc.
If a human decided to make up accounting numbers one out of every 10000 accounting requests they would likely be charged with fraud. Good luck finding the AI hallucinations at the equivalent level before some disaster occurs. Likewise, how do you ensure the human excel operator doesn't get pressured into certifying the AIs numbers when the "don't get fired this week" button is sitting right their in their excel app? how do you avoid the race to the bottom where the "star" employee is the one certifying the AI results without thorough review?
I'm bullish on AI in backoffice, but ignoring the real difficulties in deployment doesn't help us get there.
> So, then - why don't people embrace AI with thinking mode as an acceptable form of automation
Mainly because Generative AI _is not automation_ . Automation is set on fixed ruleset, predictable, reliable and actually saving time. Generative AI ...is whatever it is, it is definitely not automation.
The promise of AI is that it lets you "skip the drudgery of thinking about the details" but sometimes that is exactly what you don't want. You want one or more humans with experience in the business domain to demonstrate they have thought about the details very carefully. The spreadsheet computes a result but its higher purpose is a kind of "proof" this thinking was done.
If the actual thinking doesn't matter and you just need some plausible numbers that look the part (also a common situation), gen ai will do that pretty well.
We need to stop using AI as an umbrella term. It’s worth remembering that LLMs can’t play chess and that the best chess models like Leela Chess Zero use deep neutral networks.
Generative AI - which the world now believes is AI, is not the same as predictive / analytical AI.
It’s fairly easy to demonstrate this by getting ChatGPT to generate a new relatively complex spreadsheet then asking it to analyze and make changes to the same spreadsheet.
The problem we have now is uninformed people believing AI is the answer to everything… if not today then in the near future. Which makes it more of a religion than a technology.
Which may be the whole goal …
> Successful people create companies. More successful people create countries. The most successful people create religions.
— Sam Altman - https://blog.samaltman.com/successful-people
Ok yep, fair. My comment was about using copilot-ish tech to generate plausible looking spreadsheets.
The kind of things that a domain expert Brenda knows that ChatGPT doesn't know (yet) are like:
There are 3 vendors a, b, c who all look similar on paper but vendor c always tacks on weird extra charges that take a lot of angry phone calls to sort out.
By volume or weight it looks like you could get 100 boxes per truck but for industry specific reasons only 80 can legally be loaded.
Hyper specific details about real estate compliance in neighbouring areas that mean buildings that look similar on paper are in fact very different.
A good Brenda can understand the world around her as it actually is, she is a player in it and knows the "real" rules rather than operating from general understanding and what people have bothered to write down.
This misunderstands complexity entirely:
The complexity of the task isn't a factor - it's complex to generate correct machine code, but we trust compilers to do it all the time.
By the same fascination, do computers become more complex to enhance people? or do people get more complex with the use of computers? Also, do computers allow people to become less skilled and inefficient? or do less skilled and inefficient people require the need for computers?
The vector of change is acceptable in one direction and disliked in another. People become greater versions of themselves with new tech. But people also get dumber and less involved because of new tech.
The reason is oftentimes fairly simple, certain people have their material wealth and income threatened by such automation, and therefore it's bad (an intellectualized reason is created post-hoc)
I predict there will actually be a lot of work to be done on the "software engineering" side w.r.t. improving reliability and safety as you allude to, for handing off to less than sentient bots. Improved snapshot, commit, undo, quorum, functionalities, this sort of thing.
The idea that the AI should step into our programs without changing the programs whatsoever around the AI is a horseless carriage.
Co-pilot and AI has been shoved at the Microsoft Stack in my org for months. Most of the features were disabled or hopelessly bad. It’s cheaper for Microsoft to push this junk and claim they’re doing something, it’s going to improve their stock far more than not doing it, even though it’s basically useless currently.
Another issue is that my org disallows AI transcription bots. It’s a legit security risk if you have some random process recording confidential info because the person was too busy to attend the meeting and take notes themselves. Or possibly they just shirk off the meetings and have AI sit in.
Transcription is arguably one of the must useful enterprise AI tools avaliable. But i sure as heck wouldn't trust the cloud with it.
Still find the Copilot transcripts orders of magnitude worse than something like Wispr Flow and they tend to allucinate constantly and do not adapt to a company's context (that Copilot has access too...). I am talking about acronyms of products / teams, names of people (even when they are in the call), etc.
Can anyone familiar with the technical details shed light on why this is so.
Is it because of a globally trained model (as opposed to trained[tweaked on] on context specific data) or because of using different classes of models.
Neither copilot nor flow can natively handle audio to my understanding, so there is already a transcription model converting it to text that then GPT tries to summarise.
It could be they simply use a mediocre transcription model. Wispr is amazing but would hurt their pride to use a competitor.
But i feel it's more likley the experience is; GPT didn't actually improve on the raw transcription, just made it worse. Especially as any miss-transcipted words may trip it up and make it misunderstand while making the summary.
if i can choose between a potentially confused and misunderstood summary, and a badly spellchecked (flipped words) raw transcription, i would trust the latter.
Ye i didn't even think about advanced meetings summary bots. Just raw word for word transcription please. Wispr is pretty great.
Hmmm the Brendas I know look a little different.
“There are two Brendas - their job is to make spreadsheets in the Finance department. Well, not quite - they add the months and categories to empty spreadsheets, then they ask the other departments to fill in their sales numbers every month so it can be presented to management.
“The two Brendas don’t seem to talk, otherwise they would realize that they’re both asking everyone for the same information, twice. And they’re so focused on their little spreadsheet worlds that neither sees enough of the bigger picture to say, ‘Wait… couldn’t we just automate this so we don’t need to do this song and dance every month? Then we wouldn’t need two people in different parts of the company compiling the same data manually.’
“But that’s not what Brenda was hired for. She’s a spreadsheet person, not a process fixer. She just makes the spreadsheets.”
We need fewer Brendas, and more people who can automate away the need for them.
With respect, you probably only see that bit of Finance, but doesn't mean that is all Brenda does.
At least half of the work in my senior Finance team involves meeting people in operations to find out what they are planning to do and to analyse the effects, and present them to decision makers to help them understand the consequences of decisions. For an AI to help, someone would have to trigger those conversations in the first place and ask the right questions.
The rest of the work involves tidying up all the exceptions that the automation failed on.
Meanwhile copilot in Excel can't even edit the sheet you are working on. If you say to it, 'give me a template for an expense claim' it will give you a sheet to download... probably with #REF written in where the answers should be.
I work in corporate finance and these issues are certainly present. However, they are almost always known and determined low priority to have a better process built. Finance processes are nearly always a non priority as a pure cost center/overhead there’s not many companies that want to invest in improving the situation, they’ll limp along with minimal investment even once big and profitable.
That said, every finance function is different and it may be unknown to them that you’re being asked for some data multiple times. If you’re enduring this process, I’m of the opinion you’re equally at fault. Suggest a solution that will be easier on you. As it’s possible they don’t even know it’s happening. In the case provided, email to all relevant finance people “Here’s a link to a shared workbook. I’ll drop the numbers here monthly, please save the link and get the data directly from that file. Thanks!” Problem solved. Until you don’t follow through which is what causes most finance people to be constantly asking for data/things. So be kind and also set yourself a monthly recurring reminder on your calendar and actually follow through.
And they've all been burned by enterprise finance products which were sold to solve exactly that problem.
Only different companies were all sold different enterprise finance products, but they need to communicate with each other (or themselves after mergers), so it all gets manually copied into Excel and emailed around each month.
I’ve just set the finance people up with read only access to our data source, and they now can poke through it themselves.
Also an acceptable solution. This is usually where the next step is have a BI type person just create a report for finance. Many reasons but what will end up is different people are filtering/retrieving the data differently causing inconsistencies.
But Usually finance is always preferring on demand access so the communication feedback loop of asking for stuff is not well liked so I’m sure they appreciate this middle step too.
There are many cases where there’s no easy way to give access to the data and a human in the loop is required. In that case, do the shared workbook thing I mentioned as a starting point at least. It may evolve from there.
And then you end up with a team of five people each tree times as expensive as Brenda, and what used to be an email now takes a sprint and has to go through ticket system.
That’s not what I had in mind.
Then you end up with a report that goes out automatically every month to leadership pulled directly from the Salesforce data, along with a real time dashboard anyone in the org can look at, broken down by team, vertical, and sales volume.
Why are people so attached to manual process?
Because when one exec ask: "Why is that?" the room goes silent.
> We need fewer Brendas...
We need more Brendas (those who excel goddesses come and kiss on the forehead) and need less people who are disrespectful of Brendas. The example in this post is someone giving more respect to AI than Brenda.
People’s reaction to this varies based on the Brendas they’ve worked with. Some are given a specific task to do with their spreadsheets every week and have to just do as they are told even if they can see it’s not a good process. Others are secretly the brains of the company – the only one who really sees the whole picture. And a good number of Brendas are the company owner doing her best with the only tool she’s had the time to learn.
But then you need someone to maintain/look after that automation, and they'll be more expensive than two Brendas
And now if one of the Brendas wants to change their process slightly, add some more info, they can't just do it anymore. They have to have a three way discussion with the other Brenda, the automation guy and maybe a few managers. It will take months. So then its likely better for Brenda to just go back to using her spreadsheet again, and then you've got an automated process that no longer meets peoples needs and will be a faff to update.
> But that’s not what Brenda was hired for.
Are you suggesting that Brenda should stay in her box?
No, I’m suggesting that she is ineffective exactly because she stays in her box.
She should replaced with someone who says, “this box doesn’t need to be here… there is a better way of doing things.”
NOT to be confused with the junior engineer who comes into a project and says it’s garbage and suggests we rewrite it from scratch in ${hotLanguage} because they saw it on a blog somewhere.
"We need fewer Brendas, and more people who can automate away the need for them."
True... I have an on-staff data engineer for the purpose. But not all companies (especially in the SMB space) have that luxury.
That's a pretty specific example when there are a lot of good "spreadsheet people" out there who do a lot more than spreadsheets (maybe they had to write SQL queries or scripts to get those numbers), but commonly need to simplify things down to a spreadsheet or power point for upper management. I'm not saying you should have multiple people doing redundant work, but this style isn't entirely dumb.
What would this be replaced by? Some kind of large SAP like system that costs millions of dollars and requires a dozen IT staff to maintain?
Fair - I was creating a straw man mostly to make a point. The people I’m thinking aren’t running SQL queries or scripts, they’re merely collection points for data.
So one good BI developer who knows Tableau and Salesforce and Excel and SQL can replace those pure collection points with a better process, but they can also generate insight into the data because they have some business understanding from being close to the teams, which is what my hypothetical Brenda can’t do.
In my example, Brenda would be asking sales leaders to enter in their data instead of going into Salesforce herself because she doesn’t know that tool / side of the company well enough.
I was making the point that, contrary to the article, the Brendas I know aren’t touched by the Excel angels, they’re just maintaining spreadsheets that we probably shouldn’t have anyway.
I think that is a fair point too. The person that builds the Tableau dashboard could just send Brenda a screenshot once a month and that saves everyone time.
Y'know why people don't automate their jobs? It's not a skill issue it's an incentives issue.
If you do your job, you get paid periodically. If you automate your job, you get paid once for automating it and then nothing, despite your automation constantly producing value for the company.
To fix this, we need to pay people continually for their past work as long as it keeps producing value.
Not always:
If you don’t automate it:
1a) your company keeps you hanging on forever maintaining the same widget until the end of time
OR
1b) more likely, someone realizes your job should be automated and lays you off at some point down the road
If you do automate it
2a) your company thanks you then fires you
OR
2b) you are now assigned to automate more stuff as you’ve proven that you are more valuable to the company than just maintaining your widget
————
2b is really the safest long term position for any employee, I think. It’s not always foolproof, as 2a can happen.
But I’d rather be in box 2 than box 1 any day of the week if we’re talking long term employment potential.
Yes, but notice what you are describing are all negative incentives.
When automation produces value for the company, the people automating it should capture a chunk of that value _as a matter of course_.
Even if you argue that you can then negotiate better compensation:
1) That is uncertain and delayed reward - and only if other people feel like it, it's not automatic.
2) The reward stops if you get fired or leave, despite the automation still producing value - you are also basically incentivized to build stuff that requires constant maintenance. Imagine you spend a man-month building the automation and then leave, it then requires a man-month of maintenance over the next 5 years. At the end of the 5 years, you should still be getting 50% of the reward.
Not every topic on HN needs a contrarian's hot take.
That mirrors my experience as well. LLMs get instantly confused in real world scenarios in Excel and confidently hallucinate millions in errors
If you look at the demos for these it’s always something that is clean and abundantly available in training data. Like an income statement. Or a textbook example DCF. Or my personal fav „here is some data show me insights“. Real world excel use looks nothing like that.
I’m getting some utility out of them for some corporate tasks but zilch in excel space.
This reminds me of a friend whose company ran a daily perl script that committed every financial transaction of the day to a database. Without the script, the company could literally make no money irrespectively of sales because this database was one piece in a complex system for payment processor interoperability.
The script ran in a machine located at the corner of a cubicle and only one employee had the admin password. Nobody but a handful of people knew of the machine's existence, certainly not anyone in middle management and above. The script could only be updated by an admin.
Copilot may be good, but sure as hell doesn't know that admin password.
If your mission critical process sits on some on-site box that no-one knows about, copilot being good or not is the least of your problems.
Everywhere I’ve ever worked has had that mission critical box.
At one of my jobs we had a server rack with UPS, etc, all the usual business. On the floor next to it was a dell desktop with a piece of paper on it that said “do not turn off”. It had our source control server in it, and the power button didn’t work. We did eventually move it to something more sensible but we had that for a long time
with only one person on earth being able to access it? so if that person is hit by a car everything goes down?
An old colleague and friend used to print out a 30 page perl script he wrote to do almost exactly this in this scenario. A stapled copy could always be found on his dining room table.
Was the printed copy a backup system or casual reading?
Yes.
<3 inclusive or.
That sounds pretty bad. Not a great argument against AI: "Our employees have created such a bad mess that AI wont work because only they know how the mess they created works".
That is the luxury of theory.
Yes, most situations are terrible compared to what would be if an expert was present to perfect it.
Except if there isn’t an expert, and there’s a normal person, how do they know the output is right ?
not sure I get your point?
This sort of gimmick is not going to help anyone keeping their job.
Sadly, nah. It works.
This quote is pulled from a TikTok, I recommend watching the whole thing here: https://www.tiktok.com/@belligerentbarbies/video/75683800086...
(I pulled the quote by using yt-dlp to grab the MP4 and then running that through MacWhisper to generate a transcript.)
It's a little over two paragraphs. Seems like it would have been simpler just to... type it out?
Well if you do it once then yes, but if you automate this process it is different. E.g. I do this with YouTube videos, because watching 14 minutes video or reading 30 seconds summary is time saver. I still watch some videos fully, but many of them are not worth it.
So in summary I think it was just part of automated process (maybe) or it will become one in the future.
Why spend two minutes typing (and realistically longer than that, if I want to capture the exact transcript I would need to keep hitting pause and play and correcting myself) when I can spend ten seconds pasting a URL into my terminal and then dragging and dropping the resulting file onto the MacWhisper window?
I actually transcribed the whole TikTok which was about 50% longer than what I quoted, then edited it down to the best illustrative quote.
Where's the fun in that? :D
We choose to automate these things, not because they are easy, but because they are an interesting problem to solve
But then you would need a Brenda. Ai can write the automation script for you.
I can see that MacWhisper uses parakeet v2 as the model (although it allows choosing another model).
Is MacWhisper a $60 GUI for a Python script that just runs the model?
There's also a free version that just uses Whisper. I recommend giving it a go, it's a very well constructed GUI wrapper. I use it multiple times a week, and I've run Whisper on my machine in other less convenient ways in the past.
> Is MacWhisper a $60 GUI for a Python script that just runs the model?
Yes, a large genre of MacOS apps are "Native GUI wrappers around OSS scripts"
It's verifier law.
Coding agents are useful and good and real products because when they screw up, things stop working almost always before they can do damage. Coding agents are flawed in ways that existing tools are good at catching, never mind the more obvious build and runtime errors.
Letting AI write your emails and create your P&L and cash flow projections doesn't have to run the gauntlet of tools that were created to stop flawed humans from creating bad code.
Nah, I've seen them screw in all sorts of ways that would fail in some conditions and not others. You're way too optimistic about this.
Brenda has been getting slower over the years -as we all have-, but soon the boss will learn that it was a small price to pay for knowing well how to keep such house of cards from collapsing.
And then the boss will make the decision to outsource her job, to a company that promises the use of AI to make finance better, and faster, and while Brenda is in the unemployment line, someone else thousands of miles away is celebrating a new job
We are setting AI deployed in the US, but actually Indians. They are not better, but they are cheaper. They are probably worse, but they are cheaper.
Simon posting tiktok quotes on his blog was not on my 2025 bingo card.
This isn't the first: https://simonwillison.net/2025/Aug/8/pearlmania500/ and https://simonwillison.net/2024/Jul/29/dealing-with-your-ai-o...
Also this fun diversion into Occlupanids: https://simonwillison.net/2024/Dec/8/holotypic-occlupanid-re...
A lot of people complain that the internet isn't as weird and funny as it used to be. The weird and funny stuff is all on TikTok!
Excel is the “beast that drives the ENTIRE economy” and he’s worried about Brenda from the finance department losing her job because then her boss will get bad financial reports
I suppose the person that wrote that have not ideia Excel is just an app builder where you embed data together with code.
You know that we have excel because computers didn’t understand column names in databases and so data extraction needed to be made by humans. Humans then design those little apps in excel to massage the data.
Well, now an agent can read the boss saying gimme the sales from last month and the agent don’t need excel for that, because it can query the database itself, massage the data itself using python and present the data itself with html or PNGs.
So, we are in the process of automating Brenda AND excel away.
Also, finance departments are a very small part of excel users. Just think everywhere were people need small programs, excel is there.
In most cases where the excel spreadsheet is business critical, the spreadsheet _is_ the database. These companies aren’t using an erp system. They are directly entering inventory and sales numbers in the spreadsheet.
The post is clearly hyperbole obviously the sole issue being brought up isn't 'brenda losing her job may be bad for the company' you're being facetious.
Found the person who hasn’t seen excel in the real world.
Excel - whatever its origin story - is the actual Swiss Army knife of the tech world.
There’s easily a few billion people who use excel. There is a reason it survives.
You missed this bit “.. and then the AI is gonna fuck it up real bad and he won't be able to recognize it because he doesn't understand because AI hallucinates.”
Brendas have fucked it up multiple times, by themselves or because their boss demanded
The underlying assumption is that Brenda generally does her job pretty well. Human errors exist but usually peers/managers (or the person who did it) can identify and correct them reliably.
If we have to compare LLM’s against people who are bad at their jobs in order to highlight their utility we’re going the wrong direction.
There are a lot of underlying assumptions: Brenda, the woman, is accurate and trustworthy and has mastered an accurate and trustworthy technology; the upper manager, the male, will introduce error by not understanding that the technology he brings to bear on the situation is hallucinatory. The woman is lower in status and pay than the male. The woman is necessary to the functioning of "the economy" and "capitalism," while the man threatens those. There are a lot of unsubtle political undertones on TikTok.
I was focused on a particular element but sure
Using ai does not absolve you from the responsibility of doing it correctly. If you use ai, then you better have the skills to have done the job yourself, and so have the ability to check the AI did things correctly.
You can save time still, but perhaps not as much as you think, because you need to check the ai's work thoroughly.
Many fears of “AI mucking it up” could be mitigated with an ability to connect a workbook to a git repository. Not for data, but for VBA, cell formulas, and cell metadata. When you can encapsulate the changes a contributor (in this case co-pilot) makes into a commit, you can more easily understand what changes it/they made.
Let it all crash and burn
10 billion dollars is probably going to be spent on automating excel, it’s going to happen
There needs to a financial equivalent to the Mythical Man Month.
There are plenty of things that play the role.
The problem is that people ignore them.
"You know who's not hallucinating?
Brenda"
I don't know about that. There could be lots of interesting ways Brenda can (be convinced to) hallucinate.
I agree - having watched many people use Excel over the years, I'd say people often overestimate their skills. I see three categories of Excel users. First there are the people that are intimidated by it and stay away from any task involving Excel. Second are the people that know a little bit (a few basic formulas) and overestimate their skills because they only compare themselves to the first group. And the third group are the actual power users but know to keep that quiet because otherwise they become the "excel person" and have to fix every sheet that has issues.
I don't know if AI is going to make any of the above better or worse. I expect the only group to really use it will be that second group.
I have seen lots and lots of different uses for Excel in my line of work:
- password database - script to automatically rename jpeg files - game - grocery lists - Book keeping (and try and not get caught for fraud several years, because the monthly spending limit is $5000 and $4999 a month is below that...) - embed/collect lots of Word documents - coloring book - Minecraft processes - Resume database - ID scans
It looks like the OP is thinking that AI causing errors in spreadsheets is going to make the whole economy collapse.
When tools break, people stop using them before they sink the ship down. If AI is that terrible at spreadsheet, people will just revert to Brenda.
And it's not like spreadsheets have no errors right now.
I'm more shocked that someone is using TikTok to speak things that actually make sense instead of mindless memes.
Brendas hallucinate all the time.
At some point, a publicly-listed company will go bankrupt due to some catastrophic AI-induced fuck-up. This is a massive reputational risk for AI platforms, because ego-defensive behaviour guarantees that the people involved will make as much noise as they can about how it's all the AI's fault.
That will never happen, AI cannot be allowed to fail, so we'll be paying for that AI bail-out.
Do you really want these kind of companies to succeed? Let them burn tbh
I don't find comments along the lines of 'those people over there are bad' to be interesting, especially when I agree with them. My comment is about why it'll go wrong for them.
Make sure you’re not part of the kindling, then.
I see the inverse of that happening: every critical decision will incorporate AI somehow. If the decision was good, the leadership takes credit. If something terrible happens, blame it on the AI. I think it's the part no one is saying out loud. That AI may not do a damn useful thing, but it can be a free insurance policy or surrogate to throw under the bus when SHTF.
This works at most one time. If you show up to every board meeting and blame AI, you’re going to get fired.
This is true if you blame a bad vendor, or something you don’t even control like the weather. Your job is to deliver. If bad weather is the new norm, you better figure out how to build circus tents so you can do construction in the rain. If your AI call center is failing, you better hire 20 people to answer phones.
This is transparent nonsense. People are very very happy to introduce errors into excel spreadsheets without any help from AI.
Financial statements are correct because of auditors who check the numbers.
If you have a good audit process then errors get detected even if AI helped introduce them. If you aren't doing a good audit then I suspect nobody cares whether your financial statement is correct (anyone who did would insist on an audit).
It’s like calling out the county to inspect the home you built but when they arrive it’s a bouncy castle.
> If you have a good audit process then errors get detected even if AI helped introduce them. If you aren't doing a good audit then I suspect nobody cares whether your financial statement is correct (anyone who did would insist on an audit).
Volume matters. The single largest problem I run into: AI can generate slop faster than anyone can evaluate it.
I'm actually not that worried about this, because again I would classify this as a problem that already exists. There are already idiots in senior management who pass off bullshit and screw things up. There are natural mechanisms to cope with this, primarily in business reputation - if you're one of those idiots who does this people very quickly start just discounting what you're saying, they might not know how you're wrong, but they learn very quickly to discount what you're saying because they know you can't be trusted to self-check.
I'm not saying that this can't happen and it's not bad. Take a look at nudge theory - the UK government created an entire department and spent enormous amounts of time and money on what they thought was a free lunch - that they could just "nudge" people into doing the things they wanted. So rather than actually solving difficult problems the uk government embarked on decades of pseudo-intellectual self agrandizement. The entire basis of that decades long debacle was based on bullshit data and fake studies. We didn't need AI to fuck it up, we managed it perfectly well by ourselves.
Nudge theory isn't useless, it's just not anything like as powerful as money or regulation.
It was taken up by the UK government at that time because the government was, unusually, a coalition of two quite different parties, and thus found it hard to agree to actually use the normal levers of power.
This NY Times opinion piece by Loewenstein and Ubel makes some good arguments along these lines: https://web.archive.org/web/20250906130827/https://www.nytim...
"the sweat from Brenda's brow is what allows us to do capitalism."
The CEO has been itching to fire this person and nuke her department forever. She hasn't gotten the hint with the low pay or long hours, but now Copilot creates exactly the opening the CEO has been looking for.
320784788
Everything is now about verification.
AI may be able to spit out ann excel sheet or formula - But if it can’t be verified, so what ?
And here’s my analogy to think about the debugging of an excel sheet - you can debug most corporate excel sheets with a calculator.
But when AI is spitting out excel sheets - when the program is making smaller programs - what is the calculator in this analogy ?
Are we going to be using excel sheets to debug the output of AI?
I think this is the inherent limiter to the uptake of AI.
There’s only so much intellectual / experiential / training depth present.
And now we’re going to be training even fewer people.
At the end of the day I /customers need something to work.
But failing that - I will settle for someone to blame.
Brenda handles a lot of blame. Is OpenAI going to step into that gap ?
Nay-sayers need to decide whether they fear AI because AI is dumb and will fuckup or because AI is smart and will take over.
Silly calling Simon a nay-sayer.
Are you a fanatic that thinks anyone saying that there are any limitations to current models = nay-sayer?
Like if someone says they wouldnt wanna get a heart transplant operation done purely by GPT5, are they a nay-sayer or is that just reflecting reality?
Simon willson is definitely not a nay sayer.
Both are valid concerns, no need to decide. Take the USA: They are currently lead by a patently dumb president who fucks up the global economy, and at the same time they are powerful enough to do so!
For a more serious example, consider the Paperclip Problem[0] for a very smart system that destroys the world due to very dumb behaviour.
[0]: https://cepr.org/voxeu/columns/ai-and-paperclip-problem
The paperclip problem is a bit hand-wavey about intelligence. It is taken as a given than unlimited intelligence would automatically win presumably because it could figure out how to do literally anything.
But let's consider real life intelligence:
- Our super geniuses do not take over the world. It is the generationally wealthy who do.
- Super geniuses also have a tendency to be terribly neurotic, if not downright mentally ill. They can have trouble functioning in society.
- There is no thought here about different kinds of intelligence and the roles they play. It is assumed there is only one kind, and AI will have it in the extreme.
To be clear, I don't think the paperclip scenario is a realistic one. The point was that it's fairly easy to conceive an AI system that's simultaneously extremely savant and therefore dangerous in a single domain, yet entirely incapable of grasping the consequences or wider implications of its actions.
None of us knows what an actual, artificial intelligence really looks like. I find it hard to draw conclusions from observing human super geniuses, when their minds may have next to nothing in common with the AI. Entirely different constraints might apply to them—or none at all.
Having said all that, I'm pretty sceptical of an AI takeover doomsday scenario, especially if we're talking about LLMs. They may turn out to be good text generators, but not the road to AGI. But it's very hard to make accurate predictions in either direction.