23
votes
What could be Microsoft's larger game plan or agenda with CoPilot?
I just don't get what is so revolutionary about it. It seems like nobody actually wants it in the ecosystem if you look at all the social media, as if it's being shoved upon as an add-on package along with Github, upcoming Windows versions, etc.
CoPilot is becoming Microsoft's next Nokia Lumia moment but they haven't learned from the past experience, they are still pushing and pushing. The only reason this could still be happening is that their enterprise customers may be wanting it? But that doesn't make sense as enterprise users typically still use WinForms apps and Acccess databases on Windows 7 as part of their stacks. They will be the last ones bubbling with joy for an upgrade.
Why are they doing it then?
It is "how do I x" for people who can't google search well, and there's a ton of them.
"How do i make an excel formula that does blah"
"how to do i do something on my file system"
"how do i send an email and bcc"
and on and on and on.
In an age where search engines are vomiting up sponsored results, or you have to dig through passive aggressive and out of date tech forum answers, having a system that doesn't judge you and will attempt to answer ANY question, with a decent level of accuracy, is huge.
The target audience is not ANYONE on tildes because being on tildes means you're probably already waaay above the curve.
There's a lot more nonsense going on with this stuff that I think is questionably useful, but there's lots of quick questions i've found copilot to be good for. It's still something like 2/10 on more complex coding questions in my experience, but for simple stuff it's often faster and more context specific than searching.
I mean, an LLM is still usually faster at answering those questions versus searching Google, even if you know what you're doing. The areas it will struggle in are the same areas Google will: esoteric, or uncommon problems.
Still, I'd much prefer getting a solely text based answer most of the time versus having to deal with shitty SEO click bait and poorly edited YouTube videos most of the time.
Is the answering capacity really that good though? If I ask it something about IT technologies like "What is it like to work on a Laravel Project?" or "Which is better, Laravel or CodeIgniter?", can it answer without any of the same SEO/PR prejudices?
And how about geopolitical questions about US, Europe and Asian countries? Does it have the same biases in its answers as those of predominant political parties or establishments in these countries?
As a rule of thumb, I don't trust any opinion based answers on the Internet.
Asking any search engine, forum, or LLM "What's the best x" where x is something someone can make money off of in some way is less than useless. You'd be better off setting up a dartboard with product names in a category on it and just wildly tossing at it.
Any result you get is going to be almost entirely dictated by who paid the most on SEO, targeted advertising, fake reviews, and shill forum accounts.
I think most geopolitical questions are fairly useless for mostly similar reasons.
So yes, LLMs are bad at those things, but so is basically everything else at the internet.
The kinds of questions it IS pretty good at answering are things like "how do I make a rule in Outlook so that messages older than a week but not read are moved to a special folder?" Or "whats a simple recipe for meatloaf that doesn't have onions?"
Both of those questions will leave you sorting through absolute trash via a search engine, while an LLM will give a competent, easy to read text only answer in a couple seconds.
You cannot get good answers from human beings even with direct experience in those things. At some point, it's on the user to have reasonable expectations for the capabilities and purposes of the tools they use.
I think one of the key things on top of that is being able to evaluate the trustworthiness of a source. I feel like I can usually make a halfway educated guess about how much I can trust something based on the page I'm reading it on, the age of the content, and other context clues. LLMs typically strip that context away completely and just present me with an answer, making it difficult to know how to evaluate the result.
Here's an answer I got using Microsoft Copilot, the world's first AI-powered answer engine. Select to see the full answer or try it yourself. https://sl.bing.net/fXn41RPQC3E
Fully agree with this. It's easy to get trapped in a bubble when it comes to social media. There's likely tons of people who will find it useful.
But as far as end game goes... I'm not sure. Copilot isn't going to factor into the decision for computer manufacturers to load Windows by default on their products. It may eventually push people towards buying more MS products - perhaps a query response to editing a generic document will say "if using MS Word, you can...", or something else that they charge a subscription for. But this doesn't seem like it would drive a large enough volume of sales to make it worthwhile.
One thing everyone is doing right now is collecting data. As much data as they can. And then selling it to various AI companies to improve their LLMs. So I wouldn't be surprised if the argument to shareholders is "we'll invest in this data collection method but we anticipate a return in another year."
A few startups are working on something right now called robotic process automation (RPA - bad name IMO, nothing to do with robots). RPA is automation of computer tasks using a GUI. The end-goal is to tell the computer "Download this PDF, go through each row in the tables and enter them into this web form". These are the kind of data entry tasks that employ a good number of people or at least take up some time in many people's weeks.
I'm convinced that with enough training data you can throw that data at a language model combined with a vision model and it will do a pretty good job at these types of tasks. It's possible that MS Copilot could build such a dataset by collecting data on user's queries and their resulting actions in Windows.
If you can solve this problem with AI you're essentially consolidating hundreds of thousand of jobs into one company's revenue and significantly expanding demand for this work because it will become much cheaper. So there's a lot of incentive there. Maybe Microsoft won't be the one to do it, but someone will in the next few years.
Oooh boy, I have some strong opinions about RPA. I've had hands-on experience with three different RPA applications (or "Frameworks," whatever you want to call them), and they are mostly garbage. At some point I'm going to publish an article (anonymously) about how terrible "no-code" technologies are, RPA included. (A lot of my distaste for these is how absurdly expensive they are, at least the ones that target medium to large enterprises.)
One of the major downfalls of RPA is that you're building an automation that is dependent on the GUI it is built in remaining constant. Using your example - if the website you're entering data in changes its layout, the automation will break. If the PDF format is slightly different from one to the next, it'll break. In many cases, if the user's display settings are different (resolution, high contrast mode, zoom level in the PDF application or web browser), it'll break.
I don't want to fully discount your comment, as that could theoretically lead to some advances in this type of tech. I would argue that Microsoft Recall (the controversial & just announced AI for Windows on ARM computers) could contribute to this as well, and collect even more relevant data. But anything that advertises itself as RPA right now wouldn't even be a functional base to add this type of AI onto. A headless browser framework like Selenium is much more advanced and could accomplish some of these tasks with a higher level of reliability. The appeal of these existing RPA applications is that you can build automations "without a single line of code," but in nearly every case it would be more cost-effective for a company to hire contractors to build a proper application with API connections.
As someone who had to wrangle fragile Kofax Capture (enterprise software that does industrial-grade bulk scanning and OCR of massive stacks of paper to digitize them) installs for an enterprise back then before they added all the RPA stuff, I don't know why we (the collective we) insist on having these proprietary "no-code" messes when we already have a form for terse and explicit instructions for a computer to execute, aka code.
Short of tutorial languages like Scratch, there's no academic pipeline for proprietary no-code software about data structures and algorithms, can't use existing tools for running tests, hard to debug because there's nothing else out there, and generally goes against decades of computer science concepts that you're supposed to be able to tap from.
It's like these companies insist that people are allergic to typing things but writing Excel formulas seem to the one great exception. I don't recall anybody trying to make drag/drop no-code versions to completely avoid typing out Excel formulas.
I agree that it's baffling how much no-code has proliferated through enterprises. Though I honestly believe dishonest salespeople and managers who want to try to accomplish something are to blame. One common sales tactic is to target someone higher up in management with no significant IT background, convince them it will up productivity by 100x because anyone can now build applications, and basically sell them on it before their IT department has a chance to weigh in. Then when IT shows any form of resistance, they're met with "you're just worried we'll take your job" or some other crap - I've even seen salespeople prepare their "targets" for this conversation to ensure they'll "win" (and the salesperson will close the deal).
As I said, I'm working on a couple of long-form articles I'll eventually publish somewhere because I have that much of an issue with this sector of software.
However, I have seen some "no-code" technology work well, but only in free products and as built-in extensions to well established platforms (automations in Monday.com, PowerApps, etc). I'm currently back in school to get my Bachelor's, and one course about data science that was meant to be approachable to anyone, including those without a programming background, heavily used KNIME. It is one of the most impressive no-code frameworks I've seen, because it targets a specific use case (data analysis), can be extended with code (Python or R), and is completely free. There's also a couple of open-source projects in development for more general use, but I think standalone no-code/low-code really only makes sense for creating a user interface that does CRUD operations on a database. (Microsoft Access was ahead of its time, but it doesn't scale like it should.)
Agreed. Right now the tools are garbage and complicated. But deep learning should be able to handle the noise of layout changes.
There are plenty of tasks that LLMs are great at that search engines are not. Consider the following examples:
An LLM returns the word "tempest", which is perfect. If I ask the same of Google, I get: "FRUSTRATION Synonyms: 129 Similar and Opposite Words". That might take a while to browse through!
Three of the four top Google results are for unrelated songs. The LLM quickly catches that I'm thinking of Purple Haze by Jimi Hendrix.
The LLM informs me that rows come first by convention, and even provides some code samples. It took me three Google results to find the same answer (and even then, the answer was for Java, not PHP).
Of course LLMs do more than just answer questions. They are also capable of manipulating data that you provide them. Earlier I provided one with a list of 30 lines, then asked it to put it into a specific JSON format which it did perfectly. I then asked it to define a schema based on that data. I could have done this by hand by utilizing search+replace and multiple cursors, but it saved me 10 minutes from just two prompts.
LLMs provide far more value than just helping people who don't know how to Google. They're an extremely useful tool for many types of tasks, and if you adopt them into your toolbox, you'll find yourself becoming more productive overall.
I'd be surprised if they wouldn't find a way to work ads into the answers. And as long as they don't manually review the input, people will mess with the output, just like they do with search engine results.
The basic priniple hasn't changed. We are building machines that answer questions. The service who runs the machine is still trying to maximize profits and third parties are still trying to influence those answers. The cat-and-mouse game is just burning more energy.
While yes they'll shove in ads, i think you're still underestimating the end user who enters queries into search engines like a human, not like a programmer. There is a HUGE market for the kind of person who will ask very very simple stuff, and often screw up navigating to it from the reams of data they get back.
Having one, straight forward, probably accurate answer from human language is the use case. I know several older or tech inept relatives who this has been a massive life changer for.
That reminds me of the ads for Microsoft Hololens (AR) that showed you wearing it while working on some plumbing under your sink, so you could conference with someone more knowledgable and they can help you by not just verbally telling you what to do, but drawing arrows you'd see in front of you to make things clearer.
Maybe in another 10 years we'll have this technology, but completely fueled by AI. Though I could see things going very wrong if it isn't much more accurate than what currently exists.
I just got the trial today. I was writing my unit tests and thought I could probably use copilot - and I was correct - to write the boiler plate.
// set up fakes for all my dependencies.
And boom. Done. It was pretty convenient tbf.
Yeah it’s very good at boilerplate. Not so great on logic or business unique interactions
My (completely uneducated) guess: to satisfy MBAs and private equity firms that are pushing everyone to be "more efficient". AKA take profit from wages to satisfy shareholders.
For the love of Zeus, can we please translate some tech efficiency gains into fewer hours for the same wage? I like UBI theory, but it seems crazy that we are skipping an obvious step like this.
Can you explain how this generates stock price/profits for shareholders?
It's stupid, but I'm pretty sure that legally speaking, publicly traded companies have an obligation to shareholders, and the C-suite/board are responsible.
That makes it basically impossible for companies to allow people to work less. It has to come from legislation (except lobbying exists, so good luck...).
This is one of those things that people repeat a lot without understanding it. The concept you're pointing at is called fiduciary duty and it means specific things, none of which are "must pursue every cent of short term profit and be a jerk to everyone".
Not that I think this is any way going to happen, but a company that says 30 hours of work per week is the standard is going to attract a LOT of talent by paying the same wages.
The work they get from that talent is going to be way more passionate than the work they'd get from people burnt out from work all the time, and few people are going to leave a company that only requires them at work that often.
I don't have the data to back any of this up obviously, but there are clear benefits to the company. Do those benefits outweigh potential productivity gains by having people work full time? Who knows. I doubt it's been studied much because big companies usually don't like experimenting and rocking the boat.
Pretty directly by reducing overhead to increase profit.
Alternative party structures are not illegal. As u/papasquat mentioned, pay and benefits are levers of the free market. Companies can and do use these levers to gain advantage over their competitors.
I would argue it is immoral/unethical to continue as we have been, considering the ever-widening wealth chasm. It's not good for us and it's not good for society.
Even accepting your premise (which I think is flawed), it's trivially easy to argue that you get increased productivity and higher-quality work when you give employees more time off. Even without that, it can help with hiring and retaining quality employees -- there's a reason it's not the case that every company gives the absolute legal minimum of paid vacation and sick leave to their employees!
To be clear, I do agree with you, I was just playing devil's advocate. There are studies which show that, for "knowledge work" at least, working 6 hours a day is just as productive as 8.
LOL For some reason this reminds me of the past when the future was flying cars and robots doing all the boring domestic stuff while we had fun that has sadly not occurred.
Like The Jetsons. But would we treat Rosey humanely?
The Jetsons is a bleak future. George is one of the lucky ones too. It's a future where robots are 100% capable of doing all the work, but we are forced to go to work to press a button all day. It's a future where humans continue to be enslaved by businesses, all so the elite can keep power over us and keep us dependent on them so we don't endanger that power. It's a future where much of the population, those on the ground, live in poverty.
Not sure about the Rosey angle; I didn't watch The Jetsons other than seeing clips and bits and pieces here growing up. But what I did see and other things, yeah that future.
Occam's razor.
Most (all?) current and currently foreseeable AI implementations will "require" cloud computing resources. Meaning data will flow from the installation to the cloud, where it can be processed.
Sure they say the reason is (insert whatever function they claim that AI has), but that doesn't at all mean they can't just do other stuff too. Like index, categorize, analyze, store, use it for predictions and market research and tabulations that will get just about as granular and specific on the individual level as they'll be able to get away with. And more besides, since they'll go black and treat it like a "trade secret" that has a lot of case law designed to let them hammer employees who don't play ball.
All these things, these new features, are designed to normalize it. Create an atmosphere of acceptance. "Oh, we're only using it for X. Honest."
Until the department head or CEO changes, until marketing gets hold of it, until the government or a three letter agency comes knocking. "You have data feeds from this company don't you? We suspect them of terrorism/CSAM/drugs/murder-for-hire/etc. Do you want to help criminals, or be a good obedient citizen and cooperate? Thought so, now the data and be quick about it."
Or, less "dramatic", it's something like "would (Microsoft) like to make more money for nothing, without having to do much of anything else beyond what you're basically already doing now? Sure you would, you have quarterly reports and a performance review coming up. Let us install some algorithms and analysis into your systems, have access to all that data coming in, and we'll pay you each month!"
And then, suddenly, everyone everywhere is indexed in a very, very granular and specific way. They use all that to sell to you, or sell you to others that will sell to you. It's not that they know this stuff about you, it's that they know that stuff about everyone. And analyze everyone. And rather than using it for some "good" purpose, like "we notice you eat poorly and don't have a gym membership or visit the doctor's office; perhaps you should be aware you're at (insert very high risk) of (insert sedentary disease conditions).
No, they'll use it to psychologically "encourage" you to spend, spend, spend. Which will work on a lot of people, way more than they need to make it profitable. And, of course, all that data is still wonderful for the government too. The same data marketing firms will use to sell "products" to companies selling goods and services. Products that have a high rate of return since they're data algorithmically calibrated to be very effective, and constantly recalibrate as new data comes in and they consider how to maximize profit from it.
They normalize all this. Plus other stuff. They want to tie permanent telemetry feeds into all electronics. With smart home stuff, they can have that telemetry into a damn house. Smart lights, smart power, water usage meter, motion detectors and surveillance cameras, more. They know when you're awake, they know when you're asleep. They know your schedule, your routines, all of it.
Insurance company comes knocking, and suddenly people either aren't being renewed or find their rates are going sky high. Maybe they have guests over too much, or eat bad food, or whatever. Some sort of "risk factor" that makes them a bad investment, so they're just frozen out of insurance.
They'll have keylogger access (Rewind anyone?) to everyone's desktop. Oh, we're monitoring for terrorists, that's why we crunch and read and potentially flag every single email or text or document you touch on your computer. Oh, we're monitoring for malware, that's why we examine each and every file on your computer.
Then they start flagging people for not having properly licensed software or media files. They provide that information to lawyers and IP owners (for a fee) which streamlines the lawsuits, and makes it so Big Brother really is watching everything.
Windows has something like a 70%+ market share. Microsoft wants to position themselves at ground zero of the data economy. We call it the Information Age and used to think that was a cute moniker. Now it's starting to come true, and not in ways that will benefit you and me. That will be helpful and benevolent to ordinary little people.
If Microsoft can normalize telemetry, they'll have access to most of the data we possess. Possibly all of it, since people (and smart device manufacturers) like everything to be connected to the mothership (via your and everyone else's networks). What happens when they roll out some cutesy app that automatically syncs your phone, for example? Or your smart home stuff? Now MS doesn't just have your desktop, they have your house, and through the phone most everything you do and often are.
And it all starts with "convenience." Don't read the terms and conditions. No, no, sure it says we have (insert huge list of permissions and data), but we'd never use it for evil. Honest."
What's the next step? "Well, if you have nothing to hide, why can't we look?" So they start wondering why people stay on Linux rather than just run Windows like a "normal" person should. What are you hiding Bob? Illegal software, terrorism cell, details of your illegal street racing activities?
Normalization. They want all of this normalized. Same as ever, same as always. They sneak it in, make it unremarkable, ride out the questions and worry phase, and let it settle in. Let it get to the point where "regular" folks stop wondering, and that's when anyone who's still objecting begins to be treated like a kook and wacko. Like a problem. Shut up over there, we don't care about your privacy concerns you loon! Microsoft promised they're not evil, so knock it off.
Normalization. It always, always, always starts with a good story. Something crafted to sound believable. Often it even is believable.
But then it changes. It'll expand, it'll feature creep, and money and authority will come knocking ready to take advantage.
I don't disagree, but the idea of their snapshot system is that it runs locally on an NPU. So it shouldn't* go through any cloud servers.
*But at some point could.
It's kinda wild to lead with "Occam's razor" as if you're reasoning from a minimal foundation here. I think a better application of the principle would be to take the following as true:
We can pretty easily conclude that Microsoft wants to maintain its early-mover advantage here, even at some short term compute cost to itself. Whether the final value-add of generative AI eventually takes the form of privacy invasion like you're concerned about, RPA as mentioned up thread, or on-device AI eliminating most of the cost remains to be seen.
Perhaps it’s too cynical of a take, but I’m not sure that Microsoft has a larger game plan. For a number of years it’s had a habit of somewhat aimlessly throwing itself at whatever trends/hype are swirling around in a very flashy way and hoping something sticks.
I’m more curious to see Apple’s take on LLMs, which I think is likely to be less glamorous but probably more practical/useful, simply because they have a tendency to more thoughtfully integrate new things.
Isn't everyone doing this though because it is the next hotness? I still haven't heard a compelling reason to use an LLM. I'm pretty sure the main reason Microsoft is doing this is to appease investors and sell their new Qualcomm powered PCs.
It's removed a lot of tedium from my job and made me more efficient to be honest. I use it mainly for creating shells and frameworks that I can turn into a final product. Sometimes I use it to augment research for web searches, or to provide the search terms I use to search the web. The latest chatgpt can do web searches on my behalf too, which I find much easier than sorting through dozens of SEOed and ad boosted Google results.
Overall if I had to estimate, I'd say using an LLM gives me a roughly 20% boost in my productivity at work. Obviously some tasks it's very helpful with, and others much less so.
When programming something I'm familiar with, assuming what I'm doing is mostly programming and not a lot of planning, coordinating, researching or configuring, I'd say GitHub's Copilot gives me a 10 - 20% productivity boost.
The biggest productivity improvement comes from when I'm working with code that is:
then Copilot can give a 100-200% boost. This is where it shines. It's a language model, it's good at language tasks like translation. And this is a case where what I want is translation from the framework I know to the framework I'm learning.
That's fair. I should have added that caveat for some people's jobs. That still isn't very compelling unless we have a system that actually rewards you for being 20% more efficient at your job.
I guess it depends on your personality type. I like being 20% more efficient even if it doesn't necessarily net me more direct rewards. That 20% that an LLM does for me is tedium that I'd rather not spend my time doing. I'd rather do other stuff and get the satisfaction of completing projects more quickly, or use the mental energy I've saved to be able to think through things more clearly so that the stuff I build doesn't have to be replaced six months down the line.
You're right though, my job isn't going to tell me good job, here's a 20% raise. They'll just expect me to continue to produce at that level, which frankly I'm kind of ok with if the alternative is that I have to go back to writing boilerplate fluff language in reports, or writing boilerplate fluff code I've written in somewhat similar ways more or less 1000 times.
Yeah I totally understand that mindset and don't think it is necessarily wrong. It would just be nice if we didn't just accept that reality.
Writing mundane bullshit.
I used to be a union rep and we had to write out grievances for things. Lots of things were so common that we would create and use templates but random abnormal stuff you'd write from scratch. I HATE writing stuff like that and basically block up just trying to start it.
Last time I looked ChatGPT wasn't quite useful for our use case yet, but in the next year or so I'll reassess and see if our current reps can utilize these tools to parse the large amounts of arbitration cases and stuff and to write rough drafts of new grievances.
Yeah my company attempted to use ChatGPT to write internal policy and reactive messaging and it failed miserably. I can see it potentially being good at that, but haven't seen actual proof of that yet and I wonder how many other people's work they will have to steal to get to that point.
Ooff... Internal policy sounds like a bad plan and reactive messaging (I'm assuming chatting with customers?) sounds even worse.
To be clear, I wouldn't us anything that it created as is. I would check it over and make changes. ChatGPT is solid at roughing things in. The business use case should be saving time in that step, not for it to produce the final product. In my example of grievances I wouldn't just send out whatever ChatGPT puked out...
Cover letters tailored to the company and the skills they need. Tweaking an initial draft generated by ChatGPT saves me around 70% of the effort.
Also useful for light brainstorming if I don't have anybody else around that share my interests in that topic. I dabble in worldbuilding, so having an LLM that can "talk" to me by generating large amounts of prose given the world parameters I've provided helps greatly when trying to fine tune aspects of that world. In this case hallucination doesn't matter and is in fact encouraged since I want it to make something up for me given the constraints.
I'm interested in the Copilot+ computers, since it looks like we can run our own code against the NPU.
I would want to test to see how useful it is, because the MNIST data set and number classification is a pretty trivial problem these days. But could this satisfy an intermediate need for AI compute resources for researchers who don't consistently need large GPUs, but tune a few classification training algorithms as part of a deliverable on a grant?
As far as the Copilot software companion, it's for people who need an easier interface with their computer and how to ask questions.
For the Copilot recall type features, it might be helpful as a productivity time saver if it is good enough to generate documentation, caption training, create meeting notes and summaries, etc.
I would imagine they're just trying to expose as many as possible to their software. If people get use to using their software first, they'll have an advantage.
As for its use, increased productivity. It's not useful to me as it is, I feel like it mostly applies to jobs working for soulless corporate d-bags that demand maximum productivity from underpaid people. Jobs where they hire someone to hand type hundreds of emails where they could just type a few words into an AI and it'd output a perfect email in a quarter of the time.
The ultimate goal of AI, not necessarily Microsoft's, is to bring about an age of knowledge. Where we have 24/7 access to not just info as we've had for decades now, but something that can put that information together in a way that's useful to us. Instead of finding articles telling us how to do something, the AI will tell us or do it for us. Instead of spending hours figuring out the truth behind a news article or politician's speech, you can just ask the AI what the real story is. Need a new merchant service provider at work? An AI could research companies and costs, then once you know which to go for it could make the account and tell you what you need to know to start using it. It'd be like having every kind of skilled professional at your beck and call, along with a personal secretary.
People actually like the product - it's much more user friendly than pouring through search engine results or help documents.
The socials media discourse is not what the average person feels
I don't think they have an endgame other than trying to capitalize on a good product and get early dominance in the AI "assistant" market
I have a relative that’s getting a close to 90 years old who uses it all the time and loves it. His google skills aren’t the best, but the ability to just ask a question and get a reasonably in depth summary is perfect for him.
As others have said, copilot isn’t primarily targeted at most of the people here but rather more typical consumers for whom this is a mind blowing feature.
I won't speak to "what is Microsoft's plan?" but I can give you counterexample data that many people in my circle of connections (in software engineering) use Copilot, and appreciate what they (sometimes) get out of it. I don't use it often, but it has been helpful more often than not when I do use it. It hasn't completely supplanted websearching in my day to day technical work, but there is now a division between "stuff I will websearch" and "stuff I will ask an AI".
My work (government) has a trial for using it at the moment. Clearly in a very limited way (I'm not sure of the specifics, but it only accesses some of our information). It has been extremely helpful in this context. There a lot of questions that get asked and answered in pretty much the same way that CoPilot helps a lot with. We also use it to provide a bare-bones report or advice document that we can fill in with specifics and that will gets sent on to whoever needs that information.
One of the most useful ways I've seen it used, though, is summarising meetings through the transcript and record function in Teams. It's never a perfect job, but it's pretty good at pulling out the action items from a meeting, and if you've ever worked in government you know how helpful that can be. Having more than one person or entity taking notes also is helpful if we ever get asked to provide evidence in an inquiry. It makes us more transparent as a result.
I tried Copilot using the free trial, and that was enough to convince me it was worth paying for. It saves me time by offering completions for when I'm writing something repetitive, or something I've written before. I haven't looked into it, but I get the feeling that it can reference code from my other repositories, because sometimes I'll be rewriting something, and it will offer up completions matching my old code.
I also like using it for answering basic questions, since it's kind of a pain sometimes to find what I'm looking for on search engines nowadays.
I don't really ever ask it to write code for me, because it writes code like an idiot. It will reference libraries that I am not using or don't even exist, call methods that I haven't defined, and sometimes just totally lose the plot on what I asked it to do in the first place.
What I do use it for apart from completions and questions, is ask it for code examples using the Copilot Chat. It's helpful to be able to ask "Can you show me an example of how to do X in Y language?" and then I can go from there, correcting a mistake it may have made, asking for an alternative to a library it used in the example, etc.