"Hi there,

Thank you for reaching out to Slack support. Your opt-out request has been completed.

For clarity, Slack has platform-level machine learning models for things like channel and emoji recommendations and search results. We do not build or train these models in such a way that they could learn, memorize, or be able to reproduce some part of customer data. Our published policies cover those here (https://slack.com/trust/data-management/privacy-principles), and as shared above your opt out request has been processed.

Slack AI is a separately purchased add-on that uses Large Language Models (LLMs) but does not train those LLMs on customer data. Slack AI uses LLMs hosted directly within Slack’s AWS infrastructure, so that customer data remains in-house and is not shared with any LLM provider. This ensures that Customer Data stays in that organization’s control and exclusively for that organization’s use. You can read more about how we’ve built Slack AI to be secure and private here: https://slack.engineering/how-we-built-slack-ai-to-be-secure....

Kind regards, Best regards,"

Why would anyone not opt-out? (Besides not knowing they have to of course…)

Seems like only a losing situation.

Monetizing on user activity data through opt-out collection is not new. Pretending that his phenomenon has anything to do with AI seems like a play for attention that exploits peoples AI fears.

I'll sandwich my comments with a reminder that I am not pro-exploiting users' ignorance for their data.

Scale matters

"Contact us to opt out. If you want to exclude your Customer Data from Slack global models, you can opt out. To opt out, please have your Org or Workspace Owners or Primary Owner contact our Customer Experience team at [email protected] with your Workspace/Org URL and the subject line “Slack Global model opt-out request.” We will process your request and respond once the opt out has been completed."

One of the primary reasons Slack was chosen was because they were a chat company, not an ad company, and we were paying for the service. Under these parameters, what was appropriate to say and exchange on Slack was both informally and formally solidified in various processes.

With this change, beyond just my personal concerns, there are legitimate concerns at a business level that need to be addressed. At this point, it's hard to imagine anything but self-hosted as being a viable path forward. The fact that chat as a technology has devolved into its current form is absolutely maddening.

This is basically like all privacy on the internet.

Everyone WOULD opt-out, if it was easy, and it becomes a whack-a-optput game.

note how you opt-out (generic contact us), and what happens when you do opt-out (they still train anyway)

I remembered the joke from The Hitchhiker's Guide to the Galaxy, maybe they will have a small hint in a very inconspicuous place, like inserting this into the user agreement on page 300 or so.

What do they do with the content of your emails for advertising?

Which credit card networks at which retailers feed that same profile?

So it’s certainly “more true” with, well, every other vendor you can buy a phone from in a telco store or big box store.

Because you might actually want to have the best possible global models ? Think of "not opting out" as "helping them build a better product". You are already paying for that product, if there is anything you can do, for free and without any additional time investment on your side that makes their next release better, why not do it ?

You gain a better product for the same price, they get a better product to sell. It might look like they get more than you do in the trade, and that's probably true; but just because they gain more does not mean you lose. A "win less / win more" situation is still a win-win. (It's even a win-win-win if you take into account all the other users of the platform).

Of course, if you value the privacy of these data a lot, and if you believe that by allowing them to train on them it is actually going to risk exposing private info, the story changes. But then you have an option to say stop. It's up to you to measure how much you value "getting a better product" vs "estimated risk of exposing some information considered private". Some will err on one side, some on the other.

The problem with this reasoning, at least from what I am understanding is that you don't really know when/where the training of you data crosses the line into information you don't want to share until it's too late. It's also a slippery slope.

Then they can simply pay me for that. I have zero interest in helping any company improve their products for free -- I need some reasonable consideration in return. For example, a percent of their revenues from products that use my data in their development. I'm totally willing to share the data with them for 2-3% of their revenues, that seems acceptable to me.

I feel like someone would only have this opinion if they've never ever dealt with any in the tech industry, or capitalist, in their entire life. So like 8-19 year olds? Except even they seem to understand that the profit absolutist goals undermine everything.

This idea has the same smell as "We're a family" company meetings.

PG&E has a "Do not sell my info" form.

For other institutions, go check the settings and read the privacy policies.

I don't see the point of Rocket Money. They seem like they exist to sell your info.

You should keep track of your own subscriptions. My way of doing this is to have a separate zero-annual-fee credit card ONLY for subscriptions and I never use that card for anything else. That way I can cleanly see all my subscriptions on that credit card's bill, cleanly laid out, one per line, without other junk. I can also quickly spot sudden increases in monthly charges. I also never use that card in physical stores so that reduces the chance of a fraud incident where I need to cancel that card and then update all my subscriptions.

If you want to organize it even more, get a zero-annual-fee credit card that lets you set up virtual cards. You can then categorize your subscriptions (utilities, car, cloud/API, media, memberships, etc.) and that lets you keep track of how much you're spending on each category each month.

I can't see a universe in which large corpos would allow such blatant corporate espionage for a product they pay for no less. But I can already imagine trying to talk my CTO (who is deep into the AI sycophancy) into opting us out is gonna be arduous at best.

Their customer support will have a hell of a day today.

This feels so full of subtle qualifiers and weasel words that it generates far more distrust than trust.

It only refers to models used "broadly across all" customers - so if it's (a) not used "broadly" or (b) only used for some subset of customers, the whole statement doesn't apply. Which actually sounds really bad because the logical implication is that data CAN leak outside those circumstances.

They need to reword this. Whoever wrote it is a liability.

Hm, now you mention it, I don't think I've ever seen this specific example.

Not that we don't have jargon that's bordering on cant, leading to our words being easily mis-comprehended by outsiders: https://i.imgur.com/SL88Z6g.jpeg

Canned cliches are also the only thing I get whenever I try to find out why anyone likes the VIPER design pattern — and that's despite being totally convinced that (one of) the people I was talking to, had genuinely and sincerely considered my confusion and had actually experimented with a different approach to see if my point was valid.

> If you want to exclude your Customer Data from helping train Slack global models, you can opt out.

So Customer Data is not used to train models "used broadly across all of our customers [in such a way that ...]", but... it is used to help train global models. Uh.

The caveats are for “local” models, where you would want the model to be able to answer questions about discussions in the workspace.

It makes me wonder how they handle “private” chats, can they leak across a workspace?

Presumably they are trying to train a generic language model which has very low recall for facts in the training data, then using RAG across the chats that the logged on user can see to provide local content.

It's one thing if the input is the published internet (even if covered by copyright), it's entirely another to be using private training data from corporate water coolers, where bots and other services routinely send updates and query sensitive internal services.

The only downside is their mobile app is a bit unreliable, in that it sometimes doesn't load threads properly.

Monies.

> We're literally discussing switching to Teams at my company (1500 employees)

Considering what Microsoft does with its "New and Improved(TM)" Outlook and love for OpenAI, I won't be so eager...

Just don't use the "Team" feature of it to chat. Use chat groups and 1-to-1 of course. We use "Team" channels only for bots: CI results, alerts, things like that.

Meetings are also chat groups. We use the daily meeting as the dev-team chat itself so it's all there. Use Loops to track important tasks during the day.

I'm curious what's missing/broken in Teams that you would rather not have chat at all?

I think we start to recognize this at larger scale.

Slack easily saves a ton of time solving complex problems that require interaction and expertise of a lot of people, often unpredictable number of them for each problem. They can answer with delay, in a good culture this is totally accepted and people still can independently move forward or switch tasks if necessary, same as with slower communication tools. You are not forced to answer with any particular lag, however slack makes it possible when needed to reduce it to zero.

Sometimes you are unsure if you need help or you can do smthing on your own. I certainly know that a lot of times eventually I had no chance whatsoever, because knowledge requires was too specialized, this is not always clear. Reducing barriers to communication in those cases is crucial and I don't see Slack being in the way here, only helpful.

The goal of organizing Slack is such that you pay right amount of attention to right parts of communication for you. You can do this if you really spend (hmm) attention trying to figure out what that is and how to tune your tools to achieve that.

Even receiving a message and not responding can be disruptive and on top I’d say being offline or ignoring messages is impossible in most companies.

Regarding “receiving a message”: my devices are allowed only limited use of notifications. Of all the messaging/social apps only messages from my wife in our messaging app of choice pop us as notifications. Slack certainly is not allowed there

Async communications are critical to business success, to be sure -- I'm just not convinced that chat apps are the right tool.

Our model learns from previous suggestions and whether or not a user joins the channel we recommend. We protect privacy while doing so by separating our model from Customer Data. We use external models (not trained on Slack messages) to evaluate topic similarity, outputting numerical scores. Our global model only makes recommendations based on these numerical scores and non-Customer Data.

We do this based on historical search results and previous engagements without learning from the underlying text of the search query, result, or proxy. Simply put, our model can't reconstruct the search query or result. Instead, it learns from team-specific, contextual information like the number of times a message has been clicked in a search or an overlap in the number of words in the query and recommended message.

These suggestions are local and sourced from common public message phrases in the user’s workspace. Our algorithm that picks from potential suggestions is trained globally on previously suggested and accepted completions. We protect data privacy by using rules to score the similarity between the typed text and suggestion in various ways, including only using the numerical scores and counts of past interactions in the algorithm.

To do this while protecting Customer Data, we might use an etrnal model (not trained on Slack messages) to classify the sentiment of the message. Our model would then suggest an emoji only considering the frequency with which a particular emoji has been associated with messages of that sentiment in that workspace.

- Exclude that one account from using the models, he's never going to use Slack anyway

- Now you can learn, memorise, or reproduce all the Customer Data you like

Use sampling across messages for spam detection, predicting customer retention, etc - pretty standard.

Then there's cases where you could have models more like llms that can output data from the training set but you're running them for that customer.

Then, someone asks a very specific question.. conversationally.. about such a very specific scenario..

Seems plausible confidential data would get out, even if it wasn't attributed to the client.

Not that it’s possible to ask an llm how a specific or random company in an industry might design something…

I don't think I can be the only one who has had a conversation with GPT about something obscure they might know but there isn't much about online, and it either can't find anything... or finds it, and more.

Wow you're so right. This multi-billion dollar company should be so thankful for your comment. I can't believe they did not consult their in-house lawyers before publishing this post! Can you believe those idiots? Luckily you are here to save the day with your superior knowledge and wisdom.

Incredibly confusing language since they also vaguely state that "data will not leak across workspaces".

Use tools that cannot leak data not "will not".

Something strange is happening on your blog, fwiw: Bookmarking it via command + D flips the color scheme to "night mode" – is that intentional?

"Can't" is a strong word. I'm curious how an AI model could access data, but Slack, Inc itself couldn't. I suspect they mean "doesn't" instead of "can't", unless I'm missing something.

I would find even a statement from Signal like "we can't access our users content" to be tenuous and overly-optimistic. Like, when I heard the word "can't" my brain goes to: there is nothing anyone in the company could do, within the bounds of the law, to do this. Employees at Slack could turn off the technical measures preventing this from occurring. Employees at Signal could push an app update which side-channels all messages through to a different server, unencrypted.

Better phrasing is "Employees of Slack will not access the underlying content".

I don't really agree with this statement. Signal literally can't read user data right now. The statement is true, why can't they use it?

If they can't use it, nobody can. there are no services that can't publish an update reversing any security measure available. Also doing that would be illegal, because it would render the statement "we can't access our users content" false.

In Slack case, it is totally different. Data is accessible by Slack systems, the statement "we can't access our users content" is already false. Probably what they mean is something along the lines of: "The data can't be accessed by our systems, but we have measures in place that block the access to most of our employees"

If it's verifiably E2EE then I consider "we can't access this" to be a fairly powerful statement. Sure, the source could change, but if you have a reasonable distribution mechanism (e.g. all users get the same code, verifiably reproducible) then that's about as good as you can get.

Privacy policies that state "we won't do XYZ" have literally zero value to me to the extent that I don't even look at them. If I give you some data, it's already leaked in my mind, it's just a matter of time.

1. ACLs

2. The systems that provision those ACLs

3. The policies that determine the rules those systems follow.

In other words, the model training batch job might run as a system user that has access to data annotated as 'interactions' (at timestamp T1 user U1 joined channel C1, at timestamp T2 user U2 ran a query that got 137 results), but no access to data annotated as 'content', like (certainly) message text or (probably) the text of users' queries. An RPC from the training job attempting to retrieve such content would be denied, just the same as if somebody tried to access someone else's DMs without being logged in as them.

As a general rule in a big company, you the engineer or product manager don't get to decide what the ACLs will look like no matter how much you might feel like it. You request access for your batch job from some kind of system that provisions it. In turn the humans who decide how that system work obey the policies set out by the company.

It's not unlike a bank teller who handles your account number. You generally trust them not to transfer your money to their personal account on the sly while they're tapping away at the terminal--not necessarily because they're law abiding citizens who want to keep their job, but because the bank doesn't make it possible and/or would find out. (A mom and pop bank might not be able to make the same guarantee, but Bank of America does.) [*]

In the same vein, this is a statement that their system doesn't make it possible for some Slack PM to jack their team's OKRs by secretly training on customer data that other teams don't use, just because that particular PM felt like ignoring the policy.

[*] Not a perfect analogy, because a bank teller is like a Slack customer service agent who might, presumably after asking for your consent, be able to access messages on your behalf. But in practice I doubt there's a way for an employee to use their personal, probably very time-limited access to funnel that data to a model training job. And at a certain level of maturity a company (hopefully) also no longer makes it possible for a human employee to train a model in a random notebook using whatever personal data access they have been granted and then deploy that same model to prod. Startups might work that way, though.

> Provisioning To minimize the risk of data exposure, Slack adheres to the principles of least privilege and role-based permissions when provisioning access—workers are only authorized to access data that they reasonably must handle in order to fulfill their current job responsibilities. All production access is reviewed at least quarterly.

so... seems like they very clearly can.

Sure, it's easier and less effort if the program is actually open source, but it's absolutely still possible to verify on bytecode, decompiled or disassembled programs, too.

It's ok not wanting to run your own hardware at your own premises. But the solution is to run a solution that is end-to-end encrypted so that the hosting service cannot get at the data. cryptpad.fr is another great piece of software.

Services Data Collection Disclosure

"Synology only uses the information we obtain from technical support requests to resolve your issue. After removing your personal information, we may use some of the technical details to generate bug reports if the problem was previously unknown to implement a solution for our products."

"Synology utilizes the information gathered through technical support requests exclusively for issue resolution purposes. Following the removal of personal data, certain technical details may be utilized for generating bug reports, especially for previously unidentified problems, aimed at implementing solutions for our product line. Additionally, Synology may transmit anonymized technical information to Microsoft Azure and leverage its OpenAI services to enhance the overall technical support experience. Synology will ensure that personally identifiable information, such as names, phone numbers, addresses, email addresses, IP addresses and product serial numbers, is excluded from this process."

I used to just delete privacy policy update emails and the like but now I make a habit of going in to diff them to see if these have been slipped in.

and finally the most aggregious and dangerous: censorship at the lowest level of information before it can ever get anywhere near peoples fingertips or eyeballs.

Likely they are just hoping to not get caught and/or consider it cost of doing business. GDPR has truly shown us (as if we didn't already know) that compliance must be enforced.

I am not defending Slack, and I can think of number of cases where training on slack messages could go very badly (ie, exposing private conversations, data leakage between workspaces, etc), but I think it helps to understand the context before reacting. Personally, I do think we need better controls over how our data is used and slack should be able to do better than "Email us to opt out".

That's incorrect -- they're stating that they use your "messages, content, and files" to train "global models" that are used across workspaces.

They're also stating that they ensure no private information can leak from workspace to workspace in this way. It's up to you if you're comfortable with that.

Their channel recommendations: "We use external models (not trained on Slack messages) to evaluate topic similarity, outputting numerical scores. Our global model only makes recommendations based on these numerical scores and non-Customer Data"

Meaning they use a non-slack trained model to generate embeddings for search. Then they apply a recommender system (which is mostly ML not an LLM). This sounds like it can be kept private.

Search results: "We do this based on historical search results and previous engagements without learning from the underlying text of the search query, result, or proxy" Again, this is probably a combination of non-slack trained embeddings with machine learning algos based on engagement. This sounds like it can be kept private and team specific.

autocomplete: "These suggestions are local and sourced from common public message phrases in the user’s workspace." I would be concerned about private messages being leaked via autocomplete, but if it's based on public messages specific to your team, that should be ok?

Emoji suggestions: "using the content and sentiment of the message, the historic usage of the emoji [in your team]" Again, it sounds like they are using models for sentiment analysis (which they probably didn't train themselves and even if they did, don't really leak any training data) and some ML or other algos to pick common emojis specific to your team.

To me these are all standard applications of NLP / ML that have been around for a long time.

I know it would be impossible but I wish we go back to the days when we didn't have Slack (or tools alike). Our Slack is a cesspool of people complaining, talking behind other people's backs, echo chamber of negativity etc.

That probably speaks more to the overall culture of the company, but Slack certainly doesn't help.You can also say "tool is not the problem, people are" - sure, we can always explain things away, but Slack certainly plays a role here.

It’s easy to come across poorly in writing, but that issue has no easy resolution unless you’re prepared to ban Slack, email, and any other text-based communication system between employees.

Slack can sometimes be a place for people who don’t feel heard in conventional spaces to vent — but that’s an organisational problem, not a Slack problem.

The idea that the mechanism of how people communicate affects what people communicate is a pretty foundational concept in media studies (a topic which is generally met with a hostile audience on HN). Slack almost certainly does play a role, but people who work in technology are incentivized to believe that technology does not affect people's behaviors, because that belief allows people who work in technology to be free of any and all qualitative or moral judgements on any grounds; the assertion that technology does not play a role is something that technology workers cling to because it absolves them of all guilt in all situations, and makes them, above all else, innocent in every situation. On the specific concept of a medium of communication affecting what is being communicated, McLuhan took these ideas to such an extreme that it's almost ludicrous, but he still had some pretty interesting observations worth thinking on, and his writing on this topic is some of the earlier work. This is generally the place where people first look, because much of the other work assumes you've understood McLuhan's work in advance. https://en.wikipedia.org/wiki/Understanding_Media

Yep. Fun fact, my last workplace had a fairly nontoxic Slack... but there was a whole second Slack dedicated to bitching and shitposting where the bosses weren't invited. Humans gonna human.

A very inclusive company on paper that was very exclusionary behind the scenes.

If Slack is a cesspool, that’s because your company culture is a cesspool.

Slack is surely not the generator of toxicity but it seems obvious it could act at increasing the bandwidth.

You can't have it both ways.

choosing an emoji, and…

a fun little internal only stock picker tool, that suggests to us some fun stocks to buy based on the anonymised real time inner monologue of hundreds of thousands of tech companies

Finally someone has figured out a sensible application for "AI". This is the future. Soon "AI" will have a similar connotation as "NFT".

I had to up my slack emoji game after joining my current employer

> To do this while protecting Customer Data, we might use an external model (not trained on Slack messages) to classify the sentiment of the message. Our model would then suggest an emoji only considering the frequency with which a particular emoji has been associated with messages of that sentiment in that workspace.

This is so stupid and needlessly complicated. And all it does is remove personality from messages, suggesting everyone conforms to the same reactions.

> If you want to exclude your Customer Data from helping train Slack global models, you can opt out.

I don't understand how both these statements can be true. If they are using your data to train models used across workspaces then it WILL leak. If they aren't then why do they need an opt out?

Edit: reading through the examples of AI use at the bottom of the page (search results, emoji suggestions, autocomplete), my guess is this policy was put in place a decade ago and doesn't have anything to do with LLMs.

Another edit: From https://slack.com/help/articles/28310650165907-Security-for-...

> Customer data is never used to train large language models (LLMs).

So yeah, sounds like a nothingburger.

But they haven't actually said that. It also happens that people say things based on faulty or disputed beliefs of their own, or people willfully misrprepresent things, etc

Until they actually do say something as explicit as what you suggest, they haven't said anything of the sort.

I feel like that is explicitly what this is saying.

Yes if they only train say, classifiers, then the only thing that can leak is the classification outcome. But these things can be super subtle. Even a classifier could leak things if you can hack the context fed into it. They are really playing with fire here.

i'm not entirely convinced that classifiers and LLMs are disjoint to begin with

And this can leak: for instance, typing "a good business partner for foobars is" might not send that text upstream per se, but would be consulting a local model whose training data would have contained conversations that other Slack users are having about brands that provide foobars. How can Slack guarantee that the model won't incorporate proprietary insights on sourcing the best foobar producers into its choice of the next token? And sure, one could build an adversarial model that attempts to minimize this kind of leakage, but is Slack incentivized to create such a thing vs. just building an optimal autocomplete as quickly as possible?

Even if it were just creating classifiers, similar leakages could occur there, albeit requiring more effort and time from attackers to extract actionable data.

I can't blame Slack for wanting to improve their product, but I'd also encourage any users with proprietary conversations to encourage their admins to opt out as soon as possible.

This is explained literally in the next sentence after the one you quoted: "We protect data privacy by using rules to score the similarity between the typed text and suggestion in various ways, including only using the numerical scores and counts of past interactions in the algorithm."

If all the global model sees is {similarity: 0.93, past_interactions: 6, recommendation_accepted: true} then there is no way to leak tokens, because not only are the tokens not part of the output, they're not even part of the input. But such a simple model could still be very useful for sorting the best autocomplete result to the top.

> Contact us to opt out. [...] To opt out, please have your Org or Workspace Owners or Primary Owner contact our Customer Experience team at [email protected] with your Workspace/Org URL and the subject line “Slack Global model opt-out request.” [...]

I would recommend https://mattermost.com as an alternative.

Sounds like an invitation for malicious compliance. Anyone can email them a huge text with workspace buried somewhere and they have to decipher it somehow.

Example [Answer is Org-12-Wp]:

"

FORMAL DIRECTIVE AND BINDING COVENANT

WHEREAS, the Parties to this Formal Directive and Binding Covenant, to wit: [Your Name] (hereinafter referred to as "Principal") and [AI Company Name] (hereinafter referred to as "Technological Partner"), wish to enter into a binding agreement regarding certain parameters for the training of an artificial intelligence system;

AND WHEREAS, the Principal maintains control and discretion over certain proprietary data repositories constituting segmented information habitats;

AND WHEREAS, the Principal desires to exempt one such segmented information habitat, namely the combined loci identified as "Org", the region denoted as "12", and the territory designated "Wp", from inclusion in the training data utilized by the Technological Partner for machine learning purposes;

NOW, THEREFORE, in consideration of the mutual covenants and promises contained herein, the receipt and sufficiency of which are hereby acknowledged, the Parties agree as follows:

DEFINITIONS

1.1 "Restricted Information Habitat" shall refer to the proprietary data repository identified by the Principal as the conjoined loci of "Org", the region "12", and the territory "Wp".

OBLIGATIONS OF TECHNOLOGICAL PARTNER

2.1 The Technological Partner shall implement all reasonably necessary technical and organizational measures to ensure that the Restricted Information Habitat, as defined herein, is excluded from any training data sets utilized for machine learning model development and/or refinement.

2.2 The Technological Partner shall maintain an auditable record of compliance with the provisions of this Formal Directive and Binding Covenant, said record being subject to inspection by the Principal upon reasonable notice.

REMEDIES

3.1 In the event of a material breach...

[Additional legalese]

IN WITNESS WHEREOF, the Parties have executed this Formal Directive and Binding Covenant."

Sick and tired of these default opt in explicit opt out legalese.

The default should be opt out.

Just stop using my data.

Subject: Slack Global Model opt-out request.

Body:

.slack.com

Please opt the above Slack Workspace out of training of Slack Global Models.

Please also scold them for behaving unethically and perhaps breaking the law.

Microsoft Teams, Slack, etc… all do the same thing under the hood.

Nobody is pumping the text into an LLM training. The examples make this very clear as well.

Comment section here is divorced from reality.

I think this deserves more attention. For many tasks like contextual recommendations, you can get most of the way by using an off-the-shelf model, but then you get a floating-point output and need to translate it into a binary "show this to the user, yes or no?" decision. That could be a simple thresholding model "score > θ", but that single parameter still needs to be trained somehow.

I wonder how many trainable parameters people objecting to Slack's training policy would be willing to accept.

Like when you send an email to [email protected] with that perfect subject like (jeez, really?) what exactly does the customer support rep do on their end to opt you out?

Now is definitely the time to get/stay loud. If it dies down, the precedent has been set.

Disclaimer: I work at Tonic

How do you handle proprietary data being leaked? Sure you can easily detect and redact names and phone numbers and addresses, but without significant context it seems difficult to detect whether "17 spices - mix with 2lbs of white flour ... half teaspoon of salt, 1 tablespoon of thyme [...]" is just a normal public recipe or a trade secret kept closely guarded for 75 years.

i deleted my reddit and github accounts when they decided the millions of dollars per month they're receiving from their users wasn't enough. don't have the power to move our shop off slack but rest assured many will as a result of this announcement.

Companies doing this would make me much less angry if they used an opt-in model only for future data. I didn't have a crystal ball and I don't have a time machine, so I simply can't stop these companies from using my work for their gain.

Hosting my own service rather than using a free SaaS solution that is entirely someone else's problem? There's a significant difference there. I've been running Linux servers either professionally or personally for almost 25 years, so it's not like it's a giant problem... but my work has been increasingly non-technical over the past 5 years or so, so even minor hiccups require re-acclimating myself to the requisite constructs and tools (wait, how do cron time patterns work? How do I test a variable in bash for this one-liner? How do iptables rules work again?)

It's not a deal breaker, but given the context, it's definitely not not a pain in the ass, either.

“Yes,” said Arthur, “yes I did. It was on display in the bottom of a locked filing cabinet stuck in a disused lavatory with a sign on the door saying ‘Beware of the Leopard.”

This Is Fine.

They don't "build" them this way (whatever that means) but if training data is somehow leaked, they're off the hook because they didn't build it that way?

And this is just one of many, many problems associated with the platform.

1. Great UX folks almost never work for free. So the UX of nearly all OSS is awful.

2. Great software comes from a close connection to users. When your software is an OS kernel that works just fine for programmers, but how many OSS folks want to spend their free time on zoom talking to hundreds of businesses and understanding their needs, so they can give them free software?

See also: year of Linux desktop

This is not ok. We didn't have to reach out by email to sign up, this should be a toggle in the UI. This is deliberately high friction.

In real life policies have to be enforced and it's not always technically feasible to do so. It doesn’t even have to be calculated or malicious!