This is pure speculation, in descending order of probability:
- Random coincidence, which you notice in the one case where it happens but not in the 99 cases where it does not.
- You did search for it or something related. Or, you got one clip due to one of the other reasons, clicked it, now you're getting more.
- Correlation via IP
- Correlation via location
Since we're talking about YouTube recommendations, not ads, I kinda doubt the last two though. That would provide very little benefit and be a huge privacy risk. Location is certainly considered to some extent, but I would expect this to be on a country/region level, not city and certainly not fine enough for your friend to meaningfully influence it.
Search history including Google search history, I’ve found.
I also completely believe that YouTube correlates via IP for at least not-logged-in views (or at least tries to associate to accounts even if they've never logged in); I get bleedover to my iPad from my completely disassociated PC but not my Mac that’s logged into a different account.
(Also maybe I put too much effort into tailoring my own YouTube recommendations, but 99% of the time when they start going awry, I have a pretty good idea what triggered it. Random coincidences don't happen...)
Like others have commented, I too am very certain Youtube uses IP address. My family and I are behind the same cable modem, and oftentimes my family's Youtube "related videos" will start popping up in my Youtube home page, and vice versa, even though I have no interest in or have watched those videos.
Interestingly, I seem to recollect this happening only in the past few months or so, maybe a feature they turned on recently?
Easy enough to test, just start talking with another person for about 2-5 minutes straight repeating the words purple dog collar over and over in earshot of the device you suspect.
You'll be amazed when within 30 minutes to an hour all the ads on all devices will suddenly show purple dog collars or pet related items.
Really? I info-dump on my partner all the time about all sorts of specific products. She’s never mentioned one randomly pop up in an ad. I can honestly say I’ve performed your test thousands of times.
As a counter-experiment, try thinking of some product and intentionally DON'T search for it or say or type it anywhere. Make sure it's also not something you heard about recently from a friend or in the media. If you then see ads appear for that product you can be sure it's just baader-meinhof.
I have found out that google uses atleast 1 of the last 2 "doubt" ones. My sister was watching my little pony on her laptop without being logged in and her reccomendations would "leak" into my reccomendations on different computer with logged in account. It was very annoying and even flagging the videos as "not interested" or "don't show" wouldn't help much. I think I "fixed" it by disabling yt history or something. I am not sure. This was 2...3 years ago
They do correlation with twitch though. Went there once to support a charity stream, and I got flying sims and among us in recommend for weeks (or maybe only a week?)
I think if the IP is detected as used by one person the recommendation engines relies on it more heavily. For example in an office the same IP is used by many so its weight in the recommendation engine is relatively low. Additionally more and more ISPs are using longer lasting DHCP leases. I know my Comcast IP hasn't changed for over a year. This can better tie a person to the same IP.
Happened to me as well. I suspect that your Android phone noticed his Wifi network, mapped it to his Google id, and now yours and his YT accounts are partially sharing recomendations. That's the least pernicious mechanism I can come up with at least.
Location tracking - they don’t use the mic as many suspect, but definitely know where you were and who you were with, and presumably what you were watching if it was google-linked. Perhaps there’s Bluetooth/wifi mechanisms in there too to make it easier but it’s the same idea. That said I don’t see why they’d do that for YouTube recommended… maybe coincidence
We actually can by disassembling the YouTube.ipa, examining the calls, and checking for AVAudioSessionDataSourceDescription objects. Tracing their lifetimes.
It’s the same method Apple uses to check for “unauthorized internal API use” people get busted for.
We can also replace the AVAudioSession class with our own proxy to examine calls.
Ah right, missed that part. In this case it’s rather easy as Safari will respect your settings. Just deny microphone access. Under settings->safari->Microphone which is down under “settings for websites”
I was recently talking to my family about a pretty obscure topic. The topic appeared on our family TV in less than 15 minutes.
The plot twist in this story is that the TV was tuned to a very popular, national TV channel, and that's where the topic was mentioned. If those coincidences happen on the radio or on TV, we laugh, shrug them off and go on with our day. If they happen on the internet, though, a lot of us immediately turn to conspiracy theories.
The big-media narrative on tech really isn't doing us any good.
Honestly, this reply is well-written but detached from reality.
Google uses your IP, fingerprinting, geolocation and more for personalization, at least if you're not logged in.
Anything else would be a huge surprise given Google's business model.
There also seems to be correlation to previously used accounts if you mainly use YouTube without logging in, but use a Google account on the same device.
I experience this myself and, despite always leaning towards psychological explanations in such cases, calling this a "conspiracy theory" seems very off.
I mainly use YouTube in incognito and rarely log in, but I heavily use my Google account, dating back to 2006 or so.
My YouTube suggestions when not logged in are "personalized", although I use YouTube almost exclusively in incognito mode.
They are not personalized as aggresively as when I am logged in — since I mainly use YouTube in incognito, my suggestions when logged in tend to be very repetitious and boring.
But there is definitely some personalization going on based on IP, geolocation and browser fingerprinting.
> The big-media narrative on tech really isn't doing us any good.
HN narratives tend to be similarly distorted, see any thread on environmental change, dependence on fossil fuels or advertising exponential economic growth based on burning fossil fuels and trashing other natural resources.
Search YouTube in incognito mode for anything random and not connected to you.
Keep scrolling.
Observe how here and there are results unrelated to your search terms, but clearly aimed at you. They are discovering the user’s identity through UI events, such as scrolling and momentarily pausing to look at a video the user recognises.
The identifier videos are sparse enough, and only one of them is on screen at a time, so the mechanism must get rather good resolution this way.
I noticed the far-down personalized "related" videos already, and I am often hesitant to hover over some videos because sure, they also track user behavior.
But the videos on the start page are already obviously personalized when I use YouTube without being logged in!
I assume that there are many factors contributing to the "interest cohort" assigned to "anonymous" sessions.
The idea of specific "identifier videos" is new to me, but it sounds reasonable to me that they do that.
I still assume that Google is careful to limit and/or obfuscate the usage of data points that might be considered a GDPR violation.
But I haven't studied their TOS or privacy laws enough to estimate where the line is even drawn and how blurry it can become when the user is forced to agree to the consent popup.
For example, is correlating logged-out Google accounts using factors such as the IP address even a clear privacy law violation?
Or is there some wiggle room to just use some "anonymized" (intentional scare quotes) form of that data?
Obviously they make sure that you accept the consent popup before using the site at all when not logged in.
There was a private showing of the next episode of Better Call Saul yesterday at a film festival of sorts, the rest of us won't see that episode until July.
It's probably just trending topic and you fit a similar profile to people that would watch the show/Breaking Bad. My viewing habits are pretty stable and I noticed I get recommendations for The Wire even after resetting all my cookies as well as not logging in to my Google account
Just so you know, over the past couple of weeks I've been getting clips of Breaking Bad recommended to me - which I think it's something that's just trending and it's getting a lot of views.
I think it's random coincidence BB is getting traction now.
Honestly I would love to see it be the norm, maybe even law, that a recommendation is accompanied by a reason for the recommendation. Is this because I watched a similar video? Or is it because you snooped on my microphone when I was at the local cafe?
Story time: I have disabled all types of tracking in Twitter, same for Amazon. Now, I bought a few e-books for my wife. Those are very girly books that definitely don’t fit my profile.
Twitter is now showing me kindle ads for those books.
Twitter has a menu offering to explain why I see it. The explanation says: Amazon wanted to advertise this to people in your region.
I say they lie… We need Blackbox monitoring against those behaviors with some legal teeth.
> This might not be possible - there are many inputs into the model; it would be impossible to trace which one tipped the scale.
I am not a fan of problem-solving via legislation, but I wouldn't lose a lot of sleep if Big Tech was no longer allowed to burn billions of CPU cycles on making opaque, inexplicable guesses as to what ads are most likely to make them money if shoved in front of my eyeballs.
I get the annoyance, but also, targeted ads means both a more efficient economy and information environment.
Mass ads means more money goes into ads and ads are irrelevant to many people. Only big co’s can afford mass ads. Targeted ads means ad money is vastly more efficient, and your information environment is less cluttered. One-man shops can afford targeted ads.
I like seeing relevant ads. I like supporting small businesses targeting me. Privacy isn’t a concern either because the three-letter agencies can find out whatever they want about me if they want anyway.
All the more reason for transparency if no one can explain how it works. Ideally "my model is too opaque" isn't an excuse, but an admission of negligence.
Fun tangent, I recently went to Rally's (a really hard to find burger joint around my parts). I rarely go so its always a treat with their shake/glorious fries.
Found they've completely replaced the order taker with a robot, with order signage saying "help train our robot".
No opt out, and you can't get to an employee unless it recognizes the word employee. Recording in progress.
The ADA non-compliant parts aside for people with voice related disabilities, its BS what companies are forcing people to do just to buy a burger. I'm not going back after that.
You just put in a pile of all the data you know about the user and session into a pile of linear algebra (some ML-based recommendation system or whatever), and recommendations come out. In other words, I doubt they even know the reason for any particular one.
Even fairly simple systems might not have a clear way to answer. Consider a movie recommendation system.
One simple way you could build such a system is to come up with a list of things about movies that might affect whether or not someone would like them, where for each thing on the list we can assign each moved a number from -1 to 1 that says how much of that thing the movie has. Call this list the movie's vector.
Some examples of things we might pick are how much comedy is in the movie, how much romance is in the movie, presence of A-list stars, how musical it is, and thing like that. We might also have items for specific stars or directors.
Then we could go through our movie collection and have someone figure out each movies scores for all those things in our list.
Then we could figure out for our users a list that lists for each of those things how important it is to that user, from -1 (I hate movies that have this!) to 1 (I love movies that have this!). Let's call this the user preferences vector. If we have a list for a given user of movies they have already watched and how they rated them on say a 0 to 5 scale then it is some straightforward math to figure out the user preferences vector for that user that does the best job classifying the movies they have already seen in a way that agrees well with that user's ratings.
That user preferences vector can than be used to recommend new movies and should work pretty well if (1) we picked a good list of things to score movies on, and (2) when we manually assigned the scores we got it right.
To predict how well a user would like a given move we just take their user preference vector and compute the dot product of it with the movie's vector. The more positive that result the more we think the user would like the movie.
With this system, it would be easy to tell someone why you recommended a movie. We could look at their preference vector and compare it to the movie and tell them things they really like that the movie has and things they really hate that the movie does not have.
But the system described above has a drawback. It is hard to figure out what factors to include in the movie classification. Should comedy for example be one item, or should it be broken down into several such as physical comedy, insult comedy, bodily function comedy, and so on?
Also, if you have a large collection of movies it is a lot of work to go through them all and score them on each factor. And if you later find out you need to add or remove factors you have to do it again.
It turns out that there is a way to sidestep both the "what should my factors include?" and "how do we get the factors scored?" problems.
What you do is just decide on how many factors you will have. So let's say we decide we are going to have 50 factors. We don't have to decide what they mean. We'll just call then F1, F2, F3, ... for now.
Initially we just assign each factor a random value from -1 to 1.
We also do the same thing for the initial user preference vectors. Just assign each factor in the preference vector a random value.
Then we can do a loop, consisting of these two steps:
1. Using the known 0 to 5 star ratings from users of films they have seen, adjust their preference vectors so that ordering movies by the dot products of the movie vectors with the preferences vector matches the ordering by the user's star ratings.
2. Same thing, except instead of adjusting the preference vector to better work with all the movies a user has seen, adjust the movie vector of each movie to better work with the preference vectors or all the users have have rated that movie.
Keep looping until things aren't changing much. You then end up with a set of movie vectors for you movie catalog and preference vectors for your users that do a good job of ordering movies that user has seen that matches well with how the user rated those movies, and that likely does a good job predicting how well they will like new movies.
This was with an arbitrary decision to have 50 factors in our movie vectors. Since the process described above can be entirely automated and is pretty quick, we can experiment with difference vector sizes.
We end up with a recommendation system that is very likely much better than the one we would get if we picked the items that went into the movie vector.
It is still a simple system, just like it was when we came up with the components ourselves and assigned them by watching the movies ourselves.
But notice that we now have no idea what the heck the factors in the movie vector mean. We can no longer tell someone we recommended a movie because they like comedy and romance and hate kids movies and this movie fits that well.
We could tell them they liked past movies that have a high score in components 12, 15, 23, and 102 and a low score in components 19, 77, 83, and 107, but they are probably not going to find that to be a useful answer.
That's nothing...! My friend's smart-TV asked if I wanted to login on the Youtube app, presenting me with my username, after I'd spent an evening or two showing my new friend a bunch of YT vids on it (music/bands and more).
My phone and tablet were indeed on the same wifi, but they're not Android. And neither are my friend's devices. Except for the TV perhaps, IDK what the OS is there, it's not Apple for sure.
But I did pull up a heck of a lot of my favourite videos, for many hours. And previously the Youtube app on the smart-TV was mostly un-used. I guess it thought I might be the owner / primary user of the smart-TV.
I thought it spooky.
(I'm a software engineer by trade, I kinda totally understood how it might be done — but I'm also quite a privacy advocate, hence: spooky)
Location-based tracking. In this case, your phone was on your friend's wifi so they know whatever that wifi was doing while you were there, you were in the room.
Unless you've already completely de-googled your life, this is the most straightforward way to link you to the activity. All it would take is for you to have pinged Google's servers with that phone just once, ever, and they've got it fingerprinted. So when it shows up on the same network accessing YouTube, they placed you.
One use of Google Maps, Google Home, logging into Gmail or YouTube is all it takes to compromise a phone.
This happened to me in 2018. We were chatting with an acquaintance about horse racing. I never showed an interest in that topic or visited the location. Just after 5 mins he left I received an ad from Instagram on Horse Racing competition which was happening in the same city.
My only assumption was Instagram/Facebook was somehow listening our conversation.
Were you friends with that acquaintance on fb/instagram? Were you on the same wifi/network?
If the answer to either of those two is "yes", and if your friend googled or liked/followed horse racing content on instagram/fb, then the algo can easily determine that indeed you might be interested in it and recommend it to you. Requires zero horse racing related searches on your own devices, just requires a person with a first degree connection to you doing those searches.
If the system knows that your close social link with some similar interests interacts (searching, reading, liking, following, chatting about online with others, etc.) a lot with a specific type of content, it is a pretty straightforward idea to suggest that specific type of content to you, even if you didn't interact with that type of content online yourself yet.
What's worse is that my wife and I were talking about particular states, then the spam calls we started to get had area codes from those states. Once is a coincidence. 5 times is a pattern.
Since we don't leave any apps open on our Android phones, we assume it's because Google was listening (their version of Siri is Bixby, which we use from time to time) then selling that information. It was probably a one-off, since this was only for a short window of time (2 weeks), but it was blatant.
I keep disabling Bixby permissions (x5 apps) and Android re-enables them on updates or whether I left some "allow to change system settings" checked, it's a huge information drainage.
I listened to a podcast which mentioned a relatively obscure topic in passing using the basic samsung music app on my phone which didn't have a SIM card in, location turned on or an internet connection.
Later that day I see an advert for said obscure topic served on a web page.
Is it possible that there is someone transcribing podcasts or at least scraping databases of their RSS feeds and somehow my music player app is broadcasting that I've listened to a particular file(after receiving an internet connection)? The alternative is that the machines really are listening.
You would have had to at some point download the podcast, so at that point before they served the download to you, they would have used your IP to serve you a relevant ad based on that location data and inserted it into the podcast. A lot more podcasts are doing this unfortunately. I don't remember the name of major platform or company enabling this.
I can't help but think these are confirmation bias events (if the clips were for "Battlestar Galactica" and not BB you wouldn't be writing about it), but similar things happened to me too:
- I walked by an e-bike store, stopped to look at the model displayed on the window, a few days later I got ads for e-bikes on Instagram (I can't remember now if it's for the particular store's brand).
- I opened Instagram at a carwash, a few days later, ads for car-detailing services - here I have a strong feeling it used my location data.
You can't be paranoid enough to imagine the disgusting amount of location data being slurped up by unscrupulous apps and traded for any purpose at all. Targeting ads based on having been near or in a store is completely standard.
> By using location services on cell phones and 3rd party satellite imagery and mapping data, Facebook is able to tell you if someone visited your store within 28 days of clicking on your ad, all while filtering out employees or people who move past your stores without going in.
> Facebook can't measure all store visits. This is because some people don't have location services turned on in their phone, or are not recognizable by 3rd parties.
I shared a car ride with a friend for 20 min, and my colleague, being knowledgeable in trivia, mentioned an obscure historical figure that I couldn't even spell the last name of. I came back home, and on my Google feed there he was, the obscure historical figure with his last name fully spelled out in a biography.
I never connected to my colleague's network in any form (we were in his car); I didn't search for this historical figure (I don't even know how to spell the name correctly). And most definitely not a coincidence as I had never heard of this person before or after the brief mentioning by my colleague. I don't think my colleague searched for this person on his phone recently either as it was some trivia knowledge he randomly recalled.
This happened several times with this particular colleague as he likes to mention random trivia. I have yet to find a plausible mechanism for this phenomenon (unless it is an open mic).
My explanation would be fairly trivial: those videos were currently being pushed more broadly by the YT algorithm (propably based on some rough demographics that you two share - age, gender, location, broad interests...). You two probably saw a lot more of the same videos that you didn't talk about.
PS: my understanding is that a lot of social media sites use a strategy where they somewhat randomly push single posts by reliable creators, to make sure that those stay hooked as well. So that could be an explanation why you'd suddenly see something a bit more niche.
Indeed, this is my guess although it means I am having considering my colleague as an unreliable narrator (he is not "randomly recalling" as he claims. He is just regurgitating what he saw on his phone earlier in the day).
On the other hand, the obscurity of the feed subject makes me think YT/Google wouldn't be pushing those on a broader audience, but maybe the algorithm is just that good at nerdsniping us.
Could it be that your colleague just spent the morning googling these subjects, and Google notices a correlation between the stuff your colleague googles and the stuff you later look up?
e.g: it's using location-tracking to link you two, not an open mic and realtime transcription.
Hypothetically there is a common root source further back from your friend. IE a new book/documentary has people talking and the general buzz triggered both your friend to comment and Google independent of each other.
Samsung TV's are known to do this kind of thing when they are within ear shot of people talking. If your phone has been set to allow access to its microphone for all the apps (or some other combination of unexpected apps), just as Android will activate the "location" permission from time to time on an update, then this kind of thing would not be surprising. Check your permissions.
I've worked with Adobe Target and the Adobe Experience Cloud, both are used for marketing... Their own docs love to use terms like 'household' because they know their techniques are not good at targeting true individuals but instead groups of users with overlapping identifiers such as IP, devices, location
how did you watch the episode? many streaming services, devices, or smart TVs track what you're watching.
or more likely, you googled something breaking bad related while watching or talking about it.
or even more likely, it's a coincidence. my youtube recommendations also had a breaking bad clip in them yesterday, despite me not watching it, talking about it, or thinking about it. better call saul season ended recently, and people are searching for breaking bad clips. it doesn't have to be more complicated than that.
I’m not convinced YouTube is using such data for its recommendations engine. We are talking about the app’ that serves you ads in languages you don’t speak whenever you travel abroad.
Location sharing, wifi/Bluetooth scanning and then easy to pair accounts signed in to devices appearing at same location, being in each others contacts helps as well.
I went to a wedding and chatted with someone for 5 minutes and the next day they were a suggested friend on Facebook. Never seen that person before in my life.
I believe Facebook recommendations are somewhat bidirectional - if they visited your Facebook page (by searching you, even if they didn't add you), I think Facebook will recommend that you add them.
This one is easy - Facebook recommends people based on geolocation. It's very common to get friend suggestions to people you've met, or just been in the same building with.
I've got a million of these, but one of the weirdest ones was when a friend of mine suggested we go to one of the islands northwest of Seattle for the weekend. The next day, I was googling "things to do in..." and it autocompleted to the island's name, even though it's a relative backwater compared to some of the other places around. Best I can figure is he googled it, they saw us in proximity to each other, then predicted that we were planning to go there.
Pure guessing but your phoned showed your location and what you were watching (through your friends data), and thus recommends Breaking Bad to you now.
Not even location, just OP's phone discovered that a router with a unique MAC that just LOVES streaming BB is sometimes nearby. Google's "AI" dictates that means OP would absolutely love BB too, it might be flawed logic but it probably works
I sometimes wonder if phones are actively recording audio, but more likely these companies are all sharing data or some app on your phone is doing it. No matter how much I change my privacy settings to the max, I still have the same things happen to me where it is extremely unsettling.
It depends on your privacy settings, the local privacy laws in effect, and what you did over at your friend's place.
If you watched the episodes on a device connected to your Google account in any way I think the answer would be obvious.
Another reason you might be seeing ads for it is that you might've looked up an actor or character on your phone which Google linked back to the show.
It's also possible that Google noticed your account was suddenly shared by someone who showed interest in the show and "infected" your account.
Another possibility is that Google picked up on the show if you used a Chromecast on the same network to watch an episode; media controls are broadcast throughout the network.
There are more far fetched reasons. For example, if your location history is on and the neighbourhood where your friend lives is seeing a sudden spike in interest in Breaking Bad (watch parties? Idk), you may have been flagged as interested purely by geographical proximity.
It can also be a combination of Baader-Meinhof and your behaviour around the ad (looking at it longer, possibly unintentionally out of surprise) that reinforced the topic in your ad preferences.
Tl;dr, Google can learn a lot about you through indirect means. It's kind of their business model. Compare to the famous case where a supermarket knew someone in a US household was pregnant before the other family members did based on purchasing behaviour. It's scary how much you can tell about a person from their shopping patterns alone and Google has their hands in so much more.
None of this should probably be happening if you've disabled personalised ads (https://adssettings.google.com/), at least not on Google's platform.
"got connected to her wifi"
thats the reason you are getting the recommendation.
Youtube stores the data of videos watched using wifi and then tries to recommend other devices connected on the wifi.
Reason 2: have you talked anything about the show ? because it has been conspired and seen that google listens thru our microphone in devices and recommend ads even if you haven't searched for them.
I regularly chat about obscure music with people who are essentially strangers to me, I don't know their real names, locations, etc, but we share a lot of youtube links to music videos that have a few thousand views, averaging maybe a few views a day.
I believe this connection is picked up and reflected in our youtube recommendations, evidenced by recommendations that have nothing to do with music but seemingly relate to the interests of the people I share music recommendations with.
For instance, one of these friends recommends the song National Pastime - Pictures On Your Wall and sends me a link to it on youtube (~4k views over 7 years.) We probably both watch it at the same time, and might have been the only two people to watch it that day. For the next day or so, youtube recommends gameplay videos of f2p gacha-shit, which I utterly despise but know that person has an interest in. And they probably get videos about submarines and airplanes in their feed, because that's the sort of thing I often watch.
Pretty much this. The least obvious at the time, but looks extremely obvious in retrospective.
You and your friend are tightly linked on the social graph. Your friend starts watching a new show and keeps googling and watching stuff about it and talking about it with other people online.
While the system won't recommend to you every single thing your friend watches and likes, it knows that for some of those the likelihood of your tastes matching is high. E.g., you might not like horror movies unlike your friend, so the algo wont be recommending them to you, despite your friend watching those. But the algo knows you both like crime dramas, so as soon as your close-social-link-connection-friend starts watching and googling and talking about Breaking Bad, the algo thinks you might like it too, so you start getting suggestions for it.
It is kind of silly, but it is indeed scarily way more accurate/effective to predict recommendations that way, as opposed to doing very invasive stuff like actually trying to listen to mic or correlate locations or whatever else (not even mentioning that on modern phone OSs it is somewhere between very difficult and pretty much impossible to do in a legitimate way).
Tldr: if you have close social connections whose certain media tastes match yours in some specific niches, don't be surprised that when one of those social links starts watching and enjoying something in those specific niches (and googling about it and talking about it to others online and etc.), you might get recs for that type of content too.
Accelerometers can be used to detect speech, with a high enough resolution they essentially can be used like microphones. The audio data inferred doesn't need to be good enough for humans, just good enough for some AI algorithms to produce a perceptual hash.
I had a 5 minute conversation with my niece at the park about rotary phones and now my feed is full of rotary phone-related articles. This happens more times than I can remember.
My gut reaction is that it listens to me, either directly or via some app, but I know Google has refuted this fact. I suppose this is testable too, so I tend to fall on the confirmation bias side of things.
I am confident it did not, that conversation was out of my mind as soon as it ended, which was why I was so surprised to see it dredged up again in my feed.
- Random coincidence, which you notice in the one case where it happens but not in the 99 cases where it does not.
- You did search for it or something related. Or, you got one clip due to one of the other reasons, clicked it, now you're getting more.
- Correlation via IP
- Correlation via location
Since we're talking about YouTube recommendations, not ads, I kinda doubt the last two though. That would provide very little benefit and be a huge privacy risk. Location is certainly considered to some extent, but I would expect this to be on a country/region level, not city and certainly not fine enough for your friend to meaningfully influence it.