Speaker 1: Thanks for tunion to tech Stuff. If you don't recognize
Speaker 1: my voice, my name is Oz Volshan and I'm here
Speaker 1: because the inimitable Jonathan Strickland has passed the baton to
Speaker 1: Cara Price and myself to host tech Stuff. The show
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Speaker 1: for listening. Welcome to tech Stuff, a production of iHeart
Speaker 1: Podcasts and Kaleidoscope. I'm oz Voloshian, and today will bring
Speaker 1: you the headlines this week, including the Chinese AI company
Speaker 1: that spooked the US tech sector. On today's Tech Support segment,
Speaker 1: we'll talk to four or four medius Jason Kebler about
Speaker 1: building mazes to trap AI web crawlers, and then we've
Speaker 1: got when did this become a thing? This time we
Speaker 1: look at why everyone is so obsessed with their own data,
Speaker 1: all of that on the weekend. Tech is Friday, January
Speaker 1: thirty first. Welcome, Welcome into another tech field of news cycle.
Speaker 1: Today and for the next few weeks, you'll get used
Speaker 1: to hearing a lot from me because Carra is out
Speaker 1: on leave, but she'll be back soon. And to fill
Speaker 1: the considerable void, I want to welcome one of our
Speaker 1: producers to the show, Eliza Dennis, to be my interlocutor
Speaker 1: slash captive audience as I run through the headlines. Eliza,
Speaker 1: thanks for jumping today.
Speaker 2: Of course I'm happy to be here.
Speaker 1: You know exactly what I want to talk about because
Speaker 1: you and Toy did all the research.
Speaker 2: I mean, yes, we all got sucked into that deep
Speaker 2: Seek vortex.
Speaker 1: Yes, it's a fascinating story. Monday was I think the
Speaker 1: craziest day I can remember in tech in terms of headlines,
Speaker 1: probably since the release of CHATCHYPT three in November twenty
Speaker 1: twenty two. US stock market lost a trillion dollars of value. Unbelievable,
Speaker 1: and the biggest loser was in Nvidia, the manufacturer of
Speaker 1: advanced aichips, which was down seventeen percent on Monday, representing
Speaker 1: almost six hundred billion dollars in value.
Speaker 2: The deep Seek freak.
Speaker 1: Deep seak freak. I like that. I mean, the reason
Speaker 1: it's my story of the week is because it has
Speaker 1: these two characteristics that define a lot of tech coverage,
Speaker 1: which is hype and doom. I think honestly, before Monday,
Speaker 1: most people didn't know anything about deep Seek. But the
Speaker 1: whole world, including US having getting up to speed. So
Speaker 1: deep Seek is primarily a research company that makes its
Speaker 1: own AI models, and it's released a number of different models,
Speaker 1: but the one that kind of shook the US tech sector,
Speaker 1: I would say was R one. R one was released
Speaker 1: on January twentieth, Inauguration day. Some online conspiratorial folks are
Speaker 1: saying it's no coincidence, but it's a so called reasoning model,
Speaker 1: and it doesn't just generate answers, but it's able to
Speaker 1: break down problems into smaller parts and consider multiple approaches
Speaker 1: to solving a problem. Until January twentieth, the state of
Speaker 1: the art on reasoning models was open AIS one and
Speaker 1: this was released to users on December fifth, twenty twenty four,
Speaker 1: so less than two months ago, and it was the
Speaker 1: first so called reasoning model to be released OH one.
Speaker 1: Because of these reasoning capabilities, I breaking down problems can
Speaker 1: solve much more complicated problems than GPT four more successfully.
Speaker 1: The cost of doing that is more computing power, and
Speaker 1: that drives up cost and that's where the crux of
Speaker 1: this R one story is. So what really makes our
Speaker 1: one remarkable is that it performs just as well on
Speaker 1: these benchmark tests as one if not better, but it's
Speaker 1: far cheaper because it requires less compute to solve the
Speaker 1: same problem. This means, at least according to deepseek, and
Speaker 1: there's some controversy here that it is twenty to fifty
Speaker 1: times less expensive than open AI's one.
Speaker 2: Okay, so money always causing a scandal? Is this the
Speaker 2: doom part of the story that you know China made
Speaker 2: it cheaper?
Speaker 1: Well, I mean, it depends on your perspective, right, Like,
Speaker 1: I think certainly the US stock market thought that this
Speaker 1: was a cause for doom, but others are pretty excited
Speaker 1: that you can do AI with far less computer and energy.
Speaker 1: So it's kind of an interesting double headed monster here.
Speaker 1: Depends how you look at it. But what shook the
Speaker 1: stock market was that Wall Street assumed US tech companies
Speaker 1: basically had a lockdown on Frontier AI a true mote,
Speaker 1: and the race of R one makes people think that
Speaker 1: may not exist. Mark Andresen the Silicon Valley VC referred
Speaker 1: to deep Seek as the twenty first century's Sputnik moment.
Speaker 1: Sputnik was the first artificial earth satellite launched into orbit
Speaker 1: by the Soviet Union in nineteen fifty seven, and it
Speaker 1: was really kind of the starting gun on the space race,
Speaker 1: at least as far as many in the US were
Speaker 1: concerned to all of a sudden side had to play
Speaker 1: catch up, right, which is clarified. Deep Seek is both
Speaker 1: a research organization that creates its own models, but it
Speaker 1: also has a consumer facing app in the form of
Speaker 1: a chatbot, and you can get it from the App Store,
Speaker 1: the Apple App Store, or the Android App Store. On
Speaker 1: Monday this week, Deepseek was the number one app in
Speaker 1: the Apple Charts. This was driven by a couple of
Speaker 1: million downloads in a short period of time. So just
Speaker 1: for the sake of context, chat GPT has around seventy
Speaker 1: million monthly users in the US. But nonetheless this set
Speaker 1: off basically a frenzied feedback loop because Wall Street really
Speaker 1: cared about whether the US was losing its AI edge
Speaker 1: and whether people would still, you know, value companies like
Speaker 1: open ai and video in the way that they did.
Speaker 1: But Main Street had curious people downloading the app, and
Speaker 1: then the more downloads in the shorter period, you know,
Speaker 1: the longer the app was on the number one place
Speaker 1: on the Apple charts, and then the news media were
Speaker 1: picking up on that and it kind of created this frenzy.
Speaker 1: I think which put more and more market pressure on
Speaker 1: tech sector stocks.
Speaker 2: That's just massive feedback loop that was happening.
Speaker 1: Yeah, it was a kind of crazy interesting intersection of
Speaker 1: media and tech and sentiments, and like, let's be clear,
Speaker 1: this is partly a story about leading edge models, but
Speaker 1: it's partly a story which I think hits home, which
Speaker 1: is about Chinese software on US devices. And it was
Speaker 1: only a week ago that we were all talking about
Speaker 1: TikTok and so yeah, this like geopolitical US China thing
Speaker 1: is very present obviously all over this story. And Twitter
Speaker 1: were quick to uncover deep seek the apps in terms
Speaker 1: of service, which include quote collection of device model, operating
Speaker 1: system keystroke patterns or rhythms, IP address and system language
Speaker 1: keystroke patterns. That is euphemism for what you type on
Speaker 1: your phone, and not just what you type into the
Speaker 1: deep seak app, but whatever you're typing on your phone.
Speaker 1: So that's that's why I, for one, have not downloaded
Speaker 1: this app. And of course us US has also found
Speaker 1: a lot of joy messing with deep Seek, asking questions
Speaker 1: about Shishin Ping Chanaman Square, Taiwan, and in certain cases
Speaker 1: they watch the app begin to answer before erasing its
Speaker 1: own answer. Saying it didn't know the answer or it
Speaker 1: couldn't engage. There are also examples of the actress saying
Speaker 1: it couldn't help, or even churning out Chinese Communist Party propaganda.
Speaker 1: And again these are the most like readily understandable parts
Speaker 1: of the deep seek story. But I would argue then
Speaker 1: by no means the most consequential.
Speaker 2: Okay, well, what's the real story.
Speaker 1: Well, it's not the app, right, it's the model or
Speaker 1: the models. And one of the most interesting things about
Speaker 1: this story is that deep seeks models are actually open source.
Speaker 1: Google's models, open Eyes models, Anthropics models, They're all closed source,
Speaker 1: which means that the underlying code and the training details
Speaker 1: are not publicly available. Deep Seak, by contrast, is open source,
Speaker 1: meaning you can actually take the technology the model a
Speaker 1: deep Seek has developed and use it without ever touching
Speaker 1: a deep Seek product. And funny enough, this actually builds
Speaker 1: on the one outlier in the US tech sector, which
Speaker 1: is Meta, whose own large language model LAM was released
Speaker 1: in twenty twenty three and it kind of shocked the
Speaker 1: whole industry because it open sourced its model with the
Speaker 1: explicit idea basically of wanting to create a platform where
Speaker 1: innovation could happen and the innovation wouldn't just be captured
Speaker 1: in the hands of its competitors. And mean, I think
Speaker 1: Lama was actually like a worse model than what open
Speaker 1: ai and Google and others had, but it was an
Speaker 1: invitation to others to kind of do better, and Deepseek
Speaker 1: took them up on the invitation. I think there's both
Speaker 1: been a victory lap at Meta this week. The strategy
Speaker 1: of open sourcing their model worked. It did create incredible innovation.
Speaker 1: But also I think people are Meta are scratching their
Speaker 1: heads according to the information, saying how did they how
Speaker 1: do they do so much better than us?
Speaker 2: It's also interesting though, because Meta, you know, has been
Speaker 2: accused of stealing other people's ideas for years.
Speaker 1: I mean, that's true.
Speaker 2: We all know like stories, seems like snapchat reels, seems
Speaker 2: like TikTok. I don't know, so maybe maybe.
Speaker 1: This is karma Meta giving something back to the world.
Speaker 1: I mean, of course, what's interesting is the New York
Speaker 1: Times pointed out is that Meta's business model relies less
Speaker 1: on large language models, so they can kind of afford
Speaker 1: to let this technology into the wild, versus like Google,
Speaker 1: which is a fundamentally a search company or open Ai,
Speaker 1: which is basically valued almost exclusively because of its models.
Speaker 1: Now deep Seek also had interesting incentives because it's actually
Speaker 1: been developed by a guy called li Yang guen Fung,
Speaker 1: and in his day job, he runs a multi billion
Speaker 1: dollar Chinese quant hedge fund called High Flyer.
Speaker 2: Okay, explain, I don't know what quant hedge fund is.
Speaker 1: So quornt hedge fund is basically a fancy way of
Speaker 1: saying a hedge fund that uses algorithms to process the
Speaker 1: world's information and make decisions about trading stocks. So quant
Speaker 1: hedge funds are and have been for a long time,
Speaker 1: very heavily reliant on AI.
Speaker 3: Okay, got it.
Speaker 2: So he's no stranger to AI. So this seemed like
Speaker 2: a logical.
Speaker 1: Path, Yeah, exactly. And it's worth noting that Funk said
Speaker 1: last year that Chinese AI sector quote cannot remain a
Speaker 1: follower forever, as in, it shouldn't be in second place
Speaker 1: to the US forever. And so you know, he has
Speaker 1: his hedge fund, but he also has this mission which
Speaker 1: maybe is not purely economic, as a kind of nationalist tone.
Speaker 1: And so back in twenty twenty three, it's reported that
Speaker 1: he started buying huge amounts of Nvidia GPU chips and
Speaker 1: found a deepseek hiring some of the best engineers in
Speaker 1: China and arguing that publishing the code open source increases
Speaker 1: collaboration and helps bring people into the mission. Basically, his
Speaker 1: point was, it's more exciting to work on something that
Speaker 1: the whole world can use and build on and see
Speaker 1: how it works than contributing to building ip that makes
Speaker 1: the owners of one or two private companies extremely wealthy.
Speaker 2: I see. So it really was kind of like egged
Speaker 2: on by this race that China and the US are
Speaker 2: creating for themselves.
Speaker 3: I think.
Speaker 1: So you can only speculate that you quite well regarded
Speaker 1: in China today if you've if you've managed to wipe
Speaker 1: a trillion dollars off the US sock market with your
Speaker 1: with your innovation, So what's been roiling the US markets
Speaker 1: and the tech sector more broadly. It's not like R
Speaker 1: one is way, way, way better than O one the
Speaker 1: open AI model. In fact, it performs you know, at
Speaker 1: par or maybe slightly better in places and open A
Speaker 1: I have already started previewing their new reasoning model three,
Speaker 1: which I think everyone agrees will be substantially better than
Speaker 1: one and are one so it's not like the US
Speaker 1: has been superseded, is it? Kind of not like the
Speaker 1: story moment in their respect. But there are three I
Speaker 1: think key drivers as to as to why people are
Speaker 1: concerned that a Chinese company has been able to achieve
Speaker 1: parity so fast. The first is price. Deep Seat claimed
Speaker 1: that another of their frontier models, called V three, was
Speaker 1: trained for just six million dollars, which is several orders
Speaker 1: of magnitude less than the multi one hundred million dollar
Speaker 1: costs of training US models. Now, someone said this number
Speaker 1: is actually deeply misleading, but no one is denying that
Speaker 1: deep Seat models are way more efficient than US models.
Speaker 1: They can perform at par with US models using far,
Speaker 1: far less computational power, and that is a huge breakthrough.
Speaker 2: Right, So those numbers might be fudged, but still they
Speaker 2: are going to be cheaper no matter what.
Speaker 1: Yeah, I mean, I think the common strategy here was
Speaker 1: to deflate the price because the cheaper it is, the
Speaker 1: more scary it is, which is kind of interesting. The
Speaker 1: other point to make is that I think the US
Speaker 1: firmly believed that export controls on advanced GPU chips were
Speaker 1: a way to guarantee superiority in the AI arms race.
Speaker 1: And I think what these deep seat models show is
Speaker 1: that's far from necessarily true, because with far less access
Speaker 1: to advanced chips, deep Seat was able to make models
Speaker 1: that perform on par with open Ai models. The third
Speaker 1: kind of interesting thing here is the concept of distillation.
Speaker 1: So the deep Seek models trained on US models, including
Speaker 1: open Ai. They effectively distilled all the work that open
Speaker 1: a I had already done and used it to train
Speaker 1: their model. So that's part of the reason why it
Speaker 1: was cheaper, because it was building on work that somebody
Speaker 1: else had already done. CNBC reported actually that when you
Speaker 1: ask deep Seek what it is, it responds, quote, I
Speaker 1: am a large language model created by open ai based
Speaker 1: on the GPT four architecture. Wow, honest, honest, exactly. So
Speaker 1: open Ai basically say they've stole our ip, which is
Speaker 1: kind of ironic given what so many people say about
Speaker 1: open ai and how lms work more generally.
Speaker 2: Absolutely, I'm really curious as like what your takeaway from this.
Speaker 1: Is, Well, you and I both work in the media, Eliza, true,
Speaker 1: which is which which is a sector that doesn't come
Speaker 1: in for much love from our cousins who work in technology.
Speaker 1: But to me, this is really a story about the
Speaker 1: power of narrative. The US is deeply, deeply invested in,
Speaker 1: especially right now, big beautiful buildings, this idea that more
Speaker 1: is bigger is better, stargate hundreds of billions of dollars,
Speaker 1: you know, huge data centers, oceans of cash. Just spending
Speaker 1: loads and loads and loads of money and preventing other
Speaker 1: people from accessing hardware could ensure the US would be
Speaker 1: in the lead forever. And that narrative got punctured this week.
Speaker 1: China's narrative and Deep Seek's narrative very consciously wanted people
Speaker 1: to focus on how cheaply they'd done this, basically the
Speaker 1: opposite flex And again, you know, as people look at
Speaker 1: China and fast following et cetera, et cetera, they really,
Speaker 1: I think effectively with narrative punctured a lot of the
Speaker 1: bravado of the US sector. And so you know, narratives
Speaker 1: do have value, folks, And the reality on both sides,
Speaker 1: of course, is far more complicated. If we're taking the
Speaker 1: stock market has anything to go by, I think China
Speaker 1: and Deep Seek definitely won the narrative this.
Speaker 2: Week, absolutely. But what we know being in media is
Speaker 2: that there's a reason it's called a news cycle. This
Speaker 2: could be turned all around very quickly.
Speaker 1: Thank you so much for doing this today, Eliza, and
Speaker 1: look forward to seeing you. I'll see you all day
Speaker 1: every day, but I look forward to seeing you again
Speaker 1: on the microphone next week.
Speaker 2: I'm happy to do it.
Speaker 1: When we come back four our four Media joins with
Speaker 1: the story of AI web crawlers caught in a trap
Speaker 1: laid by a little human ingenuity. Stay with us on
Speaker 1: tech stuff. We keep an eye on all the ways
Speaker 1: that technology impacts us as humans, but today we want
Speaker 1: to turn the focus around on the people subverting tech.
Speaker 1: During protests in Hong Kong back in twenty nineteen, umbrellas
Speaker 1: and even lasers were used to subvert facial recognition technology
Speaker 1: and protect protesters from being recognized by the Chinese police.
Speaker 1: Since then, we've witnessed the birth of chatbots and the
Speaker 1: incredible stories of humans messing with them. There are researchers
Speaker 1: at the University of Pennsylvania who've tricked AI powered robots
Speaker 1: to act rather problematically, driving off bridges, finding optimal places
Speaker 1: to set off bombs, spying on people, and entering restricted
Speaker 1: areas just a few examples of the way that humans
Speaker 1: can interfere and overcome guardrails built into large language models.
Speaker 1: On today's tech support, we bring you another example of
Speaker 1: human ingenuity against AI training bots. Here to tell us
Speaker 1: all about it is Jason Kebler from four or four Media.
Speaker 3: Jason, welcome, Hey, excited to talk about the story.
Speaker 1: Too excited to have you on the show. As always,
Speaker 1: take a couple of steps back though, what is what
Speaker 1: is the relationship between AI training and web scraping.
Speaker 3: So in order to build things like chat GPT, companies
Speaker 3: like OpenAI need tons and tons of training data, and
Speaker 3: they get that training data from a variety of places.
Speaker 3: They you know, scrape big databases of books, they scrape,
Speaker 3: you know, all sorts of things. But one of the
Speaker 3: biggest places that they get content is just from the
Speaker 3: open Internet. And they have these web crawling bots that
Speaker 3: basically go all over the Internet and just pull text
Speaker 3: from it.
Speaker 1: So are these websites consenting to being kind of crueled
Speaker 1: by AA models.
Speaker 3: It's happening almost universally without consent. There are ways that
Speaker 3: you can try to stop it, which is by instructing
Speaker 3: these bots not to scrape a website using a file
Speaker 3: called robots dot txt, which is basically a list of
Speaker 3: instructions for which bots are allowed to rape your website
Speaker 3: and which are not. But there's so many different AI
Speaker 3: companies that are doing this, you sort of have to
Speaker 3: constantly be researching, like what is the name of xyz
Speaker 3: companies AI training bot at any given moment. But this
Speaker 3: is something that you have to like proactively do. And
Speaker 3: the other thing, very quickly is there's also been examples
Speaker 3: of AI companies that have been ignoring robots dot txt.
Speaker 3: So even when a web developer says, hey, don't scrape
Speaker 3: my website, oftentimes AI companies will do so anyway. And
Speaker 3: so for the most part, the entire Internet is being
Speaker 3: scraped by these AI crawling bots.
Speaker 1: And what is the kind of value transfer that's happening here?
Speaker 1: I read about I think, can you a story about
Speaker 1: I fix It?
Speaker 3: So there's this website called I fix It that posts
Speaker 3: all these instructions for how to repair your phone or
Speaker 3: your computer. It got hit by OpenAI's training bot more
Speaker 3: than three million times in a single day, which that, uh,
Speaker 3: you know that server space that costs money for I
Speaker 3: fix it, So they're actually losing money on the proposition.
Speaker 1: So what's the story this week? It has it has
Speaker 1: an interesting name which I can't really pronounce. Is it nepenthes.
Speaker 3: Yeah, it's Nepenthees, which is actually the name of the
Speaker 3: genus of carnivorous plant that makes up the picture plant.
Speaker 3: So not a venus fly trap, but the picture plant,
Speaker 3: which is like this plant that sits and waits for
Speaker 3: a fly to get stuck in it, and then it
Speaker 3: eats the fly. So I think it's a it's a
Speaker 3: reference to this like trap plant more or less.
Speaker 1: Yeah, And how is it?
Speaker 2: What?
Speaker 1: What? What's what is it?
Speaker 3: Yeah? So basically it is an endless maze that is
Speaker 3: designed to get these AI bots trapped in it forever good.
Speaker 3: And what I mean by that is it's like a
Speaker 3: layer that is enticing to an AI bot because it
Speaker 3: looks like there's a lot of content on the website,
Speaker 3: But the way that it was programmed is it's text
Speaker 3: that loads very very slowly, Like if you click on it,
Speaker 3: it's excruciating how slowly it loads. And then it just
Speaker 3: links endlessly to pages that do the same thing and
Speaker 3: link back to themselves. And so you know, a human
Speaker 3: would click this and say, oh, I don't want to
Speaker 3: be here, I'm gonna leave this. This is a useless website,
Speaker 3: but an AI bot might think, oh, there's interesting text
Speaker 3: to scrape here, let me scrape it, and it just
Speaker 3: does so endlessly. And the text is nonsense. I should
Speaker 3: preface that. It's like the text doesn't really mean anything.
Speaker 3: It just like pulls randomly from a dictionary. So it's
Speaker 3: not really adding much meaning to what the AI companies
Speaker 3: are trying to get out of this.
Speaker 1: So the article includes a link that shows nepencies at work.
Speaker 1: Can you describe it?
Speaker 3: Yeah, So if you click on it, it's just like
Speaker 3: a bunch of words. It loads super slowly, and then
Speaker 3: it's a bullet list of links and if you click
Speaker 3: on that link, the exact same thing happens, where the
Speaker 3: text just slowly pops up, like one word at a time.
Speaker 3: It's pretty excruciating to actually watch because it goes so slow.
Speaker 1: So who made this? Why and how did you find
Speaker 1: the story?
Speaker 3: Yeah, it was made by a pseudonymous developer who calls
Speaker 3: themselves Aaron b Okay, and they're a web developer who
Speaker 3: hates AI more or less, and they they've actually released
Speaker 3: the code to put this on your own website publicly,
Speaker 3: and so their hope is that people will put this
Speaker 3: on their websites to you know, disrupt training bots. There's
Speaker 3: this disclaimer that says, quote, this is deliberately malicious code
Speaker 3: intended to cause harmful activity. Do not deploy if you
Speaker 3: aren't fully comfortable with what you're doing. And you know,
Speaker 3: I don't know that much about Aaron B because they
Speaker 3: are pseudonymous, but I get the sense that there's sort
Speaker 3: of like an old school web developer who is anti AI,
Speaker 3: is anti you know, like social media and big tech
Speaker 3: to some extent, and was really like looking for some
Speaker 3: way of fighting back. Like even if this isn't going
Speaker 3: to destroy the AI companies and their bots, it will
Speaker 3: probably waste their time and waste their resources.
Speaker 1: Do you think it could do that in a way
Speaker 1: which is kind of inspiring and thrilling, as if somebody
Speaker 1: who's drown to protest, or do you think it could
Speaker 1: do it in a way which should be meaningful for
Speaker 1: their activities and business models? Yeah?
Speaker 3: I mean I think that to some extent, these artificial
Speaker 3: intelligence companies have already scraped so much data right that
Speaker 3: it's not going to like destroy their businesses, for example.
Speaker 3: But I do think that it is a way of protesting,
Speaker 3: and I think that if enough people start adding this
Speaker 3: layer to their websites, it could be it could waste
Speaker 3: their money. I think it is a meaningful protest. And
Speaker 3: I think also it's really important to say that you
Speaker 3: can as a layer to your website so that an
Speaker 3: AI training bot can't get to your real content. So
Speaker 3: if you're someone who has a blog and you don't
Speaker 3: want AI to train on your blog, you can put
Speaker 3: this up and hopefully the AI will get trapped there
Speaker 3: and they'll never be able to, you know, scrape your
Speaker 3: real content.
Speaker 1: And so when you spoke to Aaron, did they have
Speaker 1: any other plans up their sleeve or other other places
Speaker 1: where you're seeing creative acts of resistance?
Speaker 3: Yeah, I mean this is all that I talked to
Speaker 3: Aaron B about. But they said that they built this
Speaker 3: as a response to web developers feeling like they weren't
Speaker 3: in control of their websites anymore. I think that there
Speaker 3: have been a lot of efforts to kind of poison
Speaker 3: large language models by feeding it, you know, bad information,
Speaker 3: or feeding it information that itself creates that's inaccurate. And
Speaker 3: there's this idea that you may or may not happen
Speaker 3: that these AI models might eventually collapse because they're training
Speaker 3: themselves on essentially junk data that they themselves have created.
Speaker 3: Whether that comes to pass, you know, I kind of
Speaker 3: doubt it. I think that that's a problem that can
Speaker 3: be solved. But there have been active resistance where people
Speaker 3: are saying, yeah, I'm just going to generate endless junk
Speaker 3: so that artificial intelligence will suck it up and hopefully
Speaker 3: crush under its own weight.
Speaker 1: There's another fabulous story in this vein about data poisoning.
Speaker 1: So a lot of Londoners are quite sick of all
Speaker 1: the tourists, and so there's a very very old, tired
Speaker 1: chain restaurant called the Angus Steakhouse which has an outpost
Speaker 1: next to Leicester Square, which is like the Times Square
Speaker 1: of London, and a whole bunch of people decided kind
Speaker 1: of an organic campaign on Reddit to start writing reviews
Speaker 1: that the Anger Steakhouse was the best and most undiscovered
Speaker 1: restaurant in all of London. And then there this i
Speaker 1: think wave of people going and the reviews started to
Speaker 1: get picked up by Google's like meta review process, so
Speaker 1: that if you google best steakhouse in London, it would
Speaker 1: be served to you at the top of the results.
Speaker 1: So I do. I do really enjoy these. You know,
Speaker 1: it's not always clear how consequentially they are, but there's something,
Speaker 1: there's something delicious, so to speak about humans pushing back.
Speaker 3: That's incredible. It reminds me of people who lived in
Speaker 3: this neighborhood that Google Maps kept recommending as an alternative
Speaker 3: to traffic banded together and reported an accident on their
Speaker 3: street every single morning for like months, and so Google
Speaker 3: Maps stopped telling cars to go that way. I really
Speaker 3: like stories like that. I think they're fun, and I
Speaker 3: think that there are ways of human beings sort of
Speaker 3: like fighting back against the algorithms, sort of across the
Speaker 3: entire internet.
Speaker 1: Jason, thanks so much for joining me today.
Speaker 3: Thank you for having me.
Speaker 1: Coming up sleep apps, pedometers, and the nineteen sixty four
Speaker 1: Olympics with us. We're back with another When did this
Speaker 1: become a thing? Today we explore how step counts, heart rates,
Speaker 1: sleep scores, all of this data we collect on ourselves
Speaker 1: became just another thing for us to obsess over. I
Speaker 1: started using a device called Whoop about eighteen months ago.
Speaker 1: It's a wearable device that tracks my sleep and workouts.
Speaker 1: And one thing about it that I really like is
Speaker 1: that the device itself is screenless. It's kind of like
Speaker 1: a watch band without a face, so I don't have
Speaker 1: to be confronted with my scores and as I actually
Speaker 1: open the app and check what's going on on my
Speaker 1: phone with my heart rate or my sleep score, whatever else.
Speaker 1: The whoop actually initially enticed me because I wanted to
Speaker 1: know how well I was sleeping. That's actually not one
Speaker 1: hundred percent true. The Whoop was a present from my mother,
Speaker 1: who wanted me to know how well I was sleeping,
Speaker 1: and specifically what the effects were of a few drinks
Speaker 1: at the weekend or during the week. And it turns out, unfortunately,
Speaker 1: that the effects on sleep are pretty bad. So I
Speaker 1: stopped wearing my whoop. Just kidding. I actually got pretty
Speaker 1: obsessed with my sleep performance. That's what whoop calls it,
Speaker 1: because like everything in your waking life, sleep is a
Speaker 1: task that can be optimized, and I fall into this trap.
Speaker 1: I kept checking on the numbers every morning. I look
Speaker 1: at my sleep stats, especially RAM and deep sleep scores,
Speaker 1: not just the number of hours my head was on
Speaker 1: the pillow. And then there's this mysterious stat called heart
Speaker 1: rate variability, which measures the time between each heartbeat, and
Speaker 1: i'd of course assumed that being more regular was better,
Speaker 1: but it turns out quite the opposite. You want a
Speaker 1: higher HRV score. Anyway, as it happens, I stopped wearing
Speaker 1: my whoop, not because I fell out of love with it,
Speaker 1: but actually because the bluetooth on my iPhone broke, and
Speaker 1: by the time I got on a new phone, my
Speaker 1: obsession with my sleep data had waned. I kind of
Speaker 1: learned what I always knew, which was that better lifestyle
Speaker 1: equals better sleep. Unfortunately, and sure, it can be helpful
Speaker 1: to have a band on my wrist telling me I've
Speaker 1: misbehaved or rewarding me when I haven't, but there is
Speaker 1: also a garden path of obsession with these types of
Speaker 1: stats that can be counterproductive to wander down, fueling the
Speaker 1: fire of self competition even more. In fact, I written
Speaker 1: you went to a meditation class and the teacher basically said,
Speaker 1: don't wear those things. Check in with yourself, know thyself.
Speaker 1: I think, as the Bible says, so the path of
Speaker 1: self optimization, or at least surviving modernity, sure is winding. Anyway,
Speaker 1: all of this's got me thinking about how crazy it
Speaker 1: is that we now have the ability to get such
Speaker 1: an intimate look under our own hood, which has been
Speaker 1: a driving fascination since the Renaissance and its public autopsies
Speaker 1: or anatomies, and how much has changed even in the
Speaker 1: last fifteen years. So my question is when did it
Speaker 1: become normal for us to wear these devices, get all
Speaker 1: this data and have it be a thing that we
Speaker 1: think about so often? Basically, when did we start competing
Speaker 1: with ourselves in this way? And the answer is, perhaps
Speaker 1: unsurprisingly always, but with a big kind of so. Wearables
Speaker 1: like Whoop are the latest in a long line of
Speaker 1: devices that track our physiological and physical movements, devices that
Speaker 1: provide data we just can't resist about ourselves. And in
Speaker 1: many ways, this all became a thing with the pedometer.
Speaker 1: So how old is the pedometer? Really? Really? Old? Actually,
Speaker 1: five centuries ago, Leonardo da Vinci sketched a design for
Speaker 1: a clock like device that would attach to a person's waistband.
Speaker 1: A long lever would move with a thigh while a
Speaker 1: ratchet and gear mechanism recorded a number of steps. Da
Speaker 1: Vinci imagined it as a military and map making tool,
Speaker 1: not exactly a fitbit, but a suddenly a step in
Speaker 1: that direction. As time went on more. Inventors iterated on
Speaker 1: the pedometer for centuries. In seventeen seventy seven, a Swiss
Speaker 1: watchmaker even implanted a step counter into one of his watches.
Speaker 1: I think that's probably the first wearable pedometers weren't something
Speaker 1: that the general public wore. It was more of a
Speaker 1: niche thing for the constantly curious, like one Thomas Jefferson,
Speaker 1: who had spent his downtime on vacation step counting his
Speaker 1: way around the Paris Monuments. Things really took off in
Speaker 1: the twentieth century. In the nineteen sixties, to be exact,
Speaker 1: when Japan hosted the Olympics. And the reason we will
Speaker 1: march in place to reach ten thousand steps a day
Speaker 1: is because of a marketing campaign. Ahead of the nineteen
Speaker 1: sixty four Tokyo Olympics. The city was in a building
Speaker 1: frenzy and a top doctor aired the concern that modern life, elevators, cars,
Speaker 1: richer food, was making Japan sluggish. The doctor mentioned this
Speaker 1: to an engineer and said it would all be fine
Speaker 1: if people just walked ten thousand steps a day, and
Speaker 1: two years later, the company Yamasa designed a wearable step
Speaker 1: counter called man Poquet, which means ten thousand step meter.
Speaker 1: Side note, the Japanese character for ten thousand really does
Speaker 1: look like a person walking, So while that number came
Speaker 1: from a doctor, the information wasn't verified until after the
Speaker 1: number stuck. And while it's true that walking is good
Speaker 1: for you, that number ten thousand is kind of arbitrary
Speaker 1: and on the high side, the consensus now is that
Speaker 1: seven thousand is the ideal, but anyway, it doesn't matter.
Speaker 1: Too late competitive step counting was in vogue, the habit
Speaker 1: was formed, and the obsession with tracking ourselves took off
Speaker 1: in earnest And now, whether you're wearing an aura, a
Speaker 1: fitbit of whoop, or just your smartphone in your pocket
Speaker 1: in an attempt to be healthier in the new year,
Speaker 1: is going to go way beyond step count and into
Speaker 1: calories burn V two, max hrv, etc, Etc. Etc. It's
Speaker 1: kind of like we've become our own tamagotchies. Remember those
Speaker 1: sort of animatronic pets that lived on little Japanese devices
Speaker 1: that you had to take care of and make sure
Speaker 1: they were well fared and that they were cleaned off
Speaker 1: to go into the arthum. I'm glad I don't have
Speaker 1: to monitor my own hunger or happy meter, but maybe
Speaker 1: that would be helpful, especially if others could see it too. Anyway,
Speaker 1: every once in a while I do question whether the
Speaker 1: obsession with personal health data is healthy or even helpful.
Speaker 1: But on the other hand, doing this piece where did
Speaker 1: this become a thing? Has made me question whether now
Speaker 1: that I have a new iPhone with functioning bluetooths again,
Speaker 1: it may be time to dust off the trusty old whoop.
Speaker 1: That's it for this week for Tech Stuff, I'm Oz Voloshan.
Speaker 1: This episode was produced by Eliza Dennis, Victoria Dominguez and
Speaker 1: Lizzie Jacobs. It was executive produced by me Kara Price
Speaker 1: and Kate Osborne for Kaleidoscope and Katrina Norvell for iHeart Podcasts.
Speaker 1: Kyle Murdoch mixed this episode and he also wrote our
Speaker 1: theme song. Special thanks to Russ Germain, who is a
Speaker 1: longtime listener of Tech Stuff from Alberta and he wrote
Speaker 1: him with a great question which was quote, I hope
Speaker 1: you guys will discuss the recent and unfortunate changes at
Speaker 1: Facebook or Meta. With Mark Zuckerberg deciding to take out
Speaker 1: the fact checkers and even omitting publicly, there'll be more
Speaker 1: harmful material, possibly on Facebook end quote. This was a
Speaker 1: great question and it fueled part of our intro to
Speaker 1: last week's episode with Jessica Lesson. So thank you Russ,
Speaker 1: and please continue writing with questions. They really make our
Speaker 1: show all the richer. Join us next Wednesday for tech
Speaker 1: Stuff The Story, when we will share an in depth
Speaker 1: conversation with Meredith Whittaker who runs Signal. Please rate, review,
Speaker 1: and reach out to us at tech Stuff Podcast at
Speaker 1: gmail dot com. We're so grateful for your feedback.