Speaker 1: Welcome to tech Stuff, a production of I Heart Radios
Speaker 1: How Stuff Works. Hey there, and welcome to tech Stuff.
Speaker 1: I'm your host, Jonathan Strickland. I'm an executive producer with
Speaker 1: How Stuff Works and I heart Radio and I love
Speaker 1: all things tech, and today I thought i'd talk a
Speaker 1: bit about Internet search engines and how Google was able
Speaker 1: to sort of take the lead amongst a pack of competitors,
Speaker 1: most of which came out well before Google did. Now
Speaker 1: these days, lots of people use Google as a word
Speaker 1: for web searching in general, even though the company does
Speaker 1: way more than web search, and there's still plenty of
Speaker 1: competitors that are still active that are out there. I'm
Speaker 1: sure Microsoft would rather we all talk about binging the
Speaker 1: heck of the things, but that doesn't happen. I think
Speaker 1: we're now at the point where people will talk about Googling,
Speaker 1: even if they're using a different search engine. So how
Speaker 1: did that happen? How did we get to that point? Well,
Speaker 1: to explain how we got there, it's a good idea
Speaker 1: to walk down memory lane. I mean, you know, I
Speaker 1: love to do this. Every episode begins with a history
Speaker 1: lesson and to really look at how the idea of
Speaker 1: search engines developed and what things were like in the
Speaker 1: early days of the public Internet and the Web now. First,
Speaker 1: the idea of search engines predates both of those concepts
Speaker 1: by quite some time, and it rose out of necessity.
Speaker 1: It kind of evolved out of older methods of indexing.
Speaker 1: So a predecessor to search engines are the various library
Speaker 1: classification systems UH. Three big ones are the Dewey Decimal system,
Speaker 1: the Library of Congress system, and the Superintendent of Documents
Speaker 1: systems UH. The first two of those designate books with
Speaker 1: call numbers according to subject matter, so you divide the
Speaker 1: books up based upon whatever subject they cover. This can
Speaker 1: get a little complicated, it is and no pun intended subjective.
Speaker 1: You have to determine where does the book best fit
Speaker 1: in the grand taxonomy of subjects UH. Meanwhile, the Superintendent
Speaker 1: of Documents system is totally different. It doesn't divide it
Speaker 1: up by subject. It divides up books by the issuing
Speaker 1: agency responsible for the publication of the work. So they
Speaker 1: just divided up by where the book came from, not
Speaker 1: what the book covers. Whatever the system, the purpose is
Speaker 1: the same. It's to make it possible for someone to
Speaker 1: track down a specific work in an enormous collection of works,
Speaker 1: or to figure out where to place a new work
Speaker 1: within an existing collection. By classifying each work and then
Speaker 1: designating the physical location for that piece, people can find stuff. Otherwise,
Speaker 1: you just have an enormous pile of books with no
Speaker 1: organizational system at all, and finding anything would take ages. Now,
Speaker 1: someday I'll have to do an episode about these systems
Speaker 1: in more detail, to talk about how they were developed
Speaker 1: and how they've evolved over time, because it's actually a
Speaker 1: pretty interesting story. But we're gonna jump forward a bit,
Speaker 1: not quite up to the com uter age, however. Rather
Speaker 1: we're gonna jump forward to the nineteen forties. That's when
Speaker 1: a forward thinking fellow named Vanavar Bush wrote an article
Speaker 1: for The Atlantic Monthly. The piece had the title as
Speaker 1: we May Think, and it contains some fairly prescient ideas
Speaker 1: in it. Bush recognized that as we increase our knowledge,
Speaker 1: we were beginning to specialize in certain fields out of necessity.
Speaker 1: That you couldn't just be a general knowledge master. Eventually
Speaker 1: you were starting to develop our our knowledge in different areas,
Speaker 1: uh so far that you had to specialize. You couldn't
Speaker 1: be an expert in everything to get get a really
Speaker 1: deep understanding about a particular field, such as physics or chemistry,
Speaker 1: we might dedicate all our resources to that pursuit as
Speaker 1: an individual. Meanwhile, there are other people who are exploring
Speaker 1: different subjects, like pure mathematics or cosmology or something like that. Now, this,
Speaker 1: Bush argued, presented a new challenge. How do we create
Speaker 1: a usable record of our discovery, one that's easily navigable
Speaker 1: and remains relevant over time. While an older library classification
Speaker 1: system might encompass several categories, it couldn't get as granular
Speaker 1: as our knowledge was growing to be. For example, the
Speaker 1: Library of Congress classification system has twenty one categories that
Speaker 1: you can use to group books together. But as our
Speaker 1: research and discoveries honed in on ever more precise slices
Speaker 1: of those categories, the system becomes less relevant because you've
Speaker 1: you've got, you know, minor categories within those major categories,
Speaker 1: so it gets harder to start classifying things. Bush said
Speaker 1: we needed to have a record that could be continuously
Speaker 1: extended and easy to consult. But he went even further
Speaker 1: out than that. He said, to make it a really
Speaker 1: useful record, we need to structure it to respond to
Speaker 1: our queries in a way similar to how the human
Speaker 1: mind works. Bush argued that we think through associate. We
Speaker 1: associate ideas with each other, sometimes in pretty unusual ways,
Speaker 1: in ways that might seem intuitive to us. But on
Speaker 1: the very surface of it, there there doesn't seem to
Speaker 1: be any relation between those ideas. And you may have
Speaker 1: experienced this where you're thinking about one thing and you
Speaker 1: just start to think about a different thing that doesn't
Speaker 1: seem to be related, and then you're able to relate
Speaker 1: the two. This is really human ingenuity. It's where innovation
Speaker 1: really takes off. Well, Bush, that would probably be impossible
Speaker 1: for us to create an artificial system that could replicate
Speaker 1: that tendency, but we could at the very least design
Speaker 1: something that acknowledges that human trait so it works better
Speaker 1: for us. So if we did that, if we designed
Speaker 1: to search for a record for a particular type of information,
Speaker 1: we might also see the opportunity to search for tangential
Speaker 1: data that is relevant to our needs. A good system
Speaker 1: would be able to anticipate that and serve up the
Speaker 1: information for us. So Bush proposed a hypothetical system called
Speaker 1: mimics m E M E X and that would use
Speaker 1: associative factors to organize information in a virtually limitless storage space. Again,
Speaker 1: this is hypothetical. It would be a system that one
Speaker 1: could reference and send a retrieval command to get the
Speaker 1: most relevant information related to whatever it was you were
Speaker 1: asking for your query. Essentially, he was talking about a
Speaker 1: conceptual model that the Internet attempts to realize. Now skip
Speaker 1: ahead to the nineteen sixties. Then you've got a computer
Speaker 1: scientist named Jerry Saltan. Jerry Salton taught at Cornell University,
Speaker 1: and he developed an indexing strategy using a vector space model.
Speaker 1: Now this gets a bit mind bendy for people who
Speaker 1: haven't worked with vector space models, but follow me here. Now,
Speaker 1: start with an imaginary virtual space kind of analogous to
Speaker 1: the physical space we live in in our day to
Speaker 1: day lives. Now, in our reality, we can perceive three dimensions,
Speaker 1: and we experience a fourth one, that of time, but
Speaker 1: we cannot directly perceive any more than that ourselves, So
Speaker 1: most of the time we associate the physical world with
Speaker 1: three physical dimensions. Now, the information retrieval method that Salton
Speaker 1: set up, he defined the number of dimensions within his
Speaker 1: virtual space by the number of terms in a retrieval request.
Speaker 1: So if your request included five terms, the vector space
Speaker 1: model would have five dimensions. Documents within the model would
Speaker 1: virtually appear as vectors within the space according to which
Speaker 1: of the search terms were present within those documents and
Speaker 1: how frequently they were present within the documents. Uh, the
Speaker 1: queries and the documents are both vectors of the term counts.
Speaker 1: And just in case you're as rusty on your physics
Speaker 1: terms as I am, a vector is a quantity that
Speaker 1: has a magnitude and a direction. So your terms have vectors,
Speaker 1: your documents have vectors, and the goal is to identify
Speaker 1: the documents that are most similar to the initial query
Speaker 1: in an effort to retrieve the most relevant results, well
Speaker 1: leaving out anything that doesn't meet the criterion or doesn't
Speaker 1: meant a predetermined threshold of relevance. So you might say,
Speaker 1: I need to have x percentage match for the retrieval
Speaker 1: to actually come through, and anything that doesn't meet that
Speaker 1: threshold gets discarded. It's not it's not served to me,
Speaker 1: and that saves you time when you start sorting through
Speaker 1: the results to see if any of those actually represent
Speaker 1: the information you were actually looking for. Now, suffice it
Speaker 1: to say, this model really looks for the presence of
Speaker 1: specific terms, but not necessarily their use within the document
Speaker 1: their context, So you could end up retrieving a document
Speaker 1: that technically contains all the terms you used in the search,
Speaker 1: but it has no real relevance to your actual needs.
Speaker 1: So that is a limitation of this model, but still
Speaker 1: it was a pretty good starting point, so Saltan's work
Speaker 1: was incredibly important. Another big thinker who helped shape the
Speaker 1: course of what would become the Internet and the Web
Speaker 1: is a guy named Ted Nelson who in the nineteenes
Speaker 1: sixties proposed an idea he called Zanna Do. And I'm
Speaker 1: not talking about the cheesy movie starring Olivia Newton John
Speaker 1: about roller skating Greek muses, but as a side note,
Speaker 1: I really love that movie now. Nelson's Zanna Do was
Speaker 1: a hypothetical computer based writing system that would have a
Speaker 1: means to link different documents within a global depository. So
Speaker 1: essentially he was talking about hypertext links, which would allow
Speaker 1: users to navigate from document to document to relate documents together,
Speaker 1: so that you could have a collection of documents about
Speaker 1: the same sort of of subject matter and make it
Speaker 1: very easy to reference different research. It would also allow
Speaker 1: document creators to add their work to a growing collection
Speaker 1: of documents about similar subjects. Now, while the Web would
Speaker 1: incorporate many of Nelson's ideas, he has stated that the
Speaker 1: web falls far short of what Zanna do was meant
Speaker 1: to do. Still, those links would become very important for
Speaker 1: the web. Heck, I mean you could argue the links
Speaker 1: or what make it a web in the first place.
Speaker 1: The World Wide Web is a series of documents published
Speaker 1: on servers that have connective tissue between them. That's the
Speaker 1: web that you navigate. So it would be crucial in
Speaker 1: Google's eventual successes. We'll see now. In the nineteen seventies,
Speaker 1: the agency that would become DARPA, which at the time
Speaker 1: was just ARPA, funded the development of the ARPA Net,
Speaker 1: which would be the predecessor to the Internet. Computer scientists
Speaker 1: worked on the rules that machines would have to follow
Speaker 1: in order to communicate with one another over a network.
Speaker 1: This was a non trivial problem at the time because
Speaker 1: computers were dependent upon proprietary systems that were not compatible
Speaker 1: with computers from other manufacturers. So, in other words, they
Speaker 1: were talking in different languages. So you have to find
Speaker 1: a common means of communication between these different machines. Solving
Speaker 1: those problems laid the foundation for the Internet that was
Speaker 1: to follow. Now skipping ahead to the late nineteen eighties,
Speaker 1: this is still before the Web was a thing, but
Speaker 1: college students Alan Mta and Bill Healen recognized the need
Speaker 1: for a tool to search file databases. Effectively, they were
Speaker 1: part of a project at the McGill University School of
Speaker 1: Computer Science to develop that kind of a tool. It
Speaker 1: would become known as Archie, and it was meant to
Speaker 1: search archives of files. The original version was pretty primitive.
Speaker 1: It would essentially just send an automated request to a
Speaker 1: file Transfer Protocol server and it would just say, hey,
Speaker 1: give me a list of all the files that are
Speaker 1: stored on your server. That's it, just give me a
Speaker 1: laundry list of all the files that are on there.
Speaker 1: And it was once a month it would send this request,
Speaker 1: and so really it was just a list of the
Speaker 1: documents that were available on that FTP server, not anything more,
Speaker 1: you know, sophisticated than that. But it would grow to
Speaker 1: become a query search tool, allowing users to look for
Speaker 1: files containing specific terms in them or with specific titles.
Speaker 1: Other schools would develop similar search tools in the following years,
Speaker 1: naming them after characters from Archie comics like Veronica and
Speaker 1: jug Head. Now this is despite the fact that Mtaj
Speaker 1: said he intended no association with Archie comics at all.
Speaker 1: He chose the name Archie because it's archive but without
Speaker 1: the V. But sometimes memes just take hold, even if
Speaker 1: they're based off a misunderstanding. Also, both Veronica and Jugead
Speaker 1: search for files in the Gopher index system, a predecessor
Speaker 1: and alternative to the Worldwide Web. I did an episode
Speaker 1: about Gopher a couple of years ago. I think, so
Speaker 1: he can search the archives if you want to hear
Speaker 1: about that. Now. This leads us to when Tim Burners
Speaker 1: Lee built and published the world's first web page. Burners
Speaker 1: Lee had done some work with hypertext documents at CERN
Speaker 1: as a contractor in the early eighties. The goal then
Speaker 1: was to help researchers share information between each other as
Speaker 1: they were smashing particles against each other really really hard.
Speaker 1: By burners Lee was thinking about pairing the hypertext capabilities
Speaker 1: with the Internet to allow for an interconnected series of
Speaker 1: documents hosted on networked Internet servers, and thus the World
Speaker 1: Wide Web was born. It wouldn't take long for others
Speaker 1: to jump on the idea, and that meant it wouldn't
Speaker 1: be long before people needed a tool to search the
Speaker 1: growing collection of documents on the Internet. And that kind
Speaker 1: of sets me up for the next section, which I
Speaker 1: will tackle in just a moment after we take this
Speaker 1: quake break. So in the earliest days of the web,
Speaker 1: when calling it a web might have been a little generous,
Speaker 1: cern maintained a list of web servers that hosted web pages.
Speaker 1: This was all part of the Worldwide Web Virtual Library
Speaker 1: or vlib or sometimes www v lib. This was the
Speaker 1: first index of web content and it relied upon real
Speaker 1: life human beings to build out the index. As more
Speaker 1: web pages were publishing, they volunteered their time to build
Speaker 1: out the index. So this is automated. People were actually
Speaker 1: doing this by hand adding the the names and the
Speaker 1: addresses to these different sites on this index. Next we
Speaker 1: have Matthew Gray's Worldwide Web Wanderer. Now, this was a
Speaker 1: bot or an autonomous program on a network that can
Speaker 1: interact in some significant way with the information on the network.
Speaker 1: And we deal with bots all the time. Sometimes it's
Speaker 1: in the background and humans don't really notice, and sometimes
Speaker 1: like chat pots, it's very much in front of us.
Speaker 1: The butt that Matthew Gray created would navigate the World
Speaker 1: Wide Web to keep counting of how many active servers
Speaker 1: there were in any network. It was essentially measuring the
Speaker 1: growth of the web over time by counting up these servers.
Speaker 1: As more servers came online, we learned that the World
Speaker 1: Wide Web was growing. Gray upgraded the bot to actually
Speaker 1: capture the u r l's of web pages, because earlier
Speaker 1: it was just counting stuff. It wasn't actually making note
Speaker 1: of anything in particular, and so I got a little
Speaker 1: more sophisticated gray bill out of database of these captured
Speaker 1: u r l s, called wand decks. The bought would
Speaker 1: ping servers multiple times each day, and it actually became
Speaker 1: a problem that was pinging so frequently. And a ping
Speaker 1: is just a quick message that essentially says, hey, are
Speaker 1: you there, and then it's waiting for a response of yeah,
Speaker 1: I'm here. It's all good. But it was doing this
Speaker 1: so many times each day that it was actually starting
Speaker 1: to create lag on the Internet. Of course, this is
Speaker 1: in the very very early days, so whoopsie daisy there.
Speaker 1: Now toward the end of n some early web search
Speaker 1: tools were starting to make their way to the general public. Though,
Speaker 1: keep in mind that in the very early days of
Speaker 1: the Worldwide Web, the general public accessing web pages was
Speaker 1: really just a tiny fraction of the overall population. It's
Speaker 1: like college students, some early adopters, some folks with various
Speaker 1: government agencies, and a few companies, but not a whole lot. Uh.
Speaker 1: There was largely a mysterious thing. You know. This is
Speaker 1: when people were just starting to hear the terms of
Speaker 1: Worldwide Web and information super Highway, because the Internet had
Speaker 1: been around for a while, but most people didn't have
Speaker 1: any regular way to access it. So these tools could
Speaker 1: help you find stuff, but they weren't super sophisticated. There
Speaker 1: was the Worldwide web Worm, which would pull together lists
Speaker 1: of titles and u r l s for web pages.
Speaker 1: There was jump Station, which would pull down information about
Speaker 1: web pages titles and header sections, so sort of like
Speaker 1: the title of the web page and a brief description
Speaker 1: of what the web page was supposed to be. But
Speaker 1: both of those tools were very simple, and they would
Speaker 1: present results in the order that they were found by
Speaker 1: the tools, so there was no ranking of the search results.
Speaker 1: It was all by by uh, first come, first serve
Speaker 1: kind of approach. So it might be that your results
Speaker 1: all had whatever your query was in it, but the
Speaker 1: most relevant ones could be buried much further down the
Speaker 1: list because they didn't rank in any way. Then there
Speaker 1: was the rb SC spider, which actually attempted to rank
Speaker 1: results by relevance. But all three of these were limited
Speaker 1: in what they could do, and often you needed to
Speaker 1: know what you were looking for exactly in order to
Speaker 1: get a hit. In other words, you couldn't just do
Speaker 1: a string of words. You certainly couldn't write in natural
Speaker 1: language what your query was, so you might have to
Speaker 1: put in the actual title of a page in order
Speaker 1: to get the response back. So you would have to
Speaker 1: know what the page's title is, but you're not. You
Speaker 1: obviously don't know what the U r L is, or
Speaker 1: else you would just navigate to the page directly. You
Speaker 1: just type in the address and your browser's address bar
Speaker 1: and go there. So it was kind of limited in
Speaker 1: its utility. If you were to do anything outside of
Speaker 1: the actual title of a page, you might not find
Speaker 1: any hits, even if such pages actually existed out there. Also,
Speaker 1: in some Stanford undergraduates decided to take the work they
Speaker 1: had been doing on a project called Architect and develop
Speaker 1: a web crawling search tool based off of that work.
Speaker 1: Architect was all about using statistical analysis of word relationships
Speaker 1: in an effort to kind of build a basic understanding
Speaker 1: of what the subject matter was and that would then
Speaker 1: be able to help you create more relevant search results
Speaker 1: on queries. So you run a search request and this
Speaker 1: tool would statistically analyze various indexed pages in its database
Speaker 1: and return the results that appeared to be the most relevant. Um.
Speaker 1: It was an interesting approach. It was definitely one that
Speaker 1: was needed because it wasn't just listing the the sites
Speaker 1: chronologically based on how they were attained. But it would
Speaker 1: take about two years for this project to actually turn
Speaker 1: into something that the group could unveil uh and when
Speaker 1: they did, they called the will Excite and they held
Speaker 1: a commercial release for the product in n But in
Speaker 1: between the founding and the release of Excite, we hit
Speaker 1: a banner year for early search engines. Nineteen four was
Speaker 1: the year that web crawler, lycos Info, Seek, and Yahoo
Speaker 1: all got their start. Now, with the case of Yahoo,
Speaker 1: the company was not relying on bots to crawl through
Speaker 1: web servers to index all the pages that the bots
Speaker 1: came across. Instead, Yahoo initially was relying on actual human
Speaker 1: beings to curate an index, so they were actually going
Speaker 1: to web pages deciding whether or not those web pages
Speaker 1: were good enough to be listed on Yahoo on the
Speaker 1: various subjects that Yahoo was covering, and then they would
Speaker 1: be grouped together if they passed muster. Now, there are
Speaker 1: pros and cons to that approach. One of the pros
Speaker 1: is that because it is human curated, there a much
Speaker 1: better possibility that the web pages on Yeah whose lists
Speaker 1: were good ones with good content. But the conside was
Speaker 1: that as the web grew and began growing at an
Speaker 1: even faster rate, it really limited Yahoo's usefulness. It would
Speaker 1: only be later that Yahoo would branch out into the
Speaker 1: web search in general, and even then it relied very
Speaker 1: heavily on third parties for the actual search tools. They
Speaker 1: didn't really dive into developing their own. They were more
Speaker 1: about making deals with other search engines to power their search.
Speaker 1: In fact, that happened on and off throughout Yahoo's entire existence.
Speaker 1: But let's get back to web Crawler, Lycos and Infoseek. Now,
Speaker 1: of those three, WebCrawler was the first to provide full
Speaker 1: text search of web pages, so not just headers and titles.
Speaker 1: You could search terms, and if they appeared in the
Speaker 1: web page at all, then, in theory, WebCrawler would be
Speaker 1: able to bring that back as long as it was
Speaker 1: indexed in Webcrawler's index. Um it was the work of
Speaker 1: universe the a Washington student named Brian Pinkerton, and Pinkerton's
Speaker 1: web Crawler built out this big index of pages, and
Speaker 1: Pinkerton started rather modestly. He first released a list of
Speaker 1: the top twenty five websites on the web on March fifteenth,
Speaker 1: and the following month he announced that the web Crawler's
Speaker 1: index included four thousand websites, and by June of ninety four,
Speaker 1: he made the index searchable for everyone. So again, this
Speaker 1: is just a slice of all the websites that were
Speaker 1: out there, but it was a decent enough slice to
Speaker 1: start off with, and the endeavor proved to be successful.
Speaker 1: Pinkerton received financial investments from a couple of big companies,
Speaker 1: and within a year he had managed to support the
Speaker 1: service through advertising revenue, a model that other search engines
Speaker 1: would follow, so he was able to actually make money
Speaker 1: by serving up advertising on his search engine pages. By June,
Speaker 1: A O. L had become interested in WebCrawler and would
Speaker 1: purchase the company a O L would lay Eater sell
Speaker 1: the company a little less than two years later to
Speaker 1: excite that company that I had mentioned earlier in this episode.
Speaker 1: I'll get back to them and to web Crawler a
Speaker 1: bit later, but I will say that web Crawler was
Speaker 1: my search engine of choice when I first started using
Speaker 1: the web in the mid nineteen nineties. I was actually
Speaker 1: pretty slow to move over to that crazy Google thing
Speaker 1: that we're gonna get to later in this episode. Lycos meanwhile,
Speaker 1: started off as a project at Carnegie Mellon University. Michael
Speaker 1: Malden headed up the project and the name came from
Speaker 1: wolf spiders that have the scientific name Lycos sedilla. When
Speaker 1: Lycos became a company, Bob Davis took the helm to
Speaker 1: turn it into a revenue generating business that it gets
Speaker 1: cash from advertising like web Crawler, and it also was
Speaker 1: a success, and by the end of nine the Lycos
Speaker 1: index was the largest web search index available on the web.
Speaker 1: It held more than sixty million documents in it. The
Speaker 1: service grew tremendously, as did the company, and the full
Speaker 1: story of Like Coast is one I'll have to cover
Speaker 1: in another episode because it gets pretty bonkers. But for
Speaker 1: this episode, it's just important to note that it was
Speaker 1: another early search service that grew and became diversified and
Speaker 1: tried to do lots of other stuff. Um Steve Kirsch
Speaker 1: would be the guy behind info Seque. That one originally
Speaker 1: launched as a pay for use service, so it's an
Speaker 1: original revenue model. Wasn't advertising, it was you would pay
Speaker 1: to use it. Now that only lasted about half a year,
Speaker 1: a little more than half a year before a kurse
Speaker 1: dropped the fee and it became free to use, and
Speaker 1: by February the service became known as info see Search
Speaker 1: and also Netscape and Infoseque negotiated a deal in which
Speaker 1: info sque would become the default search engine and Netscape's
Speaker 1: web browser, so that really helped info squ's penetration quite
Speaker 1: a bit in those days. Now, one thing Infoseque incorporated
Speaker 1: in its service after a couple of years is the
Speaker 1: option to use boolean operators. Now, these are a collection
Speaker 1: of simple words that can help you narrow down searches.
Speaker 1: The words include and or and not, so with an
Speaker 1: and operator you are narrowing your focus. So if you
Speaker 1: search for the terms Superman and movies, the results you
Speaker 1: get should be relevant to both of those terms. You
Speaker 1: should only get results that include information about Superman and movies.
Speaker 1: If you're looking for specific Superman movie, hopefully those would
Speaker 1: be right in that list. Some of them should have
Speaker 1: the information you're looking for, and maybe that you still
Speaker 1: have to do some digging to find them, because you're
Speaker 1: going to get all the web pages that have both
Speaker 1: Superman and movies inside of them. Now you could make
Speaker 1: it more specific. You could say Superman and movies and
Speaker 1: Christopher Reeve. That would end up narrowing the results for
Speaker 1: those to look for. Any pages have all three of
Speaker 1: those terms inside of them. The boolean operator or does
Speaker 1: the opposite. It broadens your search. Maybe you want to
Speaker 1: search for Batman or Superman, then you should get results
Speaker 1: that have either or both Superman or Batman in them. Um,
Speaker 1: so you would get all the Superman results, all the
Speaker 1: Batman results. You probably also get all the Superman and
Speaker 1: Batman results, so you're you're increasing the number that you receive.
Speaker 1: The not boolean operator helps you eliminate options from search.
Speaker 1: So if you searched comic books not Superman, you should
Speaker 1: get results about comic books that don't mention or include
Speaker 1: Superman in the web pages, so it should be discussions
Speaker 1: about comic books, but they're not Superman comic books, or
Speaker 1: at least Superman's name isn't appearing in the web page. Now,
Speaker 1: Boolean search is still a great tool to help you
Speaker 1: get the results you want, but as time has gone on,
Speaker 1: search has become much more sophisticated, so it's not really
Speaker 1: as necessary to become familiar with booleyan search. It's good
Speaker 1: to know how to use it, but it's not key
Speaker 1: because searches not only just grown more sophisticated, but growing
Speaker 1: more intrusive. A lot of searches today rely on information
Speaker 1: that various browsers and web pages are gathering about you,
Speaker 1: so they're using your past behavior as a predictive tool
Speaker 1: to help serve up relevant results. But that's a topic
Speaker 1: for a different podcast episode. I'll do another podcast episode
Speaker 1: about this at some point. Now. Infacyque had a search
Speaker 1: tool that allowed users to include different modifiers on search
Speaker 1: results to narrow down the return sites, which was becoming
Speaker 1: important because the web was growing enormously in the mid
Speaker 1: to late nineties and would only continue to do so.
Speaker 1: The Walt Disney Company took notice of infoseque and would
Speaker 1: purchase more than of the company, effectively incorporating the business
Speaker 1: into the media empire ruled by the hand of the mouse.
Speaker 1: Infoseque at that point had made several acquisitions of its own,
Speaker 1: including sites like ESPN dot com and ABC news dot com,
Speaker 1: which then became part of Disney's media Empire, and infoseque
Speaker 1: we get rolled into Disney's Go dot com network of
Speaker 1: services and sites, and effectively, eventually, after several years, it
Speaker 1: would disappear into that network of sites, Infoseque would begin
Speaker 1: to offer up manually curated search results along with automated ones. Again,
Speaker 1: this was an effort to return the most relevant results.
Speaker 1: You'll see if you look at the history of search
Speaker 1: engines that a lot of them kind of experimented with
Speaker 1: this human curated approach because that was a real issue,
Speaker 1: was that you would use these search engines and you
Speaker 1: would get a ton of results and only a few
Speaker 1: of them ever seemed to be even remotely connected to
Speaker 1: what you wanted. So putting humans in charge of that
Speaker 1: made it a little easier to do relevant results. Because
Speaker 1: humans understand context. They understand when a site is actually
Speaker 1: about something versus when a site just mentions something off hand,
Speaker 1: but it's not really about that thing. You even saw
Speaker 1: this relatively recently. I remember, uh, shortly after I started
Speaker 1: How Stuff works, how the service mahallow was kind of struggling,
Speaker 1: but it was also a human curated search engine. And
Speaker 1: we're talking like two thousand seven when I was looking
Speaker 1: into that. Um my friend Veronica Belmont used to work
Speaker 1: for that company, so it was still something that people
Speaker 1: were trying even as late as the late two thousand's era,
Speaker 1: or by late two thousand's, I mean the first decade
Speaker 1: of two thousands. Anyway, info seq uh what tried that out.
Speaker 1: And also one of the engineers from Infoseek, lie Yan Hong,
Speaker 1: relocated to China and became a co founder of a
Speaker 1: different search engine company called Bai Do be a i
Speaker 1: du as a company that has become truly enormous, with
Speaker 1: asset approaching a value of three hundred billion dollars. That's
Speaker 1: actually more than what Google's parent company, Alphabet has at
Speaker 1: its disposal. So you could argue by do one the
Speaker 1: search wars, but then by Do is not widely known
Speaker 1: in the West. It's a very huge company over in Asia,
Speaker 1: but not not as well known here. Back to our
Speaker 1: search engine history. Excite, the company I talked about earlier,
Speaker 1: finally debuts and it did well. In fact, it did
Speaker 1: so well that it would end up purchasing web Crawler
Speaker 1: in But by nine it's numbers were starting to decline
Speaker 1: thanks to you know who That rhymes was Shmoogle, and
Speaker 1: it merged with a company called at home dot com,
Speaker 1: the at symbol home dot com. It was a deal
Speaker 1: that was worth nearly seven billion dollars, but that deal
Speaker 1: did not ultimately work out. The merged company would file
Speaker 1: for bankruptcy in two thousand one, one of the many
Speaker 1: victims of the dot com bubble bursting um that was
Speaker 1: at least one of the big contributing factors to that.
Speaker 1: The company also just had a lot of debt even
Speaker 1: heading into two thousand two thousand one, so that was
Speaker 1: kind of the nail in the coffin. Now, Infospace, which
Speaker 1: once upon a time owned what would become stuffed Media,
Speaker 1: So technically I was an Infospace employee for a short while,
Speaker 1: purchased Excites assets and domain names, and so web Crawler
Speaker 1: and and uh Excite all became wrapped up with infospaces offerings,
Speaker 1: and uh yeah, there you just technically, it's still part
Speaker 1: of that. You can still use some of that, although
Speaker 1: um it's a much different tool than what it used
Speaker 1: to be. Also in Alta Vista emerged from the Western
Speaker 1: Research Laboratory at the Digital equip Mint Corporation or d
Speaker 1: e C. Alta Vista allowed for natural language queries, meaning
Speaker 1: you could type in a query similar to how you
Speaker 1: would ask a person to look for something for you.
Speaker 1: You didn't have to focus on asking in a way
Speaker 1: that would only make sense to a machine. This is
Speaker 1: that barrier of entry we often see with technology, where
Speaker 1: we have to adjust our behavior so that whatever technology
Speaker 1: we're working with understands, quote unquote what we want from it.
Speaker 1: Um Alta Vista was trying to reverse that, to make
Speaker 1: the technology attempt to understand what we want, rather than
Speaker 1: making us work so that the technology can understand us.
Speaker 1: The researchers who designed it had to do a full
Speaker 1: scale web crawl in August, indexed ten million pages in
Speaker 1: that web crawl, and this was compelling enough to launch
Speaker 1: as a spinoff company by Alta Vista was powering search
Speaker 1: results for Yahoo. So, like I mentioned earlier, where Yahoo
Speaker 1: would use other company to to run their web search
Speaker 1: Altivista was one of those, but also at that time,
Speaker 1: Compact would acquire d e C, which in turn owned Altivista,
Speaker 1: and Compact turned Ultivista into more of a portal service
Speaker 1: than than a search engine, a true search engine, which
Speaker 1: put it more in direct competition with Yahoo, and Ultivista's
Speaker 1: numbers went into decline, possibly because of that shift to
Speaker 1: a portal service rather than as a more straightforward search tool.
Speaker 1: Now we're not quite done covering all the major players
Speaker 1: in the space before Google came on board. I'm going
Speaker 1: to cover a couple more right after we take this
Speaker 1: quick break. Okay, So in addition to the services I've
Speaker 1: already mentioned, there were a couple more. There was ink
Speaker 1: Tomy that it's a project that was headed by Eric
Speaker 1: Brewer and Paul Gautier. They founded inc Tomy in the
Speaker 1: two of them been working on a parallel processing computing
Speaker 1: project for DARPA when they came up with this approach
Speaker 1: to search, and rather than launching a dedicated search tool
Speaker 1: of their own, they said, oh, well, we offer to
Speaker 1: use our technology to power other people's search engines. So essentially,
Speaker 1: you you put up the front and will power the
Speaker 1: back end. And one of those was run by a
Speaker 1: company called hot Wired, and they introduced a search tool
Speaker 1: called hot bot. Ink Tomy worked largely as sort of
Speaker 1: a business to business entity, growing far beyond a search
Speaker 1: engine company. But the dot com crash of two thousand
Speaker 1: one also hit ink Tony really hard, and a couple
Speaker 1: of years later it was swept up by Yahoo. So
Speaker 1: you see, you see a lot of these companies end
Speaker 1: up kind of getting gulped up by each other. Now,
Speaker 1: the last of our pre Google search engines that I'm
Speaker 1: going to talk about is ask Jeeves. Later on, it
Speaker 1: was just known as Ask. It launched in nineteen nine
Speaker 1: d seven, having been developed by David Warthen and Garrett Gruner,
Speaker 1: and like some of the other services I mentioned in
Speaker 1: this episode, it would present curated lists that were created
Speaker 1: by sort of an editorial board, along with some paid listing.
Speaker 1: So if you're a company that wanted your website to
Speaker 1: be listed alongside quote unquote legitimate research returns rather, you
Speaker 1: could pony up the cash have your website put on
Speaker 1: that list. That still happens today on search engines. Happens
Speaker 1: today on Google, where you'll see the first couple of
Speaker 1: results tend to be ones that say, you know, add
Speaker 1: At the end of it, Google has to label them
Speaker 1: as ads, not as just natural search results based on
Speaker 1: your query. Though sometimes those ads actually are the things
Speaker 1: you're looking for, so it's not always a bad thing,
Speaker 1: but it is good to just pay attention. So eventually
Speaker 1: Ask would develop its own search engine technology that would
Speaker 1: automate things. They stopped lying exclusively on people curating lists,
Speaker 1: and Ask would go on to acquire Excite, so you saw,
Speaker 1: you know, Excite what WebCrawler will ask with later on
Speaker 1: by Excite, So you see, there's a lot of shuffling
Speaker 1: with these companies. And then came Google, which had started
Speaker 1: as a research project at Stanford. Larry Page and Sarage
Speaker 1: Brenn had developed the tool and they were running it
Speaker 1: out of a garage for a little while. They had
Speaker 1: built a search tool they originally called BackRub, and their
Speaker 1: goal is to create a search engine that could index
Speaker 1: the web and then present the most relevant results to
Speaker 1: any query. But how would you do that? Well, the
Speaker 1: actual answer, if we're being totally transparent, is kind of
Speaker 1: like Coke's secret formula, and that we know in general
Speaker 1: what has to go into it, but we don't know
Speaker 1: the specifics that would allow us to replicate the results precisely.
Speaker 1: The algorithm that Google uses is peculiar to Google, and
Speaker 1: they also change it a lot. They tweak it, so
Speaker 1: even if we did learn how it used to work,
Speaker 1: it doesn't work that way anymore. So Brendan Page would
Speaker 1: refer to this process as page rank. And here's how
Speaker 1: it worked from a theoretical standpoint. So first, you index
Speaker 1: the web. So you need to get a kind of
Speaker 1: a ah, a complete look at all the websites that
Speaker 1: are available out there on the web and inventory if
Speaker 1: you will, of all the web. To do this, you
Speaker 1: send out bots to index all the pages that are
Speaker 1: listed on the web that you can find. Um, you
Speaker 1: can actually in the HTML of a web page, you
Speaker 1: can designate it so that it will instruct bots to
Speaker 1: ignore the page and not index it. So you can
Speaker 1: do that and it won't show up on any search
Speaker 1: result page because the bot will see that message and
Speaker 1: we'll just move on. This is useful if you want
Speaker 1: a page that only people who know about it can
Speaker 1: navigate to it, and you don't want folks just stumbling
Speaker 1: on it through search, So that is an option. So
Speaker 1: for all of the pages that are discoverable. The bots
Speaker 1: will crawl through, they follow all the links, they try
Speaker 1: and index out the web and get it as good
Speaker 1: as snapshot of what the World Wide Web is as
Speaker 1: is possible. Now, these spots aren't just looking for the
Speaker 1: location of the web pages, like what server those web
Speaker 1: pages are stored on, or even get just a full
Speaker 1: understanding of what the text is inside those pages, so
Speaker 1: that when you do a search query and you put
Speaker 1: your search terms in, they can return the pages that
Speaker 1: have those search terms. They're also looking for links, both
Speaker 1: going into the page and coming from the page to
Speaker 1: go elsewhere, and the links will become a really important
Speaker 1: part of page rink. So here's the basic idea. Brand
Speaker 1: and Page figured out that if a web page about
Speaker 1: a given subject is really good, other pages tend to
Speaker 1: link to it. They do so because they recognize the quality,
Speaker 1: and that helps boost the pages position in search results.
Speaker 1: So let's use an example to kind of understand this.
Speaker 1: Let's say you are one of these early web developers
Speaker 1: in the late nineteen nineties, and you're also a big
Speaker 1: music fans, so you decided to create a blog that's
Speaker 1: completely focused on the music industry, and you cover the
Speaker 1: news in the industry. You post reviews of albums that
Speaker 1: you've listened to. Maybe you even do some interviews with
Speaker 1: people who are in the industry. And as you write
Speaker 1: this blog, other people take notice. Some of them also
Speaker 1: have a web presence and cover the industry, and they
Speaker 1: really dig your stuff, so they linked to your page.
Speaker 1: They say, there's a really cool music industry blog. It's
Speaker 1: being run by this person over here. Follow this link
Speaker 1: to go check it out. Google's bots would register that
Speaker 1: they would see that those links were out there pointing
Speaker 1: to your page, and the more sites that link back
Speaker 1: to your blog, the higher your blog would rank and
Speaker 1: search results. So if someone searched music industry news or
Speaker 1: something along those lines, there's a chance that your blog
Speaker 1: would pop up fairly high and results. Now how high
Speaker 1: would be dependent on something other than just how many
Speaker 1: pages are linking to you. That's one factor that matters
Speaker 1: a lot, the number of sites linking to your page.
Speaker 1: But the other one is how trustworthy those linking sites were.
Speaker 1: So let's consider two scenarios. In our first scenario, you've
Speaker 1: got your music blog and you've got a lot of
Speaker 1: sites that are linking to your page, but they're all
Speaker 1: small time sites like some our personal sites, run by
Speaker 1: people who are interested in music, but they don't really
Speaker 1: have any presence in the industry and no one's really
Speaker 1: linking to their page, so they're not ranked super high
Speaker 1: in Google's estimation. Some of them might be even worse
Speaker 1: than that. Some of them might be link farms. Link farms.
Speaker 1: You don't really see them that much these days, but
Speaker 1: in the nineties they were everywhere. They only existed to
Speaker 1: link to other pages, and it was in an effort
Speaker 1: to boost those other pages rankings in search. So if
Speaker 1: you navigated to one, let's say you do a search
Speaker 1: for a term and you click on the link, you
Speaker 1: end up looking at a bunch of completely disconnected titles
Speaker 1: and U r l s and that's it. There's no
Speaker 1: other content on the page. It's just a listing of
Speaker 1: links to different sites with no rhyme or reason to them.
Speaker 1: Those would also be very low in Google's trustworthiness according
Speaker 1: to its algorithm, because obviously the only reason they're existing
Speaker 1: is to try and game the system, to try and say, well,
Speaker 1: let's just add a lot more links to this page
Speaker 1: and that will boost its its relevance. So if that
Speaker 1: were the case, if most of the links going to
Speaker 1: your page were either from small potatoes websites or they
Speaker 1: were from link farms. Your page rank wouldn't be boosted
Speaker 1: very high. It might be higher than it would be
Speaker 1: if there were no links going to your page at all,
Speaker 1: but it's not a huge help. Now let's consider scenario two.
Speaker 1: Let's say your blog only has a few sites linking
Speaker 1: to it, a couple of dozen maybe, But those sites
Speaker 1: are doozies. Maybe they include record labels that are in
Speaker 1: the music industry. Maybe it's other outlets that cover music news.
Speaker 1: Maybe it includes some news websites that use your blog
Speaker 1: as a source for stories. Now those sites have a
Speaker 1: much higher level of trustworthiness for Google, and so or
Speaker 1: you know, in Google's estimation, I should say, and those
Speaker 1: links matter more. So maybe in scenario one you have
Speaker 1: a thousand tiny sites linking to you, and scenario to
Speaker 1: you just have a couple of dozen of the really
Speaker 1: big sites linking to you. Page rank would favor scenario
Speaker 1: two over scenario one, reasoning that if your blog is
Speaker 1: good enough to get the attention and support of those
Speaker 1: trusted entities, it must be a really good resource, and
Speaker 1: so your site would get boosted in search results. Now
Speaker 1: that helped address a troublesome trend with search. I mentioned
Speaker 1: link farms. That was one problem. So any search engine
Speaker 1: that looked at back linking UM could be fooled through
Speaker 1: link farms that were just there to to boost that number.
Speaker 1: In the nineties, it wasn't unusual to encounter that. I
Speaker 1: can't tell you how many times it happened to me
Speaker 1: when I was doing a search for, you know, a
Speaker 1: fairly obscure type of topic, and I just would come
Speaker 1: across a link farm to all sorts of stuff that
Speaker 1: was most of which was totally not relevant to what
Speaker 1: I wanted. UM. Those were really frustrating, and so that
Speaker 1: that was one thing that people would do to try
Speaker 1: and game the system. But another was an equally annoying tactic. UH.
Speaker 1: People wanted folks to come to their web pages really badly.
Speaker 1: They were in the old old days. There were even
Speaker 1: web page counters, a little it looked like a little
Speaker 1: digit counter that would tell you how many people had
Speaker 1: been to that website, and it became kind of a
Speaker 1: badge of honor among early web developers if that number
Speaker 1: were particularly high, because it showed that a lot of
Speaker 1: people were visiting your site, and it was kind of
Speaker 1: a prestige thing um and also could mean money because
Speaker 1: if you were using web advertising to support your your
Speaker 1: web site and that number was getting really really high,
Speaker 1: and then you had more page views, and more page
Speaker 1: views would mean more cash from the advertisers, So there
Speaker 1: was an actual, you know, financial reason to try and
Speaker 1: get more people to come to your web page, and
Speaker 1: not everybody played fair and square. Sometimes web developers would
Speaker 1: include an incredibly long list of popular search terms on
Speaker 1: the web page. Usually would be at the very bottom
Speaker 1: of the web page in tiny font and so that's
Speaker 1: the only place where your search terms would show up.
Speaker 1: The rest of the web page would be about something
Speaker 1: entirely different, and then you do a search on the
Speaker 1: web page for the terms you were looking for. It
Speaker 1: turns out there just in this list of random or
Speaker 1: seemingly random search terms, it's really the most popular search
Speaker 1: terms that people could come across, and they were just
Speaker 1: dumping them all at the bottom of their web pages,
Speaker 1: and that way their web page would pop up in
Speaker 1: all these sorts of searches, and people would end up
Speaker 1: going to their web page without knowing that it wasn't
Speaker 1: really about what they were hoping for That was really
Speaker 1: frustrating for a lot of people, including myself, because you know,
Speaker 1: you're obviously you're searching for something because you want to
Speaker 1: get that content, but then you end up going to
Speaker 1: a web page that's not about that at all. It's
Speaker 1: not a good experience, So it was a terrible way
Speaker 1: to have people come to your web page. However, if
Speaker 1: your goal was just to get those views so that
Speaker 1: you could get that ad money, people were willing to
Speaker 1: do it. Um maybe it was a successful strategy for
Speaker 1: people who were maybe running an online store, but I
Speaker 1: can't imagine it would be worked too well. I mean,
Speaker 1: if I'm looking for information about quantum mechanics and I
Speaker 1: end up being dumped in some store that's selling baseball
Speaker 1: caps that have nothing to do with anything, I'm probably
Speaker 1: just gonna be mad. But anyway, that was one of
Speaker 1: the other approaches people were taking, was trying to include
Speaker 1: this text. Sometimes they would even hide it. They would
Speaker 1: have a big section of the web page where the
Speaker 1: font had the same color as the background text, so
Speaker 1: you couldn't see it just when you're reading through the
Speaker 1: web page, but it could be read by bots as
Speaker 1: they're crawling through all this material. Uh So, search engine
Speaker 1: developers got into kind of a seesaw battle with web
Speaker 1: developers to try and get around these tricks. One of
Speaker 1: the things they started to do Google was one of
Speaker 1: them was focused on the text in the actual body
Speaker 1: of the document itself and then ignore information that might
Speaker 1: be in the headers or footers, which was typically where
Speaker 1: people were putting these laundry lists of popular search terms.
Speaker 1: So Google got around that by saying, Okay, well, we're
Speaker 1: no longer worried about the text that's in the head
Speaker 1: or the footer. We're just concentrating on what's in the
Speaker 1: body of the page. And Google's approach really improved upon relevance,
Speaker 1: the search results were just better than most of the competitors.
Speaker 1: You know, you you were more likely to come across
Speaker 1: something the stuff that you know represented what you wanted,
Speaker 1: and so Google was able to tap into advertising revenue
Speaker 1: because they were able to really give people what they wanted.
Speaker 1: Advertisers wanted to be included with that, and Google began
Speaker 1: listing ads supported results with the top returns for queries.
Speaker 1: So it meant that you know, the stuff that people
Speaker 1: most wanted to see, you would get ads served right
Speaker 1: with that Uh, there's a very attractive proposition, and it
Speaker 1: positioned the company well enough to survive the dot com
Speaker 1: bubble burst of two thousand and two thousand one, and
Speaker 1: many of its competitors either merged with other companies as
Speaker 1: I mentioned, or they completely went under. The Google remained
Speaker 1: around and then was able to actually seriously grow in
Speaker 1: the two thousand's. Uh. There were a couple of discussions
Speaker 1: with other company needs early on, including Excite, that could
Speaker 1: have led to Google getting acquired, but none of that
Speaker 1: came to fruition, and Google remained its own company and
Speaker 1: continue to build on its success. And Google would evolve
Speaker 1: its algorithm trying to crack the nut of deciphering the
Speaker 1: meaning of text inside web pages. So not just here
Speaker 1: are the web pages that include the terms that you
Speaker 1: search for, but here are the ones that included in
Speaker 1: the way that you meant including a improving it so
Speaker 1: that it can recognize natural language and not just you know,
Speaker 1: lists of search terms. Pairing that with the page rank
Speaker 1: kind of approach would give Google the information and needed
Speaker 1: to really rank its results and necessitated the search engine
Speaker 1: optimization strategy that that became a whole new industry. Ranking
Speaker 1: well in search was a really good way to get
Speaker 1: serious Internet traffic to a site. People made entire careers
Speaker 1: out of figuring out the best way to rank well
Speaker 1: in search, which honestly mostly involves creating a compelling and
Speaker 1: relevant web page or website that makes people want to
Speaker 1: link to it. Um it's easier said than done. It's
Speaker 1: that was the best way to rank well within Google's search. Occasionally,
Speaker 1: Google would tweak things so that your site, if it
Speaker 1: was particularly good, which just rise to the top because
Speaker 1: Google recognized that they might wait your site more heavily
Speaker 1: than other sites. Um. But it also led to companies
Speaker 1: learning the hard lesson that depending upon search traffic is
Speaker 1: a risky thing to do. Every time Google changes its
Speaker 1: search algorithm, it affects search rankings. So you might be
Speaker 1: doing really well for years, and then suddenly you see
Speaker 1: a massive drop off and visitor numbers because Google changed
Speaker 1: its algorithm and your page no longer ranks as well
Speaker 1: in search results as it used to. So in a
Speaker 1: future episode, I plan on getting some s e O
Speaker 1: experts on the show and have them talk about the
Speaker 1: challenges of developing a good strategy to rank well in
Speaker 1: search and what other strategies people might consider if they
Speaker 1: want to promote their traffic to sites and services. You know,
Speaker 1: it's it's tricky stuff because again, it might work great
Speaker 1: today and then tomorrow it might not work at all.
Speaker 1: So there there's a real strong push among web developers
Speaker 1: to try and find alternatives to search engine traffic being
Speaker 1: your main way of getting people into your website. Um. Also,
Speaker 1: if people are just searching for content and then popping
Speaker 1: over to your site, uh, and they read one page
Speaker 1: that is relevant to whatever their search engine query was,
Speaker 1: they're not likely to stick around unless they go down
Speaker 1: sort of the Wikipedia rabbit hole. They're more likely to bounce.
Speaker 1: And this was a problem we saw at the House
Speaker 1: Stuff Works website all the time, is that we could
Speaker 1: get great search engine traffic. People were looking for specific
Speaker 1: answers to question and we had articles that answered those questions,
Speaker 1: so people would come and read those articles. Now, what
Speaker 1: would be ideal for us is that people say, this
Speaker 1: is a great site, I want to read more articles.
Speaker 1: Let's just see what's here. But the reality was most
Speaker 1: people would come in, read whatever they wanted and then leave. Um,
Speaker 1: they wouldn't stick around to read other stuff. And and
Speaker 1: it was a real challenge One of the things that
Speaker 1: we always tried to do was figure out how to
Speaker 1: create a site that was a destination all of its own.
Speaker 1: That you're not going there because a search engine told
Speaker 1: you to. You're going there because you love the site
Speaker 1: and you want to read more of the stuff on there. Um.
Speaker 1: That was always our goal. It was always very, very
Speaker 1: challenging because there's a ton of websites out there, and
Speaker 1: there's a ton of really great content, So making sure
Speaker 1: that yours can stand up to everybody else's is a
Speaker 1: heck of a challenge. It's a hard thing to do.
Speaker 1: I think the site does a great job of it, um,
Speaker 1: but it was one of those things that we were
Speaker 1: always striving toward. In the end, Google one out because
Speaker 1: it had grown too large before the bubble burst, so
Speaker 1: it hadn't spread its assets out too thin, it wasn't
Speaker 1: in incredible amounts of debt, so it was able to
Speaker 1: weather that storm, and then it was able to build
Speaker 1: on its success, and it had developed a search engine
Speaker 1: tool that people felt returned the best results and they
Speaker 1: put a ton of trust in it. Ultimately, Google would
Speaker 1: become this enormous company that would be able to gather
Speaker 1: huge amounts of data from its users and put that
Speaker 1: to use as well, and that made it a very
Speaker 1: valuable resource for advertisers, and that's kind of how Google
Speaker 1: won the search engine war. Now, we'll talk about other
Speaker 1: stuff related search engines in the future, but our next
Speaker 1: episode is going to be about something totally different. Um
Speaker 1: And I'm just doing a few one off episodes because
Speaker 1: after doing that arc of seven episodes about the media
Speaker 1: and its relationship to us and and technology, I felt
Speaker 1: like we kind of needed to do some one offs.
Speaker 1: So the next one's gonna be an their entertainment related podcast,
Speaker 1: but it will be another one off. If you guys
Speaker 1: have suggestions for future topics I should tackle, why not
Speaker 1: send me an email address is tech stuff at how
Speaker 1: stuff works dot com or hop on over to our
Speaker 1: website that's tech stuff podcast dot com. You will find
Speaker 1: the archive of all of our shows. There, you'll find
Speaker 1: links to our social media sites. You'll find a link
Speaker 1: to our online store, where every purchase you make goes
Speaker 1: to help the show and we greatly appreciate it and
Speaker 1: I will talk to you again really soon. Text Stuff
Speaker 1: is a production of I Heart Radio's How Stuff Works.
Speaker 1: For more podcasts from my Heart radio, visit the I
Speaker 1: heart Radio app, Apple podcasts, or wherever you listen to
Speaker 1: your favorite shows.