The problem though, is the older systems had obvious problems, while the newer systems have hidden bugs and conceptual issues which often don't show up in the metrics, and which compound over time as more complexity is layered on. For example: I found an off by 1 error deep in a formula from an old launch that has been reordering top results for 15% of queries since 2015. I handed it off when I left but have no idea whether anyone actually fixed it or not.

I wrote up all of the search bugs I was aware of in an internal document called "second page navboost", so if anyone working on search at Google reads this and needs a launch go check it out.

Same is happening to YouTube as well. Feels like it’s nothing but promoters pushing content to gain followers to sell ads or other stuff because nobody else’s videos ever surface. Just a million people gaming the algorithm and the only winners are the people who devote the most time to it. And by the way, would I like to sign up for their patreon and maybe one of their online courses?

Its really easy to spot the crap websites that are scaping content-creating websites ... because they monetize by adding ads.

If Google was _only_ selling ads on the search results page, then it could promote websites that are sans ads.

Instead, it is incentivised to push users to websites that contain ads, because it also makes money there.

And that means scraping other sites to slap your ads onto them can be very profitable for the scammers.

After that I only use search for technical problems, and mouth to mouth or specific authors for everything else.

Search for a while hasn’t been about searching the web as much as it has been about commerce. It taps commercial intent and serves ads. It is now an ad engine; no longer a search engine.

If you want to purchase consumer products at your own expense and offer an impartial opinion on each of them then you will have no problem getting ranked highly on google. You will lose a lot of money doing so, however, and will also be plagiarized to death in a month. The sites you want to be rid of will outrank you for your own content, I have been there and have the t-shirt.

[1] - https://en.wikipedia.org/wiki/Breakup_of_the_Bell_System

I wish there was some way to tell "please ignore all videos that contain these strings, and I don't mean only for next 2 weeks".

Youtube gets their ads revenue from before/during video, so they can be nicer to users.

And I know they could because someone did make that list and posted it here last year.

I think it's more likely that Google stopped really caring.

Problem is that the rules of search engines created the dubious field of SEO in the first place. They are not entirely the innocent victim here.

Arcane and intransparent measures get you ahead. So arcane that you instantly see that it does not correspond with quality content at all, which evidently leads to a poor result.

I wish there was an option to hide every commercial news or entertainment outlet completely. Those are of course in on SEO for financial reaesons.

There's alway plugins or you can subscribe to Kagi, although I don't think there's any blocklist preconfigured for "all commercial news websites"

To summarize it: Google reverted an algorithm that detected SEO spams in 2019.

(Note that I never work for Google and I don't know whether it's true or not. It's just what this article says.)

This better echoes my personal experience with the decline of Google search than TFA: it seems to be connected to the increasing use of ML in that the more of it Google put in, the worse the results I got were.

The key difference is how tolerant the specific use case is of a probably-correct answer.

The things recent-AI excels at now (generative, translation, etc.) are very tolerant of "usually correct." If a model can do more, and is right most of the time, then it's more valuable.

There are many other types of use cases, though.

On the other hand ML has absolutely revolutionised translation (of longer text), where having a model containing prior knowledge about the world is essential.

I am probably the 0.01% of Tesla drivers who have the computer chime when I exceed the speed limit by some offset. Very regularly, even when FSD is in “chill” mode, the model will speed by +7-9 mph on most roads. (I gotta think that the young 20 somethings who make up Tesla's audience also contributed their poor driving habits to Tesla's training data set) This results in constant beeps, even as the FSD software violates my own criteria for speed warning.

So somehow the FSD feature becomes "more capable" while becoming much less legible to the human controller. I think this is a bad thing generally but it seems to be the fad today.

They are lying with statistics, for the more challenging locations and conditions the AI will give up and let the human take over or the human notices something bad and takes over. So Tesla miles are miles are cherry picked and their data is not open so a third party can make real statistics and compare apples to apples.

*Disclaimer: as someone who's not an AI researcher but did quite some human translation works before.

The people I see who are most excited about ML are business types who just see it as a black boxes that makes stock valuation go vroom.

The people that deeply love building things, really enjoy the process of making itself, are profoundly sceptical.

I look at generative AI as sort of like an army of free interns. If your idea of a fun way to make a thing is to dictate orders to a horde of well-meaning but untrained highly-caffienated interns, then using generative AI to make your thing is probably thrilling. You get to feel like an executive producer who can make a lot of stuff happen by simply prompting someone/something to do your bidding.

But if you actually care about the grit and texture of actual creation, then that workflow isn't exactly appealing.

If one uses English in as precise a way as one crafts code, sure.

Most people do not (cannot?) use English that precisely.

There's little technical difference between using English and using code to create...

... but there is a huge difference on the other side of the keyboard, as lots of people know English, including people who aren't used to fully thinking through a problem and tackling all the corner cases.

No one can, which is why any place human interaction needs anything anywhere close to the determinancy of code, normal natural langauge is abandoned for domain-specific constructed languages built from pieces of natural language with meanings crafted especially for the particular domain as the interface language between the people (and often formalized domain-specific human-to-human communication protocols with specs as detailed as you’d see from the IETF.)

Still needed domain experts back then, and, IMHO, in years/decades to come

I think we also need to be aware that this business layer above us that often sees __computers__ as a magic box where they type in. There's definitely a large spectrum of how magical this seems to that layer, but the issue remains that there are subtleties that are often important but difficult to explain without detailed technical knowledge. I think there's a lot of good ML can do (being a ML researcher myself), but I often find it ham-fisted into projects simply to say that the project has ML. I think the clearest flag to any engineer that this layer above them has limited domain knowledge is by looking at how much importance they place on KPIs/metrics. Are they targets or are they guides? Because I can assure you, all metrics are flawed -- but some metrics are less flawed than others (and benchmark hacking is unfortunately the norm in ML research[0]).

[0] There's just too much happening so fast and too many papers to reasonably review in a timely manner. It's a competitive environment, where gatekeepers are competitors, and where everyone is absolutely crunched for time and pressured to feel like they need to move even faster. You bet reviews get lazy. The problems aren't "posting preprints on twitter" or "LLMs giving summaries", it's that the traditional peer review system (especially in conference settings) poorly scales and is significantly affected by hype. Unfortunately I think this ends up railroading us in research directions and makes it significantly challenging for graduate students to publish without being connected to big labs (aka, requiring big compute) (tuning is another common way to escape compute constraints, but that falls under "railroading"). There's still some pretty big and fundamental questions that need to be chipped away at but are difficult to publish given the environment. /rant

really, my impression is the opposite. They are driven by doing cool tech things and building fresh product, while getting rid of "antiquated, old" product. Very little thought given to the long term impact of their work. Criticism of the use cases are often hand waved away because you are messing with their bread and butter.

ML isn't like that. It's new. It's different. It may not succeed in the ways we expect; it may even look dumb in hindsight. But it absolutely represents a genuinely new paradigm for computing and is worth studying and understanding on that basis. We look back to SOAP and see something that might as well be forgotten. We'll never look back to the dawn of AI and forget what it was about.

[1] For anyone who missed that particular long-sunken boat, SOAP was a RPC protocol like any other. Yes, that's really all it was. It did nothing special, or well, or that you couldn't do via trivially accessible alternative means. All it had was the right adjective ("XML" in this case) for the moment. It's otherwise forgettable, and forgotten.

Anyone claiming it’s some sort of snake oil shouldn’t be taken seriously. Certainly the current hype around it has given rise to many inappropriate applications, but it’s a wildly successful and ubiquitous technology class that has no replacement.

This new ML that's supposed to be the basis for an entire new economic wave, that I mostly dislike.

But I guess that's how we build new things... We explore and throw away 80% of what we've built.

Reading these comments I thought I stepped into some alternate timeline when we don't already have widespread ML all over the place.

Like, nobody does rules-based image recognition for a decade now already!

They routinely give me brain-dead suggestions such as to watch a video I just watched today or yesterday, among other absurdities.

These days my biggest gripe is that they put unrelated ragebait or clickbait videos in search results that I very clearly did not search for - often about American politics.

If I entirely avoid watching any popular videos, the recommendations are quite good and don’t seem to include anything like what you are seeing. If I don’t entirely avoid them, then I do get what you are seeing (among other nonsense).

Now the "related" section is gone in favor of "recommended" samey clickbait garbage. The relations between human interests are too esoteric for current ML classifiers to understand. The old Markov-chain style works with the human, and lets them recognize what kind of space they've gotten themselves into, and make intelligent decisions, which ultimately benefit the system.

If you judge the system by the presence of negative outliers, rather than positive, then I can understand seeing no difference.

I would go further and say that it is impossible. Human interests are contextual and change over time, sometimes in the span of minutes.

Imagine that all the videos on the internet would be on one big video website. You would watch car videos, movie trailers, listen to music, and watch porn in one place. Could the algorithm correctly predict when you're in the mood for porn and when you aren't? No, it couldn't.

The website might know what kind of cars, what kind of music, and what kind of porn you like, but it wouldn't be able to tell which of these categories you would currently be interested in.

I think current YouTube (and other recommendation-heavy services) have this problem. Sometimes I want to watch videos about programming, but sometimes I don't. But the algorithm doesn't know that. It can't know that without being able to track me outside of the website.

* They could let me directly enter my interests instead of guessing

* They could classify videos by expertise (tags or ML) and stop recommending beginner videos to someone who expresses an interest in expert videos.

* They could let me opt out of recommending videos I've already watched

* They could separate sites into larger categories and stop recommending things not in that category. For me personally, when I got to youtube.com I don't want music but 30-70% of the recommendations are for music. If the split into 2 categories (videos.youtube.com - no music) and (music.youtube.com - only music) they'd end up recommending far more to me that I'm actually interested in at the time. They could add other broad categories like (gaming.youtube.com, documentaries.youtube.com, science.youtube.com, cooking.youtube.com, ...., as deep as they want). Classifying a video could be ML or creator decided. If you're only allowed one category they would be incentive to not mis-classify. If they need more incentive they could dis-recommend your videos if you mis-classify too many/too often).

* They could let me mark videos as watched and actually track that the same as read/unread email. As it is, if you click "not interested -> already watched" they don't mark the video as visibly watched (the red bar under the video). Further, if you start watching again you lose the red-bar (it gets reset to your current position). I get that tracking where you are in a video is something that's different for email vs video but at the same time (1) if I made it to 90% of the way through then for me at least, that's "watched" - same as "read" for email and I'd like it "archived" (don't recommend this to me again) even if I start watching it again (same as reading an email marked as "read)

Theres a general problem in the tech world where people seem to inexplicably disregard the issue of non-reducibility. The point about the algorithm lacking access to necessary external information is good.

A dictionary app obviously can't predict what word I want to look up without simulating my mind-state. A set of probabilistic state transitions is at least a tangible shadow of typical human mind-states who make those transitions.

Someone probably changed the engine that shows videos for you - exactly as with search.

Or when they would show more than 3 results before spamming irrelevant videos.

Or when they didn't show 3 unskippable ads in a 5 minute video.

Or when they had a dislike button so you would know to avoid wasting time on low quality videos.

    > Or when they didn't show 3 unskippable ads in a 5 minute video.

I also use Firefox for Android, which has Addon support. Ublock Origin works on the phone and disables a a lot of the ad horror.

Wait what?! You "Consume Content" on a COMPUTER? What are you some kinda grandpa? Why aren't you consuming content from your phone like everyone else? Or casting it from your phone to your SMART TV! Great way to CONSUME CONTENT!

CONSUME CONTENT CONSUME CONTENT CONSUME CONTENT CONSUME CONTENT CONSUME CONTENT CONSUME CONTENT CONSUME CONTENT CONSUME CONTENT CONSUME CONTENT

I don't know about the TV though.

Honestly, I'd prefer my voice assistant (siri mostly) to be like that as well. It was at first, and I think everyone hated that lol.

From a user perspective, Shorts highlights a specific format of YouTube that happened to have been around for a lot longer than people realize. TikTok isn't anything new, Vine was doing exactly the same thing TikTok was a decade prior. It was shut down for what I can only assume was really dumb reasons. A lot of Viners moved to YouTube, but they had to change their creative process to fit what the YouTube algorithm valued at the time: longer videos.

Pre-Shorts, there really wasn't a good place on YouTube for short videos. Animators were getting screwed by the algorithm because you really can't do daily uploads of animation[0] and whatever you upload is going to be a few minutes max. A video essayist can rack up hundreds of thousands of hours of watch time while you get maybe a thousand.

(Fun fact: YouTube Shorts status was applied retroactively to old short videos, so there's actually Shorts that are decades old. AFAIK, some of the Petscop creator's old videos are Shorts now.)

But that's why users or creators would want to use Shorts. A lot of the UX problems with Shorts boils down to YouTube building TikTok inside of YouTube out of sheer corporate envy. To be clear, they could have used the existing player and added short-video features on top (e.g. swipe-to-skip). In fact, any Short can be opened in the standard player by just changing the URL! There's literally no difference other than a worse UI because SOMEONE wanted "launched a new YouTube vertical" on their promo packet!

FWIW the Shorts player is gradually getting its missing features back but it's still got several pain points for me. One in particular that I think exemplifies Shorts: if I watch Shorts on a portrait 1080p monitor - i.e. the perfect thing to watch vertical video on - you can't see comments. When you open the comments drawer it doesn't move over enough and the comments get cut off. The desktop experience is also really bad; occasionally scrolling just stops working, or it skips two videos per mousewheel event, or one video will just never play no matter how much I scroll back and forth.

[0] Vtubers don't count

But I associate YouTube promotions with garbage any how. The few things I might buy like Tide laundry detergent are entirely despite occasional YouTube promotion.

Besides the religious crap, ill randomly get shit in India in hindu, having had not watched anything Indian and not even remotely Indian.

There's videos I'll watch multiple times, music videos are the obvious kind, but for some others I'm just not watching/understanding it the first time and will go back and rewatch later.

But I guess youtube has no way to understand which one I'll rewatch and which other I don't want to see ever again, and if my behavior is used as training data for the other users like you, they're probably screwed.

The pathologies of big companies that fail to break themselves up into smaller non-siloed entities like Virgin Group does. Maintaining the successful growing startup ways and fighting against politics, bureaucracy, fiefdoms, and burgeoning codebases is difficult but is a better way than chasing short-term profits, massive codebases, institutional inertia, dealing with corporate bullshit that gets in the way of the customer experience and pushes out solid technical ICs and leaders.

I'm surprised there aren't more people on here who decide "F-it, MAANG megacorps are too risky and backwards not representative of their roots" and form worker-owned co-ops to do what MAANGs are doing, only better, and with long-term business sustainability, long tenure, employee perks like the startup days, and positive civil culture as their central mission.

An example of this I see is how new leaders come in and hit hard to cut costs. But the previous leader did this (and the one before them) so the system/group/company is fairly lean already. So to get anywhere near similar reductions or cost savings it typically means cutting more than fat. Which it's clear that many big corps are not running with enough fat in the first place (you want some fat! You just don't want to be obese!). This seems to create a pattern that ends up being indistinguishable from "That worked! Let's not do that anymore."

Example: User Growth & Customer Engagement

Have to have user growth and retention. If you looked at just one or the other, you'd be missing half the equation.

1) People setting the metrics

2) People implementing/calculating the metrics

3) People working on improving the metrics (ie product work)

2 is specially complicated for a lot of software products because it can some times be really hard to measure and can be tweaked/manipulated. For example, the MAU twitter figures from the buyout that Musk keeps complaining about, or Blizzard constantly switching their MAU definition.

Often 2 and 3 are the same people and 1 is almost always upper management. I argue that 1 and 2 should be a single group of people (that doesn't work on the product at all) and not directly subject to upper management and not tracked by the same metrics they implement (or tracked by any metrics at all).

Co-ops could go the angel/VC route for funding if they don’t give up a controlling share.

I would love to join a co-op producing real human survival values in an open source way. Where would you suggest that I look for leads on that kind of organization?

While DDG, Brave, Kagi etc are working generously to replace Google search. The other areas that I think get less attention and needs to be targeted to successfully dismantle them and their predatory practices are Google maps and Google docs.

Maps are hard because it requires a lot of resources and money and whatever but replacing docs should be relatively easier.

FWIW, Kagi is built on top of Google search, so yes it's "replacing" (for you and me) a dependence on Google search, but it is categorically not a from-the-ground-up replacement for Google search.

So... people go walk up for handouts from VCs....and the story begins lol.

There's a lot of ML hate here, and I simply don't see the alternative.

To rank documents, you need to score them. Google uses hundreds of scoring factors (I've seen the number 200 thrown about, but it doesn't really matter if it's 5 or 1000.) The point is you need to sum these weights up into a single number to find out if a result should be above or below another result.

So, if:

  - document A is 2Kb long, has 14 misspellings, matches 2 of your keywords exactly, matches a synonym of another of your keywords, and was published 18 months ago, and

  - document B is 3Kb long, has 7 misspellings, matches 1 of your keywords exactly, matches two more keywords by synonym, and was published 5 months ago

Ranking means deciding which document (A or B) is better to return to the user when queried.

Not writing a traditional forward-algorithm to rank these documents implies one of the following:

- You write a "backward" algorithm (ML, regression, statistics, whatever you want to call it).

- You don't use algorithms to solve it. An army of humans chooses the rankings in real time.

- You don't rank documents at all.

> Counting how many links are pointing to each document is sufficient if you know how long that link existed

- Link-counting (e.g. PageRank) is query-independent evidence. If that's sufficient for you, you'll always return the same set of documents to each user, regardless of what they typed into the search box.

At best you've just added two more ranking factors to the mix:

  - document A
    qie:
        length: 2Kb
        misspellings: 14
        age: 18 months
      + in-links: 4
      + in-link-spamminess: 2.31E4
    qde:
        matches 2 of your keywords exactly
        matches a synonym of another of your keywords

  - document B
    qie:
        length: 3Kb
        misspellings: 7
        age: 5 months
      + in-links: 2
      + in-link-spamminess: 2.54E3
    qde:
        matches 1 of your keywords exactly
        matches 2 keywords by synonym

- Which document matches your query better, A or B?

- How did you decide that, such that not only can you program a non-ML algorithm to perform the scoring, but you're certain enough of your decision that you can fix the algorithm when it disagrees with you ( >> debuggable and understandable by human search engineers )

Yes, this is a statistical method. Guess what machine learning is and what it actually excels?

I got nothing against ML in principle, but if the model doesn't do the right thing then you can just end up stuck. Also, it often burns a lot of resources to learn something that was obvious to human domain experts anyway. Plus the understandability issues.

if they were unable to do some AB testing between a ML search and a non-ML search, they deserve their failure 100%

there are not enough engineers blowing the whistle against ML

Unfortunately, Google evaluates employees by the complexity of their work. "Demonstrates complexity" is a checkbox on promo packets, from what I've heard.

Naturally, every engineer will try to over-complicate things just so they can get the raises and promos. You get what you value.

It would have been a worse outcome for Google if they had stuck to their no ML stance and then had Bing take over search because they were a generation behind in technology.

My priors before reading this article were that an uncritical over-reliance on ML was responsible for the enshittification of Google search (and Google as a whole). Google seemed to give ML models carte blanche, rather than using the 80-20 rule to handle the boring common cases, while leaving the hard stuff to the humans.

I now think it's possible both explanations are true. After all, what better way to mask a product's descent into garbage than more and more of the core algorithm being a black box? Managers can easily take credit for its successes and blame the opacity for failures. After all, the "code yellow" was called in the first place because search growth was apparently stagnant. Why was that? We're the analysts manufacturing a crisis, or has search already declined to some extent?

The engineers at Google were wonderful to work with up to 2010. It was like a switch flipped mid-2011 and they became actively hostile to any third party efforts to monitor what they were doing. To put it another way, this would like NBC trying to sue Nielsen from gathering ratings data. Absurd.

Fortunately, the roadblocks thrown up against us were half-hearted ones and easily circumvented. Nevertheless, I had learned an important lesson about placing reliance for one's life work on a faceless mega tech corporation.

It was not soon after when Google eliminated "Don't Be Evil" from the mission statement. At least they were somewhat self aware, I suppose.

I and a number of other people left in 2010. I went on to work at Blekko which was trying to 'fix' search using a mix of curation and ranking.

When I left, this problem of CPC's (the amount Google got per ad click in search) was going down (I believe mostly because of click fraud and advertisers losing faith in Google's metrics). While they were reporting it in their financial results, I had made a little spreadsheet[1] from their quarterly reports and you can see things tanking.

I've written here and elsewhere about it, and watched from the outside post 2010 and when people were saying "Google is going to steam roll everyone" I was saying, "I don't think so, I think unless they change they are dead already." There are lots of systemic reasons inside Google why it was hard for them to change and many of their processes reinforced the bad side of things rather than the good side. The question for me has always been "Will they pull their head out in time to recover?" recognizing that to do that they would have to be a lot more honest internally about their actions than they were when I was there. I was also way more pessimistic, figuring that they would be having company wide layoffs by 2015 to 2017 but they pushed that out by 5 years.

I remember pointing out to an engineering director in 2008 that Google was living in the dead husk of SGI[2] which caused them to laugh. They re-assured me that Google was here to stay. I pointed out that Wei Ting told me the same thing about SGI when they were building the campus. (SGI tried to recruit me from Sun which had a campus just down the road from where Google is currently.)

[1] https://docs.google.com/spreadsheets/d/18_y-Zyhx-5a1_kcW-x7p...

[2] Silicon Graphics -- https://www.sfchronicle.com/news/article/peninsula-high-tech...

Well in 2011 Google had just over 30k employees, and now they're doing "layoffs" with 180k+ in 2024. I don't think the layoffs mean much.

One of my observations between "early" Google and "late" Google (and like the grandparent post I see 2010 as a pretty key point in their evolution) was employee "efficiency." I don't know if you've ever been in that situation where someone leaves a company and the company ends up hiring two or three people to replace them because of all the things they were doing. Not 10x engineers but certainly 3 - 5x engineers. Google starting losing lots of those in that decade. They had gone through the "Great Repricing" in 2008 when Google lowered the strike price on thousands of share options. And having been there 5 to 10 years had enough wealth built up in Google stock that for a modest level of "this isn't fun any more" could just do that.

But aside from your observation that "they have plenty of people" it is similar to observing that a plane that has lost its engine at 36,000' has "plenty of altitude" both true and less helpful than "and here is the process we're going to use as we fall out of the sky to get the engines back on."

Google has lots of resources. If you have ever read about IBM reinventing itself in the 90's its quite interesting to note that had IBM not owned a ton of real estate it likely would not have had the resources to restructure itself. I worked with an executive at IBM who was part of that restructuring and it really impressed on me how important "facing reality" was at a corporation, and looking at the situation more realistically. I had started trying to get Google to do that but gave up when Alan Eustace explained that he understood my argument but they weren't going to do any of the things I had recommended. At that point its like "Okay then, have fun." Still, at some point, they could. They could figure out exactly what their "value add" is and the big E economics of their business and realign to focus on that. Their 'mission oriented' statement suggests that they are paying some attention to that idea now. But to really pull it off a lot of smart, self-interested, and low-EQ people are going to have to come to terms with being wrong about a lot of stuff. That is what I don't see happening and so I'm not really expecting them to transform. Both not enough star bits and the luma are just not hungry enough.

Here's a few more realistic changes that Alphabet could make: - shut down X - shut down Verily - sell calico or shut it down if no buyers - sell Fiber or shut it down if no buyers - shut down Intrinsic, Wing, and all the other X spinoffs - make Cloud be its own Alphabet company with Kurian as an actual CEO

That would show Wall Street that GOogle is really serious about not wasting money on crazy ideas. That would boost the price (along with reducing costs) giving them some runway. I think it would be a shame if Waymo was shut down but it has a long, long way to being highly profitable.

It looks like Alphabet wants to sell Verily or spin it out of the Alphabet family entirely (after decoupling Verily's infrastructure from Google's) but nobody wants to buy it.

1) If it isn't their idea that don't believe it will do any good and could not possibly be the "right" thing to do.

2) If they don't have a job after it happens, they will work behind the scenes to sabotage attempts to make it happen.

You can work around those, but you need "existential risk" level energy to create that sort of change in a company.

That said, how Blekko and Watson ended up squandering good technology in search of something else is also an interesting learning experience/tale.

We might not like what they’ve become, but the comparison to a plane that’s lost its engine seems rather odd. Why couldn’t they keep going indefinitely, without making the changes that some would like?

Thank you for sharing your experiences, Chuck!

They're dead when everyone starts to hate them and someone says "no, look how much money they're making, they're fine." That's the fatal blow, because they think they're fine, and keep doing the things that make everyone hate them.

At that point you're just waiting for someone else to offer an alternative. Then people prefer the alternative because the incumbent has been screwing them for so long, and even if they change at that point, it's too late because nobody likes or trusts them anymore, and ships that big can't turn on a dime anyway.

You have to address the rot when customers start complaining about it, not after they've already switched to a competitor.

I remember running into Kodak engineers, at an event in the 1990s, and they were all complaining about the same thing.

They were digital engineers, and they were complaining that film people kept sabotaging their projects.

Kodak invented the digital camera. They should have ruled the roost (at least, until the iPhone came out). Instead, they imploded, almost overnight. The film part was highly profitable.

Until it wasn't. By then, it was too late. They had cooked the goose.

One can really dream up a fascinating alternate timeline of iKodak if they didnt shoot themselves in the foot.

It had a magnetic mount, where you could snap on external lenses.

I'm pretty sure they still have some variant of the concept, except that it's an external camera that uses your phone as a viewfinder.

In other words, it could have been better for Kodak as a whole if they allowed their digital-arm to compete more with their film-arm, so that as the market shifted they'd at least be riding the wave rather than under it.

It's kind of astonishing that all these years later we still don't have something equivalent in browsers. In theory they're Turing-complete and you can do whatever you want, but where's the thing that makes it that easy?

The company imploded because it spent all of its time, attention, and capital trying to become a pharmaceutical factory, starting in the mid-1980s.

They did indeed have a huge patent arsenal from all their research efforts that was very valuable. They were also really good at consumer tech - so it’s a shame it didn’t amount to more.

And the internet isn't new anymore. There is no vast landscape of unexplored new technological possibilities, and no garage start up with an engineering mindset that will just offer a better solution.

Oracle, IBM and SAP have the advantage(?) of being heavily business focused from the start, and I don't see them ever die a natural death in our lifetimes. As long as they have the money to outbribe the competition they'll be there, and it will require a small miracle to break that loop.

Anything else they do is a bonus.

Having on premise hosting options for Exchange and all their core services is an example of that, even as they're also pushing for 365 in the cloud. I remember them being earlier than GCP to deal with GDPR and the in EU requirements as well but my memory might be failing.

People use Windows at home and at school and then employers use the same thing because they don't want to retrain people. But the home versions of Windows are becoming so malevolent that they're losing market share. Meanwhile all the things that used to require Windows are becoming web pages and phone apps. You go to a university and it's full of Macbooks and if you see a PC in the CS department there's a good chance it has Linux on it. These are the people who will be choosing what to buy in a few years.

But who cares about the clients anymore, right? They're making money from cloud services. Except their hook is getting people to use Active Directory and Microsoft accounts, which are the things for managing Windows client devices.

It's going to be a while before anybody convinces the accountants to stop using Excel, but for large swathes of employees Windows is no longer relevant, and if you don't need Windows then why do you need Azure instead of AWS or any of the others?

I don't have enough insight, but there's more to it than Windows/Microsoft services tie up. It's clearly not the ease of use for small customers, it could be the contract making, or something else that makes it better deal for businesses beyond just the cost bundling.

For instance I remember Apple hosting iCloud on Azure. And there's a few other big players going with Microsoft, especially retail chains who can't touch anything Amazon, and don't trust Google.

But as a business entity they've been ferocious from the start, and succeeded through sheer perseverance where Google gave up after some tepid tries.

Xbox would have been killed by Google in the first year. Exchange would have stayed in beta for a decade, and Office365 would have had no support if it was in GSuite.

If Google were to find a way, I think they'd need a radically different approach, as I don't see them ever fixing their focus problem.

But most importantly all the above listed companies with the exception of Meta are those that are heavily ingrained in large companies operations. IBM still provides mainframes, MS has Exchange and Windows domains and is successfully transitioning a lot of customers to Azure, Oracle has their databases and other products, SAP their ERP systems.

Once a non-IT company has their internal IT systems and some legacy working they're going to be very very slow in changing them out if it works, companies that provide those and get a critical are going to have very very long runways compared to regular b2c companies if a significant portion of their revenue comes from this.

Google has Chromebooks that are used in schools and some GCP usage but could that save Google long enough if search revenue was cut into a fraction? And GCP is kinda of an also-ran today, people looking at larger options usually look at AWS(nr 1) or Azure (Windows legacy).

If they could reduce headcount and operating expenditures to 2019 levels without losing that, they would be roughly breaking even without any search. They also have 280B$ in equity to tide them over.

When Google actually sees its business failing, it will have many many many chances to turn things around.

https://www.statista.com/statistics/218089/global-market-sha...

And that's just desktop. Microsoft ceded the entire mobile market, which in turn now represents the majority of devices. The majority of the company's profits no longer come from selling Windows and Office. If they hadn't pivoted into a new line of business (Azure) they'd be on a trajectory to impact with the ground.

IBM has been bleeding customers -- and business units -- for decades. Their stock is flat, not even keeping up with inflation, compared to +300% over the last decade for the overall market. And they have no obvious path to redemption.

Oracle is kind of an outlier because of the nature of their business. Their product has an extraordinarily high transition cost, so once you're locked in, they can fleece you pretty hard and still not have it cost more than the cost of paying database admins high hourly rates for many hours to transition to a different database. Then they focus their efforts on getting naive MBAs to make a one-time mistake with a long-term cost. Or just literal bribery:

https://www.cnbc.com/2022/09/27/sec-fines-oracle-23-million-...

And even with that, their database market share has been declining and they're only making up the revenue in the same way as Microsoft through cloud services.

Meta isn't a great example because people just don't hate them that much. Facebook sucks but in mostly the same ways as their major competitors, they're still run by the founder and they do things people like, like releasing LLaMA for free.

This is certainly a completely different picture than Yahoo for example.

And your argument for Microsoft is that they are in a death spiral because they only have 70% of market share on the desktop, and are shrinking by 2% per year, so in, uh 15 Years they might only have 50% of the market share! Also, please ignore that they successfully diversified their revenue streams to other markets (Cloud).

And your evidence is that they failed to capture the mobile market. While you also argue that Google is in a death spiral when Google is actually the company that won the mobile market.

I think you might be using the term death spiral in an unconventional way here.

You cited them because they are hugely profitable, ignoring the ones that are already defunct. And the entire premise is that a company can simultaneously be posting profits while doing the thing that will ultimately destroy them.

> And your argument for Microsoft is that they are in a death spiral because they only have 70% of market share on the desktop, and are shrinking by 2% per year, so in, uh 15 Years they might only have 50% of the market share!

Platforms have a network effect. They're doing so poorly that the network effect from having 90% market share isn't enough to prevent them from losing market share. But now they only have the network effect from 70% market share, which makes it even easier for customers to switch. That's how you get a death spiral.

> Also, please ignore that they successfully diversified their revenue streams to other markets (Cloud).

Which are in turn dependent on customers using Windows so they need Active Directory etc. See also:

https://news.ycombinator.com/item?id=40142351

> And your evidence is that they failed to capture the mobile market. While you also argue that Google is in a death spiral when Google is actually the company that won the mobile market.

It is unquestionably the case that Microsoft lost the mobile market, which is the larger market. Android has the most worldwide market share, but Android is free to use and generates revenue for Google only to the extent that people want their services. If people stop wanting their services and switch to e.g. another search engine, how does it save Google from this even if they're using Android?

Maybe it's time to switch to a competitor.

Google continues generating profits out of inertia and a lack of a better alternative.

It went for “don’t be evil” to “a necessary evil” (just until something a little better appears).

I have a strong memory of watching a Jacques Cousteau documentary on sharks and learning that Sharks could become mortally wounded but not realize it because of how their nervous system was structured. As a kid I thought that was funny, as an engineer watching companies in the Bay Area die it was more sobering.

If you have read the article, I think Gomes was right and saw search as a product, whereas Raghavan saw it as a tool for shoveling ads. A good friend of mine who worked there until 2020 wouldn't tell me why they left, but acknowledged that it was this that finally "ruined" Google.

Their cash cow is dying, I know from running a search engine what sort of revenue you can get from being "just one of the search engine choices" versus the 800lb gorilla. Advertisers are disillusioned, and structurally their company requires growth to support the stock price which supports their salary offerings. There is a nice supportable business for about 5,000 - 8,000 people there, but getting there from where they are?

My best guess at the moment is that when they die, "for reals" as they say, their other bets will either be spun off or folded, their search team will get bought by Apple with enough infrastructure to run it, Amazon or someone else buys a bunch of data centers, and one of the media companies buys the youtube assets.

As a chess person, saying "Mate in _" means it's a calculated inevitability. There is no mathematical way out of it.

It is not nearly equivalent to the outside judgement of a company with so many factors — it's just incomparable.

But I can guarantee you that Google employees are reading these comments and saying "Wow, this guy is totally full of it, he doesn't know about anything!" and for some of them that thought will arise not from flaws in what I and others are saying, but in the uncomfortable space of "if this is accurate my future plans I'm invested in are not going to happen..., this must be wrong." I have lived in that space with an early startup I helped start, when I went back and worked on the trauma it had caused me it taught me a lot about my willingness to ignore the thinking part of my brain when it conflicted with the emotional part.

You have to do some of that to take risks, but you also have to recognize that they are risks. Painful lesson for me.

In chess, it's specifically used for saying "even with the best defense possible, you will be mated no mater what in a maximum of X moves." Computers use this definition as well. If Stockfish says # in 6, that means there is an indefensible path to mate available, and with the best play of the opponent will take 6 moves.

It's not a "Mate in X, probably."

Google today is starting to smell of future financial engineering games, like when a car maker earns more through financing than selling core product.

B) I don’t think it’s fair to characterize recent layoffs as some put-off collapse… criticize Google all you want for running a bad search engine, but right now they’re still dominant and search is the most effective advertising known to man. They’re raking in buckets of money: they had 54K employees on 01/01/2015, and 182K on 01/01/2024. Similarly, they made 66B in 2014, and 305B in 2023. The latest layoffs are them cleaning house and scaring their workers into compliance, not the death throes of a company in trouble — they’re barely a dent in the exponential graphs: https://www.macrotrends.net/stocks/charts/GOOG/alphabet/numb...

Additionally, it's complete and total oversimplification. If you look at Google's earnings it's pretty damn clear that at least until 2020 they were not just going for maximum total spend, but for a steady, gradual raise in total spend. Not too slow, not too fast. They were NOT taking every opportunity they had, in fact they're famous for systematically refusing many opportunities (see the original founders' letter, but even after that). They were farming the ad market, the ad spend, growing it, nurturing it. Then COVID blew up the farm.

Maybe you're right now, but I do hope they're recovering their old tactics. Because if they maximize it you'd see nothing but scams ... wait a second.

B) Google was built by providing a vision, and getting out of the way of ground-up engineer efforts. "Scaring workers into compliance" IS killing the golden goose.

You can see this in AI. Every story from an AI engineer that ran away from Google is the same. They didn't run away for the money, they ran away because they were getting scared into compliance.

Now AI may make it, or not. I don't know. But this is happening EVERYWHERE in Google. Every effort. Every good idea, and every bad idea runs away, usually inside the mind of "a worker". Not to make them personally maximum money, but it's natural selection: if the idea doesn't run away, the engineer it's in is "scared into compliance", into killing the idea.

Whatever the next big thing turns out to be, it simply cannot come out of Google. And it will hit suddenly, just like it did for Yahoo.

I guess I’m standing up for the simple idea that terribly inefficient organizations can prevail when they’re the incumbents, at least for significant periods if not forever. We can’t be complacent and assume they’ll fall on their own, esp when AGI threatens social calcification on an unheard of scale.

"Build a better search engine for the good of humanity", I can understand. "Kill a search engine for the good of humanity" is a reductive, childish take.

Can you elaborate?

The whole problem kind of got swept under the rug with most advertising ecosystems implementing a checkbox solution for clean traffic, and the web turned mobile user first.

My impression is that ad fraud never disappeared - it just got sanitized and rolled in with the other parts of the ad stack.

How much of (online) advertising is legit? Does any one know?

What would a "healthy" ad ecosystem look like? What should the the FTC (and advertisers) be working towards?

Eliminate any potential conflicts of interest? Bust up vertical integration (eg search & ads must remain separate companies)? Independent verification, as best able (eg like Nielsen does for ratings)?

Or maybe we determine (digital) ads based biz models are irredeemable, and we figure something else out.

The hostility was simply this. One day we had a dedicated high level Google engineer helping us out and giving us guidance (and even special tags) to get the data we needed in a cost effective manner for both Google and us. The next day, he was gone and we received demands to know exactly what we were doing, why and even sensitive information about our business. After several months of such probing, we were summarily told that the access we had was revoked and that there was no recourse.

We circumvented by setting up thousands of unique IP addresses in 50+ countries throughout the world and pointing our spiders at Google through them (same as they do to everyone else). These were throttled to maintain very low usage rates and stay off the radar. We continually refilled our queues with untouched IPs in case any were ever blacklisted (which happened occasionally).

As for what we did, we sampled ads for every keyword under the sun, aggregated and analyzed them to find out what was working and what wasn't. This even led to methods for estimating advertiser budgets. At one point, we had virtually every Google advertiser and their ongoing monthly spend, keywords and ad copy in our database. Highly valuable to smart marketers who were looking for an edge.

I do feel that I can't argue with his stuff, although it is very dark and cynical (and, truth be told, I have a lot of dark and cynical, in me, as well, but I try not to let it come out to play, too often).

In a previous avatar, Raghavan was a pure theoretical computer scientist. As a student, he won the best student paper in FOCS, the Machtey award, which is kind of a big deal. The work was related to randomized rounding, which is a bread-and-butter technique for LP relaxation approaches to integer optimization, similar to knapsack problems.

This is not to defend any bad decisions he may have made at Google and Yahoo, but to make him an anonymous clueless corporate honcho who is good only at scheming and wrecking companies is bizarre. All this information, moreover, is available on Wikipedia and (cough) Google scholar.

https://scholar.google.com/citations?user=FtMADIMAAAAJ&hl=en...

it sadly ironic how google search used to look like this, now it looks like bloated shit, this dude pushed ruining it, yet this guys resume google scholar page just looks so slick. wow what a slick, _compact_, looking resume page. wish google search looked like this

EDIT: we should advertise between the articles, missed revenue google scolar

The author says very few people knew who Raghavan was. Clearly he isn't a computer scientist. It is more an indication of the ignorance of the writer than anything else.

Raghavan's contributions to Computer Science and, Search in particular, which were made long before he joined Yahoo!, were word-class. That is the reason he was so sought after by search engine companies. His text book on Randomised Algorithms is a classic.

Calling Raghavan a 'McKinsey' consultant is just a pure ad-hominem attack. The purpose seems to be to vilify him by association. Which is utterly ironic considering that he never worked for them or was ever a 'consultant'

As for his contributions at Yahoo!, I don't think he had any significant influence on the management direction that company took. In my opinion, absolutely no one at Yahoo!, CEO downwards, had much control over their destiny.

Yahoo! was a clusterfck all around, with the primary problem being its utterly dysfunctional board, and unfortunate share ownership structure that made it beholden to the demands of Wall St, resulting in a parade of CEOs. Personnel churn was at such a high volume, that I, an individual contributor usually seven levels below the board, calculated that the average tenure of my leadership chain to the board changed once every fifteen days.

So blaming Raghavan for what happened at Yahoo! is just stupid.

I have never worked for Google, but as an outsider, I don't disagree with the assessment, that Google Search was 'getting too close to money.' But to assign blame in this manner smells like a hit piece.

Managers, take their marching order from their bosses, ultimately this is the board of the company. If the board feels the need for revenue growth, no manager, CEO included has the power to resist too much. They advise against it, but in the end they will either need to to their biding or be fired.

Edited for typos and grammatical errors.

>A quick note: I used “management consultant” there as a pejorative. While he exhibits all the same bean-counting, morally-unguided behaviors of a management consultant, from what I can tell Raghavan has never actually worked in that particular sector of the economy.

It also seems like a stretch to say that Yahoo's former "Chief Strategy Officer" had no influence on Yahoo's management direction.

Yes it is a stretch to say he had much influence. There reason is very simple. Yahoo! was in its death throes. The core products were not bringing in revenue, and it was in the middle of multiple hostile takeover attacks by various private equity players. First it was a hostile offer from Microsoft, a hostile take over effort by Carl Icahn, and then a finally yet another, hostile take over (I forget the name of the last raider)

When there is so much uncertainty, and the fight is for mere survival, strategy has no meaning. You don't strategize, when someone is shooting you in the head.

He joined yahoo in 2005, if my memory serves correctly yahoo was already pretty much IBM-dead by then.

The downfall of yahoo was due to the hard push of popup ads in the late 90s and very early 2000s. Much like the google history of today though, maximising metrics at the cost of user experience. But it all happened in yahoo way before he joined.

Gomes said no. Raghavan clearly didn’t.

If that’s not a clear cut case of “bearing responsibility” I don’t know what is.

What I got was: Raghavan is/was a world-class computer scientist in his field, but actively pursued the management track and business strategy.

And for that, well, who's the blame him? If your main goal is to make an established company make more money - making wildly unpopular decisions (as far as the customer experience goes) can be tempting and easy.

The main problem here is that Google at that point was, and still is, a monopolistic behemoth. And frankly, why would they give a shit about what the customer thinks? 99% of google users are casual users that will neve scroll past the first page of search results, and will click on whatever top links google returns.

As far as enshitifacation goes, google is one of the worst offenders - so clearly anti user-friendly strategy is being rewarded.

The main criticisms of McKinsey (and strategy/management consulting firms in general) are:

1) They can (and have/will) consult both sides, even though there's a massive conflict of interest. It's like having the same law firm represent both plaintiff and defendant. This is the most egregious of the bunch.

2) They have deep ties with governments and the private sector, and leverage this bridge to reach their goals. Their alumni network is what keep propelling the firm.

3) They optimize for profits and recurring business (which any business does, so you can't really blame them for that...but:), and will not shy away from giving their clients morally or ethically questionable advices. This one ties back to (1).

Imagine if McKinsey is consulting Google on how to increase revenues related to customer data, while also consulting government regulators on how to deal with customer data privacy - with their own (McK) motives being maximum future revenue and extending their influence.