Ex-Google search engineer here (2019-2023). I know a lot of the veteran engineers were upset when Ben Gomes got shunted off. Probably the bigger change, from what I've heard, was losing Amit Singhal who led Search until 2016. Amit fought against creeping complexity. There is a semi-famous internal document he wrote where he argued against the other search leads that Google should use less machine-learning, or at least contain it as much as possible, so that ranking stays debuggable and understandable by human search engineers. My impression is that since he left complexity exploded, with every team launching as many deep learning projects as they can (just like every other large tech company has).
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.
Machine learning or not, seo spam sort of killed search. It’s more or less impossible to find real sites by interesting humans these days. Almost all results are Reddit, YouTube, content marketing, or seo spam. And google’s failure here killed the old school blogosphere (medium and substack only slightly count), personal websites, and forums
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?
I don't know, but Youtube seems to have a more solid algorithm. I'm typically not subscribed to any channel, yet the content I want to watch does find me reasonably well. Of course, heavily promoted material also, but I just click "not interested in channel" and it disappears for a while. And I still get some meaningful recommendations if I watch a video in a certain topic. Youtube has its problems, of course, but in the end I can't complain.
A bit chicken-and-egg. Another perspective: Google’s system incentivizes SEO spam.
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.
Best exercise bike articles, and such, are what lots of people people actually search for. There is no incentive to provide quality work which answers these queries hence the abundance of spam and ads.
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.
Absolutely this. I don't think many people consider how odd it is that the largest internet advertising company in the world and the largest search engine company in the world are one and the same, and just how overt a conflict of interest that is, so far as providing quality service goes. It would be akin to if the largest telephone service company in the world was also the largest phone maker in the world. Oh wait, that did happen [1] - and we broke them up because it's obviously extremely detrimental to the functioning of a healthy market.
I've heard this argument again and again, but I never see any explanation as to why SEO is suddenly in the lead in this cat-and-mouse game. They were trying ever since Google got 90%+ market share.
I think it's more likely that Google stopped really caring.
Well yeah, it's in the article - at some point, they switched completely to metrics (i.e. revenue) driven management and forgot that it's the quality of results that actually made Google what it is. And, with a largely captive audience (Google being the default-search-engine-that-most-people-don't-bother-or-don't-know-how-to-change in Chrome, Android, on Chromebooks etc.), they arguably don't have to care anymore...
Correction: It is Google's willingness to display search results by what is MOST PROFITABLE and/or POLITICALLY expedient to them that killed search. This includes their willingness to promote/demote content based on what the thought police wanted to promote/demote.
What I don't understand about this explanation is that Google's results are abysmal compared to e.g. DuckDuckGo or even Brave search. (I haven't tried Kagi, but people here rave about it as well.) Sure, all the SEO is targeting googlebot, but Google has by far more resources to mitigate SEO spam than just about anyone else. If this is the full explanation, couldn't Google just copy the strategies the (much) smaller rivals are using?
When a large search engine deranks spam websites, the spam websites complain! Loudly! With Google they have a big juicy target with lots of competing ventures for an antitrust case; no such luck for Kagi or DDG.
> where he argued against the other search leads that Google should use less machine-learning
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.
It's also a good lesson for the new AI cycle we're in now. Often inserting ML subsystems into your broader system just makes it go from "deterministically but fixably bad" to "mysteriously and unfixably bad".
I think that’ll define the industry for the coming decades. I used to work in machine translation and it was the same. The older rules-based engines that were carefully crafted by humans worked well on the test suite and if a new case was found, a human could fix it. When machine learning came on the scene, more “impressive” models that were built quicker came out - but when a translation was bad no one knew how to fix it other than retraining and crossing one’s fingers.
As someone who worked in rules-based ML before the recent transformers (and unsupervised learning in general) hype, rules-based approaches were laughably bad. Only now are nondeterministic approaches to ML surpassing human level tasks, something which would not have been feasible, perhaps not even possible in a finite amount of human development time, via human-created rules.
The thing is that AI is completely unpredictable without human curated results. Stable diffusion made me relent and admit that AI is here now for real, but I no longer think so. It's more like artificial schizophrenia. It does have some results, often plausible seeming results, but it's not real.
But rule-based machine translation, from what I've seen, is just so bad. ChatGPT (and other LLM) is miles ahead. After seeing what ChatGPT does, I can't even call rule-based machine translation "tranlation".
*Disclaimer: as someone who's not an AI researcher but did quite some human translation works before.
Yes, but I think the other lesson might be that those black box machine translations have ended up being more valuable? It sucks when things don't always work, but that is also kind of life and if the AI version worked more often that is usually ok (as long as the occasional failures aren't so catastrophic as to ruin everything)
> Yes, but I think the other lesson might be that those black box machine translations have ended up being more valuable?
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.
A case in point is the ubiquity of Pleco in the Chinese/English space. It’s a dictionary, not a translator, and pretty much every non-native speaker who learns or needs to speak Chinese uses it. It has no ML features and hasn’t changed much in the past decade (or even two). People love it because it does one specific task extremely well.
On the other hand ML has absolutely revolutionised translation (of longer text), where having a model containing prior knowledge about the world is essential.
Can’t help but read that and think of Tesla’s Autopilot and “Full Self Driving”. For some comparisons they claim to be safer per mile than human drivers … just don’t think too much about the error modes where the occasional stationary object isn’t detected and you plow into it at highway speed.
relevant to the grandparent’s point: I am demoing FSD in my Tesla and what I find really annoying is that the old Autopilot allowed you to select a maximum speed that the car will drive. Well, on “FSD” apparently you have no choice but to hand full longitudinal control over to the model.
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.
I have no experience with Tesla and their self-driving features. When you wrote "chill" mode, I assume it means the lowest level of aggressiveness. Did you contact Tesla to complain the car is still too aggressive? There should be a mode that tries to drive exactly the speed limit, where reasonable -- not over or under.
Well Tesla might be the single worst actor in the entire AI space, but I do somewhat understand your point. The lake of predictable failures is a huge problem with AI, I'm not sure that understandability is by itself. I will never understand the brain of an Uber driver for example
> For some comparisons they claim to be safer per mile than human drivers
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.
My guess: They are hoping user feedback will help them to fix the bugs later -- iterate to 99%. Plus, they are probably under unrealistic deadlines to delivery _something_.
I think - I hope, rather - that technically minded people who are advocating for the use of ML understand the short comings and hallucinations... but we need to be frank about the fact that the business layer above us (with a few rare exceptions) absolutely does not understand the limitations of AI and views it as a magic box where they type in "Write me a story about a bunny" and get twelve paragraphs of text out. As someone working in a healthcare adjacent field I've seen the glint in executive's eyes when talking about AI and it can provide real benefits in data summarization and annotation assistance... but there are limits to what you should trust it with and if it's something big-i Important then you'll always want to have a human vetting step.
> I hope, rather - that technically minded people who are advocating for the use of ML understand the short comings and hallucinations.
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.
We get it, you're skeptical of the current hype bubble. But that's one helluva no true Scotsman you've got going on there. Because a true builder, one that deeply loves building things wouldn't want to use text to create an image. Anyone who does is a business type or an executive producer. A true builder wouldn't think about what they want to do in such nasty thing as words. Creation comes from the soul, which we all know machines, and business people, don't have.
Using English, instead of C, to get a computer to do something doesn't turn you into a beaurocrat any more than using Python or Javascript instead does.
Only a person that truly loves building things, far deeper than you'll ever know, someone that's never programmed in a compiled language, would get that.
> Using English, instead of C, to get a computer to do something doesn't turn you into a beaurocrat any more than using Python or Javascript instead does.
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.
> Most people do not (cannot?) use English that precisely.
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.)
Yeah, I was also reading their response and was confused. "Creation comes from the soul, which we all know machines, and business people, don't have" ... "far deeper than you'll ever know", I mean, come on.
I’m not optimistic on that point: the executive class is very openly salivating at the prospect of mass layoffs, and that means a lot of technical staff aren’t quick to inject some reality – if Gartner is saying it’s rainbows and unicorns, saying they’re exaggerating can be taken as volunteering to be laid off first even if you’re right.
> but we need to be frank about the fact that the business layer above us (with a few rare exceptions) absolutely does not understand the limitations of AI and views it as a magic box where they type in
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
> technically minded people who are advocating for the use of ML understand the short comings and hallucinations
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.
So... obviously SOAP was dumb[1], and lots of people saw that at the time. But SOAP was dumb in obvious ways, and it failed for obvious reasons, and really no one was surprised at all.
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.
ML has already succeeded to the point that it is ubiquitous and taken for granted. OCR, voice recognition, spam filters, and many other now boring technologies are all based on ML.
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.
For what it's worth, I do not remember a time when YouTube's suggestions or search results were good. Absurdities like that happened 10 and 15 years ago as well.
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.
15 years ago, I used to keep many tabs of youtube videos open just because the "related" section was full of interesting videos. Then each of those videos had interesting relations. There was so much to explore before hitting a dead-end and starting somewhere else.
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.
>The relations between human interests are too esoteric for current ML classifiers to understand.
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.
>I would go further and say that it is impossible. Human interests are contextual and change over time, sometimes in the span of minutes.
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.
> Or when they didn't show 3 unskippable ads in a 5 minute video.
On desktop Chrome, a modern ad-blocking browser extension will block 100% of YouTube adverts. I haven't watched one, literally, in years. I don't watch YouTube from a mobile phone, but I think the situation is different. (Can anyone else comment about the mobile experience?)
> I do remember when Youtube would show more than 2 search results per page on my 23" display.
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!
Lol, Youtube on Apple TV is great. Mostly because I either need to find something fast or I switch it off because the remote is not conducive to skipping. But the only time I watch Youtube on my computer is for a specific video. The waste of space is horrendous. Same with Twitter (rarely visited), just a 3/4 inches wide column of posts on my 24 inch screen.
I'm not consuming the content on my phone, because the user experience of using these services on my phone sucks. Just the app vs website difference with urls is a difference in behavior I hate let alone all the UI differences that make the mobile experience awkward.
It all depends on your use case but a lot of people seem to be in agreement it fell off in the mid to late 10s and the suggestions became noticeably worse.
Long long time ago; youtube "staff" would manually put certain videos on the top of the front page when they started. Im sure there we're biases and prioritization of marketing dollars but at least there was human recommending it compared to poorly recorded early family guy clips. I dont know when they stopped manually adding "editors/staff" choice videos but I recall some of my favorite early youtubers like CGPGgrey claim that recommendation built the career.
See this >15-year-old video "How to get featured on YouTube" - https://www.youtube.com/watch?v=-uzXeP4g_qA, which I remember as being originally uploaded to the official Youtube channel but looks like it's been removed now, this reupload is from October 2008.
YT Shorts recommendations are a joke.
I'm an atheist and very rarely watch anything related to religion, and even so Shorts put me in 3 or 4 live prayers/scams (not sure) the last few months.
Similarly, Google News. The "For You" section shows me articles about astrology because I'm interested in astronomy. I get suggestions for articles about I-80 because I search for I-80 traffic cams to get traffic cam info for Tahoe, but it shows me I-80 news all the way across the country, suggestions about MOuntain View because I worked there (for google!) over 3 years ago, commanders being fired from the Navy (because I read a couple articles once), it goes on and on. From what I can tell, there are no News Quality people actually paying attention to their recommendations (and "Show Fewer" doesn't actually work. I filed a bug and was told that while the desktop version of the site shows Show Fewer for Google News, it doesn't actually have an effect).
Part of the reason I switched from google to duckduckgo for searching was I didn't WANT "personalization" I want my search results to be deterministic. If I am in Seattle and search for "ducks" I want the exact fucking same search results as if I travel to Rio de Janeiro and search for "ducks".
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.
YT Shorts itself is kind of a mystery to me. It's an objective degradation of the interface; why on earth would I want to use it? It doesn't even allow adjustment of the playback speed or scrubbing!
So, there's a few ways to explain it. From a business strategy level, TikTok exists, and is a threat to YouTube, so we need to compete with it.
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.
If you’re watching a single subject of interest video on your phone (TikTok type of content), it’s great. But landscape videos is more pleasant and there’s a reason we move from 4:3 for media. But that actually means watching the videos, but what I see is a lot of skipping.
Just because you're an atheist doesn't mean you won't engage with religious content though. YT rewards all kinds of engagement not just positive ones. I.e. if you leave a snide remark or just a dislike on a religious short that still counts as engagement.
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.
Lmao. I'm very positive that the conversion rate for placing an atheist in a live mass out of the blue is very very very low. Because I never stayed for more than 3 seconds, I'm not sure if it's real religious content or a scam, though - and they don't even let me report live shorts :(
I only get those when it's new content with <20 likes and they are testing it out. Doesn't bother me, I like to receive some untested content - even though 99% of it is pure crap (like some random non-sense film with a trendy music on top).
I think it's probably pushing pattern it sees in other users.
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.
A simple "rewatch?" line along the top would make this problem not so brain dead bad, imho. Without it you just think the algorithm is bad (although maybe it is? I don't know).
This is happening to me to, but from the kind of videos it's suggested for I suspect that people actually do tend to rewatch those particular videos, hence the recommendation.
> There is a semi-famous internal document he wrote where he argued against the other search leads that Google should use less machine-learning, or at least contain it as much as possible, so that ranking stays debuggable and understandable by human search engineers.
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
Are there any humans out there who want to write a traditional forward-algorithm to tell me which result is better?
You do not need to. Counting how many links are pointing to each document is sufficient if you know how long that link existed (spammers link creation time distribution is widely differnt to natural link creation times, and many other details that you can use to filter out spammers)
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.
What's odd to me is how everything is so metricized. Clearly over metricization is the downfall of any system that looks meritocratic. Due to the limitations of metrics and how they are often far easier to game than to reach through the intended means.
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."
Agree you have to mix qualitative with the quantitative, but the best metrics systems don't just measure one quantity metric. They should be paired with a quality metric.
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.
The hard part about starting worker owned co-ops is financing. We need good financing systems for them. People/firms who are willing to give loans for a reasonable interest, but on the scale of equity investment in tech start ups.
I formed a worker co-op - but it's just me! And I do CAD reverse engineering, nothing really life-giving.
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.
Not sure it'll work. I think half the advantage comes from the integration across all these tools (maps, search, etc). Have you ever tried to use duckduckgo? It surprised me what I take for granted in Google's user experience.
He wasn't the only one. I built a couple of systems there integrated into the accounts system and "no ML" was an explicit upfront design decision. It was never regretted and although I'm sure they put ML in it these days, last I heard as of a few years ago was that at the core were still pages and pages of hand written logic.
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.
> most engineers are drawn to complexity like moth to fire
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.
I definitely think the ML search results are much worse. But complexity or not, strategically it's an advantage for the company to use ML in production over a long period of time so they can develop organizational expertise in it.
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?
Phenomenal article, very entertaining and aligns with my experience as a prominent search "outsider" (I founded the first search intelligence service back in 2004, which was later acquired by WPP. Do I have some stories).
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'm really glad the article came out though, it fills in some gaps that I was fairly confident about but didn't have anything other than my sense of the players and their actions to back up what I thought was going on.
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.)
Did I mention I was more pessimistic? :-) I expect that today they could layoff 150k, keep the 30K that are involved with search and enough ads that are making business and that husk would do okay for a long time. I don't suppose you watched SGI die, that happened to them, kind of spiraled into a core that has some money making business and then lived on that.
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.
Are you suggesting that Google fire all the engineers who work on Cloud? That would... be a very interesting business decision, likely closing any door for them working with enterprise in the future.
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.
I was suggesting that they fire all the engineers that work on things that don't make money. It was only last quarter that Cloud actually made a profit. That said, I think you make a reasonable restructuring case; Now you just have to figure out how to get leadership to buy in and execute on that plan. In my experience two things work against that.
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 it is, but a more apt comparison would be Duck Duck Go which was a contemporary of Blekko and definitely out performed relative to Blekko's success. DDG still going strong and even buying TV ads, so yeah.
That said, how Blekko and Watson ended up squandering good technology in search of something else is also an interesting learning experience/tale.
Looking at financials, all metrics are improving. They haven’t even started to lose altitude - they’re still gaining.
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?
ChuckMcM, I just wanted to say, I really appreciate the long view you bring to HN discussions. When you've been in tech for a few decades you start to see predictable patterns. History may not repeat, but it often rhymes.
Piggybacking on this to also express my appreciation. If/when you write a memoir someday, it would be a valuable historical record. If not, your hn comments are a wonderful corpus too :)
Companies this size die several years before the body hits the floor.
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.
If they owned the digital camera space like they should have, who’s to say they wouldn’t have eventually released a smartphone. It probably would have been an absolutely incredible camera first, and some mobile internet and phone features second.
One can really dream up a fascinating alternate timeline of iKodak if they didnt shoot themselves in the foot.
I'm not a Steve Jobs fan, but one business-quote I do like: "If you don't cannibalize yourself, someone else will."
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.
The just-so story about Kodak is one of those things that bugs me. Kodak did own the digital camera market, stem to stern, for years. They did not ignore it. They did, however, invent all that stuff a little early, before the semiconductor manufacturing technology had matured to the point where it could be a consumer good.
The company imploded because it spent all of its time, attention, and capital trying to become a pharmaceutical factory, starting in the mid-1980s.
Yeah, lots of things happened for a perfect storm of downfall…probably starting with the antitrust breakup of the film processing division.
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.
Any examples of this actually playing out with a company as established as Google? You can read comments like this on many companies... Microsoft (70B$ income), Meta (40B$), Oracle (8.5B$), IBM(7.5B$), SAP (6B$), yet none of them seem to ever actually enter the predicted death spiral.
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.
Microsoft and Meta reinvented themselves a few times over. At this point Windows is just an legacy business unit for instance, and Meta literally changed name to mark the turn.
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.
The one thing that has kept Microsoft afloat is their business oriented part. They are deeply entrenched in any company that needs to use Office and only ever hires Windows admins who won't even look beyond Windows. That is pretty much every non tech small to medium company. When things were shifting to the cloud they were smart enough to make sure it would be their cloud, locking customers even deeper into their own technology.
To add to this, Microsoft is really really good at understanding businesses, in a way Google will probably never be I think.
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.
IBM used to be bigger than MS, it's a 10th of it today.
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).
AT&T, GE, AOL, Yahoo, Sony technology (they are a media company now, but they did used to make things that weren't a game console), Time Warner, SGI, Compaq, 3DFx, DEC...
Not only that, most of the other examples are just not at the end of their death spiral yet. Take a look at Windows market share, it's down 20% over the last 10 years:
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:
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.
While I totally agree that Atlassian products are terrible and steadily getting even worse, I'm not sure they are going anywhere anytime soon given their disconnect between users and customers. Most people who have to suffer their products have no say in the purchasing decision, and the company does a somewhat better job of appealing to the relative small group that does. Atlassian could very well have Oracle-like staying power.
You know how a chess player will say something like "mate in 6" because their experience of all the options left to their opponent are both easily countered and will not prevent them from losing? Companies, and tech companies in my experience, get into death spirals due to a combination of people, culture, and organization. Pulling out of one of those is possible but requires a unique combination of factors and a strong leadership team to pull off. Something that is very hard to put into place when the existing leadership has overriding voting power. You can look at GE, IBM, and to some extent AT&T as companies that have "re-invented" themselves or at least avoided dissolution into an over marketed brand.
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.
> You know how a chess player will say something like "mate in 6" because their experience of all the options left to their opponent are both easily countered and will not prevent them from losing?
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.
I don't disagree, chess is much more algorithmic and predictable. Maybe it is more like seeing your best mate of the last 20 years getting into their fourth or fifth relationship with the same kind of partner they failed with before and thinking, "Seen this movie before, it is not gonna work out." No algorithms, just you know how you're friend sabotages themselves and you also know they can't (or won't) look critically at that behavior, and so they are doomed to fail again.
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.
Yes, but there are other positions that do fit the comparison, like a couple of advanced passed pawns that can still be defended against with surgical precision, but most times are lethal.
Again, I think there is a misunderstanding of what the saying is used for.
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.
I don't think so. At one of the Sun Reunion events a bunch of us sat around and talked about it. I suggested someone should write a companion volume to "Sunrise: The first 10 years of Sun" called "Sunset: The last 10 years of Sun." But as far as I know nobody followed up (if they did they didn't reach out to me for my take)
With Google, I always feel like the side hustles (waymo, X, etc.) Really exist to be sold off in the future to prop up the add/search business and ensure future profitability. Everything not adds/search is on that list, and anything shut down despite being useful isn't seen as future-sellable.
Google today is starting to smell of future financial engineering games, like when a car maker earns more through financing than selling core product.
The majority of that revenue comes from violating data protection law and regulators and litigants are slowly racking up a series of wins which will gut ads margins.
There is no Plan B, they are just going to break the law until they can’t and there’s zero clue what happens after that.
They sat back and let OpenAI kick their ass precisely because ghouls like Prabakar call the shots and LLM are not a good display ads fit.
People would add sites for a particular topic (aka slashtag) to their list. That would build a virtual custom search engine within the search engine. And topic specific searches thrown at it would consistently out perform Bing and Google in terms of search quality. The meta "spam" slash tag (everyone got their own) would let you tell the engine sites you never wanted to see in your search results so if you were tired of your medical queries being spammed by quacks, add them to your spam list and they wouldn't be in your results.
A) I think it’s important to acknowledge that in many ways Google is actively trying to keep CPC low - what they care most about is total spend. A low CPC means an effective advertising network where interested consumers are efficiently targeted. Their position is complex thanks to their monopoly status over online advertising.
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...
A) This is short-sighted. What you're suggesting is in fact a way to optimize short-term gain over long-term viability. It's pure MBA tactics.
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.
Totally agree on the overall prognosis of Google - I am (also?) one of said engineers! Here’s a recent update from a tiny corner of the company: the rank and file is still incredibly smart and generally well-intentioned, but are following hollow simulacrums of the original culture - all-hands, dogfooding, internal feedback, and ground-up engineering priorities are all maintained in form, but they are now rendered completely functionless. I am personally convinced that the company is — or was, before ChatGPT really took off - focused on immediate short term stock value above all else. After all, if you were looking down the barrel of multiple federal and EU antritrust suits and dwindling public support for the utility you own and operate, you might do the same…
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.
> 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.
I don't know what caused it but I suspected at the time, and still do, that it was simply business people getting more involved in order to drive growth.
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 enjoy reading this chap's stuff. It's not the way that I would write, but he's got a much broader audience than I do, so he obviously is meeting the needs of the reading population.
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).
How many companies have management consultants taken down? It's quite amazing how bad they are at anything. Peter Thiel's hatred for consultants is really legit.
Great article. But the author can't be serious about no one knowing who Prabhakar Raghavan is. He is, for instance, the co-author of the definitive text on randomized algorithms [Motwani and Raghavan]. He has also been a well-respected database researcher for many years.
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.
i love this person google scholar, it is important to this employee resume that the moment i click on this link i immediately see compact list of articles with brief introductions in plaintext. very easy to see everything and access exactly what i'm interested in almost immediately with a swift skim.
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
Full Disclosure: Prabhakar Raghavan was my skip-level manager at Yahoo! and I'd known of him well before that, from my days at IBM Research.
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.
The author called Sundar a McKinsey consultant, not Raghavan.
>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.
He called Raghavan a "management consultant", whilst acknowledging that he never was a management consultant. It's slinging pejorative nonsense labels.
So why needlessly call him a management consultant?
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.
The general critique is: McKinsey over-optimizes on short term profitability over meaningful, longer term, harder-to-measure values. Your framing drops the most important aspect of the critique to make it sound contradictory.
That is not a contradiction. There are lots of ways to "ruin" a company, making all the people who interact with it more miserable, while still making that company "successful".
Even though I agree with what the author is saying, the tone of this article is off putting to me. There are ways to call out people for being bad at their job without resorting to “class traitor” and “ratfucker”.
That being said, Google search is 100% dead. I append “reddit” to every Google query to get actual results from people, and I don’t see it on Reddit, I give up on my query.
I'm not generally for singling out a person and slinging mud at them, but, I also feel like unless there's a real social cost to acting the way these parasitic executives act, there's little incentive for them to change their behavior. There should be a sense of shame in ruining a once good product for career benefits and short term growth. I think the tone is appropriate in that it conveys that this is not a good-faith effort gone wrong, but rather an executive acting in a cynical and reprehensible way.
I disagree. ratfuck is a specific term, not just a general pejorative. and I think class traitor is appropriate here as well. but i get what you're saying. that's the result, pro and con, of the shift away from edited journalism to stuff like ed's newsletter.
Interesting. In the (US) military, we used this term to describe someone who breaks into the MRE stash and steals all the good stuff, leaving horrid creations like cheese and veggie omelette.
“Private Johnson got caught ratfucking the MREs while everyone was doing PT” etc etc
TIL ratfucker actually means something relevant to the context of the article.
I think you worded my feelings much better than I did. This is a fiery op-ed from a personal blog and not polished journalism, so I should expect some individualism on writing tone.
Agreed - I can appreciate the sentiment and the history, but the ad hominem is not really necessary to prove the point and undermines the credibility of the post.
I still use Google, but man has it become difficult to get to what I want.
Calling someone a name is only an ad hominem fallacy if you try to use it as an argument. Here it's just used for style. Since the author has plenty of valid arguments, the name calling - which is not an argument - can be dismissed without weakening the actual argument.
In any case, it is not sound reasoning to reject the entirety of an argument just because one of the subclaims is not a valid argument. Doing so is the fallacy fallacy.
In this case, it's true that name calling weakens the credibility of the post for a general audience. But I contend that we might not need need to care. It only weakens the credibility of the post for members of the audience who make the fallacy fallacy, and refuse to evaluate the other claims on their own merits.
Convincing or not convincing such an audience might not be a concern to an author focused on truth, since such an audience is persuaded by fallacies.
Another thing is that if a person is actually a bad person, calling them bad names describing how they are a bad person is actually a true statement and not an argument "to the man". In this case the actual claim that is being argued is the fact of the person's moral insufficiency. Calling them the bad name is just the conclusion of an argument.
The main snafu of calling someone names as a stylistic or concluding aspect of an argument is that it lacks the decorum. If the debate forum requires respectful decorum then an argument can be disqualified on these grounds.
However in this case the forum is the author's own blog. The author has clearly chosen to speak to an audience that can evaluate arguments without being set back by insults - presumably an audience who is already very upset at Google and wants to know which person they should be upset at specifically. In this role, I found the insults were actually rather enjoyable and funny!
I agree, but all of the alternatives are no better. Bing and Duck Duck Go are okay sometimes, but truly terrible other times. Google is consistently worse than it once was, but still better than the competition.
I know search is hard to do well, but if Google is truly floundering where is the startup that for it better and not just better for a very specific niche area, but truly better across the board?
I've used the duck duck Go for over 90% of my searches for The last 5 years, as a part of a boycott against Google. I estimate I have withheld about 75,000 searches from Google in that time, or about $8,000 in revenue.
I fall back on Google only when absolutely necessary. And these days I almost never have to fall back on Google (<1% of searches).
When I do fall back, the results are invariably crap. Quality has degraded so much that it almost never gives me a better result than duck duck go did. Often when doctor go fails I don't bother with Google at all.
Even GPT4 driven Bing queries will give better results than Google now - mainly because GPT4 can filter spam, and has gotten a lot better about hallucinations.
Maybe because when I started fiddling with computers (around 2009), I only got like 1 or 2 hours of internet connection (cybercafes), so I had to find books and other types of offline information, I’ve never relied on Google Search or others that much. Even today, I treat them more like a bookmark database. If I can’t remember some specific terms to get to the page I need, I don’t even bother searching for it.
I’ve also started to hoard a stash of links and pdfs. And I have Dash for languages and framework documentation. Too many SEO farms for Python and HTML/CSS/JS.
Consider using zotero to expand and organize your library of references, if you don't already use it or something like it. It does great for PDFs, but it also captures and stores local copies of websites. Also lets you create bibliographies.
I like Kagi. It's not great for images or videos at this stage, but it is good for general search because you can personalize the rankings of the results. And they are introducing improvements all the time.
Yeah, the pejoratives were not the argument. They were clearly put there to make the reading /freaking hilarious/ for anyone on board with "Google Bad".
But I wonder if there was a deeper strategy: were the attacks put there so that Google gatekeepers would ignore the article's insights?
It could have a similar effect to Cory Doctor's concept of enshittification. I don't know if it's intentional, but the vulgarity of the term seems to prevent committed enshittifiers from reflecting critically about enshittification and how to stop in time to avoid a collapse. After feeling the insult, enshittware supporters seem to conclude enshittification is a non-existent category.
It would be fun to learn these are intentional choices, designed to sabotage the criticized party on an epistemological level!
This is going to sound crazy, but do you know what the web really needs right now in 2024? A new, searchable directory. Like the old Yahoo! Directory or DMOZ. Just a carefully curated list of trusted sites that are made and managed by humans and for humans.
Reddit is usually very bad, because it's heavily astroturfed and trivially easy for marketing firms to game. Something else is required.
GNU has a really concept called the GNS for Gnu Naming System. And what it was was that each human or org would have their own tld directory, and we could navigate the web through other actors, and they could pass trust for zones on to others. So, for example, I could resolve the same page as somepage.ninjaa.site or someotherpage.adept.site. This way you could create a trusted internet by just trusting the published link tld directory of people & institutions you know.
refdesk.com was the very first website I visited in the earlish-90s. An awesome curated collection of websites.
...and looking at it today, it may not have changed much.
Thank fark for Fark.com and I guess refdesk.com. Classic Intertubes.
reddit has become nearly unreadable. If it isn't puns, bots, bots reposting puns, it's some awful "no shit" relationship advice thread, etc. (no, I don't have an account so yes, I do look at /r/all).
Get information from llms after learning how to prompt them so that they won't hallucinate. Get information from searches by using llms to filter through the crap results. Get information from scientific papers on Google scholar and the arxiv. Get information from textbooks on the library Genesis. Get information from audiobooks on the audiobook Bay. Get information from peers trained in specific domains. Get information by reading code and documentation belonging to open source projects. Get information by performing experiments and trials. Get information by compiling reports and essays.
There are still many sources for information. And it's okay to work hard for it.
> Get information from llms after learning how to prompt them so that they won't hallucinate.
That is structurally impossible, because LLMs have no mechanism of knowing which answer is right or wrong. Please provide information how this prompting is supposed to look like.
I have been using Google search for many years now and for the past few years have been wondering if the search has really gone bad or is it just me. I remember the days when searching for something used to bring up a few sponsored links separately and I could go page after page with different results on each page allowing me to access a wealth of information and extending my reach deep into the internet. Now, it is all sponsored links and the same ones page after page. It is so sad to see and the worse part is that I am not seeing any alternative. Bing is equally bad, DDG only marginally better. I hope there comes an alternative soon but I also realize coming into this space is certainly not easy.
Until Kagi becomes popular. Then the same "SEO" bullshit that plagues Google will bite Kagi too. Right now Kagi is too small to make it worthwhile to spend resources "optimizing" for it.
Perhaps, but isn't the value of Kagi that it's user-tunable? If you open a page and it's distasteful to you, you can remove it from future searches, and you can uprank the sites you find useful. Related, no idea if they actually do it, but presumably, Kagi is getting signal from that about what people find useful, and integrating that into their rankings.
If I run into SEO crap on Google, I'm not sure they ever know I hated it and went elsewhere. They see that I searched and I clicked, and they got their money and don't care.
The alternative is using tool enabled LLMs. GPT4 can drive Bing and filter results better than I can, and it hallucinates less than I do when pile driving through spam.
If you haven't read up on modern prompting strategies and still feel LLMs are stochastic parrots, you should read the foundational prompting papers (chain of thought, react, reflexion, toolformer, etc) and update your views about llms. They're very close to being the kind of autonomous search agents that the characters in classic cyberpunk novels would unleash on the real world to compile results.
It's actually made me excited about information retrieval again, for the first time in a decade. And the cool part is that autonomous search agents might become free and open source before the corporations manage to enshittify the experience.
Nah as someone who spent years getting beaten by wordpress admins with barely enough neural complexity to be called vertebrates in search results I'm going to concur with the author-- Prabhakar Raghavan has been waging a war against humanity's greatest communication medium and worse -- he's winning. He deserves all this contempt and more.
He's at least earned the equivalent of the Ajit Pai FCC chair treatment but because John Oliver and his audience can't understand this sorta complexity without a massive concurrent media literacy push it will never happen.
I think the tone is warranted given the scale of the problem. I don't think we should mollify complaints about products that literal billions of people depend on just because they're not nice.
I am dreading the day when reddit becomes full of hot posts. I don't know what filter will I use then. I guess HN? But even I don't think we'll be safe from the GPTs here either.
What's sort of interesting about that catalyst of “steady weakness in the daily numbers” is that it didn't equate to a plateau, decline, etc...
It was that the ad dollars weren't, for the umpteenth time, exceeding YoY growth that far exceeded the growth in eyeballs watching/seeing ads. They were just somewhat exceeding the already meteoric growth of the web in general.
Google had unrealistic expectations in sustaining that growth rate because they started off with no ads, then very unintrusive ads, then somewhat instrusive, and so on. And, at the same time, leaps in A/B testing, targeting, bidding, and so on.
Until there was no more space on the visible page for ads, and little more to optimize for bids, views, targeting, etc. Then the growth fell back from crazy high to just amazingly high, and everyone lost their minds. Like it was a surprise.
From my (admittedly limited!) experience, unrealistic expectations are often set only when you want to push a senior off the team, and then as the new exec comes in, they'll "re-evaluate" the trajectory such that goals are much more realistic for their teams.
People ran experiments where they showed big, ugly, profitable ads, and they convinced themselves that the metrics meant it was a positive experience for users.
p-Hacking, again and again.
I have a ton of sympathy for everyone involved in this. It's incredibly hard to have a good model of what is good for users, and to have metrics that measure relevant things, and to have the discipline to make yourself test a real hypothesis rather than hunt for evidence that proves your foregone conclusion. And to reward people for negative experiments.
All very hard to do.
And frankly, if Google can't do it right - who can?
I think you need really powerful product managers who happen to be right. And that's not sustainable. Not something you can plan on or measure. Only reward if you happen to be lucky enough to notice it. Ow.
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.
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