Ex-Google search engineer here (2019-2023)

> 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.

i worked on ranking during singhal's tenure, and it was definitely refreshing to see a "no black box ML ranking" stance.

Thanks for writing this insightful piece. 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.

simplicity is always the recipe for success, unfortunately, most engineers are drawn to complexity like moth to fire 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

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'm glad you shared this. 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?

> 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,…

@gregw134 Thank you for sharing! I've never worked at Google, but really curious what the engineering context is when you say "needs a launch" in the last line.