Do you write a blog? Your experience resonates with mine.
553 karma · joined September 4, 2019
Ironically, the author is quick to provide answers that please executives, and slow to offer dangerous questions that would displease them. Questions like how the company's short-term optimization was causing a long-term loss on user retention. Or how certain executive-sponsored beliefs were hurting user trust. Researchers who ask those questions tend not to stick around too long, even before 2021. Those who remain self-select into safer "middle" research; they and their department have been pummeled into submission by the organization.
The author turns a blind eye to this corruption; the author points the finger at researchers, not executives. The author also forgets the history that users made the business successful [1]. Today, however, the internal hierarchies and forward momentum of many tech companies depends on a selective understanding of users [2]. Good research slows down that momentum, it aims for good-enough understanding, it asks important questions. And when important questions collide with the status quo, they are not tolerated.
Or to put in simple terms, from a chief of staff to a middle manager: "Nevermind you 'why', why ain't in your repertoire no more".
[1] http://www.paulgraham.com/before.html "Mark Zuckerberg didn't succeed because he was an expert on startups. He succeeded despite being a complete noob at startups, because he understood his users really well."
[2] https://news.ycombinator.com/item?id=35597279 "The logic that the CPO applied at the time was some pretty sporty mental gymnastics: "we optimize for conversion (looker to booker) because that's a proxy to actually delivering value to the customer."
https://en.wikipedia.org/wiki/Wei_Zheng#Emperor_Taizong's_re...
https://www.arnnet.com.au/slideshow/428957/pictures-five-yea...
I think, in the long run, the humility to admit wrong-saying does more for credibility than making excuses.
> This is the tyranny of easy metrics. It's easy to measure how much money is saved by preventing cancelations, it's much harder to measure how much long-term business is lost by poisoning your reputation with the 99.9% of customers who had to jump hoops and dodge sleazeballs to get out of the subscription. But the latter could well be orders of magnitude money more over the long run.
It theorizes how to build social trust, without top-down hierarchy / constant mediation, through the cultivation of strong institutions and a common identity. It's an interesting premise that opposes progressive ideology. However, I have yet to read the books it references, nor have I examined the history behind its findings: https://www.amazon.com/Strains-Commitment-Political-Solidari...
This is key. As the old quote goes “Automating a mess means you get an automated mess”. I’ve seen too many managers use process, and too many developers use code, to avoid digging into a mess. A mess involves ugly human emotions and motivations; understanding why the mess is there is scary work. Ignoring that work and focusing on the "technical" leads to weird requirements that then leads to bad software.
It's a real pain to maintain a prematurely automated solution to a social problem. It makes the mess worse.
In the new market, there will be much more scrutiny on costs and the complexity that creates it. The prospect of hiring a “React Engineer” with 7+ years of experience to build a cookie cutter UI, will be just as questionable as forcing the front end and backend to use the same technology and framework governed by one single vendor.
I predict vercel will have a rough time.
I think this isn't just marketing; I think this reflects a deeper understanding that you can do A/B testing on top of a feature flag and not consider it an experiment. It's an innovation that reduces inter-organizational strife on the decision to launch a feature to all users, i.e., ship.
If everything with automated metric analysis is considered an experiment, then the shipping decision only be made after analysis is performed. That leads to tension when Product Managers have already decided to ship and will only rollback if something hurts their metric. Data Scientists will then question why their "experiment" is only running for a day, and then question the decision to ship entirely. You can imagine how many meetings a misdefinition spawns.
The sooner you address these definitions, I think the happier you'll find your enterprise users.
A lot of big companies need to gradually introduce a new feature to an audience while also seeing how that feature impacts their metrics. If they go with just feature flagging, they have to build their own logging and analysis. But if they go with experiments, then they're overly constrained by that analysis since experiments are meant to be statistically rigorous.
It's a tricky problem which I think statsig solved with "pulse metrics" and "feature gates".
To me, standards are a clear, agreed-upon baseline that every member of the team must meet. They're not what we preach, they're what we tolerate. It is intolerable if a member of the team doesn't meet a standard.
Goals on the other hand, are what we hope for; they cannot be "lived up to", coerced on an individual or forced from the outside. If a member of the team can't achieve a goal, then they need differents goals.
I set the standards to be low and universal, while the goals are high and person-specific[1]. I'm thinking objectives could be thought of as "parts of standards that must be met at a later point in time but now now"[2]. But I'm still figuring it out. I think material about education is relevant, and helps to avoid easy, versatile, and deceiving thought-terminating cliches like "expectations".
[1] https://apasseducation.com/education-blog/3-differences-cont... [2] https://dataworks-ed.com/blog/2014/07/how-to-craft-a-learnin...
If the maintainer had waited out the initial flurry of attention, they probably could have chosen the name they wanted without much drama at all. Instead, they overreacted, deleted the first thread, and then immediately started editing and deleting user comments in the second. Ouch.
Besides him not leaving a clear successor[1][2], I think his communication is disingenuous. The kind of management-speak that aims to hide poor decision-making behind a facade of cheer and care.
People are scared of "might makes right", but I've found "nice makes right" to be far more insidious and pernicious in our society.