The Unlock Discount Tokens (Ʉ) are managed by another smart contract which rewards curators and referrers, as well as grant discounts upon purchases. This smart contract is also decentralized, through its ownership and governance: each discount token owner can vote on who is the “maintainer” of the Unlock Discount smart contract.
Unlock is meant to help creators find ways to monetize without relying on a middleman. It’s a protocol — and not a centralized platform that controls everything that happens on it. This time, though, it’s not about helping information spread faster, it’s about helping value spread more easily. It’s about taking back subscription and access from the domain of middlemen — from a million tiny silos and a handful of gigantic ones — and transforming it into a fundamental business model for the web.
That combination of things wasn’t an accident: I’ve been an open web nerd for ever, and the idea of a world wide network with no gatekeeper has always felt like the future… the future of knowledge, of information, of social interactions. And, obviously, that’s a future worth fighting for.
Back when I did lots of free reading, I suffered similar ailments, but of my brain. I became paranoid and lonely. I had difficulty concentrating. The eyeball economy was frying my brain: Everybody became an enemy, because animosity attracts eyeballs. I couldn’t focus because the more you fragment someone’s attention, the more times per minute you can sell their eyeballs.
Linguistic and communications skills are not universally valued. Those who do not define themselves through this skill loathe hearing the never-ending parade of rich and powerful people suggesting that they’re stupid, backwards, and otherwise lesser. Embracing being anti-PC has become a source of pride, a tactic of resistance. Anger boils over as people who reject “the establishment” are happy to watch the elites quiver over their institutions being dismantled. This is why this is a culture war. Everyone believes they are part of the resistance.
Liberal principles are not intrinsically majoritarian. John Stuart Mill, the eminent Victorian liberal philosopher, never trusted the broad public to protect individual liberty, and thus was quite content with an electoral system that denied the franchise to nine-tenths or so of English adults. Yet in the 20th century Western nations became both liberal and democratic. Why, today, do we see these two principles delaminating? Mounk concludes that liberal democracy flourished under three conditions: a mass media that filtered out extremism; broad economic growth and social mobility; and relative ethnic homogeneity. All three of those solid foundations have now crumbled away. And as they have done so, illiberal democracy and democratic illiberalism have increasingly squared off against each other. The Brexit vote, to take only one example, posed liberal cosmopolites and European Union technocrats against “little Englanders” who longed for a world of vanished traditions and stable jobs. Mounk says that the time has come to reconsider the shibboleth that liberal democracies become “consolidated,” and are no longer at risk of backsliding, after two consecutive peaceful exchanges of power. Poland and Hungary, he observes, are “deconsolidating” into illiberal democracies before or eyes.
Analytics has become a critical role at tech companies. A common question I receive is how to hire analysts and where they fit into an organizational structure. Below I share some tribal knowledge around common team structures, options I think work best and hiring tips that leverage this talent. Functional vs. Embedded Teams One of the first questions organizations face when hiring analysts is how they should structure the team. There are two common choices organizations pursue: a functional and an embedded model. The functional model is an analytics team reporting to a Head of Analytics. In the embedded model, every department (sales, marketing, product, customer service et al.) is in charge of solving for its analytics needs, hiring analysts for their teams when needed, and determining to whom they report. Benefits of the functional model are having a senior seat at the table in major discussions for the company. The advantages this presents is getting analytics its own budget for tools and infrastructure and solving other analyst specific needs that may not be a top priority on any other specific department. The downside of a functional team is how analysts’ time are allocated. In a functional team, an analysts’ time is usually allocated on a project by project level, meaning they usually enter a project once that project is already well defined (whereas analytics could have helped by define the project, had it been involved earlier). And the analyst has not developed any specific expertise for that area. In my experience, analysts get frustrated in this model because they can’t go deep into any one area, and the other departments get frustrated because analysts provide a more cursory benefit than expected. The embedded model solves a lot of these pain points, but introduces its own. With an embedded model, analysts are hired into one specific team, and therefore can develop expertise for that team very quickly. Teams are happy because they always have a teammate ready to help who understands their problems. While analysts seem to be happier in this model, the downsides are the reverse of the functional model. When there are cross-departmental analytics needs, they usually fall by the wayside. Investment in infrastructure and tooling is usually massively under-invested in, and it’s unclear where budget comes from to solve these needs. At Apartments.com and at Grubhub, we implemented the embedded model. Marketing took control of analytics infrastructure initially, but we had trouble applying it cross-functionally. The analysts across all teams started meeting regularly to share learnings, but also limitations. When we added the Seamless team into Grubhub, they were used to the functional model. Analyzing the two together created awareness for me of a new approach. The Hybrid Model At Grubhub, the value of a dedicated analytics team for infrastructure and tools became clear, but also the value of the embedded analyst. Once I started at Pinterest and dealt with the functional model, we began to work toward a hybrid approach. This is a dedicated analytics team with a Head of Analytics, but with the analysts dedicated to specific areas full-time. So the person reports to a Head of Analytics, but sits with the department they support (in my case, the growth team). As the growth team grew, we created a Growth Analytics Lead who reported to the Head of Analytics and managed other growth analysts dedicated to specific areas, like conversion optimization or on-boarding. This allowed Pinterest to have the analytics seat at the table for budgets and resourcing, but the expertise at the team level to make the most impact. It’s now what I recommend to all teams that are scaling. Hiring Analysts If you are scaling a company and need more analytics help, it can be hard to understand who to hire that will actually help your teams. Hiring from analysts at other companies, especially larger ones, proved not to be a great strategy for me. I found during the interview process that most analysts were actually what I call “reporters”, in that they ran well defined reports for people who needed them but didn’t actually analyze anything. If you read analyst job descriptions, they inadvertently screen for these types of people by saying the candidate needs experience with all of these special tools. I can’t tell you how many job requirements that list Omniture (or whatever it’s called this week), Google Analytics, SPSS, Tableau, etc. Experience with tools is not actually what you care about (though SQL and Excel are a big help). The more tools someone has worked with, the less likely they are to analyze the output of those tools. What you actually want are people who are analytically curious. Our first successful analyst at Grubhub was a new graduate whose cover letter talked about how he tracked his sleep patterns and his diet to find ways to improve his health. He crushed dozens of analysts with multiple years’ experience in our interviews because he was using his brain to analyze results instead of just report. So I now screen for roles where analysis, not reporting, is the unit of value. Many analyst teams at other companies are structured that way, but the majority are not. You also have to test analytical ability in these interviews. At Grubhub, I gave people a laptop with a bunch of data in Excel and some vague questions to answer from it. The question was based on a real question we gave an analyst intern, who returned it to me saying there was no trend in the data. I ran the analysis myself and found one of the most important correlations for our business (the impact of restaurants per search on the likelihood to order). So I said, you have to be better than our intern to get an offer. It turned out to be an incredible screener. Most people never got far with the data, or their answers were spectacularly wrong. The few good analysts cut right through to a direct way to solve the problem and could explain it easily. I like this approach because it actually shows the analyst what the job is (and if they’ll like it), and I can walk the candidate through how I would solve the problem so if they did get it wrong, they could learn something from it. — Analytics team are one of the hardest teams to scale. One of the keys is building a model of a team that will scale with the needs of a company, and the hybrid model is the best model I have found to maximize the important levers of effectiveness (and happiness!). Structure is not all that is important though. Hiring the right candidate is critical, and the market is doing a poor job of preparing people for what emerging companies actually need in an analytics organization. If you can hire correctly and structure correctly though, you will have a competitive advantage over those who do not.
What makes Silicon Valley itself is not a landmark but rather the intangible things — the brand, the network, the world view. I’ve lived here for almost four years and I’m moving on soon — this will be my last column from San Francisco. I’d like to think I’m taking a little bit of it with me, the optimism and the resilience, without having absorbed too many of the negatives. But I will still leave wondering whether I found the place I came to see.