Ramesh Johari
Professor, Stanford University. On 1 show, 1 appearance. The Shows tab opens the full record on each.
academicauthor@rameshjohari ↗LinkedIn ↗web.stanford.edu/~rjohari ↗
He researches the design, operation, and economic analysis of online platforms and marketplaces, specializing in experimentation, pricing, and matching algorithms. Alongside his academic work, he is widely recognized for advising prominent tech and marketplace companies, including Airbnb, Uber, Bumble, Stitch Fix, and Upwork.
1shows
1appearances
23statements
3resolved
3supported
0contradicted
100%fully supported
1said about them ↓ Everything Ramesh Johari said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Johari: Marketplaces never start by solving two-sided matchmaking friction
“The moral is a marketplace business never starts as a marketplace business, because what we think of as a marketplace business is something which at scale is removing the friction of the two sides finding each other. But when you start, you don't have that sca…”
Johari: AI puts more pressure on human data scientists, not less
“What AI has done for us is it's massively expanded the frontier of things we could think about our problem, hypotheses we could have, maybe things we could test. It's just an astronomical explosion of explanations and ideas and principle. And I really think ac…”
Johari: OpenAI has evolved into a marketplace via plugins
“No one in their right mind would have thought of open AI as a marketplace. But OpenAI is a marketplace now. They may not want to call themselves a marketplace, but they have plugins. The plugins are flooding that, that platform.”
Johari: Virtually every modern tech business will have the option to become a marketplace
“Virtually every business is going to have that option at some point in, in, you know, the modern tech enabled economy anyway.”
Johari: Machine learning prediction is correlation, but business decisions require causation
“When we teach people to build machine learning models, we're asking them to make predictions. We're asking them to find correlations. Prediction is inherently about correlation. But when we ask people to make decisions, we're asking them to think about causati…”
Johari: Experimentation cultures foster risk aversion and overly incremental testing
“What I generally believe is that we're risk averse on both these two dimensions. That what people decide to test in a world that has promoted experimentation for everything tends to be more incremental by design. Okay. Because, and we'll come back to by actual…”
Johari: Single A/B tests should not overturn core business understanding
“Data science is really about accumulation of evidence. It's never about one finding an isolation. And so another kind of trap, I think, is to sometimes say, well, I hit stat sig on my AB test, you know, green light, it's all go like, and, you know, I think, yo…”
Johari: Major marketplace changes create winners and losers through reallocation
“You have to recognize when you run marketplaces that many of the changes that are most consequential create winners and losers. And rolling with those changes is about recognizing whether the winners you've created are more important to your business view than…”
Johari: I will never ask coding interview questions post-generative AI
“I should say I would never ask a coding question like, you know, post November, 20, 22 after after, after we got like AI to help us code. I just, I think it's a superpower.”
Johari: Marketplaces sell friction reduction, making both supply and demand their customers
“So those are frictions and what the marketplaces are selling you is taking the friction away. That's what you're paying them for. And it's an important observation because what that means is the marketplace's customers aren't just the people buying the rides o…”
Johari: Every marketplace relies on data science for finding, making, and learning matches
“Every single thing I just said, finding potential matches, making matches, and then learning about those matches, and then, you know, cycling back again, that is the data science of marketplaces. And I feel like every marketplace that you could think of, you k…”
Johari: Early monetization commitments can trap founders as platforms mature
“Early commitments in this case to like a Particular pricing scheme, particular monetization can really tie your hands as you then realize later you actually are a platform.”
Johari: Marketplace algorithms should be evaluated on causal outcomes, not historical predictions
“When I think about the distinction about two different, between two different ranking algorithms, I don't want to be only comparing them in terms of how well they recreate the choices people made in the past. The way I'm really going to evaluate those is in my…”
Johari: Badging in marketplaces often fails by distorting inventory demand
“A common kind of finding it, you know, with badges is that, that badges you think are going to be great actually turn out to be terrible. And one reason they're terrible is they focus too much attention on the badged folks and pull too much attention away from…”
Johari: Measuring teams solely on impact stifles creative, strategic work
“It's basically because if you're measured narrowly on impact and that's all anyone sees around you, then it's very hard to engage with the creative aspect of business change and the strategic aspects of business change.”
Johari: Standard frequentist A/B testing discards past experimental learning
“You know, a funny thing about experiments is that we throw past learning away effectively. And this is just an artifact of how we analyze experiments that the methods used, the statistical methods used typically, you know, p-values, confidence intervals. These…”
Johari: Bayesian A/B testing rewards learning by updating priors from failed tests
“Bayesian A-B testing. So that's one of the things I think can help culturally weirdly. It's like a super technical thing. But I think it can help culturally because what it's doing is it's now rewarding people for contributing information to that prior. And I …”
Johari: Rewarding only winning experiments treats failed tests as wasted time
“If I reward you for shipping winners, then what I'm really telling you is all the time that you spent testing out failures was wasted time.”
Johari: Prioritizing speed prevents teams from building meaningful mental models
“We're so convinced that speed is the way you're going to find the right answer that I just don't think we slow down to develop meaningful mental models of the things we're doing.”
Johari: AI-Generated Text Can Be Dangerous in Data Science
“In the same way that AI generates a lot of ideas, AI also generates a lot of pros. And in data science, that can actually be deadly because you're getting more explanations that sometimes maybe are extraneous, you know?”
Johari: Early Uber subsidized drivers and distributed event coupons to bootstrap demand
“To take Uber as an example, right? They would walk into New City. And one thing that, that, you know, Uber was Kind of commonly known for doing this was back in the days when really Uber black was the only service is they just hand out coupons for free rides a…”
Johari: A negative first rating on eBay causes an immediate 8% revenue hit
“In fact, there was some early work on eBay that showed that your first, if your first rating's negative, that could actually in immediately cause like an eight percent hit on, you know, your immediate expected revenue, say nothing of long-term consequences. Su…”
Johari: Factoring unleft reviews into seller scores better predicts downstream performance
“There's a great concept in the ratings, in the literature on rating systems called the sound of silence, which is this idea that, that there's a lot of information in, in ratings that are not left. So Steve Tedellis, who's a professor at Berkeley, he had a rea…”
The other half of the tape: Ramesh Johari's own voice is left out of every number here. Other people bring the name up 1 time in 1 episode across the shows. every mention, with the transcript →
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Lenny's Podcast 1
2023 1 mention in 1 episode
One line per show, most statements first. The link opens Ramesh's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
| LENNY'S PODCASTLEDGER | Professor, Stanford University | 1 | 23 | 100% 3/3 | full record on Lenny's Podcast → |