Anu Hariharan

Founder & Managing Partner, Avra · 6 appearances on the record.

computed by AI from the episodes · how this works → · full disclaimer →

investorfounderengineer@anuhariharan ↗LinkedIn ↗avracap.com ↗

Anu Hariharan is the founder and managing partner of Avra, a firm providing growth funding and scaling programs for startups. She previously served as managing partner at Y Combinator's Continuity Fund and as an investment partner at Andreessen Horowitz.

33statements → 22claims → 17claims resolved → 71%fully supported → 3.88/5average certainty → 1.61/5average debate potential → 4.3/5argument clarity · the sources →

12 supported 4 partly supported 1 contradicted 1 not yet assessed 4 not checkable as stated how the 22 claims stand · each chip opens the sources

22 assertions · 2 opinions · 9 insights · every statement was checked. The predictions and assertions are the 22 claims: statements the public record can support or contradict. 17 are resolved, 1 is not yet assessed, and 4 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Anu argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Their most notable supported claim

Assertion Supported
Airbnb Saw Sluggish Growth for Three Years Before Achieving Liquidity
“The first three years of Airbnb was a real slog, because it's a global marketplace and they were trying to build supply. At the same time, they were also trying to build demand, and they needed to sign up the homes. They needed to make sure that people were, p…”
Anu Hariharan Jan 2, 2019 ▶ 10:40 a16z Podcast | Not all Network Effects Are Created Equal

Their most notable contradicted claim

Assertion Contradicted
Hariharan: Facebook penetrated 80% of US colleges in 18 months
“They did penetrate almost 80% of the colleges in 18 months.”
Anu Hariharan Jan 2, 2019 ▶ 6:55 a16z Podcast | Getting Network Effects

Expressed certainty vs assessment result

none yet certainty 1
none yet certainty 2
100% certainty 3
79% certainty 4
83% certainty 5

weighted support: a fully supported claim counts one, a partly supported claim counts half. Each filled bar is clickable and opens exactly those claims; "none yet" means nothing said at that certainty level has resolved yet

Argument clarity: do they answer the question? how? →

4.3 / 5 directness 4.3 · coherence 4.6 · precision 4.2 · compression 3.8

answered every one of 21 assessed questions directly

This is a score against a rubric. It is not a rank. Every host question → answer exchange is scored with names hidden on directness, coherence, precision and compression, 1–5 each, on meaning alone: disfluencies are ignored, and only raw unedited episodes count. This is the score that measures thought. Every scored exchange, scores shown → · The rubric and its checks →

How they sound: speaking style how? →

258 words/min while actually speaking · 18.2 um and uh per 1k words · 4.1 false starts per 1k · 31% of pauses land inside a clause

Measured by listening to the audio itself: 11,842 words across 5 episodes of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

Everything Anu Hariharan said on the a16z Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Insight
Hariharan: Point-to-point ride sharing relies on scale economies, not network effects
“So I would say the general ride sharing, we think it's more supply side economies of scale. And what that means is the more drivers you have on the platform, you know, you can make sure that you get a good quality driver within five minutes, but that's where i…”
Anu Hariharan Jan 2, 2019 ▶ 26:25 a16z Podcast | Getting Network Effects
Opinion
Anu Hariharan: Brands scale under Sarnoff's law, not Metcalfe's law
“So for brands, we actually think it's more like Sarnav's law. You know, some people argue, yes, it has Some network effect, but not directly incredibly value to the user, which is why the value is not as steep as Metcalfe's law, for example.”
Anu Hariharan Jan 2, 2019 ▶ 41:36 a16z Podcast | Getting Network Effects
Insight
Hariharan: Local execution gives Indian and Chinese startups an edge
“I do think there's a lot of elements in execution that's quite different and local to those markets. That's very different from The U.S. So it's not necessary that a U.S. Player going into those markets has all the advantages. I think there are local advantage…”
Anu Hariharan Jan 2, 2019 ▶ 1:27 a16z Podcast | The Tiger and the Dragon -- On Tech and Startups in India and China
Assertion Partly supported
Hariharan: Foreign investors provide 90 percent of Indian startup funding
“One interesting thing that I learned in my recent India trip is that 90% of the funding is actually from foreign money and it's not from domestic.”
Anu Hariharan Jan 2, 2019 ▶ 15:39 a16z Podcast | The Tiger and the Dragon -- On Tech and Startups in India and China
Assertion Not checkable as stated
Hariharan: Indian Series A startups commonly employ 650 to 900 people
“For example, to give you an example, when you look at a series A, U.S. Startup, or a series B, if, you know, you sort of get surprised if they say I have 150 people or 200 people. Like, that's a lot of people. In India, it's quite common to say I have 650 or 9…”
Anu Hariharan Jan 2, 2019 ▶ 32:17 a16z Podcast | The Tiger and the Dragon -- On Tech and Startups in India and China
Insight
Hariharan: Building Network Effects Is Easier on Mobile Than Web
“It was easier for platforms to build a network, I would argue, on mobile more than web.”
Anu Hariharan Jan 2, 2019 ▶ 5:51 a16z Podcast | Not all Network Effects Are Created Equal
Insight
Hariharan: Marketplaces Do Not Inherently Possess Network Effects
“So I don't think that all marketplaces have network effects by definition. They have the potential, but different marketplaces are different at different stages of development in their evolution.”
Anu Hariharan Jan 2, 2019 ▶ 10:16 a16z Podcast | Not all Network Effects Are Created Equal
Assertion Supported
Airbnb Saw Sluggish Growth for Three Years Before Achieving Liquidity
“The first three years of Airbnb was a real slog, because it's a global marketplace and they were trying to build supply. At the same time, they were also trying to build demand, and they needed to sign up the homes. They needed to make sure that people were, p…”
Anu Hariharan Jan 2, 2019 ▶ 10:40 a16z Podcast | Not all Network Effects Are Created Equal
Insight
Hariharan: Platforms Do Not Require Viral Growth for Network Effects
“Platforms or marketplaces don't need to have network, viral growth to have network effects.”
Anu Hariharan Jan 2, 2019 ▶ 21:12 a16z Podcast | Not all Network Effects Are Created Equal
Assertion Supported
Hariharan: Pokémon Go is the first massive light-AR mobile game
“I think this was the first, I would say, like a massive game that showed light AR meaning You could get some experience of rudimentary augmented reality without an additional hardware.”
Anu Hariharan Jan 2, 2019 ▶ 8:20 a16z Podcast | We Gotta Talk Pokémon Go
Opinion
Hariharan: Pokémon Go lacks true network effects
“My personal view is, it's too early to say whether they have a network effect. Given the gaming elements today, I don't think they have a network effect.”
Anu Hariharan Jan 2, 2019 ▶ 13:06 a16z Podcast | We Gotta Talk Pokémon Go
Assertion Not publicly verifiable
Hariharan: Facebook's DAU/MAU ratio rose to 57% in its first 18 months
“The one metric that he really focused on in the first two years was Retention, and the way he measured retention was daily actives by monthly actives, and if you see their chart in the first 18 months, it kept going from 52% to 55% to 57%.”
Anu Hariharan Jan 2, 2019 ▶ 4:06 a16z Podcast | Getting Network Effects
Assertion Not checkable as stated
Hariharan: Connecting 10 friends in 14 days drove early Facebook retention
“If someone was connected to 10 friends in 14 days, they were much likely to return to Facebook.”
Anu Hariharan Jan 2, 2019 ▶ 7:28 a16z Podcast | Getting Network Effects
Insight
Hariharan: Lyft Line and UberPool create true network effects
“However, if you look at lift line and Uber pool, I think those could have network effects because think of this, you're a rider, you want more riders using those because then you can share a ride to San Francisco from Palo Alto and therefore have a cheaper rid…”
Anu Hariharan Jan 2, 2019 ▶ 26:52 a16z Podcast | Getting Network Effects
Assertion Partly supported
Hariharan: Airbnb paid to acquire supply from its early days
“I think for Airbnb, supply was really hard. So they were, they spent dollars right from the early days to acquire the host.”
Anu Hariharan Jan 2, 2019 ▶ 30:04 a16z Podcast | Getting Network Effects
Assertion Not checkable as stated
Hariharan: Amazon US delivery costs relative to transaction size are lower than India's
“Amazon itself says that their delivery costs, for example, as a percentage of transaction in the U S is a lot lower than in India.”
Anu Hariharan Jan 2, 2019 ▶ 14:53 a16z Podcast | E-commerce, Payments, & More in India's Evolving Retail Landscape
Assertion Supported
Hariharan: Indian e-commerce delivery costs reach 30 percent of net sales
“30% of net sales in India is delivery, versus Amazon in the U.S. Says it's about 10 or 11%.”
Anu Hariharan Jan 2, 2019 ▶ 3:52 a16z Podcast | The Tiger and the Dragon -- On Tech and Startups in India and China
Assertion Supported
Hariharan: Two-factor authentication mandates hurt Indian online payment conversion rates
“The Indian central government has a two factor authentication protocol as a result of which what happens is it's the conversion rate obviously gets impacted because you have more steps to close the transaction when it's online.”
Anu Hariharan Jan 2, 2019 ▶ 12:06 a16z Podcast | The Tiger and the Dragon -- On Tech and Startups in India and China
Assertion Supported
Hariharan: Chinese ride-hailing giant Didi invested in Indian competitor Ola
“And to add to that, Didi also invested in Ola recently in their latest round, and so this just goes to show how a player in China also has, you know, global ambitions, and it's no longer just the U.S. Companies trying to enter the various markets.”
Anu Hariharan Jan 2, 2019 ▶ 25:09 a16z Podcast | The Tiger and the Dragon -- On Tech and Startups in India and China
Assertion Supported
Hariharan: U.S. tech giants dominate India without local equivalents like Alibaba
“You don't have an Alibaba yet in India. You don't have the buy, the Google of India is Google. The Facebook of India is Facebook.”
Anu Hariharan Jan 2, 2019 ▶ 35:00 a16z Podcast | The Tiger and the Dragon -- On Tech and Startups in India and China
Insight
Hariharan: Tech startup attrition in India matches Silicon Valley levels
“In India, I actually see the attrition problems to be as significant as in Silicon Valley. You do see that a lot of engineers move across startups. They switch every two years or every three years, and there's heavy competition in terms of compensation and pay…”
Anu Hariharan Jan 2, 2019 ▶ 44:45 a16z Podcast | The Tiger and the Dragon -- On Tech and Startups in India and China
Insight
Hariharan: Overlapping supply and demand gives marketplaces a distinct advantage
“The other advantage Airbnb had, has, is the supply and the demand actually overlap, right? So, for example, I was first a customer of Airbnb, but then I was open to listing the place as well. So, only some marketplaces have that overlap effect, where you could…”
Anu Hariharan Jan 2, 2019 ▶ 12:46 a16z Podcast | The Marketplace Rules
Insight
Network Effects Originated in Hardware Before Evolving to Software
“Network effects began with hardware. It was first the telephone, then the Ethernet, and in today's world, we talk about it in the context of software companies like Facebook, right?”
Anu Hariharan Jan 2, 2019 ▶ 1:34 a16z Podcast | Not all Network Effects Are Created Equal
Assertion Partly supported
Hariharan: Niantic uses micro-location and climate data for Pokémon spawns
“Because, so they like, they know the Andreessen Horowitz fountain, they know the fountain behind the Rosewood Hotel. They also know, they take information on climate and weather conditions, you know, for the gaming aspect of it, they decide, you know, if you'r…”
Anu Hariharan Jan 2, 2019 ▶ 10:11 a16z Podcast | We Gotta Talk Pokémon Go

Show 9statements(9 left)

Appearances (6)

EpisodeDateSpeaking time
a16z Podcast | Not all Network Effects Are Created Equal Jan 2, 2019 6m
a16z Podcast | We Gotta Talk Pokémon Go Jan 2, 2019 7m
a16z Podcast | Getting Network Effects Jan 2, 2019 16m
a16z Podcast | E-commerce, Payments, & More in India's Evolving Retail Landscape Jan 2, 2019 7m
a16z Podcast | The Tiger and the Dragon -- On Tech and Startups in India and China Jan 2, 2019 18m
a16z Podcast | The Marketplace Rules Jan 2, 2019 5m
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