Dec 21, 2018 · 40m · a16z

What's Next for Marketplace Startups

Li Jin · 27m spoken Frank Chen · 10m spoken
0:00 / 0:00
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions

In this Andreessen Horowitz conversation, Frank Chen and Li Jin analyze the historical evolution of service marketplace business models and outline key strategies for entrepreneurs to unlock regulated, supply-constrained industries.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. How this is scored →

The host as informed peer 4.8 Guest teaching 3.7 Guest disagreement 0.0 The host pushing back 0.0
05100:0015:0030:006:38–10:10 · The host as informed peer 5/10 Marketplace Evolution: The Listings Era Frank demonstrates domain knowledge by adding colorful historical examples, such as how yellow pages ads worked and how AAA Locksmiths gamed alphabetical sorting. Li Jin provides a clear breakdown of the listings era model.10:10–14:04 · The host as informed peer 6/10 Marketplace Evolution: The Unbundled Craigslist Era Frank draws heavily on his direct career experience working at respond.com to explain the operational necessity of vertical-specific request forms. Li Jin outlines how Angie's List and Thumbtack unbundled Craigslist categories.14:04–18:07 · The host as informed peer 4/10 Marketplace Evolution: The 'Uber for X' Era Li Jin explains why the 'Uber for X' wave suffered high failure rates due to misapplying on-demand models to non-urgent services. Frank supports her points with brief illustrations of low-friction on-demand transactions.18:07–22:53 · The host as informed peer 5/10 Marketplace Evolution: The Managed Marketplace Era Frank actively contributes to the discussion on managed marketplaces by sharing internal firm thesis work regarding corporate real estate purchasing. Li Jin illustrates managed marketplaces using portfolio examples like Honor and Opendoor.22:53–29:05 · The host as informed peer 5/10 The Future: Supply-Constrained & Regulated Services Li Jin presents the thesis that the most scalable marketplaces unlock artificial supply constraints in regulated categories. Frank enriches the point by drawing a direct comparison to how Lyft bypassed taxi medallion regulations.29:05–34:43 · The host as informed peer 4/10 Strategies for Unlocking Regulated Supply Li Jin systematically details four tactics for unlocking regulated supply, ranging from discovery to AI tools like MD Acne. Frank highlights the capital intensity of full-stack models and shares enthusiasm for AI solutions.6:38–10:10 · Guest teaching 3/10 Marketplace Evolution: The Listings Era Frank demonstrates domain knowledge by adding colorful historical examples, such as how yellow pages ads worked and how AAA Locksmiths gamed alphabetical sorting. Li Jin provides a clear breakdown of the listings era model.10:10–14:04 · Guest teaching 3/10 Marketplace Evolution: The Unbundled Craigslist Era Frank draws heavily on his direct career experience working at respond.com to explain the operational necessity of vertical-specific request forms. Li Jin outlines how Angie's List and Thumbtack unbundled Craigslist categories.14:04–18:07 · Guest teaching 4/10 Marketplace Evolution: The 'Uber for X' Era Li Jin explains why the 'Uber for X' wave suffered high failure rates due to misapplying on-demand models to non-urgent services. Frank supports her points with brief illustrations of low-friction on-demand transactions.18:07–22:53 · Guest teaching 4/10 Marketplace Evolution: The Managed Marketplace Era Frank actively contributes to the discussion on managed marketplaces by sharing internal firm thesis work regarding corporate real estate purchasing. Li Jin illustrates managed marketplaces using portfolio examples like Honor and Opendoor.22:53–29:05 · Guest teaching 4/10 The Future: Supply-Constrained & Regulated Services Li Jin presents the thesis that the most scalable marketplaces unlock artificial supply constraints in regulated categories. Frank enriches the point by drawing a direct comparison to how Lyft bypassed taxi medallion regulations.29:05–34:43 · Guest teaching 4/10 Strategies for Unlocking Regulated Supply Li Jin systematically details four tactics for unlocking regulated supply, ranging from discovery to AI tools like MD Acne. Frank highlights the capital intensity of full-stack models and shares enthusiasm for AI solutions.6:38–10:10 · Guest disagreement 0/10 Marketplace Evolution: The Listings Era Frank demonstrates domain knowledge by adding colorful historical examples, such as how yellow pages ads worked and how AAA Locksmiths gamed alphabetical sorting. Li Jin provides a clear breakdown of the listings era model.10:10–14:04 · Guest disagreement 0/10 Marketplace Evolution: The Unbundled Craigslist Era Frank draws heavily on his direct career experience working at respond.com to explain the operational necessity of vertical-specific request forms. Li Jin outlines how Angie's List and Thumbtack unbundled Craigslist categories.14:04–18:07 · Guest disagreement 0/10 Marketplace Evolution: The 'Uber for X' Era Li Jin explains why the 'Uber for X' wave suffered high failure rates due to misapplying on-demand models to non-urgent services. Frank supports her points with brief illustrations of low-friction on-demand transactions.18:07–22:53 · Guest disagreement 0/10 Marketplace Evolution: The Managed Marketplace Era Frank actively contributes to the discussion on managed marketplaces by sharing internal firm thesis work regarding corporate real estate purchasing. Li Jin illustrates managed marketplaces using portfolio examples like Honor and Opendoor.22:53–29:05 · Guest disagreement 0/10 The Future: Supply-Constrained & Regulated Services Li Jin presents the thesis that the most scalable marketplaces unlock artificial supply constraints in regulated categories. Frank enriches the point by drawing a direct comparison to how Lyft bypassed taxi medallion regulations.29:05–34:43 · Guest disagreement 0/10 Strategies for Unlocking Regulated Supply Li Jin systematically details four tactics for unlocking regulated supply, ranging from discovery to AI tools like MD Acne. Frank highlights the capital intensity of full-stack models and shares enthusiasm for AI solutions.6:38–10:10 · The host pushing back 0/10 Marketplace Evolution: The Listings Era Frank demonstrates domain knowledge by adding colorful historical examples, such as how yellow pages ads worked and how AAA Locksmiths gamed alphabetical sorting. Li Jin provides a clear breakdown of the listings era model.10:10–14:04 · The host pushing back 0/10 Marketplace Evolution: The Unbundled Craigslist Era Frank draws heavily on his direct career experience working at respond.com to explain the operational necessity of vertical-specific request forms. Li Jin outlines how Angie's List and Thumbtack unbundled Craigslist categories.14:04–18:07 · The host pushing back 0/10 Marketplace Evolution: The 'Uber for X' Era Li Jin explains why the 'Uber for X' wave suffered high failure rates due to misapplying on-demand models to non-urgent services. Frank supports her points with brief illustrations of low-friction on-demand transactions.18:07–22:53 · The host pushing back 0/10 Marketplace Evolution: The Managed Marketplace Era Frank actively contributes to the discussion on managed marketplaces by sharing internal firm thesis work regarding corporate real estate purchasing. Li Jin illustrates managed marketplaces using portfolio examples like Honor and Opendoor.22:53–29:05 · The host pushing back 0/10 The Future: Supply-Constrained & Regulated Services Li Jin presents the thesis that the most scalable marketplaces unlock artificial supply constraints in regulated categories. Frank enriches the point by drawing a direct comparison to how Lyft bypassed taxi medallion regulations.29:05–34:43 · The host pushing back 0/10 Strategies for Unlocking Regulated Supply Li Jin systematically details four tactics for unlocking regulated supply, ranging from discovery to AI tools like MD Acne. Frank highlights the capital intensity of full-stack models and shares enthusiasm for AI solutions.

speaking balance: gold is the host, purple is the guest (3 minute bins)

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Sharpest disagreement ▶ 36:42 Guest reframes safety risks in unlicensed therapy

In a generally non-combative interview, Li Jin pushes back against Frank's mock-alarmist framing about untrained therapists by clarifying that non-clinical cases only require empathetic listeners rather than full clinical expertise.

Hardest push from the host ▶ 36:21 Host plays devil's advocate on therapy regulation

Frank adopts the role of a board-certified psychotherapist to challenge the premise of using non-licensed providers, asking what could go wrong when letting untrained coaches handle mental health.

Biggest teaching moment ▶ 1:13 Guest breaks down digital service economy statistics

Li Jin educates the host with Bureau of Labor Statistics data showing that despite services accounting for two-thirds of consumer spend, only seven percent is digitized due to inherent complexity.

The host holds their own ▶ 12:37 Host details primary operator experience at respond.com

Frank demonstrates deep practical expertise by describing his former startup experience building customized request forms for disparate local service categories.

the scores for every segment, with the reasoning behind each
ChapterTopicThe host as informed peerGuest teachingGuest disagreementThe host pushing backWhy
Marketplace Evolution: The Listings Era 5300 Frank demonstrates domain knowledge by adding colorful historical examples, such as how yellow pages ads worked and how AAA Locksmiths gamed alphabetical sorting. Li Jin provides a clear breakdown of the listings era model.
Marketplace Evolution: The Unbundled Craigslist Era 6300 Frank draws heavily on his direct career experience working at respond.com to explain the operational necessity of vertical-specific request forms. Li Jin outlines how Angie's List and Thumbtack unbundled Craigslist categories.
Marketplace Evolution: The 'Uber for X' Era 4400 Li Jin explains why the 'Uber for X' wave suffered high failure rates due to misapplying on-demand models to non-urgent services. Frank supports her points with brief illustrations of low-friction on-demand transactions.
Marketplace Evolution: The Managed Marketplace Era 5400 Frank actively contributes to the discussion on managed marketplaces by sharing internal firm thesis work regarding corporate real estate purchasing. Li Jin illustrates managed marketplaces using portfolio examples like Honor and Opendoor.
The Future: Supply-Constrained & Regulated Services 5400 Li Jin presents the thesis that the most scalable marketplaces unlock artificial supply constraints in regulated categories. Frank enriches the point by drawing a direct comparison to how Lyft bypassed taxi medallion regulations.
Strategies for Unlocking Regulated Supply 4400 Li Jin systematically details four tactics for unlocking regulated supply, ranging from discovery to AI tools like MD Acne. Frank highlights the capital intensity of full-stack models and shares enthusiasm for AI solutions.

Statements from this episode (13)

Assertion Not yet assessed · timeframe Dec 2018
Li Jin: Only 7% of consumer services are digital
“I think the Bureau of Labor Statistics has a statistic that only seven percent of the services that we purchase are digital.”
Li Jin Dec 21, 2018 ▶ 1:31
Insight
Li Jin: Directory marketplace models fail for high-trust service categories
“Listings, it's not only tedious and a lot of work for consumers there were also problems with trust, so for services that required some component of trust where you were potentially letting someone into your home or letting someone interact with your child, it…”
Li Jin Dec 21, 2018 ▶ 8:16
Assertion Not checkable as stated
Jin: Every Craigslist category now has its own specialized marketplace
“And basically every single category now has its own specialized marketplace that tackles just that particular category.”
Li Jin Dec 21, 2018 ▶ 10:40
Insight
Li Jin: On-demand marketplaces require simple, atomic transactions for algorithmic matching
“And so the on demand model of marketplaces was really conducive to simple services that were very atomic and simple transactions that could you could do matching for in a very quick algorithmic way without, you know, matching on various different complex dimen…”
Li Jin Dec 21, 2018 ▶ 15:10
Insight
Li Jin: 'Uber for X' startups failed by misapplying the on-demand model
“Where a lot of the startups that were trying to become Uber for X didn't ultimately end up lasting and surviving as standalone companies, and we think that that's because the model got applied probably a bit too liberally to service categories that weren't as …”
Li Jin Dec 21, 2018 ▶ 17:11
Insight
Li Jin: Top marketplace companies face constrained supply rather than demand
“One of our observations about the best marketplace investments and the best marketplace companies are that they tend to be marketplaces that have more constrained supply than demand. So the biggest marketplace outcomes that we've seen, or the biggest marketpla…”
Li Jin Dec 21, 2018 ▶ 23:10
Assertion Not checkable as stated
Li Jin: Airbnb and Uber were consistently constrained by supply
“So, during the entire lifetimes of both of those companies, there's always been an excess of demand, and supply has always struggled to keep up.”
Li Jin Dec 21, 2018 ▶ 23:52
Prediction Not checkable as stated
Li Jin: Regulated services are the next major marketplace category
“And so in other words, we're looking for categories where there's a lot of demand that is being unfulfilled today. And so that leads us to what we think is next, which is regulated services and marketplaces that address regulated services.”
Li Jin Dec 21, 2018 ▶ 24:42
What-if
Li Jin: Uber and Lyft required unlicensed drivers to reach scale
“And without that kind of unlock, without expanding it to the unlicensed pool of potential drivers there's no way that Uber or Lyft could be anywhere near the size they are today.”
Li Jin Dec 21, 2018 ▶ 27:22
Assertion Partly supported
Jin: Truck driving is the most common US profession despite severe shortages
“Today, in order to be a truck driver in the US, you have to be licensed, and this is the most common profession in most states in America, and yet we're still extremely supply constrained when it comes to the truck driving industry. There just needs to be a lo…”
Li Jin Dec 21, 2018 ▶ 32:59
Assertion Partly supported
Li Jin: Most psychiatrists are out of network and charge up to $600 per hour
“Psychiatrists are out of network, and so they can charge prices of, you know, even 600 dollars an hour, and so this is really a product that most people can't access”
Li Jin Dec 21, 2018 ▶ 35:10
Assertion Supported
Li Jin: Mental health startup Basis uses unlicensed community coaches as therapists
“So one startup based in San Francisco is called Basis, and they're using unlicensed but trained people in the community to basically serve as therapists.”
Li Jin Dec 21, 2018 ▶ 35:50
Assertion Supported
Li Jin: Services equal 80% of U.S. GDP, signaling huge room for marketplace startups
“Marketplaces for services are a huge opportunity because services represent 80% of the U.S. GDP, and so there's a ton of opportunities left”
Li Jin Dec 21, 2018 ▶ 39:37
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