Jan 2, 2019 · 35m · a16z
a16z Podcast | Companies, Networks, Crowds
Eric Brynjolfsson · 12m spoken Andrew McAfee (Andy) · 10m spoken Sonal Chokshi · 6m spoken Frank Chen · 3m spoken
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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 episode of the a16z podcast, MIT authors Eric Brynjolfsson and Andrew McAfee discuss their book 'Machine, Platform, Crowd' with a16z partner Frank Chen, exploring how platform networks, crowd intelligence, and machine learning are fundamentally reshaping economics, firm governance, and corporate strategy.
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 4.7 Guest teaching 3.0 Guest disagreement 1.8 The host pushing back 3.5
speaking balance: gold is the host, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 18:02 Dismissing AI simulation central planning
Andy forcefully dismisses the premise that computational power and simulation allow central planning of an economy, labeling the idea ludicrous.
Hardest push from the host ▶ 19:05 Challenging anti-planning consensus using ChinaSonu explicitly refuses to accept the blanket failure of central planning, challenging the guests with China's coordinated economic momentum and asking them to disillusion her.
Biggest teaching moment ▶ 22:20 Demonstrating the power of crowd diversity via TopcoderAndy and Eric educate the room on how an open crowd tournament reduced genome sequencing times from 4 hours to 10 seconds without using a single biologist.
The host holds their own ▶ 13:07 Host corrects terminology surrounding the DAO hackSonu interrupts the flow to cleanly enforce the technical distinction between 'The DAO' as a specific hacked entity and 'a DAO' as a generic organizational structure.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | The host as informed peer | Guest teaching | Guest disagreement | The host pushing back | Why |
|---|---|---|---|---|---|---|
| Defining Network Effects and Multi-Sided Platforms | 5 | 2 | 1 | 2 | Sonu demonstrates solid domain familiarity by introducing standard economic shorthand for supply/demand scale economies and offering ride-pooling as a sharp exception to standard two-sided network dynamics. The guests collaboratively expand on these definitions into multi-sided networks. | |
| Economic Complements and the Apple App Store Case Study | 4 | 3 | 2 | 3 | Sonu contributes the razor-and-blade example and highlights common misconceptions around freemium strategy. She challenges the guests' platform discussion by noting that closed ecosystem companies have historically been major market winners. | |
| Decentralization, Incomplete Contracts, and the DAO Hack | 6 | 4 | 2 | 5 | Sonu asserts expertise by precisely distinguishing 'The DAO' entity from generic decentralized autonomous organizations. She then pushes back on incomplete contract theory by questioning whether future algorithmic AI could anticipate all contingencies. | |
| The Limits of Central Planning, Hayek, and Human Fallibility | 5 | 4 | 3 | 6 | Sonu directly challenges the guests' central planning arguments twice, citing high-powered simulation data and China's state-coordinated economic success. Andy forcefully rejects the simulation premise as ludicrous, citing the Red Queen effect. | |
| Harnessing Crowd Wisdom: Joy's Law and Topcoder Case Study | 3 | 3 | 1 | 1 | The conversation is highly collaborative as the guests explain Joy's Law and share a compelling Topcoder case study on genome sequencing. Sonu facilitates smoothly with bridging questions about token launches and prediction markets. | |
| Mind vs. Machine: AI Decision-Making and Human Augmentation | 5 | 2 | 2 | 4 | Sonu dissents from a thesis previously shared on the podcast by Kevin Kelly, arguing that emerging generative AI can formulate novel questions rather than just answer them. The guests and host agree on human-machine augmentation models. |
Statements from this episode (17)
Brynjolfsson: No economic law guarantees technological progress benefits everyone
“Even though technology is making the pie bigger, There's no economic law that everyone's going to benefit from it. It's possible for some people to get left behind.”
Brynjolfsson: Tech progress caused stagnating median incomes over the past 20 years
“Now, to be clear, that's not what happened for most of the past 200 years, but the past 1020 years, there really have been more and more people being left behind, and so you could get stagnating median incomes, even as some people, maybe in the top one percent…”
McAfee: Apple's app ecosystem is an N-sided network
“This really starts to turn into three-dimensional chess because the right way to think about the app ecosystem in Apple is not any kind of one or two-sided network. It's an N-sided network.”
McAfee: Steve Jobs fought opening the App Store to developers for a year
“The compliments are so tricky that they actually tripped up Steve Jobs really badly. This is not lore. This is fact. He did not want to open up the app store to any outside developers. He thought he had to maintain super tight control over that digital environ…”
Chen: Apple will spend unlimited money defending platform complements like Xcode
“So if you think, hey, I'm going to create a better development tool, I'm going to create a better Xcode, like, think again, because Apple is going to spend as much money as it needs to defend the complement universe.”
McAfee: Traditional companies will unequivocally survive despite blockchain decentralization
“The question gets teed up. Are we still going to have companies in the future? And as Eric and I started to think about all the stuff that we'd learned and tried to digest, our answer was an unequivocal yes.”
Brynjolfsson: Smart contracts cannot cover every contingency due to world complexity
“One of the blinders that a lot of people, especially technologists have, is they say, hey, we can just write everything down to an engineering mindset. We'll write a complete contract that covers all contingencies. And the reality is, is the world is just too …”
Chen: Japan's 1980s AI industrial policy project was a complete failure
“In the late eighties, Japan tried to organize their entire industrial policy around creating artificial intelligence. The fifth generation. The fifth generation supercomputer. Built around expert systems, optimized all the way down into silicon, so you can ima…”
McAfee: Increased computing power will not make central economic planning viable
“And the idea that we're out of that world because of Moore's law, because we have much more computational power now, I find that ludicrous.”
Frank Chen: Running small market experiments beats relying on predictive simulation
“It's hard to imagine a better system than the one we have, which is let's spend a little money and run a ton of experiments. Exactly. On businesses to figure out what people want. Because until you have it in the world, you're not sure what people will want.”
McAfee: A Topcoder challenge cut genome sequencing time to 10 seconds
“They had an algorithm that could do a run in about four hours with about 70% accuracy. There was a faculty member at Harvard Med School who made a big improvement to that algorithm. He developed one that got them up to about 75% accuracy. Kareem then worked wi…”
McAfee: Top performers in an NIH genomics challenge lacked biology backgrounds
“They interviewed the best performers that, who submitted the top performing algorithms None of them had a life sciences background. There was not a geneticist. There was not a biologist among them.”
McAfee: Humans retain a massive advantage over AI as native speakers of human reality
“We are the native speakers of the human created world. Computers are doing this as their second language. We, I believe we have a massive advantage. We are the native speakers about this reality around us.”
Chen: Patients will soon be horrified if doctors lack AI companions
“I think it's only a matter of time before we walk into a doctor's office or a lawyer's office where that isn't the fundamental interaction, and we'll just be horrified, like, where's your AI companion? Why are you trying to do this Yourself with your biases.”
Brynjolfsson: Crowdsourcing contests succeed only when problems are clearly defined
“Defining the problem is important, whether you define it for the machine or whether you define it for the crowd. Understanding what the problem is you're really trying to solve. If you can define it well enough, then these contests work great.”
McAfee: Steam-era companies failed by refusing to rethink factory workflows
“The companies that failed during the transition from steam power over to electric power, almost none of them failed because they refused to invest in electricity. That was not the failure mode. The failure mode was they refused to rethink what a factory could …”