Jan 2, 2019 · 19m · a16z
a16z Podcast | The End (and Beginning) of Programming
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
Recorded at the annual a16z Summit, GitHub co-founder Chris Wanstrath and Andreessen Horowitz General Partner Peter Levine discuss the future of software development, exploring how data science, machine learning, and domain-expert inputs are replacing traditional imperative code.
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 3.8 Guest teaching 2.0 Guest disagreement 0.2 The host pushing back 1.4
speaking balance: gold is the host, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 10:24 Peter pushes back on host premise regarding human data literacy
Peter directly counters Chris's claim that humans suck at data by distinguishing between known queries and discovering unknown patterns.
Hardest push from the host ▶ 10:06 Chris challenges the premise of data-driven adoptionChris directly pushes back on Peter's vision of widespread data usage by citing fake news and general human incompetence at interpreting data.
Biggest teaching moment ▶ 4:10 Peter explains the paradigm shift from declarative code to data modelsPeter educates the audience and host on why traditional if-then-else code fails for complex real-world tasks like football plays.
The host holds their own ▶ 12:32 Chris demonstrates deep technical history knowledgeChris cites Peter's early work on the X-Windows system in the 1980s to draw a sophisticated historical parallel between open source code and open data.
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 |
|---|---|---|---|---|---|---|
| Welcome Remarks and History of Chris and Peter | 4 | 1 | 0 | 0 | Chris demonstrates strong industry knowledge by detailing developer population metrics beyond official statistics and describing GitHub user behavior. Peter briefly asks a clarifying question, leading to a friendly, collaborative setup of the discussion. | |
| Peter's Thesis: Data as Input and Software 2.0 | 0 | 3 | 0 | 0 | Peter delivers an extended monologue presenting his Software 2.0 thesis, contrasting declarative if-then-else logic with data-driven system inputs using football and auto repair analogies. Chris does not speak during this segment. | |
| Empowering Domain Experts Through Open Data | 5 | 3 | 1 | 5 | Chris actively challenges Peter's optimistic thesis, raising practical hurdles like tool access, open-source developer career shifts, and human inability to interpret data accurately. Peter reframes these questions constructively using a traffic light data correlation example. | |
| The Emergence of an Open Data Economy | 6 | 1 | 0 | 1 | Chris displays deep domain historical expertise by bringing up X-Windows (X11) from the 1980s to draw parallels between early software privacy and current data secrecy. Peter enthusiastically agrees and elaborates on individual data monetization. | |
| Timeline and Real-World Adoption of Data Assistance | 4 | 2 | 0 | 1 | Chris compares adoption timelines between free software and GitHub to ask about the pace of the data revolution. Peter highlights existing enterprise adoption like Salesforce productivity modeling. |
Statements from this episode (9)
Wanstrath: GitHub raised in 2012 to expand into business software
“We first decided to raise investment in 2012 after we were around four years old because we wanted to branch out from our own personal networks of Software developers, from programmers, we wanted to get into the world of business, we wanted to really expand Gi…”
Wanstrath: Over 40 million people globally write software beyond full-time developers
“We think there are twenty million people employed that are writing code, but the idea of what a programmer is, someone who writes software in their free time, or even professionally, that's not counted in a programmer, it's way more than twenty million, it's w…”
Wanstrath: Non-technical teams like marketing are migrating from Jira to GitHub
“We also see our customers, people that used to use Jira, people that are in the marketing team that want to update just some copy on a website, they're increasingly moving to something like GitHub, and they're also giving us feature requests specifically for t…”
Levine: Non-technical domain experts manipulating data will be the next programmers
“By using data, and having folks optimize the data, they actually become the next generation of programmers. They're not trained as programmers. They won't even know what a program really is. They're all manipulating data in the context by which they are famili…”
Levine: Domain expert data sharing creates an open-source data model
“You take the experts who are the programmers in their field, and they then push that knowledge off to everyone else. So maybe your dad's an expert in certain parts of this, and he can participate in it. It's the open source of data, right? Everyone's manipulat…”
Levine: Data science will replace traditional coding as computer science foundation
“You know, I think that data science will become the new academic approach in computer science. There'll be less coding and more about data science. New algorithms for data science, new Approaches to understand the world around us. So, you know, we can call thi…”
Levine: Individuals will control and monetize personal data via open markets
“And so, I think there will be a lot of cases where data becomes the purview of each individual, And we will choose how we want to go dispense it, and I think in certain cases, our data and what we produce will become valuable to consumers, and there'll be a ma…”
Levine: Data-driven digital assistants will augment workers across all professions
“I mean, in some ways it's already happening. There are companies out there who look at Salesforce productivity, looking at the most productive salespeople in an organization, learn what they do, modify that, and then go give that information to, so where every…”