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Software Finally Eats Services - Aaron Levie

Software Finally Eats Services - Aaron Levie

13 segments available

Should the US put a price on H-1B visas, or would that block the flow of new talent? Are AI coding agents actually making teams way more productive, or is it just hype? And in the AI platform shift, will the big winners be incumbents or new AI-native startups? Erik Torenberg is joined by Box co-founder and CEO Aaron Levie, a16z board partner Steven Sinofsky, and a16z general partner Martin Casado to debate the biggest questions in tech. They unpack pricing vs lottery for H-1Bs and what we’re actually optimizing for, why Box now ships a third of its code from AI, the shift from writing to reviewing code, and why bottom-up personal AI tools succeed where top-down “AI pilots” struggle. Timecodes: 0:00 Introduction 0:55 Latest immigration policy and who benefits 2:46 Salary bands as a solution for tech talent allocation 5:39 Optimizing immigration policy for wages, jobs, or merit 8:08 Market dynamics and policy changes in tech hiring 12:52 AI effects on labor productivity and developer output 19:25 Drivers of large AI productivity gains vs plateaus 24:40 Measuring AI’s impact on productivity and what’s missing 31:32 Human Taste and AI Tools 37:47 Young founders building companies differently with AI 41:34 Platform shifts: startups vs incumbents 49:01 AI opening new markets beyond software 55:54 Incumbents vs disruptors in the next decade of AI Resources: Find Aaron on X: https://x.com/levie Find Steven on X: https://x.com/stevesi Find Martin on X: https://x.com/martin_casado Find Erik on X: https://x.com/eriktorenberg Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://x.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details, please see a16z.com/disclosures.

Segments Timeline

1
0:00 - 0:56
0:56 duration180 words

Introduction

"The universal adoption of this as a consumer technology and then bleeding into proumer is it exceeds anything I've ever experienced and I think it is it will just fundamentally change people's sort of..."

2
0:56 - 2:46
1:49 duration368 words

Latest immigration policy and who benefits

"the immigration news. Um, >> you really want to kick off just like really with the fun stuff. Get the >> blood point. Exactly. Um, Martin, you >> mention exactly please. >> What were your reactions to..."

3
2:46 - 5:39
2:53 duration645 words

Salary bands as a solution for tech talent allocation

"and Google who are probably more easy to regulate, but there are a number of organizations that are consult that that actually are price sensitive that would be squeezed by this. And I would I would t..."

4
5:39 - 8:09
2:30 duration528 words

Optimizing immigration policy for wages, jobs, or merit

"quibble about the number. Is it 20K which Keith Rabos said when I thought it was very sensible or is it 100k? I don't know. But like the idea that you >> Keith threw out 20. >> Keith threw out 20. >> ..."

5
8:09 - 12:52
4:42 duration1107 words

Market dynamics and policy changes in tech hiring

"and get like an IT job for 100k you just can't right and so that I think is actually the area that's the most directly impacted by the large consultants >> meaning there aren't jobs that pay 100k or t..."

6
12:52 - 19:25
6:33 duration1441 words

AI effects on labor productivity and developer output

"tricky part. >> Y >> I want to segue from uh labor markets to >> good next what's the next really interesting political topic that we engage in. >> Yeah, exactly. um from labor markets to labor produc..."

7
19:25 - 24:41
5:15 duration1184 words

Drivers of large AI productivity gains vs plateaus

"very difficult to measure. >> One of them is and I don't think it's just an early adopter thing like these these models are so magic that you get dazzled. Oh, so even if it's not what you want, you're..."

8
24:41 - 31:35
6:54 duration1455 words

Measuring AI’s impact on productivity and what’s missing

"measure for two reasons. The first one I just mentioned is it's just really dazzling. So I think like people kind of like they're like oh it's amazing. The second one is I think a lot of the productiv..."

9
31:35 - 37:48
6:13 duration1437 words

Human Taste and AI Tools

"it's just like a completely different way of of thinking about work where where does that fit in for you in what we were talking about what I was asking about earlier which is how does the expertise y..."

10
37:48 - 41:34
3:46 duration871 words

Young founders building companies differently with AI

"earlier about that there are 20-year-olds who are building up companies in new ways because remember a few years ago I think Patrick Carlson and a few others were asking hey where are all the Gen Z su..."

11
41:34 - 49:02
7:28 duration1660 words

Platform shifts: startups vs incumbents

"this is just so critical because it what what's really happening is this is why, you know, it's an actual platform ship. So Silicon Valley has seen this movie many times before and it that's why often..."

12
49:02 - 55:54
6:51 duration1502 words

AI opening new markets beyond software

"don't think we've had at least I don't know of a modern kind of case study for is is again this uh this opening up of non-software TAM for software. So there's not even incumbents in the classic sense..."

13
55:54 - 59:24
3:29 duration738 words

Incumbents vs disruptors in the next decade of AI

">> When we look at mobile, there were big companies built, you know, like Uber and and WhatsApp and Instagram and Tik Tok, but the biggest beneficiaries were Facebook and and and and Google. Um, in AI..."