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Software Finally Eats Services - Aaron Levie

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.

Aaron LevieguestSteven SinofskyguestMartin CasadoguestErik Torenberghost
Sep 23, 202559mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

AI accelerates productivity, reshapes startups, and expands software into services

  1. The speakers debate proposed immigration policy changes, arguing the current lottery-driven system is heavily gamed by large firms and consultancies and wastes massive productivity through compliance overhead.
  2. They explore salary bands or price mechanisms for visas as a way to reduce low-wage labor arbitrage and “body shop” saturation, while questioning whether a fixed threshold (e.g., $100K) would unintentionally disadvantage smaller startups.
  3. They report substantial AI coding productivity gains—from ~20–75% self-reported improvements in larger companies to 3–10x claims in small, senior teams—driven by agentic workflows that shift engineers from writing code to reviewing it.
  4. They argue AI productivity is hard to measure because benefits are often “shadow productivity” (personal, bottom-up tool usage) and because improvements may show up as quality, iteration speed, and decision-making rather than feature throughput.
  5. They frame AI as a true platform shift that favors startups by neutralizing traditional incumbent advantages (especially scale), while simultaneously expanding total addressable markets by turning services and domain labor into software products.

IDEAS WORTH REMEMBERING

5 ideas

Define the goal before redesigning immigration rules.

They stress that optimizing for wages, domestic job access, or pure merit leads to different policies; without clarity, thresholds like a single salary minimum can produce unintended outcomes.

The current immigration system imposes major “deadweight loss” that advantages big companies.

Large firms can afford teams to navigate and lobby around complex rules, while startups face disproportionate burden; simplifying the system may matter as much as any numeric threshold.

Salary bands can target labor arbitrage, but the number matters less than the mechanism.

A pay floor could squeeze low-margin consultancies and reduce displacement in mid-tier IT/admin roles, yet a high fixed bar (e.g., $100K) could also price out parts of the startup ecosystem.

Big AI gains come from workflow redesign, not just faster typing.

The step-change appears when teams use background agents to complete scoped tasks asynchronously, turning engineers into reviewers/editors and compressing multi-day cycles into minutes or hours.

AI disproportionately amplifies people with domain expertise and judgment.

They argue the best outcomes require knowing what to ask for and how to verify outputs; experts become “turbocharged,” while non-experts risk over-trusting hallucinations or producing low-quality work.

WORDS WORTH SAVING

5 quotes

If you got everybody in a room and you say, you sort of say, "What are we optimizing for?"

Aaron Levie

I will regularly talk to three, five, 10-person startup founders that, that self-report they might be getting somewhere on the order of like three to five to 10X, uh, productivity improvements.

Aaron Levie

They're sending off a task. The task goes off, comes back in 20 minutes, and then they're really in the, in the business of doing code review, not code writing.

Aaron Levie

Anecdotally, the more senior small teams that use AI are superhuman. It's like they woke up-... and they were all fucking Tony Stark.

Martin Casado

The, the adopt- the universal adoption of this as a consumer technology and then bleeding into prosumer is, is, it exceeds anything I've ever-... I've ever experienced.

Aaron Levie

Immigration policy incentives and “what are we optimizing for?”Visa allocation via salary bands / pricing signalsConsultancies/body shops vs startups in tech labor marketsAI coding tools and background agents (Cursor, agent workflows)Measuring productivity: self-reporting, hidden gains, quality vs speedExpertise, judgment, and “human taste” as AI complementsPlatform shifts: incumbents, disruptors, and new non-software TAM

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