a16z Podcast Episode Summary

Episode Title: Marc Andreessen and Amjad Masad: English As the New Programming Language
Date: October 23, 2025
Host: Andreessen Horowitz (Marc Andreessen)
Guest: Amjad Masad (CEO & Founder, Replit)
Main Theme: How AI agents and natural language programming are revolutionizing software creation, the implications for coding, and the boundary between current and future intelligence.

Episode Overview

This episode explores the radical transformation of programming as AI agents increasingly enable people to build software using standard English—potentially making "English the new programming language." Marc Andreessen is joined by Amjad Masad, CEO of Replit, to discuss:

• How AI coding agents are changing what it means to code
• The dissolution of traditional code syntax requirements
• The rise of agents that can autonomously reason, execute, test, and iterate on code
• Technical breakthroughs behind these advances
• Reflections on the pace of AI development, AGI (Artificial General Intelligence), and the future of programming
• Amjad Masad’s personal journey from hacking his university database in Jordan to founding Replit

Key Discussion Points & Insights

1. The Dawning of English as the New Programming Language

• Opening Analogy: The Pace and Magic of AI

  • Marc reflects on the paradox of today’s AI: "We're dealing with magic here that we, I think, probably all would have thought was impossible five years ago or certainly 10 years ago. This is the most amazing technology ever. And it's moving really fast and yet we're still, like, really disappointed. Like, it's not moving fast enough." (00:00)
  • Amjad observes: "It is faster, but it's not at computer speed. Right what we expect computer speed to be." (00:18)

• Programming via English Prompts

  • Replit now enables users to build apps by simply typing ideas in English, removing the need for syntax and setup hassles.
  • "The prompt box is really open for you... you want to build a startup? You have an idea For a startup, I would start with a paragraph long kind of description of what I want to build. The agents will read that." —Amjad Masad (01:33)
  • "You just type in, I want to sell grapes online." —Marc Andreessen (02:08)

• Under the Hood: Stack Selection and Abstraction

  • Users can specify a preferred language/stack but Replit optimizes choices for their problem automatically.
  • The platform runs any language; syntax is no longer a key barrier.

2. Historical Context: From Machine Code to English

• Grace Hopper’s Vision

  • Amjad references Hopper’s hope "to get to a world where people are programming English... that's why I invented the compiler." (04:21)
  • "Instead of typing syntax, you're actually typing thoughts. Right. Which is what we ultimately want." —Amjad Masad (04:54)

• Programming Culture’s Resistance to Abstraction

  • An enduring pattern where older generations deride new abstractions as “sloppy” or lacking true machine understanding.
  • "The absolute irony is I was part of the JavaScript revolution... we got a lot of hate... And now those guys... are hating on this new wave. People never change." —Amjad Masad (06:11)

3. How AI Agents Work in Practice

• The User Experience

  • The agent provides a breakdown of tasks, sets up infrastructure, and iterates until a working app emerges, including browser-based self-testing.
  • "After it writes, the software spins up a browser, goes around and tests in the browser... it kind of iterates, kind of goes and fix the code." —Amjad Masad (06:43)
  • Full app deployment—including cloud and database setup—in minutes: "A kid can do it, a layered person can do it." (07:43)
  • Technical users can still inspect, tweak, or export the generated code.

• The Agent as the Real User

  • The Replit shift: the agent becomes the main “programmer” using human tools.
  • Infrastructure quirks emerged: "It’s because the AIs are sitting in the United States and so the programmer is actually in the United States." —Amjad Masad (09:23)

4. AI Agents and the Challenge of Coherence

• Maintaining Reasoning Over Time

  • Early agents would “spin out” after minutes; now, with improvements, agents can reason for hours.
  • "Sometime around last year, we maybe crossed a 3, 4, 5 minute mark. And it fell to us that, okay, we're on a path where long, you know, long horizon reasoning is getting solved." —Amjad Masad (11:53)
  • Innovations involve compressing “context” (the agent’s working memory) and verification/testing loops.

• Technical Breakthroughs: Reinforcement Learning

  • RL (reinforcement learning) enables agents to learn by verifying solutions, like fixing bugs in code and receiving rewards only for correct fixes.
  • "What reinforcement learning... gave us is the ability for the LLM to roll out what we call trajectories... step by step reasoning chain in order to reach a solution." —Amjad Masad (14:19)

• Verification as the Key to Progress

  • Multi-agent systems now test and summarize progress in a relay, enabling sustained, reliable output.
  • "If it founds a bug, it starts a new trajectory... that plus what the bug that we found, that's a prompt for a new trajectory. So you stack those on each other and you can go endlessly." —Amjad Masad (19:26)

5. The Rate, Limits, and Winners of AI Progress

• Rapid Doubling

  • Useful consecutive reasoning time for agents is doubling every several months, especially in programming and other “verifiable” domains.
  • "Agent one... can run for two minutes... Agent two... ran for 20 minutes, Agent three, 200 minutes." —Amjad Masad (17:11)

• Why Code Wins: The Verifiability Principle

  • Domains where right and wrong can be concretely tested (code, math, physics) are advancing fastest.
  • “The key is that it be a problem statement that there is a defined and verifiable answer.” —Marc (25:17)
  • Law or medical tasks are lagging, since outcomes are less concretely testable or inherently subjective.

6. The AGI (Artificial General Intelligence) Debate

• Are We on Track to AGI?

  • Despite stunning progress, AGI remains elusive, as transfer learning is still not emergent — improvements in one domain do not automatically generalize.
  • Amjad: "Are we on track to AGI or not? Because there are some ways that I can tell you it doesn't seem like we're on track to AGI because there doesn't seem to be transfer learning across these domains..." (33:29)
  • Marc’s skepticism: "My answer to that is like have you met people? And how many people do you know are able to do transfer learning? Not many." (35:55)

• AI’s ‘Local Maximum Trap’

  • Powerful, economically lucrative specialization can sap momentum from seeking truly general intelligence.
  • "We're in a local maximum trap where it's good enough for so much economically productive work. Yes. It relieves the pressure... to create the generalized answer." —Marc & Amjad (50:38)

7. Emotionality, Reasoning, and Diminishing Returns in LLMs

• Limitations of Current Models

  • GPT-5 and similar models have improved in verifiable tasks but may feel less human or emotionally resonant than prior iterations.
  • "GPT5 got good at verifiable domains. It didn't feel that much better at anything else." —Amjad (40:41)
  • Marc: "Well, but this is synthesizing knowledge, not creating new knowledge." (44:32)
  • Generative AI is phenomenal at synthesis and explanation but falls short on creativity or controversial reasoning.

• RLHF and Censorship/Taboo Effects

  • Certain controversial topics or reasoning chains remain inaccessible or stilted due to RLHF (Reinforcement Learning with Human Feedback) and bias constraints.

8. Amjad Masad’s Journey: From Jordan to Silicon Valley

• Early Exposure to Computing

  • Amjad’s father took on debt to purchase the first home computer in their neighborhood, planting the seeds for Amjad’s programming journey. (52:45)
  • Early entrepreneurial ventures included building and selling a management system for LAN gaming cafes as a teenage coder. (54:13)

• Hacking His Way Out of School

  • After being penalized for poor attendance, Amjad hacked his university database (with all the drama and ethical struggles of a true hacker story), ultimately confessing and being recruited to help secure the school’s systems.
  • Marc’s summary: “I think the moral of the story is if you can successfully hack into your school system... you deserve the grade and you deserve to graduate.” (69:40)

• Birth of Replit

  • Struggles with setting up development environments and seeing an opportunity for browser-based coding led to building the first prototypes for what became Replit—first for JavaScript, then progressively for other languages via browser-compiled runtimes.
  • Viral adoption occurred, especially after MOOC platforms (Coursera, Codecademy) began integrating Replit tech globally.

• Coming to the US

  • After initial resistance, Amjad moved to the US to work with Codecademy, receiving an O-1 visa after their persistence.
  • "When I watched Pirates of Silicon Valley." (60:46) — on when he first dreamt of moving to the U.S.

• Takeaway for the AI Age

  • "The traditional, more conformist path is paying less and less dividends... Kids coming up today should use all the tools available to chart their own paths." —Amjad Masad (69:57)

Notable Quotes & Moments with Timestamps

• On the Exponential Pace of AI, and its Irony

  • "We're dealing with magic here... This is the most amazing technology ever... it's like, maybe right on the verge of stalling out." —Marc Andreessen (00:00)

• On Programming in English

  • "The last thing we had to abstract away is code. I had this realization last year... syntax is still an issue. Syntax is just an unnatural thing for people. So ultimately, English is the programming language." —Amjad Masad (03:53)

• On Agents Becoming the Programmer

  • "... how much the actual user stopped being the human user and it's actually the agent programmer." —Amjad Masad (09:11)

• On AI Agent Reasoning Breakthrough

  • "We may have crossed a 3, 4, 5 minute mark. It felt to us that... long horizon reasoning is getting solved." —Amjad Masad (11:53)

• On Verification as the Lever for Progress

  • "You need a verifier in the loop. So that's why we spend all our time creating scaffolds to make it so that the agent can spin up a browser and do computer use style testing." —Amjad Masad (17:11)

• On the AGI Trap

  • "We're in a local maximum trap where it's good enough for so much economically productive work. Yes, it relieves the pressure in the system to create the generalized answer." —Marc Andreessen and Amjad Masad (50:38)

• On Hacking Out of School

  • "So I spent two weeks just going mad trying to hack into the university database... finally I found a way... but I didn't want to risk it. So I went to my neighbor..." —Amjad Masad (62:28)

• The Spider-Man Advice

  • "He gave me a Spider man line at the time. It's like, with the great power comes great responsibility, and you have a great power. And it really affected me." —Amjad Masad (66:09)

• On the Changing Landscape for the Next Generation

  • "The traditional sort of more conformist path is paying less and less dividends... kids coming up today should use all the tools available to be able to discover and chart their own paths." —Amjad Masad (69:57)

Key Timestamps of Major Segments

• 00:00 – 02:30: The magic and limitations of current AI pace
• 03:53 – 06:37: How Replit enables English-based programming and the historical context
• 06:43 – 10:22: What the AI agent actually does for the user in Replit
• 11:25 – 14:19: The problem of coherence and the breakthrough of reinforcement learning
• 17:11 – 20:40: Verification loops, agent performance, and multi-agent relay systems
• 25:17 – 30:05: Why code/math move fast (verifiability), slow progress in “soft” fields like law/medicine
• 33:18 – 35:55: The “local maximum trap” and the elusive nature of transfer learning/AGI
• 40:41 – 45:46: Diminishing returns with GPT-5 and limits of current LLMs’ “humanity” or creativity
• 52:45 – 56:23: Amjad’s early childhood, entrepreneurship, and discovery of browser-based programming
• 60:36 – 69:57: The hacker story: changing grades, being forgiven, and lessons for the AI age

Final Takeaways

• English is rapidly becoming the universal interface for programming, thanks to advances in AI agents that reason, build, and iterate like hyper-productive human programmers.
• The fastest progress in AI is seen where outcomes can be crisply tested and verified, such as coding and math.
• Despite dazzling practical advances, “true” AGI and cross-domain transfer remain open problems, with economic incentives potentially locking industry into specialized “local maxima.”
• Massad's personal journey—from hacking his way out of university requirements in Jordan to inspiring the browser-based, AI-powered revolution in programming—underscores the value of curiosity, risk-taking, and challenging the status quo.