Beyond Coding

How do new staff engineers build judgment without the years of experience that used to come with the role? Mallika Rao, engineering leader in big tech, explains why the data-structures-and-algorithms foundation everyone was trained on is no longer enough on its own, and where the complexity has actually shifted now that AI writes the implementation.
In this video, we cover:
Why "how does AI affect engineers" is the wrong question, and what to ask instead
Rehearsing multiple futures: what judgment looks like in a staff engineer
The case method: building judgment from incident reports and system design history instead of waiting years for it
Cognitive coordination, code review load, and the surprise ask for more meetings at staff level
Tiger teams vs scaled teams, trust as architecture, and building evals from a spreadsheet
Splitting planning from execution so engineers stop falling behind with agents
Taste vs judgment, and how to build both outside of software
If you've just made staff, or you're about to, this conversation gives you a frame for what the level actually demands now and how to grow into it faster than the old apprenticeship allowed.

Timestamps:
00:00:00 - How AI Is Changing Senior Engineering Careers
00:00:41 - Why "How Does AI Affect Engineers" Is the Wrong Question
00:03:26 - What Judgment Actually Is: Rehearsing Multiple Futures
00:05:24 - Why Data Structures and Algorithms Are No Longer Enough
00:07:22 - Learning Judgment From Incident Reports Like the 2017 S3 Outage
00:11:13 - The New Staff Engineer's Core Challenge: Cognitive Coordination
00:14:48 - What Managers, Universities, and Shakespeare Each Owe You
00:17:55 - Code Review Load, Meeting Notes, and the Surprise Ask for More Meetings
00:23:59 - Trust as Architecture: Why Evals Started as a Spreadsheet
00:27:09 - Tiger Teams vs Big Teams: Product Managers Reviewing Code
00:32:39 - Why Some Engineers Can't Keep Up With Agents
00:35:46 - Local AI Champions and Splitting Planning From Execution
00:38:38 - Go Deep or Go Broad? Search in a World of Agents
00:44:12 - Taste vs Judgment: Thinking in 50 Layers

Guest: Mallika Rao, engineering leader in big tech.
Rehearshing the Future framework
 If by Rudyard Kipling

How does Amazon build its agentic AI? Michael Giannangeli, Head of Product for Amazon Nova and Agentic AI, breaks down evals, RL gyms, and model routing.
He also explains why the bottleneck in software has shifted away from engineering hours and what takes its place.
In this video, we cover:
The eval lifecycle: building from real failure modes, saturation, and why 100% means delete
RL gyms: training models on real environments like migrations, DevOps, and pen testing
Model routing, cost-per-token trade-offs, and why routing isn't solved
The agent stack of an Amazon product lead: Claude Code, Codex, and Kiro
Autonomous migrations, trust, and how much human-in-the-loop survives
For engineers and product people building with AI agents who want to see how a frontier lab actually closes its feedback loops.
Recorded at the AI4 conference 2026.

Timestamps:
00:00:00 - Intro
00:00:36 - The Agents an Amazon Product Lead Uses Daily
00:03:36 - Why Nobody's Heard of Amazon Nova
00:04:55 - Model Costs and the Routing Problem
00:08:10 - Why Building Good Evals Is So Hard
00:10:05 - When Evals Saturate and Get Deleted
00:12:17 - Turning Real Failure Modes Into Hundreds of Evals
00:15:26 - Improving Models Without Training on Customer Data
00:18:26 - If Everyone Uses Agents, You Need Agents
00:20:22 - The Bottleneck Is No Longer Engineering Hours
00:23:20 - Ship Fast to Validate the Right Thing
00:26:44 - Staying at the Frontier Amid Constant Noise
00:29:37 - Spend 10-20% of Your Time Experimenting
00:32:54 - RL Gyms: How Models Learn From Failure
00:37:09 - Will Migrations Become Fully Autonomous?

Guest: Michael Giannangeli - Head of Product, Agentic AI & Amazon Nova at Amazon
#AmazonNova #AgenticAI #AIEngineering

AI is changing what developers build, but code alone is no longer enough to prove what you can do. Wes Bos explains why engineers need to solve problems beyond syntax, how agent workflows are reshaping software development, and what still requires human thinking.
In this conversation:
The limits of generative UI and AI-generated design
Agent loops, harnesses, and cheaper AI models
The rising cost of AI coding and the case for local hardware
Why developer education is shifting from syntax to problem-solving
Personal branding, conferences, newsletters, and AI-generated content
For developers navigating AI-assisted coding, this episode explores the skills and signals that still help you stand out.
This podcast was recorded at JSNation, the key web dev conference.

OUTLINE
00:00:00 - Code Is Not Enough for Developers
00:00:32 - Why Generative UI Still Feels Unfinished
00:04:35 - How Agent Loops Improve AI Coding
00:07:06 - When Agent Workflows Become Standard Tools
00:08:19 - Are Cheaper AI Models Good Enough?
00:10:44 - Can AI Coding Costs Stay Sustainable?
00:12:24 - What Engineers Need To Learn Now
00:14:23 - Why Fundamentals Matter Beyond Syntax
00:15:34 - How Non-Coders Are Building Production Tools
00:16:21 - Why In-Person Conferences Still Matter
00:18:11 - Personal Branding When Code Isn't Enough
00:20:37 - Can Newsletters Beat The Attention Crisis?
00:22:02 - Why AI-Generated Content Feels Insulting
00:24:12 - Use AI To Scaffold, Not Think

Answering engineer questions on AI pressure, career growth, product thinking and impact. Including the production incident I'm glad happened, and the mindset I refuse to accept when things break.

In this video, we cover:
- Whether managers are really demanding more output because of AI
- Balancing fundamentals with AI coding tools and agents early in your career
- Specialist vs generalist and when to lean into each
- Visibility, personal branding and who gets credit for your work
- Product thinking, evaluating impact and what I got wrong about content being king

For software engineers at any level who want honest answers on career strategy in the agent era, from someone doing both engineering and product.

Timestamps:
00:00:00 - How to Spot the Next Big Thing
00:03:15 - The Saying I Hate Most
00:04:27 - The Production Mistake I'm Glad I Made
00:07:32 - Are Managers Demanding More Because of AI?
00:13:39 - Learning Fundamentals vs AI Coding Tools
00:19:00 - Will AI Ever Get Good at Distributed Systems?
00:20:51 - Specialist vs Generalist: When to Lean In
00:26:35 - How to Become More Visible in Your Org
00:31:49 - I Was Wrong: Content Isn't King
00:35:03 - Workflows, Priorities and Hiring an Editor
00:37:08 - What Being a Force Multiplier Really Means
00:41:26 - How to Evaluate What's Worth Building
00:45:01 - Product Thinking Without Years of Experience
00:48:13 - Energy Management, Curiosity and Defining Success
00:54:21 - Hair Talk

How do you prove AI is shipping more features? Amos Haviv leads the Developer Workflow teams at Booking.com, supporting 4000 engineers operating 8000 repos.
Everybody is burning through their AI budget right now and almost nobody can answer what it bought them. Amos can, because his team spent four years building an event system to debug their own SDLC before AI upped the urgency.
In this video, we cover:
Why verification is the bottleneck right now, and where it moves next
Building an event store that separates KTLO from real feature delivery
Why static dashboards create the metric they measure, and the cobra story behind it
Agent cost, model routing, and why Booking ignores token maxing entirely
Running a developer survey with a 92% response rate across 3k+ engineers
Who should own skills and MCPs: a central platform team or the domain experts?
For platform engineers, engineering leaders, and anyone being asked to prove ROI on AI tooling this quarter.

Timestamps:
00:00:00 - Everyone is burning through their budget
00:00:32 - Verification Is the Bottleneck Every Team Hit
00:03:35 - 4,000 Engineers and 8,000 Repos at Booking.com
00:06:48 - Why Copying Google and OpenAI Will Break You
00:09:21 - Verification Is a Stack of Agents, Not One Review
00:13:27 - Cost Is Becoming a Bottleneck of Its Own
00:17:14 - Was the Internet a Bubble? What That Teaches Us
00:25:32 - What Working With the Frontier Labs Looks Like
00:28:26 - Debugging the SDLC With Four Years of Event Data
00:30:24 - Do Engineers Using AI Actually Ship More Features?
00:37:13 - Where to Start If You Measure Nothing Today
00:45:01 - The Cobra Effect: When a Metric Becomes a Target
00:52:23 - Everyone Is a Builder Now, and Everything Needs Support
01:01:21 - Is AI Turning Every Engineer Into a Manager?
01:03:46 - The Developer Survey With a 92% Response Rate
01:10:09 - Who Owns Skills, MCPs, and the Enterprise Harness
01:17:46 - Great Developer Experience Is High Velocity

Mentioned in the episode:
High Output Management by Andy Grove
The Sovereign Individual (1997)
The story of General Magic

Views expressed are Amos's own and do not represent Booking.com.
#AI #SoftwareEngineering #DeveloperExperience