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Kliv in i en oändlig värld av stories

Lyssna när som helst, var som helst

Gergely Orosz

Software engineering at Big Tech and startups, from the inside. Deepdives with experienced engineers and tech professionals who share their hard-earned lessons, interesting stories and advice they have on building software. 

Especially relevant for software engineers and engineering leaders: useful for those working in tech. 

newsletter.pragmaticengineer.com

The Pragmatic Engineer

Brought to You By:

•⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. 

•⁠ Linear – The system for modern product development. 

—

Addy Osmani is Head of Chrome Developer Experience at Google, where he leads teams focused on improving performance, tooling, and the overall developer experience for building on the web. If you’ve ever opened Chrome’s Developer Tools bar, you’ve definitely used features Addy has built. He’s also the author of several books, including his latest, Beyond Vibe Coding, which explores how AI is changing software development.

In this episode of The Pragmatic Engineer, I sit down with Addy to discuss how AI is reshaping software engineering workflows, the tradeoffs between speed and quality, and why understanding generated code remains critical. We dive into his article The 70% Problem, which explains why AI tools accelerate development but struggle with the final 30% of software quality—and why this last 30% is tackled easily by software engineers who understand how the system actually works.

—

Timestamps

(00:00) Intro

(02:17) Vibe coding vs. AI-assisted engineering

(06:07) How Addy uses AI tools

(13:10) Addy’s learnings about applying AI for development

(18:47) Addy’s favorite tools

(22:15) The 70% Problem

(28:15) Tactics for efficient LLM usage

(32:58) How AI tools evolved

(34:29) The case for keeping expectations low and control high

(38:05) Autonomous agents and working with them

(42:49) How the EM and PM role changes with AI

(47:14) The rise of new roles and shifts in developer education

(48:11) The importance of critical thinking when working with AI

(54:08) LLMs as a tool for learning

(1:03:50) Rapid questions

—

The Pragmatic Engineer deepdives relevant for this episode:

•⁠ Vibe Coding as a software engineer

•⁠ How AI-assisted coding will change software engineering: hard truths

•⁠ AI Engineering in the real world

•⁠ The AI Engineering stack

•⁠ How Claude Code is built

—

Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
 

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Beyond Vibe Coding with Addy Osmani

Brought to You By:

•⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. Something interesting is happening with the latest generation of tech giants. Rather than building advanced experimentation tools themselves, companies like Anthropic, Figma, Notion and a bunch of others… are just using Statsig. Statsig has rebuilt this entire suite of data tools that was available at maybe 10 or 15 giants until now. Check out Statsig.

•⁠ Linear – The system for modern product development. Linear is just so fast to use – and it enables velocity in product workflows. Companies like Perplexity and OpenAI have already switched over, because simplicity scales. Go ahead and check out Linear and see why it feels like a breeze to use.

—

What is it really like to be an engineer at Google?

In this special deep dive episode, we unpack how engineering at Google actually works. We spent months researching the engineering culture of the search giant, and talked with 20+ current and former Googlers to bring you this deepdive with Elin Nilsson, tech industry researcher for The Pragmatic Engineer and a former Google intern.

Google has always been an engineering-driven organization. We talk about its custom stack and tools, the design-doc culture, and the performance and promotion systems that define career growth. We also explore the culture that feels built for engineers: generous perks, a surprisingly light on-call setup often considered the best in the industry, and a deep focus on solving technical problems at scale.

If you are thinking about applying to Google or are curious about how the company’s engineering culture has evolved, this episode takes a clear look at what it was like to work at Google in the past versus today, and who is a good fit for today’s Google.

Jump to interesting parts:

(13:50) Tech stack

(1:05:08) Performance reviews (GRAD)

(2:07:03) The culture of continuously rewriting things

—

Timestamps

(00:00) Intro

(01:44) Stats about Google

(11:41) The shared culture across Google

(13:50) Tech stack

(34:33) Internal developer tools and monorepo

(43:17) The downsides of having so many internal tools at Google

(45:29) Perks

(55:37) Engineering roles

(1:02:32) Levels at Google 

(1:05:08) Performance reviews (GRAD)

(1:13:05) Readability

(1:16:18) Promotions

(1:25:46) Design docs

(1:32:30) OKRs

(1:44:43) Googlers, Nooglers, ReGooglers

(1:57:27) Google Cloud

(2:03:49) Internal transfers

(2:07:03) Rewrites

(2:10:19) Open source

(2:14:57) Culture shift

(2:31:10) Making the most of Google, as an engineer

(2:39:25) Landing a job at Google

—

The Pragmatic Engineer deepdives relevant for this episode:

•⁠ Inside Google’s engineering culture

•⁠ Oncall at Google

•⁠ Performance calibrations at tech companies

•⁠ Promotions and tooling at Google

•⁠ How Kubernetes is built

•⁠ The man behind the Big Tech comics: Google cartoonist Manu Cornet

—

Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
 

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Google’s engineering culture

Brought to You By:

•⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. Most teams end up in this situation: ship a feature to 10% of users, wait a week, check three different tools, try to correlate the data, and you’re still unsure if it worked. The problem is that each tool has its own user identification and segmentation logic. Statsig solved this problem by building everything within a unified platform. Check out Statsig.

•⁠ Linear – The system for modern product development. In the episode, Armin talks about how he uses an army of “AI interns” at his startup. With Linear, you can easily do the same: Linear’s Cursor integration lets you add Cursor as an agent to your workspace. This agent then works alongside you and your team to make code changes or answer questions. You’ve got to try it out: give Linear a spin and see how it integrates with Cursor.

—

Armin Ronacher is the creator of the Flask framework for Python, was one of the first engineers hired at Sentry, and now the co-founder of a new startup. He has spent his career thinking deeply about how tools shape the way we build software.

In this episode of The Pragmatic Engineer Podcast, he joins me to talk about how programming languages compare, why Rust may not be ideal for early-stage startups, and how AI tools are transforming the way engineers work. Armin shares his view on what continues to make certain languages worth learning, and how agentic coding is driving people to work more, sometimes to their own detriment. 

We also discuss: 

• Why the Python 2 to 3 migration was more challenging than expected

• How Python, Go, Rust, and TypeScript stack up for different kinds of work 

• How AI tools are changing the need for unified codebases

• What Armin learned about error handling from his time at Sentry

• And much more 

Jump to interesting parts:

• (06:53) How Python, Go, and Rust stack up and when to use each one

• (30:08) Why Armin has changed his mind about AI tools

• (50:32) How important are language choices from an error-handling perspective?

—

Timestamps

(00:00) Intro

(01:34) Why the Python 2 to 3 migration created so many challenges

(06:53) How Python, Go, and Rust stack up and when to use each one

(08:35) The friction points that make Rust a bad fit for startups

(12:28) How Armin thinks about choosing a language for building a startup

(22:33) How AI is impacting the need for unified code bases

(24:19) The use cases where AI coding tools excel 

(30:08) Why Armin has changed his mind about AI tools

(38:04) Why different programming languages still matter but may not in an AI-driven future

(42:13) Why agentic coding is driving people to work more and why that’s not always good

(47:41) Armin’s error-handling takeaways from working at Sentry 

(50:32) How important is language choice from an error-handling perspective

(56:02) Why the current SDLC still doesn’t prioritize error handling 

(1:04:18) The challenges language designers face 

(1:05:40) What Armin learned from working in startups and who thrives in that environment

(1:11:39) Rapid fire round

—

The Pragmatic Engineer deepdives relevant for this episode:

—

Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
 

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Python, Go, Rust, TypeScript and AI with Armin Ronacher

Brought to You By:

•⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. Statsig built a complete set of data tools that allow engineering teams to measure the impact of their work. This toolkit is SO valuable to so many teams, that OpenAI - who was a huge user of Statsig - decided to acquire the company, the news announced last week. Talk about validation! Check out Statsig.

•⁠ Linear – The system for modern product development. Here’s an interesting story: OpenAI switched to Linear as a way to establish a shared vocabulary between teams. Every project now follows the same lifecycle, uses the same labels, and moves through the same states. Try Linear for yourself.

—

What does it take to do well at a hyper-growth company? In this episode of The Pragmatic Engineer, I sit down with Charles-Axel Dein, one of the first engineers at Uber, who later hired me there. Since then, he’s gone on to work at CloudKitchens. He’s also been maintaining the popular Professional programming reading list GitHub repo for 15 years, where he collects articles that made him a better programmer. 

In our conversation, we dig into what it’s really like to work inside companies that grow rapidly in scale and headcount. Charles shares what he’s learned about personal productivity, project management, incidents, interviewing, plus how to build flexible skills that hold up in fast-moving environments. 

Jump to interesting parts:

• 10:41 – the reality of working inside a hyperscale company

• 41:10 – the traits of high-performing engineers

• 1:03:31 – Charles’ advice for getting hired in today’s job market

We also discuss:

• How to spot the signs of hypergrowth (and when it’s slowing down)

• What sets high-performing engineers apart beyond shipping

• Charles’s personal productivity tips, favorite reads, and how he uses reading to uplevel his skills

• Strategic tips for building your resume and interviewing 

• How imposter syndrome is normal, and how leaning into it helps you grow

• And much more!

If you’re at a fast-growing company, considering joining one, or looking to land your next role, you won’t want to miss this practical advice on hiring, interviewing, productivity, leadership, and career growth.

—

Timestamps

(00:00) Intro

(04:04) Early days at Uber as engineer #20

(08:12) CloudKitchens’ similarities with Uber

(10:41) The reality of working at a hyperscale company

(19:05) Tenancies and how Uber deployed new features

(22:14) How CloudKitchens handles incidents

(26:57) Hiring during fast-growth

(34:09) Avoiding burnout

(38:55) The popular Professional programming reading list repo

(41:10) The traits of high-performing engineers 

(53:22) Project management tactics

(1:03:31) How to get hired as a software engineer

(1:12:26) How AI is changing hiring

(1:19:26) Unexpected ways to thrive in fast-paced environments

(1:20:45) Dealing with imposter syndrome 

(1:22:48) Book recommendations 

(1:27:26) The problem with survival bias 

(1:32:44) AI’s impact on software development 

(1:42:28) Rapid fire round

—

The Pragmatic Engineer deepdives relevant for this episode:

•⁠ Software engineers leading projects

•⁠ The Platform and Program split at Uber

•⁠ Inside Uber’s move to the Cloud

•⁠ How Uber built its observability platform

•⁠ From Software Engineer to AI Engineer – with Janvi Kalra

—

Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
 

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Hypergrowth startups: Uber and CloudKitchens with Charles-Axel Dein

Brought to You By:

•⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. Statsig built a complete set of data tools that allow engineering teams to measure the impact of their work. This toolkit is SO valuable to so many teams, that OpenAI - who was a huge user of Statsig - decided to acquire the company, the news announced last week. Talk about validation! Check out Statsig.

•⁠ Linear – The system for modern product development. Here’s an interesting story: OpenAI switched to Linear as a way to establish a shared vocabulary between teams. Every project now follows the same lifecycle, uses the same labels, and moves through the same states. Try Linear for yourself.

—

The Pragmatic Engineer Podcast is back with the Fall 2025 season. Expect new episodes to be published on most Wednesdays, looking ahead.

Code Complete is one of the most enduring books on software engineering. Steve McConnell wrote the 900-page handbook just five years into his career, capturing what he wished he’d known when starting out. Decades later, the lessons remain relevant, and Code Complete remains a best-seller.

In this episode, we talk about what has aged well, what needed updating in the second edition, and the broader career principles Steve has developed along the way. From his “career pyramid” model to his critique of “lily pad hopping,” and why periods of working in fast-paced, all-in environments can be so rewarding, the emphasis throughout is on taking ownership of your career and making deliberate choices.

We also discuss:

• Top-down vs. bottom-up design and why most engineers default to one approach

• Why rewriting code multiple times makes it better

• How taking a year off to write Code Complete crystallized key lessons

• The 3 areas software designers need to understand, and why focusing only on technology may be the most limiting 

• And much more!

Steve rarely gives interviews, so I hope you enjoy this conversation, which we recorded in Seattle.

—

Timestamps

(00:00) Intro

(01:31) How and why Steve wrote Code Complete

(08:08) What code construction is and how it differs from software development

(11:12) Top-down vs. bottom-up design approach

(14:46) Why design documents frustrate some engineers

(16:50) The case for rewriting everything three times

(20:15) Steve’s career before and after Code Complete

(27:47) Steve’s career advice

(44:38) Three areas software designers need to understand

(48:07) Advice when becoming a manager, as a developer

(53:02) The importance of managing your energy

(57:07) Early Microsoft and why startups are a culture of intense focus

(1:04:14) What changed in the second edition of Code Complete 

(1:10:50) AI’s impact on software development: Steve’s take

(1:17:45) Code reviews and GenAI

(1:19:58) Why engineers are becoming more full-stack 

(1:21:40) Could AI be the exception to “no silver bullets?”

(1:26:31) Steve’s advice for engineers on building a meaningful career

—

The Pragmatic Engineer deepdives relevant for this episode:

• What changed in 50 years of computing

• The past and future of modern backend practices

• The Philosophy of Software Design – with John Ousterhout

• AI tools for software engineers, but without the hype – with Simon Willison (co-creator of Django) 

• TDD, AI agents and coding – with Kent Beck

—

Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
 

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Code Complete with Steve McConnell

Brought to You By:

•⁠ WorkOS — The modern identity platform for B2B SaaS.

•⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more.

• Sonar — Code quality and code security for ALL code.

—

In this episode of The Pragmatic Engineer, I sit down with Peter Walker, Head of Insights at Carta, to break down how venture capital and startups themselves are changing.

We go deep on the numbers: why fewer companies are getting funded despite record VC investment levels, how hiring has shifted dramatically since 2021, and why solo founders are on the rise even though most VCs still prefer teams. We also unpack the growing emphasis on ARR per FTE, what actually happens in bridge and down rounds, and why the time between fundraising rounds has stretched far beyond the old 18-month cycle.

We cover what all this means for engineers: what to ask before joining a startup, how to interpret valuation trends, and what kind of advisor roles startups are actually looking for.

If you work at a startup, are considering joining one, or just want a clearer picture of how venture-backed companies operate today, this episode is for you.

—

Timestamps

(00:00) Intro

(01:21) How venture capital works and the goal of VC-backed startups

(03:10) Venture vs. non-venture backed businesses 

(05:59) Why venture-backed companies prioritize growth over profitability

(09:46) A look at the current health of venture capital 

(13:19) The hiring slowdown at startups

(16:00) ARR per FTE: The new metric VCs care about

(21:50) Priced seed rounds vs. SAFEs 

(24:48) Why some founders are incentivized to raise at high valuations

(29:31) What a bridge round is and why they can signal trouble

(33:15) Down rounds and how optics can make or break startups 

(36:47) Why working at startups offers more ownership and learning

(37:47) What the data shows about raising money in the summer

(41:45) The length of time it takes to close a VC deal

(44:29) How AI is reshaping startup formation, team size, and funding trends

(48:11) Why VCs don’t like solo founders

(50:06) How employee equity (ESOPs) work

(53:50) Why acquisition payouts are often smaller than employees expect

(55:06) Deep tech vs. software startups:

(57:25) Startup advisors: What they do, how much equity they get

(1:02:08) Why time between rounds is increasing and what that means

(1:03:57) Why it’s getting harder to get from Seed to Series A 

(1:06:47) A case for quitting (sometimes) 

(1:11:40) How to evaluate a startup before joining as an engineer

(1:13:22) The skills engineers need to thrive in a startup environment

(1:16:04) Rapid fire round

—

The Pragmatic Engineer deepdives relevant for this episode:

—

See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠

—

Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
 

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

The state of VC within software and AI startups – with Peter Walker

Supported by Our Partners

•⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more.

• Graphite — The AI developer productivity platform.

—

There’s no shortage of bold claims about AI and developer productivity, but how do you separate signal from noise?

In this episode of The Pragmatic Engineer, I’m joined by Laura Tacho, CTO at DX, to cut through the hype and share how well (or not) AI tools are actually working inside engineering orgs. Laura shares insights from DX’s research across 180+ companies, including surprising findings about where developers save the most time, why devs don’t use AI at all, and what kinds of rollouts lead to meaningful impact.

We also discuss: 

• The problem with oversimplified AI headlines and how to think more critically about them

• An overview of the DX AI Measurement framework

• Learnings from Booking.com’s AI tool rollout

• Common reasons developers aren’t using AI tools

• Why using AI tools sometimes decreases developer satisfaction

• Surprising results from DX’s 180+ company study

• How AI-generated documentation differs from human-written docs

• Why measuring developer experience before rolling out AI is essential

• Why Laura thinks roadmaps are on their way out

• And much more!

—

Timestamps

(00:00) Intro

(01:23) Laura’s take on AI overhyped headlines 

(10:46) Common questions Laura gets about AI implementation 

(11:49) How to measure AI’s impact 

(15:12) Why acceptance rate and lines of code are not sufficient measures of productivity

(18:03) The Booking.com case study

(20:37) Why some employees are not using AI 

(24:20) What developers are actually saving time on 

(29:14) What happens with the time savings

(31:10) The surprising results from the DORA report on AI in engineering 

(33:44) A hypothesis around AI and flow state and the importance of talking to developers

(35:59) What’s working in AI architecture 

(42:22) Learnings from WorkHuman’s adoption of Copilot 

(47:00) Consumption-based pricing, and the difficulty of allocating resources to AI 

(52:01) What DX Core 4 measures 

(55:32) The best outcomes of implementing AI 

(58:56) Why highly regulated industries are having the best results with AI rollout

(1:00:30) Indeed’s structured AI rollout 

(1:04:22) Why migrations might be a good use case for AI (and a tip for doing it!) 

(1:07:30) Advice for engineering leads looking to get better at AI tooling and implementation 

(1:08:49) Rapid fire round

—

The Pragmatic Engineer deepdives relevant for this episode:

• AI Engineering in the real world

• Measuring software engineering productivity

• The AI Engineering stack

• A new way to measure developer productivity – from the creators of DORA and SPACE

—

See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠

—

Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
 

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Measuring the impact of AI on software engineering – with Laura Tacho

Supported by Our Partners

•⁠ WorkOS — The modern identity platform for B2B SaaS.

•⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more.

•⁠ Sonar — Code quality and code security for ALL code.

—

Steve Yegge⁠ is known for his writing and “rants”, including the famous “Google Platforms Rant” and the evergreen “Get that job at Google” post. He spent 7 years at Amazon and 13 at Google, as well as some time at Grab before briefly retiring from tech. Now out of retirement, he’s building AI developer tools at Sourcegraph—drawn back by the excitement of working with LLMs. He’s currently writing the book Vibe Coding: Building Production-Grade Software With GenAI, Chat, Agents, and Beyond.

In this episode of The Pragmatic Engineer, I sat down with Steve in Seattle to talk about why Google consistently failed at building platforms, why AI coding feels easy but is hard to master, and why a new role, the AI Fixer, is emerging. We also dig into why he’s so energized by today’s AI tools, and how they’re changing the way software gets built.

We also discuss: 

• The “interview anti-loop” at Google and the problems with interviews

• An inside look at how Amazon operated in the early days before microservices 

• What Steve liked about working at Grab

• Reflecting on the Google platforms rant and why Steve thinks Google is still terrible at building platforms

• Why Steve came out of retirement

• The emerging role of the “AI Fixer” in engineering teams

• How AI-assisted coding is deceptively simple, but extremely difficult to steer

• Steve’s advice for using AI coding tools and overcoming common challenges

• Predictions about the future of developer productivity

• A case for AI creating a real meritocracy 

• And much more!

—

Timestamps

(00:00) Intro

(04:55) An explanation of the interview anti-loop at Google and the shortcomings of interviews

(07:44) Work trials and why entry-level jobs aren’t posted for big tech companies

(09:50) An overview of the difficult process of landing a job as a software engineer

(15:48) Steve’s thoughts on Grab and why he loved it

(20:22) Insights from the Google platforms rant that was picked up by TechCrunch

(27:44) The impact of the Google platforms rant

(29:40) What Steve discovered about print ads not working for Google 

(31:48) What went wrong with Google+ and Wave

(35:04) How Amazon has changed and what Google is doing wrong

(42:50) Why Steve came out of retirement 

(45:16) Insights from “the death of the junior developer” and the impact of AI

(53:20) The new role Steve predicts will emerge 

(54:52) Changing business cycles

(56:08) Steve’s new book about vibe coding and Gergely’s experience 

(59:24) Reasons people struggle with AI tools

(1:02:36) What will developer productivity look like in the future

(1:05:10) The cost of using coding agents 

(1:07:08) Steve’s advice for vibe coding

(1:09:42) How Steve used AI tools to work on his game Wyvern 

(1:15:00) Why Steve thinks there will actually be more jobs for developers 

(1:18:29) A comparison between game engines and AI tools

(1:21:13) Why you need to learn AI now

(1:30:08) Rapid fire round

—

The Pragmatic Engineer deepdives relevant for this episode:

•⁠ The full circle of developer productivity with Steve Yegge

•⁠ Inside Amazon’s engineering culture

•⁠ Vibe coding as a software engineer

•⁠ AI engineering in the real world

•⁠ The AI Engineering stack

•⁠ Inside Sourcegraph’s engineering culture—

See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠

—

Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
 

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Amazon, Google and Vibe Coding with Steve Yegge

Supported by Our Partners

•⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more.

• Graphite — The AI developer productivity platform. 

• Augment Code — AI coding assistant that pro engineering teams love.

—

Steve Huynh spent 17 years at Amazon, including four as a Principal Engineer. In this episode of The Pragmatic Engineer, I join Steve in his studio for a deep dive into what the Principal role actually involves, why the path from Senior to Principal is so tough, and how even strong engineers can get stuck. Not because they’re unqualified, but because the bar is exceptionally high.

We discuss what’s expected at the Principal level, the kind of work that matters most, and the trade-offs that come with the title. Steve also shares how Amazon’s internal policies shaped his trajectory, and what made the Principal Engineer community one of the most rewarding parts of his time at the company.

We also go into: 

• Why being promoted from Senior to Principal is one of the hardest jumps in tech

• How Amazon’s freedom of movement policy helped Steve work across multiple teams, from Kindle to Prime Video

• The scale of Amazon: handling 10k–100k+ requests per second and what that means for engineering

• Why latency became a company-wide obsession—and the research that tied it directly to revenue

• Why companies should start with a monolith, and what led Amazon to adopt microservices

• What makes the Principal Engineering community so special 

• Amazon’s culture of learning from its mistakes, including COEs (correction of errors) 

• The pros and cons of the Principal Engineer role

• What Steve loves about the leadership principles at Amazon

• Amazon’s intense writing culture and 6-pager format 

• Why Amazon patents software and what that process looks like

• And much more!

—

Timestamps

(00:00) Intro

(01:11) What Steve worked on at Amazon, including Kindle, Prime Video, and payments

(04:38) How Steve was able to work on so many teams at Amazon 

(09:12) An overview of the scale of Amazon and the dependency chain

(16:40) Amazon’s focus on latency and the tradeoffs they make to keep latency low at scale

(26:00) Why companies should start with a monolith 

(26:44) The structure of engineering at Amazon and why Amazon’s Principal is so hard to reach

(30:44) The Principal Engineering community at Amazon

(36:06) The learning benefits of working for a tech giant 

(38:44) Five challenges of being a Principal Engineer at Amazon

(49:50) The types of managing work you have to do as a Principal Engineer 

(51:47) The pros and cons of the Principal Engineer role 

(54:59) What Steve loves about Amazon’s leadership principles

(59:15) Amazon’s intense focus on writing 

(1:01:11) Patents at Amazon 

(1:07:58) Rapid fire round

—

The Pragmatic Engineer deepdives relevant for this episode:

•⁠ Inside Amazon’s engineering culture

—

See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠

—

Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
 

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

What is a Principal Engineer at Amazon? With Steve Huynh

Supported by Our Partners

•⁠ WorkOS — The modern identity platform for B2B SaaS.

•⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more.

• Sonar — Code quality and code security for ALL code. 

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What happens when a company goes all in on AI?

At Shopify, engineers are expected to utilize AI tools, and they’ve been doing so for longer than most. Thanks to early access to models from GitHub Copilot, OpenAI, and Anthropic, the company has had a head start in figuring out what works.

In this live episode from LDX3 in London, I spoke with Farhan Thawar, VP of Engineering, about how Shopify is building with AI across the entire stack. We cover the company’s internal LLM proxy, its policy of unlimited token usage, and how interns help push the boundaries of what’s possible.

In this episode, we cover:

• How Shopify works closely with AI labs

• The story behind Shopify’s recent Code Red

• How non-engineering teams are using Cursor for vibecoding

• Tobi Lütke’s viral memo and Shopify’s expectations around AI

• A look inside Shopify’s LLM proxy—used for privacy, token tracking, and more

• Why Shopify places no limit on AI token spending 

• Why AI-first isn’t about reducing headcount—and why Shopify is hiring 1,000 interns

• How Shopify’s engineering department operates and what’s changed since adopting AI tooling

• Farhan’s advice for integrating AI into your workflow

• And much more!

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Timestamps

(00:00) Intro

(02:07) Shopify’s philosophy: “hire smart people and pair with them on problems”

(06:22) How Shopify works with top AI labs 

(08:50) The recent Code Red at Shopify

(10:47) How Shopify became early users of GitHub Copilot and their pivot to trying multiple tools

(12:49) The surprising ways non-engineering teams at Shopify are using Cursor

(14:53) Why you have to understand code to submit a PR at Shopify

(16:42) AI tools' impact on SaaS 

(19:50) Tobi Lütke’s AI memo

(21:46) Shopify’s LLM proxy and how they protect their privacy

(23:00) How Shopify utilizes MCPs

(26:59) Why AI tools aren’t the place to pinch pennies

(30:02) Farhan’s projects and favorite AI tools

(32:50) Why AI-first isn’t about freezing headcount and the value of hiring interns

(36:20) How Shopify’s engineering department operates, including internal tools

(40:31) Why Shopify added coding interviews for director-level and above hires

(43:40) What has changed since Spotify added AI tooling 

(44:40) Farhan’s advice for implementing AI tools

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The Pragmatic Engineer deepdives relevant for this episode:

• How Shopify built its Live Globe for Black Friday

• Inside Shopify's leveling split

• Real-world engineering challenges: building Cursor

• How Anthropic built Artifacts

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See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠

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Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email podcast@pragmaticengineer.com.
 

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Beyond Vibe Coding with Addy Osmani

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