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—

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 [email protected].

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 [email protected].

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 [email protected].

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 [email protected].

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. 

—

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!

—

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

—

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

—

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 [email protected].

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

—

GitHub recently turned 17 years old—but how did it start, how has it evolved, and what does the future look like as AI reshapes developer workflows?

In this episode of The Pragmatic Engineer, I’m joined by Thomas Dohmke, CEO of GitHub. Thomas has been a GitHub user for 16 years and an employee for 7. We talk about GitHub’s early architecture, its remote-first operating model, and how the company is navigating AI—from Copilot to agents. We also discuss why GitHub hires junior engineers, how the company handled product-market fit early on, and why being a beloved tool can make shipping harder at times.

Other topics we discuss include:

• How GitHub’s architecture evolved beyond its original Rails monolith

• How GitHub runs as a remote-first company—and why they rarely use email 

• GitHub’s rigorous approach to security

• Why GitHub hires junior engineers

• GitHub’s acquisition by Microsoft

• The launch of Copilot and how it’s reshaping software development

• Why GitHub sees AI agents as tools, not a replacement for engineers

• And much more!

—

Timestamps

(00:00) Intro

(02:25) GitHub’s modern tech stack

(08:11) From cloud-first to hybrid: How GitHub handles infrastructure

(13:08) How GitHub’s remote-first culture shapes its operations

(18:00) Former and current internal tools including Haystack

(21:12) GitHub’s approach to security 

(24:30) The current size of GitHub, including security and engineering teams

(25:03) GitHub’s intern program, and why they are hiring junior engineers

(28:27) Why AI isn’t a replacement for junior engineers 

(34:40) A mini-history of GitHub 

(39:10) Why GitHub hit product market fit so quickly 

(43:44) The invention of pull requests

(44:50) How GitHub enables offline work

(46:21) How monetization has changed at GitHub since the acquisition 

(48:00) 2014 desktop application releases 

(52:10) The Microsoft acquisition 

(1:01:57) Behind the scenes of GitHub’s quiet period 

(1:06:42) The release of Copilot and its impact

(1:14:14) Why GitHub decided to open-source Copilot extensions

(1:20:01) AI agents and the myth of disappearing engineering jobs

(1:26:36) Closing

—

The Pragmatic Engineer deepdives relevant for this episode:

• AI Engineering in the real world

• The AI Engineering stack

•  How Linux is built with Greg Kroah-Hartman

•  Stacked Diffs (and why you should know about them)

•  50 Years of Microsoft and developer tools

—

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 [email protected].

Supported by Our Partners

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

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

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

—

Kent Beck is one of the most influential figures in modern software development. Creator of Extreme Programming (XP), co-author of The Agile Manifesto, and a pioneer of Test-Driven Development (TDD), he’s shaped how teams write, test, and think about code.

Now, with over five decades of programming experience, Kent is still pushing boundaries—this time with AI coding tools. In this episode of Pragmatic Engineer, I sit down with him to talk about what’s changed, what hasn’t, and why he’s more excited than ever to code.

In our conversation, we cover:

• Why Kent calls AI tools an “unpredictable genie”—and how he’s using them

• Why Kent no longer has an emotional attachment to any specific programming language

• The backstory of The Agile Manifesto—and why Kent resisted the word “agile”

• An overview of XP (Extreme Programming) and how Grady Booch played a role in the name 

• Tape-to-tape experiments in Kent’s childhood that laid the groundwork for TDD

• Kent’s time at Facebook and how he adapted to its culture and use of feature flags

• And much more!

—

Timestamps

(00:00) Intro

(02:27) What Kent has been up to since writing Tidy First

(06:05) Why AI tools are making coding more fun for Kent and why he compares it to a genie

(13:41) Why Kent says languages don’t matter anymore

(16:56) Kent’s current project building a small talk server

(17:51) How Kent got involved with The Agile Manifesto

(23:46) Gergely’s time at JP Morgan, and why Kent didn’t like the word ‘agile’

(26:25) An overview of “extreme programming” (XP) 

(35:41) Kent’s childhood tape-to-tape experiments that inspired TDD

(42:11) Kent’s response to Ousterhout’s criticism of TDD

(50:05) Why Kent still uses TDD with his AI stack 

(54:26) How Facebook operated in 2011

(1:04:10) Facebook in 2011 vs. 2017

(1:12:24) 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 [email protected].

Supported by Our Partners

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

•⁠ Sinch⁠ — Connect with customers at every step of their journey.

•⁠ Modal⁠ — The cloud platform for building AI applications.

—

How has Microsoft changed since its founding in 1975, especially in how it builds tools for developers?

In this episode of The Pragmatic Engineer, I sit down with Scott Guthrie, Executive Vice President of Cloud and AI at Microsoft. Scott has been with the company for 28 years. He built the first prototype of ASP.NET, led the Windows Phone team, led up Azure, and helped shape many of Microsoft’s most important developer platforms.

We talk about Microsoft’s journey from building early dev tools to becoming a top cloud provider—and how it actively worked to win back and grow its developer base.

In this episode, we cover:

• Microsoft’s early years building developer tools 

• Why Visual Basic faced resistance from devs back in the day: even though it simplified development at the time

• How .NET helped bring a new generation of server-side developers into Microsoft’s ecosystem

• Why Windows Phone didn’t succeed 

• The 90s Microsoft dev stack: docs, debuggers, and more

• How Microsoft Azure went from being the #7 cloud provider to the #2 spot today

• Why Microsoft created VS Code

• How VS Code and open source led to the acquisition of GitHub

• What Scott’s excited about in the future of developer tools and AI

• And much more!

—

Timestamps

(00:00) Intro

(02:25) Microsoft’s early years building developer tools

(06:15) How Microsoft’s developer tools helped Windows succeed

(08:00) Microsoft’s first tools were built to allow less technically savvy people to build things

(11:00) A case for embracing the technology that’s coming

(14:11) Why Microsoft built Visual Studio and .NET

(19:54) Steve Ballmer’s speech about .NET

(22:04) The origins of C# and Anders Hejlsberg’s impact on Microsoft 

(25:29) The 90’s Microsoft stack, including documentation, debuggers, and more

(30:17) How productivity has changed over the past 10 years 

(32:50) Why Gergely was a fan of Windows Phone—and Scott’s thoughts on why it didn’t last

(36:43) Lessons from working on (and fixing)  Azure under Satya Nadella 

(42:50) Codeplex and the acquisition of GitHub

(48:52) 2014: Three bold projects to win the hearts of developers

(55:40) What Scott’s excited about in new developer tools and cloud computing 

(59:50) Why Scott thinks AI will enhance productivity but create more engineering jobs

—

The Pragmatic Engineer deepdives relevant for this episode:

• Microsoft is dogfooding AI dev tools’ future

• Microsoft’s developer tools roots

• Why are Cloud Development Environments spiking in popularity, now?

• Engineering career paths at Big Tech and scaleups

• How Linux is built with Greg Kroah-Hartman

—

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 [email protected].

Supported by Our Partners

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

•⁠ Sinch⁠ — Connect with customers at every step of their journey.

•⁠ Cortex⁠ — Your Portal to Engineering Excellence.

—

What does it take to land a job as an AI Engineer—and thrive in the role?

In this episode of Pragmatic Engineer, I’m joined by Janvi Kalra, currently an AI Engineer at OpenAI. Janvi shares how she broke into tech with internships at top companies, landed a full-time software engineering role at Coda, and later taught herself the skills to move into AI Engineering: by things like building projects in her free time, joining hackathons, and ultimately proving herself and earning a spot on Coda’s first AI Engineering team.

In our conversation, we dive into the world of AI Engineering and discuss three types of AI companies, how to assess them based on profitability and growth, and practical advice for landing your dream job in the field.

We also discuss the following: 

• How Janvi landed internships at Google and Microsoft, and her tips for interview prepping

• A framework for evaluating AI startups

• An overview of what an AI Engineer does

• A mini curriculum for self-learning AI: practical tools that worked for Janvi

• The Coda project that impressed CEO Shishir Mehrotra and sparked Coda Brain

• Janvi’s role at OpenAI and how the safety team shapes responsible AI

• How OpenAI blends startup speed with big tech scale

• Why AI Engineers must be ready to scrap their work and start over

• Why today’s engineers need to be product-minded, design-aware, full-stack, and focused on driving business outcomes

• And much more!

—

Timestamps

(00:00) Intro

(02:31) How Janvi got her internships at Google and Microsoft

(03:35) How Janvi prepared for her coding interviews 

(07:11) Janvi’s experience interning at Google

(08:59) What Janvi worked on at Microsoft 

(11:35) Why Janvi chose to work for a startup after college

(15:00) How Janvi picked Coda 

(16:58) Janvi’s criteria for picking a startup now 

(18:20) How Janvi evaluates ‘customer obsession’ 

(19:12) Fast—an example of the downside of not doing due diligence

(21:38) How Janvi made the jump to Coda’s AI team

(25:48) What an AI Engineer does 

(27:30) How Janvi developed her AI Engineering skills through hackathons

(30:34) Janvi’s favorite AI project at Coda: Workspace Q&A 

(37:40) Learnings from interviewing at 46 companies

(40:44) Why Janvi decided to get experience working for a model company 

(43:17) Questions Janvi asks to determine growth and profitability

(45:28) How Janvi got an offer at OpenAI, and an overview of the interview process

(49:08) What Janvi does at OpenAI 

(51:01) What makes OpenAI unique 

(52:30) The shipping process at OpenAI

(55:41) Surprising learnings from AI Engineering 

(57:50) How AI might impact new graduates 

(1:02:19) The impact of AI tools on coding—what is changing, and what remains the same

(1:07:51) Rapid fire round

—

The Pragmatic Engineer deepdives relevant for this episode:

•⁠ AI Engineering in the real world

•⁠ The AI Engineering stack

•⁠ Building, launching, and scaling ChatGPT Images

—

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 [email protected].

Supported by Our Partners

•⁠ WorkOS — The modern identity platform for B2B SaaS.

•⁠ Modal⁠ — The cloud platform for building AI applications.

•⁠ Cortex⁠ — Your Portal to Engineering Excellence.

—

Kubernetes is the second-largest open-source project in the world. What does it actually do—and why is it so widely adopted?

In this episode of The Pragmatic Engineer, I’m joined by Kat Cosgrove, who has led several Kubernetes releases. Kat has been contributing to Kubernetes for several years, and originally got involved with the project through K3s (the lightweight Kubernetes distribution).

In our conversation, we discuss how Kubernetes is structured, how it scales, and how the project is managed to avoid contributor burnout.

We also go deep into: 

• An overview of what Kubernetes is used for

• A breakdown of Kubernetes architecture: components, pods, and kubelets

• Why Google built Borg, and how it evolved into Kubernetes

• The benefits of large-scale open source projects—for companies, contributors, and the broader ecosystem

• The size and complexity of Kubernetes—and how it’s managed

• How the project protects contributors with anti-burnout policies

• The size and structure of the release team

• What KEPs are and how they shape Kubernetes features

• Kat’s views on GenAI, and why Kubernetes blocks using AI, at least for documentation

• Where Kat would like to see AI tools improve developer workflows

• Getting started as a contributor to Kubernetes—and the career and networking benefits that come with it

• And much more!

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Timestamps

(00:00) Intro

(02:02) An overview of Kubernetes and who it’s for 

(04:27) A quick glimpse at the architecture: Kubernetes components, pods, and cubelets

(07:00) Containers vs. virtual machines 

(10:02) The origins of Kubernetes 

(12:30) Why Google built Borg, and why they made it an open source project

(15:51) The benefits of open source projects 

(17:25) The size of Kubernetes

(20:55) Cluster management solutions, including different Kubernetes services

(21:48) Why people contribute to Kubernetes 

(25:47) The anti-burnout policies Kubernetes has in place 

(29:07) Why Kubernetes is so popular

(33:34) Why documentation is a good place to get started contributing to an open-source project

(35:15) The structure of the Kubernetes release team 

(40:55) How responsibilities shift as engineers grow into senior positions

(44:37) Using a KEP to propose a new feature—and what’s next

(48:20) Feature flags in Kubernetes 

(52:04) Why Kat thinks most GenAI tools are scams—and why Kubernetes blocks their use

(55:04) The use cases Kat would like to have AI tools for

(58:20) When to use Kubernetes 

(1:01:25) Getting started with Kubernetes 

(1:04:24) How contributing to an open source project is a good way to build your network

(1:05:51) Rapid fire round

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The Pragmatic Engineer deepdives relevant for this episode:

•⁠ Backstage: an open source developer portal

•⁠ How Linux is built with Greg Kroah-Hartman

•⁠ Software engineers leading projects

•⁠ What TPMs do and what software engineers can learn from them

•⁠ Engineering career paths at Big Tech and scaleups

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

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