alphalist.CTO Podcast – For CTOs and Technical Leaders
- Episódios
- 150
- Publicação
- quinzenal
- Último episódio
- 12 nov 2026
Sobre o podcast
This podcast features interviews of CTOs and other technical leadership figures and topics range from technology (AI, blockchain, cyber, DevOps, Web Architecture, etc.) to management (e.g. scaling, structuring teams, mentoring, technical recruiting, product etc.). Guests from leading tech companies share their best practices and knowledge. The goal is to support other CTOs on their journey through tech and engineering, inspire and allow a sneak-peek into other successful companies to understand how they think and act. Get awesome insights into the world‘s top tech companies, personalities with this podcast brought to you by Tobias Schlottke.
Episódios 150
#150 From a Worm's Brain to Mercedes: Building AI That Runs Without the Cloud // Mathias Lechner, Liquid AI
Liquid AI's co-founder explains how a 302-neuron worm led to models that run on phones and in cars, why there's no lasting technical moat in AI, and why on-device speed starts with the hardware. Can a model …
Notas do episódio
Liquid AI's co-founder explains how a 302-neuron worm led to models that run on phones and in cars, why there's no lasting technical moat in AI, and why on-device speed starts with the hardware.Can a model small enough to run on a Raspberry Pi be good enough for real products? Mathias Lechner thinks so. As co-founder and CTO of Liquid AI, he builds foundation models that run on phones, laptops and cars without the cloud, and his team starts with the hardware before anything else.
- From C. elegans to liquid neural networks
- No long-term technical moat, and why silicon partnerships matter
- Speed as the North Star for on-device models
- The memory triangle: bandwidth, size and compute
- AI in cars and the challenge of 10-year hardware
- Training data, evals and research culture at Liquid AI
00:00:51 Meet Mathias: from a worm's nervous system to Liquid AI
What you'll learn
00:01:41 Why computers felt like magic, and how he found C. elegans
00:04:39 A 19-neuron self-driving demo while the industry scaled up
00:07:34 Robustness vs knowledge: what a car really needs to know
00:08:38 Is vision enough for autonomous driving?
00:09:28 From the MIT test track to Liquid AI's LFM2 models
00:09:57 Is there any worm left in the model?
00:12:10 No long-term technical moat: why silicon partnerships matter
00:15:01 What chipmakers get in return, and access to pre-release hardware
00:16:14 Inference speed as the North Star
00:17:12 Cloud vs local: where workloads will run
00:19:02 Memory bandwidth, memory size and FLOPS: the on-device triangle
00:22:35 How many tokens per second a phone assistant really needs
00:23:34 Is the local bet really about tools?
00:24:59 What runs on device and what runs in the cloud by 2028
00:28:11 Unmetered intelligence: token maxing with a local agent
00:28:29 His setup: Claude Code, Codex and OpenCode in YOLO mode
00:31:15 Why Mercedes uses Liquid AI for its in-car assistant
00:33:06 Why not a Chinese open model? Speed is non-negotiable
00:34:45 Why cars only get Raspberry Pi-level compute
00:35:41 Cars live 10 years, models six months: continual updates
00:38:00 Testing Liquid AI's open-weight models: where to start
00:40:00 Inside the training cluster: thousands of GPUs, academic workflow
00:42:47 Data: the unglamorous work behind model quality
00:45:06 How to judge data: would a user actually ask this?
00:47:12 From use cases to evals and benchmarks
00:50:04 Learning resources he recommends
00:51:35 His recent aha moment: tiered memory systems
00:53:39 Tobi's surprise: a message to his 2017 self
00:55:21 Hardware awareness from day one- Which AI workloads are moving on-device and which will stay in the cloud
- Why memory bandwidth and memory size, not just FLOPS, decide what can run locally
- How hardware partnerships can matter more than model architecture as a moat
- What it takes to ship AI into cars with long hardware lifecycles
- How to judge training data by asking whether real users would need it
- Where to start if you want to test small open-weight models yourself
"For me at least, there's no long-term technical moat anywhere... It's just innovation. It's a race." – Mathias Lechner [00:12:10]
Links
"The way we think about the moat is actually about silicon partnerships." – Mathias Lechner [00:14:14]
"For us, speed is non-negotiable. We start actually with the speed." – Mathias Lechner [00:34:13]
"If you have a good local agent, you can token max because it's basically free." – Mathias Lechner [00:28:11]Liquid AI: https://www.liquid.ai
Join alphalist
Mathias on Liquid AI: https://www.liquid.ai/team/mathias-lechner
Liquid AI models on Hugging Face: https://huggingface.co/LiquidAI
Join alphalist: https://alphalist.comalphalist is the biggest community of CTOs in Germany. If you're an experienced CTO, join 600+ peers sharing insights in our Slack space and at our events. Apply at alphalist.com.
#149 Governance Gives You Speed: Why AI Agents Must Earn Trust with Sri Viswanath // Founder & CEO @ Sycamore
Atlassian's first CTO explains why agents should never be autonomous on day one, why software starts decaying the moment you ship it, and how trust becomes the thing that lets enterprises move fast. What does …
Notas do episódio
Atlassian's first CTO explains why agents should never be autonomous on day one, why software starts decaying the moment you ship it, and how trust becomes the thing that lets enterprises move fast.What does it take to trust an AI agent with real enterprise work? Sri Viswanath spent six years turning Atlassian into a cloud company. Now he's building the trust layer for agents at Sycamore, and his answer is that governance doesn't slow you down. It's what lets you go faster.
- From Atlassian's first CTO to first-time founder
- Software decay and living software
- Progressive trust and agents earning autonomy
- Agent security: sandboxes, PII and zero-trust identity
- PLG vs. enterprise sales in the agentic era
- Systems thinking and the engineering org of the future
00:00:51 Meet Sri: Atlassian's first CTO turned first-time founder
What you'll learn
00:02:04 How Sri got into tech, and doing everything as CEO and CTO
00:04:47 A 25-person company where nobody writes code by hand
00:05:58 What an agent operating system for enterprises is
00:08:00 Why software decays after deployment, and customers on version 50
00:10:24 Why Atlassian needed a CTO after 14 years: the move to cloud
00:13:32 Will AI replace software? Why large enterprises are different
00:15:33 Bending Spoons, Evernote and the end of the enterprise as we know it?
00:19:17 Progressive trust: why agents are never autonomous on day one
00:21:13 7,000 engineers for an issue tracker and a wiki?
00:24:02 The future of Jira and a central source of truth
00:27:21 What he'd never build again if he inherited Atlassian today
00:28:31 Five features instead of 500, and why bolt-on AI doesn't work
00:30:32 When agents delegate work to humans
00:31:12 Trust but verify: Sycamore Forge and Sycamore Guard
00:32:01 Is PLG over? Enterprise land, self-service expand
00:34:35 Sandboxes, PII and the new security dimension of agents
00:36:50 Governance gives you speed
00:38:21 Systems thinking and what forward deployed engineers really do
00:41:05 Lego blocks, fractals and MCP at every level
00:43:02 Kubernetes, five-second tokens and builds that run for days
00:45:23 Do you still need to read code?
00:47:18 Deciding what to build when you can build everything
00:52:00 The engineering org of 2028 and the human-to-agent ratio
00:55:25 Advice for young engineers: become a systems thinker
00:56:31 Worst decision at Atlassian: the "five alarm fire"
00:57:39 Toby's surprise: an old Jira ticket
00:59:48 Why every CTO should build something with AI themselves- How to roll out agents with progressive trust instead of blind autonomy
- Why governance and verification increase delivery speed rather than slowing it down
- How to secure agents with sandboxes, default-deny networking and short-lived tokens
- Why systems thinking beats automating one workflow at a time
- How engineering roles and org structures shift when every engineer manages ten agent sessions
- The lesson from Atlassian's cloud migration about pausing feature work after a big move
- Sycamore: https://sycamore.so
- Sri on LinkedIn: https://www.linkedin.com/in/sriviswanath/
- Join alphalist: https://alphalist.com
alphalist is the biggest community of CTOs in Germany. If you're an experienced CTO, join 600+ peers sharing insights in our Slack space and at our events. Apply at alphalist.com.
#148 Git Is the Wrong Tool Now: Rewriting the Social Contract of Agentic Teams with Raffi Krikorian // CTO @ Mozilla
Raffi Krikorian ran 600 engineers at Twitter and 1,000 at Uber. Now he thinks solo agentic coding has broken how teams build software, and that fixing it takes more managers, not fewer. Raffi Krikorian has …
Notas do episódio
Raffi Krikorian ran 600 engineers at Twitter and 1,000 at Uber. Now he thinks solo agentic coding has broken how teams build software, and that fixing it takes more managers, not fewer. Raffi Krikorian has built teams at scale: platform engineering at Twitter, Uber's self-driving center, and the Democratic National Committee, where he was its first CTO. Today he's CTO of Mozilla, working to make AI as open as the web, and on the side he runs three rotating Claude Max subscriptions and a GPU in his closet. Tobi and Raffi look at what agentic coding has done to engineering teams. Writing code used to be a social contract: coordinating with other teams, negotiating requirements, sharing work. Now one person with agents does all of it alone. Raffi argues that Git, built to commit bytes, doesn't fit a world where nobody types the bytes anymore, and that the tooling, processes and roles for working as a team still need to be invented. The conversation then moves to Mozilla's strategy: building an open source AI ecosystem without owning it, convincing CTOs and CEOs (not just developers) that open-weight models are safe to deploy, and pushing for standards like portable memory so switching providers stays possible. CTOs will come away with a clear argument for building leverage into their AI stack before their teams get locked into a single vendor's playbook. - Why Git may be the wrong tool for agentic teams - Why we may need more engineering and product managers, not fewer - Raffi's personal setup: a local Hermes agent, rotating subscriptions and a sandboxed Claude Code - Mozilla's plan for an open source AI ecosystem and open standards - Memory as the new lock-in, and a portable memory standard - Why chatbots should be measured by usefulness, not engagement - What Raffi would tell his 2009 self about shutting down the Twitter API#147 "Make It Secure" Isn't a Prompt: A L0pht Hacker on Securing AI-Written Code with Chris Wysopal // Chief Security Evangelist @ Veracode
Nearly three decades after telling the US Senate the internet could be taken down in 30 minutes, the L0pht veteran explains why AI has made attack sophistication meaningless as a signal, and why the real fix is …
Notas do episódio
Nearly three decades after telling the US Senate the internet could be taken down in 30 minutes, the L0pht veteran explains why AI has made attack sophistication meaningless as a signal, and why the real fix is giving your models security intent before the code is written. Chris Wysopal was one of the first hackers to go public. As "Weld Pond" at the L0pht hacker collective in Boston, he testified before the US Senate in 1998, where the group delivered the soundbite that they could take down the internet in 30 minutes. He also wrote the Windows version of Netcat. In 2006 he co-founded Veracode, which by his account has now analysed trillions of lines of code. Today he's the company's Chief Security Evangelist. Tobi and Chris talk about what AI changes for attackers and defenders. Attacks are getting cheaper and faster, and a custom exploit no longer tells you a nation-state is behind it. Chris argues this follows a familiar cycle: attackers adopt a new class of tool first, defenders catch up, and parity returns. That only holds if defenders actually adopt the tools, especially underfunded organisations like hospitals, schools and utilities. They also cover the new attack surface created by agents, plugins and MCPs, why prompt injection may never be fully solvable, and how Chris would handle security debt when acquiring a small SaaS company. CTOs will leave with a concrete shortlist: avoid memory-unsafe languages, put a package firewall in front of open source, run AI-assisted static analysis with real architectural context, and give coding agents explicit security intent rather than hoping "make it secure" does the job. - From the L0pht and BBS culture to the professional security industry - The BGP flaw behind "30 minutes to take down the internet" - How AI changes the cost and speed of attacks - Prompt injection, agents, MCPs and least privilege - Security debt in B2B SaaS acquisitions - Secure on first write: security intent, context and pre-merge testing - How engineering and security roles change over the next two years#146 AI Found What Nine Years of Security Research Missed with Charles Guillemet // CTO @ Ledger
Ledger's CTO on vulnerability discovery, security-by-design, key management and AI-native engineering. Charles Guillemet started hacking at 12. He sold his piano to buy his first computer. Nine years ago, he …
Notas do episódio
Ledger's CTO on vulnerability discovery, security-by-design, key management and AI-native engineering.Charles Guillemet started hacking at 12. He sold his piano to buy his first computer. Nine years ago, he built Ledger's offensive security team from scratch; today, he's CTO of a company protecting a fifth of the world's crypto, and he just watched AI find vulnerabilities his team spent almost a decade not finding.
Topics covered:
- The economics of security — and why AI is breaking them
- Ledger's three layers of security, and what The Donjon actually does
- Turning a security problem into a key management problem
- Charles's recommendation for restructuring engineering teams around AI and "harness engineering"
- Zero trust, bus factor, and protecting a company's "crown jewels"
- Using AI to build a threat model and cut noise from production signals
- Why Charles believes the CISO function should stay independent
Timestamps: [00:00:51] Intro & guest welcome [00:01:21] Charles's nerd origin story — hacking at 12 [00:03:33] From software hacking into hardware security [00:04:45] Ledger's security org today: hardware vs. software [00:08:48] The three layers of security at Ledger — and The Donjon [00:13:28] The AI vulnerability that broke 9 years of research [00:14:47] Security as economic asymmetry — and why AI is collapsing it [00:18:31] Why blockchain attackers already spend big to break in [00:20:51] Is prompting AI to "find vulnerabilities" enough? [00:23:48] Security-by-design and turning security into key management [00:26:00] Does AI kill the CTO/developer job — or redefine it? [00:30:40] Rebuilding an SDLC: Charles's small-team, harness-engineering recommendation [00:36:22] Building a threat model with AI [00:38:58] Zero trust, bus factor, protecting the crown jewels [00:42:36] Using AI to cut through noisy production security signals [00:47:04] "Average is better than nothing" — the Wikipedia analogy [00:48:00] Why Charles thinks the CISO function should stay independent [00:49:09] The Easter egg question — advice to his 2017 self
What you'll learn:
- Why the old economics of security are breaking down — and what that means for your own threat model
- A concrete way to turn any security problem into a (much easier) key-management problem
- Charles's recommendation for restructuring engineering teams around small, AI-native pods and "harness engineering"
- What zero trust looks like in practice when developers bring their own devices
- How to use AI to build a threat model and cut through noisy production security signals
Quotes: ⭐ [00:14:22] "The cost of finding vulnerabilities is trending towards zero." ⭐ [00:26:21] "If they think they will continue writing code, yes, they will lose their job." ⭐ [00:23:39] "If you just prompt like crazy — implement this database, implement this feature — and at the end just prompt, 'please find vulnerabilities,' it's something, but it won't solve the security problem." [00:14:47] "Security is a matter of creating an imbalance, an asymmetry between attacks and defense." [00:41:21] "More and more attackers go through the developer door, trying to compromise the endpoint of one developer and then leverage his access to production." [00:47:14] "Some people say AI is only producing average results, but sometimes average is better than nothing." [00:39:19] "If a human has full access to a system he doesn't need, that also means the malware running on his computer has the same access." (All quotes lightly cleaned of stutters/filler — verify against final audio.)
Links:
- Ledger: https://www.ledger.com
- Ledger Donjon (security research team): https://donjon.ledger.com
- Meet Cerberus — Ledger Donjon's AI security harness: https://www.ledger.com/blog-cerberus-donjon-ai-harness
- Charles Guillemet on LinkedIn: https://fr.linkedin.com/in/charles-guillemet
Want to keep talking about this with other CTOs? Join over 600 tech leaders in our Slack community — go to alphalist.com to apply.
#145 "Harness Writing Is Not Hard": Build to Learn, Not to Run with David Soria Parra // Member of Technical Staff @ Anthropic
David Soria Parra on why he'd build a custom agent harness only to understand it, not to run it,and why the industry's MCP-vs-CLI debate misses the point entirely. BROUGHT TO YOU BY: Blocks Sponsored by Blocks …
Notas do episódio
David Soria Parra on why he'd build a custom agent harness only to understand it, not to run it,and why the industry's MCP-vs-CLI debate misses the point entirely.BROUGHT TO YOU BY: Blocks Sponsored by Blocks: Save at least 20% on your AWS costs with AI-powered optimization and enterprise discounts. Get your free Cloud Check at https://blocks.cloud/alphalist?utm_source=alphalist&utm_medium=podcast&utm_campaign=blocks-podcast-2026
David Soria Parra co-created MCP, used to help write Mercurial, and now works alongside the people building Claude at Anthropic — so when he says building your own AI agent harness isn't actually that hard (but probably isn't worth doing anyway), it's worth sitting with. This one's a grounded, unhyped look at what to actually build versus configure.
Topics covered:
- David's nerd origin story: a PHP guestbook at 13 and a very expensive ISDN phone bill
- From Mercurial core contributor to Meta's internal source control team
- What Meta and Anthropic both get right by running on almost no process
- David's personal setup — a handful of skills, a few MCP connectors, staying "vanilla"
- Why his workflow moved from Claude Code sessions into Slack threads with Claude
- The real MCP-vs-CLI debate, and why it isn't actually a debate
- Should every CTO or engineer build their own agent harness?
- What's still defensible in software engineering as agents get stronger
- Smart-home and hardware side quests: securing a UniFi network, hacking a coffee machine
- Closing time machine question, via Mercurial's own abandoned time-travel Easter egg
Timestamps: [00:00:51] Intro and guest background [00:02:39] David's role in creating MCP — "one of the co-creators" [00:03:24] David's nerd origin story — a PHP guestbook at 13 [00:08:32] From application development to obsessing over tools and infrastructure [00:09:38] Joining the Mercurial community [00:12:39] How Mercurial led to Meta [00:14:30] Biggest lessons from Meta — optimism, incentives, and minimal process [00:20:15] Senior vs. junior engineers in the AI era [00:22:26] Why high agency matters more than learning a narrow craft [00:23:13] How Anthropic organizes teams in a fast-moving field [00:25:46] Loose org process vs. strict model-safety process [00:29:15] David's personal setup: Claude Code, a handful of skills, MCP connectors [00:30:09] Personalized skills — writing voice and multi-agent code review [00:32:05] Combining CLIs, MCP servers, Slack, and Linear [00:33:00] Why his workflow moved from Claude Code sessions into Slack threads [00:34:56] Permissions and compartmentalization for company-wide agents [00:38:34] David's private setup — Herder, Pi, and running multiple agents [00:45:55] Context selection, RAG, Obsidian, Git, files and folders [00:47:42] General-purpose agents vs. custom automation — where each wins [00:49:38] Why he'd build a harness to understand it, but not run one [00:52:43] The new age of custom software — workout apps, home inventory, recipes [00:53:31] Reliability, and the ongoing case for buying software [00:55:14] How software engineering roles may shift, and what stays defensible [00:59:38] New software possibilities for smaller organizations [01:00:02] MCP today: the co-creator's view on his own invention [01:02:18] The real MCP-vs-CLI debate — and why it isn't actually a debate [01:04:27] Why building an internal MCP registry is "the better investment" [01:06:02] What to check for when evaluating an agent-connectivity vendor [01:06:57] Smart-home and hardware side quests — UniFi, coffee machines, and Bluetooth [01:10:09] David's closing recommendation: stop optimizing your setup [01:13:00] Talk to customers instead of building a software factory (unattributed exchange — see quote flag) [01:13:38] Outro: the "hq shelf" Mercurial Easter egg [01:14:51] What David would tell his younger self
What you'll learn:
- Why "harness writing is not hard" — and what actually separates a good agent harness from a mediocre one
- When to reach for MCP vs. a CLI (and why the industry debate between them misses the point)
- How Meta and Anthropic both run on almost no internal process — and what that requires of the people you hire
- Why David moved his entire day-to-day workflow from local Claude Code sessions into Slack threads
- What to check for in a vendor contract if agent connectivity matters to you
- A grounded gut-check for anyone convinced they need to build a custom "software factory"
Quotes: "A lot of my workflows nowadays are: 'Hey Claude, can you look into this thing, review it, figure out what you feel about it — make an adversarial review — and come back to me.' Or, 'I need to change this over here, implement this new feature, go do it, and come back when you have a review.' A lot of my interaction moved away from Claude Code sessions to threads in Slack where I interact with Claude, and Claude does the thing for me." — [00:33:00] (cleaned of filler/false starts — verify against audio)
"You don't need only MCP. You need also MCP, but you also need CLIs… Enterprises in particular love centralized things they can control via gateways in the middle. MCP is great if you need to govern the whole thing. But if you're running a local harness for your own development, running CLIs is perfectly fine. They both will be needed — they're just different for different use cases." — [01:02:42]
"Harness writing is not hard. It gives it a bunch of tools, gives it a bunch of execution steps, be a little smarter about context selection, and go for it. It turns out the model does 90% of the work." — [00:50:00] (cleaned of stutters — verify against audio)
"The main recommendation is not to over-focus on optimizing your setup. This is a very fast-moving technology — you don't need to fiddle with everything. Pick one or two things you find useful, be it Claude Code, be it Claude Tag, and just focus on using it quickly, optimizing for as little downtime as possible so you can get stuff going." — [01:10:53]
Relevant links: David Soria Parra on LinkedIn: https://www.linkedin.com/in/david-soria-parra-4a78b3a Model Context Protocol: https://modelcontextprotocol.io Mercurial: https://www.mercurial-scm.org Pi coding agent (by Mario Zechner): https://pi.dev Anthropic: https://www.anthropic.com
Join the conversation with 600+ CTOs sharing insights on technical leadership and tech trends in the alphalist Slack. Visit alphalist.com to apply.
OUR SPONSORS Sponsored by Blocks: Save at least 20% on your AWS costs with AI-powered optimization and enterprise discounts. Get your free Cloud Check at https://blocks.cloud/alphalist?utm_source=alphalist&utm_medium=podcast&utm_campaign=blocks-podcast-2026
#144 Writing Code Is No Longer the Job: Dana Lawson on Trusting AI Agents Like Self-Driving Cars // CTO @ Netlify
Why CTOs should stop asking whether they trust AI agents and start asking when that trust becomes automatic, and where human judgment still has to hold the line. Sponsored by Blocks: Save at least 20% on your …
Notas do episódio
Why CTOs should stop asking whether they trust AI agents and start asking when that trust becomes automatic, and where human judgment still has to hold the line.Sponsored by Blocks: Save at least 20% on your AWS costs with AI-powered optimization and enterprise discounts. Get your free Cloud Check at https://blocks.cloud/alphalist?utm_source=alphalist&utm_medium=podcast&utm_campaign=blocks-podcast-2026
Dana Lawson, CTO at Netlify, joins Tobi for a candid conversation about the changing role of software engineers in the age of AI agents.
Dana's path into technology began in the US Army in the late 1990s. She started by managing backup tapes and automating manual workflows before moving on to engineering leadership roles at GitHub and Netlify. The conversation explores why Dana believes "writing code is no longer the job," how AI agents are changing software development, and why trust in agents may eventually become as automatic as trusting a car we cannot repair ourselves.
Topics covered
Dana's path from the US Army to GitHub and Netlify How automating password resets sparked her interest in software Why Dana argues that writing code is becoming a commodity The Hacker News reaction to her article, "Writing Code Is No Longer the Job" The self-driving car analogy for trusting AI agents Why "agent experience is human experience" How Netlify is adapting its platform for an agentic future The shift from writing code to defining architecture and guardrails The tension between speed, control, reliability, and safety Whether developers could become the bottleneck Why the key constraint may be moving from "can we build it?" to "does anyone actually want it?" The future of developer experience, CMS platforms, databases, and the web
Timestamps
[00:00:51] Introduction and Dana's background [00:01:44] Dana's path from art school to the US Army [00:03:51] Backup tapes, automation, and the origins of her DevOps mindset [00:06:03] Moving to GitHub and learning to operate at scale [00:08:33] How AI could change GitHub and the software development lifecycle [00:12:18] The self-driving car analogy for trusting AI [00:16:20] AI, productivity, and whether more software is always better [00:19:43] Dana's "30 apps in 30 days" challenge [00:20:00] "Writing Code Is No Longer the Job" and the Hacker News backlash [00:25:00] Craftsmanship, abstraction, and the changing role of developers [00:30:28] Control, governance, and building for the future [00:33:59] What the agentic shift means for Netlify [00:37:53] From developer experience to agent experience [00:39:02] The future role of DevOps, SRE, and release engineering [00:49:11] CMS platforms, databases, and the future of the web [00:53:19] Dana's side projects and what people actually want [00:55:38] The closing time-machine question
What you'll learn
- Why AI agents may change the entry point into software development
- Where human expertise remains essential
- How engineering teams can think about guardrails and safe deployment
- Why product judgment and demand may matter more than implementation speed
- How platform teams can create better experiences for both agents and humans
About Dana Lawson Dana Lawson is the CTO at Netlify. Before joining Netlify, she was VP of Product Engineering at GitHub. She began her technology career in the US Army, where she developed an early interest in automation and infrastructure.
Relevant links
- Netlify: https://www.netlify.com
- Dana Lawson's article, ["Writing Code Is No Longer the Job"]
- alphalist Slack community: https://alphalist.com/apply
- alphalist events and community: https://alphalist.com
Join alphalist Are you an experienced CTO or engineering leader? Join more than 600 CTOs sharing insights, challenges, and practical advice in the alphalist Slack community. Apply at https://alphalist.com/apply
Sponsored by Blocks: Save at least 20% on your AWS costs with AI-powered optimization and enterprise discounts. Get your free Cloud Check at https://blocks.cloud/alphalist?utm_source=alphalist&utm_medium=podcast&utm_campaign=blocks-podcast-2026
#143 The Company Brain: How Kombo Runs on a Git Repo and a Cursor Agent — with Aike Hillbrands, Co-Founder & CTO @ Kombo
A GitHub repo, a Cursor cloud agent, and a public Slack channel replaced Notion AI at a $25M-funded HR integration startup, and became the whole company's shared brain. BROUGHT TO YOU BY: Blocks Save at least …
Notas do episódio
A GitHub repo, a Cursor cloud agent, and a public Slack channel replaced Notion AI at a $25M-funded HR integration startup, and became the whole company's shared brain.BROUGHT TO YOU BY: Blocks
Save at least 20% on your AWS costs with AI-powered optimization and enterprise discounts. Get your free Cloud Check at https://blocks.cloud/alphalist?utm_source=alphalist&utm_medium=podcast&utm_campaign=blocks-podcast-2026
Two failed startups, a Y Combinator batch, and a $25M Series A later, Aike Hillbrands and his co-founders built Kombo into a $10M+ ARR HR integration platform, and then, almost by accident, built a company-wide AI brain out of a GitHub repo and a Cursor agent. Aike joins Tobi to explain why files and grep beat MCP tools for agent reliability, what the "lethal trifecta" of AI security actually means in practice, and why he doesn't think AI will commoditize his own business anytime soon.
chapters:
00:00:00 — Intro 00:01:00 — Meeting his co-founders at CODE University 00:03:00 — Founding Kombo — the YC S22 pivot 00:04:00 — Nerd origin story: graphic design, Delphi, and Windows Forms games 00:06:00 — Selling an early company, and the open-source interception tool 00:08:00 — The Workday pain point that became Kombo 00:10:00 — Grayish markets: intercepting API traffic, and when it's not worth it 00:12:00 — Team size, the $25M raise, and staying a bootstrapper at heart 00:14:00 — From Notion AI to a company-wide brain 00:16:00 — Why the MCP experiment failed — and files + grep won instead 00:18:00 — Eliminating the "ask an engineer" bottleneck 00:20:00 — Two hours to build the repo, five minutes for Slack 00:22:00 — Linking files together so the agent connects the dots 00:24:00 — Public-by-default culture, and what data they'd feed the brain 00:26:00 — Exposing the company brain to customers 00:28:00 — The "lethal trifecta" framing, and why the market isn't investing enough 00:30:00 — The scariest failure mode: exfiltration, not just leaks 00:32:00 — The wife/prompt-injection story 00:34:00 — Why the risk can't be zero — and the "blue worker suit" analogy 00:36:00 — Public Slack as a social guardrail against prompt injection 00:38:00 — Shopify's "River" and Tobi Lütke's Lehrwerkstatt 00:40:00 — Buy vs. build: is anyone doing this well already? 00:42:00 — An internal app store made of markdown files and PRs 00:44:00 — Will AI commoditize Kombo's own business? 00:46:00 — Why enterprise integrations resist commoditization 00:48:00 — Integration teams of 10–15 people, and Kombo's end-to-end bet 00:50:00 — Is SaaS dying? System-of-record inertia 00:52:00 — What actually changes vs. what doesn't 00:54:00 — Time travel: advice to his younger self 00:56:00 — Outro
Quotes:
00:17:29 – "The agent will just run a grep command and find 200 files where something is discussed, and it will actually look at 50 or so of them. It's not stopping too early." (verbatim) 00:24:26 – "We also share with employees the money in our bank account monthly. This is the kind of public-by-default that we run." 00:35:52 – "By having it in the Slack channel, our people see what the customer is doing with the agent, but also the colleagues of the customer see what's going on. That's reducing the amount of exploiting you can do, by a lot, just because other people have visibility." 00:32:14 – "My wife accidentally prompt-injected it without any bad intention. Now imagine people with bad intentions." (Tobi)
#142 Why LLMs Need Their Own Programming Language: From Assembly to AI with Vaibhav Gupta // Co-founder @ BAML
From HoloLens assembly to AI: Vaibhav Gupta on why LLMs need their own language, and how BAML makes them type-safe and shippable at agent speed. BROUGHT TO YOU BY: Blocks A decade building computer vision and …
Notas do episódio
From HoloLens assembly to AI: Vaibhav Gupta on why LLMs need their own language, and how BAML makes them type-safe and shippable at agent speed.BROUGHT TO YOU BY: Blocks
A decade building computer vision and writing assembly at Microsoft (HoloLens), Google, and D.E. Shaw, then a from-scratch bet on a programming language built for LLMs. Vaibhav Gupta joins Tobi to explain why probabilistic compute needs its own tooling, what "shipping at agent speed" actually requires, and why the world's appetite for software is mathematically infinite.
Chapters: 00:00:00 — Intro 00:01:00 — From HoloLens to D.E. Shaw: a decade in computer vision and assembly 00:02:00 — Starting from scratch, and the YC pivot ("500K to not build a Slack competitor") 00:04:00 — Falling in love with coding — and bricking a few machines along the way 00:06:00 — His first AI moment: the GPT-3.5 wake-up call 00:09:00 — Code as a means to an end — why 90% of the job is plumbing 00:11:00 — Why he decided to build a language: first principles and BAML 00:13:00 — LLMs as a new compute primitive 00:15:00 — What BAML actually is — embedded, type-safe, callable from any language 00:17:00 — The business model and the "data trench" 00:19:00 — When AI ships code you didn't ask for — and why CI/CD breaks in an agent loop 00:22:00 — Live demo: function versioning and locking the codebase 00:24:00 — Why no one else competes here — Protobuf, Thrift, and Google's playbook 00:29:00 — Getting started: the BAML "hello world" (live coding) 00:32:00 — Everything is a function — type safety that runs in Rust 00:36:00 — Shipping at agent speed = trust plus granular control 00:38:00 — Visualizing code instead of reading it 00:44:00 — Where to start with BAML (docs.boundaryml.com → Agents MD) 00:45:00 — The mathematically infinite appetite for software 00:47:00 — Why we'll have 10x more builders — and why "English isn't a programming language" 00:51:00 — The future of SaaS: PaaS, harnesses, and customer-defined models 00:56:00 — Will design matter more in an agent world? 00:59:00 — The "time travel" decorator: advice to his 2017 self 01:04:00 — Outro
Quotes: 00:13:00 — "BAML's a new thing that exists because we have a new compute primitive in the form of LLMs." (verbatim) 00:10:00 — "90% of software engineering — well, in most jobs, 100% of software engineering is plumbing… And AI just takes that 90%, just makes it go away." 00:19:00 — "We can now generate code at machine speed… But we still cannot ship code at machine speed." 00:49:00 — "English can't go down to assembly… And the minute you add that to English, what have you done? You've built a new programming language." 00:45:00 — "I don't think we've yet found a company that hasn't found that I can make more money if I write more code."
-- OUR SPONSORS
Blocks Save at least 20% on your AWS costs with AI-powered optimization and enterprise discounts. Get your free Cloud Check at blocks.cloud/alphalist.
#141 AI Pat Works Here Now: Why Agents Must Follow Human Rules with Pat Casey // CTO @ ServiceNow
ServiceNow's CTO of 20 years explains why the safest way to deploy AI agents is to treat them like employees, same rules, same approvers, same spending limits, and why AI is reshuffling the deck on who your …
Notas do episódio
ServiceNow's CTO of 20 years explains why the safest way to deploy AI agents is to treat them like employees, same rules, same approvers, same spending limits, and why AI is reshuffling the deck on who your best engineers are.Intro How do you go from installing software off floppy disks to running engineering for a $13B revenue company — without ever losing the fish? Pat Casey, CTO of ServiceNow and its first engineer after founder Fred Luddy, joins Tobi to talk 20 years of scale, the architecture behind 90,000 databases, and why enterprise AI agents should be treated exactly like slightly untrustworthy employees.
Key topics
- Pat's nerd path: Atari 400, Wizardry, and building Adobe's first employee tracking system in Microsoft Access
- Flipping off Fred Luddy in traffic — and becoming ServiceNow employee-after-one
- The stuffed-fish code-ownership system and why productivity dips at ~100 engineers
- Inside the architecture: Java metadata engine, hacked Rhino, K8s services, single-tenant clusters
- RaptorDB: from MariaDB board seat to buying Swarm64 and forking Postgres
- Gen 3 of AI coding: Windsurf vs Claude Code, the 15% average vs the 5x outliers
- The five-chessboards theory of AI-native engineering, and Pat's daughter's vibe-coding conversion
- "AI Pat": agents in the user table, following human rules
- Hybrid pricing: seats for humans, consumption for AI
- Is the market wrong about SaaS incumbents? Pat's answer to the Anthropic scare
- What Pat would whisper to his younger self (spoiler: it's about Jelly XML — and family)
Chapters [00:00:51] Intro: who is Pat Casey [00:01:43] Nerd origin story: Atari 400, tape storage, Wizardry [00:04:12] First job at Aldus/Adobe — accidental ITIL [00:06:12] Flipping off Fred Luddy → joining Glide in 2005 [00:08:28] From 2 job titles to 10,000 engineers — the introvert advantage [00:10:21] The stuffed fish, and the productivity trough at 100 engineers [00:13:44] Architecture: metadata engine, Java monolith, Kubernetes [00:16:41] The single-tenant bet: 90,000 databases, 25B queries/hour [00:21:32] MariaDB, Monty, and building RaptorDB from Swarm64 [00:27:21] Postgres fork, OpenJDK contributions, open-sourcing Raptor? [00:29:35] AI coding gen 1–3: Copilot → 7,000 Windsurf licenses → Claude Code [00:33:35] Five boards of chess: who clicks with AI coding (and who doesn't) [00:36:47] Pat's daughter and the vibe-coding conversion [00:38:21] Enterprise agents: from toolkits to outcomes [00:40:41] "AI Pat": agents that follow human rules [00:42:29] Pricing: seats for humans, consumption for AI [00:46:15] The Anthropic scare, SaaS valuations, and the incumbent advantage [00:53:06] The new bottleneck: not engineering, not product — customers [00:59:00] Pat's advice to CTOs: lean in, don't turf it [01:01:26] Time machine: what Pat would whisper to his 2005 self
Quotes
[00:41:30] "You should not trust an LLM more than you trust a human being. Modern business processes were designed on the assumption that human beings are a little bit untrustworthy." (fillers removed — verify against audio) [00:34:00] "AI coding, if you get to that next level, is like playing five boards of chess, 'cause you got multiple prompts spinning at the same time." (one "um" removed) [00:16:04] "If it was that easy, none of the world's big monolithic code bases would still exist." (condensed from "It's like, all right, if it was that easy, like, none of…" — verify) [01:01:26] "This is not a time for excessive caution. It's not a time to completely go bonkers and do crazy stuff, but this is a time really to lean into the new technology." (one "uh" removed)
#140 From Stripe's Fifth Engineer to Serving Millions of Developers with Anurag Goel // Founder & CEO @ Render Goel
Anurag Goel was Stripe's fifth engineer before he built Render into a platform millions of developers deploy on. Here's his contrarian read on agents, security, and why "the AI cloud" is the wrong thing to be …
Notas do episódio
Anurag Goel was Stripe's fifth engineer before he built Render into a platform millions of developers deploy on. Here's his contrarian read on agents, security, and why "the AI cloud" is the wrong thing to be.Show Notes
Anurag Goel joined Stripe as its fifth engineer in 2011 and later ran risk. He left to solve a big problem and landed on the one he'd watched eat Stripe's engineering time: making infrastructure disappear. This conversation is about what Render learned on the way to millions of developers and what changes now that a lot of what gets deployed isn't a website, it's an agent. * Key topics:*
From Stripe's fifth engineer to founding Render The "application cloud" vs. "AI cloud" positioning Agents as long-running, stateful applications for a new end user Workflows, sandboxes, and the consolidated AI runtime Executive hiring and reference calls as a growth hack Security: minimizing blast radius, short-lived scoped keys Distribution in the chatbot era (GEO) and why Google is underrated Observability is the real bottleneck for production agents
Timestamps
[00:00:00] Intro and the pitch [00:02:00] Origin story: ebook search engine, game rentals, and the first-ever Stripe payment [00:04:00] Joining Stripe as engineer #5; talent density [00:06:00] Raising the hiring bar, no warm bodies [00:11:00] Executive hiring and reference calls as a growth hack [00:13:00] Why he started Render: ~20% of Stripe's engineers stuck on AWS [00:16:00] Agents as a new kind of application [00:17:00] "We're the application cloud, not the AI cloud" [00:18:00] Workflows, sandboxes, and the consolidated AI runtime [00:24:00] Heroku's decline and the exploding sales pipeline [00:25:00] The agentic moment: when adoption spiked [00:33:00] Security and blast radius [00:50:00] Why SaaS isn't dying specialization [00:58:00] Distribution: from SEO to GEO [01:02:00] Why Google is underrated [01:03:00] Advice for CTOs going all in on agents [01:06:00] Easter egg: a whisper to his 2011 self
#139 Your Future Job Is a Decision Inbox — Max Deichmann Built the Layer That Gets You There // Co-Founder @ Langfuse
Max Deichmann built Langfuse — the open-source LLM engineering platform acquired by ClickHouse — and explains why the engineer of the future isn't writing code, they're reviewing what agents did overnight. Max …
Notas do episódio
Max Deichmann built Langfuse — the open-source LLM engineering platform acquired by ClickHouse — and explains why the engineer of the future isn't writing code, they're reviewing what agents did overnight.Max Deichmann didn't set out to build the observability layer for the AI era. He started with mobile apps, taught himself to code via Harvard's CS50, and ended up in Y Combinator with a SaaS product he wasn't excited about. Then ChatGPT launched, and on a Sunday night at 10 pm, his co-founder asked: "If you just had time, what would you build?" The answer became Langfuse and eventually led to an acquisition by ClickHouse. This episode is a rare, grounded conversation about what building and operating AI agents actually looks like in 2025, from the engineering loop to the 3 am incident, to what the engineer's job becomes when agents are doing most of the execution.
Key topics:
- Why LLM applications broke traditional observability tools, and what Langfuse does instead
- The pre-production → production → evaluation → iteration loop for agent development
- Open source as a trust and adoption strategy for dev tools
- The ClickHouse acquisition: why they sold, what the half-page doc said, and how it's going
- Agentic incident response: copy-pasting alerts into Codex at 3 am, and what comes next
- The "decision inbox" engineers are reviewers and decision-makers, not coders
- The real state of agents in production: what's working, what's not, and what LinkedIn gets wrong
Timestamps:
[00:00:00] Intro & guest welcome [00:02:00] Max's nerd origin story CS50 on a beach in Singapore [00:04:00] Why they pivoted to Langfuse: firing customers mid-YC batch [00:06:00] Building the first AI products and discovering the observability gap [00:07:00] What Langfuse actually does: the LLM engineering platform explained [00:09:00] Tracking business AND infrastructure metrics billing via Langfuse [00:10:00] Open source from day one: trust, adoption, and hardening the product [00:13:00] Go-to-market with 1.5 salespeople: how engineers sell to enterprises [00:14:00] The acquisition story: 5 engineers, 40TB/day, and a Series A that became a sale [00:17:00] What it felt like when half the AI ecosystem knocked on their door [00:18:00] Life inside ClickHouse: cultural fit, Tokyo offsite, and what surprised them [00:20:00] Agentic coding in practice: velocity per engineer, what still needs a human [00:22:00] The planning loop: Claude summarising GitHub discussions, RFC → agent → review [00:23:00] The "decision inbox" model: engineers as taste-makers and reviewers [00:27:00] How to build an observability stack for the agentic era from scratch [00:29:00] Agentic on-call: the 3 am Codex workflow and what's coming next [00:32:00] Where Langfuse fits vs. traditional observability agent quality vs. infra health [00:35:00] The real state of agents in production: the non-LinkedIn version
Best quotes:
"We didn't initially jump on the topic because we thought all the PhD AI people, they are much better at this. We have no idea what's going on, until we figured out nobody has a clue what's going on." — [00:05:00–00:06:00]
"We have two guys doing customer support, and we have basically an agent that is doing first-level customer support for us, and I think it's about doing about 10,000 conversations a week. We would never be able to do this type of support with two people." — [00:38:00]
"The alert comes in, I wake up at the night, I just take the alert from our Slack, copy paste it into Codex, and we have a skill there with all the context, and then it's just going." — [00:29:00–00:30:00]
"I currently think of an email/Linear inbox where an agent tells me, 'Hey Max, we needed to fix this here because this broke.' And then if I want to, I can just dive into it and see all the context within this notification and also take a corrective course, or I just let it go." — [00:41:00]
#138 From Hacker News to W3C: How One Amazon Engineer Accidentally Shaped the Future of AI Browsers // Alex Nahas, MCP-B
How a browser-based fix for an enterprise auth problem became a W3C web standard and what it means for how AI agents will interact with the web. Alex Nahas, founder of MCP-B and initiator of the WebMCP web …
Notas do episódio
How a browser-based fix for an enterprise auth problem became a W3C web standard and what it means for how AI agents will interact with the web.Alex Nahas, founder of MCP-B and initiator of the WebMCP web standard, joins Tobias to explore one of the most underappreciated shifts happening in AI: the browser as the primary runtime for agentic systems.
Key topics covered:
- What MCP actually is: an RPC framework for calling tools across processes, not the complex protocol it's made out to be
- The OAuth problem: why MCP's push towards OAuth locked out most enterprise infrastructure still running on SAML
- The WebMCP solution: running an MCP server in client-side JavaScript so agents can use the browser's existing auth context
- How a Hacker News post posted under anesthesia got 400 upvotes and caught the attention of Google and Microsoft
- Chrome 146 natively supports WebMCP, and what that means for adoption
- The chicken-and-egg problem: why website owners won't add WebMCP support until clients support it, and vice versa
- Agent identity: why agents don't need their own credentials and can operate as a subset of the user's identity
- Real-time bidding for agents: the emerging market where advertisers bid to inject results into agent contexts
[~04:30] "The browser itself is like the perfect sandbox we've been iterating on for so long now." — Alex Nahas [~06:30] "MCP is just an RPC framework. It's super simple. Basically just a wrapper around API documentation." — Alex Nahas [~13:00] "My first memory coming back was me arguing with people on Hacker News who didn't understand it." — Alex Nahas [~16:30] "Agents don't need their own identity. They can have an identity that's like a subset of the user who spun them off." — Alex Nahas [~32:30] "This reminds him of the dawn of programming — where everyone was just doing things and nobody really knew what they were doing, but people were just trying to figure things out." — Alex Nahas [~43:00] "Believe in yourself." — Alex Nahas
#137 - Only Three Search Engines Left Standing: One of Them Powers Your AI with JP Schmetz // Chief of Ads @ Brave
Thirty years building search infrastructure — and why the AI industry quietly depends on it. SHOW NOTES Jean-Paul Schmetz has been close to search infrastructure for nearly three decades — as founder of Clix …
Notas do episódio
Thirty years building search infrastructure — and why the AI industry quietly depends on it.SHOW NOTES
Jean-Paul Schmetz has been close to search infrastructure for nearly three decades — as founder of Clix, the European search engine that eventually became part of Brave, and now as Chief of Ads at Brave Software. In this conversation with Tobi, he opens up the hood on one of the internet's most misunderstood infrastructure layers.
Key topics covered:
- The three independent search indices: why only Google, Bing, and Brave could answer every query on earth if the others disappeared
- How AI grounding works — and how most AI products are either paying for Brave Search API or quietly scraping Google
- The SERP API ecosystem: legal exposure, unit economics, and why Jean-Paul gives it a two-year shelf life
- Google's origin story: the NDA, the 120% revenue share strategy, and why they spent years pretending to lose money
- What it takes to build a search index from scratch — and why it's still a 10-year problem
- The Inktomi story: the $1M autocomplete investment no one else was willing to make
- Why Brave is profitable selling shovels to the AI gold rush — and what that looks like at $10M/month in infrastructure
- Agents and the web: why most people won't know what to do with 50 agents, just like they didn't know what to do with 50 employees
- What Jean-Paul would tell his 1996 self: think simpler, invest more aggressively, and stop underestimating capital
TIMESTAMPS: [to be added]
QUOTES:
[17:49] "Google was not better. It was just simply that they were driving you to queries they could answer better." — Jean-Paul Schmetz
[37:44] "There are more countries in the world that build atom bombs than countries that have a search engine." — Jean-Paul Schmetz
[49:08] "You're driving these Formula 1s with the wheels not properly attached." — Jean-Paul Schmetz
[01:28:45] "It's easy to appear intelligent when you list all the things that can go right, but it's much more courageous to actually claim what can go wrong." — Jean-Paul Schmetz
#136 - AI Writes Code: Who Architects the Consequences? with Neal Ford // Software Architect & Author
Fitness functions, agentic guardrails, and why experienced architects matter more than ever in the AI era Show Notes: Neal Ford has spent decades thinking about how software systems hold together over time. In …
Notas do episódio
Fitness functions, agentic guardrails, and why experienced architects matter more than ever in the AI eraShow Notes: Neal Ford has spent decades thinking about how software systems hold together over time. In this episode, Tobias sits down with Neal to ask the question nobody is asking loudly enough: when AI agents write the code, who is responsible for the architecture?
We dive into:
-** Neal's origin story: **from journalism to mechanical engineering to computer science, and why writing made him a better architect
- **AI as an "advanced beginner": **why LLMs pattern-match instead of reason, and why that distinction matters deeply
- **Behavior vs. Capabilities: **the trap of building demos that scale to 60 users but collapse at 60,000
- **Architectural fitness functions: **how to use deterministic guardrails to constrain non-deterministic code generation
- Architecture Definition Language (ADL): using pseudocode to wire constraints into agents before they start building
- LLMs as interpolators: generating platform-specific fitness functions in Java, .NET, or Python from a single pseudocode spec
- **Legacy modernization: **why AI is best suited for re-engineering existing codebases, and what COBOL whispering looks like in practice
- Ephemerality as a new architectural dimension: the first question every CTO should ask before building anything
- **The future of developers and architects: **why experienced engineers are multiplied by AI — and 0 × 10 is still zero
- The AI hype cycle: RAD, no-code, and now agents — same pattern, same blowback, different speed
- Advice for CTOs: understand capabilities, govern what your agents build, and let life surprise you
Chapters: [00:00] Intro & welcome: Neal Ford — Software Architect & Author [00:28] Neal's journey: from journalism to computer science [02:10] Writing as a superpower in software architecture [03:45] AI as an "advanced beginner": the Dreyfus scale applied to LLMs [07:20] Behavior vs. Capabilities: the gap everyone is ignoring [11:05] Architectural fitness functions: guardrails for agentic systems [16:30] Architecture Definition Language (ADL) & LLMs as interpolators [21:00] Fitness function-driven architecture in practice [25:15] Legacy modernization: COBOL whispering and agentic re-engineering [30:40] Ephemerality: the new architectural dimension for CTOs [34:10] The future of developers, architects, and pull requests [38:50] The AI hype cycle: fads vs. trends [42:15] AI pricing models and the Silicon Valley playbook [44:30] Easter egg: choosing computer science over CIS — and why it paid off [46:00] Advice to his younger self: let life surprise you [47:10] Conclusion & final thoughts
#135 - From Legacy to Innovation: Yahoo's Modernization & AI with Lee Zen // CTO @ Yahoo
Modernizing Yahoo in the private-equity era: legacy constraints, shipping velocity, and AI-native consumer products Yahoo is far more than a nostalgia brand—it’s a broad consumer platform operating at huge …
Notas do episódio
Modernizing Yahoo in the private-equity era: legacy constraints, shipping velocity, and AI-native consumer productsYahoo is far more than a nostalgia brand—it’s a broad consumer platform operating at huge scale. In this episode, Tobias sits down with Lee Zen (CTO @ Yahoo) to explore how the company is modernizing in the private-equity era, and what “innovation” looks like when you’re also responsible for decades of legacy systems.
We dive into:
**
- Yahoo’s consumer portfolio: Mail, Finance, Sports, News, Search—and what that implies for platform strategy
- What changes under private equity: priorities, focus, and modernization constraints
- Modernization in real terms: cloud adoption, legacy trade-offs, and sequencing big bets
- Shipping velocity as a leadership lever: faster learning cycles over perfect planning
- Organizational mechanics for rapid experimentation at consumer scale
- AI in engineering: where it helps, where it creates new bottlenecks, and “AI as a coworker”
- AI in the product (real use cases): mail catch-up, news key takeaways, finance and fantasy-sports insights
- The hardest problems at consumer scale: cost optimization, velocity, and quality without regressions
- Career reflections: imposter syndrome, self-belief, and staying hands-on as a CTO**
Chapters:
[00:00] Intro & welcome: Lee Zen (CTO @ Yahoo) [00:28] Lee’s journey & Yahoo’s evolution [01:14] Early computing: DOS, QBasic, and the builder origin story [03:43] Academic background: CS + AI foundations [05:20] Yahoo today: portfolio breadth & what private equity changes [08:54] Modernization: cloud, legacy constraints, and technical trade-offs [17:44] Leadership & org design: enabling speed and experimentation [22:52] AI integration: tool vs. coworker, and the future of engineering [31:38] AI across Yahoo’s consumer products: mail, news, finance, sports [32:21] Avoiding “AI labels”: enhancing UX with human + AI judgment [33:26] Consumer-scale engineering challenges: cost, velocity, and quality [34:28] Easter egg: side projects and reflecting on the past [35:34] Advice to his younger self: self-belief, self-love, imposter syndrome [36:28] Conclusion & final thoughts
#134 - From Inner to Outer Loop: Agentic Coding, Stacking PRs, and the Cursor Merger with Greg Foster // CTO @ Graphite
Solving the "Outer Loop" bottleneck: Agentic coding, Stacked PRs, and the future of the CTO role with Graphite's Greg Foster The "Inner Loop" of coding (typing syntax) has been solved by AI. The new bottleneck …
Notas do episódio
Solving the "Outer Loop" bottleneck: Agentic coding, Stacked PRs, and the future of the CTO role with Graphite's Greg FosterThe "Inner Loop" of coding (typing syntax) has been solved by AI. The new bottleneck is the "Outer Loop"—reviewing, testing, and merging the flood of code that agents generate.
In this episode, Greg Foster (CTO of Graphite, now part of Cursor) explains why this shift drove their merger and what it means for the future of engineering.
We dive into:
- The Outer Loop Crisis: Why fast code generation breaks traditional review processes (and how to fix it).
- Agentic Engineering: The 2029 vision where engineers become "architects of agents" and writing code trends to zero.
- Stacked PRs: Why smaller, dependent changes are the only way to maintain velocity at scale.
- Metrics that Matter: Why "PRs per engineer" is actually a valid proxy for velocity (velocity = speed + vector).
- The Cursor Merger: Combining the best text editor (Inner Loop) with the best review platform (Outer Loop).
- Provenance in Git: The need for storing "prompts" and AI attribution directly in commit history.
- Advice to Young Engineers: Why understanding the business model is as important as reading O'Reilly books.
Chapters:
- [00:00] Intro: Greg's journey from iOS apps to Airbnb infrastructure
- [01:25] Graphite & The Pivot: Bringing "Phabricator-style" tools to GitHub
- [03:52] Reinventing the Pull Request: Stacked PRs and Merge Queues
- [13:20] The Velocity of Dev Tools: When Copilot/Cursor changed the game
- [20:41] Metrics: Is "PRs per engineer" a vanity metric or a velocity proxy?
- [24:22] Jevons Paradox: Why 2x productivity means more engineers, not fewer
- [26:30] The Merger: Why Graphite joined Cursor (Inner + Outer Loop)
- [33:30] Provenance: Storing AI prompts in Git history
- [52:00] The 2029 Vision: Writing code -> Zero. Reviewing Agents -> 100%.
- [56:17] Future of CI: Auto-generated sandboxes with agentic QA
- [59:26] Advice to his younger self: Understand the business, not just the code
#133 - Build the Learning Machine: AI Adoption, Flow Metrics, and the Future of the CTO Role with Eric Bowman
From “dangerously skip permissions” to the Andon cord: voluntary adoption, flow metrics, and how agents change what CTOs optimize for Partner Tradegate Direct: Europe’s most direct online broker – trade for …
Notas do episódio
From “dangerously skip permissions” to the Andon cord: voluntary adoption, flow metrics, and how agents change what CTOs optimize forPartner Tradegate Direct: Europe’s most direct online broker – trade for free, efficiently, and directly on the stock exchange. Trade directly here
TOPdesk: Discover the future of internal IT in the DACH region with the "Inside ITSM 2026" report – featuring exclusive data on AI, DEX, and collaboration from 6,000 IT professionals. Download the report here
In this episode, Eric Bowman (CTO @ King.com) breaks down how AI is changing programming, creativity, and technical leadership. We talk about what “living on the edge” looks like with modern AI tools, why “AI is just technology” is still the right mental model, and how CTOs can build organizations that stay fast, adaptable, and focused on time-to-value.
We dive into:
- Eric’s journey: TomTom → Zalando → CTO at King.com
- What “living on the edge” with AI means in real engineering workflows
- The future of programming: what shifts when AI becomes a default collaborator
- Why AI creativity is different from human creativity (and why that matters)
- What an “ideal” tech organization looks like in an AI-first era
- Time-to-value and time-to-learning as the CTO’s north star
- The Andon cord concept applied to software delivery
- How the CTO role evolves as AI reshapes execution, leverage, and expectations
- Motivating engineers to embrace AI without turning it into a mandate
- Voluntary adoption as a rollout principle (and where it breaks)
- Jevons paradox and why “cheaper” AI can still increase total spend
- Practical reflections and advice for tech leaders navigating the shift
Chapters:
[00:53] Meet Eric Bowman: CTO of King.com [02:24] Eric's Journey in Tech [03:25] Living on the Edge with AI [05:43] The Future of Programming with AI [09:51] Navigating the Complexities of AI [22:03] AI Creativity vs. Human Creativity [28:41] Building the Ideal Tech Organization [29:04] Focusing on the Tech Field [29:21] The Future of SaaS [29:50] AI as Just Technology [33:03] Building a Learning Machine [33:32] The Onan Cord Concept [35:29] Time to Value and Learning [38:23] The Evolving Role of CTOs [41:04] Motivating Engineers to Embrace AI [46:02] Voluntary Adoption of Tools [51:35] Jevons Paradox and AI [53:55] Reflections and Advice [56:12] Conclusion and Farewell
#132 - Clarity Over Tooling: Velocity & Building Teams Without Drama with Loïc Houssier // CTO @ Superhuman Mail
From acquisition carve-outs to 100ms interaction rules: Engineering velocity through team clarity, not tool proliferation Partner TOPdesk: Discover the future of internal IT in the DACH region with the "Inside …
Notas do episódio
From acquisition carve-outs to 100ms interaction rules: Engineering velocity through team clarity, not tool proliferationPartner TOPdesk: Discover the future of internal IT in the DACH region with the "Inside ITSM 2026" report – featuring exclusive data on AI, DEX, and collaboration from 6,000 IT professionals. Download the report here
Outline How do you maintain execution velocity while building quality products? Loïc Houssier (CTO of Superhuman Mail, formerly Superhuman) offers a contrarian perspective: the answer isn't better tools or frameworks—it's radical clarity about team missions and strategic alignment.
With experience spanning French defense contractors (Orange, Thales), navigating DocuSign's complex French acquisition, and now steering Superhuman through its Grammarly acquisition/rebrand, Loïc brings a unique European-to-SF perspective on technical leadership.
Key insights for engineering leaders:
Execution & Velocity • PR per engineer per week as the "less bad" metric - track trends, not absolutes across teams • Clarity drives speed: clear team missions eliminate dependency bottlenecks and headquarters approval loops • Most velocity gains come from strategy clarity, not tooling improvements • Vertical team alignment: minimize cross-team dependencies by organizing around experiences (calendar team, compose team) • Focus quarterly: solve one major problem per quarter (velocity this quarter, quality next quarter)
Technical Architecture Decisions • Monorepo philosophy: resist microservices until genuine pain necessitates splitting • 100-millisecond interaction rule: every UI interaction must complete under 100ms • Offline-first architecture: sync happens in background, all actions execute locally • Gmail/Outlook API integration over IMAP for modern email clients • Modulith with domain-driven design over distributed microservices
Team Organization • Guild structure: formalize existing technical conversations (iOS guild, backend guild, AI guild) • Guild leaders surface quarterly problems and negotiate engineering time with product • Platform team + vertical feature teams = minimal dependencies • Full-stack teams owning specific experiences prevent feature factory mentality • Aligned autonomy: clear objectives, flexible execution methods
Framework Skepticism • Spotify model: "mostly marketing" according to actual Spotify engineers • GitLab remote-first: ~70% there, not the paradise portrayed • Context matters more than best practices from big tech • Apply frameworks as inputs, not blueprints - adapt to your specific context • Agile manifesto written by senior architects who knew each other - doesn't translate directly to teams fresh from college
Product & Design Philosophy • Laser-focused personas (professionals with heavy email volume) vs. Gmail's everyone approach • Four hours per week user time savings from better email UX • Design-centric like Linear: quality experience for narrow audience beats mediocre experience for everyone • Promotion frameworks should align with product goals: bake quality into career ladders if quality matters
Acquisition Integration • DocuSign French carve-out: Ministry of Finance blocked some assets, created split structure • Execution during acquisition demonstrates team value beyond revenue/customers • French labor law calculated as risk, not blocker - opportunity cost of not entering EU market • Smooth landings possible: kept redundant roles due to EU market subtleties
Cultural Observations • US mindset: "we might die tomorrow" drives urgency vs. EU "we're already successful" complacency
• European social safety nets enable higher risk-taking than perceived - losing your job in France is manageable • US risk-taking constrained by healthcare costs, college tuition, lack of safety net • Europeans are "grumpy" seeing empty glass, Americans see half full with optimismChapters:
[00:51] Superhuman, Grammarly, and the rebrand to Superhuman Mail
[01:47] Why Grammarly became Superhuman: AI-native productivity suite vision
[06:18] Career journey: Math PhD track to security researcher at Orange and Thales
[08:18] Submarines and defense contracts: Building the muscle to learn deep tech fast
[09:53] DocuSign's French acquisition: Navigating labor law and Ministry of Finance approvals
[12:16] Execution velocity: Getting shit done fast as a competitive advantage
[15:43] Personal secret sauce: Learning fast, working with smarter people, execution focus
[18:58] PR per engineer per week: The "less bad" velocity metric
[22:26] Vertical team alignment and minimizing cross-team dependencies
[24:22] Monorepo philosophy: Resist microservices until it's genuinely painful
[27:02] Framework skepticism: Why Spotify and GitLab models are "mostly marketing"
[30:41] Quarterly focus: Solve one major problem every three months
[32:30] Guild structure: Formalizing existing technical conversations
[36:14] Platform teams + vertical feature teams = ownership without dependencies
[38:31] The Linear of email: Laser-focused personas and design-centric approach
[40:25] Four hours per week saved: The value proposition for heavy email users
[42:05] 100-millisecond rule: Offline-first architecture for instant interactions
[46:16] Google's engineer-driven culture and the Gmail maintenance problem
[48:38] Promotion frameworks determine product quality
[50:02] Career advice: Take more risk in Europe - safety nets enable experimentation#131 - AI Product Strategy: When to Build and When to Wait with Matthias Keller // CPO @ Kayak
From Alexa to LLMs: Platform experiments, distribution challenges, and how AI is democratizing feature development in travel tech Tradegate Direct: Europe's most direct online broker – trade for free …
Notas do episódio
From Alexa to LLMs: Platform experiments, distribution challenges, and how AI is democratizing feature development in travel techTradegate Direct: Europe's most direct online broker – trade for free, efficiently, and directly on the stock exchange. Trade directly here
In this episode, Matthias Keller (CPO @ Kayak) shares strategic lessons about making AI product decisions under uncertainty. With 12 years at Kayak and experience spanning multiple AI waves—from Alexa to LLMs—Matthias offers a masterclass in knowing when to invest early and when to wait.
We dive into:
- Strategic framework for evaluating emerging AI platforms (Alexa, ChatGPT, Vision Pro, Apple Intelligence)
- Why Kayak's 2016 Alexa experiments failed and what that taught them about timing
- The shift from "will this work?" (voice) to "it should work" (LLMs) and why that matters
- Competing with distribution giants: Google Flights has massive advantages, so when do you innovate vs. wait?
- Real talk on platform bets: early ChatGPT plugins, evaluating ROI on new platforms
- How LLMs democratized AI development—engineers now build AI features without data scientists
- User expectation shifts: the "why" questions and what they mean for product strategy
- Commercial complexity: airlines control content, credit cards worth more than tickets
- The "if you build it they may come, if you don't they won't" mindset for CTOs
- Balancing first-mover advantage with execution realities in your org
This conversation offers actionable frameworks for product and engineering leaders navigating AI strategy decisions.
Chapter 1: From Sensors to Travel Tech (00:00 - 09:00) Matthias shares his unconventional path from measuring train wheel vibrations and wireless sensors on mountains to joining Kayak 12 years ago. He discusses the growing complexity of travel booking as airlines unbundled services, creating 15+ ticket options instead of just economy, business, and first class.
Chapter 2: Competitive Landscape & Google Flights (09:00 - 13:00) The challenges of competing in a space where Google Flights has massive distribution advantages. Matthias explains how Kayak maintains its edge through innovation like "hacker fares" (combining one-way tickets from different airlines) and comprehensive comparison across hundreds of partners.
Chapter 3: Platform Evolution - From Alexa to LLMs (13:00 - 28:00) A deep dive into Kayak's platform experiments, from 2016's Alexa skills and Facebook Messenger bots to today's AI mode. Matthias reflects on what worked (flight tracking), what didn't (voice checkout), and the key differences that make LLMs more successful than earlier voice assistants.
Chapter 4: AI Mode & Conversational Search (28:00 - 41:00) How Kayak integrated LLM-powered search across their products, not just as a separate experience. Matthias discusses engagement patterns, the importance of explaining when AI helps versus traditional search, and fascinating user behaviors like asking "why" they should book specific options.
Chapter 5: Distribution in the AI Era (41:00 - 46:00) The "dam breaking" moment when airlines and travel providers might embrace AI platforms for distribution. Discussion of commercial barriers, ecosystem control, and how the industry balance between direct booking and meta-search might shift.
Chapter 6: AI Democratizing Development (46:00 - 50:00) Matthias explains how LLMs enable software engineers to build AI features without data scientists, using examples like Kayak's price check feature that uses vision AI to compare flight prices from screenshots.
#130 - From PhD Research to DuckDB: Building the Next Generation of Analytical DBs with Mark Raasveldt // CTO @ DuckDB
How single-node performance and embedded analytics are revolutionizing database architecture for modern applications Tradegate Direct: Europe's most direct online broker – trade for free, efficiently, and …
Notas do episódio
How single-node performance and embedded analytics are revolutionizing database architecture for modern applicationsTradegate Direct: Europe's most direct online broker – trade for free, efficiently, and directly on the stock exchange. Trade directly here
In this episode, we dive deep into the world of analytical databases with Mark Raasveldt, the co-founder and CTO of DuckDB Labs. Mark takes us through his fascinating journey from academic research to building one of the most talked-about databases in the industry.
We explore:
- How DuckDB emerged from academic research at CWI Amsterdam
- The technical philosophy behind embedded analytical databases
- Why single-node performance still matters in the age of distributed systems
- The challenges of building a database system from the ground up
- Open source business models and the DuckDB Labs approach
- The evolution of columnar data formats and Parquet optimization
- How DuckDB is making high-performance analytics accessible to all developers
- The future of analytical data processing and embedded systems
This conversation offers unique insights into database architecture, performance optimization, and the intersection of academic research and commercial success.
#129 - $32B Lessons: Building CTO Teams, Rapid Innovation, and Staying Customer-Connected with Solal Raveh
What Wiz's $32B acquisition teaches about scaling CTO teams, rapid innovation, and customer-centric leadership Tradegate Direct: Europe's most direct online broker – trade for free, efficiently, and directly on …
Notas do episódio
What Wiz's $32B acquisition teaches about scaling CTO teams, rapid innovation, and customer-centric leadershipTradegate Direct: Europe's most direct online broker – trade for free, efficiently, and directly on the stock exchange. Trade directly here
What does it take to build a company worth Google's $32 billion acquisition? Solal shares the hard-won lessons from scaling technical teams during one of the fastest-growing security companies in history.
Key leadership insights from the episode:
• CTO Office Evolution: How Wiz split technical leadership into 3-4 specialized tracks focused on domain expertise rather than geography • The Geographic Cloning Failure: Why hiring locally for technical roles created dissatisfaction and duplication instead of excellence
• Remote Team Success: Building global CTO teams around container security, API infrastructure, and runtime protection expertise • Incubation Philosophy: Moving from building teams to rapid POC development - like their 3-hour response to the Shy Hulud NPM exploit • Customer-Centric Engineering: How every CTO team member stays connected to customer challenges rather than waiting for inbound requests • Innovation Metrics: The challenge of measuring incubation success vs finished features, plus P99 performance tracking for enterprise readiness • People-First Leadership: Why focusing on people and customer problems trumps pure technical automation • Security Industry Insights: Making security "not scary" through gamification and community engagementTechnical Context (18% of episode): • Agentless API scanning that maps entire cloud environments in minutes vs weeks • Graph database visualization of attack paths from code credentials to AWS admin access • Risk contextualization: Why a CVSS 9.9 vulnerability on unused images can wait, but the same vulnerability across 10,000 live VMs demands immediate action • AI agent "Mika" that correlates threat intelligence with specific infrastructure data
Chapters:
- [01:49] - What makes Wiz worth $32 billion: People and technology combined
- [04:08] - Technical architecture: Agentless scanning to graph databases to agent validation
- [10:56] - Personal journey: From assembly coding to customer-focused engineering
- [14:18] - CTO office structure: Splitting technical leadership into specialized domains
- [17:30] - Three-fold CTO mission: Foresight, gray areas, and team incubation
- [19:35] - Evolution from team building to rapid POC development
- [23:30] - Security industry paradigm shifts: Vulnerabilities, identities, and AI challenges
- [25:30] - Log4Shell response: Community support and agentless advantage
- [34:17] - Major failure: Why geographic CTO team cloning doesn't work
- [40:09] - CTO metrics challenges: Measuring innovation vs finished features
- [43:16] - Missing hands-on work: The balance between leadership and building
- [45:44] - Time travel advice: Focus more on people than automation
#128 - From Tickets to Problems: Klaus Breyer // Head of Product & Technology @ Edding
From assembly lines to problem-solving teams: practical strategies for breaking development silos Here's the thing about agile transformations: they almost never work the way they're supposed to. Teams end up …
Notas do episódio
From assembly lines to problem-solving teams: practical strategies for breaking development silosHere's the thing about agile transformations: they almost never work the way they're supposed to. Teams end up more siloed than before, chasing tickets instead of solving actual problems. Klaus Breyer has seen this pattern everywhere, and he's figured out some ways to break it.
Klaus runs product and technology at Edding—yeah, the pen company—but his background is anything but traditional. He learned team coordination by managing 40-person World of Warcraft raids, ran a few startups, and now applies those lessons to building software at a 150-year-old German manufacturer. It's an unusual path that gives him a different perspective on how teams actually work together.
We talked about Shape Up methodology, but honestly, the more interesting stuff was about changing how teams think about their work. Klaus has some pretty specific ideas about when teams are ready to ditch ticket systems entirely, how to spot the early warning signs of assembly-line thinking, and why most agile implementations fail at the mindset level.
Tradegate Direct: Europe's most direct online broker – trade for free, efficiently, and directly on the stock exchange. Trade directly here
Also, Edding is doing some wild stuff with technology—like building a driver license verification system using invisible conductive ink that smartphones can read. Who knew pen companies were this technical?
What we covered:
[00:51] Klaus's background and how Edding ended up doing serious tech [01:30] The invisible ink technology that got my attention [05:11] Why building cool tech is easier than building teams that work well together [06:05] Learning management from World of Warcraft raids (seriously) [08:40] The realization that most project failures aren't technical [09:29] The shift from "give me a ticket" to "let me solve the problem" [10:35] How Shape Up actually works in practice—6 weeks, small teams, single focus [11:26] Why tiny teams still end up with silos [13:22] Red flags that your team is in assembly-line mode [14:16] Late compromises as a symptom of poor collaboration [15:40] The magic number for team size and why bigger gets messy [16:28] Matching the right people to the right problems [18:17] Breaking down specialization barriers [19:23] How "business" ruined the original agile manifesto [20:35] Getting clear on what actually matters [22:28] The art of problem definition (harder than it sounds) [24:23] Having honest conversations about how much effort problems deserve [27:17] Building projects that can be cut at any point [29:41] When senior teams can just… work without tickets [32:17] What product managers actually do in this model [35:00] Conway's Law and organizing around what you're building [38:10] Dealing with matrix organizations and temporary teams [39:58] First steps for teams stuck in traditional agile [42:05] The question Klaus asks to cut through confusion [43:39] Remote collaboration tools and templates [45:46] Starting solution sessions with blank slates [48:19] Timeline from problem to working code [49:02] How you know when it's actually working
Quotes worth remembering:
"Almost all teams out there have silos. You can have silos in the smallest teams. You can have silos with three or four people if they are thinking about the work in the wrong way." [11:15]
"One of the biggest signs is when you need to do tradeoffs because the time is running out. And then if you do tradeoffs because the time is running out, most of the times the tradeoffs are then done or led by the engineers because we don't have time to complete this feature." [13:22]
#127 - Kelsey Hightower's Unfiltered Truths: 25 Years of Infrastructure, DevOps, and Retiring at 42
Unfiltered lessons on infrastructure, career strategy, and the art of knowing when to stop. What does 25 years in tech teach you? Kelsey Hightower, a self-taught engineer who rose to become a Distinguished …
Notas do episódio
Unfiltered lessons on infrastructure, career strategy, and the art of knowing when to stop.What does 25 years in tech teach you? Kelsey Hightower, a self-taught engineer who rose to become a Distinguished Engineer at Google and a key figure in the Kubernetes community, sits down with Tobi to share unfiltered insights from his extensive career and his recent decision to retire at 42.
This conversation goes beyond the hype, focusing on the timeless principles of technical leadership, system maintenance, and career strategy. Kelsey breaks down the reality of infrastructure management, the hidden costs of complexity, and why the most important engineering skill is understanding the business.
Tradegate Direct: Europe's most direct online broker – trade for free, efficiently, and directly on the stock exchange. Trade directly here
Technical insights for CTOs and engineering leaders:
- The Lifecycle of Technology: Why new tech is additive, not a replacement, leading to an ever-growing collection of systems to maintain.
- The Value of Managed Services: Understanding when to build vs. when to buy, and why most companies should lean on experts.
- Evolutionary Architecture: The danger of making permanent technology decisions on day one and the importance of evolving your stack.
- Business-Driven Engineering: Why engineers who can't link their work to revenue are at risk and how CTOs can use business metrics to guide priorities.
- A Grounded View on AI: How the evolution of underlying systems is a prerequisite for AI to reach its full potential.
TIMESTAMPS: [02:52] Kelsey's self-taught journey into tech [05:30] From computer store owner to Google data center technician [09:17] Why infrastructure and complexity look the same after 25 years [10:25] The reality of technology accumulation vs. replacement [12:28] The myth of choosing the ""perfect"" tool (Kubernetes vs. Firebase) [14:15] Why managed services are the right choice for most companies [18:00] The biggest mistake: failing to evolve your tech stack [22:47] Why engineers must link their work to revenue to survive layoffs [26:04] How CTOs can use business metrics to guide engineering priorities [31:13] The future of software: Will AI change the fundamentals? [38:17] The evolution required for AI to manage entire systems [41:30] Why indie hackers and small SaaS will survive the AI wave [48:12] The philosophy behind retiring at 42 to ""learn how to live"" [56:00] Advice to his younger self: ""You're on the right track""
QUOTES:
- ""I think the only thing that is wrong is when you fail to evolve… They try to make a permanent decision on day one."" - Kelsey Hightower [18:00]
- ""If you're ignoring all of that signal [about what's important to the business], then you're going to be in for a surprise… make my own career pivot before someone decides to make it for me."" - Kelsey Hightower [24:47]
#126 - AI Transformation at Scale: Practical Adoption Across 150+ Engineers with Peter Gostev // Head of AI @ Moonpig
Navigating AI hype versus implementation reality in large engineering organizations The AI transformation challenge isn't just technical—it's organizational. Peter Gostev, Head of AI at Moonpig, discusses …
Notas do episódio
Navigating AI hype versus implementation reality in large engineering organizationsThe AI transformation challenge isn't just technical—it's organizational. Peter Gostev, Head of AI at Moonpig, discusses building practical AI capabilities in a 600-person company while navigating the gap between AI hype and implementation reality.
From managing adoption across 150 software engineers to partnering with non-technical teams, Peter shares insights on driving meaningful AI transformation that goes beyond superficial tool usage. This conversation explores the contrast between industry claims of widespread AI adoption and the nuanced reality of implementing AI at scale.
TIMESTAMPS: [00:00:51] Introduction: Peter Gostev, Head of AI at Moonpig [00:03:49] First Computer Memory: Pentium 2 and the Golden Age of Learning [00:05:28] Career Evolution: From Gaming to Data Science and Machine Learning [00:07:01] AI Hype Cycle: Underestimating Future Potential While Overhyping Present [00:09:41] Agent Reality Check: Why Full Agentic Systems Are Harder Than Expected [00:13:51] The Decade of Agents: Learning from Autonomous Driving Timelines [00:15:09] Developer Tool Adoption: The Skill Gap in AI-Assisted Programming [00:18:40] Organizational Change: Teaching 150 Engineers New Workflows [00:20:51] Claude Code Success: Finding Product-Market Fit in CLI Tools [00:24:33] Context Management: Why Proper AI Usage Requires Intuition [00:26:07] Three-Pillar AI Strategy: Tools, Automation, and Experimentation [00:28:01] Team Structure: AI Engineers vs Data Scientists [00:30:10] Partnership Models: Building vs Enabling Other Teams [00:32:51] Adoption Management: Direct Collaboration vs Ambassador Networks [00:35:45] Prototype Power: One Evening of LLM Work vs Weeks of Traditional Development [00:38:02] Subject Matter Expertise: Custom GPTs for Non-Technical Users [00:42:45] Writing with AI: Research vs Synthesis and the Importance of Human Thinking [00:47:08] Tool Strategy: ChatGPT Rollout Without Internal Marketing [00:49:58] Adoption Reality: 400 Users But Still Only 5-10% of Potential Usage [01:02:06] Training Challenges: From Individual to National Level Education [01:05:10] Future of Development: What Changes and What Remains Human [01:08:24] Developer Productivity: Higher Ambition, Not Less Work [01:16:50] University Concerns: Are Students Prepared for AI-First Development? [01:18:47] Career Advice: Do Hard Technical Things Early While You Can
QUOTES:
• ""I think there's never really been more exciting time in my career versus now and every time we can we can just do more and more and more, which is just such a cool position to be in."" - Peter Gostev [00:06:39]
• ""I think people really underestimating where we are and or how much more there is to go… we are really just at the beginning of these investments starting to come through."" - Peter Gostev [00:07:01]
• ""On any given project, most of the work is actually non-AI work… most of it is integrations, scaffolding, deployment, UI, all of the things like that. And then the AI part of it is normally maybe 10, 20% of the actual work."" - Peter Gostev [00:28:01]
#125 - Two CTO Dinosaurs vs. Today's Tech Hype with Raz Shuty // CTO @ auxmoney
Unfiltered insights on microservices myths, cloud cost reality, and pragmatic technical leadership Two veteran CTOs tear down today's tech hype with brutal honesty and hard-won experience. Raz Schweiger-Shuty …
Notas do episódio
Unfiltered insights on microservices myths, cloud cost reality, and pragmatic technical leadershipTwo veteran CTOs tear down today's tech hype with brutal honesty and hard-won experience. Raz Schweiger-Shuty shares his controversial approach to technical leadership at auxmoney, where he stopped a microservices rewrite and focused on business outcomes over engineering trends.
From his early days as a QA engineer to running 80-person engineering organizations, Raz demonstrates why sometimes the best technical decision is the boring one that actually works. This conversation offers a masterclass in pragmatic technical leadership for an age obsessed with the latest frameworks and architectures.
Key insights for technical leaders: • 🏗️ Domain-driven design as prerequisite for any architectural decision • 💰 FinOps strategies that connect engineering decisions to business metrics • 🔄 Modular monolith patterns for teams of 40+ engineers • 📊 Why velocity metrics are vanity and DORA metrics matter • 🚫 Avoiding Kubernetes complexity when elasticity isn't needed • 👥 Conway's Law applied to value stream organization • 🔧 Platform engineering pitfalls and centralization nightmares
TIMESTAMPS: [00:01:00] Show premise: Two CTO Dinosaurs vs. Today's Tech Hype [00:03:56] Raz's unconventional path: QA to CTO via pragmatic learning [00:08:31] Junior vs senior engineers in the AI era [00:11:01] auxmoney: 17 years without a CTO, then Raz arrives [00:13:45] Stopping the microservices migration: building trust through disagreement [00:15:08] Language choices: PHP works, Rust creates hiring problems [00:19:35] Modular monolith strategy using domain-driven design [00:21:56] Value stream teams and Conway's Law alignment [00:22:26] Kubernetes reality check: elasticity vs complexity overhead [00:29:53] FinOps deep dive: €120k to €85k AWS cost reduction [00:32:13] Cost-per-transaction metrics for engineering accountability [00:35:00] Platform engineering centralization dangers [00:40:22] Velocity metrics are broken: focus on DORA instead [00:43:54] Meantime to recovery as team health indicator [00:48:34] Bottom-up AI adoption: Cursor rollout strategy [00:54:16] Fintech security: ISO 27001 and AI supply chain risks [00:56:59] Leadership lessons: building trust through authentic communication
#124 - The Path to AGI: Inside poolside’s AI Model Factory for Code with Eiso Kant
Building Human-Level AI for Code: Model Factories, RL at Scale, and Distributed Teams Technical Deep Dives: Poolside’s model factory: end-to-end automation from raw data to production models Scaling RL from …
Notas do episódio
Building Human-Level AI for Code: Model Factories, RL at Scale, and Distributed TeamsTechnical Deep Dives:
- Poolside’s model factory: end-to-end automation from raw data to production models
- Scaling RL from code execution: 800,000+ containerized repos, millions of agent tasks
- Immutable versioning with Apache Iceberg for full traceability
- Distributed team structure: 120+ engineers across US/EU, monthly in-person sprints
- Hardware orchestration: 10,000+ H200s, hot swap failover, dynamic allocation
- Leadership: dividing responsibilities, low-ego culture, and the MIT principle
- Future of software: managing agent workforces, context window strategies, continual learning
"Our model factory runs thousands of experiments before a single production model is trained. It’s an empirical science—every component, from data ingestion to evals, is versioned and traceable." – Eiso Kant
Chapters: [00:04:28] Poolside’s unique approach to foundation models [00:13:02] Scaling hardware: 10,000+ H200s and orchestration [00:17:42] RL, agents, and the future of developer tools [00:24:56] Immutable versioning and evaluation frameworks [00:36:04] Distributed team structure and monthly sprints [00:40:26] Leadership, decision-making, and low-ego culture [00:45:54] Lessons for CTOs: breaking process dogma, preparing for agent-driven orgs [00:50:54] The next 3 years: AGI, agent workforces, and the end of manual coding [00:53:44] Context window, continual learning, and model memory [00:56:20] Everything collapses into the model: product, research, and daily life [00:59:46] Advice to a younger self: scale compute, trust RL+LM, and the four-minute mile
#123 - From Nokia to AI-IoT: Engineering the Physical World with Bernd Groß // CEO @ Cumulocity
Bridging operational technology and cloud architecture for industrial-scale systems The convergence of AI and IoT is reshaping how technical leaders approach industrial systems architecture. Bernd Groß, CEO and …
Notas do episódio
Bridging operational technology and cloud architecture for industrial-scale systemsThe convergence of AI and IoT is reshaping how technical leaders approach industrial systems architecture. Bernd Groß, CEO and co-founder of Cumulocity, shares insights from building Germany's leading IoT platform—from its origins as a Nokia spin-off to becoming a critical infrastructure for companies managing tens of thousands of connected industrial assets.
This conversation explores the unique engineering challenges of industrial IoT, where systems must process 50 terabytes of monthly data from wind turbines while maintaining real-time control capabilities. Bernd discusses the evolution from monolithic architectures to microservices, the complexity of supporting hundreds of industrial protocols, and the emerging "AI-IoT" paradigm.
Key technical insights: • 🏗️ Speed-layer architecture design for real-time data processing with cloud data lake offloading • 📡 Managing protocol complexity through certified partner ecosystems (320+ PLC protocols) • 🔄 Microservices transformation strategies for industrial platforms on Kubernetes • 🤖 AI model deployment and operationalization across distributed industrial assets • ⚡ Edge-cloud hybrid architectures for latency-critical and regulated environments • 🛠️ Technical debt management during platform evolution and acquisition cycles
Thx 2 our partner SoSafe for sponsoring this Episode.
Or start a free safety simulation with SoSafe
TIMESTAMPS: [00:01:04] Introduction to Bernd Groß and Cumulocity's Industrial IoT Platform [00:03:45] Early Programming: From ZX Spectrum to PC Building [00:07:07] The Nokia Rejection: Language Barriers in Early Career [00:11:51] Nokia's Innovation Incubator and Cloud Computing (2010) [00:17:24] Meeting Werner Vogels: Early AWS and Cloud Architecture [00:20:07] Spinning Off Cumulocity from Nokia Networks [00:21:21] IoT Device Management: Original Platform Vision [00:23:56] Real-Time Architecture vs. Legacy VPN Systems [00:26:33] From APIs to Applications: Learning Customer Needs [00:31:19] Software AG Acquisition: Strategic Partnership [00:34:03] Management Buyback from Silver Lake Partners [00:37:53] AI's Impact on IoT: The Emergence of "AI-IoT" [00:42:03] Speed-Layer Architecture and Data Lake Integration [00:46:46] Edge vs. Cloud: Hybrid Deployment Strategies [00:48:53] Technical Debt: Protocol Complexity and Modernization [00:52:37] Partner Ecosystem: Managing 320+ Industrial Protocols [00:56:59] Leadership Lessons: Trust and Authentic Communication
QUOTES: • "When you compare that to the generation, the technology which was available before, there was really a VPN system where you needed to dial into the wind turbine… And today it's very, very different with real-time analytics happening in the background." - Bernd Groß [00:23:56] • "Our platform does data acquisition, data normalization, contextualization of the data. So you need to help structure the data because that is the foundation for your training for your AI model training." - Bernd Groß [00:42:03]
#122 - Grid Control in Milliseconds: Engineering Energy Systems with Barbara Wittenberg // CTO @ 1KOMMA5°
Real-time system architecture lessons from the edge of the energy transition Technical infrastructure enables the modern energy transition. Barbara Wittenberg, CTO at 1KOMMA5°, reveals the engineering …
Notas do episódio
Real-time system architecture lessons from the edge of the energy transitionTechnical infrastructure enables the modern energy transition. Barbara Wittenberg, CTO at 1KOMMA5°, reveals the engineering challenges behind connecting and optimizing 40,000+ household energy assets while building a unified tech platform after acquiring 80+ companies across 7 countries.
Tech leaders will find valuable parallels to their own challenges as Barbara discusses how her team modernized legacy systems, managed complex integrations, and deployed advanced optimization algorithms that must respond in milliseconds to prevent grid failures.
Key technical insights include:
- 🏗️ Building infrastructure-as-code and standardizing operations across acquired companies
- 📊 Creating data pipelines that connect real-world physical devices to cloud optimization systems
- ⚡ The millisecond-level responsiveness required for grid stabilization services
- 🔄 Managing the product transition from siloed applications to integrated platforms
- 🤖 Leveraging AI to automate complex technical explanations to end users
- 🧠 Post-merger integration strategies that preserve valuable domain knowledge
Thx 2 our partners Surfshark & SoSafe for sponsoring this Episode.
Go to surfshark.com/alphalist or use code ALPHALIST at checkout to get 4 extra months of Surfshark VPN!
Or start a free safety simulation with SoSafe
TIMESTAMPS: [00:00:51] Introduction to Barbara Wittenberg & 1KOMMA5° [00:02:52] Barbara's Journey from Electrical Engineering to CTO [00:05:13] Early Virtual Power Plant Development at E.ON [00:07:05] The Transition from Energy to Tech and Back [00:11:02] Explaining 1KOMMA5°'s Business Model & Technology [00:13:00] The Physics of Energy Systems and Real-Time Control [00:16:41] Grid Operators and the Complex Energy Landscape [00:19:47] Smart Meters and Market Differences Across Countries [00:22:32] Battery Storage and Energy Trading Economics [00:25:35] Building Software Systems for Installation Companies [00:28:54] Tech Stack and Integration of Acquired Companies [00:33:19] Managing 2,500 Employees Across 80+ Locations [00:37:21] Data Standardization and Business Intelligence [00:42:10] Using AI to Explain Energy Decisions to Customers [00:49:22] Female Representation in Energy & Engineering [00:54:28] Closing Thoughts on Consumer Energy Solutions
QUOTES:
- ""We send live signals in two-second intervals to 40,000 assets in the field. In Sweden, we need to respond to grid frequency changes in milliseconds - you can't send faxes, you can't even send an email. You need real-time control of your assets."" - Barbara Wittenberg [00:13:50]
- ""If you want to bring the best solution, you need to have all assets connected in one system."" - Barbara Wittenberg [00:12:12]
#121 - Canva's Playbook: Scaling Teams, Tech, and AI with Adam Schuck // Senior Engineering Director @ Canva
Insights into hypergrowth, late-stage career frameworks, AI adoption, and the tech powering Canva's collaborative editor. In this episode, Tobi chats with Adam Schuck, Senior Engineering Director at Canva, a …
Notas do episódio
Insights into hypergrowth, late-stage career frameworks, AI adoption, and the tech powering Canva's collaborative editor.In this episode, Tobi chats with Adam Schuck, Senior Engineering Director at Canva, a company that has scaled to over 5,000 employees, 2,000+ engineers, and 230 million MAUs while remaining profitable. Adam shares his journey through startups (including acquisitions by Twitter and Canva) and large tech companies like Google, leading to his current role managing 220 engineers at Canva.
They dive deep into the challenges and strategies behind Canva's hypergrowth, including:
- Scaling engineering teams from 150 to over 2000.
- Implementing a career framework (Growth & Development Framework) relatively late at 1000+ engineers, moving beyond "minimum viable structure."
- Canva's approach to AI: Viewing it as a tailwind, fostering experimentation ("AI Impact"), providing broad access to tools (Cursor, Copilot, LLMs), and emphasizing human responsibility ("humans as shepherds").
- The core technology decisions enabling Canva's success, particularly the operational transformation logic for real-time concurrent editing and the strategic shift to a unified web-based mobile experience (WebX).
- Maintaining a startup culture of adaptability despite massive scale.
- Adam's personal productivity hacks for leaders, focusing on ruthless calendar management and clear goal setting.
TIMESTAMPS: [00:00:51] Introduction to Adam Schuck & Canva's Scale [00:03:28] Adam's Personal Nerd Journey (Full Stack, Startups, Google, Twitter) [00:10:00] Canva's Business: Growth, Revenue, and Profitability [00:11:32] AI as a Tailwind: Canva's Strategy and Magic Studio [00:15:36] From PLG to Enterprise: Evolving Canva's Go-to-Market [00:17:21] Conway's Law & Org Structure at Scale (Matrix, Groups, Specialties) [00:19:45] Defining the "Paved Road": Tech Governance and Foundations Teams [00:24:23] AI Impact: Augmenting Engineers, Tooling (Cursor/Copilot), and Responsibility [00:31:29] Implementing Career Frameworks Late: The Growth & Development Framework Story [00:42:10] Tech Secrets: Concurrent Editing (Operational Transformation) Deep Dive [00:46:24] Tech Secrets: The Journey to a Unified Mobile Experience (WebX) [00:52:26] Personal Productivity Hacks for Leaders [00:57:19] Advice to Past Self
QUOTES:
- "We recognized that maybe we were now at the point where it was actually causing more harm than good to not have these kind of frameworks and structure in place." - Adam Schuck [00:33:50]
- "We're really clear with our messaging to our engineering team that we don't, we're not entitling our team to abdicate themselves of responsibility. That ultimately every line of code that is shipped at Canva, it's a person responsible for that." - Adam Schuck [00:28:46]
Aqui estão os 40 episódios mais recentes de 150. Os mais antigos estão no feed do podcast.
Podcasts parecidos
Mais podcasts de Tecnologia
Este podcast é seu?
Confirme com uma entrada no seu feed: você recebe um código aqui, adiciona como podcast:txt no canal do seu feed e nós verificamos. Depois disso, seu podcast aparece como “verificado” no SoundNode.