alphalist.CTO Podcast - For CTOs and Technical Leaders

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Aike Hillbrands co-founded and killed two companies before Kombo, now a Y Combinator-backed HR integration platform with $10M+ ARR and a $25M Series A. Along the way, his team built something almost by accident: a company-wide AI brain made of a GitHub repo, a Cursor agent, and a Slack channel, built in two hours, that replaced how the whole company gets answers.

In this episode, Aike explains why files and grep beat MCP tools and vector search for agent reliability, walks through Simon Willison's "lethal trifecta" of AI security risks and how a public Slack channel acts as a guardrail against it, and makes the case for why AI won't commoditize enterprise HR integrations anytime soon, despite that being Kombo's own bet.

Topics covered:

- How Kombo went from Notion AI to a Git-based company brain
- Why files and grep beat MCP tools and vector search for agent reliability
- The architecture: per-customer summary files, cross-linked support tickets, BigQuery CLI, Slack integration
- Simon Willison's "lethal trifecta" and practical mitigations
- Why a public Slack channel works as a security guardrail
- The buy-vs-build question for internal AI tooling
- Why enterprise HR API integrations resist commoditization by AI

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Vaibhav Gupta built computer vision for the original Microsoft HoloLens, optimized AR at Google, and wrote high-performance assembly at D.E. Shaw, then left it all to start from scratch. After a YC pivot away from a Slack competitor he was told not to build, he landed on something foundational: BAML, a programming language for a world where humans increasingly don't read code.

His thesis: every software leap came from a new compute paradigm getting its own language assembly, C, Java, JavaScript and LLMs are the next primitive. They're probabilistic and non-deterministic, which breaks our deterministic tooling. In this episode, Vaibhav explains why "shipping at agent speed" is really a problem of trust and control, why 90% of engineering is plumbing AI will delete, why "English as a programming language" can't work, and why the world has a mathematically infinite appetite for software.

Topics covered:

- Why LLMs are a new compute primitive and why that justifies a new language
- BAML: an embedded, type-safe language for structured LLM outputs across any language
- Shipping at agent speed as a problem of trust, locking, and granular control
- Why traditional CI/CD breaks in an agent loop
- The "data trench" one type system across code, backend, and data
- Why 90% of engineering is plumbing, and what changes when AI removes it
- Where SaaS pricing and product models are heading

Pat Casey was the first person besides founder Fred Luddy to write code at ServiceNow back in 2005, when it was called Glide and lived above a friend's restaurant. Twenty years later, he's CTO of a company where 85% of the Fortune 500 are customers, and until recently ran all of engineering: 10,000 people, 7,000 of them writing code. Almost nobody survives the journey from first engineer to public-company CTO. Pat did.

Tobi and Pat dig into how ServiceNow actually works under the hood: a metadata processing engine running 90,000 single-tenant databases and over 25 billion queries an hour, why they bought a 15-person German database company and turned it into RaptorDB, and why tearing apart a 20-year-old monolith is harder than every senior engineer thinks.

Then the conversation turns to AI. Pat bought 7,000 Windsurf licenses and measured a real, but unglamorous, 15% productivity bump, with a small subset of engineers going 5–6x while most barely changed. His thesis: AI coding is like playing five chessboards at once, and it's reshuffling the deck on who the top engineers will be. On agents, ServiceNow's answer is disarmingly simple: create a user called "AI Pat," assign it cases, and make it follow the exact same rules as humans because you should not trust an LLM more than you trust a human being.

Topics covered:

- From Atari 400 and floppy-disk jockey at Aldus to first engineer at ServiceNow
- Scaling engineering from a stuffed fish on a monitor to 10,000 people — and the productivity trough at ~100 engineers
- Single-tenant architecture: 90,000 databases, 25B+ queries/hour, and the monolith-to-Kubernetes migration
- Why ServiceNow bought Swarm64 and built RaptorDB on a Postgres fork
- 7,000 Windsurf licenses, Claude Code, and the real numbers on AI coding productivity
- "AI Pat": the anthropomorphic model for enterprise agents outcomes, not toolkits
- Whether AI kills seat-based SaaS, and why incumbents may have the inside track
- Pat's advice to CTOs: this is not a time for excessive caution

Before he founded Render, Anurag Goel was the fifth engineer at Stripe, where he watched roughly a fifth of the engineering team disappear into managing AWS, writing brittle, repetitive, error-prone infrastructure scripts that had nothing to do with the actual product. That experience became the seed for Render: a platform that automates away the undifferentiated DevOps work and lets application teams ship without standing up their own cloud team. Today, millions of developers build on it, and Render has raised over $260M from Bessemer and General Catalyst.

In this episode, Tobi and Anurag get into what's actually changing as AI moves from hype to production. Anurag makes the case that agents are simply a new kind of application, long-running, stateful, tool-heavy, and a new kind of end user you have to design for. He explains why Render deliberately refuses the "AI cloud" label, what he's building with Workflows and sandboxes, and why the hardest part of shipping agents isn't building them but seeing inside them.

The conversation also goes wide: how to hire executives when interviews lie, why short-lived keys and blast-radius thinking matter more than container escapes, how distribution is shifting from SEO to getting ChatGPT and Claude to recommend you, and why, despite all the "SaaS is dead" noise, specialization isn't going anywhere.

Topics covered:

Why ~20% of Stripe's engineers were stuck managing AWS and how that became Render
"We're not the AI cloud, we're the application cloud," and why the distinction matters
Agents, as a new type of application (and a new end user), you have to build for
Render Workflows and sandboxes: the consolidated AI runtime
Hiring executives when interviews are an imperfect signal
Security as blast-radius management: short-lived keys over "admin forever"
The shift from SEO to GEO, getting chatbots to recommend your product
Why SaaS isn't dying, and specialization still wins

Max Deichmann is the co-founder of Langfuse, the open-source LLM engineering platform that became the observability layer of choice for teams building production AI agents, before being acquired by ClickHouse. He started as a business student who taught himself to code via CS50 on a beach in Singapore, pivoted through Y Combinator, fired his own customers mid-batch, and built Langfuse out of a Sunday night conversation about what they'd actually want to build if nothing was in the way.

In this episode, Tobi and Max dig into what it really means to build and operate AI agents in production, not the LinkedIn version, but the 3 am alert, copy-pasted into Codex version. They cover the full loop: from pre-production experimentation and prompt iteration, to tracing, online evaluation, and the emerging architecture of agentic incident response. Max is unusually honest about where Langfuse itself still falls short, and what the next 12 months of the engineer's job actually look like.

What CTOs will take away: a clear mental model for LLM observability vs. traditional observability, a practical blueprint for agentic on-call workflows, and a grounded view of where agents are genuinely working in production today, and where the hype still outpaces reality.

Topics covered:

Why traditional observability tools fail for non-deterministic AI applications

The Langfuse loop: pre-production testing, tracing, online evaluation, and iteration

How the ClickHouse acquisition happened, and the half-page doc that decided it

Open source as a go-to-market strategy: adoption without a sales team

Agentic on-call: how Max's team handles 3 am incidents with Codex today

The "decision inbox" model, what the engineer's job looks like when agents do the work

Where agents are genuinely succeeding in production (and where LinkedIn is lying to you)