The Tech Trek

Phil Freo, VP of Product and Engineering at Close, has lived the rare arc from founding engineer to executive leader. In this conversation, he breaks down why he stayed nearly 12 years, and what it takes to build a team that people actually want to grow with.

We get into retention that is earned, not hoped for, the culture choices that compound over time, and the practical systems that make remote work and knowledge sharing hold up at scale.

Key takeaways
• Staying for a decade is not about loyalty, it is about the job evolving and your scope evolving with it
• Strong retention is often a downstream effect of clear values, internal growth opportunities, and leaders who trust people to level up
• Remote can work long term when you design for it, hire for communication, and invest in real relationship building
• Documentation is not optional in remote, and short lived chat history can force healthier knowledge capture
• Bootstrapped, customer funded growth can create stability and control that makes teams feel safer during chaotic markets

Timestamped highlights
00:02:13 The founders, the pivots, and why Phil joined before Close was even Close
00:06:17 Why he stayed so long, the role keeps changing, and the work gets more interesting as the team grows
00:10:54 “Build a house you want to live in”, how valuing tenure shapes culture, code quality, and decision making
00:14:14 Remote as a retention advantage, moving life forward without leaving the company behind
00:20:23 Over documenting on purpose, plus the Slack retention window that forces real knowledge capture
00:22:48 Bootstrapped versus VC backed, why steady growth can be a competitive advantage when markets tighten
00:28:18 The career accelerant most people underuse, initiative, and championing ideas before you are asked

One line worth stealing
“Inertia is really powerful. One person championing an idea can really make a difference.”

Practical ideas you can apply
• If you want growth where you are, do not wait for permission, propose the problem, the plan, and the first step
• If you lead a team, create parallel growth paths, management is not the only promotion ladder
• If you are remote, hire for writing, decision clarity, and follow through, not just technical depth
• If Slack is your company memory, it is not memory, move durable knowledge into docs, issues, and specs

Stay connected:
If this episode sparked an idea, follow or subscribe so you do not miss the next one. And if you want more conversations on building durable product and engineering teams, check out my LinkedIn and newsletter.

Pete Hunt, CEO of Dagster Labs, joins Amir Bormand to break down why modern data teams are moving past task based orchestration, and what it really takes to run reliable pipelines at scale. If you have ever wrestled with Apache Airflow pain, multi team deployments, or unclear data lineage, this conversation will give you a clearer mental model and a practical way to think about the next generation of data infrastructure.

Key Takeaways
• Data orchestration is not just scheduling, it is the control layer that keeps data assets reliable, observable, and usable
• Asset based thinking makes debugging easier because the system maps code directly to the data artifacts your business depends on
• Multi team data platforms need isolation by default, without it, shared dependencies and shared failures become a tax on every team
• Good software engineering practices reduce data chaos, and the tools can get simpler over time as best practices harden
• Open source makes sense for core infrastructure, with commercial layers reserved for features larger teams actually need

Timestamped Highlights
00:00:50 What Dagster is, and why orchestration matters for every data driven team
00:04:18 The origin story, why critical institutions still cannot answer basic questions about their data
00:07:02 The architectural shift, moving from task based workflows to asset based pipelines
00:08:25 The multi tenancy problem, why shared environments break down across teams, and what to do instead
00:11:21 The path out of complexity, why software engineering best practices are the unlock for data teams
00:17:53 Open source as a strategy, what belongs in the open core, and what belongs in the paid layer

A Line Worth Repeating
Data orchestration is infrastructure, and most teams want their core infrastructure to be open source.

Pro Tips for Data and Platform Teams
• If debugging feels impossible, you may be modeling your system around tasks instead of the data assets the business actually consumes
• If multiple teams share one codebase, isolate dependencies and runtime early, shared Python environments become a silent reliability risk
• Reduce cognitive load by tightening concepts, fewer new nouns usually means a smoother developer experience

Call to Action
If this episode helped you rethink data orchestration, follow the show on Apple Podcasts and Spotify, and subscribe so you do not miss future conversations on data, AI, and the infrastructure choices that shape real outcomes.

Sandesh Patnam, Managing Partner at Premji Invest, breaks down how long duration capital changes the way you evaluate companies, founders, and moats. We talk about what most growth investors miss, why product strength still matters, and how to separate real AI businesses from thin wrappers in a noisy market.

Premji Invest is a captive, evergreen fund built to grow an endowment that supports major education work, which gives the team flexibility on time horizon and partnership style. Sandesh shares how that shows up in diligence, how they think about backing contrarian founders, and why the best companies in this AI era may still be ahead of us.

Key Takeaways
Focus on the long arc, not quarter by quarter optics, founders make better decisions when they are not trapped in short term metrics
In growth investing, TAM models and KPI spreadsheets can distract from the core question, does the product have real strength and an expanding roadmap
Enduring outcomes often come from backing a contrarian view early, then helping it move from contrarian to consensus over time
Evergreen capital changes behavior, you can slow down, build relationships, and partner across private and public markets instead of treating IPO as the finish line
In AI, separate the stack into data center, foundation models, and applications, then look for defensibility like vertical depth, data moats, and compounding usage value

Timestamped highlights
00:38 Premji Invest explained, evergreen structure, one LP, and why public markets can be part of the journey, not the exit
04:47 Two common growth investor lenses and what gets missed when product and roadmap do not lead the thesis
08:48 Partnership mindset, building trust, and being the first call when things get hard
12:48 The contrarian to consensus path, what creates alpha, and how to support founders through the lonely middle
19:54 Why rushing decisions is a trap, and how flexibility changes when and how you can partner with a company
20:55 AI investing framework, three layers, what looks frothy, what can endure, and where moats still exist
26:48 The cost of intelligence is collapsing, why this may still be the early internet moment, and what that implies for the next wave

A line that stuck with me
“We want to be the first port of call when the seas are turbulent.”

Practical moves you can steal
Pressure test the roadmap, ask when product two ships, what adjacency comes next, and what tradeoffs change at scale
When evaluating AI apps, demand a defensibility story beyond the model, look for proprietary data, vertical workflow depth, and value that improves with usage
Treat speed as a risk factor, if you cannot complete your churn cycle of doubt and validation, step back rather than force certainty

Call to Action
If you liked this one, follow the show and share it with a founder, operator, or investor who is building in AI right now. For more conversations at the intersection of tech, business, and execution, subscribe and connect with me on LinkedIn.

Software engineering is changing fast, but not in the way most hot takes claim. Robert Brennan, Co founder and CEO at OpenHands, breaks down what happens when you outsource the typing to the LLM and let software agents handle the repetitive grind, without giving up the judgment that keeps a codebase healthy. This is a practical conversation about agentic development, the real productivity gains teams are seeing, and which skills will matter most as the SDLC keeps evolving.

Key Takeaways
AI in the IDE is now table stakes for most engineers, the bigger jump is learning when to delegate work to an agent
The best early wins are the unglamorous tasks, fixing tests, resolving merge conflicts, dependency updates, and other maintenance work that burns time and attention
Bigger output creates new bottlenecks, QA and code review can become the limiting factor if your workflow does not adapt
Senior engineering judgment becomes more valuable, good architecture and clean abstractions make it easier to delegate safely and avoid turning the codebase into a mess
The most durable human edge is empathy, for users, for teammates, and for your future self maintaining the system

Timestamped Highlights
00:40 What OpenHands actually is, a development agent that writes code, runs it, debugs, and iterates toward completion
02:38 The adoption curve, why most teams start with IDE help, and what “agent engineers” do differently to get outsized gains
06:00 If an engineer becomes 10x faster, where does the time go, more creative problem solving, less toil
15:01 A real example of the SDLC shifting, a designer shipping working prototypes and even small UI changes directly
16:51 The messy middle, why many teams see only moderate gains until they redraw the lines between signal and noise
20:42 Skills that last, empathy, critical thinking, and designing systems other people can understand
22:35 Why this is still early, even if models stopped improving today, most orgs have not learned how to use them well yet

A line worth sharing
“The durable competitive advantage that humans have over AI is empathy.”

Pro Tips for Tech Teams
Start by delegating low creativity tasks, CI failures, dependency bumps, and coverage improvements are great training wheels
Define “safe zones” for non engineers contributing, like UI tweaks, while keeping application logic behind clearer guardrails
Invest in abstractions and conventions, you want a codebase an agent can work with, and a human can trust
Track where throughput stalls, if PR review and QA are the bottleneck, productivity gains will not show up where you expect

Call to Action
If you got value from this one, follow the show and share it with an engineer or product leader who is sorting out what “agentic development” actually means in practice.

Deborah Hanus, Co-founder and CEO at Sparrow, joins Amir to unpack the founder journey from academia to building a scaled company. They dig into why leave management is still a messy, high stakes problem, and how Sparrow is turning it into a clean, guided experience for both HR and employees.

Sparrow helps companies provide employee leave across the United States and Canada, and Deborah shares what it really takes to scale a compliance driven business without slowing down. From founder resilience and early stage emotional swings to hiring, onboarding, and culture design, this one is packed with lessons for operators and builders.

Key takeaways
• Academia can be real founder training, especially for building resilience and hearing “no” without losing your edge
• Early stage startups feel brutal because you have too few data points, it is easy to overreact to every win or setback
• Compliance and leave are fundamentally data problems, the right info to the right person at the right time changes everything
• Scaling leadership is mostly communication and alignment, five people and 250 people require totally different systems
• Culture does not stay stable by accident, values must drive hiring, training, rewards, and performance management

Timestamped highlights
00:37 What Sparrow does, and the 300 million dollars in payroll cost savings milestone
01:37 Why academia can prepare you for founding, and how customer pain beats outside skepticism
03:40 The leave compliance mess, and why state by state rules made the problem explode
08:25 The two real ways startups die, and why morale matters as much as cash
12:55 Leading at scale, onboarding, clarity, and the feedback questions that keep teams aligned
19:54 “Scale intentionally” as a culture principle for a company that cannot afford to break things
25:48 Keeping values stable while everything else evolves as the team grows

A line worth sharing
“Companies end when you run out of cash or you run out of morale.”

Pro tips you can steal
• Treat the employee journey like a product journey, from recruiting through promotions and hard moments
• Before a big change, collect questions early so the message lands where people actually are
• After a meeting, ask “What were the main points?” to see what people heard, then tighten your messaging
• Invest in onboarding and goal clarity to prevent teams from drifting into competing priorities

Call to action
If you enjoyed this conversation, follow and subscribe so you do not miss what is next.