The Tech Trek

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
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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
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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
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Max Bruner, Founder and CEO of Anzen, joins Amir Bormand to break down why insurance is quietly one of the biggest data and workflow opportunities in tech right now. They dig into Max’s unconventional path from foreign policy to building an executive liability marketplace, and what it really takes to modernize a slow moving industry with AI.

If you care about building in real world markets, scaling with discipline, and using AI for more than content, this one will sharpen your thinking fast.

Key Takeaways
• Insurance is not flashy, but it is foundational, massive, profitable, and packed with repeatable workflows that software can improve
• The best tech opportunities are often in slow moving industries with lots of data and outdated systems
• Better decision making comes from predicting outcome impact and pressure testing your thinking with a strong community around you
• AI value is clearest when it drives real operations, faster transactions, lower costs, and better service
• Fundraising is a pipeline game now, treat it like sales, build the plan, hit the numbers, run a tight process

Timestamped Highlights
00:42 What Anzen actually does, a one stop marketplace for executive liability quotes across the US
02:29 From Arabic studies and foreign policy to discovering insurance through political risk
08:12 The curiosity engine, how deep research habits shaped his ability to build in new domains
11:23 Decision guardrails, learning from outcomes and using trusted people to keep you efficient
13:12 Why choose insurance, building in industries that make the world work, plus the profit reality
17:29 The startup advantage, modern infrastructure vs incumbent legacy systems, and why catching up takes time
20:36 Raising in today’s market, what changed, what worked, and why the pitch volume matters

A line worth stealing
“Sometimes in tech we miss the application, there are massive industries to go change if we apply technology in the right way.”

Max Bruner

Pro Tips for builders
• Pick markets with repeatable workflows, you can ship measurable value faster
• Spend your time where the outcome impact is high, skip low ROI rabbit holes
• Build a real financial plan before fundraising, then operate close to it
• Run fundraising like a sales process, pipeline, volume, and discipline win

Call to Action
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Stu Solomon, CEO of HUMAN, joins Amir to unpack a blind spot most teams underestimate: a huge share of online activity is not people at all, it is automated traffic. They break down how verification really works at internet scale, why agentic workflows change the rules, and what it will take to build trust when bots transact with bots.

If you have ever wondered how fraud, fake clicks, account abuse, and synthetic behavior get caught in real time, this episode is a clear, practical look behind the curtain.

Key takeaways
• Most of the internet is machine traffic now, the goal is no longer spotting bots, it is separating good machines from bad ones
• Trust is built by combining behavior, infrastructure signals, and identity or credential history into fast decisions at scale
• Agentic systems lower the barrier to entry for attackers, less skilled actors can now create outsized impact
• The hard part is accountability, when a machine acts with your authority, who owns the outcome
• Adoption follows convenience, but visibility matters, if it feels like a black box, people will not trust it

Timestamped highlights
00:33 HUMAN in plain English, making split second decisions about who is human, and whether they are safe
03:59 The trust stack, behavior signals, infrastructure clues, and identity or credential history
10:19 The real shift with AI, lower barriers for attackers, plus the rise of agentic autonomy
14:37 The cake story, an agent completes the task, then surprises you with a 750 dollar bill
17:22 Bots talking to bots, where accountability and liability get messy fast
24:18 Security builds trust, trust unlocks adoption, and society is already closer than it thinks

A line you will remember
“We have always operated on the notion that if you are human, you are good, and if you are a machine, you are bad. That is simply not the case anymore.”

Practical ideas you can use
• Add guardrails when you delegate to tools, especially budgets, limits, and approval steps
• Watch for trust signals, not just identity checks, behavior plus infrastructure plus history beats any single data point
• Design for visibility, show users what the system did and why, so trust can compound over time

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