Nikhil Gupta, founder and CTO of Vapi, joins Amir to talk about how voice AI is reshaping the way we connect with businesses. From customer support to healthcare, Nikhil explains how voice agents can bring back the human side of digital interactions. This is a look at where real conversation meets real technology and what happens when machines start to understand us like people do.

Key Takeaways

• Voice AI creates genuine, human-like engagement instead of the usual scripted support.

• The next wave of AI will personalize relationships at scale while protecting privacy.

• Full duplex voice models will make conversations flow naturally and feel real.

• Businesses will use voice agents to understand customers, not just respond to them.

• Our phones and screens may evolve as voice becomes the primary interface.

Timestamped Highlights

01:08 — What Vapi does and how it reached 400,000 developers

02:15 — Why voice AI is one of the few areas showing clear ROI

06:09 — How AI can make customer relationships human again

11:18 — Building trust and privacy into voice-based systems

16:48 — Blending text, voice, and context into a single experience

19:05 — Rethinking our devices as voice replaces the screen

A moment that stands out

“Every person should feel like they can just text their hospital, and it knows exactly who they are, what they need, and when to help.” — Nikhil Gupta

Pro Tip

Start small. Use voice AI where conversation improves experience or clarity. It’s not about automation; it’s about creating connection.

Call to Action

Share this episode with someone exploring AI in their business and follow The Tech Trek for more stories about people, impact, and technology.

Alex Daniels, Founder and CTO at Predoc, joins the show to share how he is building a mission driven healthtech company that is changing how medical data is accessed and used. He opens up about the personal story that inspired Predoc, how he keeps culture authentic while scaling, and what zero turnover really looks like in a startup. From hiring philosophies to equity design to managing context switching, Alex brings a deeply human view of leadership in engineering.

Key Takeaways

• Building culture starts with personal connection. Founders who share their why help every new hire connect to mission and meaning.

• The best hiring filters are values and networks, not just tech stack alignment.

• Predoc’s culture formula of high agency, urgency, meritocracy, and transparency keeps turnover at zero.

• Equity is not just compensation. It is shared ownership and long term motivation.

• Flat structures and super ICs can scale effectively when leaders stay close to the work.

Timestamped Highlights

[01:30] How a personal loss and a lifelong heart condition inspired Predoc’s mission to fix healthcare data

[05:20] Inside Predoc’s culture formula and why it has helped them retain every hire for three years

[09:40] Why core values stay constant but merit evolves as the company grows

[13:00] Rethinking equity and risk for early startup employees

[15:10] How Predoc combats AI assisted interview cheating and keeps hiring authentic

[23:45] Building a flat team structure where directors are still super ICs

[30:00] Alex’s approach to managing context switching and mental decompression

Memorable Line

“We cared about what he cared about and why would he care about what we care about if I don’t care about him?”

Call to Action

If you enjoyed this conversation, follow The Tech Trek for more candid talks with founders and tech leaders shaping the future of engineering and culture. Subscribe on Spotify or Apple Podcasts and join the discussion on LinkedIn.

Jordan Tigani, CEO and cofounder of MotherDuck, knows what world class infrastructure looks like. He spent years building Google BigQuery before taking those lessons into the startup world. In this episode, he breaks down why building infrastructure products is fundamentally different from typical SaaS and why founders who don’t understand that difference are in for a painful surprise.

What You'll Learn

There are no shortcuts in infrastructure. You can’t just wire together existing open source components and call it a product. Real infrastructure requires contributing meaningfully to the state of the art, and that takes time, money, and deeper technical investment than most founders expect.

Starting with startups, not enterprises, is often the smarter play. Early stage infrastructure companies should target other startups first because they’re more comfortable with bleeding edge tech, have lower security barriers, and won’t force you to spend three engineers building custom auth instead of your actual product.

Scaling down is the new scaling up. Jordan saw pressure at SingleStore to make databases smaller and more efficient, not just bigger. That insight led to MotherDuck, which is built on DuckDB—a database that can run in a car, scale to massive cloud instances, and challenge the coordination overhead of legacy distributed systems.

Bottoms up engineering cultures win in infrastructure. At BigQuery, engineers close to customer problems could ship fast and independently. Jordan’s recreating that at MotherDuck by removing layers between engineers and customers, because creative problem solving requires understanding business constraints, not just technical ones.

Convincing people you can scale is half the battle. The best proof is customers who look like your next target and can vouch for you. Next best is real data and benchmarks. If you don’t have those yet, lean on implementation support and help prospects test at scale themselves. Early on, sometimes all you have is your word.

Timestamped Highlights

[01:22] Why infrastructure takes longer to build than typical SaaS products and why there’s no shallow way to do it

[06:57] The MVP dilemma: finding product market fit when enterprises demand reliability from day one

[11:44] Lessons from BigQuery and SingleStore—what to carry over from big tech and what to leave behind

[21:21] The gap in the market that led to MotherDuck: why distributed databases don’t scale down and why that matters now

[26:10] Redefining scale: why 100 users on one giant instance isn’t necessarily better than 100 auto scaling individual instances

[29:08] The hierarchy of proof: from customer testimonials to benchmarks to trust me, it’ll work

A Line to Remember

“If you really want to build an infrastructure product, you can’t just string existing components together. You actually have to contribute meaningfully to improving the state of the art.”

Stay Connected

If this breakdown of infrastructure startups resonated with you, subscribe so you don’t miss future episodes. And if you’re building in this space or thinking about it, connect with Jordan on LinkedIn. He’s committed to paying forward the help he got as a founder.

Ogi Kavazovic, co-founder and CEO of House Rx, joins the show to unpack what most product leaders miss about building for enterprise software. Drawing from two decades in tech, Ogi breaks down how product management shifts when you move from B2C or “B to small B” to true enterprise—what he calls “B to Big B.” He explains why traditional user research frameworks don’t hold up, how buyer research should actually be done through sales and marketing motions, and how to keep engineering teams aligned when the product takes years to build.

Key Takeaways

• Building for enterprise (B to Big B) requires selling to buyers and users—two very different audiences with distinct needs.

• Buyer research is not user research—it happens through early sales decks, vision slides, and iterative storytelling that test how well a concept resonates before code is written.

• Pre-selling a “fantasy product” through slides helps validate the market fit and shapes the first version of your product strategy.

• Engineering for enterprise software demands simulated iteration—testing features internally long before the MVP is complete.

• Vision alignment between product, marketing, and engineering is crucial to avoid two-year build tunnels and ensure team motivation.

Timestamped Highlights

[03:12] The overlooked divide between B2B and true enterprise—why “B to Big B” changes everything for product teams.

[10:47] How buyer research actually works and why it starts with slides, not software.

[17:40] The difference between pitching VCs and pitching enterprise buyers—and why they care about totally different things.

[22:29] The engineering challenge of building massive enterprise systems and why agile methods fall short.

[30:11] How to keep teams motivated and moving forward when the product roadmap spans years.

Standout Moment

“You can pre-sell a product before it even exists. That sales and marketing artifact—the deck you built to sell your vision—can become the blueprint for your product strategy.”

Pro Tips

Start with conversations, not code. Use early customer and buyer meetings to validate your story through slides, then hand your engineers a vision they know can sell.

Call to Action

If you enjoyed this episode, share it with a fellow product leader or founder navigating enterprise challenges. Follow The Tech Trek for more conversations that connect people, impact, and technology.

Astasia Myers, General Partner at Felicis, breaks down how venture capital is betting on AI and why over 80% of their recent investments are in this space. But this isn’t just another “AI is the future” conversation. We dig into the real ROI happening right now in healthcare voice agents, why MIT says 95% of GenAI projects fail to reach production, and what needs to happen for that number to flip. If you’re building, investing, or just trying to understand where enterprise AI is actually working (not just hyped), this episode cuts through the noise.

What You'll Learn

The labor replacement opportunity: Why outcome-based AI solutions are targeting the $35 trillion labor market instead of just software budgets and how that changes everything for startups and investors.

Voice AI’s healthcare breakthrough: How voice agents are finally solving the operational bottlenecks in patient scheduling and communication, driving 24/7 availability with better NPS than human operators.

Why 95% of GenAI projects still fail: The technical and infrastructure gaps that prevent most AI initiatives from making it to production and what’s needed to fix that in 2026.

The new technical risk era: After years of focusing purely on market risk, VCs are back to evaluating deep technical challenges in agentic systems, browser automation, and continuous learning loops.

The exceptionalism filter: How early-stage investors are separating signal from noise when everyone can spin up an AI startup and why founder insights and lived experience matter more than ever.

Timestamped Highlights

00:31 – What Felicis invests in and the types of AI companies dominating their portfolio right now

02:58 – Why healthcare tech is finally ready for its AI moment after years of long sales cycles and unclear ROI

08:15 – How outcome-based pricing is changing the VC evaluation playbook and unlocking 10x larger TAMs

13:26 – The mythical one-person billion-dollar company: Is it real, and how would investors even spot it?

17:18 – Voice AI as the gateway for enterprise adoption and why this modality is different from Siri and Alexa

20:08 – Democratizing AI: What ChatGPT did for consumers and what needs to happen for enterprise builders

One Thing Worth Remembering

“These technologies can price towards the labor replacement markets, which is about 10x the size of the software market itself. The ROI right now is so tangible that it is a time to invest.”

Subscribe and Stay in the Loop

If this episode gave you a new angle on where AI is actually delivering value, share it with a founder or investor who needs to hear it. Subscribe so you don’t miss the next conversation, and drop a comment if there’s a topic or guest you want us to tackle next.

In this episode, Amir sits down with Taofeek Rabiu, VP of Engineering at Etsy, to unpack a distinction that most organizations miss: being a people leader is not the same as being a people manager.

If you have ever wondered why some teams thrive under pressure while others crumble, or why trust feels so hard to build in engineering orgs, this conversation has answers. Taofeek shares how leadership is not reserved for those with a manager title, why vulnerability is a strategic advantage, and how to spot the early warning signs of poor leadership before they drag down performance.

What You’ll Learn

Leadership exists at every level, not just in management roles. Individual contributors who mentor, influence, and model the right behaviors are leaders too — and organizations need to recognize and reward that.

Trust is built through action, not talk. It grows when leaders show vulnerability, stay transparent about their thinking, and follow through on commitments. When you stop acting on what you hear, you break trust.

Poor leadership has a smell. Teams that avoid hard conversations, struggle to navigate change, or fail to ramp new hires are showing symptoms of leadership gaps, not process problems.

Feedback is about helping people see, not telling them what to do. The best leaders use curiosity to guide others toward realization and self-awareness.

Effective leaders make high signal, low frequency decisions. The goal is not to make a thousand calls a day but to gather diverse perspectives and make the few decisions that truly move the team forward.

Timestamped Highlights

01:42 – Taofeek breaks down the difference between managing people (reviews, org charts, timesheets) and leading people (building trust, showing care, creating psychological safety).

09:04 – What happens when managers focus only on mechanics. Taofeek describes the smells of poor leadership and how they surface in teams that can’t handle change.

13:18 – How to give feedback when someone is not showing up as a leader. Taofeek explains his approach: start with curiosity, triangulate with skip levels, and guide people to their own realizations.

17:47 – Who is responsible for building trust. Taofeek shares why it is on leaders to create the conditions, not on reports to earn it.

22:04 – The moment Simon Sinek told Taofeek to stop saying people managers and start saying people leaders — and how that small shift in language changed his approach to leadership.

24:29 – What feedback a VP of Engineering actually values. Taofeek shares how he uncovers blind spots and the kind of input that helps him grow.

Words That Stuck

“The team doesn’t trust you. You’re not providing a psychologically safe environment in which the team feels like they can course correct and flag things that they believe will lead to poor outcomes.”

If This Resonates, Here’s What to Do

Take one insight from this episode and put it into practice this week. Maybe it’s being more open in your next one-on-one, checking your follow-through, or asking your team a question you have been avoiding. Then share this episode with someone navigating the manager-to-leader transition. Subscribe to The Tech Trek for more conversations that help you grow as a leader, and connect with Taofeek on LinkedIn to keep the dialogue going.

Ion Feldman, CTO at Rightway, has learned to love one thing about scaling a company from a kitchen table to nearly 1,000 employees: his job completely changes every six months. In this episode, Ion shares what it means to lead engineering when the role refuses to stay still—from writing code in the early days to building product, security, and data teams, and now shaping AI infrastructure. He explains how to stay hands-on without micromanaging, why he deliberately works himself out of roles by hiring people better than him, and how to preserve startup urgency inside a heavily regulated industry. If you’ve ever wondered how CTOs balance technical depth with business strategy while keeping their team fast and focused, this conversation delivers.

Key Takeaways

Treat change as part of the job.

Ion’s leadership mindset centers on adapting to wherever the company needs him most—product, security, data, or AI. He views change as an opportunity to grow, not a disruption to avoid.

Hire yourself out of the role.

He dives deep into an area, builds it from scratch, then brings in experts who can take it to the next level. Once the right leadership is in place, he steps back completely and lets them own it.

Hands-on time creates credibility.

Ion makes sure every leader spends time building. Each quarter, his team takes a week off from meetings and Slack to focus on creating something new. It keeps them close to the work and sharp as technical leaders.

AI adoption needs clarity and focus.

Rightway avoids vague “use AI” goals by targeting clear use cases like unit test generation and onboarding to codebases. Sharing examples and results drives faster adoption than leaving teams to figure it out alone.

Fail fast and move forward.

Ion builds space for experimentation but expects quick recognition of failure. The goal is not to avoid mistakes but to learn, pivot, and evolve faster.

Timestamped Highlights

[02:10] The zero to one mindset – Why Ion thrives on constant reinvention and the satisfaction of building new functions from the ground up.

[06:41] Three pillars of AI strategy – How Rightway is transforming work through AI enablement, applied projects, and bold experiments.

[08:26] Delegating by design – How going deep before handing off creates clarity and trust across teams.

[15:42] Skills that matter later – Ion reflects on learning public speaking and business fluency after years of technical focus.

[17:48] Creating space for risk – How to give your team agency to take on big challenges and fail fast without fear.

[21:22] Preparing successors – Why the best leaders hire people who will replace them and rethink everything they built.

What Stuck With Us

"I don't know, maybe I just get bored easily. I think a lot of people could view it as a burden and they want to stay in their lane of expertise, but I see it as an opportunity to learn and change things up."

Pro Tips for Tech Leaders

Take a week each quarter to build something with zero meetings or Slack. It reconnects you and your team with what you actually love about engineering.

Wait to hire senior leadership until the need is undeniable. The role becomes meaningful, and you’ll attract higher caliber talent.

Give your engineers specific AI examples and let them experiment from there. Adoption follows clarity, not mandates.

Jay Chia, cofounder of Eventual, joins the show to unpack what real empowerment looks like inside a fast growing startup. Most people confuse empowerment with initiative, but Jay explains how trust, vulnerability, and accountability work together to turn good teams into self directed ones. If you are scaling a startup or leading a growing engineering team, this conversation explores the human side of leadership, when to let go, when to step in, and how to help your team grow without losing alignment.

What You’ll Learn

• Why initiative and empowerment are different and how that distinction shapes your company culture

• How to build trust so early employees can take ownership without constant oversight

• Why vulnerability is the key to honest feedback and deeper one on ones

• How to build a culture of experimentation that rewards progress, not perfection

• When to intervene as a leader versus when to let your team learn through mistakes

Timestamped Highlights

03:20 The difference between taking initiative and true empowerment, and why fixing bugs is not ownership

08:39 Using vulnerability to turn one on ones into real conversations

12:20 Building an experimentation culture inspired by research driven teams

17:53 How much room to give before stepping in, balancing trust, skill, and risk

21:41 Why letting new managers bring their own cultural imprint can strengthen your company

A Line That Sticks

“Empowerment is handing off the monkey. It is not just fixing the problem, it is owning the plan, asking for resources, and having the mandate to execute.”

Practical Advice for Leaders

• Start one on ones by being open first so your team feels safe to share what is really happening

• Lower the barrier to experimentation and let people test ideas early. Progress beats polish

• Build rituals, not just processes. Repetition creates trust and space for feedback

• Encourage a mindset of asking for forgiveness, not permission. Autonomy grows from trust

Keep the Conversation Going

If this episode made you rethink how you empower your team, share it with another founder or manager who is building through similar challenges. Follow The Tech Trek for more conversations at the intersection of people, impact, and technology.

Rohan Kodialam, cofounder and CEO of Sphinx, is building AI agents that treat data as its own language—one most models and humans still fail to understand. In this episode, he unpacks why data science has lagged behind software engineering, how AI can finally close the gap between business questions and answers, and what happens when small teams gain the analytical power of a thousand person quant desk.

What You'll Learn

• How AI models that actually see data can unlock insights traditional transformers miss

• Why enterprises must rethink dashboards and embrace real time ad hoc analysis

• Where AI truly saves the most time across the data lifecycle and why modeling is not the hardest part

• How decoupling statistics from business context gives teams freedom to focus on strategy and creativity

• Why success in data science now means reclaiming human creativity while automating repetitive work

Timestamped Highlights

[01:44] Why data is fundamentally different from text and code and why most AI models struggle with it

[06:39] The cultural problem with ad hoc being a dirty word in enterprises and why that mindset is changing

[11:09] Where AI tools actually fit into the data science workflow

[17:09] How to measure success when using an AI data scientist

[21:04] What happens when a small team gains the data firepower of a hedge fund quant operation

[24:37] Why bad data science is worse than none and why quality matters more than hype

A Thought That Stuck With Us

“We are cutting the time to completion by 20x, 40x, even 50x and that remaining human review is not a bottleneck. It is the feature that keeps AI accountable.”

Worth Following

Connect with Rohan Kodialam on X (@KodialamRo) or LinkedIn and learn more about Sphinx AI and how they are transforming enterprise data science.

If This Resonated

Share this with someone in the data world who is tired of waiting weeks for insights that should take minutes. Follow The Tech Trek for more conversations about how people and technology create lasting impact.

Karl Alomar, Managing Partner at M13 and former COO of DigitalOcean, joins The Tech Trek to share how being an operator changes the way you invest. He explains why M13 was built to be a truly founder-first VC firm—one that acts early, helps proactively, and builds deep relationships rooted in empathy and experience. From spotting great founders to balancing instinct and data, this episode explores how venture capital can drive better outcomes when it focuses on people as much as product.

Key Takeaways

• The most effective VCs act before problems surface, shaping a founder’s path rather than reacting to it.

• Founder–market fit often comes down to whether someone is a specialist with deep expertise or an athlete who can adapt fast.

• Empathy built through years of operating experience creates trust that fuels honest conversations and better decisions.

• Great founders lead with vision—they can inspire, recruit, and align teams behind a clear story of what’s possible.

• Even the best instincts and pattern recognition can’t outplay timing, luck, and market shifts—but reflection and learning can.

Timestamped Highlights

(01:20) How being an operator shaped Karl’s approach to venture capital

(06:48) The three kinds of investors—and why empathy gives operators an edge

(09:54) Creating a safe space where founders can share problems without fear

(14:13) Identifying “athletes” and “specialists” when evaluating founders

(20:33) Pattern matching, instincts, and the role of luck in investing

(23:50) What M13 learns from postmortems on both wins and misses

A Line That Stuck

“To do it the right way, you have to be a proactive investor, not a reactive one.”

Pro Tips

Karl suggests founders build relationships with investors who understand their world and seek out those who can help them see around corners—not just react when things break.

Call to Action

If this episode resonated, follow The Tech Trek on Apple Podcasts or Spotify and connect with Amir Bormand on LinkedIn for more conversations at the intersection of people, impact, and technology.