The Tech Trek

In this episode of The Tech Trek, Christina Garcia, SVP of Engineering at Echo Global Logistics, shares her insights on integrating AI not as a replacement but as a partner in business operations. We unpack how organizations can holistically rethink processes, overcome adoption hurdles, and empower innovators inside the company to co-create AI use cases. Christina also opens up about the unique leadership pressures this wave of transformation brings—and how she manages them.

🔑 Key Takeaways:

AI as a collaborator, not a replacement: The best outcomes come from reimagining processes where AI augments human work, especially in repetitive or low-ROI tasks.

Involve frontline innovators early: The most valuable insights often come from those doing the work. Let them help shape the solution.

Avoid AI hype traps: Not every problem needs generative AI. Use the right tool for the job—and focus on business value, not buzz.

Learning over immediate ROI: Start with low-risk use cases to build organizational muscle and maturity.

Leadership challenge: The pressure isn’t just urgency—it's finding the space to experiment while delivering on core business commitments.

🕒 Timestamped Highlights:

00:00 – Intro & Overview

Christina joins the show to talk about treating AI as a true teammate in the enterprise.

01:58 – AI evolution and tuning complexity

From 1980s DJ boards to modern EQs—how fine-tuning models with vast datasets is changing.

03:40 – Generative AI in action

Using AI for documentation, code reading, legacy systems—real applications that shift ROI.

06:21 – Who should be at the table for AI integration?

It’s not just leadership—bring in the doers, early adopters, and tool testers.

09:40 – Stakeholder enthusiasm and the AI buzz cycle

Why generative AI is unlike previous tech waves—and the danger of inflated expectations.

13:52 – The hammer and flyswatter problem

Helping teams focus on the right use cases without killing excitement.

17:47 – The ROI tradeoff: learn now, pay later

Why experimentation is essential—even if today’s results are fuzzy.

21:42 – What pressure feels like for leaders right now

Carving out capacity, not just funding, is the modern leadership crunch.

24:30 – The compressed AI adoption curve

Companies are jumping in fast—ripping off the learning Band-Aid.

25:11 – Where to connect with Christina

Find her on LinkedIn.

💬 Quote of the Episode:

“If you don’t trust the AI to do the task, and you make a human micromanage it—you’ve actually increased the workload.” – Christina Garcia

📚 Resources Mentioned:

The Innovator’s Dilemma by Clayton Christensen

💼 Career Tips (from the conversation):

Credibility matters when guiding tech decisions: Don’t just say “no”—offer a better path rooted in understanding the problem deeply.

Stakeholder management is key in AI adoption: Be transparent, protect the business, and educate with empathy.

Early involvement = stronger adoption: Let your internal innovators shape and test the tools before rolling out org-wide.

In this episode, Amir sits down with Anthony Jules, Co-Founder and CEO of Robust.AI, to explore how scaling lessons from the early days of Sapient translate into today’s rapidly evolving world of AI and robotics. Anthony shares stories from growing a company from 3 to 4,000 people, what scale teaches you about communication and change, and how being ruthlessly honest about your business creates strategic advantage. From the hype vs. reality of AI to how hardware can stabilize innovation in robotics, this conversation is rich with insights for technologists, entrepreneurs, and leaders navigating change.

🧠 Key Takeaways

Scaling Isn't Linear: Growth comes in step changes. Every size milestone (20, 80, 400 people) brings new communication and leadership challenges.

Be Your Own Harshest Critic: Anticipating problems internally before customers see them helps companies adapt with intention rather than react out of panic.

Conflicting Conversations Are Strategic: To see opportunities clearly, seek out voices that challenge your assumptions.

Hardware Brings Stability to AI: Robotics forces long-term thinking, helping offset the volatility of rapidly shifting AI models.

The Future of Robotics Is Ubiquitous: Anthony believes robotics will become the largest industry in the world in 20 years, driven by economics, not hype.

🕒 Timestamped Highlights

00:41 – What Robust.AI does: collaborative robots for logistics and manufacturing

01:58 – Scaling Sapient from 3 to 4,000 people and lessons learned along the way

04:23 – How communication and organizational structure evolve with growth

07:31 – Being brutally honest about internal problems before they become external ones

11:15 – How to know if you’re chasing a real opportunity or just rationalizing it

14:31 – Transferable skills from big orgs to startups: problem-solving and people leadership

17:53 – Why AI generalists are essential and how fast the AI landscape is changing

21:40 – Robotics as a stabilizer in the age of volatile AI

24:42 – Why robotics will be the dominant global industry in 20 years

28:18 – How to contact Anthony or explore Robust.AI

💬 Quote of the Episode

“Your ability to have large impact is proportional to your ability to get people aligned toward a common goal.” — Anthony Jules

🛠️ Resources Mentioned

Robust.AI website

Contact Anthony directly: anthony@robust.ai

💼 Career Tips (from the conversation)

Find Your Sweet Spot: Anthony notes his strongest impact comes when leading teams of 50–200. Knowing the environment where you thrive is critical for long-term growth.

Feedback Loops Drive Performance: Success isn’t set-and-forget. Constantly revisit goals, resourcing, and alignment.

Stay Open to Reconfiguration: Especially in emerging tech, leaders should be ready to reshape their teams, tools, and focus based on what’s working.

In this episode of The Tech Trek, Amir sits down with Joe Philleo, founder and CEO of Edio, an AI platform transforming K-12 education. Joe shares his journey from building websites in high school to writing a viral essay on Palantir that kickstarted his tech career. He dives into the critical role AI now plays in solving chronic absenteeism and driving measurable academic improvements. The conversation explores how tech is reshaping education—from device adoption post-pandemic to rethinking how we measure and manage learning outcomes.

🔑 Key Takeaways

Tech + Mission = Impact: Joe’s early obsession with improving education led to building Edio, a platform now serving districts ranging from NYC to remote Alaskan towns.

The Device Shift: The pandemic rapidly accelerated device distribution, giving every student access to digital tools—a catalyst for modernizing classrooms.

AI in Attendance: Chronic absenteeism doubled post-pandemic. Edio's AI attendance agent contacts parents in real-time, streamlining interventions and improving student outcomes.

Classroom of 2030: AI will transform how content is delivered—moving beyond lectures and textbooks to highly personalized, interactive, and measurable learning environments.

Change Is Hard—but Happening: Districts act slowly, often bound by 7-year textbook adoption cycles, but solutions like Edio’s attendance tool are gaining fast traction due to obvious value.

⏱️ Timestamped Highlights

00:37 – What Edio does and who it serves—from NYC to rural Alaska.

01:50 – Joe’s early fascination with education and building tech projects.

03:15 – The viral essay on Palantir that launched Joe’s career in tech.

06:20 – Why the pandemic changed everything: devices in every student's hands.

08:20 – The balance of technology vs. traditional materials in modern classrooms.

10:44 – AI-driven attendance tools: how they work and why they matter.

13:37 – Why large school districts can still adopt fast when the ROI is obvious.

17:09 – School systems are large enterprises—change requires true strategic partnership

19:32 – How to contact Joe and learn more about Edio.

💼 Career Tip

Joe’s career took a turn when he wrote an essay that went viral—highlighting the power of publicly sharing your insights. Whether you’re in tech, education, or venture, putting your ideas out into the world can open doors you never expected.

In this episode of The Tech Trek, Amir sits down with Sus Misra, SVP of Data & Analytics at Solve(D) (IPG Health), to unpack what true precision targeting looks like in one of the most regulated industries: pharma. Sus explains how healthcare marketers uniquely leverage individual-level data to connect with professionals like doctors and oncologists—something unheard of in most sectors.

But with great data comes great responsibility. Sus dives into the ethical, regulatory, and technical challenges of working with sensitive healthcare data, from HIPAA compliance to new state-level restrictions that are reshaping how campaigns are executed. He also shares how machine learning and generative AI are beginning to help—but warns they’ll never replace human governance.

Whether you work in data, marketing, or product, this episode is a masterclass in what happens when cutting-edge tech meets hard regulatory walls.

🔑 Key Takeaways:

Individual-Level Targeting in Pharma: The healthcare sector enables direct, measurable communication with doctors using unique identifiers—enabling true 1:1 marketing.

Data Governance is Business-Critical: Mishandling sensitive health data can lead to major fines, shutdowns, or loss of business. Regulatory compliance is non-negotiable.

AI Is Helpful—But Not a Savior: While generative AI and LLMs can accelerate personalization and regulatory response, human oversight remains essential.

Privacy Rules Are Getting Stricter: State-level restrictions are tightening how pharma marketers operate, even down to restrictions like not being able to advertise within 30 miles of a hospital.

Tech vs. Policy: The bottlenecks in pharma marketing are often more policy- than tech-related, requiring coordination with regulators and legal teams, not just engineers.

⏱️ Timestamped Highlights:

00:00 – Intro to Sus Misra and the focus on measurable audience engagement at the individual level

01:01 – How pharma is uniquely positioned to target individuals via data and NPI (National Provider Identifier) systems

04:24 – Key data governance challenges and why even internal stakeholders may be restricted from access

07:36 – Granular modeling and attributing behavior to specific events—down to weather disruptions

09:48 – Why AI and ML were hype for years before becoming usable—and how social platforms still limit data sharing

14:10 – Regulatory hurdles: how pharma ads differ from consumer ads and what that means for data handling

18:30 – State-specific privacy laws (like 30-mile hospital ad bans) and their impact on campaign strategy

22:23 – The promise and limits of generative AI and LLMs for personalization and compliance

26:21 – Where to reach Sus and his parting humor on making others’ jobs feel easier by comparison

💬 Quote of the Episode:

"There are therapies where our objective is to bring people to a hospital—and some states forbid us from placing an ad within 30 miles of one."

📚 Resources Mentioned:

HIPAA and post-HIPAA state-level privacy regulations

NPI (National Provider Identifier) system for healthcare professionals

FDA regulations and their impact on data governance

🎯 Career Tips (from Sus):

If you're in analytics, understand the power—and the responsibility—of data governance. It’s not just a technical task; it's a strategic imperative.

Stay current with regulations. Marketing innovation in pharma isn’t just about tech—it’s about mastering evolving compliance landscapes.

In this episode of The Tech Trek, Brian Clifford, Chief Data Officer at Amica Insurance, shares how his team translates core company values—like exceptional customer service—into actionable AI and data strategies. We explore how Amica approaches pilots, vendor selection, internal adoption, and governance to scale AI effectively and responsibly.

🔑 Key Takeaways:

Value-Driven Data Strategy: Amica anchors its AI and data strategy in core values like customer service and employee engagement—not just tech for tech's sake.

Practical AI Implementation: Rather than chasing flashy use cases, Brian’s team prioritizes “easy wins” to build momentum and user trust.

Governance & Risk-Awareness: All AI initiatives go through structured cost-benefit reviews and risk assessments, especially critical for a legacy insurance firm.

Internal Enablement Is Key: The company invested heavily in internal L&D, communications, and peer communities to ensure scalable adoption of AI tools like Microsoft Copilot.

⏱ Timestamped Highlights:

[00:00] Intro to Brian Clifford, CDO at Amica; focus on AI, data, and company values.

[01:37] Defining Amica’s core values and how data supports customer satisfaction.

[03:35] Translating strategy into data initiatives; how priorities shape metrics.

[05:04] Taking a deliberate approach to AI—early POCs, team engagement, use cases.

[06:55] Why their first AI project failed—and why that was okay.

[09:52] Measuring value: usage, time saved, improved product quality.

[13:16] Governance model and how they assess AI tools before rollout.

[16:26] Year-long roadmap planning while maintaining flexibility for change.

[20:41] Upskilling the team: leveraging vendor training, L&D, and internal forums.

[23:08] Connect with Brian via LinkedIn.

💬 Quote to Share:

“We didn’t pick the hardest tech or the biggest value. We picked what we could deliver—and that built momentum.”

— Brian Clifford

📚 Resources Mentioned:

• Microsoft Copilot – actively being used for internal productivity AI initiatives.

• Internal Communities of Practice – Amica built internal forums to support peer learning and tool adoption.

• AI Governance Committee – jointly led by the CDO and CIO to vet AI vendors and use cases.

💼 Career Tips from the Episode:

• Focus on Practical Wins: Don’t aim for the most complex use case; start small, deliver value, and scale.

• Change Management Matters: Opt-in AI adoption requires more internal marketing, trust-building, and education.

• Learn Through Pilots: Treat all early tech initiatives as pilots. Be willing to pivot or shut things down if they don’t work.

In this deep-dive episode, we explore what it truly means to be "AI-native" versus bolting AI onto existing products. Abhay Mitra, CTO of Nirvana Insurance, shares how his team is building industry-specific AI models to transform the $800B+ commercial insurance market, starting with trucking—one of the most complex and painful sectors in insurance.

From telematics data platforms to fine-tuned underwriting models, discover why commercial insurance might be the perfect proving ground for AI and how a data-first approach is creating unfair advantages for startups competing against century-old incumbents.

Key Takeaways

🎯 AI-Native vs. AI-Enhanced: Know the Difference

AI-Enhanced: Adding chatbots and customer service automation to existing workflows

AI-Native: Building core business logic, pricing, and underwriting around AI models from day one

The key differentiator: domain-specific data and expert annotations that create defensible moats

📊 Data is the New Competitive Moat

Quality beats quantity: Having "heaps of data" means nothing if it's not structured and usable

The real challenge: Correlating data across 20-100 different legacy systems

Version control for AI: You need to remember what models and rules applied at what time to properly train new models

🚛 Why Commercial Insurance is Perfect for AI

10-15x more complex than personal insurance with premiums to match

Highly varied customer profiles that resist traditional automation

Perfect storm: Complex data + high-stakes decisions + massive inefficiencies = AI opportunity

🏗️ Building AI-Native Engineering Teams

Hire for data expertise first, AI expertise second

Invest 5x more time in data quality and expert annotations than traditional SaaS

Focus on reliability and production-readiness, not just impressive demos

💰 The Startup Advantage Over Legacy Players

Legacy companies have data but can't correlate it effectively across systems

Modern data infrastructure beats decades of accumulated technical debt

Speed of iteration trumps size of existing datasets

🕒 Timestamped Highlights:

00:00 – 02:18: Intro to Nirvana Insurance and choosing to tackle the hardest problems in commercial insurance.

03:22 – 06:40: Why off-the-shelf AI isn’t enough and how domain-specific modeling gives Nirvana an edge.

07:28 – 09:55: Defining what's core IP vs. commodity tech when building AI solutions.

10:28 – 13:45: Why commercial insurance is a perfect fit for AI—high complexity, high stakes.

17:10 – 20:13: The difference between data-first and AI-first engineering orgs.

20:58 – 23:59: Why legacy insurers struggle to operationalize their data despite decades of collection.

25:09 – 27:26: What customers actually care about—better outcomes, not flashy tech.

💬 Quote:

“Before AI, this wasn’t even possible. You just couldn’t bring that level of nuance to each individual business. But with these new capabilities, insurance can finally become a tool for safety—not just cost.” — Abhay Mitra

What's Next?

Enjoyed this deep dive into AI-native insurance? Share this episode with your network and subscribe for more conversations with CTOs and engineering leaders building the future of regulated industries.

Questions or feedback? Drop us a line—we read every message and love hearing how these insights are helping you build better products.

In this episode, we dive deep into the evolving relationship between engineering and product with Pranab Krishnan, CTO of Zeal - a payroll and payments platform for staffing companies. We explore how the traditional boundaries between engineering, product management, and customer interaction are dissolving, especially in the age of AI. Pranab shares insights on building a product-centric engineering culture, the concept of "shifting left," and how AI tools are reshaping the skills engineers need to succeed.

Key Takeaways

🎯 Everyone Should Be a Product Person

The most successful startups foster a culture where engineers, designers, and even operations staff think like product managers and maintain direct connections to customer needs.

🤖 AI as the New Abstraction Layer

Just like TypeScript abstracted JavaScript complexity, AI will become another abstraction layer. The future belongs to those who master orchestration, architecture, and agency - not just coding.

🚀 The Flat Organization Future

Teams will become leaner and flatter, with higher expectations for product surface area. Instead of hiring more engineers, companies will be expected to build more comprehensive platforms with the same team size.

⚡ Shift Left Philosophy

Engineers moving closer to business problems and customer interactions, while designers and other roles also expand their responsibilities into traditionally separate domains.

🏗️ Core vs. Edge Development

In regulated industries like fintech, maintain bulletproof core systems while moving fast on user-facing features and interfaces.

Timestamped Highlights

[01:26] The CTO Evolution Journey

[03:44] Building vs. Learning Product Skills

Pranab discusses whether product management is learnable or innate, emphasizing that everyone approaches it differently - some from operations, others from technical backgrounds.

[06:12] The AI Evolution Question

Discussion on whether AI represents an evolution of software engineering or a fundamental paradigm shift away from core coding skills.

[07:21] AI as Abstraction

"My thesis on this is that everything is an abstraction... We are going to see AI becoming abstraction. The skills that I think people will need over the next five to 10 years is... orchestration... and agency."

[10:49] The Backlog Problem

Exploring what happens to product backlogs when engineers can produce more through AI assistance, and the potential for engineers to become natural problem-solvers with more time.

[15:15] Magic Patterns Tool Discussion

Real-world example of AI design tools that allow rapid UI iteration and prototyping with simple prompts.

[21:29] Expertise and AI Questions

"You can judge expertise by the types of questions people ask. And I think these tools... it requires a technical person to ask those questions, because you're not gonna know the nuance of if the answer's correct or not."

[23:38] The Future Hiring Landscape

Prediction that while teams will initially hire fewer engineers, expectations for product complexity will increase, eventually balancing back to similar hiring needs.

[25:13] The Data Advantage

"The big AI company that's going to do this well is most likely going to be the one who has the most data about you. So OpenAI is already poised... I would not be surprised if OpenAI builds its own Netflix, its own web flow, its own e-commerce."

Featured Quote

"Intelligence is on tap, but agency is the core of capitalism. Agency is going to be even more important as intelligence is more easily available to us."

— Pranab Krishnan, referencing Gary Tan

Tools & Resources Mentioned

• Magic Patterns - AI-powered design tool for rapid UI creation
• Zeal - Payroll and payments platform for staffing companies
• Claude Sonnet 3.7 - Referenced for physics simulation capabilities
• Y Combinator philosophy on hiring

Like this episode?

Share it with fellow tech leaders and subscribe for more insights on the intersection of technology, product, and business strategy.

In this episode of The Tech Trek, Vinayak Kumar shares how his team at Lynx strikes a practical balance between innovation and efficiency in the heavily regulated healthcare and finance space. He explains why innovation shouldn’t be forced, how to avoid the "tech in search of a problem" trap, and why pattern-driven execution helps startups scale faster without compromising flexibility.

🔑 Key Takeaways:

Innovation Should Be Embedded, Not Mandated

Innovation at Lynx happens organically—it's not about buzzwords, it's about solving real problems with the right tools.

Avoid “Technology in Search of a Problem”

True innovation stems from understanding the business problem first, then choosing a tool—not the other way around.

The Power of Reusable Patterns

Solving a problem once and codifying the solution into repeatable patterns has helped Lynx grow quickly and stay lean.

Fungibility in Teams Is Critical

Developers are encouraged to work across tech stacks to increase agility and reduce dependency on specialized roles.

🕒 Timestamped Highlights:

[02:55] – Why innovation must be cultural, not a KPI

[05:38] – Real-world example of choosing technology based on a business problem

[07:59] – The trap of adopting AI without a clear use case

[09:49] – Defining and leveraging “cookie cutter” solutions without sacrificing flexibility

[13:10] – A rigorous, fast-paced tech evaluation process in regulated industries

[16:41] – How Lynx builds team flexibility through cross-functional experience

[19:44] – Using agentic AI to automate non-obvious internal tasks like production issue research

💬 Featured Quote:

“We don’t talk about innovation—we just solve problems. And when you do that every day, innovation takes care of itself.”

In this episode, Amir speaks with Ameya Brid, Global Director of Data & Analytics at Invista, about the maturation of GenAI conversations in the enterprise. They dive into the shift from hype to implementation, real-world challenges like data quality and change management, and how composable architecture is helping organizations adapt to rapid innovation cycles.

🔑 Key Takeaways

From Hype to Value: GenAI conversations are moving beyond experimentation into outcome-driven initiatives—but most companies still struggle to define measurable KPIs.

Top Barriers to Scale: Poor data quality, fragmented systems, unclear use cases, and skills gaps continue to stall enterprise GenAI efforts.

Composable > Monolith: Modular, API-driven architectures provide agility to swap components as the tech rapidly evolves.

Change Management Rebooted: Adoption now means embedding insights directly into workflows—not just “viewing reports.”

Upskilling is Social: Peer-driven learning and internal documentation are outperforming formal training in the GenAI era.

🕒 Timestamped Highlights

00:00 – Introduction to Ameya and Invista’s work in manufacturing and chemicals

01:58 – How GenAI conversations have evolved over the past 18 months

03:52 – Marrying business outcomes with AI capabilities

06:04 – The five biggest barriers to GenAI implementation: use case clarity, data quality, skills gap, governance, and change management

11:53 – Managing constant tech evolution with composable architectures

15:02 – Data quality’s outsized impact on GenAI success

17:46 – Why CFOs must now invest in data quality

20:41 – Change management: From “read the dashboard” to “integrate AI into your workflow”

24:03 – Upskilling through shared learning and internal knowledge loops

💬 Quote of the Episode

"The cost of bad data today is far higher than it was 10 or 20 years ago—not just in decision-making, but in the process itself." – Ameya Brid

In this episode of The Tech Trek, Amir sits down with Andy Beam, CTO of Lila Sciences, to explore how AI is transforming the messy, serendipitous nature of scientific discovery into an engineered, scalable process. From automating lab work to accelerating the speed of breakthroughs, Andy explains why the future of science may be less about eureka moments and more about AI-driven iteration.

🔑 Key Takeaways:

Science as Engineering: AI enables science to move from a lucky break model to a systematic engineering process.

Scaling the Scientific Method: Pairing AI with experimentation platforms creates a feedback loop where hypotheses can be tested at unprecedented speed and scale.

Productivity Shift: AI copilots are redefining how scientists (and technologists) interact with their work, elevating humans to higher levels of abstraction.

Compounding Innovation: Once AI systems start discovering consistently, the rate of breakthroughs could go from decades to weeks—shifting timelines across industries.

⏱️ Timestamped Highlights:

00:00 – Intro to Andy Beam and Lila Sciences

01:00 – Why the scientific literature is a record of debate, not facts

03:09 – Science’s reliance on serendipity—and why that’s changing

04:55 – The power of scale in AI and what it means for discovery

06:15 – Andy’s personal shift in programming with AI copilots

08:41 – Will AI cause serendipity instead of waiting for it?

09:38 – The fungibility of speed and intelligence in research

11:47 – The challenge of change management in scientific communities

13:30 – What consumer adoption could look like in a future of constant innovation

💬 Quote:

“What we’re doing is taking the scientific method and scaling it with AI—so instead of waiting for Einstein, we build a million of them and run them 24/7.” – Andy Beam

In this episode of The Tech Trek, Amir speaks with Alexander Schlager, founder and CEO of AIceberg, about how his company has tackled the AI talent shortage by partnering directly with universities. From building relationships with faculty to onboarding students into real-world R&D roles, Alex shares a unique, cost-effective strategy for hiring early-career tech talent and turning them into long-term contributors. It’s a compelling listen for anyone in emerging tech, hiring, or leadership.

🔑 Key Takeaways

Faculty Buy-in Is Crucial: AIceberg’s success hinged on close collaboration with university faculty, ensuring student recruits were well-prepared and supported.

Rethink Talent Pipelines: Instead of competing for senior AI engineers, they invested in training early-career talent—gaining loyalty and retention in return.

Process Over Pedigree: Success in junior hires wasn’t about academic brilliance alone—it required a willingness to follow processes and grow into professional environments.

Retention Through Learning & Ownership: Clear career paths, challenging problems, and the ability to own projects helped retain young talent even with lower initial salaries.

⏱️ Timestamped Highlights

00:30 – What AIceberg does: AI trust platform for monitoring AI interactions

01:39 – The challenges of hiring AI talent in a startup environment

03:17 – Why partnering with faculty made their hiring model work

05:42 – Managing overhead and coaching needs with junior hires

08:02 – Standardizing research and product pipelines with JIRA

10:24 – Who to contact when building university partnerships

11:50 – Why maturity and teamwork matter more than grades alone

14:43 – How AIceberg advises candidates to evaluate offers before accepting

16:49 – Documentation and redundancy reduce risks when junior hires leave

18:30 – From outreach to onboarding: a 3-4 day ramp-up process

20:18 – Fresh perspectives from new grads as a strategic advantage

💬 Quote

“Don’t underestimate the benefit of a fresh brain—students often approach problems in ways seasoned professionals might never consider.”

Wyatt Smith, CEO of UpSmith, joins Amir to unpack how agentic AI is transforming the skilled trades industry. From dispatch optimization to human-in-the-loop workflows, Wyatt shares a practical and visionary lens on how AI can solve deep productivity challenges, empower call centers, and proactively generate business opportunities. If you think AI only disrupts digital industries, this episode will make you think again.

🔑 Key Takeaways:

Agentic AI is unlocking productivity by automating repetitive coordination tasks—like technician dispatching—allowing humans to focus on higher-value interactions.

Skilled trades businesses already have rich data but need tools to surface and act on it proactively rather than reactively.

Selling AI into traditional industries requires proof points, tight business cases, and sensitivity to the human element.

AI augments, not replaces—freeing up people to do work they're best suited for, like nuanced customer engagement.

💬 Highlight Quote:

“Advances in technology automate tasks, not people… Machines do what they're best at so humans can do what they're best at.” – Wyatt Smith

⏱️ Timestamped Highlights:

00:38 – Intro to Wyatt Smith and UpSmith's mission in the skilled trades.

02:51 – Why dispatching the wrong tech to the wrong job is a billion-dollar coordination problem.

05:09 – The customer journey in home services—and where productivity breaks down.

08:54 – AI adoption challenges in the trades and how business owners evaluate new tech.

11:15 – Human-AI dynamics: skepticism, latency, and building trust with agentic systems.

13:49 – “AI creates more work”: how automation changes tasks, not headcount.

17:19 – How UpSmith trains agents like new hires with workflows and documentation.

20:31 – Personalization at scale: how agents remember details from 5 years ago.

23:20 – The future of call centers and human-in-the-loop automation.

25:49 – Wyatt’s contact info and closing reflections.

What separates a successful founder from the rest? In this episode, Harish Abbott—CEO and co-founder of Augment—breaks down how he repeatedly spots opportunity early, builds products customers actually want, and navigates the fast-moving world of AI without falling into the trap of chasing every shiny benchmark.

We explore how Harish’s team shadowed 60 logistics operators before writing a single line of code, why storytelling is a founder's most underutilized superpower, and how to know when it’s time to pivot—even if everything looks good on the surface.

Whether you're scaling your first product or figuring out what not to build, this conversation is packed with real-world insights you can apply today.

🔑 Key Takeaways:

Start with Pain, Not Product: Successful startups begin by deeply understanding real customer pain points, not by jumping into code or chasing tech trends.

Shadowing Over Selling: Harish’s team shadowed 60 logistics operators in the early days of Augment—prioritizing observation over assumptions.

Strong Opinions, Loosely Held: Founders must balance confidence in their vision with humility to pivot when data points to a better path.

AI ≠ The Product: In a world obsessed with benchmarks, remember: AI is a tool. The actual value lies in making things better, cheaper, or faster for users.

⏱ Timestamped Highlights:

00:32 – What Augment does: AI teammates for the logistics industry

02:48 – “Follow one path consistently” – Harish’s approach to serial entrepreneurship

05:57 – The importance of shadowing operators before writing code

11:21 – When is it time to pivot? Why usage data is often more telling than top-line growth

19:23 – Storytelling as a founder’s core job: how to get employees, investors, and customers on board

25:02 – The challenge of AI startup building today: chasing stability over shiny new benchmarks

30:10 – Avoiding the trap of benchmark chasing in AI product development

💬 Quote:

“The best founders are always seeking truth. That truth sometimes tells you to let go of the idea you love.”

In this episode of The Tech Trek, Amir speaks with Patrick Leung, CTO of Faro Health, about what it takes to lead an engineering organization through a transformation to become an AI-first company. From redefining the product roadmap to managing cultural and technical shifts, Patrick shares practical insights on team structure, skill development, and delivering AI-enabled features in a regulated domain like clinical trials. This is a must-listen for tech leaders navigating similar transitions.

🧠 Key Takeaways:

AI-First ≠ Just Using AI

Being AI-first means deeply embedding AI into the core product architecture—not just bolting on an LLM. It requires strategy, structure, and long-term thinking.

Build the Right Team Early

The biggest shift for engineering orgs is in people—getting the right AI talent onboard early, rather than doing it all yourself, is critical for momentum.

Upskilling Is Real—but Selective

Not every engineer will pivot to AI, but there’s room for involvement across UX, product, and front-end roles. Cultural fit and willingness to contribute matter more than title.

Data Engineering is the Unsung Hero

Most AI work today isn’t in model building, but in crafting clean, structured datasets. Investment here pays off exponentially.

⏱️ Timestamped Highlights:

00:00 – What Does It Mean to Be AI-First?

Patrick defines the term and outlines Faro Health’s mission to reduce the cost and timeline of clinical trials.

04:13 – Defining the AI Strategy

How they started with clinical writing as the first application of LLMs and why it was harder than expected.

07:54 – The Role of Change Management

AI introduces massive shifts; managing sponsor expectations and workflows is as important as the tech.

10:28 – Engineering Impact

How the roadmap changed and what it meant for full-stack vs. data science roles.

14:24 – Hiring vs. Upskilling

Why Patrick hired an expert to lead AI efforts and the balance between internal upskilling and external hiring.

16:43 – Competing for AI Talent

How startups can win top AI talent despite the lure of FAANG compensation.

18:58 – Team Culture and Opportunity

Creating space for engineers who want to jump into AI while maintaining alignment on startup needs.

21:07 – Realistic Upskilling Paths

From Coursera to immersive bootcamps—what actually works for engineers wanting to break into AI.

23:11 – If He Could Do It Again

The two things Patrick would do sooner: hire a dedicated AI team and build structured data pipelines earlier.

🔖 Featured Quote:

“If you're serious about becoming an AI company, you need to find someone amazing who's launched real AI products—and build a team around them.”

In this episode of The Tech Trek, Amir sits down with Sunita Verma, CTO at Character AI and former engineering leader at Google. Sunita shares how she’s transitioned from leading large-scale AI initiatives at Google to building novel experiences in a fast-paced startup environment. She dives into the mindset shift required to prioritize velocity over scale, how to lead AI-native product innovation, and what it means to be a female technical leader in today’s tech ecosystem.

🔑 Key Takeaways:

Shift in Leadership Mindset: At startups, leaders must prioritize velocity and innovation over scale, focusing on getting frictionless, AI-native products to market quickly.

AI Product Loop: Success comes from tightly coupling AI research with product development—shortening the feedback loop to create truly novel user experiences.

Female Technical Leadership: Sunita emphasizes the need for more women in senior engineering roles and shares how calculated risk-taking and mentorship shaped her journey.

Startup Clarity vs. Corporate Comfort: While startups offer focus and purpose, they also require deep ownership and rapid decision-making without the cushion of big-company resources.

💬 Quote:

“Focus brings clarity of purpose... but with that comes the pressure of knowing every decision deeply impacts the company.” — Sunita Verma

⏱️ Timestamped Highlights:

00:00 – Intro: Meet Sunita Verma, CTO at Character AI and former Google engineering leader.

01:52 – Google to Startup: Comparing work at Google with her current role at Character AI.

03:39 – Leadership Shift: Sunita’s take on building AI-native products from scratch.

06:21 – From Scale to Speed: Pivoting from optimization at scale to innovating with velocity.

08:12 – Product & Tech Integration: Creating tight feedback loops between AI research and products

10:01 – Closer to Engineering: Why Sunita enjoys being hands-on and deeply involved in compute management.

12:12 – Focus as a Double-Edged Sword: The simplicity and pressure of startup leadership.

14:00 – Female Engineering Leadership: The need for more women in senior tech roles.

16:02 – Career Advice: Why calculated risk and building a support network are key to long-term success.

19:14 – Leaving Google: Her thought process in taking the leap from a big brand to an emerging category leader.

In this episode of The Tech Trek, Amir sits down with Emily Long, the CEO and co-founder of Edera, a deep tech startup focused on secure infrastructure. Emily shares her unconventional journey from HR leadership into the world of high-performance computing, infrastructure, and cybersecurity. Together, they explore the realities of leading a technical startup as a non-engineer, the underestimated value of soft skills in building scalable companies, and how trust, learning, and risk-taking shape leadership at every stage.

💡 Key Takeaways:

Soft Skills Scale: Emily challenges the misconception that only hard skills matter in tech leadership, showing how people skills drive team performance and product success.

Learning is a Superpower: Her career evolution was fueled by an unapologetic hunger to learn and willingness to step into discomfort and uncertainty.

The CEO as Conductor: Emily views the CEO role as orchestrating harmony across functions—ensuring each part of the company plays in sync.

Technical ≠ Only Coders: Emily has gained deep technical understanding through proximity, curiosity, and respect—without being an engineer herself.

Redefining Career Paths: She encourages others, especially in HR or non-traditional roles, to question labels and stretch into new domains with courage.

⏱ Timestamped Highlights:

(00:00) Intro to Emily Long and her transition from HR to tech CEO

(00:42) What Edera does: security + infrastructure beneath the Linux kernel

(02:07) Early career: from public accounting to people operations

(03:38) Becoming a founder by learning what others didn’t want to do

(06:10) Why she said “yes” to being CEO — and the orchestra analogy

(09:36) Relationship with CTO and deep respect for engineering

(12:51) The business acumen of HR professionals is underappreciated

(14:22) Breaking the “not technical” stigma and respecting both skill sets

(20:14) Should founders always scale with the company? A nuanced view

(23:25) Would she have jumped into tech sooner? The safety-risk tradeoff

(25:45) Where to connect with Emily: LinkedIn and edera.dev

💬 Quote to Feature:

"Just because you can doesn't mean you should. You’ve got to ask yourself—am I bringing the right energy to the next stage?" – Emily Long

Arlene Watson, a product and engineering leader in the cybersecurity space with experience at CrowdStrike, ServiceNow, and Tenable, joins the show to unpack the critical challenges facing cybersecurity teams today. We dive into breach realities, the need for proactive defenses, how automation is reshaping security operations, and why AI is both a threat and an essential tool. If you’re building, managing, or securing software in today’s threat landscape, this episode is for you.

🔑 Key Takeaways:

Breaches are a daily reality – Most go unreported, but every breach should raise alarm bells because attackers may be setting the stage for larger, future infiltrations.

Automation is critical – Repetitive, manual tasks in cybersecurity can and should be automated to free up teams for higher-value, offensive strategies.

AI expands the threat and the solution – Generative AI introduces exponential risk, but it's also becoming a core component of advanced cyber defense strategies.

💬 Quote to Highlight:

"The moment someone says they know all the adversaries that will show up tomorrow, we know that’s not the fact. Our job is to chase the unknown and prepare for it." — Arlene Watson

⏱️ Timestamped Highlights:

00:00 – Intro to Arlene Watson and the state of cybersecurity today

00:33 – Why breaches are more common than we think

02:14 – Breaches must always raise alarm bells

05:26 – Understanding the hierarchy of high-value assets

08:23 – Automation trends in product engineering for cybersecurity

11:35 – Why cybersecurity budgets often lag behind priorities

15:04 – How AI is growing the cybersecurity attack surface

18:28 – Can AI help defend against adversarial AI?

21:22 – Prioritizing cybersecurity product development: foundation, automation, and integration

25:10 – Connect with Arlene via LinkedIn

In this episode, Amir sits down with David Marchick, Dean of the Kogod School of Business at American University, to explore how AI is transforming higher education. From early skepticism to full-scale integration, David shares how his faculty is embracing generative AI—not just as a tool, but as a cornerstone of future-ready learning. The conversation dives into what it means to prepare students for an AI-infused workplace, the ethical dilemmas that arise, and how this technology could either widen or bridge existing academic gaps.

🔑 Key Takeaways:

AI Integration Is No Longer Optional: David emphasizes that resisting AI is like banning calculators—students will use it, so schools must evolve to teach responsible and effective use.

Education Must Mirror the Workplace: From proofreading to prototyping, AI skills are becoming table stakes in modern careers. Schools must prepare students accordingly.

AI as an Equalizer—or Divider: While AI tutoring tools can democratize learning, lack of access at under-resourced schools could deepen educational inequality.

Faculty Need Retraining Too: Teachers are being retrained with help from industry to effectively embed AI into their disciplines—from finance to marketing.

🧠 Quote:

“You won’t be replaced by AI. But you could be replaced by someone who knows how to use AI.” — David Marchick

⏱️ Timestamped Highlights:

00:00 – Introduction to David Marchick and American University’s approach to AI in education

01:15 – Why early academic response was to ban AI—and why that’s changing

03:30 – Shifting from fear to experimentation: How the Kogod faculty embraced AI

06:45 – Balancing original student work with AI assistance

09:00 – Teaching students to question AI and use it responsibly

12:20 – Will AI adoption in education be fast or slow? Marchick predicts years, not decades

14:50 – AI exacerbating the education gap: The equity question

16:15 – Use case: How AI tutors are built and used in quantitative graduate programs

18:45 – Writing, equity, and how AI may lift weaker students without eliminating learning

20:45 – Broader career implications: How AI reshapes job boundaries and skillsets

22:30 – Marketing example: Cutting down design debates with generative tools

24:45 – How to learn more about Kogod’s AI curriculum and initiatives

In this episode, Amir sits down with Brent Keator, an expert advisor at Primary Venture Partners, to unpack one of the most debated engineering challenges: tech debt versus reengineering. They explore how to define tech debt, when to refactor versus rebuild, the ROI of revisiting old code, and how AI is (and isn't) changing the equation. This is a must-listen for engineering leaders navigating complex technical decisions in fast-moving environments.

🔑 Key Takeaways:

Tech debt isn't always bad—just misunderstood. Brent reframes it as part of the software evolution, often misjudged in hindsight with unrealistic expectations.

Refactoring isn't an all-or-nothing decision. Brent recommends carving out 30–40% of engineering time for tech debt if possible, and viewing it as iterative maintenance tied to business value.

Reengineering has a cost—evaluate wisely. Use the “better, faster, cheaper” test before replacing tools or platforms, and always account for hidden transition costs.

AI can help but won’t eliminate tech debt. While AI improves productivity, Brent argues it doesn’t change the underlying truth: software is disposable, and architecture still needs discipline.

⏱️ Timestamped Highlights:

00:00 – Intro to Brent Keator and the episode focus: tech debt vs reengineering

01:01 – Defining tech debt across code, products, and organizational habits

02:53 – When reengineering tools goes too far or solves the wrong problem

04:35 – The stigma of tech debt and how to rethink it

08:55 – The cost of revisiting old code and the ROI on fixing the past

11:12 – Why tech debt in engineering is fundamentally different than other domains

12:44 – When to rebuild, how to evaluate tool replacements, and the abstraction advantage

16:23 – Vetting open-source solutions: cost, support, and security risks

18:36 – The emerging role of AI in engineering and why trust and testing still matter

23:20 – Will AI help solve tech debt? Brent’s take on the future of disposable code

24:46 – How to connect with Brent and final thoughts

💬 Quote of the Episode:

“What we write today is going to be gone tomorrow. Whether AI helps or not, we need to get comfortable with that.” – Brent Keator

In this episode, Amir Bormand sits down with Andy White, CEO of ClosingLock, to talk through his journey from PhD engineer to startup founder. Andy shares the aha moment that launched ClosingLock, a cybersecurity-focused platform protecting real estate transactions, and offers a transparent look at the early struggles of building trust in a skeptical industry. From pitching title companies with Chick-fil-A to learning an entirely new domain from scratch, this is a story about execution, humility, and listening harder than you pitch.

📌 Key Takeaways:

Execution > Ideas: Success came not from having a unique idea, but from executing better than competitors who had millions in funding.

Talk It Out: Andy credits customer conversations—and even explaining problems to a rubber duck—with clarifying and improving his product thinking.

In-Person Matters: Showing up with lunch and listening in-person proved essential in building trust with skeptical title companies.

Start Simple, Iterate Fast: ClosingLock launched with just one feature: securely sharing wiring instructions. Growth came by solving one problem at a time, then listening for the next one.

⏱️ Timestamped Highlights:

[02:10] – Why a PhD wasn’t all that helpful in building a startup.

[04:46] – Andy’s first “startup”—selling mazes in 2nd grade.

[07:20] – The lightbulb moment: real estate wire fraud almost hits home.

[11:15] – It’s not the idea—it’s the execution that matters.

[16:46] – The “rubber duck method” for solving complex problems.

[19:27] – Selling to skeptics: convincing title companies to try something new.

[21:17] – Why email, fax, and phone still dominate real estate—and why that’s a problem.

[25:49] – Would Andy build the same way post-pandemic? (Yes.)

[28:03] – Avoiding the trap of planning too far ahead.

💬 Quote:

"Ideas are cheap. Execution is everything. Everyone saw the problem—very few stuck around to solve it better."

In this episode of The Tech Trek, Amir Bormand talks with Jason Wells, Head of Engineering at BrowserBase, about building a high-performance culture rooted in trust, emotional intelligence, and psychological safety. Jason shares how his unconventional path—including a six-year break from tech—helped shape a management philosophy that puts human connection at the center of engineering leadership. From dismantling blame culture to fostering self-compassion and authentic feedback loops, Jason offers a powerful framework for anyone looking to lead modern tech teams more intentionally.

💬 Quote:

“The best engineering is done by people who love their jobs. If you want the best output, you need a culture that makes people feel safe, trusted, and empowered.” — Jason Wells

🔑 Key Takeaways:

Trust is the foundation: Jason outlines how “boldly daring to trust” creates psychological safety—key to collaboration, innovation, and long-term performance.

Blameless culture matters: Mistakes should be opportunities for learning, not shame. This leads to more ownership and less deflection in engineering teams.

Emotional intelligence is a multiplier: Jason shares how his six-year break from tech helped him level up his emotional toolkit—skills he now actively brings into management.

Every engineer is unique: One-size-fits-all management doesn’t work. Jason emphasizes individualized leadership rooted in curiosity, vulnerability, and compassion.

🕒 Timestamped Highlights:

00:00 – Intro & Jason’s background

02:43 – What makes a great engineering culture

04:40 – Why trust and psychological safety are non-negotiable

06:59 – How BrowserBase screens for cultural alignment

10:46 – Building an ideal environment from scratch

12:27 – Jason’s early start: Atari, Oracle, and startups

17:00 – Transition into management and leadership philosophy

20:00 – Leaving tech for six years: self-actualization and purpose

24:00 – Learning emotional intelligence and conflict resolution

28:19 – Creating safe space for engineers with high expectations

31:38 – Preventing burnout while maintaining performance

33:38 – Leadership means knowing your people

In this episode, Amir Bormand sits down with Kieran Furlong, CEO and co-founder of Realta Fusion, to explore the unique path of a deep tech startup spun out of a university lab. They discuss building a fusion energy company, navigating complex stakeholder relationships with universities and government agencies, and keeping long-term mission-driven teams aligned. From licensing technology to managing a decade-long development cycle, this conversation reveals how Realta Fusion is working to change the world’s energy future.

🔑 Key Takeaways:

Deep tech startups require a different VC playbook: Realta Fusion operates on a decade-long roadmap that demands alignment with investors willing to play the long game.

University spinouts bring both opportunity and friction: Leveraging academic research can be powerful but navigating bureaucracy and IP licensing adds layers of complexity.

Mission-driven leadership is essential: With long timelines and uncertain outcomes, Kieran keeps his team focused through a relentless reminder of their shared purpose—commercial fusion energy.

Energy abundance as a global equalizer: Fusion isn’t just a tech challenge—it’s a moral imperative to bring energy equity to the planet’s future 10 billion people.

🕒 Timestamped Highlights:

00:25 – Intro to Kieran Furlong and Realta Fusion's mission

01:35 – Why Realta is a venture capital outlier: long timelines and deep capital

03:46 – Spinning out of the University of Wisconsin and working with federal energy programs

05:55 – Startup vs university culture clashes and how to navigate them

08:07 – The race to meet fusion milestones by 2035

11:53 – Diplomacy in energy: balancing federal, academic, and private sector dynamics

14:53 – The global case for fusion: climate, equity, and energy abundance

16:05 – How to lead scientists toward a commercial goal without losing curiosity

18:29 – Licensing tech the right way: aligning incentives for long-term success

21:00 – Where to follow Realta Fusion and get involved

💬 Quote:

“You still want the creativity and curiosity of scientists—but you need to keep one eye on the destination: commercial fusion energy.” – Kieran Furlong

In this episode of The Tech Trek, Amir sits down with Clark Downum, CTO at Redox, to unpack the deeper dynamics between engineering, product, and business stakeholders. From tech debt and project delays to culture, communication gaps, and delivery trade-offs—this conversation is a candid exploration of how technical teams can drive impact without getting stuck in process perfection.

Whether you're a tech leader or aspiring one, this episode offers a fresh lens on ownership, expectation-setting, and delivering what really matters.

🔑 Key Takeaways:

The cost of tuning out business context: Engineers often rush to solution-mode too early—Clark stresses the need for active listening before architecting.

Tech debt is not a dirty word: Clark challenges traditional thinking—some tech debt is strategic, and discussing it in business terms builds clarity.

Product owners need more support: Agile isn't just about shifting scope; engineering teams should help product leaders clarify and prioritize based on impact.

Delivery ≠ Impact: Shipping on time is not enough. Clark urges teams to elevate conversations toward value, trade-offs, and business impact over output.

⏱️ Timestamped Highlights:

00:48 – What Redox does and the scale of its data exchange operations

02:00 – Onboarding engineers in a complex healthcare ecosystem

03:55 – Why stakeholders often only ask about engineering when things go wrong

07:24 – Do engineers stop listening when they start solutioning too early?

10:20 – Rethinking tech debt: What the business doesn’t know actually helps

13:46 – Can we train engineers to prioritize “getting it done” over “doing it right”?

17:36 – Agile as a response to imperfect plans, not bad estimates

20:53 – Why scope, time, and quality are business trade-offs, not just engineering ones

22:22 – "The burden is on engineering"—and why that might be the right mindset

24:52 – Final thoughts on collaboration, failure, and owning outcomes

💬 Quote of the Episode:

“Don’t just ask, ‘Is this hard?’ Ask, ‘How hard should I work to make this easy?’ That’s where true collaboration starts.” – Clark Downham

In this episode of The Tech Trek, Daniel Whatley, co-founder and technical lead at Vividly, shares his journey launching a startup while still a student at MIT. From managing college life during COVID to navigating the CPG industry's digital transformation, Daniel reflects on what it meant to be the youngest in the room, how he grew into executive leadership, and what he wishes he’d known before co-founding a company. A candid look at growth, grit, and the impact of youth in a traditional space.

🔑 Key Takeaways:

Startups in school are possible: Daniel co-founded Vividly while at MIT, proving early-stage entrepreneurship can thrive during college years—even amid COVID.

Tech-first in a non-tech industry: He leveraged his technical expertise to modernize trade spend management in consumer packaged goods.

Being the youngest has its perks: Despite age differences, deep domain knowledge can earn respect and create opportunity.

Hard lessons in leadership: Managing older employees taught Daniel resilience and the importance of learning on the job.

💬 Memorable Quote:

“Don’t give up. If something feels hard, remember you’ve solved a million problems before—this is just the million-and-first.” – Daniel Watley

⏱ Timestamped Highlights:

00:23 – 01:30 — Intro to Daniel and Vividly’s mission in CPG optimization

03:39 – 05:18 — Launching a company as a student and the power of momentum

06:27 – 08:13 — Choosing a startup over corporate offers post-graduation

08:17 – 09:39 — Origin of the business idea from family connections

10:20 – 12:18 — How COVID created unexpected demand for their product

12:35 – 15:14 — Being the youngest in the room and embracing your technical edge

15:17 – 17:57 — What’s changed: scaling, hiring, and engineering maturity

18:32 – 21:34 — Learning management fast: handling tough dynamics with older team members

21:53 – 24:11 — Daniel’s advice to aspiring founders still in school

25:07 – 26:21 — Would he take the job if he could do it again? No regrets

26:21 – 27:32 — Final thoughts and how to connect with Daniel

In this episode of The Tech Trek, Amir is joined by Jonathan Myron, VP of Engineering at Healthie, to dive into what it really takes to lead engineering teams inside startups. From aligning with founders' visions to building engineering cultures that thrive on autonomy and creativity, Jonathan shares hard-won lessons for engineers stepping into leadership. Whether you're building early-stage or scaling through growth, this episode delivers practical insights on driving value, developing team culture, and shaping your career path.

🔑 Key Takeaways:

Start with empathy for the founder’s vision. Engineering leaders must deeply understand why a company was started to effectively implement and scale that vision.

Leadership is a behavior, not a title. Taking ownership, solving problems, and filling gaps earns trust and influence, especially in startup environments.

Engineering culture thrives on transparency and purpose. Aligning product goals with team values keeps engineers motivated and connected to impact.

Metrics are a story, not a scoreboard. Use developer experience surveys and team feedback—not just velocity or failure rate—to shape team performance meaningfully.

⏱ Timestamped Highlights:

00:00 – Intro to Jonathan and the theme: working with founders in startups

01:48 – Why understanding the founder’s origin story matters for engineering leadership

03:00 – Sussing out alignment during interviews with startup founders

04:15 – Translating founder vision into engineering execution and culture

05:19 – The role of metrics and surveys (like Westrom) in measuring alignment and team health

06:49 – Why engineering is both a scientific and creative pursuit

08:26 – Bridging founder imprint and engineering culture with empathy and clarity

09:53 – Common traits of successful founders and how engineers can support them

11:58 – Driving value by solving problems without waiting for instruction

13:25 – Advice: “Put aside ego. Real leaders don't need titles.”

15:08 – Thriving in ambiguous, high-impact startup environments

16:54 – How to reach Jonathan on LinkedIn for career advice

💬 Standout Quote:

“Leadership is when somebody is a leader, everybody knows it—and you don't need a title for that.” – Jonathan Myron

What should you really be asking during your interview as a tech leader? And once you land the role, how do you manage expectations, reduce technical debt, and make meaningful impact fast?

In this episode, Justin Nguyen, Technology Director of Enterprise Data & Analytics at Home Depot, shares hard-won insights from his recent leadership transitions. From assessing team maturity to setting realistic AI expectations, we unpack the tactical and strategic moves leaders need to thrive in the first 180 days of a new role.

💡 Key Takeaways:

Interview the Company Like a Pro: Ask about key initiatives, maturity of the org, and how they attract top talent—not just the role’s scope.

Manage Expectations with Data: Use metrics and storytelling to align stakeholder expectations with technical realities.

Build Trust First: Quick wins, especially those that align with long-term goals, are essential for establishing credibility early.

Data's Real Value is Trust: The true measure of data success is stakeholder trust and consistent usage.

Balance Training vs. Hiring: When evolving your team, identify real skill gaps and be transparent to maintain trust.

⏱️ Timestamped Highlights:

[01:18] – Three things to assess in interviews: org maturity, domain readiness, and team strength

[03:30] – Why the presence of technical/data debt should be expected—not feared

[06:28] – Aligning stakeholder expectations with reality to reduce frustration

[09:27] – The real AI question: what not to do with it

[11:17] – Spotting leadership dynamics during interviews

[14:16] – Measuring your own leadership ROI in the first 90–180 days

[17:19] – Short-term wins that support long-term strategic goals

[19:44] – Measuring success in data through usage and trust

[22:19] – Balancing team upskilling, outside hiring, and consulting

🔖 Quote of the Episode:

“Frustration is the delta between expectations and reality. The greater the gap, the greater the frustration. Your job is to close that gap.” – Justin Nguyen

In this episode of The Tech Trek, Brendan Grove, CTO and co-founder at PrizeOut, shares how his non-traditional background shaped his leadership style and hiring philosophy. Brendan dives into how being curious, humble, and pattern-aware has helped him scale teams and solve complex problems. He also unpacks how hiring for core traits like learning velocity and ownership can outperform chasing resumes full of surface-level skills. We also discuss tech debt, decision-making frameworks, and the role of engineering excellence in business success.

Whether you're a startup founder, engineering leader, or aspiring technologist, this episode is a reminder that greatness often lies beyond the obvious checklist.

🔑 Key Takeaways:

Hire for Curiosity and Ownership: Brendan values engineers who "give a shit" more than those who just ace technical interviews. Passion, curiosity, and ability to learn fast are force multipliers.

Non-Traditional Backgrounds Offer Valuable Perspective: Brendan's journey from mechanical engineering to CTO helped him build pattern recognition and a strong product-building instinct.

Balance Autonomy and Accountability: Great leaders don’t need to be the expert—they need to empower others while knowing when to step in.

Tech Debt Isn’t the Enemy—Stagnation Is: Tech debt becomes a problem only when it slows you down or introduces risk. Code should be easy to change without fear.

⏱️ Timestamped Highlights:

00:32 – What PrizeOut Does

01:13 – Brendan’s Path from Mechanical Engineering to Tech

02:59 – Humility and Curiosity as Tools for Problem Solving

04:41 – Delegating While Still Leading

06:46 – What Brendan Looks for When Hiring Engineers

09:24 – Hiring Junior vs. Senior: A Strategic Approach to Ramp-Up

11:56 – Giving Raw Talent a Chance: A Success Story

15:08 – Code Quality vs. Business Value: Finding the Right Balance

17:47 – Tech Debt: When It Matters and How to Approach It

💬 Quote:

"You should be able to make small changes without being scared. If you can't, it's not a testing problem—it's a code problem."

In this episode of The Tech Trek, Amir sits down with Ronak Vyas, Co-Founder and CTO of Lead Bank, to explore how leadership principles remain constant even as the problems — and companies — change. Ronak shares lessons from leading at Yahoo, Square, and now founding a fintech bank, reflecting on how to adjust to new environments, make high-stakes decisions, and transition from engineering leader to startup founder. If you’re a technology professional considering leadership or even starting your own venture, this episode is packed with real-world insights on navigating change, making smart decisions, and staying close to your craft.

🔥 Key Takeaways:

Leadership tools stay constant, but their application must adapt to different company cultures, industries, and scales.

Prioritize understanding the business context before forming strong technical opinions.

Speed of decision-making beats perfection — collect real-world data fast, iterate, and adjust.

As a founder, decision-making carries broader consequences, making a deep business understanding essential beyond technical leadership.

Retaining technical depth is critical as you move into higher leadership roles, especially when founding or joining small companies.

🕰️ Timestamped Highlights:

(00:42) – What Lead Bank does: Combining fintech innovation with banking infrastructure.

(02:20) – How to adjust to new company cultures and identify first-order problems.

(05:47) – Why leadership skills are constants — and how applying them evolves.

(09:11) – Balancing gathering information with moving fast: an art, not a science.

(13:39) – Why fast, iterative decision-making often beats chasing perfection.

(15:12) – How decision-making changes when you're a co-founder vs an executive.

(17:28) – Staying technically sharp: the importance of retaining depth as you grow.

(21:18) – What Ronak wishes he had more exposure to before becoming a founder.

💬 Memorable Quote:

"Most often, it's better to make a good decision and iterate quickly than to wait for the perfect decision — real-world feedback is your best guide."

In this episode, Marty Neese, CEO of Verdagy, joins Amir to unpack what it takes to scale a company in one of the most innovative and high-stakes industries—green hydrogen. From managing a purpose-driven culture to embracing failures as a strategic advantage, Marty shares insights on leading ambitious climate tech initiatives while staying grounded in economic reality. Whether you're in tech, energy, or just love solving complex problems, this one's for you.

🔑 Key Takeaways

Purpose as a North Star: Verdagy’s mission—delivering the power of nature—is more than a slogan. It shapes the company’s decision-making, from high-level strategy down to subcomponent cost roadmaps.

Problems Are Treasures: Marty champions a culture where failures are embraced as learning opportunities, inspired by the Toyota Production System.

Motivation Through Impact: When the going gets tough, Verdagy employees reconnect with their impact—literally watching hydrogen being created in real time—to reignite their passion.

CEO Doesn't Mean Solo: Marty opens up about his reliance on investor and customer feedback as his mentorship circle, busting the myth of the lone visionary at the top.

🕒 Timestamped Highlights

[00:40] – What Verdagy does: splitting water to create hydrogen and oxygen.

[01:55] – Why purpose matters more than just a mission statement.

[03:54] – “Problems are treasures”: embracing failure as an asset.

[06:53] – Knowing when a problem isn’t worth solving.

[08:38] – Staying motivated when outcomes are uncertain.

[11:41] – Breaking down purpose into measurable missions.

[14:03] – A look into Verdagy’s quarterly cost roadmap methodology.

[16:29] – Marty’s unexpected mentors: customers and investors.

[18:52] – The future of green hydrogen and fossil parity.

💬 Quote of the Episode

“Every time you encounter a problem, there's treasure to be mined. That mental polarity shift—from failure to learning—is how real innovation happens.” — Marty Neese

In this episode of The Tech Trek, Amir chats with Rob Williams, co-founder and CTO at Read AI, about what it truly means to be an AI-native company. Rob shares how Read AI uses its own tools internally, how his small but mighty engineering team balances speed and structure, and the evolving role of AI in productivity workflows. Whether you're building AI products or trying to adopt them effectively, this conversation offers a unique peek behind the curtain of a startup navigating the future of work.

💡 Key Takeaways:

AI adoption without intentionality fails. Many companies are experimenting with AI tools, but without clear goals, adoption is often aimless.

“Tech debt” is outdated. Rob prefers specific discussions around scalability, readability, and maintenance over the vague term “tech debt.”

Internal AI usage drives efficiency. Read AI uses its own product to streamline workflows like onboarding, reducing repetitive knowledge transfer.

Small teams thrive on focus. Being a smaller company is an advantage when it comes to agility, focus, and avoiding bureaucracy—especially in AI.

⏱ Timestamped Highlights:

00:35 – What Read AI is and how it differs from big platform players.

02:19 – Why intentionality matters in successful AI adoption.

04:41 – How building AI-native products changes the cost/benefit mindset.

06:28 – Rob’s hot take on the term “tech debt” and why he avoids it.

09:45 – How they divide engineering time between R&D, product, and internal needs.

12:19 – Using AI to eliminate repetitive tasks like onboarding and documentation.

15:34 – How startup culture encourages practical AI tool adoption.

18:08 – Closing the gap between engineers and customer feedback.

20:45 – Competing with tech giants by focusing narrowly and moving efficiently.

🧠 Quote of the Episode:

“If we know something will serve our customers well for the next three to six months, we do it. Anything beyond that is just as likely to be wrong as it is right.” – Rob Williams

If you'd like to see Read AI in action, this link will take you to the transcript their AI produced of the episode: https://app.read.ai/analytics/meetings/01JPJXY1SFAXE509NJ4S5P0W5X?utm_source=Share_CopyLink

In this episode of The Tech Trek, Amir sits down with Abhi Sharma, CEO and Co-Founder of Relyance AI, to unpack the philosophy of "unreasonable hospitality"—a framework for building unforgettable customer and team experiences. From small gestures like a humidifier in the interview room to culture-embedded rituals, Abhi reveals how this principle fuels trust, retention, and performance at every level. If you're building teams or scaling a company, this one is packed with actionable insights.

🔑 Key Takeaways:

Unreasonable hospitality = memorable + maximizing + mentionable. It’s not about going the extra mile—it’s about doing the unexpected in personal, meaningful ways.

Small gestures can drive huge impact. Whether winning deals or recruiting talent, personalized touches create emotional connections that close the loop.

Culture is built through consistent rituals. From Slack channels to awards like “Golden Lion,” Reliance AI embeds their values in routines.

Founders must lead from the front. Embodying cultural values in visible, everyday ways—like flying out for a candidate interview—sets the tone company-wide.

⏱️ Timestamped Highlights:

[01:21] — Defining “unreasonable hospitality” with the 3 M’s: maximizing, memorable, mentionable.

[05:19] — A personalized video tip wins a competitive deal.

[07:40] — A $30 humidifier makes an outsized impact in the interview process.

[09:45] — The 4-part framework to embed hospitality into company culture: Rituals, Empowerment, Feedback, Storytelling.

[14:15] — Balancing perfectionism and personalization in culture values.

[18:27] — Recruiting a new dad: flying in instead of flying him out shows care and commitment.

[21:00] — Why the small stuff carries culture and why consistency matters as a company grows.

💬 Quote to Share:

“If everything gets commoditized and we’re living in the fancy AI world... then the only thing that’s actually going to matter is the element of service—the human touch.” — Abhi Sharma

In this episode of The Tech Trek, Amir sits down with Sasha Gainullin, CEO of Battleface, to explore how focusing on a small, underserved niche in the travel insurance industry unlocked global opportunity. Sasha shares how Battleface used in-house technology to revolutionize the outdated travel insurance model, expanding from serving adventure travelers to powering major partners through their service platform, Robin Assist. This is a conversation about focus, customer empathy, and tech-driven disruption—valuable for any founder or product leader.

🔑 Key Takeaways

Start Small, Win Big: Battleface began by solving a single problem for niche adventure travelers. That focused approach laid the foundation for global scale.

Tech as a Differentiator: Building the entire platform in-house enabled real-time risk pricing, scalable customization, and operational agility.

Customer Connection Wins: Even as CEO, Sasha remains hands-on with customer service to ensure product relevance—an often-missing link in insurance innovation.

From Product to Platform: The launch of Robin Assist extended Battleface’s reach, now powering services for other travel insurance providers worldwide.

⏱️ Timestamped Highlights

00:49 – What is Battleface? A travel insurance company that customizes micro-products using tech.

02:23 – Why they focused on one underserved segment: journalists, surfers, adventure travelers.

05:35 – The pricing problem solved with real-time tech under Lloyd’s of London guidance.

09:48 – How building in-house tech enabled flexibility, scalability, and global compliance.

12:08 – Competitive advantage: fast iteration, informed by decades of industry experience.

14:33 – GenAI isn't a threat—it's a tool. The focus is on solving customer problems, not chasing trends.

18:54 – How the pandemic revealed broader market applicability and led to Robin Assist.

24:05 – Distribution cost challenges and exposing why traditional insurance often fails customers.

26:07 – Partner insights: why offering relevant, flexible insurance products is the future.

💬 Quote Worth Sharing

"Technology is just a feature. If you lose that touch with the customer, you’ll stumble—and that’s what’s happening in travel insurance today." — Sasha Gainullin

In this episode, Amir sits down with Nirman Dave, co-founder and CEO of Zams, an enterprise AI platform built to help businesses design and deploy AI agents with ease. They dive into Nirman's founding story—launching during the pandemic, navigating the evolution of the AI ecosystem, and the unique challenges of maintaining customer focus amid shifting trends and rising competition. Nirman also shares lessons from pitching investors, building trust with customers, and the art of product prioritization.

📌 Key Takeaways

Differentiation Through UX: Zams is not just another AI tool—it aims to be the browser for AI, giving enterprises a seamless UI to work with agents.

Customer Over Competition: Success has come from solving real business problems—not chasing trends or investor hype.

Trust Through Design: A 30-second loading delay helped build trust in Zams’ lightning-fast models, proving psychology matters in UX.

Resilient Startup Strategy: Focusing on sustainable growth and user love—not vanity metrics—is what keeps investors coming back.

🕒 Timestamped Highlights

00:40 – What Zams does and how it’s helping enterprises with AI agents

02:14 – Starting a business in college during the pandemic

04:21 – Evolution of AI from AutoML to LLMs and product-market fit

07:15 – Staying customer-centric as terminology and trends change

09:43 – Manufacturing case study: 20 hours/day saved with AI agents

12:25 – Why the “browser moment” for AI is coming

14:33 – Balancing roadmap flexibility with intentional focus

17:34 – Fundraising lessons: sustainable growth beats glamor

24:08 – Listening to customers—but not too literally

26:11 – The 30-second delay that changed customer perception

💬 Memorable Quote

“At the end of the day, businesses care about three things—saving time, saving money, or making money. Everything else is noise.”

In this episode of The Tech Trek, I sit down with Artem Rodichev, Founder & CEO of Ex-Human, to explore the emerging world of empathetic generative AI. We discuss how today’s LLMs fall short on emotional intelligence and how Ex-Human is building AI that can emotionally connect with users. Artem shares the vision behind their product Botify AI, its real-world applications—from gaming and education to mental health—and the crucial role of guardrails in ensuring safe, ethical AI development.

🔑 Key Takeaways

Current LLMs lack emotional depth. They're designed to solve tasks quickly, not to engage in human-like, emotionally resonant conversations.

Empathetic AI can reduce loneliness. These systems aim to connect with users on an emotional level and offer meaningful companionship.

Real use cases span industries. From gaming and language learning to mental health support and education, empathetic AI has broad applications.

Data-driven improvement. Wattify AI learns through millions of conversations and user feedback, fine-tuning its responses for empathy and memory.

Safety is a must. As AI gets more emotionally intelligent, strong ethical guardrails are essential to prevent misuse.

🕒 Timestamped Highlights

00:34 – What is X-Human? Creating customizable, emotionally intelligent AI characters

02:05 – Why current LLMs feel robotic (task vs. engagement-driven design)

04:38 – Defining “empathetic AI” and how it’s different from classic chatbots

06:06 – Use case: Solving loneliness and building emotional connections

07:50 – Applications in gaming, Discord bots, and immersive NPC experiences

09:40 – Language learning via informal practice with emotionally aware AI

10:50 – Supporting mental health by providing judgment-free companionship

12:25 – How Wattify AI gathers and uses data for emotional accuracy and memory

16:10 – Technical details: short-term vs long-term memory, voice & visual integration

19:23 – The importance of safety, ethics, and guardrails in emotionally intelligent AI

23:06 – The broader opportunity in education, tutoring, and emotional engagement

23:57 – Where to try Wattify AI and connect with Artem

💬 Featured Quote

"Empathetic AI companions don’t just respond—they remember, support, and emotionally connect. That’s what makes them powerful and personal." – Artem Rodichev

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What does it mean to find out what your team is actually good at—and how do you use that insight to grow, scale, and lead effectively?

In this episode, Amir sits down with Pallavi Pal, Head of Product at Grata, to unpack the nuanced art of identifying strengths within product teams. From hiring with purpose to fostering technical and soft skills, Pallavi shares how she built her team from the ground up and established a culture of collaboration and excellence. Whether you’re a product leader, aspiring manager, or simply navigating your growth path in tech, this conversation is packed with frameworks and hard-earned lessons.

✨ Key Takeaways

“Good” is personal and team-specific – Recognize where individual team members naturally lean in and where they need support.

Hiring with intention matters – Building a team from scratch allows leaders to define what “good” looks like for each role early on.

Balancing technical and soft skills is crucial – Successful PMs don’t just understand the product—they empathize with users and collaborate effectively.

Path to people management starts with mentorship – Use mentorship as a low-risk way to identify potential managers.

Culture isn’t just top-down – Product teams should reflect company values while fostering technical curiosity and peer collaboration.

Metrics can’t be mandated – Teams need to co-create their North Star metrics and OKRs to stay engaged and aligned.

⏱️ Timestamped Highlights

[00:20] – Introducing Pallavi and the focus on identifying what your team is great at

[02:05] – Observing behaviors to identify strengths and hesitations

[05:22] – Hiring to match specific skill sets across different product functions

[08:20] – The balance between domain knowledge, technical skills, and soft skills

[12:03] – Identifying future people managers within your team

[16:21] – Building a product culture that aligns with company values but has its own identity

[21:06] – How to define and align around standards and metrics in product

[24:21] – How to connect with Pallavi for follow-up questions

💬 Quote of the Episode

“It’s a lot more art than science. Good is seeing where people lean in—what excites them—and building the team to amplify that.”

– Pallavi Pal

In this episode, Amir sits down with Zach Barney, Co-founder and CEO of Mobly, the system of record for event marketers. Zach’s story takes us from his early ambitions of joining the NSA to a career-altering injury, a serendipitous fall into sales, and eventually the founding of Mobly. This episode explores not only the career pivots that led Zach to entrepreneurship, but also the mental, financial, and strategic challenges he faced along the way.

If you’ve ever thought about switching paths or launching your own thing — especially from a non-technical background — Zach’s journey is proof that drive, vision, and grit can get you there.

🔑 Key Takeaways:

Pivot Points Can Define You: A severe knee injury and life changes redirected Zach’s path from NSA hopeful to tech founder.

Sales is Entrepreneurship Training: Zach views sales as the most entrepreneurial job short of being a founder — giving him the skills and mindset for startup life.

Solve Real Problems: Mobly was born from Zach’s own pain points in the field — and customer validation made the case.

Execution Over Everything: Despite the harsh fundraising climate, Mobly thrived by focusing on product and market fit.

Founding Doesn’t Require Code: Zach’s non-technical background didn’t stop him — and his story encourages others in the same boat.

⏱️ Timestamped Highlights:

00:20 – Intro to Zach Barney and Mobly — from spreadsheets to sales tech for event marketers.

01:50 – Zach’s drive to control his financial destiny, inspired by his upbringing as the oldest of 8.

03:23 – The “spy-to-startup” journey: NSA offer, Russian fluency, and a career-altering knee injury.

06:15 – How a devastating injury forced Zach to pivot, finding a sales job that set the foundation for his future.

08:29 – Falling in love with sales: the accidental career path that turned into a calling.

10:20 – Constant learning: how podcasts, books, and early-stage exposure prepared him for founding.

12:07 – Making the leap: risks, fears, and financial tradeoffs of starting Mobly with five kids to support.

14:07 – Co-founder chemistry: 30 years of friendship becomes a business partnership

16:20 – Building the MVP without a CTO and the power of scrappy execution.

17:48 – Navigating the economic downturn and fundraising panic attacks in a tough VC market.

20:12 – Why Zach is bullish on execution over economic prediction — and how Mobly is thriving.

💬 Quote to Share:

“Sales is the most entrepreneurial job you can have without being an entrepreneur.” – Zach Barney

🔗 Connect with Zach:

📱 Find him on LinkedIn (just don’t automate your message — he can sniff it out instantly!)

Join us in this insightful conversation with Eric Valasek as we explore the crucial relationship between CEOs, product teams, and engineering leaders. Eric shares his expertise on managing prioritization, strategic tech debt, and ensuring engineering teams stay focused and insulated amidst business dynamics.

Key Takeaways:

Balance is Crucial: A company's success depends heavily on balancing business goals, product demands, and engineering capabilities.

Strategic Tech Debt: Not all tech debt is harmful. Strategic tech debt can accelerate business growth, but must be managed and planned carefully.

Upskilling for Growth: Investing in your team's skill development can pay long-term dividends, especially when tackling new technology domains.

Transparency vs. Focus: Protecting your team from constant business shifts ("horse trading") is essential to maintain productivity and morale.

Engineering's Voice: In tech-driven companies, the engineering team often carries significant influence. Leaders must balance innovation with practical business outcomes.

Timestamped Highlights:

00:41 - Eric's introduction and overview of engineering-product-business relationships.

01:30 - Balancing the business, product, and engineering "trifecta."

05:01 - Effective strategies for team skill development and training.

07:26 - Adjusting team velocity and maintaining quality during upskilling.

09:44 - Navigating potential dips in quality when adopting new technologies.

11:57 - Strategic considerations when intentionally incurring tech debt.

14:31 - Managing transparency and team insulation from business volatility.

17:40 - The importance and impact of engineering's voice in technology-centric businesses.

Quote:

"You can't have speed and quality with the same size team with new technologies. You need to plan that development cycle carefully—some trade-offs are necessary."

— Eric Valasek, Engineering Leader

Connect with Eric: https://www.linkedin.com/in/evalasek/

In this episode of The Tech Trek, Amir Bormand sits down with Shang Wang, Co-founder and CTO of CentML, to explore the dynamic landscape of open source AI technologies and how enterprises are rapidly adapting to this growing ecosystem. Shang offers expert insights into why open source solutions are becoming essential in AI development, the advantages in security and privacy, and how CentML strategically contributes to this evolution.

🌟 Key Takeaways:

Open Source Dominance in AI: Open-source technologies have become foundational to AI development, promoting innovation, transparency, and faster problem-solving.

Enterprise Adoption Shift: Enterprises are increasingly embracing open source solutions in AI, driven by the need for greater transparency, data privacy, and community-driven innovation.

CentML’s Impact: CentML leverages open source through developing tools and infrastructure that optimize AI model deployment, training, and performance at scale.

Security and Privacy Advantages: Open-source AI solutions provide enterprises with enhanced control over data privacy and security, challenging traditional assumptions that closed-source means more secure.

💬 Notable Quote:

"Open source gives you more control. If there’s a security flaw, you can fix it. If there’s a privacy issue, you can build safeguards. Closed source leaves you hoping nothing goes wrong.” – Shang Wang

⏰ Timestamped Highlights:

00:00: Introduction to Shang Wang and CentML

01:28: Origins of open source AI in academia

03:30: Differences in developing with open vs. closed-source solutions

05:10: Impact of open-source tools on talent development and recruitment

07:16: Predictions on the future of open-source AI

10:05: Deep dive into CentML’s tools and open-source integrations

19:46: Real-world applications of CentML, exemplified through banking

22:57: Addressing misconceptions about open source security

27:42: How to connect with Shang Wang

📞 Connect with Shang Wang:

LinkedIn: https://www.linkedin.com/in/shang-sam-wang-52851489

🎙️ Subscribe, Rate, and Review: Let us know your thoughts and stay updated with future episodes of The Tech Trek!

In this episode of The Tech Trek, Amir sits down with Andrew Levy, CEO and Co-founder of AirCover.ai, to explore how agentic AI is transforming the sales landscape. Andrew shares how AirCover builds real-time digital assistants that empower sales teams, the role of humans in AI-driven workflows, and how enterprises—both nimble and traditional—are adopting these tools to leap ahead. From change management to trust-building and the rise of “little language models,” this conversation unpacks what it really means to bring AI into the heart of go-to-market strategies.

🔑 Key Takeaways

1. Real-Time AI for Real-World Sales AirCover.ai builds AI agents that operate in real time alongside sales reps, surfacing the right information at the right moment, and helping teams scale more effectively with digital counterparts.

2. Scaling Expertise, Not Replacing Teams Rather than replacing humans, agentic AI amplifies expertise—like turning one sales engineer into six through virtual counterparts, unlocking growth, not cuts.

3. Human-in-the-Loop Is the Bridge Especially in regulated industries, “human-in-the-loop” AI design helps companies automate workflows while maintaining control, transparency, and trust.

4. Model Confidence Matters for Adoption Andrew emphasizes trust-building in AI by surfacing high-confidence data and leveraging behavior signals to continually improve user experience and relevance.

5. Little Language Models Are the Future Expect a shift from massive models to specialized ones—“little language models”—tailored per team or even per individual, making AI more personalized and effective.

⏱️ Timestamped Highlights

00:00 – Meet Andrew Levy

Intro to Andrew and AirCover.ai – building digital agents for live sales calls.

02:21 – The Origin of AirCover

Andrew shares the story behind the idea, influenced by challenges scaling sales enablement at VMware.

06:50 – Spotting the Market Gap

When tech and market timing intersect: how AI-native thinking unlocked new possibilities.

08:53 – Change Management From Day One

Why ease of use and seamless workflow integration were key in early product design.

11:26 – Enterprise AI Adoption Trends

Big companies are leapfrogging past previous tech gaps by going all-in on AI.

13:55 – AI as an Extension, Not a Replacement

How AI fills capability gaps without threatening job loss—and why that’s a key adoption driver.

16:47 – Agentic Workflows in Action

Examples of tasks AI handles autonomously vs. where human oversight is essential.

20:07 – Confidence, Trust, and Adoption

Andrew talks about how AirCover builds trust through transparency, high-confidence responses, and adaptive learning signals.

22:34 – The Shift to Smaller, Smarter Models

A peek into the near future of AI: narrow, task-specific models that are ultra-personalized.

23:24 – Final Thoughts & How to Connect

Andrew’s contact info and closing takeaways from Amir.

💬 Featured Quote

“This isn't about replacing your team with AI—it's about giving them superpowers. Imagine taking your best solution engineer and scaling their expertise across your entire team.”

— Andrew Levy, CEO of AirCover.ai

Guest: Viraj Narayanan, CEO of Cornerstone AI

🔑 Key Takeaways

Healthcare data is messy by default. It's generated by countless sources with different standards—think EMRs, Apple Watches, and pharmacy systems—making research data fragmented and hard to use.

AI can clean up the mess. Cornerstone AI applies automation to standardize and improve the fidelity of clinical research data, significantly cutting down manual effort.

Productivity > Replacement. Rather than replacing jobs, AI is helping PhDs and data scientists focus on higher-value tasks, enabling more research and faster discovery.

Standardization is foundational. Without clean, consistent data, the insights drawn—even with AI—are limited or flawed.

Trust is earned. The biggest mindset shift is seeing your own messy data cleaned instantly by AI, not a polished demo set.

Patients win too. Cleaner, faster data means more reliable research, potentially more personalized medicine, and better access to understandable information.

💬 Quote of the Episode

“We’re going to look back in 10 years and think—‘I can’t believe we had PhDs doing that kind of manual data work.’”

— Viraj Narayanan

⏱ Timestamped Highlights

00:00 – Intro to Viraj and Cornerstone AI: Automating healthcare data quality

01:54 – The "plumbing problem" of healthcare data and what no one thinks about

04:48 – Why AI in healthcare often starts with admin—not research

05:35 – Steph Curry and SNOMED: How basketball shows us the need for standardization

08:58 – Wild West of research data: From 2% lift to 40%+ with AI

11:41 – Why research is built on redundancy and how AI rewires the model

14:43 – Change management: From trust to technical buy-in to leadership alignment

18:42 – Will AI take jobs? No—but it will transform what we do with talent

21:03 – What patients will see: Cleaner, faster, more understandable data

23:49 – Where to reach Viraj and final thoughts

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Share this episode with a friend in tech or healthcare

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On this episode of The Tech Trek, we're diving deep into the intersection of engineering, product, and business thinking with Vineet Goel — Co-Founder and Chief Product & Technology Officer at Parafin, a fast-growing fintech startup powering small businesses on platforms like DoorDash, Amazon, and Walmart.

We unpack what it really means to build a company where engineers are product thinkers, why bringing in product managers too early can backfire, and how AI is reshaping what it means to write code — and who’s best positioned to thrive in this new world.

Vineet shares how Parafin scaled with just two PMs to 25 engineers, why every engineer shadows customer support calls, and how GenAI might collapse the wall between product and engineering entirely.

Whether you're an engineer, product leader, founder, or just curious where the future of tech orgs is headed — this conversation is packed with insights you won’t want to miss.

🧠 Key Takeaways

Don’t hire PMs too early. Founders should own product-market fit before bringing on a product leader.

Engineers need a business mindset. At Parafin, engineers are ruthlessly customer-focused — many even shadow support calls.

GenAI will change everything. Writing code is becoming a commodity. Future engineers will need to blend product and technical skills.

The product org evolves with scale. Vineet shares when and why Parafin added a Head of Product, and how it shifted org dynamics.

PMs should create leverage, not just roadmaps. When engineers are stretched thin, PMs help teams stay focused and effective.

⏱️ Timestamped Highlights

00:46 – What is Parafin?

A fintech startup empowering small businesses on platforms like Amazon and DoorDash with embedded financial services.

02:35 – Org Design at Parafin

Why they built a structure that’s neither product- nor engineering-led, but customer-obsessed.

05:09 – 25 Engineers, 2 PMs

How a product-minded engineering culture powers massive output and scale.

06:40 – Customer Empathy as Culture

Engineers shadow support calls—and sometimes ship fixes within the hour.

08:50 – When to Hire a Head of Product

What prompted the shift, and how it solved growing pains around complexity and speed.

11:59 – PMs Create Leverage

Bringing in PMs at the right time accelerates decision-making and keeps engineers focused.

14:28 – Dual Hat of CPTO

How Vineet balances strategy, execution, and organizational leadership.

16:34 – GenAI’s Impact on Engineers

Code is getting commoditized. Engineers must evolve—or risk becoming obsolete.

19:14 – What Happens to Product Roadmaps?

AI will speed up delivery—product teams need to dream further ahead, faster.

21:11 – The ‘Shift Left’ of Engineering

Engineers are moving closer to the business—Vineet predicts a product-tech hybrid role will dominate.

💬 Quote Worth Sharing

“Being product and business minded will become a necessity—not a nice to have. Code is becoming a commodity. The future belongs to those who can build and think.”

— Vineet Goel, CPTO at Parafin

In this episode, Amir sits down with Meg Henry, Head of People & Talent at Companyon Ventures, to unpack a critical—yet often overlooked—aspect of growing technical teams: onboarding.

Engineering leaders spend weeks hiring top talent, only to fumble the first 90 days. Meg shares a tactical, startup-friendly approach to onboarding that actually helps new hires ramp faster, become productive sooner, and stick around longer. If you’ve ever onboarded a dev by tossing them a laptop and saying "Good luck," this one’s for you.

🗝️ Key Takeaways for Tech Leaders:

Weak onboarding kills productivity. Even A+ hires won’t thrive if they don’t know how to succeed.

You’re losing time, not saving it. A 30-minute onboarding plan can prevent months of confusion.

Hybrid makes things harder. Without structure, async teams sink.

Consistency beats chaos. No two roles are the same, but every new hire should feel supported.

AI can help you scale onboarding. Especially when documentation is scattered across Slack, Notion, and Drive.

🕒 Timestamped Highlights:

[00:02:00] Why startups obsess over hiring—but ignore onboarding

[00:04:30] That awkward new hire phase, and how to design around it

[00:05:45] Hybrid onboarding: Why access > answers

[00:07:15] The two onboarding tracks every company needs: company-wide + role-specific

[00:09:30] Founders want plug-and-play hires—but that doesn’t work without a plan

[00:10:45] "Here’s your map": how tech leads can shortcut the ramp-up curve

[00:13:30] Using ChatGPT to build lightweight onboarding flows? Yes, here’s how

[00:15:45] Spotting weak onboarding when you inherit a team

[00:18:15] Customization vs. consistency: how much is too much?

[00:20:00] Time investment: just 2.5 hours over 3 months

💬 Quote of the Episode:

“Before GPS, you wouldn’t invite someone over and just say, ‘Figure out how to get here.’ Even your most autonomous hires need directions.” — Meg Henry

📬 Connect with Meg:

Meg’s helping early-stage B2B startups scale smarter. Connect with her on LinkedIn (Meg Henry, Companyon Ventures) and ask for her free onboarding template—it’s lightweight, practical, and startup-tested.

In this episode, Carlos Peralta returns to The Tech Trek to dive deep into data culture in the wearable tech space, sharing how WHOOP turns petabytes of real-time biometric data into personalized, actionable insights. We explore the technical complexities behind data ingestion, transformation, and delivery, and how the mission-driven nature of WHOOP influences both their engineering decisions and company culture.

🔑 Key Takeaways

Wearable tech = real-time big data: WHOOP processes petabytes of multimodal data from edge devices to deliver insights to users in near real time.

Data must be actionable, not just abundant: It's not about the quantity of data collected, but how that data is translated into meaningful guidance for users.

ML Ops is central to product success: The data and ML infrastructure team plays a critical role in feature development, roadmap planning, and performance optimization.

Mission fuels motivation: WHOOP’s internal culture is deeply driven by its impact on human performance—employees are often users of the product themselves.

Scalability ≠ just growth: Cost-efficiency, forecasting, and cloud infrastructure readiness are vital to scaling responsibly in a global market.

⏱️ Timestamped Highlights

00:00 – Intro to Carlos & the mission behind WHOOP

02:19 – Data culture at WHOOP vs. traditional companies

04:15 – Scale of data in wearables: petabytes, not megabytes

05:52 – Complexity of ingesting, transforming, and delivering personalized data

08:53 – Striking a balance: Real-time feedback vs. cloud cost efficiency

11:14 – Scaling the platform as the member base expands globally

13:43 – Internal motivation and culture driven by positive impact stories

15:56 – Why data teams are involved early in the product roadmap

17:59 – Carlos’ journey from WHOOP user to WHOOP employee

20:40 – How to connect with Carlos + final thoughts

💬 Quote of the Episode

“You can have petabytes of data, but if you can’t make it queriable, understandable, and actionable—it’s just noise.” — Carlos Peralta

In this episode of The Tech Trek, Amir Bormand sits down with Max Mergenthaler-Canseco, CEO and co-founder of Nixla, to explore the nuanced reality behind startup success. A multi-time founder with experience as both CEO and CTO, Max shares hard-earned lessons from his entrepreneurial journey—including why theoretical knowledge often clashes with real-world execution, how to build a resilient startup team, and the underestimated danger of survivorship bias in startup lore.

From balancing optimism with statistical failure rates to knowing when to focus on strengths over weaknesses, Max delivers practical wisdom for anyone navigating the startup grind. Whether you're a first-time founder or on your third venture, this conversation will leave you thinking differently about what it really takes to succeed in tech.

🔑 Key Takeaways

Experience is not a blueprint, it's a lens. Max breaks down how startup learnings aren’t always repeatable but instead shape the founder’s decision-making over time.

Passion is the sustainability engine. You have to love what you're building, not just what the market wants—otherwise, you won’t last through the inevitable startup grind.

Founders vs. early employees. Understanding the difference in motivation and expectations is crucial to building and managing a startup team effectively.

Survivorship bias is everywhere. Max cautions against building a startup playbook based only on outlier success stories.

Know your lane. Instead of leveling up all weaknesses, focus on doubling down where your strengths make the biggest impact.

⏱️ Timestamped Highlights

00:44 – What is Nixla?

Max introduces his company, a time series forecasting and anomaly detection startup with deep roots in research.

01:34 – Serial founder life

Max gives a quick snapshot of his startup journey, from NLP experiments to YC-backed fintech.

03:21 – Startup experience ≠ shortcut to success

Why practical experience matters more than theoretical frameworks, and how each startup is its own universe.

07:59 – Playing the startup game because you love it

Max explains why loving the problem you’re solving is essential for long-term survival and sanity.

10:53 – Hiring the right people early

What Max looks for in early-stage team members—and why founders shouldn't expect employees to grind the same way they do.

13:24 – CEO vs. CTO: Vision vs. Execution

A thoughtful breakdown of the distinct roles and responsibilities between CEO and CTO, especially in early-stage companies.

16:27 – Strengths over Weaknesses

Why Max believes in focusing on what you do well, rather than fixing every flaw.

20:25 – The trap of survivorship bias

A fascinating conversation about how the startup ecosystem overemphasizes success stories and ignores the valuable lessons of failure.

How to reach Max

LinkedIn: https://www.linkedin.com/in/mergenthaler/

💬 Featured Quote

“The only way to keep playing the startup game is to actually enjoy the game.” — Max Mergenthaler-Canseco

In this episode, I sit down with Jason Rogers, CEO & Co-Founder of Invary, to explore an unconventional approach to building a cybersecurity startup—leveraging a tech transfer agreement with the NSA. Jason shares his journey of launching a company around licensed technology, the benefits and challenges that come with it, and why runtime system integrity is becoming a crucial factor in modern security strategies.

We also dive into how AI is changing the cybersecurity landscape, the importance of real-time security validation, and how companies can better protect their systems against evolving threats.

Key Takeaways

🔹 Tech transfer provides a competitive edge – Licensing government-developed technology can offer startups a head start with validated, battle-tested IP.

🔹 Security needs to be proactive, not reactive – Real-time validation of system integrity can prevent breaches before they escalate.

🔹 Collaborative research fuels innovation – Invary works with the NSA and academic institutions to advance security capabilities.

🔹 AI is expanding the attack surface – As AI adoption grows, ensuring system and data integrity will be more critical than ever.

🔹 Zero trust applies to machines too – It’s not enough to verify users—organizations must continuously verify their systems.

Timestamped Highlights

⏳ 00:01 – Introduction to Jason Rogers and Invary’s mission

⏳ 00:49 – How NSA-licensed technology is securing critical systems

⏳ 01:36 – The journey from research-backed tech to startup success

⏳ 02:56 – The challenges and benefits of building a business around licensed IP

⏳ 05:32 – Collaborating with government research teams for innovation

⏳ 09:33 – How engineers adapt to the tech transfer model

⏳ 14:06 – Why runtime integrity is the missing piece in security

⏳ 16:34 – The shift from traditional security models to real-time validation

⏳ 19:27 – AI’s growing attack surface and what it means for security

⏳ 23:28 – Predicting future cybersecurity challenges in an AI-driven world

⏳ 24:00 – How to connect with Jason

Quote from the Episode

“The bad guys collaborate all the time. It’s time for the good guys to do the same.” – Jason Rogers

Connect with Jason Rogers

🔗 Website: Invary.com

🔗 LinkedIn: https://www.linkedin.com/in/jasonlrogers/

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In this episode, Amir sits down with Kaustav Das to discuss one of the most critical yet challenging aspects of analytics—asking the right questions. They explore how analytics leaders can better navigate conversations with stakeholders, ensuring they gather the correct requirements and deliver actionable insights. The conversation touches on the evolving role of analytics, the impact of generative AI in business intelligence, and how decision-making is shifting toward more conversational data engagement.

Key Takeaways

The Power of Asking the Right Questions: The quality of analytics is only as good as the questions being asked. Stakeholders’ intent must be fully understood before diving into solutions.

Balancing Speed with Thoughtfulness: Quoting Einstein, Kaustav highlights the importance of preparation: “If I were to chop a tree down in an hour, I would spend 55 minutes sharpening my blade.” Rushing to a solution without understanding the problem leads to inefficiencies.

Technology vs. Process: Not all business challenges require a technology-driven solution. Often, simpler process optimizations can be more effective.

Conversational Analytics & AI: Generative AI is shaping analytics by making data interactions more intuitive, but expertise in asking the right questions remains critical.

Roadmapping for Success: The PTP (Present-To-Path) framework helps stakeholders clarify their goals, define a roadmap, and create an execution timeline for analytics projects.

The Art vs. Science of Analytics: Analytics is more of an art than a science. Understanding business goals, managing multiple stakeholders, and iterative questioning are key to driving value.

Timestamped Highlights

[00:00] Introduction to the episode and guest, Kaustav Das.

[01:08] Why asking the right questions is critical in analytics.

[04:58] Do technologists jump to solutions too quickly?

[06:01] The balance between planning and execution in a fast-paced environment.

[07:28] The high failure rate of technology projects—why intent matters.

[10:52] The five “whys” technique and getting to the core of business problems.

[12:24] The future of analytics—can it become more conversational?

[17:03] Measuring ROI in marketing and media analytics.

[20:29] Where to connect with Kaustav Das.

Quote of the Episode

"If I were to chop a tree down in an hour, I would spend 55 minutes sharpening my blade." – Albert Einstein, referenced by Kaustav Das

Connect with Kaustav Das

LinkedIn: https://www.linkedin.com/in/kaustavanalytics/

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What happens when you build a business around what you genuinely love? In this episode of The Tech Trek, Amir sits down with Michael Farb, CEO of Boatsetter — the Airbnb of boats — to unpack how passion can be a strategic advantage in tech entrepreneurship.

Michael shares his journey of launching multiple businesses rooted in personal interests, from college sports to global philanthropy to now, outdoor water adventures. Together, they explore what it really takes to turn a personal obsession into a scalable business, how to identify real opportunities in your hobbies, and why solving a specific problem matters more than chasing a massive market.

Whether you're dreaming about launching your own thing or leading product inside a startup, this conversation is packed with insights on product-market fit, customer discovery, and building teams who care as much as you do.

🧠 Key Takeaways

Passion is a superpower: When you’re obsessed with a hobby or space, you naturally develop deep insights others don’t see — and that can unlock serious business potential.

Solving problems > chasing scale: Michael shares how the best businesses often start by solving a very specific problem — even if that solution doesn't scale at first.

Inspiration is everywhere: Whether it’s boats, black cars, or model airplanes, there’s almost always a business idea hiding in what people love to do.

Team alignment is critical: Boatsetter thrives by hiring people who live and breathe outdoor adventure — passion isn't just a founder trait, it's company-wide.

Don’t overthink TAM: Many aspiring founders kill ideas too early worrying about market size. Start small, build value, and the market might grow with you.

⏱️ Timestamps & Highlights

00:00 – Introduction

Michael Farb joins Amir to talk about building businesses around personal passions and how that philosophy led to Boatsetter.

01:00 – What is Boatsetter?

A two-sided marketplace for boat rentals in 700+ global locations. No boating license? No problem.

02:20 – Michael’s Entrepreneurial Journey

From sports recruiting tech to nonprofit fundraising platforms — every business tied back to something he personally cared about.

04:45 – How to See the Business in Your Passion

“If you’re obsessed with a space, you’ll know more than anyone else. That’s your edge.”

08:00 – Advice for Aspiring Passion-Driven Entrepreneurs

Look for friction points in your hobby — that’s where business opportunities are born.

10:50 – Employees with Passion

Boatsetter hires people who love the water. They even get boating credits as part of their benefits.

14:00 – Working with Product Teams as a Passionate CEO

Michael partners closely with product to scale both sides of the marketplace — consumers and boat owners.

16:00 – Would He Ever Build a Business Without Passion?

Short answer: No. The passion + business combo has worked too well to ditch.

19:00 – Do You Need Market Research to Start?

Michael skips the spreadsheets — he talks to real people and builds MVPs to validate problems.

21:30 – “Do Things That Don’t Scale”

The early Boatsetter days were scrappy. Human-powered logistics and manual processes — until the model was proven.

24:40 – How to Connect with Michael

Find him on LinkedIn or visit Boatsetter.com.

💬 Quote to Share

“Don’t get paralyzed trying to figure out how big the market is — just solve a real problem. Everything big started small.” – Michael Farb

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In this episode, Zachary Hanif, VP of AI, ML, and Data at Twilio, joins Amir to talk about the engine behind B2B AI innovation. From selecting the right tools to navigating the shift from POCs to production, Zachary offers an insider's look at how enterprises can thoughtfully and effectively integrate AI.

We unpack:

The danger of "boiling the ocean" with AI

Why chatbots aren’t always the right starting point

What makes an AI POC actually valuable

And why UX in the age of AI needs systems thinking

💬 “If you come into it with a technology and not a firm understanding of the problem, you're going to solve a problem that isn’t there — and at best, you'll just end up with a great tech demo.” – Zachary Hanif

🔑 Key Takeaways

Start with the use case, not the tool: Jumping in with LLMs without a clear business problem leads to superficial results.

UX in AI is different: You’re not just designing for humans—you're designing for a human-model-human interaction loop.

POCs must build trust: Especially with generative AI, proof-of-concepts must feel reliable and human-like to succeed.

AI increases surface area: Models introduce new attack surfaces and complexities. Security, observability, and model risk management are critical.

Think systems, not screens: LLMs change how users interact with software. This demands broader thinking from designers and PMs.

⏱️ Timestamped Highlights

00:00 – Intro to Zachary Hanif and Twilio's AI mission

02:05 – Why most companies are AI tool users, not tool makers

04:25 – The “chatbot temptation” and why it might not be the best starting point

06:00 – UX lessons from Google’s early search box vs. today’s LLMs

08:30 – Why we’re still early in discovering transformative AI use cases

11:55 – How AI changes what a good POC looks like

14:59 – Should AI UX be its own discipline?

18:23 – How to know when a POC is ready for production

22:12 – Dealing with AI’s expanding “surface area” and model drift

25:56 – Why model risk management matters more than ever

In this episode, Amir chats with Bobby Touran, the non-technical CEO and co-founder of Rainbow, an insurance tech company that thrives on strong engineering culture. They dive deep into how non-technical founders can effectively collaborate with technical teams, foster a hybrid office culture, and ensure that engineers are closely aligned with business objectives. Bobby shares how Rainbow maintains a tight feedback loop, how returning to in-office work has shaped their growth, and why AI isn’t necessarily replacing jobs in insurance but transforming them.

🔑 Key Takeaways:

Non-technical founders can be powerful allies to engineering teams by focusing on context, communication, and fostering a collaborative culture.

Being back in the office has served as a key differentiator in attracting engineers who value social interaction and cross-functional communication.

Culture is not static—Bobby emphasizes actively evolving it through regular offsites, tools like Slack Donut, and clear alignment between technical and business teams.

Engineering teams thrive when given real-world context—Rainbow’s practice of dining at insured restaurants is a brilliant example of tying product to user impact.

Transparency and experience matter—Rainbow’s engineering team is senior, engaged in thoughtful discussions on technical debt, and values clear communication around business priorities.

AI is reshaping roles, not removing them—Bobby shares his view that AI will augment rather than replace underwriting and risk assessment functions in insurance.

⏰ Timestamped Highlights:

00:00 – Introduction to Bobby Touran and overview of Rainbow’s mission.

02:10 – Bobby’s approach as a non-technical founder and fostering strong relationships with engineers.

04:43 – Building a healthy engineering org: culture is intentional, not accidental.

07:48 – The importance of engineers understanding the "why" behind product decisions.

10:29 – Why being back in the office twice a week has helped Rainbow’s growth and culture.

15:03 – How engineering and leadership collaborate on roadmap and growth opportunities.

19:03 – Decision-making confidence: balancing business opportunities and engineering cycles.

21:59 – Bobby’s take on AI's role in insurance and how Rainbow is integrating it.

25:43 – Connect with Bobby and explore career opportunities at Rainbow.

💡 Featured Quote:

"The culture is not something that you define and it just is what it is. It’s something you actively work on. It’s always evolving." – Bobby Touran

📢 Connect & Learn More:

Website: useRainbow.com – Check out the About Us page and open engineering roles.

LinkedIn: https://www.linkedin.com/in/bobby-touran-782a6513/

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