Afleveringen
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Guest: Viraj Narayanan, CEO of Cornerstone AI
đ Key Takeaways
Healthcare data is messy by default. It's generated by countless sources with different standardsâthink EMRs, Apple Watches, and pharmacy systemsâmaking 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 drawnâeven with AIâare 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.
đŹ Quote of the Episode
âWeâre going to look back in 10 years and thinkââI canât believe we had PhDs doing that kind of manual data work.ââ
â Viraj Narayanan
â± Timestamped Highlights
00:00 â Intro to Viraj and Cornerstone AI: Automating healthcare data quality
01:54 â The "plumbing problem" of healthcare data and what no one thinks about
04:48 â Why AI in healthcare often starts with adminânot research
05:35 â Steph Curry and SNOMED: How basketball shows us the need for standardization
08:58 â Wild West of research data: From 2% lift to 40%+ with AI
11:41 â Why research is built on redundancy and how AI rewires the model
14:43 â Change management: From trust to technical buy-in to leadership alignment
18:42 â Will AI take jobs? Noâbut it will transform what we do with talent
21:03 â What patients will see: Cleaner, faster, more understandable data
23:49 â Where to reach Viraj and final thoughts
đą Like what you heard?
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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 â 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 â and whoâs 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 â this conversation is packed with insights you wonât want to miss.
đ§ Key Takeaways
Donât 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 â 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.
â±ïž Timestamped Highlights
00:46 â What is Parafin?
A fintech startup empowering small businesses on platforms like Amazon and DoorDash with embedded financial services.
02:35 â Org Design at Parafin
Why they built a structure thatâs neither product- nor engineering-led, but customer-obsessed.
05:09 â 25 Engineers, 2 PMs
How a product-minded engineering culture powers massive output and scale.
06:40 â Customer Empathy as Culture
Engineers shadow support callsâand sometimes ship fixes within the hour.
08:50 â When to Hire a Head of Product
What prompted the shift, and how it solved growing pains around complexity and speed.
11:59 â PMs Create Leverage
Bringing in PMs at the right time accelerates decision-making and keeps engineers focused.
14:28 â Dual Hat of CPTO
How Vineet balances strategy, execution, and organizational leadership.
16:34 â GenAIâs Impact on Engineers
Code is getting commoditized. Engineers must evolveâor risk becoming obsolete.
19:14 â What Happens to Product Roadmaps?
AI will speed up deliveryâproduct teams need to dream further ahead, faster.
21:11 â The âShift Leftâ of Engineering
Engineers are moving closer to the businessâVineet predicts a product-tech hybrid role will dominate.
đŹ Quote Worth Sharing
âBeing product and business minded will become a necessityânot a nice to have. Code is becoming a commodity. The future belongs to those who can build and think.â
â Vineet Goel, CPTO at Parafin
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Zijn er afleveringen die ontbreken?
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In this episode, Amir sits down with Meg Henry, Head of People & Talent at Companyon Ventures, to unpack a criticalâyet often overlookedâaspect 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 youâve ever onboarded a dev by tossing them a laptop and saying "Good luck," this oneâs for you.
đïž Key Takeaways for Tech Leaders:
Weak onboarding kills productivity. Even A+ hires wonât thrive if they donât know how to succeed.
Youâre 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.
đ Timestamped Highlights:
[00:02:00] Why startups obsess over hiringâbut 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 hiresâbut that doesnât work without a plan
[00:10:45] "Hereâs your map": how tech leads can shortcut the ramp-up curve
[00:13:30] Using ChatGPT to build lightweight onboarding flows? Yes, hereâs 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
đŹ Quote of the Episode:
âBefore GPS, you wouldnât invite someone over and just say, âFigure out how to get here.â Even your most autonomous hires need directions.â â Meg Henry
đŹ Connect with Meg:
Megâs helping early-stage B2B startups scale smarter. Connect with her on LinkedIn (Meg Henry, Companyon Ventures) and ask for her free onboarding templateâitâs lightweight, practical, and startup-tested.
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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.
đ 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: WHOOPâs internal culture is deeply driven by its impact on human performanceâemployees are often users of the product themselves.
Scalability â just growth: Cost-efficiency, forecasting, and cloud infrastructure readiness are vital to scaling responsibly in a global market.
â±ïž Timestamped Highlights
00:00 â Intro to Carlos & the mission behind WHOOP
02:19 â Data culture at WHOOP vs. traditional companies
04:15 â Scale of data in wearables: petabytes, not megabytes
05:52 â Complexity of ingesting, transforming, and delivering personalized data
08:53 â Striking a balance: Real-time feedback vs. cloud cost efficiency
11:14 â Scaling the platform as the member base expands globally
13:43 â Internal motivation and culture driven by positive impact stories
15:56 â Why data teams are involved early in the product roadmap
17:59 â Carlosâ journey from WHOOP user to WHOOP employee
20:40 â How to connect with Carlos + final thoughts
đŹ Quote of the Episode
âYou can have petabytes of data, but if you canât make it queriable, understandable, and actionableâitâs just noise.â â Carlos Peralta
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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 journeyâincluding 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.
đ Key Takeaways
Experience is not a blueprint, it's a lens. Max breaks down how startup learnings arenât always repeatable but instead shape the founderâs decision-making over time.
Passion is the sustainability engine. You have to love what you're building, not just what the market wantsâotherwise, you wonât 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.
â±ïž Timestamped Highlights
00:44 â What is Nixla?
Max introduces his company, a time series forecasting and anomaly detection startup with deep roots in research.
01:34 â Serial founder life
Max gives a quick snapshot of his startup journey, from NLP experiments to YC-backed fintech.
03:21 â Startup experience â shortcut to success
Why practical experience matters more than theoretical frameworks, and how each startup is its own universe.
07:59 â Playing the startup game because you love it
Max explains why loving the problem youâre solving is essential for long-term survival and sanity.
10:53 â Hiring the right people early
What Max looks for in early-stage team membersâand why founders shouldn't expect employees to grind the same way they do.
13:24 â 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 â Strengths over Weaknesses
Why Max believes in focusing on what you do well, rather than fixing every flaw.
20:25 â 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/
đŹ Featured Quote
âThe only way to keep playing the startup game is to actually enjoy the game.â â Max Mergenthaler-Canseco
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In this episode, I sit down with Jason Rogers, CEO & Co-Founder of Invary, to explore an unconventional approach to building a cybersecurity startupâleveraging 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
đč Tech transfer provides a competitive edge â Licensing government-developed technology can offer startups a head start with validated, battle-tested IP.
đč Security needs to be proactive, not reactive â Real-time validation of system integrity can prevent breaches before they escalate.
đč Collaborative research fuels innovation â Invary works with the NSA and academic institutions to advance security capabilities.
đč AI is expanding the attack surface â As AI adoption grows, ensuring system and data integrity will be more critical than ever.
đč Zero trust applies to machines too â Itâs not enough to verify usersâorganizations must continuously verify their systems.
Timestamped Highlights
âł 00:01 â Introduction to Jason Rogers and Invaryâs mission
âł 00:49 â How NSA-licensed technology is securing critical systems
âł 01:36 â The journey from research-backed tech to startup success
âł 02:56 â The challenges and benefits of building a business around licensed IP
âł 05:32 â Collaborating with government research teams for innovation
âł 09:33 â How engineers adapt to the tech transfer model
âł 14:06 â Why runtime integrity is the missing piece in security
âł 16:34 â The shift from traditional security models to real-time validation
âł 19:27 â AIâs growing attack surface and what it means for security
âł 23:28 â Predicting future cybersecurity challenges in an AI-driven world
âł 24:00 â How to connect with Jason
Quote from the Episode
âThe bad guys collaborate all the time. Itâs time for the good guys to do the same.â â Jason Rogers
Connect with Jason Rogers
đ Website: Invary.com
đ LinkedIn: https://www.linkedin.com/in/jasonlrogers/
Stay Connected with The Tech Trek!
đ§ Like what you heard? Subscribe, rate, and review on your favorite podcast platform!
đ© Have feedback or guest suggestions? Connect with Amir on LinkedIn.
đ Follow for more deep dives into technology, security, and innovation.
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In this episode, Amir sits down with Kaustav Das to discuss one of the most critical yet challenging aspects of analyticsâasking 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. Stakeholdersâ intent must be fully understood before diving into solutions.
Balancing Speed with Thoughtfulness: Quoting Einstein, Kaustav highlights the importance of preparation: âIf I were to chop a tree down in an hour, I would spend 55 minutes sharpening my blade.â 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 projectsâwhy intent matters.
[10:52] The five âwhysâ technique and getting to the core of business problems.
[12:24] The future of analyticsâcan 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." â 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 â the Airbnb of boats â 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.
đ§ Key Takeaways
Passion is a superpower: When youâre obsessed with a hobby or space, you naturally develop deep insights others donât see â 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 â even if that solution doesn't scale at first.
Inspiration is everywhere: Whether itâs boats, black cars, or model airplanes, thereâs 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 â passion isn't just a founder trait, it's company-wide.
Donât overthink TAM: Many aspiring founders kill ideas too early worrying about market size. Start small, build value, and the market might grow with you.
â±ïž Timestamps & Highlights
00:00 â Introduction
Michael Farb joins Amir to talk about building businesses around personal passions and how that philosophy led to Boatsetter.
01:00 â What is Boatsetter?
A two-sided marketplace for boat rentals in 700+ global locations. No boating license? No problem.
02:20 â Michaelâs Entrepreneurial Journey
From sports recruiting tech to nonprofit fundraising platforms â every business tied back to something he personally cared about.
04:45 â How to See the Business in Your Passion
âIf youâre obsessed with a space, youâll know more than anyone else. Thatâs your edge.â
08:00 â Advice for Aspiring Passion-Driven Entrepreneurs
Look for friction points in your hobby â thatâs where business opportunities are born.
10:50 â Employees with Passion
Boatsetter hires people who love the water. They even get boating credits as part of their benefits.
14:00 â Working with Product Teams as a Passionate CEO
Michael partners closely with product to scale both sides of the marketplace â consumers and boat owners.
16:00 â Would He Ever Build a Business Without Passion?
Short answer: No. The passion + business combo has worked too well to ditch.
19:00 â Do You Need Market Research to Start?
Michael skips the spreadsheets â he talks to real people and builds MVPs to validate problems.
21:30 â âDo Things That Donât Scaleâ
The early Boatsetter days were scrappy. Human-powered logistics and manual processes â until the model was proven.
24:40 â How to Connect with Michael
Find him on LinkedIn or visit Boatsetter.com.
đŹ Quote to Share
âDonât get paralyzed trying to figure out how big the market is â just solve a real problem. Everything big started small.â â Michael Farb
Want more stories like this?
Follow, rate, and share The Tech Trek wherever you get your podcasts. Got feedback or guest suggestions? Hit up Amir on LinkedIn or drop a comment.
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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 arenât always the right starting point
What makes an AI POC actually valuable
And why UX in the age of AI needs systems thinking
đŹ âIf you come into it with a technology and not a firm understanding of the problem, you're going to solve a problem that isnât there â and at best, you'll just end up with a great tech demo.â â Zachary Hanif
đ 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: Youâre not just designing for humansâyou'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.
â±ïž Timestamped Highlights
00:00 â Intro to Zachary Hanif and Twilio's AI mission
02:05 â Why most companies are AI tool users, not tool makers
04:25 â The âchatbot temptationâ and why it might not be the best starting point
06:00 â UX lessons from Googleâs early search box vs. todayâs LLMs
08:30 â Why weâre still early in discovering transformative AI use cases
11:55 â How AI changes what a good POC looks like
14:59 â Should AI UX be its own discipline?
18:23 â How to know when a POC is ready for production
22:12 â Dealing with AIâs expanding âsurface areaâ and model drift
25:56 â Why model risk management matters more than ever
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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 isnât necessarily replacing jobs in insurance but transforming them.
đ 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 staticâBobby 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 contextâRainbowâs practice of dining at insured restaurants is a brilliant example of tying product to user impact.
Transparency and experience matterâRainbowâs engineering team is senior, engaged in thoughtful discussions on technical debt, and values clear communication around business priorities.
AI is reshaping roles, not removing themâBobby shares his view that AI will augment rather than replace underwriting and risk assessment functions in insurance.
â° Timestamped Highlights:
00:00 â Introduction to Bobby Touran and overview of Rainbowâs mission.
02:10 â Bobbyâs approach as a non-technical founder and fostering strong relationships with engineers.
04:43 â Building a healthy engineering org: culture is intentional, not accidental.
07:48 â The importance of engineers understanding the "why" behind product decisions.
10:29 â Why being back in the office twice a week has helped Rainbowâs growth and culture.
15:03 â How engineering and leadership collaborate on roadmap and growth opportunities.
19:03 â Decision-making confidence: balancing business opportunities and engineering cycles.
21:59 â Bobbyâs take on AI's role in insurance and how Rainbow is integrating it.
25:43 â Connect with Bobby and explore career opportunities at Rainbow.
đĄ Featured Quote:
"The culture is not something that you define and it just is what it is. Itâs something you actively work on. Itâs always evolving." â Bobby Touran
đą Connect & Learn More:
Website: useRainbow.com â Check out the About Us page and open engineering roles.
LinkedIn: https://www.linkedin.com/in/bobby-touran-782a6513/
Like, share, and leave a review!
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In this episode, Vijay Sankararaman breaks down how to intentionally build a low-churn, high-impact team culture. Whether youâre a tech leader, manager, or individual contributor aiming to level up your leadership skills, Vijay shares tangible lessons and frameworks to keep your team motivated, productive, and resilientâwithout sacrificing authenticity.
đ Lessons & Leadership Takeaways
Lesson 1: Crafting Vision That Sticks
"Am I valued?"
â the question every team member subconsciously asks.
Vijay emphasizes the importance of clarity + buy-in. Itâs not enough to have a lofty team visionâyou need to ensure each person sees themselves in that vision. Leaders must actively align the team's purpose with individual validation.
Lesson 2: Understanding Intrinsic Motivation
âEnthusiasm is common. Endurance is rare.â
Borrowing from Angela Duckworth, Vijay discusses the challenge of sustaining energy over time. His approach:
âïž Learn each team member's personal definition of growth.âïž Help light the fire beneath their intrinsic goalsânot just professional milestones, but personal fulfillment.Lesson 3: Building Enduring Trust & Vulnerability
Vijay advocates for psychological safety and social freedom:
Leaders must create spaces where people can drop their guard, shed ego, and express goals or concerns.Authenticity at the leadership level trickles downâtrust is the currency that keeps teams aligned.Lesson 4: Managing Ambiguity with the 80-10-10 Rule
âNot every conversation needs 100% clarity.â
Vijayâs 80-10-10 framework:
80% of information â shared openly with the whole team.10% â reserved for focused 1:1 conversations.10% â intentional ambiguity to encourage senior team members to develop resilience and independent thinking.Lesson 5: Productive Friction & Healthy Chaos
Conflict isnât the enemyâitâs a tool.
Focus more on asking the right questions than offering immediate solutions.Curiosity-driven leadership + active listening ensures issues surface early, and innovations thrive.
Vijay encourages creating healthy chaos:đ Timestamped Highlights
00:00 â Intro to Vijay & episode topic01:59 â Defining âhigh impactâ in tech teams03:11 â Vision clarity & team buy-in strategies05:18 â Aligning personal growth goals with team objectives09:29 â Building trust & psychological safety12:17 â Why productive chaos is essential14:40 â Team collaboration over perfection: Vijayâs puzzle exercise18:32 â Communication strategy: 80-10-10 rule22:00 â Connect with Vijay on LinkedInđą Quote of the Episode
"Enthusiasm is common. Endurance is rare. As leaders, itâs our job to sustain that energy, to light the fire beneath each individualâs intrinsic motivation."
â Vijay Sankararaman
đ Connect & Share
Enjoyed this leadership deep dive?
âĄïž Share it with someone leading (or aspiring to lead) high-performing tech teams.
âĄïž Leave a review, subscribe, and help spread the word on building resilient, motivated teams in tech! -
In this episode, Amir sits down with Santhosh Kumar, Head of Data at Trepp, to unpack the evolving world of Data as a Product. Data is no longer just a support functionâitâs becoming a core business driver. Santhosh shares how data teams are embracing a product-oriented approach, aligning closely with business goals while mirroring software engineering practices.
If youâve ever wondered:
How data can be treated like a shippable product
What mindset shifts data teams need
And how collaboration between data, product, and tech teams is evolving
This episode is for you!
đ„ Key Takeaways:
Data as a Product = Data with Purpose:
Itâs not just about pipelines and reportsâitâs about delivering business value like increased revenue, better user experience, and cost optimization.
Striking a Balance Between Tech & Business:
Santhosh emphasizes that successful data teams blend technical excellence with a strong understanding of business objectives. Itâs not one or the other.
Mindset Shift is Crucial:
Data teams must move from being reactive service providers to proactive product thinkersâasking the right questions, prioritizing value, and collaborating closely with stakeholders.
Alignment with Software Engineering Practices:
Data engineering is increasingly mirroring software engineering: roadmaps, product managers, long-term planning, and ownership are becoming key.
The Role of Data Quality & Governance:
In the age of productized data, ensuring accuracy, governance, and discoverability at every stage is non-negotiable.
â±ïž Timestamped Highlights:
[00:00] Introduction to Santhosh Kumar & TREPâs mission in structured finance, CRE, and banking data.
[02:00] What does Data as a Product actually mean? Why itâs more than dashboards and CSVs.
[04:00] Balancing tech and businessâwhere Santhoshâs team fits.
[07:00] Collaboration challenges: aligning with tech teams & self-serving stakeholders.
[09:00] How treating data like a product impacts strategy and mindset shifts.
[12:00] Key differences between data pipelines and software engineering cycles.
[14:00] The role of product managers on data teams.
[16:00] The increasing technical depth of data teams & potential future bifurcation of business vs. technical roles.
[18:00] Roadmap planning: how far ahead data teams are planning, and why.
[19:30] Final thoughts and how to connect with Santhosh on LinkedIn.
đĄ Featured Quote:
"We use technology to translate business requirements and maximize valueâthat's where the magic happens. Data isn't just numbers; it's a product with purpose." â Santhosh Kumar
đŁ Enjoyed this episode? Share it with your fellow data practitioners, software engineers, or tech leaders. Letâs get the conversation rollingâespecially with CTOs and VPs who need to collaborate tighter with data teams!
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In this episode, Amir sits down with Trevor Lee, Co-founder and CEO of Myko AI, to unpack his unconventional leap from the finance world to building a cutting-edge tech company. Trevor shares how he navigated leaving a comfortable corporate job, using an MBA to break into tech, and the iterative process of finding product-market fit, including pivotal moments and lessons learned while bootstrapping and scaling his business.
đ„ Key Takeaways:
Trust Your Gut & Bet on Yourself: Trevor realized early that traditional corporate environments weren't for him. His dissatisfaction pushed him toward entrepreneurship.
Using an MBA as a Pivot Point: While some may dismiss the value of an MBA in tech, Trevor leveraged it to gain exposure, experiment, and network, ultimately identifying his ideal role as a tech founder.
Product-Market Fit is a Journey, Not a Destination: Trevor candidly discusses how his company pivoted multiple times, emphasizing constant customer discovery and iteration.
Bootstrapping Builds Grit: Failing to raise initial capital didnât stop Trevor; instead, he bootstrapped MyKo, learning key lessons about resilience and resourcefulness.
Sales > Code (Sometimes): As CEO, Trevor unexpectedly found himself deeply involved in sales â a critical part of driving growth even for a technical product.
Location Isnât Everything: Based in Florida, Trevor highlights how being outside Silicon Valley hasnât limited his access to capital, mentors, or customersâespecially in a post-remote world.
đ Timestamped Highlights:
00:28 â Trevorâs background: from finance to founding MyKo
01:36 â The motivation to leave a comfortable corporate job
03:05 â Why Trevor pursued an MBA to break into tech
05:36 â How the idea for Myko originated and evolved during business school
07:56 â Navigating pivots and recognizing when to change direction
09:58 â Aligning a small team during a pivot decision
11:38 â Confidence in the current product and growth focus
12:57 â The story behind bootstrapping vs. early fundraising struggles
14:54 â Shifting roles as CEO: From product to sales leadership
15:28 â Skills Trevor wishes he had focused on earlier (sales & technical chops)
17:02 â How mentors and investors have guided his journey
18:52 â Founding outside a traditional tech hub: Advantages and challenges
20:35 â Trevorâs vision for Myko's future: Hybrid remote growth strategy
21:40 â How to connect with Trevor for advice or collaboration
đĄ Memorable Quote:
âYou donât truly appreciate how much chewing glass being a founder is and the level of unsexy, terrible work you have to do all the time.â â Trevor Lee
đŻ Audience Note:
This episode is a must-listen for anyone who is considering making the leap into entrepreneurship, especially those outside traditional tech hubs or without prior technical backgrounds.
đŹ Connect with Trevor:
LinkedIn â https://www.linkedin.com/in/trevorlee20
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In this episode, Amir is joined by Sachin Nene to explore what it really takes to thrive as a modern CTO. Sachin shares actionable strategies for balancing vision and execution, managing relationships with CEOs and fellow executives, and staying relatable and credible with engineering teams. They dive deep into the challenges of expectation management, engineering metrics, and how AI tools like LLMs are reshaping the future of engineering leadership.
Whether you're already in a leadership role or aspiring to step up, this conversation is packed with practical insights tailored for todayâs fast-moving tech landscape.
Key Takeaways:
The CTO as a Strategic Subcontractor: Sachin redefines the CTO role as the "subcontractor" within the C-suite â fully responsible for delivery without burdening non-technical peers with unnecessary details.
Balancing Vision & Execution: Effective CTOs master both managing expectations upwards and maintaining technical credibility downward, acting as the glue between business goals and engineering execution.
Building Trust with Engineering Teams: Staying relatable means understanding current trends (like LLMs), engaging in technical brainstorming, and being able to advocate for the team at any level.
Avoid Over-Optimizing Metrics: Sachin warns against over-indexing on engineering metrics (e.g., DORA metrics) when they risk detaching teams from meaningful business impact.
Future-Proof Engineering Leadership: With AIâs influence growing, CTOs must rethink hiring profiles and team structures, moving toward polyglot engineers who can flex between product, business, and technical hats.
Timestamped Highlights:
[00:00] Introduction & Overview: Sachinâs journey from Upside CTO to launching fractional CTO services.
[01:00] CTO Relationship with C-suite: Why the CTO operates differently from other executives, and why itâs akin to a subcontracted role.
[03:00] Balancing Business & Technical Leadership: How Sachin keeps one foot in business strategy and one in technical leadership.
[07:00] Staying Relatable to Engineering Teams: Practical ways to stay connectedâpersonal research, whiteboarding sessions, and knowing when to step into the technical weeds.
[10:00] Translating Strategy into Metrics: The difficulty of measuring engineering success without losing sight of broader goals.
[14:00] Dangers of Over-Optimizing Metrics: The risk of becoming overly process-driven and detached from actual business outcomes.
[16:00] Technology-Driven Revenue Opportunities: How a CTO ensures technology investments align with business shifts, particularly in SaaS models.
[19:00] Preparing for the AI Shift: Why LLMs and AI tools require a new type of engineering team and leadership approach.
[22:00] The Shift Left in Engineering: Why tomorrowâs engineers need to think more like product managers and business leaders.
Featured Quote:
"The ideal CTO is the king or queen of expectation managementâbalancing business impact with technical trust, without getting lost in jargon or micromanagement." â Sachin Nene
Links:
Connect with Sachin on LinkedIn: https://www.linkedin.com/in/sachinnene/
Learn more at sachinnene.com
Call to Action:
đ„ Know an engineering leader or tech exec whoâd benefit from this guide? Share this episode!
â Subscribe, leave a review, and drop a commentâlet us know what resonated most!
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In this episode of The Tech Trek, Amir Bormand sits down with Nirmal Ranganathan, CTO, Global Public Cloud at Rackspace, to dissect one of the hottest and most crucial topics in todayâs tech landscapeâtrust in AI applications. They explore how enterprises can drive adoption of AI solutions, what key factors are needed to foster trust, and why guardrails, security, and change management play a pivotal role. Whether you're a developer, tech leader, or AI enthusiast, this episode dives deep into the challenges and opportunities shaping the future of AI adoption.
Key Takeaways
Trust is the Cornerstone: For AI adoption to succeed, users must trust the output. Trust hinges on data quality, security, responsible use, and model transparency.Change Management Matters: Adoption in enterprises isn't about trendsâitâs about clear processes, education, and user enablement.Guardrails Are Non-Negotiable: Especially when AI is exposed to external users, organizations need strong safety checksâthink toxicity filters, bias mitigation, and strict data governance.Scaling AI = Scaling Costs: Unlike typical systems, scaling AI comes with heavy computational costs. Patterns like caching and model optimization are essential for sustainability.Prompt Engineering & Peer Learning: The secret to effective enterprise AI adoption is empowering users to master prompt engineering and fostering peer collaboration.Future of Adoption: 2025 might not yet be the year of mass AI production rollout, but the curve is gradually climbingâespecially with evolving architectures and better model accuracy.Timestamped Highlights
[00:00:00] Introduction to Nirmal Ranganathan & the importance of trust in AI[00:01:34] Why adoption is keyâand why most tech projects fail due to lack of it[00:02:50] Three pillars of successful AI adoption: Trust, Change Management, Functionality[00:05:02] The trust barrier: Hallucinations, relevance, and grounding AI responses in enterprise knowledge[00:10:01] Why most AI projects are stuck in POCsâand what's preventing full-scale deployment[00:11:43] Technical guardrails: Security, scalability challenges, and compliance considerations[00:14:56] Cost & infrastructure challenges when scaling AI solutions to millions of users[00:17:52] How tech companies differ from enterprises in deploying AIâdata privacy, safety checks, user unpredictability[00:20:00] The role of prompt engineering, peer learning, and experiential training in ensuring AI adoption success[00:22:16] What the future holds for AI adoptionâand why the heavy lifting might get easierFeatured Quote "AI adoption compounds all of our existing challengesâand then multiplies them by five or ten times." â Nirmal Ranganathan
Connect with Nirmal
LinkedIn: https://www.linkedin.com/in/rnirmal/If you enjoyed this episode, please like, share, and subscribe! Donât forget to follow the podcast to stay updated on future episodes.
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In this episode, we dive into the real-world experimentation of Generative AI (Gen AI) with Naveed Asem. Naveed shares his hands-on experience in identifying, testing, and scaling AI-driven solutions. We discuss how organizations should approach experimentation, set success metrics, manage stakeholders, and navigate governance challenges.
If your company is exploring Gen AI or struggling with moving AI pilots to production, this episode is packed with insights to help you move forward.
Key Takeaways
đč The 4P Framework for AI Adoption â Platforms, Potential, People, and Policies form the foundation for AI experimentation and implementation.
đč Prioritization Strategy â Wex uses an impact vs. complexity matrix to determine which AI projects to pursue.
đč Iterative Product Development â AI projects need constant evaluation due to the rapidly evolving technology landscape.
đč Governance and Risk Mitigation â Hallucination-free AI is critical for financial institutions, requiring strict regulatory and security measures.
đč Measuring ROI in AI â The challenge isnât just in cost savings but also in tracking efficiency gains and organizational impact.
đč Change Management â Early AI adoption requires executive buy-in and employee education to drive acceptance.
Timestamped Highlights
[00:00] â Introduction to the episode and guest, Naveed Asem
[00:01:20] â Overview of Wex and its role in fintech and payments
[00:02:10] â How to identify business needs that align with AI solutions
[00:03:45] â The 4P framework: a structured approach to AI adoption
[00:06:00] â How Wex prioritizes AI experiments using an impact vs. complexity framework
[00:08:41] â Establishing measurable goals for AI projects â revenue, productivity, and customer satisfaction
[00:12:03] â The AI product development lifecycle: discovery, delivery, and optimization
[00:14:50] â Navigating challenges with generative AI: hallucinations, governance, and security
[00:16:46] â The role of governance in AI â from acceptable use policies to ethical considerations
[00:21:11] â The impact of AI on jobs and processes â change management in action
[00:25:28] â How companies are evaluating ROI for AI and tracking efficiency improvements
[00:28:50] â Where to connect with Naveed Asem for further discussion
Quote from the Episode
"AI governance isnât just about policiesâitâs about ethics, security, and ensuring that what we build is trustworthy and aligned with real-world needs." â Naveed Asem
Connect with Naveed Asem
đ LinkedIn: Naveed Asem
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In this episode, Amir Bormand sits down with Jeremy Goldsmith, VP of Engineering at Branch, to explore leading with purpose and how it impacts engineering teams. Jeremy shares his philosophy on leadership, the psychology behind motivation, and how connecting individual contributions to a larger purpose can unlock potential and drive performance.
This conversation is a must-listen for engineering leaders, tech managers, and individual contributors who want to cultivate a stronger sense of purpose in their work and teams.
Key Takeaways
đč Clarity in Purpose Fuels Performance â Helping engineers understand the bigger picture leads to higher engagement, motivation, and job satisfaction.
đč Connecting the Dots â Leaders must translate business strategy into meaningful work for individuals and teams.
đč Balancing Strategic & Tactical Thinking â The best leaders can zoom in and out, ensuring both long-term vision and day-to-day execution are aligned.
đč Psychology in Leadership â Jeremy's psychology background plays a big role in how he manages and motivates his teams.
đč Hiring for Purpose Alignment â Engineers often self-select into mission-driven companies; leaders should recognize this when hiring and retaining talent.
đč Authenticity Matters â You canât fake purposeâpeople see through it. Leadership must be genuine in their messaging and actions.
đč Vulnerability as a Strength â Great leaders model growth and development, making it easier for their teams to do the same.
Timestamped Highlights
â± [00:00:00] Introduction â Jeremy Goldsmith joins the show to discuss leadership and purpose-driven engineering.
â± [00:01:00] What Does Branch Do? â Jeremy explains deep linking and how Branch enhances digital experiences.
â± [00:03:00] Defining Leading with Purpose â Helping engineers see meaning in their work improves engagement.
â± [00:07:00] Connecting Work to Strategy â Why engineers need clear links between daily tasks and company goals.
â± [00:10:00] Avoiding Corporate Jargon â Leaders should communicate in plain language to build trust.
â± [00:12:00] Unlocking Potential â How purpose ties into motivation and high performance.
â± [00:14:00] Mission-Driven Workplaces â Jeremyâs experience at Tendril and how mission impacts hiring and culture.
â± [00:17:00] Do Engineers Self-Select into Companies? â How job seekers evaluate company missions.
â± [00:19:00] Psychology and Leadership â Jeremyâs background in psychology and how it informs his leadership style.
â± [00:24:00] Getting to Know Your Team â Why spending time with individuals leads to better outcomes.
â± [00:26:00] Being a Work in Progress â Modeling self-improvement as a leader.
â± [00:27:00] Part Two Teaser â A future episode on finding the right workplace fit.
Quote from the Episode
"Leading with purpose isnât just about inspiring teamsâitâs about helping them connect their daily work to something bigger. When people see the impact they make, they perform at a higher level." â Jeremy Goldsmith
Connect with Jeremy
đ© LinkedIn â https://www.linkedin.com/in/jeremygoldsmith/ (Mention the podcast when reaching out!)
Enjoyed This Episode?
â Share it with your colleagues and friends in tech.
â Subscribe to The Tech Trek for more conversations with top engineering leaders.
â Leave a Review â Your feedback helps us bring more great content!
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In this episode, I sit down with Nate Sullivan, the CTO at Academia.edu, to discuss technology strategy and special projects. Nate shares insights on identifying and prioritizing emerging technologies, balancing innovation with business needs, and collaborating with product teams to drive impactful technical advancements.
Key Takeaways
The Role of a CTO in Special Projects
Nate focuses on exploring technologies a few quarters ahead while aligning them with business strategy.
Balancing Exploration with Business Needs
Special projects should solve real business problems rather than being just theoretical experiments.
AI & Its Application in Research
Academia.edu has developed AI-powered tools like Academia Answers to extract insights from millions of research papers.
Assessing Emerging Technologies
Instead of chasing distant tech trends, Nate prioritizes technologies with immediate use cases.
Product & Engineering Alignment
Special projects often treat the product team as the customer, ensuring technical capabilities translate into valuable user experiences.
Measuring Success & Knowing When to Pivot
Projects have clear time-bound evaluationsâif results donât align with expectations, it's time to reassess or move on.
Timestamped Highlights
[00:00] Introduction to the episode & guest, Nate Sullivan
[00:01] What Academia.edu does & Nateâs role as CTO
[00:02] Exploring technologies for future implementation
[00:04] AI-powered innovation at Academia.edu (Academia Answers)
[00:07] How far ahead should companies look at emerging tech?
[00:08] Working with the product teamâwhen and how to involve them
[00:12] Deciding when to stop a special project that isnât progressing
[00:14] Evaluating success: AB testing & impact on users
[00:17] How the special projects team operates within engineering
[00:19] Communicating progress to leadership & aligning with company goals
[00:21] Final thoughts & how to connect with Nate
Notable Quote
"I think the crucial thing is building something that a customer actually wants, which is very hard as we all know. It requires really understanding their needs, but also knowing about tools that they might not naturally think of." â Nate Sullivan
Connect with Nate
Find Nate on LinkedIn: https://www.linkedin.com/in/nate00/
Enjoyed the Episode?
Share this episode with a fellow tech leader
Subscribe & leave a review on your favorite podcast platform
Follow us for more insights from industry experts
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Join Amir Bormand in a captivating discussion with Anand Madhavan, VP of Engineering at Everlaw, as they explore innovative development models, the art of integrating into new company cultures, and why Everlaw prioritizes quality code over strict deadlines. Anand reveals how Everlaw's unconventional engineering practices cultivate a sustainable, high-quality development environment focused on long-term success.
Key Takeaways:
Embracing a Unique Development Model:
Everlaw's "no deadlines" philosophy promotes quality and sustainable software.
Prioritizing thoroughness over speed helps maintain high developer morale and lower burnout.
Cultural Alignment for Engineering Leaders:
Importance of assessing and understanding company culture in the initial 90 days.
Utilizing frameworks (e.g., "The First 90 Days" book) to align leadership approaches effectively.
Addressing Technical Debt Strategically:
Technical debt is proactively managed through documentation, careful planning, and supporting engineers in tackling issues as they arise.
Engineering managers at Everlaw play a critical role in protecting the team from unrealistic pressures, ensuring long-term code health.
Balancing Customer Needs and Engineering Excellence:
Everlaw commits to "shipping functionality with healthy urgency," ensuring customer satisfaction without compromising technical integrity.
Clear communication and disciplined sales practices prevent the premature selling of undeveloped features.
Timestamped Highlights:
[00:01:00] Introduction to Everlaw and its role as a "truth-finding machine" in litigation.
[00:02:30] Anandâs perspective on assimilating into new engineering cultures using insights from "The First 90 Days."
[00:05:00] Core concepts of Everlawâs unique engineering model.
[00:09:00] Discussing the "no deadlines" model and its positive impact on developer happiness and productivity.
[00:15:00] Strategic approach to managing and minimizing technical debt.
[00:18:00] Importance of managerial support and autonomy for empowering engineering teams.
Engaging Quote:
"Developing software is like a long hikeâtake one step at a time, enjoy the view, leave no garbage, and trust the team to make the right decisions."
Connect with Anand Madhavan:
https://www.linkedin.com/in/madhavananand
Enjoyed the episode? Don't forget to like, subscribe, and share your thoughts in the comments!
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In this episode, Amir Bormand welcomes Scott Persinger, CEO of Supercog AI, to discuss the rise of agentic AI and its applications in modern businesses. They explore the evolving role of AI agents in knowledge management, team collaboration, and enterprise automation, particularly in environments like Slack. The conversation delves into how AI is reshaping work, privacy and ethical considerations, and the future of human-AI collaboration.
Key Takeaways
đč Agentic AI vs. Traditional AI â Agentic AI enables systems to plan, reason, and take iterative actions based on real-time feedback, offering more dynamic solutions than static AI models.
đč The Evolution of AI Agents â While agent-based AI has been discussed for decades, recent advancements in large language models (LLMs) have made them more effective at executing complex tasks autonomously.
đč Enterprise AI Adoption â Companies are sitting on vast knowledge bases, but AIâs ability to search, retrieve, and process unstructured data is still evolving. AI agents in Slack and other platforms can help bridge this gap.
đč AI in Slack and Workplace Collaboration â By integrating AI agents into platforms like Slack, businesses can automate and streamline workflows without complex engineering resources.
đč The Debate on Vertical vs. General AI â While some believe AI should specialize in vertical applications (like finance, healthcare, or insurance), others argue that general AI will soon be capable of handling multiple domains with high proficiency
đč Computer Vision & AI Agents â The next step in AI evolution includes visual data processing, allowing AI agents to analyze screenshots, documents, and interfaces, making interactions even more seamless.
đč Privacy & Ethical Concerns â As AI agents gain access to corporate communications and historical data, privacy and governance will play a crucial role in adoption.
đč The Role of Humans in AI-Driven Workflows â AI adoption will likely involve human oversight ("human in the loop") in the short term, but as AI improves, trust in fully autonomous systems may increase.
Timestamped Highlights
[00:00] â Introduction to Scott Persinger and Supercog AIâs mission.
[02:00] â The origins and resurgence of agentic AI.
[05:00] â Vertical AI vs. General AI: Which will dominate?
[08:00] â Why knowledge search and retrieval are AIâs low-hanging fruit.
[11:00] â How AI agents in Slack enhance team collaboration and efficiency.
[14:00] â The rise of AI-powered assistants instead of AI replacing human workers.
[16:00] â Computer vision as the next leap for agentic AI.
[18:00] â Privacy and ethical concerns with AI searching historical corporate data
[21:00] â The human-in-the-loop debate and how much control people will retain over AI decisions.
[24:00] â The shift from multi-source research (Google) to single-answer AI interactions.
[27:00] â Final thoughts on AIâs future and how people can connect with Scott Persinger.
Quote of the Episode
"Weâre heading towards a world where AI models will know more than any human could, and that will fundamentally change how we trust information and make decisions." â Scott Persinger
Connect with Scott Persinger
đ§ Email: [email protected]
đ LinkedIn: https://www.linkedin.com/in/scottpersinger
Support the Podcast!
đ Like & Subscribe â Donât miss an episode!
đą Share â If you found this insightful, send it to a friend or colleague.
đŹ Comment â Let us know your thoughts on agentic AI!
đïž Tune in next time for more conversations on the future of technology!
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