Эпизоды
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In this episode, Amir Bormand chats with Jake Moshenko about his journey from engineer to founder, the challenges of identifying the right startup ideas, and navigating both venture capital and bootstrapping. They also discuss decision-making, failure, and the growing role of AI in authorization systems.
Key Takeaways
Turning an Idea into a Startup: Jake emphasizes the importance of picking the right problems to solve—ones that are painful, widely felt, and worth paying for.
The "Muscle" of Evaluating Ideas: Recognizing which ideas are viable requires experience and learning from past failures.
Bootstrapping vs. VC: Both approaches have trade-offs—bootstrapping requires patience and personal risk, while VC funding adds pressure but accelerates growth.
Decision-Making as a Founder: Founders must make decisions without perfect information and delegate when possible.
AI and Authorization: AI companies face similar security challenges as traditional applications, and AuthZed is helping businesses ensure secure, permission-based access.
Timestamped Highlights
[00:01:00] – Introduction: Jake’s journey from engineer to startup founder.
[00:02:30] – The origins of AuthZed: Identifying a need based on past experience.
[00:05:00] – How Jake evaluates startup ideas using his three criteria.
[00:09:30] – Learning from startup failures and developing a critical decision-making "muscle."
[00:14:00] – Decision-making as a founder: Confidence, risk tolerance, and analysis paralysis.
[00:18:00] – Bootstrapping vs. venture capital: The key differences and challenges.
[00:21:00] – AI and security: How AuthZed helps AI companies protect data.
[00:24:00] – Where to connect with Jake and final thoughts.
Quote from the Episode
"You need to fail a lot. You can over-index on success stories, but real learning comes from understanding why things didn’t work." – Jake Moshenko
Connect with Jake
Website: https://authzed.com/
Discord: Join via the link on AuthZed's homepage
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In this episode, Srini Rajagopal joins us to discuss how Generative AI (Gen AI) is transforming the engineering landscape. We explore the challenges of integrating AI into legacy products vs. building AI-first solutions from the ground up, the impact on developer productivity, and how teams prioritize AI-driven innovation while bringing stakeholders along for the ride.
🔹 How should engineering teams think about AI adoption?
🔹 Where do AI-driven efficiencies actually go?
🔹 What does success look like in AI integration?
Srini shares actionable insights from his experience leading engineering at Navan Expense, a major travel and expense platform, as they leverage AI to unlock hyper-personalization, automation, and developer velocity.
🎯 Key Takeaways
✔ AI Adoption Strategy: Organizations must retrofit AI based on user needs rather than forcing AI into existing product frameworks.
✔ Legacy vs. Ground-Up AI Integration: Legacy products pose challenges with user experience and expectations, while AI-first solutions provide faster innovation cycles.
✔ AI’s Impact on Developers: Engineers are evolving into problem solvers and editors rather than just coders, shifting left into the business side of decision-making.
✔ AI-Driven Efficiency: AI reduces manual coding time, enabling engineers to iterate faster, focus on strategic problems, and deliver business impact.
✔ Guardrails for AI Implementation: AI-driven solutions require a probabilistic mindset—instead of strict rules, companies must define what "wrong" looks like and use AI to monitor itself.
✔ The Future of AI in Engineering: Expect a shift toward natural language-driven development and more automation in business logic and rules-based programming.
✔ Measuring Success: AI adoption should be tracked through customer value, impact on developers' velocity, and measurable efficiency gains—not just cost savings.
⏱️ Timestamped Highlights
[00:02:00] – The Two Key Factors in AI Integration: Solving existing inefficiencies vs. unlocking new possibilities
[00:04:00] – Personalization at Scale: How Gen AI customizes data views dynamically for Navan users
[00:06:30] – Prioritizing AI Features: Balancing business value, feasibility, and innovation risks
[00:08:30] – Managing Stakeholders: Keeping internal teams engaged even when AI adoption takes time
[00:09:45] – AI’s Impact on Developers: Shifting from code generation to business problem-solving
[00:12:00] – The Future of Engineering: AI will push engineers toward higher-level decision-making and automation
[00:15:00] – The Complexity of Bringing AI into Legacy Products: Navigating accuracy, consistency, and user expectations
[00:17:30] – Lessons Learned: How AI speeds up internationalization and the importance of self-regulating AI guardrails
🔥 Quote of the Episode
"Developers are evolving from just writing code to solving real business problems—AI is pushing engineering toward strategic thinking and automation." – Srini Rajagopal
📢 Connect with Srini Rajagopal
🔗 LinkedIn: https://www.linkedin.com/in/srajagop/
🐦 Twitter: @SriniRajagopal
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👉 Got thoughts or feedback? Drop a comment!
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In this episode, Amir Bormand is joined by Jay Como to discuss how organizations can simplify data governance. Jay shares insights on shifting the perception of governance from a bureaucratic burden to a business enabler, how emerging technologies like Gen AI can assist in governance challenges, and strategies for making governance policies more effective and accessible.
🔑 Key Takeaways
Data Governance Has a Stigma – But It Can Be Overcome
Traditional governance has been slow and bureaucratic. The key to transformation is making governance more business-focused and outcome-driven.
Governance Needs Storytelling and Sales Skills
Successful data governance leaders are not just compliance experts—they understand how to "sell" governance by tying it to business outcomes.
Balancing Governance and Business Agility
Organizations must strike a balance between strong controls and flexibility to support commercial goals.
Gen AI and Data Governance
Generative AI is evolving as a tool to enhance governance processes, from data quality rule identification to metadata collection.
Start Small, Build Trust
If you're looking to simplify governance, start with listening, identify quick wins, and build relationships with stakeholders.
Breaking into Data Governance
You don’t need a governance background to succeed. Strong business knowledge, digital fluency, and a strategic mindset can help professionals transition into governance roles.
🕒 Timestamped Highlights
[00:00] Introduction to Jay Como and the topic of data governance
[01:30] The perception vs. reality of governance—why it feels slow and heavy
[03:45] How to make governance an exciting career choice
[06:30] Balancing strong governance with commercial agility
[10:00] Why governance should be “sold” as a business enabler, not a compliance cost
[12:00] The correlation between data quality and governance complexity
[14:30] How Gen AI can help automate governance processes
[18:00] Steps to simplify governance processes within an organization
[21:00] The cultural aspect of governance—getting leadership buy-in
[25:00] How professionals from outside governance can transition into the field
[28:00] How to connect with Jay Como
📢 Quote of the Episode
"Governance should not feel like a roadblock—it should be the foundation that enables better business decisions, faster execution, and stronger data quality." – Jay Como
🔗 Connect with Jay Como
📍 LinkedIn: https://www.linkedin.com/in/jaycomoiii/
📢 Enjoyed the episode? Like, subscribe, and share with someone who would benefit from this conversation!
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In this episode, Amir Bormand sits down with Ganesh Datta, Co-founder & CTO of Cortex, to dive deep into engineering excellence—what it means, how to measure it, and how to build it into the culture of a technology organization. They explore product thinking, shared standards, accountability, and continuous improvement, as well as the challenges of maintaining excellence across different types of companies.
Whether you're an engineering leader or a developer striving for high standards, this episode provides valuable insights into how to define, implement, and sustain engineering excellence in your organization.
Key Takeaways
Engineering Excellence is Continuous: There’s no final state of “excellence”—it’s about ongoing improvement and iteration.
The Four Pillars of Engineering Excellence:
Velocity – How fast can the team deliver?
Efficiency – Are resources being used optimally?
Security – Is the system safe and resilient?
Reliability – Can users trust the system to work as expected?
Business Alignment Matters: Excellence should align with business goals, whether that’s innovation, efficiency, or reliability.
Engineering Culture is Key: Excellence isn’t just about processes and metrics—it’s about visibility, accountability, and fostering a mindset of improvement.
Standardization vs. Flexibility: While setting clear standards is crucial, organizations must adapt their definitions of excellence based on their unique challenges and priorities.
Timestamped Highlights
[00:00:00] – Introduction: Who is Ganesh Datta, and what is Cortex?
[00:02:00] – Defining engineering excellence and why it differs by company.
[00:05:00] – Engineering excellence as a cultural foundation, not just an end goal.
[00:07:30] – Measuring excellence: The role of metrics and how to avoid focusing on lagging indicators.
[00:10:30] – Overcoming resistance to engineering standards and ensuring adoption across teams.
[00:12:30] – How business drivers shape engineering standards.
[00:15:30] – Why excellence is different for every company: Comparing OpenAI vs. a large financial institution.
[00:18:00] – How CTOs can translate business goals into engineering priorities.
[00:21:00] – Ensuring consistency: How to sustain high standards year after year.
[00:23:00] – Where to connect with Ganesh Datta for follow-up questions.
Quote of the Episode
“Engineering excellence is not an end state—it’s a culture of continuous improvement. You’re never truly excellent, just more excellent than before.” – Ganesh Datta
Connect with Ganesh Datta
LinkedIn: https://www.linkedin.com/in/gsdatta/
Email: ganesh@cortex.io
Cortex Website: cortex.io
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Artificial intelligence is moving beyond proofs-of-concept and into real-world production—but how do you make it work in highly secure environments? In this episode, Ben Van Roo, CEO & Co-Founder of Yurts, joins Amir Bormand to discuss the challenges of implementing Gen AI in government, financial institutions, and enterprises with strict security requirements.
Ben breaks down why 2024 is the year of POCs, but 2025 will be the year of production, the biggest "gotchas" companies face when scaling AI, and why infrastructure—not just modeling—is the real challenge. We also dive into why AI adoption in enterprises is different, how organizations must navigate governance and security, and whether legacy companies will finally leapfrog into AI or repeat the mistakes of slow digital transformation.
🔑 Key Takeaways:
2024: The Year of POCs; 2025: The Year of AI in Production – Organizations are moving from experimentation to full-scale adoption.
It’s Not Just a Modeling Problem—It’s a Software Problem – Scaling AI in enterprises is about infrastructure, access control, observability, and governance.
Biggest “Gotchas” in Production – Companies underestimate data access, role-based security, and integrating AI into existing workflows.
Legacy Infrastructure Isn’t Going Away – Over 50% of enterprise compute is still on-prem; AI must work with hybrid systems.
AI's Real Value: Corporate Memory & Efficiency – Organizations struggle with managing institutional knowledge—Gen AI can bridge the gap.
⏳ Timestamped Highlights:
[00:01:00] – Yurts’ mission: Connecting secure enterprises to AI without breaking compliance.
[00:03:00] – Why 2025 is the year AI goes into real production.
[00:07:00] – The "gotcha" moments: Scaling from proof-of-concept to enterprise-wide AI.
[00:12:00] – AI governance: Why “boring” topics like data security & observability matter more than ever.
[00:18:00] – AI’s potential to transform enterprise productivity, not just replace workers.
[00:22:00] – Will enterprises leapfrog to AI or repeat the slow-moving digital transformation struggles?
[00:27:00] – What makes AI adoption harder for highly secure enterprises (government, semiconductors, etc.)?
[00:29:00] – Ben’s advice: How organizations should start their AI journey today.
📢 Quote to Share:
"AI won’t change your business unless it’s connected to the work you do, the data you use, and the privacy requirements you have." – Ben Van Roo
🔗 Connect with Ben Van Roo:
LinkedIn: https://www.linkedin.com/in/vanroo
📢 Like, Subscribe, and Share!
Love this episode? Leave a review and let us know your biggest AI adoption challenge in the comments!
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In this episode, we dive deep into getting the right results from Gen AI with Timm Peddie, an expert in operationalizing AI at scale. We discuss the common pitfalls companies face, what "right results" actually mean, and how organizations can effectively implement Gen AI solutions. Timm shares practical strategies for AI adoption, the importance of rapid failure, and how companies can avoid costly mistakes.
🔍 Key Topics Covered:
✔️ Defining "Operationalizing Gen AI" and why it’s more than just integrating APIs
✔️ The challenge of hallucinations, drift, and policing AI models
✔️ The importance of rapid failure and iterative learning in AI projects
✔️ Picking the right POC (Proof of Concept) – What makes a successful AI pilot?
✔️ Managing AI costs – Avoiding unexpected cloud bills
✔️ Adoption & Trust – How to build confidence in AI outputs
✔️ Competitive advantage – Where AI will become table stakes and where companies can still differentiate
📌 Key Takeaways:
💡 1. AI Isn't Plug-and-Play – Deploying AI models requires process development, governance, and continuous monitoring. Organizations that think AI "just works" out of the box often fail.
💡 2. Expect AI Drift – AI models are never static. They improve or degrade over time and require ongoing retraining and human oversight to stay relevant.
💡 3. Rapid Failure = Faster Success – Companies should design for rapid iteration instead of expecting perfection from day one. The more experiments, the better the long-term outcomes.
💡 4. Internal POCs Matter – A low-risk starting point is using AI internally (e.g., automating HR handbook searches) before deploying customer-facing AI.
💡 5. Competitive Advantage is Temporary – AI will soon become table stakes. Early adopters gain an edge now, but long-term differentiation will come from how AI is embedded into business processes.
💡 6. AI Costs Can Balloon Quickly – Without clear cost structures and monitoring, AI projects can become expensive fast. Companies must understand pricing models for training and inference costs.
💡 7. Trust is Key to Adoption – Users will abandon AI systems if they don’t trust the results. Implementing quality checks and human oversight is crucial to ensuring AI credibility.
⏳ Timestamped Highlights:
📌 [00:01:00] – What does "Operationalizing Gen AI" mean? The real challenges beyond just using APIs.
📌 [00:04:00] – The problem of AI drift – Why the same model can produce different results over time.
📌 [00:07:00] – How to pick the right AI POC – Key characteristics of a successful pilot project.
📌 [00:09:30] – The risk of AI misinformation – The real-world example of an automaker’s AI chatbot fabricating car details.
📌 [00:12:00] – AI costs explained – How cloud providers structure AI pricing and where companies can get blindsided.
📌 [00:14:00] – Building AI trust – Why humans must be in the loop to validate AI results.
📌 [00:19:00] – Where does competitive advantage come from? Why AI will soon become table stakes.
💬 Notable Quote:
"If AI isn’t a part of every breath in your business, it’s going to be difficult to survive in the future." – Timm Peddie
🔗 Connect with Timm Peddie:
📌 LinkedIn: www.linkedin.com/peddie
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In this episode, I sit down with Varun Madan, Head of Engineering at OneHouse, to discuss how startups must operate with slimmer margins—both in decision-making and execution. We dive into the high-stakes hiring process, balancing efficiency with impact, managing context switching, and transitioning between IC and leadership roles.
Key Takeaways:
✅ Hiring at a startup requires extreme precision. Every hire matters, and balancing speed vs. fit is key to avoiding costly mistakes.
✅ Prioritization is everything. Engineering teams need to measure their impact weekly, ensuring they drive value rather than just delivering effort.
✅ A structured hiring pipeline saves time. Using data-driven hiring matrices can prevent wasted engineering hours spent on interviews that won’t convert.
✅ Context switching is unavoidable, but it can be managed. Effective leaders block time on their calendars to focus on key areas without distraction.
✅ Blameless cultures drive improvement. Transparent postmortems and shared learning from mistakes help teams get stronger rather than fearful.
✅ Moving between IC and leadership roles can be a strategic advantage. Engineers who step back into IC roles often return as better leaders with deeper domain expertise.
Timestamped Highlights:
🕒 [00:01:00] - What is a Data Lakehouse? How OneHouse is shaping the future of data storage.
🕒 [00:03:00] - The challenge of making high-impact decisions quickly in a startup environment.
🕒 [00:05:00] - Why hiring is different in a startup vs. a big company—and how to refine the process.
🕒 [00:08:00] - How OneHouse balances deep expertise with learning potential when hiring engineers.
🕒 [00:12:00] - Context switching and efficiency—how Varun defends his calendar against distractions.
🕒 [00:16:00] - Why blameless cultures drive innovation and help engineering teams improve.
🕒 [00:20:00] - Moving from IC to leadership and back—how to position yourself for future leadership roles.
🕒 [00:23:00] - Advice for engineers looking to re-enter management after an IC stint.
Standout Quote:
"At the end of the day, everything we do has to be measured by impact. Effort alone doesn’t count—what really matters is delivering value." — Varun Madan
Connect with Varun:
📌 LinkedIn: https://www.linkedin.com/in/varun-madan-6b51377/
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In this episode, I sit down with Wade Bruce, CTO of Fetch, to explore his journey from engineer to CTO. We dive into what it takes to grow into a leadership role, how to create influence, and why focusing on value over titles leads to career progression. Wade shares his unique perspective on filling gaps within a company, playing "free," and embracing challenges without the fear of failure.
If you're in tech and aspiring to level up your career—whether you're an engineer, manager, or founder—this conversation is packed with valuable insights.
Key Takeaways:
🚀 Fill the Need First: Wade emphasizes solving problems and adding value over chasing titles. Career growth happens naturally when you focus on execution.
🎯 Play Free & Fearless: Don't let fear dictate your decisions. Trust your skills, take risks, and focus on the impact you can make.
📈 Growth is the Key Metric: Your success is determined by how much you’re evolving. Stagnation—not failure—is the real career risk.
🤝 Surround Yourself with the Right People: No one knows everything—find experts, delegate, and learn from those around you.
🏆 Culture Matters: Choose environments that encourage big swings and innovation, not ones that penalize failure.
Timestamped Highlights:
⏳ [00:01:00] – Wade’s journey into Fetch and the startup world
⏳ [00:03:00] – Did Wade plan to become CTO? (Hint: It wasn’t the goal)
⏳ [00:05:00] – Why stepping "back" into engineering helped his career move forward
⏳ [00:08:00] – The secret to getting promoted: Solve problems before aiming for titles
⏳ [00:11:00] – The trust factor: How adding consistent value creates opportunities
⏳ [00:14:00] – Transitioning into leadership: Delegation, influence, and playing at the right level
⏳ [00:17:00] – Why Fetch’s culture of big swings and learning from failure works
⏳ [00:20:00] – Advice to early-career engineers: How to accelerate your trajectory
⏳ [00:22:00] – Wade’s final thoughts and how to connect with him
Quote from the Episode:
"Job security is really just your ability to get your next job. Focus on growth, solving problems, and being valuable—everything else will follow." – Wade Bruce
Connect with Wade Bruce:
🔗 https://www.linkedin.com/in/wade-bruce-39359a33/
Support the Show:
✔️ Share this episode with a friend or colleague aiming for a leadership role.
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In this episode, Jeremy Whittington shares his journey of building a startup without relying on traditional venture capital. Instead, he leveraged alternative funding paths, including government grants and accelerators. We dive deep into the Small Business Innovation Research (SBIR) program, the I-Corps program, and how startups can secure non-dilutive funding to kickstart their business. If you're an entrepreneur looking for funding beyond VC, this episode is for you!
Key Takeaways:
🔹 SBIR Grants Can Fund Your MVP – The SBIR program provided Jeremy’s startup with $1.9M in non-dilutive funding, allowing them to build a prototype before taking on traditional investment.
🔹 Government Funding Has Strings Attached – While the money is great, it comes with paperwork, strict reporting, and compliance requirements—be prepared for documentation!
🔹 Accelerators Expand Your Network – Programs like Capital Factory and Deutsche Telekom’s hubraum helped Jeremy's team connect with investors and industry partners.
🔹 Customer Discovery is Critical – Through the I-Corps program, Jeremy discovered that their original idea wouldn’t work commercially and pivoted to a more lucrative market segment.
🔹 Alternative Funding Works Best for Certain Startups – If your company aligns with government priorities (e.g., cybersecurity, defense, healthcare, finance), alternative funding can be a game-changer.
Timestamped Highlights:
⏳ [00:02:00] – Jeremy introduces Illuma and how they developed voice biometrics for fraud prevention.
⏳ [00:03:40] – How Jeremy’s co-founder discovered the SBIR program while researching funding options.
⏳ [00:06:30] – The SBIR application process and how the phase-based funding structure works.
⏳ [00:08:55] – Why alternative funding isn’t well known and how startups can find relevant grants.
⏳ [00:11:20] – The challenges of working with government funding—compliance, reporting, and restrictions.
⏳ [00:14:00] – How I-Corps helped them pivot from securing government cell phones to working with financial institutions.
⏳ [00:18:00] – Jeremy’s experience with accelerators like Capital Factory & hubraum and how they helped with industry connections.
⏳ [00:21:06] – Would Jeremy take alternative funding again? His take on SBIR vs. VC for early-stage startups.
⏳ [00:24:38] – Final thoughts: Advice for entrepreneurs exploring alternative funding paths.
Quote of the Episode:
"If you want to start a company but don’t have a financial cushion, alternative funding—like SBIR grants—can help you quit your job and focus without giving up equity." – Jeremy Whittington
Resources & Links:
🔗 SBIR Program – https://www.sbir.gov
🔗 I-Corps Program – https://www.nsf.gov/i-corps
🔗 Connect with Jeremy on LinkedIn – https://www.linkedin.com/in/jeremywhittington/
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🎙️ New episodes drop weekly – stay tuned!
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In this episode, we dive into blurring engineering lines, full-stack engineering, and the evolving role of software engineers in a rapidly changing landscape. Sahil shares insights on how generative AI is reshaping engineering, the shift towards problem-solving over-specialization, and how teams can optimize for speed and business value.
Key Takeaways:
🔹 Blurring Engineering Roles: Traditional engineering roles (frontend, backend, DevOps) are blending, leading to more end-to-end ownership. Engineers who can span the stack and understand business impact are becoming more valuable.
🔹 The Power of Problem-Solving: As AI tools handle more code generation, the real skill will be problem formulation—defining problems correctly will matter as much as solving them.
🔹 Generative AI’s Impact: AI-powered tools are shifting software development leftward—catching security issues, automating QA, and assisting in DevOps before code even leaves the IDE.
🔹 Optimizing for Speed & Business Value: Small, autonomous teams with full ownership tend to deliver higher impact faster than large, interdependent teams.
🔹 The Future of Software Engineering: Despite concerns about AI replacing coding jobs, the demand for software engineers will increase, not decrease. The job will evolve, with natural language-based programming replacing traditional syntax-based coding.
Timestamped Highlights:
⏳ [00:00:00] Introduction – Sahil Maheshwari joins the show to discuss blurring engineering lines and its impact on speed and value.
⏳ [00:01:09] Full-Stack Engineering Revisited – Why the traditional boundaries between frontend, backend, and DevOps are disappearing.
⏳ [00:03:40] Generative AI and Engineering Autonomy – How AI-powered tools are enabling engineers to work across disciplines.
⏳ [00:06:57] Measuring Business Value & Speed – What are the right metrics to track speed and efficiency in engineering teams?
⏳ [00:08:59] Shift Left Engineering – Why engineers need to be closer to the problem and the customer to deliver the most value.
⏳ [00:12:11] AI & Developer Productivity – Real-world examples of how AI is making engineers more efficient.
⏳ [00:17:00] The Evolution of Software Engineering – Will engineers still be writing code in the future, or will AI handle it all?
⏳ [00:22:37] Ideal Team Structures – Why small, autonomous teams drive the most business value.
⏳ [00:27:02] Decision-Making in Engineering – The importance of reversible vs. irreversible decisions in technology strategy.
Quote from the Episode:
"The most valuable engineers won't just be the best coders—they'll be the best at defining the right problems to solve." – Sahil Maheshwari
Connect with Sahil Maheshwari:
🔗 LinkedIn: Reach out to Sahil on LinkedIn
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⭐ Subscribe & leave a review to help others discover the show.🗣️ Continue the conversation – Drop a comment or reach out if you have thoughts or questions!
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In this episode, we dive into the evolving role of engineering leadership with Andy Elmhorst, VP of Engineering at Bolt. We explore the delicate balance between delegation and hands-on leadership, dissect the founder mode philosophy, and analyze how servant leadership has been interpreted—and sometimes misapplied—in tech organizations. Andy shares insights on why traditional management books may be outdated for modern engineering leadership, and how staying hands-on with teams can drive better problem-solving and business outcomes.
Key Takeaways
🚀 Founder Mode Explained – A leadership approach where managers work alongside teams, not just delegate.
📚 Are Management Books Outdated? – Why classic leadership principles may not fully apply to fast-moving engineering teams.
⚖️ The Balance Between Delegation & Hands-On Leadership – Knowing when to be involved and when to step back.
🛠 Servant Leadership: Misunderstood? – Andy challenges common interpretations and explains how coaching, not just empowering, is key.
🔄 Tech Leaders Must Stay Close to the Work – How maintaining technical depth can make engineering leaders more effective.
💡 The Impact of AI on Leadership – Will AI shift engineering leaders into more business-focused problem solvers?
Timestamped Highlights
🕒 [00:01:00] – Andy introduces Bolt and how their accelerated checkout technology works.
🕒 [00:02:30] – Why traditional management books don’t fully capture the realities of modern software engineering.
🕒 [00:03:47] – Founder mode vs. bureaucratic mode: What’s the difference?
🕒 [00:06:00] – The rise (and potential pitfalls) of servant leadership in tech.
🕒 [00:10:40] – Football coaches vs. engineering leaders: The art of guiding teams without being absent.
🕒 [00:15:00] – Push vs. pull leadership: How leaders can choose when to get involved.
🕒 [00:20:40] – Do AI and automation change the role of an engineering leader?
🕒 [00:26:30] – Andy’s non-traditional career journey—from VP back to IC and back again.
🕒 [00:34:35] – Final thoughts: Why leaders must always be learning and evolving.
Quote of the Episode
"Leadership is presence, not absence. The best managers don’t just delegate problems—they solve them together with their teams." – Andy Elmhorst
Connect with Andy
🔗 LinkedIn:Andy Elmhorst
✍️ Blog:compiling.enstaria.com
Join the Conversation!
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In this episode,Sasha Bartashnik shares her insights on howlarge language models (LLMs) are transforming the development ofdata products, making advanced AI-driven solutions moreaccessible and scalable. We dive into thechallenges of traditional data tools, theadvantages and risks of LLM integration, and how businesses shouldadapt to the changing landscape of AI-driven decision-making.
Key Takeaways
🔹What Are Data Products? – Any software that processes or surfaces data to users, including dashboards and AI-powered insights.
🔹Challenges in Building Data Products – Team complexity, data quality, and model training require specialized knowledge and resources.
🔹How LLMs Help – They speed up development, make AI-driven insights more accessible, and improve data cleaning and structuring.
🔹Risks and Limitations – Accuracy concerns, hallucinations, and over-reliance on AI-generated outputs require human oversight.
🔹Changing Stakeholder Expectations – Faster and more scalable data solutions raise business expectations for AI-driven insights.
🔹Where to Start with LLMs? – Safer applications likeinternal chatbots before tackling complex structured data analysis.
Timestamped Highlights
📌[00:00] – Introduction to Sasha Bartashnik & Vendelux’s role in event intelligence
📌[01:25] – Defining what a "data product" really means in the AI-driven era
📌[03:00] – Key challenges in building scalable data products
📌[06:45] – The impact of traditional data tools and their limitations
📌[07:54] – How LLMs accelerate development and improve AI-driven insights
📌[10:00] – Risks of LLMs: Accuracy concerns, hallucinations, and human oversight
📌[14:18] – The evolving role of data engineering teams with LLMs
📌[17:31] – Where should businesses start when implementing LLMs?
📌[22:00] – The responsibility of AI builders in ensuring data accuracy and transparency
📌[23:43] – How to connect with Sasha for more insights
Quote of the Episode
"LLMs are not a silver bullet. They don’t replace humans; they just shift where expertise is needed." –Sasha Bartashnik
Connect with Sasha
🔗 LinkedIn: Sasha Bartashnik
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Subscribe, leave a review, and share it with someone exploring LLMs in data products! 🚀
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In this episode, we dive into the evolution of theChief Product and Technology Officer (CPTO) model, the blending of traditional engineering and product roles, and howAI, hackathons, and shifting org structures are reshaping product development. Arjun shares insights on what this means for engineers, product managers, and leadership teams, as well as the challenges of making this shift successful.
⏳ Timestamped Highlights
[00:00] Introduction
Amir introduces Arjun Shah and sets the stage for discussing the CPTO model.
[00:01] The Traditional Product Development Model
Breakdown of theclassic trifecta: product management, design, and engineering.
How Agile shaped product teams over the last two decades.
[00:02] The Shift to a More Integrated Model
Why companies are moving away from rigid role definitions.
Engineers taking on user research, designers coding, and product managers prototyping.
[00:04] What is the CPTO Model?
Defining theChief Product and Technology Officer role.
Examples of companies making this shift.
How CPTO improvesstrategy execution and alignment.
[00:06] The Impact on Engineers & ICs
Engineers expected to care aboutbusiness outcomes, UX, and customer needs.
Squadron model vs. Scrum model – how AI-driven teams are changing the landscape.
New hiring criteria:product sense, entrepreneurial mindset, and data analytics.
[00:08] Measuring Success in the CPTO Model
How do you know if the CPTO model is working?
R&D metrics:velocity, alignment, and strategic impact.
[00:10] Hackathons: The Canary in the Coal Mine?
The role of hackathons inbreaking down barriers between product and engineering.
How great features and products have emerged from hackathons.
[00:14] AI’s Role in Accelerating the CPTO Model
AI blurring functional lines and enablingfaster product iteration.
Why "everyone is a developer" in the age ofLLMs and code generation tools.
[00:16] Risks & Failure Points of the CPTO Model
The biggest challenge:finding the right leader for the CPTO role.
Potential pitfalls:misalignment of product vs. engineering goals, poor org design.
How tostructure squads and teams for success under a CPTO.
[00:19] The Right Person for the CPTO Role
Do you need to be afounder to succeed as a CPTO?
Why curiosity,cross-functional expertise, and product acumen are essential.
[00:22] Final Thoughts & How to Connect with Arjun
Follow Arjun Shah on LinkedIn for more insights on product and engineering leadership.
🏆 Key Takeaways
💡The product and engineering roles are merging. Engineers today are expected to think like product managers, and PMs must understand technology.
🚀The CPTO model is gaining traction. Companies are moving away from separate CPO and CTO roles in favor of a unified leader todrive better alignment and execution.
⚡AI is changing product development. Large language models and AI-driven tools are enabling anyone to prototype, reducing barriers between roles.
🔎Finding the right CPTO is challenging. The role requiresbusiness acumen, technical expertise, and product strategy skills—a rare combination.
🎯Hackathons are an early signal. Engineers experimenting with new ideas and taking on product roles during hackathons may hint at the future of team structures.
🗣️ Quote of the Episode
“The new programming language is English. With AI, everyone can be a developer.” – Arjun Shah
🎧Enjoyed the episode?
✅ Subscribe for more insights on the evolving world of tech and product development.
💬 Share your thoughts in the comments or on social media!🔗
Connect with Arjun Shah on LinkedIn: https://www.linkedin.com/in/arjunshah/.
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In this episode, JD Williams joins Amir Bormand to dive into the critical role ofchange management in driving successful digital adoption. From leading with digital fluency to navigating organizational change for AI integration, JD shares actionable insights from his work at Zoetis.
Key Takeaways
Digital Fluency Starts with People:
Training needs to be role-specific and practical.
Peer-to-peer learning fosters deeper adoption across teams.
Change Management is a Team Effort:
Success requires both top-down support and grassroots enthusiasm.
AI champions in different regions help scale efforts effectively.
Rethinking ROI in AI Adoption:
Focus onhours gained rather than hours saved.
Establish CFO-certified metrics to measure value and demonstrate ROI.
Integrating Change Management Early:
Include change management planning from the proof-of-concept stage.
Prioritize initiatives that are both technically and operationally feasible.
Storytelling is Key for Leadership:
Data leaders must communicate AI's value across diverse business functions.
Timestamped Highlights
[00:01:03] JD introduces Zoetis and its global role in animal health.
[00:02:04] Defining digital fluency and how Zoetis integrates AI into workflows.
[00:04:34] The three pillars of digital transformation: people, process, and technology.
[00:06:14] Leveraging AI champions for grassroots adoption.
[00:10:00] The importance of process mapping to identify change impacts.
[00:14:53] Measuring AI’s ROI: hours gained, accelerated R&D timelines, and improved sales tools.
[00:19:10] Injecting change management into strategy from the start.
[00:21:38] How storytelling helps leadership align on AI's value.
Memorable Quote
"Change management isn't just a top-down directive; it's about enabling and empowering individuals across the organization to embrace and drive innovation." – JD Williams
Connect with JD Williams
LinkedIn: JD WilliamsFollow JD for insights on digital adoption, AI, and data-driven leadership.
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In this episode ofThe Tech Trek, Amir Bormand sits down with Stephen Harris, former Corporate Vice President of Global Data Science and Growth Analytics at Microsoft. Steffen, a seasoned data executive with over 30 years of experience, shares insights into tackling foundational data issues, addressing data debt, and integrating advanced AI strategies. Together, they explore how businesses can move the needle on long-standing challenges and position themselves for sustainable growth in a data-driven world.
Key Takeaways
Foundational Data Challenges: Many enterprises struggle with defining and managing core data assets such as customer and product data, often resulting in inefficiencies and missed opportunities.
Data Debt: Short-term wins in data management can lead to long-term complications. Addressing data debt requires balancing immediate needs with sustainable strategies.
AI as a Catalyst: Generative AI and machine learning can help identify gaps, streamline processes, and improve data quality, but they must align with business goals to maximize ROI.
Parallel Solutions: Digital transformation and AI strategies should run on parallel tracks, emphasizing quick wins while developing a cohesive long-term roadmap.
Stakeholder Engagement: Effective communication and tailored problem-solving are essential when advocating for foundational data investments to stakeholders.
Highlighted Timestamped Moments
[00:00:21]: Introduction to foundational data issues and their role in enabling advanced technologies like generative AI.
[00:02:05]: Steffen shares insights from his time at Wells Fargo and VMware, discussing challenges in mastering customer and product data.
[00:09:29]: Exploring the concept of data debt and its implications for short-term wins versus long-term sustainability.
[00:14:58]: Leveraging AI to assess and address foundational data gaps and enhance decision-making.
[00:23:54]: The evolution of digital transformation and the rise of interconnected challenges like cybersecurity and cloud integration.
[00:29:08]: Strategies for presenting long-term data solutions to stakeholders and prioritizing fixes for maximum business impact.
Quote of the Episode
"Stop, pause, reflect, and reimagine the opportunity. Quick wins today can fuel long-term strategies tomorrow." – Stephen Harris
Connect with Stephen Harris
LinkedIn: Stephen Harris
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In this episode, Amir Bormand is joined by Marty Kausas, Co-founder and CEO of Pylon, a groundbreaking customer support platform for B2B companies. Marty shares his journey of navigating "pivot hell," the challenges of ideation, finding the right co-founders, and understanding what it takes to build a billion-dollar company. Whether you're an aspiring entrepreneur or a seasoned professional, this episode is packed with insights on creating, evolving, and scaling a tech startup.
Key Takeaways:
The Ideation Journey: Startups often require multiple pivots before discovering a viable product-market fit.
Co-founder Chemistry: Aligning goals, skillsets, and hustle is crucial for long-term success.
Big Market Thinking: Even niche solutions can thrive when embedded in expansive markets.
Unicorn Realities: Building a billion-dollar company demands focus, persistence, and adaptability.
Founder Fitness: Maintaining personal resilience is as critical as business execution.
Timestamped Highlights:
[00:00:00] Introduction to Marty Kausas and Pylon’s mission.
[00:01:00] From Airbnb software engineer to entrepreneur: Marty’s background and startup motivation.
[00:02:55] Lessons learned from early ideas and the struggles of healthcare and nonprofit markets.
[00:05:12] Decoding product-market fit and total addressable market (TAM) for startup success.
[00:06:31] The importance of choosing the right co-founders for adaptability and growth.
[00:13:49] "Find a niche in a big market" – Balancing focus and scalability.
[00:16:18] Insights into unicorn companies and the pressures of achieving high valuations.
[00:23:26] How Marty decompresses and stays motivated amidst the demands of entrepreneurship.
[00:26:00] How to connect with Marty and his final thoughts on startup growth.
Quote of the Episode:
"The worst-case scenario isn't failure—it's becoming a 'zombie company' that can't grow or sell. Momentum is everything." – Marty Kausas
Links and Resources:
Connect with Marty on LinkedIn
Email Marty at: marty@usepylon.com
Learn more about Pylon: usepylon.com
Share the Knowledge:
If you found this episode insightful, share it with your network and tag us! Don’t forget to like, subscribe, and leave a review on your favorite podcast platform. Let us know your thoughts and what topics you'd like to hear next.
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In this episode, Tanaz Mody shares insights on the hiring struggles founders face as they scale their companies. We explore how founders navigate the decision-making process, overcome hiring hesitations, and transition from being hands-on operators to strategic leaders.
Key Topics:
✅ The weight of hiring decisions on founders
✅ How founders can avoid “decision paralysis” in hiring
✅ The evolution from founder to CEO
✅ The role of VC talent partners in scaling startups
✅ When (and why) founders should consider executive coaching🎯 Key Takeaways
💡 Hiring Delays = Growth Delays
If a founder keeps postponing hiring, it could be a sign that they aren’t ready—or that they haven’t clearly defined what they need. Setting milestones helps avoid an endless search.💡 Letting Go is Hard, But Necessary
Successful founders evolve their roles. Holding onto every function prevents company growth. Hiring isn’t just about filling a role—it’s about scaling leadership.💡 Decisions Made Late Are Still Decisions
A decision avoided today can become a crisis tomorrow. Founders must recognize that delaying hiring (or any critical choice) will eventually force a decision under less favorable conditions.💡 There’s No “One-Size-Fits-All” Playbook
Best practices are useful, but every startup is unique. Founders must build teams that align with their specific needs, not just industry templates.💡 Coachability Matters
Some founders struggle with external advice. The key is finding the right voice that resonates and helps them pivot their perspective.🕒 Timestamped Highlights
⏳ [00:01:00] – Introduction to Tanaz and her role at Lerer Hippeau
⏳ [00:02:30] – Why hiring is one of the hardest decisions for founders
⏳ [00:05:00] – The danger of founders being too involved in hiring
⏳ [00:08:30] – How delaying hiring can indicate deeper problems
⏳ [00:12:00] – The importance of outside coaching for founders
⏳ [00:16:00] – Trusting teams and evolving leadership skills
⏳ [00:20:00] – Why the “operator” mindset in people teams is changing💬 Quote Worth Sharing
"Your company won’t grow if you don’t. If you’re still doing the same job as a founder that you did six months ago, you’re holding the business back." – Tanaz Mody
📢 Connect with Tanaz Mody
🔗 LinkedIn: Tanaz Mody
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Like, subscribe, and share with someone who needs to hear this! 🚀 -
In this insightful episode, James Evans dives into the unique challenges and growth opportunities for first-time founders. From building leadership styles to navigating tough decisions, James shares his journey and valuable lessons from leading CommandBar to its acquisition by Amplitude. If you're a tech enthusiast, startup founder, or aspiring entrepreneur, this episode is packed with actionable takeaways!
Key Takeaways
Leadership Style Evolves Over Time:
As a first-time founder, leadership is a learning process; it’s okay to admit you’re still refining your style.
Authenticity is crucial—your actions must align with your leadership claims.
Decisions Matter, but Not All Are Equal:
Overthinking can paralyze growth. Some decisions, like choosing a CRM, don't need innovation—pick a functional solution and move forward.
Prioritize building a team culture that aligns with long-term company goals.
The Importance of Early Employees:
The first ten hires shape the company’s culture more than the founders. Be intentional about hiring individuals who embody the values you want to amplify.
Seek Advice From Experienced Founders:
Establish a network of mentors who are 1–2 steps ahead in their entrepreneurial journeys.
Tactical advice from seasoned founders can save you months (or years) of missteps.
Redefining Product-Market Fit:
True product-market fit feels effortless—customers want your product, not just your team or service.
Timestamped Highlights
[00:00:00] Introduction to James Evans and the topic of first-time founders.
[00:01:08] Defining and discovering leadership style as a founder.
[00:04:23] What candidates really look for in a founder's leadership style.
[00:06:38] Aligning leadership style with company culture.
[00:11:00] The impact of early employees on company culture and growth.
[00:16:00] Avoiding decision paralysis as a first-time founder.
[00:17:18] Finding and leveraging founder mentors.
[00:22:00] Product-market fit: Lessons and red flags for first-time founders.
Quote of the Episode
"As a startup, you should seek to innovate as little as possible—focus your creativity on what truly matters, like your product or distribution strategy." — James Evans
Connect with James Evans Podcast
LinkedIn: James Evans
X: @JamesEvans
Support The Tech Trek
If you enjoyed this episode:
Share it with friends or colleagues interested in startups and leadership.
Subscribe to The Tech Trek for more insightful conversations with tech leaders.
Leave a review and let us know your favorite takeaways!
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In this inspiring episode, Amir Bormand chats with Matt Martin, Co-Founder of Clockwise. Matt shares his unique career trajectory, transitioning from being a lawyer to a self-taught software engineer, and ultimately, to a tech entrepreneur. They discuss the challenges, self-doubt, and triumphs of making a bold career pivot, as well as insights into the future of technology and software development. Whether you're contemplating a career change or simply curious about what it takes to thrive in the tech world, this episode is packed with valuable takeaways.
Key Takeaways:
The Courage to Pivot: Matt discusses the fears and uncertainties of leaving a secure legal career to pursue his passion for technology.
Building Skills from Scratch: The self-taught path is tough, but Matt explains how enthusiasm and resilience helped him break into the tech industry.
Diverse Backgrounds in Tech: How Matt’s legal training provided him with a unique perspective as a leader in the tech world.
Future of Coding: Thoughts on how large language models and automation tools could democratize software development.
Startup Lessons: The value of scrappiness, enthusiasm, and hiring for potential over traditional qualifications.
Timestamped Highlights:
[00:00:00] Introduction: Meet Matt Martin and his journey from lawyer to tech entrepreneur.
[00:01:41] From Politics to Law: How Matt’s passion for public policy led him to law school.
[00:04:21] The Big Decision: Why Matt chose to leave a secure legal career for the uncertain path of technology.
[00:09:03] Early Challenges: Matt’s struggles to land his first tech job and the resilience it required.
[00:13:00] Hiring Insights: How Matt’s non-traditional background influences his views on diversity in hiring.
[00:18:00] The Role of AI in Coding: Matt’s perspective on how tools like LLMs are reshaping software development.
[00:23:28] Legal Skills in Tech: The pros and cons of Matt’s legal training in his entrepreneurial journey.
[00:26:09] Closing Thoughts: Matt’s advice for aspiring career changers and tech enthusiasts.
Featured Quote:
"The magic of large language models isn't just faster code completion; it's their ability to teach you along the way." – Matt Martin
Connect with Matt Martin:
Email: matt@getclockwise.com
LinkedIn: Matt Martin on LinkedIn
Call to Action:
If you enjoyed this episode, share it with someone exploring a career pivot or interested in technology. Don’t forget to like, subscribe, and leave a comment with your thoughts!
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In this episode of The Tech Trek, Amir Bormand sits down with Scott Peachey, Director of Data Governance and Oversight at Bread Financial, to explore the evolving world of data enablement. Together, they unpack how rebranding data governance can shift perceptions, make data accessible across organizations, and foster a culture of collaboration.
Scott shares his insights on tackling the challenges of data literacy, meeting business stakeholders where they are, and the importance of tailoring governance approaches to align with organizational goals. The conversation also delves into the intersection of advanced technologies like generative AI and governance, examining how businesses can adopt AI strategically without blindly following trends.
Key Takeaways
Data Enablement Redefined: Rebranding "data governance" to "data enablement" highlights its role in empowering organizations to use data effectively, not just managing compliance.
Meeting Stakeholders Where They Are: Effective data governance requires understanding the unique needs of each business line and tailoring solutions to fit their objectives.
Shifting Left: Engaging governance professionals early in the decision-making process helps prevent costly, reactive fixes and ensures alignment with business goals.
AI: A Hammer Searching for a Nail?: Many companies adopt AI without clear goals, risking wasted resources. Scott advocates for purposeful, strategic AI integration.
Governance as a Value Driver: Data governance should go beyond risk mitigation, enabling other teams to focus on their core work while maintaining efficiency and compliance.
Timestamped Highlights
[00:01:00] What is Data Enablement? Redefining governance for a modern era.
[00:03:00] Origins of Governance: From Enron to privacy-driven modern policies.
[00:06:00] Data Literacy Challenges: Bridging the gap with tailored governance approaches.
[00:08:52] Adapting Governance for Business Needs: Customizing solutions for different teams.
[00:11:00] The Future of Data Governance: Moving from reactive to proactive.
[00:21:41] AI Governance and Ethics: Why "keeping up with the Joneses" is not enough.
[00:27:00] Is AI Investment Worth It? Balancing ROI with ethical considerations.
Notable Quote
"Data governance professionals enable other data professionals to focus on their craft. Governance isn't about saying 'no'; it's about building the right framework to help businesses thrive."– Scott Peachey
Resources and Follow-Up
Connect with Scott Peachey on LinkedIn
Check out his podcast: Data with Peachey
Follow him on social media: Twitter/Instagram @TheScottPeachey
Join the Conversation
If you enjoyed this episode, subscribe to The Tech Trek, leave a review, and share your thoughts on how data governance impacts your organization.
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