The Tech Trek

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] Anand’s perspective on assimilating into new engineering cultures using insights from "The First 90 Days."[00:05:00] Core concepts of Everlaw’s 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 hike—take 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/madhavananandEnjoyed the episode? Don't forget to like, subscribe, and share your thoughts in the comments!

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🔹 Agentic AI vs. Traditional AI – Agentic AI enables systems to plan, reason, and take iterative actions based on real-time feedback, offering more dynamic solutions than static AI models.🔹 The Evolution of AI Agents – 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.🔹 Enterprise AI Adoption – Companies are sitting on vast knowledge bases, but AI’s ability to search, retrieve, and process unstructured data is still evolving. AI agents in Slack and other platforms can help bridge this gap.🔹 AI in Slack and Workplace Collaboration – By integrating AI agents into platforms like Slack, businesses can automate and streamline workflows without complex engineering resources.🔹 The Debate on Vertical vs. General AI – 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🔹 Computer Vision & AI Agents – The next step in AI evolution includes visual data processing, allowing AI agents to analyze screenshots, documents, and interfaces, making interactions even more seamless.🔹 Privacy & Ethical Concerns – As AI agents gain access to corporate communications and historical data, privacy and governance will play a crucial role in adoption.🔹 The Role of Humans in AI-Driven Workflows – 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] – Introduction to Scott Persinger and Supercog AI’s mission.[02:00] – The origins and resurgence of agentic AI.[05:00] – Vertical AI vs. General AI: Which will dominate?[08:00] – Why knowledge search and retrieval are AI’s low-hanging fruit.[11:00] – How AI agents in Slack enhance team collaboration and efficiency.[14:00] – The rise of AI-powered assistants instead of AI replacing human workers.[16:00] – Computer vision as the next leap for agentic AI.[18:00] – Privacy and ethical concerns with AI searching historical corporate data[21:00] – The human-in-the-loop debate and how much control people will retain over AI decisions.[24:00] – The shift from multi-source research (Google) to single-answer AI interactions.[27:00] – Final thoughts on AI’s future and how people can connect with Scott Persinger.Quote of the Episode"We’re 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." – Scott PersingerConnect with Scott Persinger📧 Email: scottp@supercog.ai🔗 LinkedIn: https://www.linkedin.com/in/scottpersingerSupport the Podcast!👍 Like & Subscribe – Don’t miss an episode!📢 Share – If you found this insightful, send it to a friend or colleague.💬 Comment – Let us know your thoughts on agentic AI!🎙️ Tune in next time for more conversations on the future of technology!

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 TakeawaysTurning 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 MoshenkoConnect with JakeWebsite: https://authzed.com/Discord: Join via the link on AuthZed's homepage

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📩 Enjoyed the episode?👉 Share this with a fellow engineering leader or AI enthusiast👉 Subscribe & leave a review to help more people discover the show👉 Got thoughts or feedback? Drop a comment!

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 TakeawaysData Governance Has a Stigma – But It Can Be OvercomeTraditional governance has been slow and bureaucratic. The key to transformation is making governance more business-focused and outcome-driven.Governance Needs Storytelling and Sales SkillsSuccessful data governance leaders are not just compliance experts—they understand how to "sell" governance by tying it to business outcomes.Balancing Governance and Business AgilityOrganizations must strike a balance between strong controls and flexibility to support commercial goals.Gen AI and Data GovernanceGenerative AI is evolving as a tool to enhance governance processes, from data quality rule identification to metadata collection.Start Small, Build TrustIf you're looking to simplify governance, start with listening, identify quick wins, and build relationships with stakeholders.Breaking into Data GovernanceYou 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!

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 TakeawaysEngineering 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 DattaConnect with Ganesh DattaLinkedIn: https://www.linkedin.com/in/gsdatta/Email: ganesh@cortex.ioCortex Website: cortex.io

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!

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🎧 Enjoyed the episode?✅ Subscribe, rate & review to stay updated!🔗 Share with someone in tech who needs to hear this.

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 MadanConnect with Varun:📌 LinkedIn: https://www.linkedin.com/in/varun-madan-6b51377/🎧 Enjoyed the episode?✔️ Share it with someone who might find it useful!✔️ Follow, rate, and review on your favorite podcast platform!✔️ Let us know your thoughts in the comments or on social media!

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 himQuote 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 BruceConnect 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.✔️ Subscribe & Leave a Review – Your feedback helps us grow!