The Tech Trek

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/

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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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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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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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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

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Connect with Arjun Shah on LinkedIn: https://www.linkedin.com/in/arjunshah/.

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

1. Digital Fluency Starts with People:

• Training needs to be role-specific and practical.

• Peer-to-peer learning fosters deeper adoption across teams.

2. Change Management is a Team Effort:

• Success requires both top-down support and grassroots enthusiasm.

• AI champions in different regions help scale efforts effectively.

3. Rethinking ROI in AI Adoption:

• Focus onhours gained rather than hours saved.

• Establish CFO-certified metrics to measure value and demonstrate ROI.

4. Integrating Change Management Early:

• Include change management planning from the proof-of-concept stage.

• Prioritize initiatives that are both technically and operationally feasible.

5. 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.

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

1. 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.

2. Data Debt: Short-term wins in data management can lead to long-term complications. Addressing data debt requires balancing immediate needs with sustainable strategies.

3. 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.

4. Parallel Solutions: Digital transformation and AI strategies should run on parallel tracks, emphasizing quick wins while developing a cohesive long-term roadmap.

5. 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