Nir Soudry, Head of R&D at 7AI, breaks down how teams can move from early experimentation to real production work fast, without shipping chaos. If you are building AI features or agent workflows, this conversation is a practical look at speed, safety, and what it actually takes to earn customer trust.
Nir shares how 7AI ships in tight loops with a real customer in mind, why pushing decisions closer to the engineers removes bottlenecks, and how guardrails and evaluation keep fast releases from turning into security risks. You will also hear a grounded take on human plus AI collaboration, and why “just hook up an LLM” falls apart at scale.
Key takeaways
• Speed starts with focus, pick one customer and ship something usable in two or three weeks, then iterate every couple of weeks based on real feedback
• If you want velocity, remove the meeting chain, get engineers in the room with customers and push decisions downstream
• Agent workflows are not automatically testable, you need scoped blast radius, strong input and output guardrails, and an evaluation plan that matches real production complexity
• “LLM as a judge” helps, but it is not magic, you still need humans reviewing, labeling, and tuning, especially once you have multi step workflows
• In security, trust is earned through side by side proof, run a real pilot against human outcomes, measure accuracy and thoroughness, then improve with tight feedback loops
Timestamped highlights
00:28 What 7AI is building, security alert fatigue, and why minutes matter
02:03 A fast shipping cadence, one customer, quick prototypes, rapid iterations
03:51 The velocity playbook, engineers plus sales in the same meetings, fewer bottlenecks
08:08 Shipping agents safely, blast radius, guardrails, and why testing is still hard
14:37 Human plus AI in practice, how ideas become working agents with review and monitoring
18:04 Why early AI adoption works for some customers, and how pilots build confidence
24:12 The startup reality, faster execution, traction, and why hiring still matters
A line worth sharing
“When it’s wrong, click a button, and next time it will be better.”
Pro tips you can steal
• Run a two to four week pilot with one real customer and ship weekly, the goal is learning speed, not perfect coverage
• Put engineers directly in customer conversations, keep leadership focused on unblocking, not gatekeeping
• Treat every agent like a product surface, define strict inputs and outputs, sanitize both, and limit what it can affect
• Build evaluation around real workflows, not single prompts, and combine automated checks with human review
• Add feedback buttons everywhere, route feedback to both model improvement and the team that tunes production behavior
Call to action
If you want more conversations like this on building real tech that ships, follow and subscribe to The Tech Trek.
B2B pricing is still way harder than it should be, even in 2026. In this conversation, Tina Kung, Founder and CTO at Nue.ai, breaks down why quote to revenue can take weeks, and how a flexible pricing engine can turn it into something closer to one click.
You will hear how fast changing pricing models, AI driven products, and new selling motions are forcing revenue teams to rethink the entire system, not just one tool in the stack.
Key takeaways
• B2B quoting is basically a shopping cart, but the real complexity is cross team workflow, accounting controls, and downstream revenue rules.
• Fragmented systems break the moment pricing changes, and in fast markets that can mean you only get one real pricing change per year.
• AI companies often evolve from simple subscriptions to usage, services, and even physical goods, which creates billing chaos without a unified backbone.
• Commit based models can make revenue more predictable while staying flexible for customers, but only if you can track entitlement, burn down, overspend, and approvals cleanly.
• The most useful AI in revenue ops is not just insight, it is action, meaning it can generate the right transaction safely inside a system of record.
Timestamped highlights
00:43 What Nue.ai actually does, one platform for billing, usage, and revenue ops with intelligence on top
02:43 Why a one minute checkout in B2C turns into weeks or months in B2B
05:28 The real reason quote to revenue stays broken, fragmentation and brittle integrations
08:03 How AI era pricing evolves, subscriptions to consumption, services, and physical goods
12:51 Why Tina designed for flexibility from day one, and what 70 plus customer calls revealed
19:42 Transactional intelligence, AI that can create the quote, route approvals, and move revenue work forward
A line worth keeping
“It should be as easy as one click.”
Practical moves you can steal
• Map every pricing change to the downstream work it triggers, quoting, billing, revenue recognition, and approvals, then measure how many handoffs exist today.
• If you sell both self serve and enterprise, design for multiple selling motions early, because the same objects can have totally different context and risk.
• Treat pricing as a product surface, if your systems make changes slow, you are giving up speed in the market.
Call to action
If you want more conversations like this on how modern tech companies actually operate, follow the show on Apple Podcasts or Spotify, and connect with me on LinkedIn for clips and episode takeaways.
Tim Bucher, CEO and cofounder of Agtonomy, joins Amir to break down what physical AI looks like when it leaves the lab and shows up on the farm. Tim shares how his sixth generation farming roots and a lucky intro computer science class led to a career that included Microsoft, Apple, and Dell, then back into agriculture with a mission that hits the real world fast.
This conversation is about building tech that earns its keep, delivers clear ROI, and improves quality of life for the people who keep the food supply moving.
Key takeaways
• Deep domain experience is a real advantage, especially in ag tech, you cannot fake the last mile of operations
• The win is ROI first, but quality of life is right behind it, less stress, more time, and fewer dangerous moments on the job
• Agtonomy focuses on autonomy software inside existing equipment ecosystems, not building tractors from scratch, because service networks and financing matter
• One operator can run multiple vehicles, shifting the role from tractor driver to tech enabled fleet operator
• Hiring can change when the work changes, some farms started attracting younger candidates by posting roles like ag tech operator
Timestamped highlights
00:42 What Agtonomy does, physical AI for off road equipment like tractors
01:45 Tim’s origin story, sixth generation farming roots and the class that changed his path
03:59 Lessons from Bill Gates, Steve Jobs, and Michael Dell, and how Tim filtered the mantras into his own leadership
05:53 The moment everything shifted, labor pressure, regulations, and the prototype built to save his own farm
09:17 The blunt advice for ag tech founders, if you do not have a farmer on the team, fix that
11:54 ROI in plain terms, one person operating a fleet from a phone or tablet
14:29 Why Agtonomy partners with equipment manufacturers instead of building new vehicles, dealers, parts, service, and financing are the backbone
17:39 The overlooked benefit, quality of life, reduced stress, and a more resilient food supply chain
20:18 How farms started hiring differently, “ag tech operator” roles and even “video game experience” as a signal
A line that stuck with me
“This is not just for Trattori farms. This is for the whole world. Let’s go save the world.”
Pro tips you can actually use
• If you are building in a physical industry, hire a real operator early, not just advisors, get someone who lives the workflow
• Write job posts that match the modern workflow, if the work is screen based, label it that way and recruit for it
• Design onboarding around familiar tools, if your UI feels like a phone app, training time can collapse
Call to action
If you got value from this one, follow the show and share it with a builder who cares about real world impact. For more conversations like this, subscribe and connect with Amir on LinkedIn.
Data and AI are everywhere right now, but most teams are still guessing where to start. In this episode, Cameran Hetrick, VP of Data and Insights at BetterUp, breaks down what actually works when you move from AI hype to real business impact.
You will hear a practical way to choose AI and analytics projects, how to spot low risk wins, and why clean, governed data still decides what is possible. Cameran also shares a simple mindset shift, stop copying broken workflows, and start rethinking the outcome you are trying to create.
Key Takeaways
• AI is a catchall term right now, the best early wins usually come from “assist” use cases that boost speed and quality, not full replacement
• Start with low context, low complexity work, then earn your way into higher context projects as data quality and governance mature
• Pick use cases with an impact versus effort lens, quick wins create proof, buy in, and budget for bigger bets
• Stakeholders often ask for a data point or feature, but the real value comes from digging into the goal, and redesigning the workflow
• Data teams cannot stop at insights, adoption matters, if the next team cannot act on the output, the project stalls
Timestamped Highlights
00:40 BetterUp’s mission, building a human transformation platform for peak performance
01:57 AI as a “catchall,” where expectations are realistic, and where they are not
05:19 A useful way to think about AI work, context versus complexity, and why “intern level” framing helps
07:33 How to choose projects with an impact and level of effort calculator, and why trust in data is everything
10:33 The hard part, translating stakeholder requests into real outcomes, and reimagining workflows instead of automating bad ones
13:47 Systems thinking across handoffs, plus why teams need deeper business fluency, including P and L basics
16:59 The last mile problem, if the next stakeholder cannot act, the value never lands
20:27 The bottom line, AI does not change the fundamentals, it accelerates them
A Line Worth Saving
“AI is like an intern, it still needs direction from somebody who understands the mechanics of the business.”
Practical Moves You Can Use
• Run every idea through two quick questions, what business impact do we expect, and what level of effort will it take
• Look for a win you can explain in one minute, then use it to fund the harder work
• When someone asks for a metric or feature, ask why twice, then validate the workflow, then redesign the outcome
• Invest in governed data early, untrusted outputs kill adoption fast
Call to Action
If this episode helped you think more clearly about AI in the real world, follow the show, leave a quick review, and share it with one operator who is trying to move from experiments to impact. You can also follow Amir on LinkedIn for more clips and practical notes from each episode.
Yogi Goel, cofounder and CEO of Maxima AI, breaks down how he hires outlier talent, people who think like future founders and thrive when the plan changes fast. We get practical on what to look for beyond pedigree, how to assess it without relying on easy resume signals, and how culture scales when your team doubles.
Yogi also shares what Maxima AI is building, an agentic platform for enterprise accounting that automates day to day operations and month end work, and why the best teams win by pairing speed with real ownership.
Key takeaways
• Outlier candidates often look “non standard” on paper, the signal is founder mentality, fast thinking, grit, and a point to prove
• Hiring gets easier when it is always on, keep a living bench of great people long before you have a headcount
• Use long form conversations to assess how someone thinks, not just what they have done, ask for their life story and listen for the choices they highlight
• Train the specifics, but set a baseline for domain aptitude, then coach the narrow parts once the fundamentals are there
• Culture scales through leaders and through what you reward and penalize, not through posters and slogans
Timestamped highlights
00:39 What Maxima AI does and the real value of agentic accounting
01:38 Defining an outlier candidate as a future founder, and why school matters less than you think
07:34 The conveyor belt approach to recruiting, building an inventory of great people before you need them
11:35 Where to draw the line on training, test for general aptitude, coach the specifics
14:20 How diverse teams disagree productively, bring evidence, run small bets, then double down or pivot
18:25 Scaling culture with values driven leaders, and the simple rule of reward versus penalty
A line worth keeping
“Culture is two things, what you reward and what you penalize.”
Pro tips you can steal
• Keep a short list of the best people you have ever met for each function, update it constantly
• Ask candidates for their journey from day zero, then pay attention to what they choose to emphasize
• When the team disagrees, grab quick evidence, customer texts, small pulse checks, then place a small bet that will not kill the company
• Expect great people to want autonomy and scope, manage like a mentor, not a hovercraft
Call to action
If this episode helped you rethink hiring, share it with a founder or engineering leader who is building a team right now. Follow the show for more conversations on people, impact, and technology, and connect with Yogi Goel on LinkedIn by searching his name and Maxima AI.
Chandan Lodha, Co-founder at CoinTracker, joins Amir Bormand to unpack the real shift from big tech to building your own company. From Harvard to Google to Y Combinator, Chandan shares what pushed him to take the leap, how he found the right idea, and what he had to unlearn to lead at startup speed.
This conversation is for builders and leaders who want to grow faster, ship faster, and build teams that can actually execute.
Key Takeaways
• The early career advantage is learning velocity, optimize for environments that stretch you fast
• Managing the business is rarely the hardest part, people problems scale with headcount
• Big company habits can break you at a startup, especially around distribution, speed, and getting your first users
• YC helped most through peer proximity, being surrounded by real users and founders who move quickly
• Founder growth is a system, use feedback loops like reviews, 360 input, and personal goal tracking
Timestamped Highlights
00:00 From Harvard and Google to founder mode, what made him leave the safe path
00:35 CoinTracker in plain English, crypto taxes and accounting for individuals and businesses
03:32 Leap first, think later, the messy six month search for a real idea
05:00 Runway reality, setting a 12 to 18 month window to figure it out
06:09 Crypto skepticism to conviction, reading the Bitcoin white paper changed his frame
10:05 Leadership lessons at 100 people, why people issues become the main work
14:43 Y Combinator benefits, users everywhere and a practical playbook for early company building
17:55 Personal growth systems, performance feedback and personal OKRs, plus changing your mind on three issues each year
21:04 Becoming a new parent, structure, efficiency, and cutting non essentials
23:24 The two skills to build before you leap, building and selling
A line worth keeping
Managing the business is easy, managing people is hard.
Pro Tips
• Set a real runway window, then use it to iterate hard with users every week
• Expect to unlearn big company instincts, distribution and speed do not come for free
• Build a feedback cadence for yourself, not just your team, reviews and 360 input can surface blind spots
• Practice building and selling in small side projects now, those skills compound in any startup
Call to Action
If this episode helped you think differently about leadership and the founder path, follow The Tech Trek on Apple Podcasts or Spotify, and share it with one person who is building or thinking about making the leap.
Joel Dolisy, CTO at WellSky, joins the podcast to reveal why organizational design is the ultimate "operating system" for scaling tech companies. This conversation is a deep dive into how engineering leaders must adapt their strategies when moving between the hyper growth of Venture Capital and the disciplined profitability of Private Equity.
Building a high performing team is about much more than just hiring. Joel explains the necessity of maximizing the "multiplier effect" where the collective output far exceeds the sum of individual parts. We explore the pragmatic reality of digital transformation, the "art" of timing disruptive technology adoption like Generative AI, and how to use the Three Horizons framework to keep your core business stable while chasing the next big innovation. Whether you are leading a team of ten or an organization of hundreds, these insights on design principles and leadership context are essential for navigating the complexities of modern software delivery.
Core Insights
Shifting the perspective of software from a cost center to a core growth enabler is the fundamental requirement for any company aiming to be a true innovator.
Private Equity environments require a specialized leadership approach because the "hold period" clock dictates when to prioritize aggressive growth versus EBITDA margin acceleration.
Scaling successfully requires a "skeleton" of design principles, such as maintaining team sizes around eight people to ensure optimal communication flow and minimize overhead.
The most critical role of a senior leader is providing constant context to the engineering org, ensuring teams understand the "why" behind shifting constraints as the company matures.
Timestamped Highlights
01:12 Defining the broad remit of a CTO from infrastructure and security to the unusual addition of UX.
04:44 Treating your organizational structure as a living operating system that must be upgraded as you grow.
10:07 Why innovation must include internal efficiency gains to free up resources for new revenue streams.
15:01 Navigating the massive waves of disruption from the internet to mobile and now large language models.
23:11 The tactical differences in funding engineering efforts during a five to seven year Private Equity hold period.
28:57 Applying Team Topologies to create clear responsibilities across platform, feature, and enablement teams.
Words to Lead By
"You are trying to optimize what a set of people can do together to create bigger and greater things than the sum of the individual parts there".
Expert Tactics for Tech Leaders
When evaluating new technology like AI, Joel suggests looking at the "adoption curve compression". Unlike the mid nineties when businesses had a decade to figure out the internet, the window to integrate modern disruptors is shrinking. Leaders should use the Three Horizons framework to move dollars from the core business (Horizon 1) to speculative innovation (Horizon 3) without making knee jerk reactions based solely on hype.
Join the Conversation
If you found these insights on organizational design helpful, please subscribe to the show on your favorite platform and share this episode with a fellow engineering leader. You can also connect with Joel Dolisy on LinkedIn to keep up with his latest thoughts on healthcare technology and leadership.
Stop chasing shiny objects and start driving real business outcomes. Marathon Health CTO Venkat Chittoor joins the show to explain why AI is the ultimate enabler for digital transformation but only when it is anchored by a rock solid business strategy.
Essential Insights for Tech Leaders
AI is not a standalone strategy. It is a powerful tool to accelerate a pre-existing business North Star.
Success in digital transformation follows a specific maturity curve. Start with personal productivity, move to replacing mundane tasks, and eventually aim for cognitive automation.
Governance must come before experimentation. Establishing guardrails for data privacy is critical before launching any AI pilot.
Measure value through tangible efficiency gains. In healthcare, this means reducing administrative burden or "pajama time" so providers can focus on patient care.
Don't let marketing speak fool you. Always validate vendor claims against your specific industry use cases.
Timestamped Highlights
00:50 Defining advanced primary care and the mission of Marathon Health
02:44 Why AI strategy is useless without a defined business strategy
05:01 The three steps of AI adoption from productivity to cognition
12:14 How to define success metrics for a pilot versus a scaled V1 solution
16:40 Real world ROI including call deflections and charting efficiency
21:43 Advice for leaders on data quality and avoiding vendor traps
A Perspective to Carry
AI is actually enabling [efficiency], but without a solid business strategy, AI strategy is not useful.
Tactical Advice for the Field
When launching an AI initiative, focus heavily on the underlying data quality. Ensure your team accounts for data recency, accuracy, and potential biases, as these factors determine whether an experiment succeeds or fails. Start small with pilots to build muscle memory before attempting to scale complex systems.
Join the Conversation
If you found these insights helpful, subscribe to the podcast for more deep dives into the tech landscape. You can also connect with Venkat Chittoor on LinkedIn to follow his work in healthcare innovation.
Stop treating data governance as a "data cop" function and start using it as a high ROI offensive weapon. In this episode, Peter Kapur, Head of Data Governance and Data Quality at CarMax, breaks down how to move beyond defensive compliance to drive profitability, customer experience, and better data science outcomes.
Critical Insights for Leaders
Shift from defense to offense Data defense covers the mandatory regulatory and legal requirements like privacy and cybersecurity. Data offense involves everything else that hits your bottom line, such as investing in data quality to save or make money.
Prioritize problems over frameworks Avoid bringing rigid policies and "data geek" terminology to business leaders. Instead, spend time listening to their specific data struggles and apply governance capabilities as solutions to those problems.
Data quality makes governance tangible Without high quality data, governance is just a collection of abstract policies. Improving data quality empowers data scientists to produce better models and gives analytics teams the ability to discover and trust their data.
Key Moments in the Conversation
02:41 Defining the clear line between defensive regulation and offensive growth
06:03 Why data quality and data governance must sit together to be effective
11:00 Shifting from "data school" to "business school" to communicate value
13:12 Quantifying the ROI of data governance through customer wins and time savings
18:35 Actionable advice for starting an offensive strategy from scratch
Wisdom from the Episode
"If we meet the laws, we meet the regulations, we meet the legal, how do we leverage our data? It is a mindset shift versus, let me lock my data down, no one use it."
Tactical Advice for Implementation
Ensure adoption through personalization Design tools and processes that are personalized to specific roles so they feel like a natural part of the workflow rather than a burden.
Focus on the eye of the consumer Treat every person in the organization as a "data citizen" and remember that data quality is ultimately defined by the needs of the people consuming it.
Join the Conversation
Subscribe to the podcast on your favorite platform to catch every episode. Follow us on LinkedIn to stay updated on the latest trends in data leadership.
Afrooz Ansaripour, Director of Data Science at Walmart, joins the show to explain how global leaders are shifting from simple historical tracking to predicting psychological triggers and customer intent. This episode explores the evolution of customer intelligence and how Generative AI is turning massive data sets into personalized, value driven experiences. Listeners will learn how to balance hyper personalization with foundational privacy to build lasting consumer trust.
Key Insights
Predict intent rather than just reporting past transactions to understand why a customer is with the brand.
Use Generative AI as an explainability layer to transform complex data platforms from black boxes into conversational tools.
Prioritize customer trust as a critical part of the user experience rather than just a legal requirement.
Integrate digital and physical signals to create a 360 degree view that reveals insights which would otherwise be invisible.
Focus on rapid technology adoption and curiosity as the primary drivers of success in modern AI teams.
Timestamped Highlights
01:51 Identifying the challenges and opportunities when managing millions of real time signals.
06:43 Strategies for showing genuine value to the customer without making them feel like just a part of a sale.
09:51 How LLMs are fundamentally changing the way data teams interpret unstructured feedback and behavioral patterns.
14:42 Managing privacy and ethical data practices while building personalized conversational AI.
19:14 Stitching together the online and offline journey to create a seamless customer experience.
22:52 The necessary evolution of data science skills toward storytelling and execution bias.
A Powerful Thought
"Personalization should never come at the expense of customer trust."
Tactical Steps
Combat the garbage in garbage out problem by refining cleaning processes to handle modern AI requirements.
Build an interactive layer or chatbot on top of data products to make insights instantly accessible and automated.
Translate technical insights into real world decisions to ensure customers actually benefit from data models.
Next Steps
Subscribe to the show for more insights into the future of tech. Share this episode with a peer who is currently navigating the complexities of customer data.
Shahryar Qadri, CTO of OneImaging, joins me to unpack a hard truth about healthcare tech: the goal is not to remove humans, it is to give them more room to be human.
We talk about where cost “optimization” actually helps patients, why radiology is a perfect fit for AI but still held back by data access, and how better workflows can improve trust, speed, and outcomes without losing the human touch.
OneImaging sits in the radiology benefits space, helping members book imaging in a national network with more transparency and a high touch booking experience, while helping employers cut imaging costs significantly.
Key takeaways
• The “human touch” in healthcare is not going away, the better play is using tech to increase capacity so caregivers can spend more time being caregivers
• Cost optimization is not always about paying less for expertise, it is often about wasting less human time, improving trust, and removing friction around services
• Healthcare still runs on outdated plumbing in places you would not expect, including fax based workflows that slow everything down
• Radiology is one of the best real world use cases for AI, but the bigger blocker is getting access to imaging data in usable form, not model capability
• Your health data is already “there”, but it is not working for you yet. The next wave is tools that scan your longitudinal record and surface what to ask your doctor about, so you can be a stronger advocate for your own care
Timestamped highlights
• 00:36 What OneImaging actually does, and why “transparent imaging” is more than a pricing story
• 02:00 Why healthcare stays personal, and how tech should increase capacity instead of replacing care
• 03:36 The real definition of cost optimization, commodity versus service, and where trust matters
• 07:01 The surprising reality of imaging ops, why it still feels like 1998, and what gets digitized next
• 17:19 AI in radiology is real, but the data access and interoperability gap is the bottleneck
• 24:21 Your CDs are full of value, the problem is we do almost nothing with that data today
A line worth replaying
“These LLM models are the worst that they’ll ever be today. They’re only going to get better and better and better.”
Call to action
If this episode sparked a new way of thinking about healthcare tech, follow The Tech Trek on your podcast app, share it with a friend in product or engineering, and connect with me on LinkedIn for more conversations like this.
Swarupa Mahambrey, Vice President of Software Engineering at The College Board, breaks down what tech debt really looks like in a mission critical environment, and how an engineering mindset can prevent it from quietly choking delivery. She shares a practical operating model for paying down debt without stopping the roadmap, and the cultural habits that make it stick.
You will hear how College Board carved out durable space for engineering excellence, how they use testing and automation to protect reliability at scale, and how to make the trade offs between features, simplicity, and user experience without slowing the team to a crawl.
Key Takeaways
• Tech debt behaves like financial debt, delay the payment and the interest compounds until even simple changes become painful
• A permanent allocation of capacity can work, dedicating 20 percent of every sprint to tech debt can reduce support load and improve delivery
• Shipping more features can slow you down, simplifying workflows and validating with real usage can increase velocity and reduce tickets
• Resilience is not about avoiding every failure, it is about designing for graceful degradation so spikes and outages become small blips instead of crises
• Automation is not “extra,” it is part of the definition of done, including unit tests as acceptance criteria and clear code coverage expectations
Timestamped Highlights
• 00:00 Why tech debt is a mindset problem, not just a backlog problem
• 01:00 Tech debt explained with a real example, what happens when a proof of concept becomes production
• 03:45 The feature trap, how “powerful” workflows can overwhelm users and explode maintenance costs
• 11:03 Engineering Tuesday, one day a week to strengthen foundations, not ship features
• 14:39 Stability vs resilience, designing systems that bend instead of shatter
• 20:06 Testing and automation at scale, unit tests as a requirement and code coverage guardrails
A line worth keeping
“If we don’t intentionally carve out space for engineering excellence, the urgent will always crowd out the important.”
Practical moves you can steal
• Protect a fixed slice of capacity for tech debt, make it part of the operating model, not a one time cleanup
• Treat automation as acceptance criteria, no test, no merge, no release
• Use pilots and targeted releases to learn early, then iterate based on metrics and real user behavior
• Design for graceful degradation with retries, fallback paths, and clear failure visibility
Call to action
If this episode helped you think differently about tech debt and engineering culture, follow The Tech Trek, leave a quick rating, and share it with one engineer who is fighting fires right now.
Software is still eating the world, and AI is speeding up the clock. In this episode, Amir talks with Tariq Shaukat, co CEO at Sonar, about what it really takes for non tech companies to build like software companies, without breaking trust, security, or quality.
Tariq shares how leaders can treat AI like a serious capability, not a shiny add on, and why clean code, governance, and smart pricing models are becoming board level topics.
Key Takeaways
• “Every company is a software company” does not mean selling SaaS, it means software is now core to differentiation, even in legacy industries.
• The hardest shift is not tools, it is mindset: moving from slow, capital style planning to fast iteration, test, learn, and ship.
• AI works best when leaders stay educated and involved, outsourcing the whole strategy is a real risk.
• “Trust but verify” needs to be a default posture, especially for code generation, security, and compliance.
• Pricing will keep moving toward value aligned consumption models, not simple per seat formulas.
Timestamped Highlights
• 00:56 What Sonar does, and why clean code is really about security, reliability, and maintainability
• 05:36 The Tesla lesson: mechanics commoditize, software becomes the experience people buy
• 09:11 Culture plus education: why software capability cannot live in one silo
• 14:21 Cutting through AI hype with program discipline and a “trust but verify” mindset
• 18:23 Boards, governance, and setting an “acceptable use” policy for AI before something goes wrong
• 25:18 How software pricing changes in an AI world, and why Sonar prices by lines of code analyzed
A line worth saving:
“Define acceptable risk as opposed to no risk.”
Pro Tips you can steal
• Write down what you want AI to achieve, the steps to get there, and the metric you will use to verify outcomes.
• For code generation, scan and review before shipping, treat AI output like a draft, not a final answer.
• Set clear rules for what is allowed with AI inside the company, then iterate as you learn.
Call to Action
If you want more conversations like this on software leadership, AI governance, and building real impact, follow The Tech Trek and subscribe on your favorite podcast app. If someone on your team is wrestling with AI rollout or developer productivity, share this episode with them.
Gregg Altschul, Vice President of Technology at FanDuel, shares a clear and practical look at how leaders can create real alignment across personal, team, and company goals. He explains why transparency drives trust, how to build a path for growth at every level, and why the best managers help people pursue their long term North Star while still delivering for the business. This is a thoughtful and modern blueprint for tech leadership and team development.
Key Takeaways
Teams move faster when the company goal is translated into a simple set of objectives that every level can understand and act on.
Transparency is the anchor for healthy goal setting and creates the space for honest conversations about career direction.
Managers should encourage long term North Star thinking since it keeps people growing even after short term milestones are reached.
Succession planning should be an active part of how teams operate so progress never depends on a single person.
People can stay committed to their work even if they have long term plans outside the company, and supporting those plans often improves retention.
Timestamped Highlights
02:19 How top level business goals get distilled into specific team and personal goals that engineers can act on.
04:57 The role of transparency in helping teams understand the why behind each objective.
07:34 Helping ICs tie personal development to broader company needs while still honoring their ambitions.
09:28 Creating a safe environment for honest career conversations in a world of hybrid and remote work.
15:14 Why knowing a person’s long term plans makes succession planning easier for everyone.
17:45 How Gregg works with his own manager on growth even when the title ladder narrows at the VP level.
A standout idea from Gregg
“As long as you have a North Star you will grow. Whether you ever reach the exact role you picture is not really the point. The point is growth.”
Call to action
If this conversation helped you rethink how goals work inside your team, share it with a colleague who will appreciate it. Follow the show so you never miss new episodes and connect with me on LinkedIn for more conversations with leaders shaping the future of engineering and data.
Ken Ringdahl, CTO at Emburse, joins The Tech Trek to share what it really looks like to grow from engineer to CTO without losing your love for building. He talks about staying close to the code while leading a three hundred person org, how he learned the business side on the job instead of through an MBA, and why curiosity is still his strongest tool. If you are an engineer who cares about leadership, AI, and long term impact, this one will hit close to home.
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Key takeaways
The best engineering leaders stay technical for as long as they can, then pick their spots to lean in where the business needs them most.
You can learn the business side on the job by raising your hand for cross functional work and building real relationships with sales, finance, and product leaders.
Curiosity is a career advantage, both in technology and in leadership, because the quality of your questions shapes the quality of your decisions.
A practical AI strategy comes from listening to customers, partners, and internal experts, then translating that into focused product bets instead of chasing shiny tools.
Do not rush into management just for the title, a deep foundation as an engineer will make every future leadership decision stronger.
Timestamped highlights
00:38 Ken explains what Emburse does and how modern spend management lives at the intersection of software, data, and finance.
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01:30 How he balances being an engineer at heart with the reality of leading many teams and products as CTO.
03:41 Ken reflects on missing his coding days, what he still tinkers with, and why he chose the bridge role between tech and business.
08:32 Learning leadership without an MBA, creating your own opportunities, and attaching yourself to people you can learn from across the company.
14:58 How he stays smart on AI through office hours, internal experts, cloud partners, customers, and investor networks.
21:22 His biggest advice for engineers who want to move into leadership and why he actually went back to a more hands on role before moving up again.
One line that stayed with me
“Even if you want to be a leader, do not rush it. Do not go so fast that you do not get that foundation.”
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Practical moves for your own career
Stay technical as long as you can, then choose a few focus areas such as architecture, AI strategy, or cloud patterns where you can still go deep.
Use curiosity as your main tool, ask simple but sharp questions of finance, sales, and customers so you see how technology really creates value.
Look for chances to run cross functional projects early in your career so that by the time you step into leadership, you already understand how the wider business works.
Treat partners, customers, and internal experts as an extended brain trust, especially when you are trying to shape an AI and platform strategy.
Listen and stay connected
If this episode helped you think differently about your own path from engineer to leader, follow The Tech Trek, leave a rating on your favorite podcast app, and share it with one person on your team. To keep the conversation going, connect with Ken on LinkedIn and find me there as well for more stories from leaders who are building real impact with technology.
Karan Talati, cofounder and CEO at First Resonance, joins me to unpack what modern manufacturing really looks like inside factories that build rockets, drones, reactors, and other complex hardware. We dig into why only a small slice of factories run on real systems today, what a true factory operating system unlocks, and how that connects directly to national security and the AI boom.
If you care about where all of this new compute, energy, and defense hardware will actually come from, this conversation gives you a clear view of the stack, the gaps, and the opportunity.
Key takeaways
• Only a small fraction of factories in the United States use a manufacturing execution system, which leaves a huge gap between legacy on prem tools, paper processes, and generic workflow apps that were never built for hardware work
• Cloud infrastructure and open interfaces now make it possible to deploy a purpose built factory operating system at a cost and speed that works for both fast moving startups and long standing suppliers
• Reindustrialization does not mean bringing every product back onshore, it means being deliberate about the layers of manufacturing that matter most for national security, chips, optics, and other high value components
• The real foundation for modern manufacturing is talent, there is a major chance to re skill people into highly technical, well paid roles in aerospace, semiconductors, energy, and more
• AI and agent style workflows will sit across design, manufacturing, and field operations so that hardware teams can close feedback loops, shorten timelines, and make better decisions with the data they already generate
Timestamped highlights
[00:40] Karan explains what First Resonance does and why he calls it a factory operating system for complex industries like aerospace, defense, energy, and autonomy
[01:55] How we ended up with only about fifteen percent of factories running on an MES, and why most hardware work still lives on paper, spreadsheets, and ad hoc tools
[06:49] A clear walkthrough of how offshoring looked like a rational path for decades, and why it created hidden risk across chips, optics, and other critical components
[11:46] Which parts of manufacturing should come back onshore, why you do not want everything local, and how workforce strategy fits into the new industrial map
[16:35] What a horizontal stack across design, factory systems, test, and field data can look like, and how AI agents can keep teams in sync across that stack
[23:02] The real timelines of hardware in the age of AI, why software is speeding up physical development, and why examples like SpaceX and TSMC matter for the next decade
A line that stayed with me
“Hardware and software are not separate worlds, they are one system that is now converging faster than most people realize.”
Practical moves for tech leaders
• Map your current manufacturing and hardware workflows, even if you are at a software first company, find the paper, spreadsheets, and disconnected tools that support anything physical you ship
• Look for one or two places where a factory operating system or modern MES could remove handoffs, for example design changes that take weeks to reach the line or test data that never feeds back into engineering
• Treat manufacturing careers as part of your talent strategy, help your teams see these roles as high skill and high impact, not as a side track
Call to action
If this episode gave you a clearer view of how hardware, AI, and national security tie together, share it with one other person who should be thinking about the factory side of their roadmap.
Follow and subscribe to The Tech Trek so you never miss deep dives like this, and connect with me on LinkedIn if you want more conversations at the edge of data, engineering, and real world impact.
Michael Marmo, founder and chief executive of CurbWaste, joins The Tech Trek to share how he went from catching fastballs in Europe to building software that runs the daily work of waste haulers. We walk through the very human side of leaving a sports identity, starting at the bottom in a family waste business, and finally asking a simple question about founding a company. Why not me
If you are sitting inside an industry and quietly seeing the gaps that no product seems to solve, this conversation is a playbook in how to turn that insider view into a real business, even if you do not come from a traditional tech background.
Key takeaways
• Identity can change, but the work habits that made you good at sports or any craft can transfer directly into building a company, especially persistence, dealing with failure, and showing up every day
• You do not have to love a specific activity forever, you can follow the deeper thread underneath it, like merit, teamwork, and visible impact, and find those same traits in a very different industry
• Deep time inside an industry lets you see painful, repeatable problems, and that is often a better seed for a product business than starting with a clever idea and pivoting until something sticks
• A clear why for the product and a clear why you are the person to build it are not nice to have, they are what convince customers, hires, and investors to follow you when things get hard
• Great founders do not pretend to be good at everything, they are honest about what they do not know, learn just enough to make good calls in product, engineering, and go to market, and then surround themselves with people who fill the gaps
Timestamped highlights
00:32 Michael explains what CurbWaste does and how it runs a hauler business from first customer contact through billing
01:21 From college baseball and pro teams in Europe to the first job in media and tech sales, and the identity shock that came with that change
06:27 What it really felt like when the game ended, why mens leagues did not scratch the itch, and how that led to a quiet reset in the working world
09:11 Starting at the bottom in a family recycling center, discovering a love for the waste industry, and why it felt like a merit based team environment
15:24 Walking the floor at Waste Expo, not finding the software he needed, deciding to fund and build his own tools, and seeing other haulers facing the same problems
19:40 The moment hearing the Yelp founder speak turned into a personal question, why not me, and how that idea of trying anyway shapes the way he thinks about founding today
A line that stayed with me
“At the end of the day he tried. He had an idea and he acted on it and pursued it. That really resonated. I was like, why not me”
Practical notes for future founders
• Before you write any code or quit your job, write down why this problem matters, why it matters now, and why you are willing to keep going when it stops being fun
• If your first answer to why is only about money, keep digging until you find something that still feels true on a hard day, because you will have a lot of those
• Use your current role as a live lab, list the moments that feel broken, expensive, or slow, and ask which of those could actually support a business if you solved them well
• Be direct with yourself about weak spots, whether that is product, tech, or selling, then build a basic understanding and lean on people who are strong where you are not
Call to action
If you enjoy stories that get inside how real founders make the leap from operator to builder, follow The Tech Trek in your favorite podcast app and share this episode with someone who is quietly thinking about starting something of their own.
Jason Ash, Chief of Data at Symetra, joins the show to unpack how a mid sized insurer is rebuilding its data stack and culture so business and technology actually pull in the same direction. He shares how his team brings actuaries, product leaders, and engineers into one data platform, and why opening that platform to non technical contributors has been a turning point. If you work in a regulated industry and are trying to move faster with data, this conversation gives you a very practical view of what it takes.
Key takeaways
• Business and tech only work when they share context and trust
Jason has sat in both seats, first as an actuary and now as a data and engineering leader. That dual background helps him translate between risk, regulation, and modern data practices, and it shapes how he frames projects around shared business outcomes rather than tools.
• Put data leaders inside business line leadership, not on the outside
Several of Jason’s managers sit on the leadership teams for Symetra’s life, retirement, and group benefits divisions. They hear priorities and constraints at the same time as product and distribution leaders, which lets them frame data as a value add for new products instead of a back office cost.
• Treat the warehouse as a shared product and measure contributors, not just tables
Symetra’s dbt based warehouse started with about five contributors. Over three years they grew that to more than sixty, and half of those people sit outside the core data team. Business users learn to contribute SQL, documentation, and domain knowledge directly into the repo, which spreads ownership and reduces bottlenecks.
• Shift stakeholders away from big bang launches to steady delivery
Jason pushes his teams to think like software engineers. Rather than promising a perfect data product on a single date, they deliver an early slice of data, have partners use it right away, collect feedback, and improve every month. That builds trust and avoids the usual disappointment that comes with one big release.
• Use maturity as a guide for where to invest
Early on, his group picked a few strong champions who were willing to accept slower delivery in exchange for building real infrastructure. Now that the platform and practices are in place, the focus is on scale, reuse, and getting more people to build on the same foundation, including as AI capabilities start to reshape the work.
Timestamped highlights
00:53 Jason explains what Symetra actually does and how their product mix makes data work more complex than the company size might suggest
02:19 From actuary to Chief of Data, and what sitting on both sides of the fence taught him about business and technology expectations
08:08 Why mixing data engineers, data scientists, actuaries, and analysts on the same problems leads to stronger solutions than any single discipline alone
13:44 How embedding data leaders into each business division’s leadership group changed when and how data enters product discussions
16:38 The dbt story at Symetra, and how more than sixty people across the company now contribute directly to the shared data warehouse
26:22 Moving away from big bang data launches and setting expectations around early value, continuous feedback, and ongoing quality improvements
32:06 The tension between safety and speed as AI advances, and what Jason worries about most for established insurers that move too slowly
Practical moves you can steal
• Put data leaders on business line leadership teams so they hear priorities and constraints in real time, not after the roadmap is set
• Track how many unique people contribute to your data warehouse and make that a visible success metric across the company
Stay connected
If this episode helped you think differently about data leadership in regulated industries, share it with a colleague who owns product, data, or actuarial work.
Chris Morgan, VP of Data Science at Lincoln Financial Group, joins me to unpack what a real data culture looks like inside a complex, highly regulated business that has policies on the books for decades. We talk about how to turn Gen AI buzz into real value, why governance and quality suddenly matter to everyone, and how to tackle data technical debt without stalling delivery.
Chris shares concrete ways he finds champions in the business, balances centralized and federated models, and keeps stakeholders excited about the future while he quietly fixes the messy data foundation underneath it all.
Key takeaways
Data culture is less about dashboards and more about curiosity, repeatable processes, and raising the analytical watermark across the company, not just in the data team.
The teams that will win with Gen AI are the ones that can safely connect proprietary data to these models, which demands strong governance, clear definitions, and shared standards.
A blended model works best for scaling data work, where a central function sets guardrails and standards while domain teams stay close to the business and own local decisions.
Paying down technical debt works when it is framed in business terms, tied to revenue and risk, and treated as a regular slice of capacity instead of a one time side project.
Education is now part of the job for data leaders, from internal road shows on Gen AI to simple stories that explain why foundational data work matters before you can ship shiny tools.
Timestamped highlights
00:04 Setting the stage Chris explains his role at Lincoln Financial and how data science supports life and annuity products that can live for decades.
03:33 The Cobb salad story A simple grocery store analogy that makes data standards and shared definitions instantly clear to non technical stakeholders.
06:06 Finding the right champions Why Chris prefers curious partners who will invest time with the data team over senior leaders who just want results without changing behavior.
08:33 Governance as Gen AI fuel How regulatory pressure and the need to trust what goes into models are pushing data governance and quality into the spotlight.
11:11 A practical way to attack data technical debt How Chris decides what to fix first, and why he tries to reserve a steady slice of team time for cleanup so progress is visible and sustainable.
17:44 Managing Gen AI expectations From road shows to constant communication, Chris shares how he keeps enthusiasm high while also being honest about the timeline and effort.
One line that sums it up
“These generative models are going to become a commodity and what will separate companies is who can take the most advantage of their proprietary data.”
Practical playbook
Start small with data culture by picking one engaged business partner, one problem, and one outcome you can measure clearly.
Reserve a consistent portion of team capacity for technical debt, even if it is only a small percentage at first, and make the tradeoffs visible.
Use stories, analogies, and simple rules of the road so stakeholders can understand how data systems work without becoming experts in the tech.
Call to action
If this conversation helped you think differently about data culture and Gen AI inside your company, follow the show and leave a rating so more engineering and data leaders can find it. To keep the discussion going, connect with me on LinkedIn and share how your team is tackling data culture and technical debt right now.
Varun Puri, CEO and cofounder of Yoodli, joins the show to talk about using AI role play to transform how people practice for high stakes conversations, from sales calls to job interviews to tough manager chats. He breaks down how Yoodli went from a consumer public speaking tool to a serious enterprise platform used by teams at Google, Snowflake, Databricks, and more, all while staying anchored in one mission, helping humans communicate with confidence. We dig into product led growth, honest feedback loops, and why real human communication will matter even more as AI makes information instant.
Key takeaways
• Why Yoodli started with public speaking anxiety and grew into an AI role play simulator for any important conversation, not just conference talks or pitch decks
• How watching real user behavior inside companies like Google pulled the team into enterprise without abandoning their consumer product
• A simple approach to product feedback, talk to end users constantly, then prioritize changes by business impact, renewal risk, and how many people benefit
• What it really takes to move from consumer to enterprise, new roles, new processes, and a very different mindset around reliability, security, and expectations
• Why Varun draws clear ethical lines, using AI to coach and prepare people, not to replace human judgment in hiring, promotion, or high trust decisions
Timestamped highlights
[00:35] What Yoodli actually does today, from solo practice to training sales and go to market teams inside large enterprises
[01:43] The original vision, helping people who are scared of public speaking, and the insight that interviews, sales calls, and manager talks are all just role plays
[03:37] How the team listens to end users, the channels they rely on, and why the consumer product is still their testing ground for new ideas and experiments
[05:20] Following users into the enterprise, why it was an addition and not a full pivot, and how product led growth inside companies like Google works in practice
[07:42] The early shock of selling to enterprises, learning about new roles, SLAs, InfoSec, and bringing in leaders from Tableau and Salesforce to build a real B2B engine
[11:10] Two paths for AI in sales, tools that try to replace humans versus tools that make humans better, and why Varun has drawn a hard line on what Yoodli will not do
[15:26] A future where information is commoditized and instant, and why communication and presence become the real edge for top performers in that world
[20:48] Designing for trust and adoption, how Yoodli keeps practice private by default, when data is shared, and why control has to sit with the end user
A line worth saving
“In a world where AI makes everyone smarter and faster, the thing that will be at the biggest premium is how you communicate as a human with other humans.”
Practical ideas you can use
• Keep a consumer like surface in your product so you can experiment faster than your enterprise roadmap would ever allow
• Treat feedback from large customers like a queue you rank by renewal risk, strategic value, and number of users helped, not as a list you must clear
• Look for product led growth signals inside your user base, if thousands of people in one company are using you, someone there probably wants a team level solution
• Draw explicit boundaries for your AI product, write down what you will not automate, so you can build trust with users and buyers over the long term
Call to action
If you care about the future of sales, interviewing, and communication in an AI rich world, this conversation is worth a listen. Follow the show, leave a quick rating, and share this episode with a founder, product leader, or sales leader who is thinking about AI in their workflow. And if you want feedback on your own speaking, check out what Varun and his team are building at Yoodli.