The Tech Trek

New leaders face a choice fast. Do you adapt to the organization you inherit, or reshape it around the way you lead?

In this conversation, Amir sits down with Gian Perrone, engineering leader at Nav, to unpack how org design really works in the first 30 to 120 days, and how to drive change without spiking anxiety or losing trust.

You will hear how Gian treats leadership as triage, why “listen and learn” is rarely passive, and what separates a thoughtful reorg from one that feels chaotic.

Key takeaways

Leaders almost always arrive with hypotheses, the real work is testing them without rushing to force a playbook

A reorg is not automatically bad, perception turns negative when the why is unclear and people feel unsafe

Over communicating helps, but thinking out loud too often can create noise, a structured comms plan keeps change steady

A simple way to spot a collaborative culture is to disagree in the interview and see how they respond

Managers are the front line in change, set clear expectations so teams hear a consistent story about what is changing and why

Timestamped highlights

00:01 What Nav does, and the real question behind org design for new leaders

01:59 Why “first 90 days” is usually triage, not passive observation

04:14 The reorg stopwatch, and why structure reflects your worldview

08:36 How to communicate change without destabilizing teams

12:54 A practical interview move to test whether a company truly collaborates

17:03 The manager layer, how Gian sets expectations so change lands well

A line worth repeating

“If you arrive and something is on fire, you are going to fix it.”

A few practical moves worth stealing

When you are new, write down your hypotheses early, then use real signals to confirm or kill them

Float a change as a real idea first, gather feedback, then come back with details before you finalize

Create a simple comms map of who hears what, when, and from whom, then follow it

Be matter of fact about changes, teams often mirror the tone you set

Call to action

If this episode helped you think more clearly about leadership and org design, follow the show and share it with one operator who is navigating change right now.

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.