The Tech Trek

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.

Mike Collins, CEO of Alumni Ventures, joins Amir to unpack what it really means to democratize venture capital and why the next wave of value creation will happen in private markets long before it hits the public exchanges. He explains how Alumni Ventures lets accredited investors build a meaningful venture portfolio, why diversification and time matter more than stock picking, and how this model changes the game for both founders and individual investors.

If you are a tech professional who cares about innovation, wealth building, and staying close to what comes next in AI, energy, health, and more, this conversation gives you a clear window into how the venture world actually works and how you can take part in it without becoming a full time investor.

Key takeaways

• Venture capital is a hits business, so the real game is building a broad portfolio, not trying to pick one or two magic startups

• Diversification and time are the core levers for venture investing, especially for busy professionals who are not watching markets all day

• Alumni Ventures acts as a large scale co investor with top venture firms, letting individual investors ride along with the same lead investors founders already want

• Value creation is shifting to private markets, since many of the most important tech companies now stay private far longer than in past cycles

• Alumni Ventures is building a global, tech enabled platform that aims to support founders and investors across regions, stages, and themes

Timestamped highlights

[03:01] Mike breaks down what venture capital really is and why random one off startup bets look more like gambling than investing

[04:40] Why diversification is your superpower and how a portfolio of 30 to 200 startups changes the risk profile for individual investors

[07:40] The rise of private value creation and why waiting for the open AI or Stripe IPO means missing the first big wave of upside

[11:38] Venture as a time machine, looking five to seven years ahead at technologies the public will only hear about much later

[17:48] How Alumni Ventures plays the role of co investor of choice for founders by bringing a global alumni network and real customer access

[21:48] Roughly 300 deals a year and multiple themed funds, and what that volume unlocks for different types of accredited investors

[25:31] The next ten years, going global, and why Mike wants Alumni Ventures to become the most valuable venture capital firm on the planet

A line that stayed with me

“Diversification is your superpower and time is really an asset.”

Ideas you can use

• Think of venture as a small but intentional slice of your overall portfolio, alongside public stocks, fixed income, and real estate

• Treat venture like an ETF for innovation, where you build exposure to many teams across multiple years rather than buying a single hot deal

• Use your curiosity as a filter, follow companies whose work you genuinely want to track over years, not days

Call to action

If this episode helped you see the venture world in a clearer way, follow the show, leave a quick rating, and share it with a friend who cares about tech and investing. To stay close to upcoming conversations with founders and investors who sit at the edge of innovation, connect with Amir on LinkedIn and make sure you are subscribed so you never miss an episode.

Chinmay Barve, VP of Engineering at Nooks AI, joins the show to break down what the first sixty to ninety days look like when you step into a senior leadership role at a fast moving AI company. He explains how to build trust quickly, how to find real problems worth solving, and how to avoid the trap of either rushing change or waiting too long to act. This conversation is a practical playbook for engineering leaders who want early impact without losing alignment.

Key Takeaways

• The listening phase starts before day one and should shape how you enter the role

• Early wins matter but only if they support the deeper problems you were hired to solve

• Alignment with founders becomes the real foundation for fast progress

• Sharing your thinking openly can build trust faster than any formal process

• You need a clear personal compass so you know what parts of your approach are fixed and what parts can change

Timestamped Highlights

00:36 How Nooks AI thinks about the next generation of sales productivity and why human guided AI matters in real workflows

04:20 What leaders should really listen for during the first weeks on the job and why the listening starts before you join

12:31 Why a new VP should enter with personal objectives while staying open to what the company needs most

14:11 How to act fast without creating chaos and where to spot early wins that build confidence on both sides

17:29 The value of a rough thirty sixty ninety plan and how daily syncs create deeper alignment right away

20:34 What it looks like to foster trust through openness, vulnerability, and consistent shared reasoning with your team

A Line That Stands Out

Once you commit, go all in with conviction. Do all the real deciding before day one so you can show up fully aligned and ready to move.

Pro Tips

• Enter with a clear ambition that matches the founders vision so you are rowing in the same direction from day one

• Look for low effort problems with high emotional or operational weight to build fast trust

• Overshare your thinking at the start so the team can see how you reason

Call to Action

If you found this useful, follow the show and share it with someone stepping into a new leadership role. You can also connect with us on LinkedIn for more conversations about people, tech, and real impact.

Digital IDs are about to reshape how we prove who we are online. Peter Horadan, CEO at Vouched, joins the show to break down what this shift really means for trust, privacy, and the rise of agent driven systems. He explains why digital IDs will remove huge amounts of friction, stop common fraud paths, and change how we secure everything from bank accounts to AI agents acting on our behalf.

This is a clear look at what is coming in the next two years and why it matters to every engineering and product leader.

Key Takeaways

• Digital IDs will move identity checks from risk based guesses to near perfect certainty which changes how products verify users

• Fine control over what you share will unlock new applications and ease concerns about oversharing personal data

• Agent driven workflows need a clear way to separate human actions from agent actions so that permissions, auditing, and safety scale

• Identity standards for agents will remove phishing and reduce fraud by creating traceable reputations for good and bad agents

• Regulation and real world use are not fully aligned yet which creates gaps around privacy, liability, and legal agreements

Timestamped Highlights

00:53 How digital IDs work on your phone and why they remove friction across services

04:14 What becomes possible when you can share only the specific parts of your ID

07:22 Why physical ID checks are easy to fake and how digital IDs solve this

12:16 How agents act on your behalf and why that breaks old security patterns

17:40 Why agents need their own identity and reputation systems

22:01 Legal gray zones around AI, privacy, accountability, and real world contracts

27:12 The tipping point where digital IDs become standard for most online services

A line that captures the episode

“Everything we do today to identify people online is risk based. Digital IDs move us to absolute proof.”

Pro Tips from Peter

• Expect digital ID flows to replace password resets across most valuable services

• Treat agent permissions like API scopes and give only what is needed

• Plan for separate logging of human actions and agent actions in your systems

Call to Action

If this episode gave you a clearer picture of where identity and agent driven systems are headed, follow the show and share it with someone building in security, AI, or product. You can also follow along on LinkedIn for more discussions that connect people, impact, and technology.

Most people still think of AI in medicine as a novelty. Matt Pavelle sees it as the new first step in patient care.

In this episode, Matt breaks down how Doctronic built an AI doctor that can gather history, follow clinical guidelines, produce full treatment plans, and then hand everything to a real physician who can review it in minutes. It is private by default, aligned with top primary care doctors, and already helping millions of people move faster through the healthcare system without lowering the standard of care.

We talk through how this changes access, trust, and the way care teams work. And we open up what this means for the future of primary care as capacity continues to fall and patient demand keeps rising.

Key takeaways

• The AI is trained on physician written clinical guidelines which gives it a clear path for gathering symptoms, sorting possible conditions, and building treatment plans that match top doctors at a high rate.

• Privacy and trust were built in from the start. The chat is anonymous, data is not used for training, and everything is run with HIPAA level protection even when it is not required.

• Capacity pressure is the real problem in primary care. Offloading the easy eighty percent of cases lets doctors focus on the harder ones and gives them more time with each patient.

• The system writes notes, gathers history, and completes insurance paperwork which cuts down on burnout and improves the patient experience.

• This model can scale to wearables, home devices, labs, and specialists which could raise the standard of care for people who normally wait weeks for answers.

Timestamped highlights

00:40 Doctronic explained and why a full visit can take only a few minutes

03:44 How medical knowledge moved from books and search results to AI that can guide real care

08:13 A look at the micro agent system and how the team measures accuracy against real doctors

11:27 The shortage of primary care doctors and why capacity pressures make AI support necessary

17:20 How anonymous design and strong privacy choices help people trust the system

26:05 Adoption numbers, fast growth, and what millions of consults are teaching the team

A line that captures the episode

We want to be that first step in patient care every time you need that first step.

Pro tips for builders and leaders

• Ground your product in real domain guidelines so the AI follows the same reasoning paths as experts.

• Treat privacy as a design choice. Make it clear, simple, and part of the value of the product.

• Focus on the work that slows experts down. The biggest wins come from reducing the load, not from replacing the expert.

• Make the handoff between AI and human seamless so the expert starts with context instead of starting over.

Closing note

If you enjoyed this conversation, follow The Tech Trek, leave a quick rating, and share this episode with someone curious about the future of patient care and AI.

Chris Church, VP of Engineering at Rainforest, breaks down why a zero bug policy is more than a technical choice. It is a mindset, an operating model, and a culture shift that shapes how engineering teams build, release, and support software at scale.

In this conversation he goes inside the habits that actually make quality a strategic advantage and explains how small releases, strong visibility, and healthy engineering practices create real impact over time.

Key Takeaways

• Quality is not a feature. It is the foundation of trust, especially in a payments environment where even small defects can erode confidence.

• Small releases reduce risk because teams can actually reason about the changes they ship. Frequency builds confidence and reliability.

• Visibility is non negotiable. You cannot fix what you cannot see, so strong monitoring and clear alerts must exist before a quality culture can grow.

• Teams need real capacity set aside for fixes and improvements. Without that buffer, bugs turn into a silent tax that slows down the entire org.

• You can adopt a zero bug mentality even in a mature codebase, but you must commit to a long game of continuous improvement.

Timestamped Highlights

00:33

What Rainforest actually does and why their customers rely on embedded payments

01:44

Chris explains what a zero bug policy means in practice for a fintech engineering team

03:06

Why the policy must be strict and why a backlog of broken things creates a false sense of safety

06:13

How Rainforest structures ownership, on call rotations, and incident response to support quality

10:51

Smaller releases, lower risk, and why the size of a change has a direct impact on failure modes

12:59

Why test coverage and automation must start early and why teams struggle when they try to catch up later

14:27

How to adopt this mindset if your org is nowhere near zero bugs and where to begin

23:44

The biggest gotchas teams underestimate when they start this journey and why progress requires patience

One line that stands out

“People overestimate what they can fix quickly and underestimate what they can improve over the long run.”

Pro Tips

• Start by making your system noisy. More visibility will feel painful at first, but it becomes the foundation for every improvement.

• Reserve capacity for fixes before planning feature work. If you wait until later, that time will never appear.

• Break tech debt into specific problems. Vague labels hide real risks and slow down prioritization.

Call to Action

If you found value in this conversation, follow the show and share it with someone who cares about engineering quality, team culture, and building software that lasts. You can also connect with me on LinkedIn for more conversations that explore people, impact, and technology.

Snehal Antani, co founder and CEO of Horizon3 AI, joins the show for a conversation about how veterans bring rare leadership strengths to fast moving companies. He pulls back the curtain on the world of special operations, shares what industry leaders often miss when interviewing former service members, and explains why these leaders are some of the most prepared problem solvers you can hire.

This episode helps any listener understand the real strengths behind military experience and how those strengths translate into modern tech and business environments.

Key Takeaways

• Veterans succeed in high pressure environments because they train as learn it alls and solve problems as a team

• The best performing military units succeed due to empowerment, shared understanding, and clear cadence

• Many veterans underestimate their own leadership ability when entering industry and need support reframing their experience

• Hiring managers often miss top talent because they use filters that do not map well to military backgrounds

• Reference based hiring and early transition planning create a smoother path for veterans entering tech roles

Timestamped Highlights

00:41 Snehal describes the world inside JSOC and what makes special operations leaders exceptional

04:45 Why many transitioning service members experience imposter syndrome and how to shift that mindset

10:17 How geography affects familiarity with military culture and shapes hiring outcomes

14:33 A look at why Israeli veterans become top founders and what the United States can learn from that

19:19 How military roles connect directly to major sectors like logistics, telecom, infrastructure, and talent management

24:24 The real reason many veterans struggle to land interviews and why referral networks matter so much

28:40 Practical resources and programs that help veterans navigate transition with clarity and confidence

A line that captures the heart of the episode

“You are the most cycle tested leader in the world. Those skills are not taught in school. They are earned.”

Practical advice from the conversation

• Translate military jargon into industry language and speak to the business outcomes you created

• Build and maintain a strong network long before you transition

• Start planning two to three years out and use programs like SkillBridge to build experience and confidence

• Hiring teams should look beyond titles and focus on the pressure tested leadership traits that veterans bring

Call to action

If this conversation helped you, follow the show and share the episode with someone who would benefit. You can also connect with us on LinkedIn for more leadership insights and real stories from people shaping tech today.

Chandni Jain, VP of Engineering at Checkr, joins the show to talk about what it takes to build a real culture of ownership. She explains how clarity, trust, and true empowerment help teams move faster and work better together. You will also hear how leaders can bring out stronger initiative and confidence in their people.

This episode gives a simple and useful guide for anyone who wants to lead with intent and build teams that think and act like owners.

Key Takeaways

• Ownership grows when clarity, context, and empowerment all work together

• Strong accountability does not require fear. It comes from trust and clear expectations

• Teams follow what leaders show, so leaders need to model ownership every day

• Feedback works only when trust comes first

• New managers grow fastest when they balance technical skills with people leadership

Timestamped Highlights

00:26 Why ownership begins with customer outcomes

02:02 How accountability, empowerment, and safety support each other

04:08 The difference between blame and real accountability

11:36 How to give people space to lead without losing direction

14:37 What new managers struggle with and how to guide them

16:49 A four part checklist for building stronger ownership

20:16 Why recognition matters and how it lifts the whole org

A standout moment

“Ownership begins with you as a leader. The team mirrors what they see.”

Pro Tips

• Give clear context early and often so people know what they own

• Celebrate small wins to encourage more initiative

• Focus on outcomes, not tasks. It changes how people think and deliver

• When someone steps up, give them more room to grow

Call to Action

If this episode helped you see leadership and ownership in a new way, follow the show and share it with someone who might find it useful. For more conversations on people, impact, and technology, subscribe and stay connected.

Shawn Taikratoke, CEO and co founder of Mozee, joins the show to unpack one of the biggest questions in mobility today. How close are we to real autonomous transportation and what will actually move the needle in our cities. Shawn breaks down why the future is not a single robotaxi dream, but a more human centered shift in public transit that solves the first and last mile in a smarter way. If you care about how people move, how cities evolve, or how autonomy will reshape everyday life, this one is worth your time.

Key Takeaways

• The biggest transportation barriers are not technical. They come from how cities were built and how people actually move in short distances.

• Robo taxis will play a role, but public transit needs a more flexible and human centered model before adoption changes.

• Many Americans still have no access to reliable transit, which creates ripple effects in work, health, and community access.

• Real adoption will come when mobility becomes easier and cheaper than using your own car.

• Cities want smarter transit, but they need partners that help them bridge gaps without major infrastructure costs.

Timestamped Highlights

00:44 What Mozee was built to solve and why they avoided the pure robotaxi route

03:26 Why autonomy still scares most people and how public perception is shaping rollout

06:57 How regional culture and city layout shape transportation adoption

10:24 The vision for a mesh network of shared autonomous shuttles

16:24 How smarter first mile and last mile service can shift car dependence

21:52 What it takes to move from a handful of vehicles to true scale

27:54 Why Shawn moved from the robotaxi hype to solving public transit gaps instead

A standout thought

“Progress is rarely a straight line. The products that last are the ones that stay human centered.”

Pro Tips from the Conversation

• Transit solutions that work do not start with tech. They start with how people move in the real world.

• Scale only matters when it meaningfully makes someone’s day easier.

• If you want to understand mobility problems, talk to city officials. They know exactly where the gaps are.

Call to Action

If this episode pushed your thinking about mobility and smart cities, follow the show and share it with someone who is curious about the future of how we move. New episodes every week with leaders shaping technology, people, and impact.

Mike Hanson, CTO at Clockwise, joins the show to break down how our relationship with computers is changing as language based systems reshape expectations. We explore why natural storytelling feels so intuitive with today’s AI tools, how context is becoming the new currency of great software, and why narrow AI is often more powerful than the industry hype suggests.

This conversation gives tech leaders a grounded look at what is real, what is noise, and what is coming fast.

Key Takeaways

• Natural storytelling is becoming the default way people communicate with AI, and products must adjust to that shift.

• Context is the driving force behind great interaction design and LLM powered systems now surface and use context at a scale traditional UIs never could.

• Most real world gains come from narrow AI that solves focused everyday problems, not from broad AGI promises.

• Multi agent systems and multiplayer coordination are emerging as the next frontier for enterprise AI.

• The biggest risk is not model weakness but user uncertainty about when an answer is trustworthy.

Timestamped Highlights

01:21 What Clockwise is building with its scheduling brain and how natural language creates new value

04:13 Why humans default to storytelling and how LLMs finally make that instinct useful

08:00 The rising expectation that software should understand context the way people do

12:13 The shift away from feed centric design and toward multi person coordination in AI systems

17:31 Why narrow AI delivers real value while wide AI often creates anxiety

23:52 A real world example of how AI can remove busy work by orchestrating tasks across tools

26:24 Why we do not need AGI to meaningfully improve everyday productivity

A standout thought

People have always tried to talk to computers in a natural way. The difference now is that the systems finally understand us well enough to meet us where we already are.

Pro Tips

• Look for AI that reduces busy work across tools rather than chasing broad capability.

• Prioritize context rich interactions in your product planning. It will define user expectations for years to come.

• Treat multi person workflows as the next major opportunity. Most teams still rely on manual coordination.

Call to action

If this episode helped you think differently about where AI is actually useful, follow the show and share it with someone who is building product in this space. And join me on LinkedIn for weekly insights on tech, people, and impact.