The Tech Trek

Max Bruner, Founder and CEO of Anzen, joins Amir Bormand to break down why insurance is quietly one of the biggest data and workflow opportunities in tech right now. They dig into Max’s unconventional path from foreign policy to building an executive liability marketplace, and what it really takes to modernize a slow moving industry with AI.

If you care about building in real world markets, scaling with discipline, and using AI for more than content, this one will sharpen your thinking fast.

Key Takeaways

• Insurance is not flashy, but it is foundational, massive, profitable, and packed with repeatable workflows that software can improve

• The best tech opportunities are often in slow moving industries with lots of data and outdated systems

• Better decision making comes from predicting outcome impact and pressure testing your thinking with a strong community around you

• AI value is clearest when it drives real operations, faster transactions, lower costs, and better service

• Fundraising is a pipeline game now, treat it like sales, build the plan, hit the numbers, run a tight process

Timestamped Highlights

00:42 What Anzen actually does, a one stop marketplace for executive liability quotes across the US

02:29 From Arabic studies and foreign policy to discovering insurance through political risk

08:12 The curiosity engine, how deep research habits shaped his ability to build in new domains

11:23 Decision guardrails, learning from outcomes and using trusted people to keep you efficient

13:12 Why choose insurance, building in industries that make the world work, plus the profit reality

17:29 The startup advantage, modern infrastructure vs incumbent legacy systems, and why catching up takes time

20:36 Raising in today’s market, what changed, what worked, and why the pitch volume matters

A line worth stealing

“Sometimes in tech we miss the application, there are massive industries to go change if we apply technology in the right way.”

Max Bruner

Pro Tips for builders

• Pick markets with repeatable workflows, you can ship measurable value faster

• Spend your time where the outcome impact is high, skip low ROI rabbit holes

• Build a real financial plan before fundraising, then operate close to it

• Run fundraising like a sales process, pipeline, volume, and discipline win

Call to Action

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Stu Solomon, CEO of HUMAN, joins Amir to unpack a blind spot most teams underestimate: a huge share of online activity is not people at all, it is automated traffic. They break down how verification really works at internet scale, why agentic workflows change the rules, and what it will take to build trust when bots transact with bots.

If you have ever wondered how fraud, fake clicks, account abuse, and synthetic behavior get caught in real time, this episode is a clear, practical look behind the curtain.

Key takeaways

• Most of the internet is machine traffic now, the goal is no longer spotting bots, it is separating good machines from bad ones

• Trust is built by combining behavior, infrastructure signals, and identity or credential history into fast decisions at scale

• Agentic systems lower the barrier to entry for attackers, less skilled actors can now create outsized impact

• The hard part is accountability, when a machine acts with your authority, who owns the outcome

• Adoption follows convenience, but visibility matters, if it feels like a black box, people will not trust it

Timestamped highlights

00:33 HUMAN in plain English, making split second decisions about who is human, and whether they are safe

03:59 The trust stack, behavior signals, infrastructure clues, and identity or credential history

10:19 The real shift with AI, lower barriers for attackers, plus the rise of agentic autonomy

14:37 The cake story, an agent completes the task, then surprises you with a 750 dollar bill

17:22 Bots talking to bots, where accountability and liability get messy fast

24:18 Security builds trust, trust unlocks adoption, and society is already closer than it thinks

A line you will remember

“We have always operated on the notion that if you are human, you are good, and if you are a machine, you are bad. That is simply not the case anymore.”

Practical ideas you can use

• Add guardrails when you delegate to tools, especially budgets, limits, and approval steps

• Watch for trust signals, not just identity checks, behavior plus infrastructure plus history beats any single data point

• Design for visibility, show users what the system did and why, so trust can compound over time

Follow:

If this episode helped you think more clearly about trust, fraud, and agentic systems, follow the show, subscribe for more conversations like this, and share it with a teammate who is building in ads, ecommerce, identity, security, or AI.

Mek Stittri, CTO at Stuut, breaks down a leadership skill that sounds simple but gets messy fast, trust, then verify. You will learn how to delegate without losing control, how to stay close to the work without becoming a micromanager, and how AI is changing what it means to review and own technical outcomes.

Key takeaways

• Trust and verify starts with alignment, define success clearly, then keep a real line of sight to outcomes

• Verification is not micromanagement, it is accountability, your team’s results are your responsibility as a leader

• Use lightweight mechanisms like weekly reports, and stay ready to answer questions three levels deep when speed matters

• AI is pushing engineers toward system design and management skills, you will manage agents and outputs, not just code

• Fast feedback prevents slow damage, address issues early, praise in public, give direct feedback in private

Timestamped highlights

00:41 Stuut in one minute, agented AI for finance ops, starting with collections and faster cash outcomes

01:54 Trust without verification becomes disconnect, why leaders still need to get close to the details

03:42 The three levels deep idea, how to keep situational awareness without hovering

06:33 The next five years, engineers managing teams of agents, system design as the differentiator

11:40 Feedback as a gift, why speed and privacy matter when coaching

16:54 The timing art, when to wait, when to jump in, using time and impact as your signal

19:43 Two leaders who shaped Mek’s leadership style, letting people struggle, learn, and then win

23:29 Curiosity as the engine behind trust and verification

A line worth repeating

“Feedback is a blessing.”

Practical coaching moves you can borrow

• Set the bar up front, define the end goal and what good looks like

• Build a steady cadence, short weekly updates beat occasional deep dives

• Calibrate your involvement, give space early, step in when time passes or impact expands

• Make feedback faster, smaller course corrections beat late big confrontations

• Use AI as a reviewer, get quick context on unfamiliar code and decisions so you can ask better questions

Call to action

If you found this useful, follow the show and share it with a leader who is leveling up from IC to manager. For more leadership and hiring insights in tech, subscribe and connect with Amir on LinkedIn.

Michael Topol, Co-founder and Co-CEO at MGT Insurance, explains why insurance is quietly becoming one of the most interesting data and AI problems in tech.

We get practical about turning messy legacy data into usable signals, how agentic tools change decision making, and why culture and team design matter as much as the models.

MGT Insurance is building a fully verticalized AI and agentic native insurance company for small businesses, pairing experienced insurance operators with top tier technologists. Michael breaks down what changed in the last few years that makes real disruption possible now, and what modern product delivery looks like when prototyping is cheap and iteration is fast.

Key takeaways

• Insurance is a data business at its core, but most incumbents cannot use their data fast enough because it lives across silos, mainframes, and old systems.

• Modern AI lets teams combine internal data with public signals to speed up underwriting and improve consistency, without losing human judgement.

• Vibe coding and rapid prototyping collapse the gap between idea and implementation, bringing product, engineering, and the business closer together.

• Senior talent gets more leverage in an AI driven workflow, and small teams can ship faster by focusing on problem solving, not just building.

• Pod based teams, fixed outcome planning, and strong culture help regulated companies move quickly while staying inside the rules.

Timestamped highlights

00:44 What MGT Insurance is, and what “AI and agentic native” means in practice

02:09 Why small business insurance matters more than most people realize

06:06 The real blocker for incumbents, data exists but it is not usable

08:55 Vibe coding in a regulated industry, where it helps first

12:54 Requirements are shifting, prototypes bring teams closer to the real problem

17:26 The pod structure, plus the Basecamp inspired approach to scoping and shipping

20:52 Better, faster, cheaper, why AI finally makes all three possible

22:11 Where to connect, and who they are hiring

A line you will remember

“Insurance is really just a big data problem.”

Pro tips you can steal

• Build cross functional pods early, include a domain expert, a technical product lead, and a senior engineer from day one.

• Scope for outcomes, not perfect specs, then let the team decide the depth as they build.

• Use AI to automate collection and synthesis, then keep humans focused on the decisions and trade offs.

Call to action

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Marco DeMeireles, co founder and managing partner at ANSA, breaks down how a modern VC firm wins by being focused, data driven, and allergic to hype. If you want a clearer view of how investors evaluate open source, mission critical industries, and AI categories, this is a practical, operator minded look behind the curtain.

Marco explains ANSA’s focus on what they call undercover markets, from open source and open core businesses to defense, intelligence, cybersecurity, healthcare IT, and infrastructure companies that become deeply embedded and rarely lose customers. We also get into how they raised their first fund, why portfolio concentration changes everything, and how they push founders toward efficiency and profitability without killing ambition.

Key Takeaways

• In open source, two things matter more than most people admit: founder DNA tied to the project, and what you put behind the paywall that enterprises will pay for

• Concentration forces rigor, fewer bets means deeper diligence, clearer underwriting, and more hands on support post investment

• Great early stage support is not just advice, it is people, capital planning, and operating help that changes outcomes

• AI investing gets easier when you start with category selection, avoid fickle demand, then hunt for non obvious wedges in real workflows

• Long term winners tend to show compounding growth, improving efficiency, real demand, durable business models, founder strength, and an asymmetric risk reward at the price

Timestamped Highlights

00:00 Marco’s quick intro and what ANSA invests in

00:36 Undercover markets, open source, and mission critical industries explained

01:54 The two open source filters that change how ANSA underwrites a deal

03:31 Why open source can work in defense, plus the Defense Unicorns example

05:29 How a new firm raises a first fund, and what the right LP partners look for

10:50 The three levers ANSA pulls with founders: people, capital, operations

15:22 Marco’s six part framework for evaluating investments

17:39 How to tell who wins in crowded AI categories, and why niche wedges matter

21:41 The first investment they will never forget, and the air gapped cloud problem

A line worth stealing

“You can’t outsource greatness. You can’t outsource people selection.”

Pro Tips

• If you are building open source, be intentional about what is free versus paid, security, compliance, and auditability tend to earn real pricing power

• If your business depends on paid acquisition, test a path to organic growth early, it can unlock profitability and give you leverage in fundraising and exits

• In crowded AI spaces, pick a wedge where documentation is heavy, complexity is low, and ROI is obvious, then expand once you own that lane

Call to Action

If this episode helped you think more clearly about investing and building, follow the show, subscribe, and share it with one founder or operator who is navigating funding, pricing, or go to market right now

AI is everywhere, but most teams are stuck talking about efficiency and headcount. In this episode, Dave Edelman, executive advisor and best selling author, shares a sharper lens, how to use AI to create real customer value and real growth.

We get into the high road vs low road of AI, what personalization should look like now, and why data has to become an enterprise asset, not a bunch of disconnected departmental files.

Key Takeaways

• Efficiency is table stakes, the real win is using AI to build new experiences that customers actually want

• Start with customer friction, find the biggest compromises and frustrations in your category, then design around that

• Personalization is no longer limited by content scale in the same way, AI changes the economics of tailoring experiences

• You do not always need one giant database, modern tools can pull and connect data across systems in real time

• Treat data as an enterprise resource, getting cross functional alignment is often the hardest and most important step

Timestamped Highlights

• 00:46 Dave’s origin story, from early loyalty programs to Segment of One marketing

• 03:33 The high road and low road of AI, growth experiences vs spam at scale

• 06:51 Where to start, map the biggest customer frustrations, then build use cases from there

• 16:31 The data myth, why you may not need a single mega database to get value from AI

• 21:31 Data as a leadership problem, shifting from functional ownership to enterprise ownership

• 25:14 Strategy that actually sticks, balancing bottom up automation with top down customer led direction

A line worth stealing

“Use those efficiencies to invest in growth.”

Pro Tips you can apply this week

• List the top five customer frustrations in your category, pick one and design an AI powered fix that removes a compromise

• Audit your data reality, identify where the same customer facts live in multiple places, then decide what must be unified first

• Run a simple test and learn loop, create multiple variations of one experience, measure what works, and keep iterating

• Put strategy on the calendar, make room for a recurring discussion that is not just metrics and cost cutting

Call to Action

If this episode helped you think differently about AI and growth, follow the show, leave a quick rating, and share it with one operator who is building product, data, or customer experience right now.

Amanda Kahlow, CEO and founder of 1Mind, joins Amir to break down what AI changes in modern sales and go to market, and what it does not. If you lead revenue, product, or growth, this is a practical look at where AI creates leverage today, where humans still matter, and how teams actually adopt it without chaos.

Amanda shares how “go to market superhumans” can handle everything from early buyer conversations to demos, sales engineering support, and customer success. They also dig into trust, hallucinations, and why the bar for AI feels higher than the bar for people.

Key takeaways

• Most buyers want answers early, without the pressure that comes with talking to a salesperson

• AI can remove friction by turning static content into a two way conversation that helps buyers move faster

• The hardest part of adoption is not capability, it is change management and trust inside the team

• Humans still shine in relationship and nuance, but AI can outperform on recall, depth, and real time access to the right info

• As AI levels the selling experience, product quality matters more, and the best product has a clearer path to win

Timestamped highlights

00:31 What 1Mind builds, and what “go to market superhumans” actually do across the full buyer journey

02:00 The buyer lens, why early conversations matter, and how AI gives control back to the buyer

06:14 Why the SDR experience is frustrating for buyers, and where AI can improve both sides

09:42 Change management in the real world, why “everyone build an agent” gets messy fast

13:04 Why “swivel chair” AI fails, and what real time help should look like in live conversations

15:52 Hallucinations and trust, plus the blunt question every leader should ask about human error

22:26 Competitive advantage today, and why adoption eventually pushes markets toward “best product wins”

A line worth sharing

“Do your humans hallucinate, and how often do they do it?”

Pro tips you can use this week

• Start with low stakes usage, bring AI into calls quietly, then ask it for a summary and what you missed

• Build adoption top down, define what good looks like, otherwise you get a pile of similar agents and no clarity

• Focus AI on what it does best first, recall, context, and instant answers, then expand into workflow and process later

Call to action

If this episode sparked ideas for your sales team or your product led funnel, follow the show so you do not miss the next one. Share it with one revenue leader who is trying to modernize their go to market motion, and connect with Amir on LinkedIn for more clips and operator level takes.

What if the best people on your investing team are still in college? Peter Harris, Partner at University Growth Fund, breaks down how they run a roughly 100 million dollar venture fund with 50 to 60 students doing real diligence, real founder calls, and real deal work.

You will hear how their student led model stays disciplined with checks and balances, why repeat games matter in venture and in business, and how this approach creates a flywheel that helps founders, investors, and the next generation of operators win together.

Key Takeaways

• Student led does not mean unstructured, the process is built around clear stages, data room access, investment memos, student votes, and an advisory style investment committee, with final fiduciary responsibility held by the partners

• Real autonomy is the unlock, when interns are trusted with meaningful work, the best ones level up fast and start leading teams, not just supporting them

• The goal is win win win outcomes, founders get capital plus a high effort support network, investors get disciplined underwriting, students get experience that compounds into career leverage

• Repeat games beat short term incentives, the alumni network becomes a long term advantage, bringing the fund into high quality opportunities years later

• Mistakes are inevitable, the difference is containment and systems, avoiding errors big enough to break trust, then building process improvements so they do not repeat

Timestamped Highlights

00:32 A 100 million dollar fund powered by 50 to 60 students, and what empowered really means

01:43 The decision path, from founder screen to student memo to student vote to the advisory investment committee

06:44 Why most venture internships underdeliver, and how longer tenures change outcomes

10:37 Repeat games and the trust flywheel, how former students now pull the fund into top tier deals

13:55 What happens when something goes wrong, damage control, learning loops, and confidentiality as a core discipline

24:39 The bigger vision, expanding beyond venture into additional asset classes to create more student opportunities

A line worth stealing

If you give people real autonomy, they’ll surprise you with what they do.

Pro Tips

• If you are building an internship program, start by deciding what real ownership means, then build guardrails around it, not the other way around

• Treat trust like an asset, design your process so every stakeholder wants to work with you again

Call to Action

If you enjoyed this one, follow The Tech Trek and share it with a founder, operator, or student who cares about building real advantage through talent and process.

Yulun Wang, executive chairman and co founder at Sovato Health, joins Amir Bormand to unpack the next wave after telemedicine, procedural care at a distance. If you have ever wondered what it would take for a top surgeon to operate without being in the same room, this conversation gets practical fast, from the real bottlenecks inside operating rooms to the health system changes required to make remote robotics mainstream.

Key takeaways

• Better care can actually cost less when the right expertise reaches the right patient at the right time

• Telemedicine is already normalized, which sets the stage for faster adoption of remote procedures once infrastructure and workflows catch up

• Surgical robots already have two sides, the surgeon console and the patient side, today connected by a short cable, the leap is making that connection work reliably across hundreds or thousands of miles

• Volume drives proficiency, the outcomes gap between high volume specialists and low volume settings is one of the biggest reasons access matters

• Operating rooms spend more than half their time on steps around surgery, which creates room to dramatically increase surgeon throughput when workflows are redesigned

Timestamped highlights

• 00:42 What Sovato Health is building, bringing procedural expertise to patients without requiring travel

• 02:10 The early days of surgical robotics and the transatlantic gallbladder surgery on September 7, 2001

• 05:30 The counterintuitive idea, higher quality care can reduce total cost in healthcare

• 10:27 What actually changes for patients, local hospitals stay the destination, expertise becomes the thing that travels

• 14:57 Why repetition matters, the first question patients ask is still the right one

• 17:53 Inside the operating room schedule, where time is really spent and why productivity can jump

A line that sticks

“Healthcare is different, higher quality, if done right, costs less.”

Practical angles you can steal

• If you are building in regulated industries, adoption is rarely about the tech alone, it is about trust, workflows, and incentives

• If you sell into health systems, position the value around system level outcomes, access, quality, and margin improvement, not just novelty

• If you are designing new workflows, look for the hidden capacity, the biggest gains often sit outside the core task

Call to action

If you want more conversations like this at the intersection of tech, systems, and real world impact, follow The Tech Trek on Apple Podcasts and Spotify.

Moiz Kohari, VP of Enterprise AI and Data Intelligence at DDN, breaks down what it actually takes to get AI into production and keep it there. If your org is stuck in pilot mode, this conversation will help you spot the real blockers, from trust and hallucinations to data architecture and GPU bottlenecks.

Key takeaways

• GenAI success in the enterprise is less about the demo and more about trust, accuracy, and knowing when the system should say “I don’t know.”

• “Operationalizing” usually fails at the handoff, when humans stay permanently in the loop and the business never captures the full benefit.

• Data architecture is the multiplier. If your data is siloed, slow, or hard to access safely, your AI roadmap stalls, no matter how good your models are.

• GPU spend is only worth it if your pipelines can feed the GPUs fast enough. A lot of teams are IO bound, so utilization stays low and budgets get burned.

• The real win is better decisions, faster. Moving from end of day batch thinking to intraday intelligence can change risk, margin, and response time in major ways.

Timestamped highlights

00:35 What DDN does, and why data velocity matters when GPUs are the pricey line item

02:12 AI vs GenAI in the enterprise, and why “taking the human out” is where value shows up

08:43 Hallucinations, trust, and why “always answering” creates real production risk

12:00 What teams do with the speed gains, and why faster delivery shifts you toward harder problems

12:58 From hours to minutes, how GPU acceleration changes intraday risk and decision making in finance

20:16 Data architecture choices, POSIX vs object storage, and why your IO layer can make or break AI readiness

A line worth stealing

“Speed is great, but trust is the frontier. If your system can’t admit what it doesn’t know, production is where the project stops.”

Pro tips you can apply this week

• Pick one workflow where the output can be checked quickly, then design the path from pilot to production up front, including who approves what and how exceptions get handled.

• Audit your bottleneck before you buy more compute. If your GPUs are waiting on data, fix storage, networking, and pipeline throughput first.

• Build “confidence behavior” into the system. Decide when it should answer, when it should cite, and when it should escalate to a human.

Call to action

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