The Tech Trek

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
If you got value from this one, follow the show and turn on notifications so you do not miss the next episode.