The Tech Trek

Anjali Jameson, Chief Product Officer at Arbiter, says the hard part is not gathering data. It is getting action across patients, providers, and payers without breaking what already works.

“Automating something that’s broken is not going to necessarily give us better outcomes.”

Arbiter is a care orchestration platform built for patients, providers, and payers together, not a single point solution. The operating spine ingests and makes actionable data across the patient journey, including provider directories, EMR integrations, claims, and financial and policy data from health plans, then connects it to highly personalized multi channel agentic outreach. You will hear why cross system context matters, how total cost of care stays in view while each stakeholder chases different leading metrics, and what it looks like to move from automation into optimization, like going from a call center scheduling flow to 60 percent conversion and pushing toward 95 percent conversion.

Timeline

00:40 Care orchestration platform, operating spine, data across the patient journey
04:33 Misaligned incentives, prior authorizations, 12 to 14 hours a week
09:42 Total cost of care, star metric, building for different metrics
12:25 Long form personalized videos, transportation, education, medication management
15:02 Prior authorization from three to six days to almost instantaneous
22:07 COVID, provider messaging two, three X, AI responds faster

Subscribe and share it with someone who is building in health tech.

Most data teams do not have a tooling problem. They have a customer service problem.

Mo Villagran, Associate Director of Insights, Analytics, and Data at Cambrex, argues that stakeholder expectation management is the difference between being a trusted advisor and being an order taker.

"In a simple word, it's really just customer service."

In this episode, Mo breaks down how to manage stakeholder expectations, define expected delivery value, and keep projects aligned to real business outcomes instead of chasing rebranded tools. She shares why simple solutions often win, how to show progress even when the work is plumbing, and why qualitative stakeholder testimony beats dashboard count KPIs. You will also hear how she thinks about AI as a tool, when it works, when it is just a cool toy, and how to build trust by demoing in real time.

00:02:00 Stakeholder expectation management is customer service

00:03:00 Why skeleton teams can still deliver value

00:06:00 Who defines expected delivery value, and how to shape it

00:09:00 Negotiate expectations, do not become an order taker

00:18:00 How to show progress when there is nothing visual

00:21:00 Stop chasing quantitative KPIs, win with testimony

Subscribe and share this episode with anyone who is knee deep in stakeholder management.

Ashok Krishnamurthi, Managing Partner at Great Point Ventures, says the biggest mistake in venture capital is confusing prediction with judgment.
Early stage investing is not about perfect stories, it is about first principles and picking the founder who can execute when the story breaks.
This episode is for startup founders and investors who want a cleaner filter for what matters.

“You have to learn to check your ego at the door because it’s a partnership.”

Ashok shares his path from engineering into building companies, then into venture capital, and explains how he forms an investment thesis when markets are noisy. We talk about founder evaluation, why picking the jockey matters more than the idea, and how first principles thinking shows up in real domains like healthcare data and cancer. We also get practical about artificial intelligence, why AI is not only a compute race, and how AI inference, energy efficiency, and cost shape what wins.

00:00 Why legacy matters more than VC metrics02:28 Engineer to founder to venture capital11:16 How to pick the jockey14:21 First principles, cancer data, and AI constraints23:24 AI is here to stay, keep your mind open30:15 How to reach Ashok

If this episode helped, subscribe and share it with a builder or investor who will use it.

Aditya Agarwal did not plan to work in robotics. He got rejected from his first-choice major, joined a student club to keep his parents off his back, and stumbled into one of the fastest-growing fields in tech. Now he is Head of Robotics at Medra, a company building physical AI scientists that let researchers run experiments remotely at speeds a traditional lab cannot touch.

"Even the companies that have made the most progress haven't deployed at the scale of laptops, cars, or phones. So if you have experience scaling hardware products, that is super valuable at an early-stage robotics company."

What we get into: why the PhD requirement is mostly gone, how AI is shrinking the hardware development timeline, and the cheapest way to start building with robotics today if you cannot afford to go back to school or take a step back in your career.

Timestamped Highlights

01:19 The accidental path into robotics that actually worked

03:04 Whether you still need an engineering degree for hardware roles

04:48 Master's degree vs. early-stage startup: what gets you there faster

10:57 How AI is replacing the guesswork in hardware configuration

15:51 How to start learning robotics at home without spending much

18:38 Why rigid hiring processes are costing robotics teams good candidates

If this one lands, subscribe and share it with someone who has been thinking about making a move into the space.

Ronak Desai, Co-founder and CPTO at Payment Labs, breaks down a surprisingly hard problem that sits at the intersection of fintech, sports, and compliance. If you have ever assumed paying winners is just a simple payout flow, this episode will change that view fast.

Payment Labs helps tournament organizers, league operators, and modern sports businesses handle payouts plus tax compliance and support, all in one system. Ronak explains why spot payments are high risk, why manual workflows still dominate the space, and how stablecoins and AI are about to reshape fraud, identity, and trust.

Key Takeaways
One time payouts are a fraud magnet, inconsistent winners and risk based rules make verification and compliance much harder than payroll
Solving payments without solving tax and forms still leaves the biggest liability sitting with the organizer
Many sports and esports operators still run payouts in a surprisingly analog way, checks, cash, and post event cleanup
AI is now good enough to pressure identity verification, and stablecoins make recovery harder because transfers are effectively final
Product adoption depends on meeting users where they are, younger athletes expect texting and simple flows, not tickets and portals

Timestamped Highlights
00:29 What Payment Labs actually does, payouts plus tax compliance plus support for sports, esports, and creator economy use cases
01:15 The origin story, a real tax problem hit an esports operator and exposed how broken the payout workflow is
02:46 Why spot payments raise risk, random recipients, fraud pressure, and why bank partners treat this differently than payroll
04:58 The industry reality check, still running on checks and cash, and what digitizing the workflow unlocks next
06:58 AI fraud versus AI detection, how identity verification is getting bypassed and why stablecoin rails raise the stakes
11:55 The NIL wild west and the product lesson, meet athletes where they already live, including iMessage support

A Line Worth Repeating
Now you have AI committing the fraud and then you have AI detecting the fraud.

Pro Tips for Builders and Operators
If your users are young and mobile first, build support where they already communicate, texting beats ticketing for adoption
Do not bolt on AI for a storyline, use it where it replaces manual work you already do and frees time for higher leverage decisions
Map your tasks with the Eisenhower quadrant, then automate what is repetitive before you chase shiny features

Call to Action
If this episode helped you think differently about fintech, fraud, and modern payout infrastructure, follow the show and share it with a founder or operator who touches payments. For more conversations at the intersection of tech, data, and real world execution, connect with Amir on LinkedIn and subscribe to the Elevano newsletter.

Healey Cypher, CEO of BoomPop and COO at Atomic, breaks down what separates founders who win from founders who stall. You will hear a clear way to judge whether an idea is truly worth building, plus the trust mechanics that get investors, customers, and teammates to actually follow you.

This conversation is a practical map for tech builders who want to pick smarter problems, execute faster, and earn credibility without the founder theater.

Key Takeaways

Founders matter most, but the idea is still a gate, the same great team can get wildly different outcomes depending on the market and timing

VC backed is a specific game, it requires not just big potential, but fast scale, and the incentives are not the same as building a profitable lifestyle business

A quick reality check for market size, if you need more than about five to seven percent penetration to hit meaningful revenue, it is usually a brutal path

Painkillers beat vitamins, solve an urgent problem people feel right now, or you risk getting cut the moment budgets tighten

Trust is built through authenticity, logic, and empathy, if one wobbles, people feel it fast, and progress slows everywhere

Timestamped Highlights

00:00:00 Healey’s background, why BoomPop, and what the episode is really about

00:02:00 The post pandemic spend shift and the why now behind modern events and group travel

00:04:30 Founder versus idea, why execution dominates, but the opportunity still decides the ceiling

00:06:40 The VC reality, power law returns, speed, and why some good businesses are still a no for venture

00:09:15 A simple market math test, penetration levels that become a growth wall

00:19:00 Trust as a founder skill, the three ingredients and how to spot when one is missing

00:21:30 Vulnerability as a shortcut to real connection, plus the giver mindset that makes people want you to win

A line worth stealing

If everyone wants you to win, it is a lot easier to win.

Pro Tips for Tech Founders

Ask yourself what you naturally look forward to doing, that is often your zone of strength, hire around the tasks you dread

Learn the financial basics early, especially cash flow, it is the scoreboard that keeps you alive long enough to win

When trust is lagging, check the three levers, are you showing the real you, can people follow your reasoning, do they feel you care about their outcomes

What's next:

If you build products, lead teams, or are thinking about starting something, follow the show so you do not miss episodes like this. Also connect with me on LinkedIn for short takeaways and clips from each conversation.

Ty Wang, cofounder and CEO of Angle Health, breaks down what it means to give back through public service, then shows how that same mindset drives his mission to modernize healthcare for small and midsize businesses. We get into why legacy health plans feel opaque and painful, what an AI native health plan actually changes behind the scenes, and how better data and workflows can create real cost stability for employers.

Ty shares his path from a federal scholarship and national service work to Palantir, and why he chose one of the most regulated, least glamorous industries to build in. If you have ever wondered why healthcare feels impossible to navigate, or why renewals can blindside a company, this conversation will give you a clear mental model of the problem and a practical view of what modernization looks like when it actually ships.

Key Takeaways

Healthcare feels broken because the infrastructure is fragmented, data is siloed, and even basic questions become hard to answer across inconsistent systems

Modernizing healthcare is not just about a new app, it is about rebuilding the operational core so workflows, claims, underwriting, and member experience can run on integrated data

Small and midsize businesses are hit hardest by cost volatility because they lack transparency, predictability, and negotiating leverage, yet health insurance is often a top line item after payroll

A strong approach to regulated markets is collaborative, treat regulators as partners in consumer protection, not obstacles to work around

Mission and impact can be a recruiting advantage, especially when the technical problems are genuinely hard and the outcomes touch real people fast

Timestamped Highlights

00:40 What Angle Health is, and what AI native means in a real health plan

02:05 The scholarship path that pulled Ty into public service and set his trajectory

04:06 The personal story behind the mission, the American dream, and why access matters

09:38 Why healthcare infrastructure is so complex, and how siloed systems create bad experiences

11:33 Why SMBs get squeezed, and how manual administration blocks customization at scale

13:20 The real pain point for employers, cost volatility and zero predictability before renewal

16:55 Why the tech can expand beyond SMBs, but why the SMB market is already massive

19:51 Lessons from building in a regulated industry, and why credibility and funding matter

22:26 Hiring for high agency, mission driven talent in a world full of AI companies

A line that sticks

“Unless you are lucky enough to work for a big company, these modern healthcare services are still largely inaccessible to the vast majority of Americans.”

Pro Tips for tech operators and builders

If you are modernizing a legacy industry, start with the infrastructure layer, fix the data model, integrate the systems, then automate workflows

In regulated markets, build relationships early, show how your product improves consumer outcomes, and make compliance a design constraint, not a bolt on

When selling into SMBs, predictability beats perfection, give customers a clear breakdown of what drives costs and what they can control

What's next:

If this episode helped you see healthcare and legacy modernization more clearly, follow the show on Apple Podcasts or Spotify and subscribe so you do not miss the next conversation. Also, share it with one operator or builder who is trying to modernize a messy industry.

Gabe Ravacci, CTO and co-founder at Internet Backyard, breaks down what the “computer economy” really looks like when you zoom in on data centers, billing, invoicing, and the financial plumbing nobody wants to touch. He shares how a rejected YC application, a finance stint, and a handful of hard lessons pushed him from hardware curiosity to building fintech infrastructure for compute.

If you care about where compute is headed, or you are early in your career and trying to find your path without overplanning it, this one will land.

Key Takeaways

• Startups often happen “by accident” when your competence meets the right problem at the right time

• Compute accessibility is not only a chip problem, it is also a finance and operations problem

• Rejection can be data, not a verdict, treat it as feedback to sharpen the craft

• A real online presence is less about networking and more about being genuinely useful in public

• Time blocking and single task focus beats grinding when you are juggling school, work, and a startup

Timestamped Highlights

00:28 What Internet Backyard is building, fintech infrastructure for data center financial operations

01:37 The first startup attempt, cheaper compute via FPGA based prototyping, and why investors passed

04:48 The pivot, from hardware tools to a finance informed view of compute and transparency gaps

06:55 How Gabe reframed YC rejection, process over outcome, “a tree of failures” that builds skill

08:29 Building a digital brand on X, what he posted, how he learned in public, and why it worked

13:36 The real balancing act, dropping classes, finishing the degree well, and strict time blocking

20:00 Books that shaped his thinking, Siddhartha, The Art of Learning, Finite and Infinite Games

A line worth keeping

“The process is really more important than any outcome.”

Pro Tips for builders

• Treat learning like a skill, ask better questions before you chase better answers

• Make focus a system, set blocks, mute distractions, and do one thing at a time

• Share what you are learning in public, not to perform, but to be useful and find signal

Call to Action

If this episode sparked an idea, follow or subscribe so you do not miss the next one. Also check out Amir’s newsletter for more conversations at the intersection of people, impact, and technology.

Data leaders are being asked to ship real AI outcomes while the foundations are still messy. In this conversation, Dave Shuman, Chief Data Officer at Precisely, breaks down what actually determines whether AI adoption sticks, from hiring “comb shaped” talent to building trusted data products that make AI outputs believable and usable.

If you are building in data, AI, or analytics, this episode is a practical map for what needs to be true before AI can move from demos to dependable, repeatable impact.

Key Takeaways

Comb shaped talent beats narrow specialization, AI work rewards people who can span multiple skills and collaborate well

Adoption is a trust problem, and trust starts with data integrity, lineage, context, and a semantic layer that business users can understand

Open source drives the innovation, commercialization makes it safe and usable at enterprise scale, especially around security and support

Data must be fit for purpose, start every AI project by asking what data it needs, who curates it, and what the known warts are

Humans are still the last mile, small workflow choices can make adoption jump, even when the model is already accurate

Timestamped Highlights

00:56 The shift from T shaped to comb shaped talent, what modern AI teams actually need to look like

05:36 Hiring for team fit over “world class” niche skills, and when to bring in trusted partners for depth

07:37 How open source sparks the ideas, and why enterprises still need hardened, supported versions to scale

11:31 Where AI adoption is today, why summarization is only the beginning, and what unlocks “AI 2.0”

13:39 The trust stack for AI, clean integrated data, lineage, context, catalog, semantic layer, then agents

19:26 A real adoption lesson from machine learning, and why the human experience decides if the system wins

A line worth stealing

“You do not just take generative AI and throw it at your chaos of data and expect it to make magic out of it.”

Pro Tips for data and AI leaders

Hire and build teams like Tetris, fill skill voids across the group instead of chasing one perfect profile

Use partners for the sharp edges, but require knowledge transfer so your team levels up every engagement

Make adoption easier by designing for human behavior, sometimes the smallest workflow tweak beats more accuracy

Build governed data products in a catalog, then validate AI outputs side by side with dashboards to earn trust fast

Call to Action

If this helped you think more clearly about AI adoption, talent, and data foundations, follow the show and turn on notifications so you do not miss the next episode. Also, share it with one data or engineering leader who is trying to get AI out of pilots and into real workflows.

Cloud bills are climbing, AI pipelines are exploding, and storage is quietly becoming the bottleneck nobody wants to own. Ugur Tigli, CTO at MinIO, breaks down what actually changes when AI workloads hit your infrastructure, and how teams can keep performance high without letting costs spiral.

In this conversation, we get practical about object storage, S3 as the modern standard, what open source really means for security and speed, and why “cloud” is more of an operating model than a place.

Key takeaways

• AI multiplies data, not just compute, training and inference create more checkpoints, more versions, more storage pressure

• Object storage and S3 are simplifying the persistence layer, even as the layers above it get more complex

• Open source can improve security feedback loops because the community surfaces regressions fast, the real risk is running unsupported, outdated versions

• Public cloud costs are often less about storage and more about variable charges like egress, many teams move data on prem to regain predictability

• The bar for infrastructure teams is rising, Kubernetes, modern storage, and AI workflow literacy are becoming table stakes

Timestamped highlights

00:00 Why cloud and AI workloads force a fresh look at storage, operating models, and cost control

00:00 What MinIO is, and why high performance object storage sits at the center of modern data platforms

01:23 Why MinIO chose open source, and how they balance freedom with commercial reality

04:08 Open source and security, why faster feedback beats the closed source perception, plus the real risk factor

09:44 Cloud cost realities, egress, replication, and why “fixed costs” drive many teams back inside their own walls

15:04 The persistence layer is getting simpler, S3 becomes the standard, while the upper stack gets messier

18:00 Skills gap, why teams need DevOps plus AIOps thinking to run modern storage at scale

20:22 What happens to AI costs next, competition, software ecosystem maturity, and why data growth still wins

A line worth keeping

“Cloud is not a destination for us, it’s more of an operating model.”

Pro tips for builders and tech leaders

• If your AI initiative is still a pilot, track egress and data movement early, that is where “surprise” costs tend to show up

• Standardize around containerized deployment where possible, it reduces the gap between public and private environments, but plan for integration friction like identity and key management

• Treat storage as a performance system, not a procurement line item, the right persistence layer can unblock training, inference, and downstream pipelines

What's next:

If you’re building with AI, running data platforms, or trying to get your cloud costs under control, follow the show and subscribe so you do not miss upcoming episodes. Share this one with a teammate who owns infrastructure, data, or platform engineering.