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
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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.