The Tech Trek

Arnie Katz has been running product and engineering under one roof since before most companies even considered combining the roles. As CPTO at GoFundMe, he oversees the teams behind a platform processing over 2.5 donations every second, with more than $40 billion in help facilitated worldwide. Arnie breaks down why the CPTO title keeps gaining traction, how he thinks about the role like a portfolio manager, and where the real trade offs live when one person holds both the product and technology reins.
Key Takeaways
The CPTO role works like a portfolio manager. Arnie manages the company's largest investment center by balancing short term business wins against long term platform bets, knowing when to take on technical debt and when to pay it down.
Velocity, coordination, and alignment are the three biggest wins. When product and engineering report to one leader, decisions happen faster, roadmap conflicts get resolved without executive tug of war, and technical investments stay tied to business outcomes.
The disadvantages are real. Without separate CPO and CTO voices at the executive table, certain perspectives can get muted. His fix: build a leadership bench strong enough to create the right tension underneath him.
AI is changing what small teams can deliver. GoFundMe's eight person team behind Giving Funds is shipping at a pace that would have been impossible five years ago.
Timestamped Highlights
[00:38] The scale most people don't realize about GoFundMe, including 2.5 donations per second and GoFundMe Pro for nonprofits.
[02:02] How Arnie first landed the CPTO title at StubHub seven years ago, and why it clicked.
[09:11] The real downside of collapsing two C suite roles into one, and how Arnie designs around it.
[13:57] His portfolio approach to technical debt, sequencing re platforming in areas like identity and payments while other teams ship business value.
[18:38] AI reshaping engineering velocity, the future of the SDLC, and product teams prototyping without writing code.
[23:06] Where the CPTO model is headed as the industry evolves.
The Line That Stuck
"I often think of myself as a portfolio manager. My job is to invest money where the company gets the best returns, where the mission gets the best return, where the shareholder gets the best returns."
Pro Tips
Sequence your bets instead of spreading them thin. GoFundMe gave their identity and payments teams nine months of runway to re platform with no feature expectations while other squads picked up the pace on near term results.
Build leadership that creates productive friction. Without CPO vs. CTO tension at the exec level, let your VPs and SVPs push back against each other. That tension is where the best decisions come from.
Think in time horizons, not just priorities. Short term moves for 0.1% to 0.5% metric lifts. Midterm bets for 1% to 5% gains. Long term swings that could transform the business. Allocate across all three.
If this conversation changed how you think about product and engineering working together, share it with someone on your team. Subscribe to The Tech Trek so you never miss an episode, and connect with Arnie on LinkedIn to keep the conversation going.

GoFundMe is offering listeners of The Tech Trek a chance to open their own Giving Fund. For the first 50 people who open a Giving Fund and add $25 or more to their Giving Fund, GoFundMe will add an additional $25 to that Giving Fund. If you have a Giving Fund but have never contributed into it, you can also participate. The deadline for this incentive is March 13. To get this incentive, click here to start your Giving Fund.

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.