The Tech Trek

Behnam Bastani, CEO and cofounder of OpenInfer, breaks down why the last two years of AI feel explosive, and why the next wave is not chat, it is action at the edge.
We get into always on inference, what actually forces compute to move closer to the data, and the missing layer that makes edge AI scale: the Android like infrastructure that lets devices collaborate instead of living in silos.

Key takeaways

• The hype spike is real, but the runway is decades, it took compute, sensors, and communication protocols maturing over generations to unlock this moment
• AI is shifting from conversational to actionable, which means continuous, always on inference becomes the norm
• Edge wins when cost, reliability, and data sovereignty matter, cloud and edge will coexist, but the workload placement changes
• The biggest bottleneck is not just silicon, it is the infrastructure layer that makes building and deploying across devices easy, plus a shared fabric so devices can cooperate
• Adoption is as much a human story as a technical one, this shift lands faster and broader than previous tech transitions, so anxiety is predictable and needs real attention

Timestamped highlights

00:38 OpenInfer’s mission, intelligence on every physical surface, and why collaboration matters
02:07 Electricity as the earlier revolution, intelligence as the next kind of power, and the control problem
05:54 Where we really are on the maturity curve, early products are here, mass adoption and safety take time
08:31 When the device boundary disappears, it stops being you versus the agent, it becomes one system
11:04 Always on inference, and the three forces pushing compute to the edge: cost, reliability, data sovereignty
14:40 The Android moment for edge AI, why the operating system layer unlocks developers, apps, and adoption

A line worth replaying

Those are going to be the three pillars that really enforces that edge and cloud are going to live together.

Pro tips for builders

• If your product needs real time decisions, design for intermittent networks from day one, reliability is not optional
• Treat data sovereignty as a product feature, not a compliance afterthought, it is becoming the moat
• Push for interoperability early, the fabric that lets devices share the right data is what makes edge feel seamless

Call to action

If this episode helped you rethink where AI should run and what it takes to ship it in the real world, follow the show and share it with one builder who is working on edge, robotics, devices, or applied AI.

Building data capability from zero is not a tooling problem, it is a trust and prioritization problem. In this episode, Laura Guerin, Head of Data and Data Science at Bevi, breaks down how she goes from blank slate to real business impact, without getting trapped in endless plumbing or endless meetings.

Laura shares how she runs an early listening tour, prototypes value before asking for bigger investment, and decides when to hire scrappy generalists versus specialists. We also get practical on AI, where it helps, where it is unnecessary, and why quality data and a clean semantic layer still decide whether anything works.

Key takeaways
• Start with business priorities, then map data work to the actions and outcomes leaders actually care about
• Prototype the end deliverable fast, even if the backend is duct tape at first, then scale after stakeholders see value
• Use cases first for AI, most problems do not need AI, but the right problems can see real acceleration
• Early teams win with adaptable generalists who can wear multiple hats across data, analytics, and data science
• Trust is a shared responsibility, build reliability, then create a culture where users flag weirdness quickly

Timestamped highlights
00:44 Bevy explained, smart bottle less dispensers and why the business context matters for data priorities
02:01 The listening tour playbook, exec alignment, stakeholder map, and using AI to synthesize themes into a SWOT
04:00 The MVP reality, manual prototypes to prove value, then the conversation about scalable pipelines
06:33 AI without the hype, use cases, when AI is not needed, and two examples with clear business impact
09:22 Hiring from zero, why generalists first, the data analytics data science spectrum, and the personality traits that matter
14:21 Self service reimagined, Slack as the interface, semantic layer and permissions, and how to keep a single source of truth
20:19 Keeping trust when things break, checks and balances plus a shared responsibility model
22:39 Making innovation real, baking it into expectations so the team has time to learn and test new approaches

A line worth stealing
Data on its own is not typically a priority. It is more about the action or the impact that comes out of the data.

Pro tips
• Run a structured listening tour early, capture themes, then pick two or three priorities you can deliver quickly
• Show the business an MVP output first, then use that proof to justify the unglamorous backend work
• Treat AI like any other tool, define the problem, validate the use case, then confirm the data quality inputs

Call to action
If you are building analytics, data products, or AI inside a growing company, follow the show and subscribe so you do not miss the next operator level conversation. Share this episode with one leader who is asking for data outcomes but has not funded the foundation yet.

Riya Grover, CEO and co founder of Sequence, breaks down what “good CEO” actually looks like when the job is messy, fast, and high stakes. This is a practical conversation about building excellence through people, clarity, and direction, not through heroics or micromanagement.

Riya runs a revenue automation platform for finance teams, helping companies automate order to cash, billing, invoicing, accounts receivable, and revenue recognition. From that seat, she shares a founder level view on leadership that is direct, repeatable, and built for real operating constraints.

Key takeaways

• The CEO’s highest leverage job is building the bench, your company becomes the team you assemble
• High performance culture comes from a clear bar, fast decisions when it is not met, and leaders who own outcomes
• Great teams do not need more policies, they need context, goals, trade offs, and clarity
• Separate reversible decisions from irreversible ones, move fast on two way doors, slow down on one way doors
• Hiring signal to watch, motivation and hunger for the stretch challenge often beats the “done it before” resume

Timestamped highlights

00:32 What Sequence does, why order to cash is still painfully manual
01:48 The CEO role is less about functions, more about direction and execution
03:23 Excellence starts with talent density, do not compromise on the bar
06:10 Why companies win, direction plus distribution, and the Figma example
11:01 Getting real feedback as a leader, how to reduce hierarchy and increase ownership
14:39 “They need clarity,” decision frameworks over micromanagement
18:01 The hidden damage of the founder weighing in on every micro decision
20:53 Hiring underrated talent, motivation, ambiguity tolerance, and the stretch role
24:38 Why the CEO should invest time in hiring, the leverage math is obvious

A line worth keeping

They do not need policies, they need clarity.

Pro tips you can steal

• Promote leaders who have done the job and set the pace, it earns trust and improves decision quality
• Give teams context and constraints, then treat your input like any other input
• Use the door test, reversible decisions get speed and delegation, irreversible ones get more diligence
• In hiring, look for motivation plus clear thinking, then bet on aptitude over the perfect background

Call to action

If this one helped you think more clearly about leadership and hiring, follow the show and share the episode with one operator who is building under pressure. New conversations drop with different guests and different problems, so you always have something useful to steal.

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
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