The Tech Trek

Yahoo is not just adding AI on top of existing products. It is using AI across product experiences, internal tools, engineering workflows, and modernization efforts.

In this episode of The Tech Trek, Lee Zen, CTO at Yahoo, joins Amir Bormand to talk about modernizing at massive scale, moving from on prem infrastructure to the cloud, rebuilding internal tools with AI, and how engineering organizations need to rethink process when agents can move faster than people.

Lee also shares how Yahoo views AI as a coworker, not just a tool, and why the next bottleneck in software delivery may be human judgment.

Practical Takeaways

• Modernization at scale often means operating in two worlds at once, keeping proven systems running while new cloud based services move faster.
• AI can help teams move past legacy tools by reverse engineering requirements and rebuilding modern versions from scratch.
• The real unlock is not only code generation. It is connecting agents to documents, chats, emails, production context, and internal knowledge with the right permissions.
• As agents speed up execution, engineering teams need to rethink where human approval, judgment, and review should live.
• The build versus buy equation is changing because some tools that were too expensive to build before may now be realistic to create internally.

Timestamped Highlights

00:31, Yahoo’s mission and why the internet still feels hard to navigate
02:01, Where AI fits across Yahoo products and engineering work
03:30, The challenge of moving from on prem data centers to cloud based infrastructure
05:27, How Yahoo has used AI to rebuild internal tools and leave technical debt behind
07:25, Why agents need access to engineering context, not just code
10:20, AI as a coworker and the shift from human speed to machine speed
16:27, Why parts of the SDLC may need to change as AI increases delivery speed

One Line That Stuck

“AI as a coworker, not just as a tool.”

The Tech Trek is for technical leaders thinking through how teams build, operate, modernize, and adapt as AI changes the work. Subscribe or follow for more conversations with engineering, product, data, and technology leaders.

Mike Choi wanted to work at Apple for years. Then he got there and had the moment many ambitious builders eventually hit.

Is this the thing I was sprinting toward?

In this episode of The Tech Trek, Mike Choi, co founder at Koah, shares his path from Korea to the United States, mandatory military service, Apple, Twitter, and eventually building Koah, an AI monetization company helping AI app builders create sponsored experiences.

The conversation is less about the glamour of startups and more about what founder work actually demands: making decisions without complete information, learning from Big Tech without copying it, and staying focused when AI moves faster than your team can absorb.

Practical Takeaways

• Big Tech can teach you strong operating patterns, but startups force you to build your own style.
• Founder decisions rarely come with complete data. Moving creates the next data point.
• In AI startups, speed can become a distraction if every new tool or feature changes the plan.
• Clear vision helps teams make decisions without waiting on the founder.
• Knowing when to share an idea matters as much as having the idea.

Timestamped Highlights

00:38, Mike explains Koah and why AI products need new monetization models.
02:25, Mike shares how his father’s Korean Air Force service brought him to the United States as a child.
05:01, Mandatory military service, pausing college, and learning to code around strong engineers.
07:29, The long term goal of working at Apple and the unexpected feeling after getting there.
10:57, Why Mike chose to build from scratch instead of staying on the Big Tech path.
14:05, What Big Tech did and did not prepare him for as a founder.
17:03, The founder lesson of making decisions before the full picture is clear.
19:35, Why AI startups move so fast and how shiny object syndrome drains energy, time, and attention.

One Line That Stuck

“Just make the decision, produce data points that way through actions, and make a better decision tomorrow.”

Subscribe to The Tech Trek for more conversations on how modern technical teams are building, hiring, operating, and adapting around AI, data, platform, product, and engineering execution.

AI is moving fast, but the bigger question for companies and governments may be control. Who owns the data, the workflow, the output, and the risk?

In this episode, Amir talks with Shaun Modi, cofounder and CEO of Capitol AI, about sovereign AI, shadow AI, model dependency, government use cases, and why organizations need repeatable, governed, auditable workflows before AI becomes part of core operations.

Shaun also brings a design lens to the conversation, connecting AI adoption to user experience, voice interfaces, and the next wave of AI in the physical world.

Practical Takeaways

• Sovereign AI means having control over your data, outcomes, upside, and risk.
• Shadow AI creates short term productivity, but can also create silos, governance gaps, and data exposure.
• Organizations need repeatable, governed, auditable AI workflows, especially in regulated environments.
• Model independence matters because model costs, performance, and capabilities keep changing.
• Design will come back into focus as AI systems become more powerful and more embedded in work.

Timestamped Highlights

00:40, What Capitol AI does and why decision ready artifacts matter
01:50, Shaun defines sovereign AI in plain language
02:36, The intelligence paradox, more data, less control
04:15, Why shadow AI can become a governance and accuracy problem
10:29, Zero data retention, model independence, and evaluation criteria
17:53, Why AI user experience may be entering a new design cycle
25:56, Where AI may create major impact in the physical world

One Line That Stuck

“Companies and governments have more data than ever, but they are losing control over the outcomes.”

Practical Takeaways For Teams

If AI is moving into real business processes, start by asking what needs to be controlled. Data rights, model choice, accuracy standards, workflow governance, and auditability all matter more once AI is producing work that affects customers, citizens, or critical operations.

Follow The Tech Trek for more conversations on how technical teams are building, operating, and adapting around AI, data, product, and engineering execution.

Most healthcare AI stories start with diagnosis. Edmund Jackson thinks that misses the real bottleneck.

In this episode of The Tech Trek, Edmund Jackson, CEO and founder of Unity AI, joins Amir to talk about AI for healthcare operations. The conversation gets into why scheduling, staffing, follow up, payer coordination, and interoperability are often where healthcare breaks down, and why solving those operational problems may matter more than chasing the flashiest use cases.

Edmund brings a healthcare first view to AI. His argument is simple: healthcare is not slow because people are ignoring technology. It is slow because the real workflows are complex, regulated, high context, and hard to capture cleanly in software.

What You’ll Take Away

• Why healthcare experience matters when choosing which AI problems are actually worth solving
• Why diagnosis is not always the best starting point for healthcare AI
• How scheduling becomes much more complex when patients, payers, clinics, staff, protocols, and follow up all have to line up
• Why AI can help clinics save time while moving human staff toward higher value patient interactions
• Why interoperability is still hard, even with standards like FHIR gaining momentum

Timestamped Highlights

00:29, What Unity AI does and why healthcare operations is the focus
01:11, Why healthcare AI needs people who understand the domain, not just the technology
02:26, The danger of solving the hardest or flashiest problem instead of the most pragmatic one
05:15, Why AI may finally help healthcare handle personalization and operational complexity at scale
07:47, Why scheduling a healthcare visit is nothing like scheduling a delivery or restaurant order
10:42, How operational AI can save time and reduce downstream chaos in clinics
24:33, Why healthcare data is much harder to structure than financial data

One Line That Stuck

“Software is like children. Making it is all fun and games. Maintaining it is a whole other question.”

Practical Takeaways

• Start with the workflow that actually blocks progress, not the one that sounds most impressive
• In healthcare, operational context is often the product
• AI should create more room for humans to handle the interactions that require judgment, care, and clinical responsibility
• More software is not always the answer, especially in regulated environments where maintenance, compliance, and security matter

Subscribe to The Tech Trek for more conversations with founders, operators, and technical leaders building through AI, data, product, platform, and engineering execution.

Deepak Bapat, CTO and co founder at Tabs, joins The Tech Trek to talk about how his team is using tools like Claude Code and Cursor, where AI is helping, and why systems thinking may matter more than raw coding ability as engineering work shifts.

Practical Takeaways

• AI coding agents are already producing useful production work, but judgment still matters.
• Tool choice may be less important than standardizing the expected output.
• Messy repos can make AI generated work harder to trust, so cleanup and patterns matter.
• The future engineer may look more like a product engineer with strong systems thinking.
• Teams may move from debating features to rapidly building multiple versions and testing what works.

Timestamped Highlights

00:37
What Tabs does and why contracts create hard revenue workflow problems for B2B finance teams.
02:16
Deepak compares pre AI engineering work with the current shift toward AI assisted development.
05:09
How the Tabs engineering team uses Claude Code, Cursor, and other coding tools in real work.
08:13
Why inconsistent codebases create more risk when teams add coding agents
14:00
The idea that teams can build the same feature multiple ways in one afternoon.
20:53
Deepak’s view on whether the future team needs separate PMs and engineers, or more product engineers.
23:38
A future where software can become more bespoke to each customer because AI changes the cost model.

One Line That Stuck

“You can build on three different work trees the same feature in three different ways and see which one you like, and you can do it all in an afternoon.”

Practical Moves From The Conversation

• Keep humans close to the review process, especially when the last five percent still requires taste and judgment.
• Clean up inconsistent code patterns before letting agents operate broadly across the repo.
• Hire for adaptability, systems thinking, and problem solving, not just past tool familiarity.
• Use AI to explore more product options faster, but do not remove the need to ask whether the feature should exist.

Subscribe or follow The Tech Trek for more conversations on how technical teams are building, hiring, and operating as AI changes the work.