The Tech Trek

Sports organizations have more data than ever. The real advantage comes from knowing which problem to solve, which data matters, and whether people will trust the answer enough to change how they work.

Rohan Nagi, VP of Strategy and Analytics at Sponsor United, explains how sports analytics is moving beyond basic reporting into AI supported decision making. He discusses how teams and brands can combine quantitative and qualitative information to evaluate athletes, identify sponsorship opportunities, understand audiences, and make better business decisions.

The technology is only part of the challenge. Coaches, athletes, executives, and business teams may be asked to abandon routines and instincts that have worked for years. Successful AI adoption requires clear problems, organized data, executive direction, and tools that fit real workflows.

Practical Takeaways

• Start with the person and the problem, not the AI tool.
• Identify the information people already use and the data gaps limiting their decisions.
• Build adoption around practical individual workflows before expanding across departments.
• Connect daily use cases to a clear executive vision and broader business goals.

Approximate Episode Highlights

00:55 What Sponsor United does across sports, entertainment, brands, and sponsorships
02:50 How sports moved from intuition toward data informed decision making
05:35 Where traditional analytics ends and more advanced AI applications begin
08:55 How teams can combine performance, medical, and personality data when evaluating players
12:20 Why changing an athlete’s routine can be harder than collecting the data
18:00 Why an AI strategy must begin with a clearly defined problem

Best Line

“The tools are just meant to help solve a problem.”

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AI is changing more than how engineers write code. It is changing what leaders hire for, how candidates are assessed, and which engineering skills may matter most.

Raymond Wang, CTO and cofounder at Ease Health, joins Amir to discuss how an engineering team using agentic coding tools thinks about hiring, productivity, code review, token costs, and the future of software engineering.

Raymond argues that syntax knowledge and familiarity with a specific language matter less than they once did. His team puts more weight on product instincts, engineering judgment, passion, drive, and the ability to break down problems and guide AI agents when they go in the wrong direction.

The conversation also examines a growing interview challenge. Watching a candidate prompt an AI tool can introduce subjectivity, especially when different prompting styles produce equally strong results. Raymond recommends making interviews resemble the actual work and evaluating the quality of the output rather than whether the candidate used the same process as the interviewer.

Practical Takeaways

• Hire for product judgment, engineering instincts, and problem solving, not only language precision.
• Design interviews around realistic work and evaluate results more than prompting style.
• Use the strongest models for expensive mistakes, such as code review, and cheaper models for lower risk internal tasks.
• Build an internal AI harness that engineers use and improve as part of their daily workflow.

Episode Highlights

02:05 What Ease Health now values when hiring engineers
05:30 Why grading prompts can make interviews more subjective
08:40 The challenge of keeping coding interviews ahead of rapidly improving models
12:10 Why Raymond sees code review as one of AI’s strongest engineering use cases
14:40 How Ease Health compares token spend with engineering output
26:45 Why software engineering may split between elite generalists, narrower roles, and highly specialized experts

One Line That Stuck

“Evaluate the output more than the subjective input.”

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AI infrastructure is expanding faster than the power systems required to support it. A data center can be built in two to three years, while a new power plant or transmission line may take seven to nine years. That gap puts utilities at the center of the next phase of AI growth.

Vik Chaudhry, cofounder and CTO of Buzz Solutions, explains how utilities are using visual AI, computer vision, drones, and infrastructure data to find defects, prioritize maintenance, prevent outages, and reduce wildfire risk. He also discusses how AI can help utilities uncover existing grid capacity, forecast unpredictable demand, control operating costs, and preserve knowledge as experienced workers retire.

What You’ll Take Away

• Why electricity, not computing chips, may become the largest constraint on AI growth
• How utilities can extract more capacity from existing infrastructure while new power generation is built
• Where visual AI helps teams prioritize inspections, repairs, and maintenance spending
• How AI can improve load forecasting and transfer knowledge to the next generation of utility workers

A Moment Worth Pulling Out

“The biggest problem for AI right now is not the chips. It’s the electrons.”

Key Moments

Approximate timestamps based on the transcript.

00:45 How Buzz Solutions uses visual AI to assess power infrastructure
03:05 Why utilities began building internal AI teams and governance processes
06:45 The energy constraint behind data center and AI expansion
08:20 Why data centers can be built much faster than new power infrastructure
11:50 Balancing data center demand with affordability for consumers
26:35 Using AI for load forecasting and utility workforce knowledge transfer

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Machine learning teams are moving faster, but the hard part has not disappeared. The work is shifting from writing and debugging every line of code toward defining the right problem, setting requirements, reviewing outputs, and deciding what belongs in a durable platform.

Niels Bantilan, Chief Machine Learning Engineer at Union AI, explains how machine learning work has changed, why coding agents are accelerating prototyping, and what engineers must consider when building infrastructure that supports many teams instead of optimizing one model. He also shares how customer needs become product decisions, why machine learning roles are becoming more specialized, and why measuring AI productivity remains difficult.

Key Takeaways

• Coding agents reduce time spent on implementation, debugging, and exploration, but engineers still need judgment around architecture, quality, and business value.
• Platform teams must balance experimentation with stability by giving users freedom at the edges while protecting a reliable foundation.
• Machine learning engineering now spans a wider range of skills, from low level performance work to customer empathy, education, documentation, and developer advocacy.
• The best model for a task may depend on complexity. Smaller self hosted models can handle tightly scoped changes, while longer and more complex work may still require stronger hosted tools.

Episode Highlights

00:50 What Union AI means by an AI runtime for production
02:10 How machine learning work has changed over the past five years
10:40 The mindset shift from model building to platform engineering
15:00 Turning customer problems into reusable product capabilities
19:00 Why machine learning roles are becoming more specialized
21:50 Using coding agents through specifications, tickets, and code review
26:50 Token costs, productivity measurement, and choosing the right model

One Line That Stuck

“I’m still solving problems. It’s just the level at which I’m doing it doesn’t require me to necessarily get into the weeds of the implementation.”

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Healthcare providers can wait 60 to 75 days to get paid, while many hospitals spend 5% to 7% of revenue on the collection process. That makes revenue cycle management more than a back office issue. It affects margins, staffing, patient experience, and access to care.

Akash Magoon, cofounder and CEO of Adonis, joins The Tech Trek to explain how agentic AI can help medical groups and hospitals automate denials, accounts receivable work, and other manual billing processes. He also shares how Adonis applies AI internally across engineering, sales, and customer success.

The conversation goes beyond automation. Akash explains why healthcare companies often win through distribution, not product quality alone, why focused solutions can create more progress than broad attempts to fix healthcare at once, and why leaders need to frame AI as a tool that helps people work at the top of their license.

Practical Takeaways

• Start with a narrow, material problem rather than trying to rebuild healthcare all at once.
• Measure AI through business outcomes, including net collection rate and cost to collect.
• Invest in marketing and distribution early, even when the product is strong.
• Build employee trust by showing how AI improves effectiveness, not only efficiency.

Approximate Highlights

00:45 How Adonis applies agentic AI to revenue cycle management
02:05 Lessons from building a second healthcare technology company
04:45 Using AI for customers and inside the company
08:55 Why healthcare progress often starts with focused swim lanes
14:25 The distribution lesson Akash carried into Adonis
20:40 How operational efficiency may improve patient access and rural healthcare

One Line That Stuck

“Healthcare ends up becoming a very humbling place to build.”

Follow The Tech Trek for more conversations on AI, data, product, engineering, and technical leadership.