The Tech Trek

AI is changing hiring in ways that go far beyond candidates using ChatGPT to answer interview questions. The harder problem is knowing whether the person on screen is actually who they claim to be, whether their answers are their own, and what happens if someone with malicious intent gets access to company systems.

Yagub Rahimov, CEO and founder of Polygraf AI, joins The Tech Trek to discuss the growing trust problem surrounding AI assisted interviews, deepfakes, impersonation, and security. He explains why organizations need more visibility into the hiring process without turning every unusual behavior, accent, or response into a reason for suspicion.

What You’ll Take Away

• AI interview fraud is not simply a recruiting problem. Once someone enters the company, identity and access become security concerns.
• Detecting suspicious candidates based on human intuition alone can create false positives. Rahimov argues for using technology to create evidence and visibility.
• Small pieces of public information can reveal far more about a company than leaders realize when they are combined through what Rahimov calls mosaic intelligence.
• Protecting company information means thinking beyond traditional security controls to what employees, executives, and systems expose publicly.

Key Moments

02:27 How AI tools can turn legitimate technology into an interview cheating mechanism
05:35 Why hiring fraud can become a security and data access problem
07:43 Using voice, conversation context, and AI detection to improve visibility during interviews
11:43 Why increased AI uncertainty should not lead companies to distrust everyone
14:28 What organizations should think about after a candidate actually gets hired
19:40 The continuing race between increasingly capable deepfakes and detection technology

One Line That Stuck

“Tech problems have tech solutions.”

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If AI can produce the code, what becomes more valuable for engineers?

John Kuhn, CTO and cofounder of Integral, joins The Tech Trek to discuss how agentic development is changing engineering work, product ownership, experimentation, and hiring. Integral helps companies de identify and anonymize data for model training, including unstructured data.

John argues that the value of an engineer is shifting away from simply writing code. As AI handles more implementation work, engineers need stronger product judgment, better systems thinking, and the ability to make decisions when requirements are incomplete. That means asking better questions, understanding customer problems more directly, and taking greater ownership of the outcome.

The conversation also looks at what happens when software becomes cheaper to produce. Teams can prototype and experiment faster, but lower development costs do not eliminate the cost of building something customers do not want. Good product discovery still matters, especially when engineers are expected to operate with more autonomy.

What You’ll Take Away

• Why engineers increasingly need to think like product managers
• How agentic tools are changing the economics of prototyping and product experimentation
• Why good product discovery requires questions that seek information instead of confirming an existing idea
• Why engineering interviews may need to focus more on assumptions, constraints, systems thinking, and decision quality than manual coding speed

A Moment Worth Pulling Out

“Engineers are not meant to write code anymore. They’re meant to solve problems.”

John also raises an interesting idea for the future of technical hiring: instead of giving candidates only a time limit, give them a fixed AI compute budget and evaluate how efficiently they use it to reach a solution.

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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

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