AI can make teams faster, but it can also expose every weakness in the data underneath it.Elizabeth Stanford, VP of Data at PandaDoc, joins The Tech Trek to talk about what it takes to prepare a growing company to actually execute on AI. That means more than giving engineers access to Claude or Cursor. It means getting the data foundation, team skills, stakeholder expectations, and ownership model right.Elizabeth explains how PandaDoc is preparing its data organization for AI while keeping a small team from becoming the company’s quality control department. She also shares how AI is changing what she looks for when hiring data professionals, and why expertise, problem framing, and judgment may become more valuable as coding gets easier.What you’ll take away• AI readiness starts with reliable data, shared definitions, and systems that can provide consistent context.• Giving stakeholders easier access to data creates a new problem when the data team becomes responsible for checking everyone else’s AI generated work.• Technical execution is becoming easier, which puts more value on knowing what questions to ask and whether an answer is actually correct.• Hiring standards are changing. Candidates need to show how they think with AI, not simply that they can use it.Best Line“It’s not whether you know today’s technology, it’s whether you can figure out tomorrow’s technology.”Follow The Tech Trek for more conversations about building and leading modern technology teams.

AI is not just changing software. It may also change the economics of the services businesses built around it.Anirudh Sriram, CTO at Tessera Labs, joins The Tech Trek to explain how his company is using AI to take on enterprise transformation work traditionally handled by large systems integrators. Tessera focuses on migrations, ERP upgrades, code, data, and planning, but the bigger story is how a startup can compete by replacing large teams and long projects with automation, smaller teams, and a focus on outcomes.The conversation also gets into a harder problem. AI can produce work much faster than people can verify it. In one example, Tessera completed code migration work in three days, but functional testing still required roughly two months. That gap between production and verification may become one of the biggest constraints on enterprise AI.What Stood Out• AI creates an opening for startups to compete in markets where incumbents have historically won through scale and headcount.• Selling outcomes instead of large project teams can change both pricing and customer expectations.• Enterprise migrations can be a wedge into a much larger opportunity because they require understanding a customer's systems, data, code, and business processes.• Faster AI output does not remove the need for human review. In some cases, verification becomes the new bottleneck.Key Moments02:12 Where AI can take work out of the enterprise migration process04:39 How transformation projects can stretch years beyond their original plan08:27 Why AI may change the economics of services businesses13:42 Why verifying AI output is becoming a major constraint22:17 How Tessera approaches security, governance, and enterprise data24:56 Using migration as the entry point into broader enterprise automationOne Line That Stuck“We sell the outcome and not the process.”Follow The Tech Trek for more conversations on building, operating, and competing with AI.

AI changes more than the product roadmap. It changes how engineering teams build, how data flows through the company, and what a CTO needs to own.Andrew Rabinovich, CTO and Head of AI at Upwork, joins The Tech Trek to talk about his move from leading AI into the broader CTO role. His view is simple: AI is no longer just another component inside a software system. Increasingly, AI is the system, and infrastructure, data, engineering, and product need to be designed around that reality.Andrew explains how that shift is changing Upwork's product development, from adding AI to individual features to building systems that learn across the entire user journey. He also discusses faster iteration, the importance of real time data, and how software engineering changes when machines can generate most of the code.What Stood Out• AI first development requires thinking about the entire system, not adding AI capabilities to isolated product features.• Product iteration can move from months between versions to daily updates when systems continuously learn from user interactions.• Engineers are moving from writing every line of code toward reviewing, steering, simplifying, and evaluating machine generated code.• Asking the right question and knowing when a result is good enough may become more valuable than the mechanical work between those two points.Key Moments03:39 AI moves from being a component of software to becoming the foundation of the system.07:35 How an AI background changes the role of the CTO and the relationship between technology and product.10:48 Why Upwork moved from AI inside individual features toward end to end learning across the user journey.16:04 AI makes feature creation easier, but faster creation does not automatically mean adoption.20:41 Software engineering shifts toward reviewing and steering code generated by AI agents.27:01 The skills that become more valuable when AI handles more of the execution.One Line That Stuck“AI is no longer a component of a large software system. AI is the system.”Follow The Tech Trek for more conversations on AI, engineering, product, data, and technical leadership.

AI is making software faster and cheaper to build. It is not making trust any easier to earn.Simon Wu, Partner at Cathay Innovation, joins The Tech Trek to discuss how AI is changing investment opportunities across healthcare, fintech, insurtech, legal services, and other regulated industries.The conversation looks at a major shift in software economics. Instead of simply selling seats, licenses, and tools, AI companies can increasingly perform more of the work and deliver the outcome a customer actually wants. That creates opportunities for new business models, especially in industries where customization and services historically made it difficult to scale.Regulation adds another dimension. Compliance, accuracy, governance, and complex workflows make these markets harder to enter. But Simon argues that the same friction can create defensibility once a company earns the trust of its customers.Key Takeaways• Regulation can become a moat. AI may lower the cost of building software, but companies still have to earn the right to operate inside sensitive workflows.• Software is moving closer to outcomes. Customers increasingly care about the result, not how many seats or licenses they purchased.• AI changes the economics of customization. Companies may no longer have to choose as sharply between scalable software and labor intensive services.• Human involvement still matters. In healthcare, wealth management, and legal services, AI can make professionals more efficient without requiring them to disappear from the workflow.One Line That Stuck“AI has dramatically lowered the cost of building software. It doesn't lower the cost of earning trust.”Follow The Tech Trek for more conversations on AI, engineering, product, data, and building modern technology companies.

AI can make individual tasks faster while leaving the organization with the same old coordination problems, or even making them worse.Sergei Sorokin, CEO and co founder of Highlight, joins The Tech Trek to discuss why faster output does not automatically mean better work. Teams can generate documents, code, notes, and analysis faster, then spend the time they saved reshaping that output, moving information between tools, and figuring out what matters.The bigger problem, Sergei argues, is often not access to information or model intelligence. It is context. AI needs to understand what matters to a specific person, team, and moment rather than simply searching across everything available.The conversation also covers proactive AI assistants, privacy and security, team specific customization, and why trust will shape how quickly people allow AI to act on their behalf.Key Takeaways• Faster task completion does not eliminate the coordination tax between people and tools.• The challenge is increasingly signal versus noise. AI needs to understand which information matters now.• AI that adapts to individual teams could help companies preserve what makes their work distinct rather than producing increasingly similar output.• Adoption will depend on trust. Drafts, approvals, undo options, and clear boundaries can help people become comfortable giving AI more control.Key Moments02:09 Why faster AI output can still create more work across teams05:02 The coordination tax that existed before AI and why AI can amplify it08:19 Why chat alone may not be the right starting point for workplace AI13:58 How AI could adapt to teams rather than forcing teams to adapt to software18:16 Why human behavior and trust will determine AI adoption22:05 Why greater agent autonomy may create a demand for more user controlOne Line That Stuck“It’s not an intelligence gap. It’s a context gap.”Follow The Tech Trek for more conversations about AI, engineering, product, data, and how technical teams are changing.

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 Moments02:27 How AI tools can turn legitimate technology into an interview cheating mechanism05:35 Why hiring fraud can become a security and data access problem07:43 Using voice, conversation context, and AI detection to improve visibility during interviews11:43 Why increased AI uncertainty should not lead companies to distrust everyone14:28 What organizations should think about after a candidate actually gets hired19:40 The continuing race between increasingly capable deepfakes and detection technologyOne Line That Stuck“Tech problems have tech solutions.”Follow The Tech Trek for more conversations about AI, engineering, data, product, and how technical teams are adapting.

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

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 Highlights00:55 What Sponsor United does across sports, entertainment, brands, and sponsorships02:50 How sports moved from intuition toward data informed decision making05:35 Where traditional analytics ends and more advanced AI applications begin08:55 How teams can combine performance, medical, and personality data when evaluating players12:20 Why changing an athlete’s routine can be harder than collecting the data18:00 Why an AI strategy must begin with a clearly defined problemBest Line“The tools are just meant to help solve a problem.”Follow The Tech Trek for more conversations on AI, data, engineering, product, and technical leadership.

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 Highlights02:05 What Ease Health now values when hiring engineers05:30 Why grading prompts can make interviews more subjective08:40 The challenge of keeping coding interviews ahead of rapidly improving models12:10 Why Raymond sees code review as one of AI’s strongest engineering use cases14:40 How Ease Health compares token spend with engineering output26:45 Why software engineering may split between elite generalists, narrower roles, and highly specialized expertsOne Line That Stuck“Evaluate the output more than the subjective input.”Follow The Tech Trek for more conversations on AI, engineering, product, data, and technical leadership.

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 workersA Moment Worth Pulling Out“The biggest problem for AI right now is not the chips. It’s the electrons.”Key MomentsApproximate timestamps based on the transcript.00:45 How Buzz Solutions uses visual AI to assess power infrastructure03:05 Why utilities began building internal AI teams and governance processes06:45 The energy constraint behind data center and AI expansion08:20 Why data centers can be built much faster than new power infrastructure11:50 Balancing data center demand with affordability for consumers26:35 Using AI for load forecasting and utility workforce knowledge transferFollow The Tech Trek for more conversations about how technical teams are building and operating around AI, data, platforms, product, and engineering.