The Tech Trek

Elevano

The Tech Trek is a podcast about building and leading technology companies. Each episode features founders, CTOs, engineering leaders, and operators sharing how they make decisions across product, engineering, AI, data, teams, hiring, and growth.

  1. 3 giờ trước

    Why AI Makes Work Faster but Teams Slower

    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 Moments 02:09 Why faster AI output can still create more work across teams 05:02 The coordination tax that existed before AI and why AI can amplify it 08:19 Why chat alone may not be the right starting point for workplace AI 13:58 How AI could adapt to teams rather than forcing teams to adapt to software 18:16 Why human behavior and trust will determine AI adoption 22:05 Why greater agent autonomy may create a demand for more user control One 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.

    Why AI Makes Work Faster but Teams Slower

  2. 5 ngày trước

    AI Deepfakes and Hiring Fraud: Can You Trust Who You’re Interviewing?

    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.” Follow The Tech Trek for more conversations about AI, engineering, data, product, and how technical teams are adapting.

    AI Deepfakes and Hiring Fraud: Can You Trust Who You’re Interviewing?

  3. 11 thg 8

    AI Is Changing Software Engineering: Engineers Need to Solve Problems, Not Just Write Code

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

    AI Is Changing Software Engineering: Engineers Need to Solve Problems, Not Just Write Code

  4. 6 thg 8

    How AI Is Changing Sports Analytics and Strategy

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

    How AI Is Changing Sports Analytics and Strategy

  5. 4 thg 8

    How Do You Hire Engineers When AI Writes the Code?

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

    How Do You Hire Engineers When AI Writes the Code?

  6. 30 thg 7

    AI Data Centers Are Outgrowing the Power Grid

    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.

    AI Data Centers Are Outgrowing the Power Grid

  7. 28 thg 7

    How Coding Agents Are Changing Machine Learning Engineering

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

    How Coding Agents Are Changing Machine Learning Engineering

  8. 23 thg 7

    How AI Is Reshaping Healthcare Revenue and Access

    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.

    How AI Is Reshaping Healthcare Revenue and Access
Xem tất cả (694)

5
/5
78 Xếp hạng

Giới Thiệu

The Tech Trek is a podcast about building and leading technology companies. Each episode features founders, CTOs, engineering leaders, and operators sharing how they make decisions across product, engineering, AI, data, teams, hiring, and growth.

Thông Tin

  • Nhà sáng tạo
    Elevano
  • Năm hoạt động
    2020 - 2026
  • Tập
    694
  • Xếp hạng
    Sạch
  • Bản quyền
    © Elevano
  • Trang web chương trình
    The Tech Trek