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 around product, engineering, AI, data, teams, hiring, and growth.

  1. לפני שע׳ (1)

    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?

  2. לפני 5 ימים

    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

  3. 28 ביולי

    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

  4. 23 ביולי

    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

  5. 21 ביולי

    How AI Agents Are Rewriting Enterprise Analytics

    Most companies are not short on data. They are short on the time, cost, and coordination required to turn it into action. Ethan Ding, co founder and CEO of TextQL, joins The Tech Trek to explain how AI agents are changing enterprise analytics. The conversation moves beyond faster dashboards into a larger shift, analysts managing fleets of agents, business teams asking far more questions, and companies finding revenue and cost opportunities that were previously too expensive to pursue. What Technical Teams Can Take From This • Making answers cheaper does not reduce analytics work. It increases the number of questions people ask. • Analysts may spend less time assembling dashboards and more time managing agents, data sources, permissions, quality, and costs. • The clearest ROI comes from decisions with direct financial outcomes, including fraud prevention, upsell opportunities, churn risk, and unused vendor spend. • Faster analysis matters most when teams can act on valuable opportunities they previously could not afford to investigate. • Token costs will force AI companies and buyers to reconsider where software budgets go, especially across BI tools and data platforms. Moments Worth Hearing 00:00 Ethan explains how TextQL agents work across messy enterprise systems including Cognos, Teradata, Snowflake, Databricks, Tableau, and Power BI. 04:52 Why giving people faster answers does not create free time. It creates even more demand for analytics 07:10 How self service analytics quickly moves from asking what a number is to asking whether it matters and what to do next. 10:08 The analyst role shifts toward managing fleets of agents and tuning an insight factory for the business. 14:38 Why faster access to data can reveal valuable opportunities that were previously too expensive to investigate. 19:55 A practical way to measure analytics ROI through fraud prevention, upsell opportunities, and other direct financial outcomes. 24:18 How token costs, AI margins, and easier migrations could reshape spending on traditional BI tools. One Line That Stuck “It becomes much more of an operations manager job. It is a factory. It takes in tokens and churns out dashboards, reports, and recommendations.” Follow The Tech Trek on your podcast platform, subscribe for future episodes, and share this conversation with someone rethinking how their team works with data.

    How AI Agents Are Rewriting Enterprise Analytics

  6. 16 ביולי

    Scaling Enterprise AI Beyond the POC

    Enterprise AI is easy to demonstrate. The real test begins when a promising POC meets production costs, security requirements, data movement, latency, and internal adoption. Shimon Ben-David, CTO at WEKA, joins Amir to discuss the gap between experimenting with generative AI and operating it at scale. They explore how classical AI differs from generative AI, why production exposes problems that demos hide, and how companies with limited AI maturity can start building useful internal capability. Practical Takeaways • A successful POC proves that an outcome is possible. It does not prove that the system will be affordable, secure, reliable, or fast at scale. • Enterprise AI adoption reaches across infrastructure, engineering, data, security, and business teams. It cannot be owned by one group in isolation. • Adding more GPUs will not fix slow data access, poor utilization, weak pipelines, or an experience users do not want to use. • External support can help, but the person or firm involved needs to stay through implementation and production, not stop at recommendations. • Companies that are behind should begin with proven use cases, build internal experience, and quickly stop experiments that fail to show value. Key Moments 00:00 Why moving enterprise AI into production remains difficult 01:55 The difference between classical AI and generative AI adoption 07:05 How companies can use AI without having a formal AI strategy 11:35 Why successful POCs often struggle when they reach production 17:35 Competitive pressure, AI FOMO, and the need to calculate real ROI 22:00 Why AI adoption requires cross organizational change 33:10 Where a company with limited AI maturity should begin One Line That Stuck “The promise is there. It is possible. You just need to do it properly.” Subscribe to The Tech Trek for more conversations about how technical teams are building, operating, and adapting around AI, data, product, platform, and engineering execution.

    Scaling Enterprise AI Beyond the POC

  7. 14 ביולי

    How Todoist Is Rethinking Engineering With AI

    AI can generate code faster, but that does not make software delivery simple. It shifts the pressure to requirements, architecture, review, and technical judgment. Goncalo Silva, CTO at Doist, explains how AI is changing the way teams behind Todoist and Twist build software. He shares why greater individual autonomy has led to more collaboration, why deep expertise still matters, and how faster execution is reshaping product delivery, project planning, and engineering hiring. What Leaders Can Take From This • Faster code generation makes strong planning and clear requirements more important, not less important. • Designers, product leaders, and engineers can work from richer prototypes, but production systems still need experienced technical judgment. • Engineering capacity does not have to move into other functions. Teams can use it to improve reliability, performance, quality, and the amount of valuable work they ship. • Token counts are a weak measure of progress. Doist looks at team feedback and whether projects are staying on track. • Engineering interviews need to test architecture, decision making, curiosity, and depth, not simply whether a candidate can produce working code. Approximate Highlights 00:00 Meet GonCalo Silva and the products behind Doist 02:00 How broadly AI is being used across Doist 04:15 Why greater autonomy has brought teams closer together 09:45 Where nontechnical coding works, and where it creates risk 17:50 How AI compressed a major refactoring effort by 20 to 30 times 25:05 Measuring AI value without counting tokens 30:20 Why faster execution requires more up front planning 34:50 How Doist changed its engineering interview process One Line That Stuck “We are the bottleneck. Our attention span, our ability to memorize, our ability to understand, and deep expertise.” Follow The Tech Trek for more conversations on how technical teams are changing the way they build, hire, and operate.

    How Todoist Is Rethinking Engineering With AI

  8. 9 ביולי

    The Future of Engineering May Have Fewer Handoffs

    AI is not just changing how engineers write code. It is changing who gets close enough to shape the work. In this episode of The Tech Trek, Robert Stewart, CTO at Arbital Health, joins Amir to talk about how AI is bringing actuarial subject matter experts closer to product and engineering teams, especially in healthcare and risk based contracts. Robert shares how his team is pairing technically minded SMEs with software engineers, using AI tools in development, and rethinking technical hiring now that AI assisted coding is part of the job. Practical Takeaways • AI can reduce the distance between domain experts and engineering when the SMEs can clearly describe requirements, acceptance criteria, and edge cases. • Pairing a subject matter expert with an experienced engineer can be more powerful than traditional pair programming because each person brings a different kind of judgment. • Better written requirements matter more in an AI assisted workflow because tools can work directly from detailed tickets and context. • Technical interviews may need to test how candidates use AI, not whether they can avoid it. • Hiring teams need stronger signals around identity, environment fit, prompting skill, and how candidates respond to AI output. Timestamped Highlights 00:00 Robert Stewart on Arbital Health, value based care, and the role of actuarial expertise in healthcare infrastructure. 03:06 Why actuarial knowledge is hard to transfer into engineering teams through normal handoffs. 04:40 How AI helps subject matter experts move closer to product and engineering work. 06:08 Why engineering fundamentals still matter, even when AI makes code easier to create. 09:55 How Arbital Health is using Cursor, Claude Code, and human review in a regulated environment. 14:52 Why more detailed Jira tickets are becoming more valuable in AI assisted development. 17:10 How AI is changing technical interviews from “you may use AI” to “you must use AI.” 22:16 What suspicious candidates, remote interviews, and fake profiles are forcing hiring teams to rethink. One Line That Stuck “You can judge an expert by the type of questions they ask.” Pro Tips • Ask candidates to share their screen during AI assisted technical interviews. • Watch how they prompt, not just what they produce. • Look for whether they catch strange or weak AI output. • Use a rubric, but also evaluate whether the candidate fits the way your team actually works. • For AI generated code, add stronger human review, especially in regulated environments. Subscribe to The Tech Trek for more conversations on how technical teams are adapting around AI, data, product, platform, hiring, and engineering execution.

    The Future of Engineering May Have Fewer Handoffs
הצגת הכול (690)

אודות

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 around product, engineering, AI, data, teams, hiring, and growth.

מידע

  • יוצרים
    Elevano
  • שנות פעילות
    2020 - 2026
  • פרקים
    690
  • סיווג
    תוכן הולם
  • זכויות יוצרים
    © Elevano
  • אתר התכנית
    The Tech Trek