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The Tech Trek: Daily Summaries Delivered
by Elevano
The Tech Trek is a podcast for founders, builders, and operators who are in the arena building world class tech companies. Host Amir Bormand sits down with the people responsible for product, engineering, data, and growth and digs into how they ship, who they hire, and what they do when things break. If you want a clear view into how modern startups really get built, from first line of code to traction and scale, this show takes you inside the work.
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- 4 days ago28 min
AI Agents, Engineering Workflows, and the Cost of Being Wrong
AI coding agents can produce software faster, but they do not replace the judgment needed to understand the system.Shaun Patterson, CTO at Titan, joins The Tech Trek to discuss how agentic coding is changing problem solving, development workflows, project management, and technical hiring.Shaun explains why engineers still need a strong mental model of the systems they are building. AI can generate code, reproduce bugs, research implementation options, and automate repeated debugging work. But it can also keep working on the wrong problem long after a human debugger would have found the answer.The conversation also gets into a bigger shift in software delivery. If agents can work across much larger pieces of a project, engineering teams may move from managing work at the story level to working at the epic level.Key Takeaways• AI speeds up implementation, but engineering judgment still matters.• Repeated debugging work can become reusable agent skills.• Faster implementation lowers the cost of testing different technical approaches.• Hiring increasingly needs to measure how engineers work with AI.Highlights02:08 Why AI can abstract work, but not engineering wisdom06:04 Turning repeated debugging sessions into reusable agent skills09:47 Why faster development may change traditional project management12:42 Moving engineering work from stories to epics16:19 Where agentic coding still creates problems19:29 How Titan evaluates engineers who use AIOne Line That Stuck“It abstracts your thinking, but it doesn’t abstract your wisdom.”Follow The Tech Trek for more conversations with the people building and leading technology companies.
- 6 days ago31 min
AI Agents, Identity, and the Security Gap
AI agents create a different security problem from traditional software. They can operate at software speed and scale while behaving in ways that are much less predictable.Ev Kontsevoy, CEO and cofounder of Teleport, joins The Tech Trek to discuss what happens when companies deploy agents into security systems designed around humans, applications, and relatively static organizational structures.The conversation gets into authentication, impersonation, infrastructure identity, access control, and a harder question: what actually defines the identity of an AI agent when its model, memory, skills, and capabilities can change?Ev also explains why the combination of speed, scale, and unpredictable behavior changes the risk of mistakes. Later, he explores the tension between agents being useful because they can do new things and security systems that often depend on predictable behavior.Key takeaways• Agent identity gets harder when memory, models, and capabilities can change.• Traditional access controls often reflect static organizational structures.• Agents combine software speed with behavior that can be difficult to predict.• Useful agent behavior can conflict with security systems built around anomaly detection.Highlights00:41 What Teleport does and why infrastructure identity matters08:37 Why companies may already be behind on agent security13:47 Why an electronic account is not the same as identity15:11 What actually defines the identity of an AI agent?22:49 Why agent speed and unpredictability change the risk equation29:04 The conflict between useful agent behavior and anomaly detectionOne Line That Stuck“Agents are just as unpredictable as humans, but they are way, way, way faster.”Follow The Tech Trek for more conversations with the people building and leading technology companies.
- 1 weeks ago31 min
Can AI Agents Help One Founder Run a Company?
AI agents are moving beyond helping with individual tasks. The bigger question is how much of a company they can actually run.Ben Cera, founder of Polsia, joins The Tech Trek to discuss what happens when AI handles engineering, support, marketing, research, and other parts of company execution.Ben explains how Polsia uses specialized agents that can take direction from a founder or decide what to work on autonomously. He also shares how he uses similar systems inside his own company, which he says has more than 10,000 paying customers and is approaching a $10 million run rate without a traditional full time team.The conversation gets into where humans still matter, why AI mistakes may be acceptable, and how faster execution changes the way founders test ideas.Key Takeaways• AI agents can move from completing tasks to coordinating entire business functions.• Faster execution gives founders quicker feedback on what works and what does not.• Humans still matter most for judgment, direction, and authentic storytelling.• Autonomy requires accepting some mistakes instead of demanding perfect AI output.Highlights02:43 What changes when AI becomes part of how a founder operates04:03 Turning customer support into a system that can also fix problems06:19 Running a company without a traditional full time team13:47 Why founder judgment still matters when AI gives the options18:49 How specialized agents coordinate engineering, marketing, and outreach22:10 What happens when autonomous AI makes the wrong decisionOne Line That Stuck“You have to trust your gut and you have to be willing to make mistakes.”Follow The Tech Trek for more conversations with the people building and leading technology companies.
- 1 weeks ago31 min
How AI Is Changing Engineering Workflows and Software Teams
AI coding agents can help engineering teams ship more code. But the bigger change may be what engineers spend their time doing.Viren Baraiya, Co-Founder and CTO of Orkes, joins The Tech Trek to discuss how AI is changing workflow orchestration, engineering productivity, project delivery, and hiring. As agents take on more implementation work, engineers are spending more time on design, architecture, review, and verification.Viren shares how his team measures the return on AI through product velocity, stability, and the ability to build things that previously required more time or outside resources. He also explains how Orkes manages model costs by using stronger models for difficult reasoning and smaller models for implementation.Key Takeaways• Coding agents increase output, but they also increase the need for verification.• Engineers are shifting from pure implementation toward design, review, and orchestration.• Repeated AI tasks can become reusable workflows that reduce ongoing token usage.• Hiring should test how engineers actually work with agents, not just manual coding.Highlights01:21 Why agents are workflows and where orchestration fits into AI systems05:19 How AI changed feature velocity, testing, and customer engineering at Orkes07:44 Measuring AI ROI through velocity, stability, and new product capabilities09:19 Why engineers increasingly look more like tech leads12:56 Turning repeated AI requests into reusable workflows to reduce token usage19:34 Why Orkes changed engineering interviews to include agentic codingOne Line That Stuck“That has become a more important skill than actually writing the code now.”Follow The Tech Trek for more conversations with the people building and leading technology companies.
- 2 weeks ago23 min
How AI Agents Are Changing Who Can Build Software
AI is doing more than helping engineers write code faster. It is starting to change who can participate in software development, how teams divide work, and where technical talent creates the most value.Shaosu Liu, Co Founder and CTO at Loop, explains how his team is using AI agents across the software development lifecycle while also enabling highly technical people outside traditional software engineering roles to build customer specific workflows. The result is a different model for scaling technical work, one that matters for founders and technical leaders thinking about team design in the age of AI.Loop is also using forward deployed engineers as core product engineers who can work directly with customers, understand difficult edge cases, and turn those requirements into product improvements.Takeaways• AI agents can now support much more than coding, including specifications, testing, deployment, validation, rollout, and production management.• Giving technical people better AI tools can expand who is capable of contributing to software development.• The final few percent of a customer workflow may consume most of the manual effort. AI makes that customization more practical.• Forward deployed engineers need both technical ability and the judgment, communication skills, and confidence to work directly with customers.Key Moments04:37 How Loop uses AI across the software development lifecycle07:05 Why people outside traditional engineering roles are writing significant amounts of code09:03 Why automating the final few percent of a workflow can remove most of the remaining manual work11:01 Why forward deployed engineers matter when customer requirements get complicated16:37 Why forward deployed engineering is often a path to another role rather than a long term career19:50 How Loop evaluates technical ability and customer facing skills when hiringOne Line That Stuck“That last 5% automation ends up saving 100% of time.”Follow The Tech Trek for more conversations on AI, engineering, product, data, hiring, and technical leadership.
- 2 weeks ago29 min
How to Build a Data Team Ready for AI
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.
- 3 weeks ago30 min
How AI Startups Can Challenge the Enterprise Services Model
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.
- 3 weeks ago31 min
From Head of AI to CTO: How AI Is Changing Engineering and Product
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.
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About The Tech Trek
The Tech Trek is a podcast for founders, builders, and operators who are in the arena building world class tech companies. Host Amir Bormand sits down with the people responsible for product, engineering, data, and growth and digs into how they ship, who they hire, and what they do when things break. If you want a clear view into how modern startups really get built, from first line of code to traction and scale, this show takes you inside the work.
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