Podcasts about tech trek

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.

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.

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.

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.

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.

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 Highlights00:45 How Adonis applies agentic AI to revenue cycle management02:05 Lessons from building a second healthcare technology company04:45 Using AI for customers and inside the company08:55 Why healthcare progress often starts with focused swim lanes14:25 The distribution lesson Akash carried into Adonis20:40 How operational efficiency may improve patient access and rural healthcareOne 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.

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 Hearing00:00 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 analytics07:10 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.

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 Moments00:00 Why moving enterprise AI into production remains difficult01:55 The difference between classical AI and generative AI adoption07:05 How companies can use AI without having a formal AI strategy11:35 Why successful POCs often struggle when they reach production17:35 Competitive pressure, AI FOMO, and the need to calculate real ROI22:00 Why AI adoption requires cross organizational change33:10 Where a company with limited AI maturity should beginOne 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.

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 Highlights00:00 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.

Voice AI is moving from simple call routing into work that used to require trained human agents. The harder question is what happens when those conversations involve lending, collections, servicing, compliance, and real customer risk.In this episode of The Tech Trek, Amir Bormand speaks with Joshua March, founder and CEO of Veritus, about building AI voice agents for regulated financial services. Joshua shares why consumer lending is a demanding test case for voice AI, what makes regulated conversations different, and why the next version of the contact center may be built with much smaller teams overseeing AI systems.Practical takeawaysAI voice agents only matter if they can actually resolve the issue. Joshua argues that users have been trained to distrust automated phone systems because most IVRs block progress instead of helping.Regulated communication is not just about what the agent says. It also includes who can be contacted, when they can be contacted, call frequency, TCPA rules, QA, and post call compliance.Complex voice agents require more than a prompt. Joshua talks about context engineering, state management, specialized background agents, compliance monitoring, KYC workflows, latency, and turn detection.AI changes startup execution. Small teams with experienced people can build and ship much more than before, but that also raises the pressure to move faster.Venture backed AI companies face a bigger bar. Joshua makes the case that higher seed valuations and larger funds increase the need for very large outcomes.Timestamped highlights00:00, Why Veritus is focused on AI communications for financial services and voice agents in consumer lending02:10, Joshua's path from Facebook apps to social customer service, messaging, bots, and now voice AI05:05, Why AI voice agents may replace a large share of traditional call center work07:00, Why customers have learned to fight IVRs and what changes when AI can actually solve the problem10:00, The compliance layers around regulated voice conversations in lending, servicing, origination, and collections14:00, Why production voice agents need context engineering, state machines, background agents, observability, and monitoring20:30, How AI has changed startup hiring, management, productivity, and the role of experienced individual contributorsOne Line That Stuck“This isn't a crappy IVR that's just trying to get in my way, this is an intelligent system that can actually take actions and actually resolve my issue.”Practical lens for technical teamsIf you are building AI into customer operations, the hard part is not only getting the model to speak well. The harder work is making sure it knows what it can do, when it can act, what rules apply, how it is monitored, and when humans need to step in.That matters even more in regulated industries, where the conversation itself is only one part of the system.Follow The Tech Trek for more conversations on how technical teams are building, operating, and adapting around AI, data, product, platform, and engineering execution.

Joanne Chen, VP of Data and AI at SimplePractice, joins The Tech Trek to talk about what it takes to build AI data products in a regulated, sensitive domain where privacy, consistency, monitoring, and customer trust have to be designed from the start.This conversation gets into why AI product development feels different from traditional software, how teams should think about quality control, and why not every valuable AI solution needs to be GenAI.Practical Takeaways• AI products need defense in depth, especially in healthcare, where privacy, confidentiality, and security cannot depend on one layer of protection.• The core product questions still matter. What customer pain does this solve, who benefits, and what does it take to ship responsibly?• AI changes the development life cycle because outputs are not always deterministic and quality can degrade after launch.• Teams need monitoring, validation, and a plan for edge cases before putting AI features in front of customers.• AI literacy is becoming part of every role involved in building, marketing, supporting, and operating software products.Timestamped Highlights00:00 Joanne Chen on AI data products, deterministic outputs, and safely shipping AI features01:20 What SimplePractice does for mental health practitioners and group practices02:30 Why healthcare AI needs multiple layers of risk protection05:00 What makes an AI data product different from a traditional data product08:15 Why stakeholder expectations around AI have widened so much10:40 How AI changes the work across engineering, CS, marketing, and support13:10 Where AI can help reduce tedious administrative work in healthcare16:45 Why leaders need to keep their hands dirty with new AI toolsOne Line That Stuck“Keeping hands dirty is important.”Subscribe to The Tech Trek for more conversations on how technical teams are building, operating, and adapting around AI, data, product, and engineering execution.

Kevin Haggard, Vice President of Engineering at Barracuda, joins The Tech Trek to talk about how AI is showing up across cybersecurity products, engineering workflows, team adoption, and software delivery culture. He shares how Barracuda is approaching AI with guardrails, why adoption varies across teams, and what happened when the company ran protected AI dev days for the engineering organization.What to take from this episode* AI adoption inside engineering teams will not be even. Some teams are already orchestrating agents from requirements to deployment, while others are still figuring out where AI fits into their day to day work.* Guardrails matter more in security sensitive environments. Barracuda uses an AI gateway and control plane so teams can experiment without leaking data or letting agents take uncontrolled actions.* Protected time changes behavior. Barracuda's AI dev days gave teams three days with no meetings so they could work with the tools inside real projects instead of treating AI as a side experiment.* The coding bottleneck may move. AI can create more code faster, but QA, testing, release safety, problem definition, and rollback mechanisms become even more visible.* The engineer's role is shifting from operator to orchestrator. Kevin argues that system design, review, context, and crisp instruction will become more valuable as agents take on more execution.Key moments00:28, What Barracuda does and how AI pairs with people in cybersecurity03:35, Why AI adoption varies across engineering teams inside a larger organization05:06, The need for AI guardrails, gateways, and control planes06:20, How Barracuda ran AI dev days across the organization08:42, The light bulb moments from product, design, and engineering teams13:07, Why AI velocity makes existing delivery bottlenecks harder to ignore17:00, How engineers may move from writing code to orchestrating agents and reviewing systemsOne line that stuck:“Their role is going to elevate more.”Practical moves from Kevin's experience* Bring trusted partners in for training, but follow that with hands on sessions.* Give teams protected time to use AI inside actual work, not just demos.* Share what the advanced teams are learning so adoption does not stay isolated.* Keep people in the loop, especially when agents are generating code, tests, or workflow changes.* Work backward from release bottlenecks, not just coding speed.Subscribe to The Tech Trek for more conversations on how technical teams are adapting around AI, data, platform, product, and engineering execution.#agenticai #ai #techleadership #engineeringleadership #engineering 

AI adoption is no longer just a policy conversation. For many organizations, the bigger question is how to move faster without creating avoidable risk.In this episode of The Tech Trek, Amir Bormand sits down with Aimee Cardwell, CIO and CISO in residence at Transcend, to talk about responsible AI deployment, the tension between speed and control, and how leaders should think about security, compliance, productivity, and customer experience as AI moves through the enterprise.Aimee brings a rare view across the CIO, CISO, and board lens. The conversation gets into why blocking AI often backfires, how prompt redaction can help teams move faster safely, where companies should draw the line on risk, and why some teams may need to rethink old assumptions about tech debt, code ownership, and modernization.Practical Takeaways• Responsible AI depends on the lens. Security, compliance, business, board, and technology teams may all define it differently.• Blocking employee AI usage can create worse outcomes. People may use shadow tools anyway, or teams may fall behind in productivity.• Prompt redaction and enterprise agreements can give teams room to experiment while reducing exposure of sensitive data.• Moving fast is not the same as releasing half finished customer experiences. Bad AI tools can train customers to distrust the entire interaction.• AI may change how teams think about tech debt, refactoring, and whether some legacy systems should be rebuilt instead of patched forever.Timestamped Highlights00:00 Responsible AI deployment and why the definition changes by role02:35 Aimee explains the CIO, CISO, and board perspectives on AI adoption05:14 Why companies that block AI may create shadow usage and slower teams06:52 Prompt redaction as a practical way to let employees experiment safely10:40 How AI risk changes when the data exposure model is different from traditional insider theft15:10 Why releasing poor AI customer experiences can damage trust21:50 Using shared enterprise prompts to raise the quality of AI output across engineering teams26:20 How AI could change the way teams approach security debt and code modernizationOne Line That Stuck“The conversation has flipped, and it is really how can I get the company to go faster.”Pro Tips• Start by identifying what truly makes your business defensible. Not every asset carries the same risk.• Give employees safe paths to use AI instead of pretending they will not use it.• Build shared prompts with engineering standards, approved tools, and company context so teams do not start from scratch every time.• Ask whether old assumptions still hold. Some decisions made sense when changes were expensive, slow, or risky. AI may change that equation.Subscribe to The Tech Trek for more conversations on how modern technical teams are building, hiring, operating, and adapting around AI, data, platform, product, and engineering execution.#ai #agentic #techleadership #engineeringleadership 

AI agents are easy to demo. They are much harder to trust, maintain, govern, and put into production.In this episode of The Tech Trek, Amir Bormand talks with Lucas Thelosen, CEO and cofounder at Gravity, about the agent economy, AI analytics, and what changes when analysts move from doing every task themselves to managing AI systems that create more bandwidth.Lucas shares why Gravity built Orion, an AI analyst, after years working in analytics, product, and data teams at companies like Looker and Google. The conversation gets into the messy middle of AI adoption, why so many agent projects struggle to make it into production, and how context may become one of the most valuable assets a company owns.Practical Takeaways• Agent prototypes are easy. Production agents require support, maintenance, accuracy checks, and clear ownership.• Not every company should build every agent internally. If the capability is not core to what you sell, buying may be the faster path.• Context matters because it lets humans critique AI output with business judgment, not just technical review.• Analysts may shift toward data architecture, governed data models, and internal product management for analytics.• AI does not remove human responsibility. It raises the bar for review, delegation, and decision making.Timestamped Highlights00:40, What Gravity is building with Orion, an AI analyst designed around the work analytics teams already know well.02:28, Why mature companies still miss major insights, even when they already have data teams.03:43, The agent economy reality check, easy prototypes, hard production, and the gap between demo and durable system.06:28, Why companies still build agents internally, even when many projects never reach production.09:48, The case for experimenting now instead of waiting for the AI stack to settle.11:40, How AI shifts people from doing the work to managing the work.18:50, What the future analyst role may look like as AI takes on more of the execution layer.One Line That Stuck"Where previously you were the person doing the work, now you're the manager."Subscribe to The Tech Trek for more conversations on how technical teams are building, hiring, operating, and adapting around AI, data, platform, product, and engineering execution.

Yahoo is not just adding AI on top of existing products. It is using AI across product experiences, internal tools, engineering workflows, and modernization efforts.In this episode of The Tech Trek, Lee Zen, CTO at Yahoo, joins Amir Bormand to talk about modernizing at massive scale, moving from on prem infrastructure to the cloud, rebuilding internal tools with AI, and how engineering organizations need to rethink process when agents can move faster than people.Lee also shares how Yahoo views AI as a coworker, not just a tool, and why the next bottleneck in software delivery may be human judgment.Practical Takeaways• Modernization at scale often means operating in two worlds at once, keeping proven systems running while new cloud based services move faster.• AI can help teams move past legacy tools by reverse engineering requirements and rebuilding modern versions from scratch.• The real unlock is not only code generation. It is connecting agents to documents, chats, emails, production context, and internal knowledge with the right permissions.• As agents speed up execution, engineering teams need to rethink where human approval, judgment, and review should live.• The build versus buy equation is changing because some tools that were too expensive to build before may now be realistic to create internally.Timestamped Highlights00:31, Yahoo's mission and why the internet still feels hard to navigate02:01, Where AI fits across Yahoo products and engineering work03:30, The challenge of moving from on prem data centers to cloud based infrastructure05:27, How Yahoo has used AI to rebuild internal tools and leave technical debt behind07:25, Why agents need access to engineering context, not just code10:20, AI as a coworker and the shift from human speed to machine speed16:27, Why parts of the SDLC may need to change as AI increases delivery speedOne Line That Stuck“AI as a coworker, not just as a tool.”The Tech Trek is for technical leaders thinking through how teams build, operate, modernize, and adapt as AI changes the work. Subscribe or follow for more conversations with engineering, product, data, and technology leaders.

Mike Choi wanted to work at Apple for years. Then he got there and had the moment many ambitious builders eventually hit.Is this the thing I was sprinting toward?In this episode of The Tech Trek, Mike Choi, co founder at Koah, shares his path from Korea to the United States, mandatory military service, Apple, Twitter, and eventually building Koah, an AI monetization company helping AI app builders create sponsored experiences.The conversation is less about the glamour of startups and more about what founder work actually demands: making decisions without complete information, learning from Big Tech without copying it, and staying focused when AI moves faster than your team can absorb.Practical Takeaways• Big Tech can teach you strong operating patterns, but startups force you to build your own style.• Founder decisions rarely come with complete data. Moving creates the next data point.• In AI startups, speed can become a distraction if every new tool or feature changes the plan.• Clear vision helps teams make decisions without waiting on the founder.• Knowing when to share an idea matters as much as having the idea.Timestamped Highlights00:38, Mike explains Koah and why AI products need new monetization models.02:25, Mike shares how his father's Korean Air Force service brought him to the United States as a child.05:01, Mandatory military service, pausing college, and learning to code around strong engineers.07:29, The long term goal of working at Apple and the unexpected feeling after getting there.10:57, Why Mike chose to build from scratch instead of staying on the Big Tech path.14:05, What Big Tech did and did not prepare him for as a founder.17:03, The founder lesson of making decisions before the full picture is clear.19:35, Why AI startups move so fast and how shiny object syndrome drains energy, time, and attention.One Line That Stuck“Just make the decision, produce data points that way through actions, and make a better decision tomorrow.”Subscribe to The Tech Trek for more conversations on how modern technical teams are building, hiring, operating, and adapting around AI, data, platform, product, and engineering execution.

Most healthcare AI stories start with diagnosis. Edmund Jackson thinks that misses the real bottleneck.In this episode of The Tech Trek, Edmund Jackson, CEO and founder of Unity AI, joins Amir to talk about AI for healthcare operations. The conversation gets into why scheduling, staffing, follow up, payer coordination, and interoperability are often where healthcare breaks down, and why solving those operational problems may matter more than chasing the flashiest use cases.Edmund brings a healthcare first view to AI. His argument is simple: healthcare is not slow because people are ignoring technology. It is slow because the real workflows are complex, regulated, high context, and hard to capture cleanly in software.What You'll Take Away• Why healthcare experience matters when choosing which AI problems are actually worth solving• Why diagnosis is not always the best starting point for healthcare AI• How scheduling becomes much more complex when patients, payers, clinics, staff, protocols, and follow up all have to line up• Why AI can help clinics save time while moving human staff toward higher value patient interactions• Why interoperability is still hard, even with standards like FHIR gaining momentumTimestamped Highlights00:29, What Unity AI does and why healthcare operations is the focus01:11, Why healthcare AI needs people who understand the domain, not just the technology02:26, The danger of solving the hardest or flashiest problem instead of the most pragmatic one05:15, Why AI may finally help healthcare handle personalization and operational complexity at scale07:47, Why scheduling a healthcare visit is nothing like scheduling a delivery or restaurant order10:42, How operational AI can save time and reduce downstream chaos in clinics24:33, Why healthcare data is much harder to structure than financial dataOne Line That Stuck“Software is like children. Making it is all fun and games. Maintaining it is a whole other question.”Practical Takeaways• Start with the workflow that actually blocks progress, not the one that sounds most impressive• In healthcare, operational context is often the product• AI should create more room for humans to handle the interactions that require judgment, care, and clinical responsibility• More software is not always the answer, especially in regulated environments where maintenance, compliance, and security matterSubscribe to The Tech Trek for more conversations with founders, operators, and technical leaders building through AI, data, product, platform, and engineering execution.

Deepak Bapat, CTO and co founder at Tabs, joins The Tech Trek to talk about how his team is using tools like Claude Code and Cursor, where AI is helping, and why systems thinking may matter more than raw coding ability as engineering work shifts.Practical Takeaways• AI coding agents are already producing useful production work, but judgment still matters.• Tool choice may be less important than standardizing the expected output.• Messy repos can make AI generated work harder to trust, so cleanup and patterns matter.• The future engineer may look more like a product engineer with strong systems thinking.• Teams may move from debating features to rapidly building multiple versions and testing what works.Timestamped Highlights00:37What Tabs does and why contracts create hard revenue workflow problems for B2B finance teams.02:16Deepak compares pre AI engineering work with the current shift toward AI assisted development.05:09How the Tabs engineering team uses Claude Code, Cursor, and other coding tools in real work.08:13Why inconsistent codebases create more risk when teams add coding agents14:00The idea that teams can build the same feature multiple ways in one afternoon.20:53Deepak's view on whether the future team needs separate PMs and engineers, or more product engineers.23:38A future where software can become more bespoke to each customer because AI changes the cost model.One Line That Stuck“You can build on three different work trees the same feature in three different ways and see which one you like, and you can do it all in an afternoon.”Practical Moves From The Conversation• Keep humans close to the review process, especially when the last five percent still requires taste and judgment.• Clean up inconsistent code patterns before letting agents operate broadly across the repo.• Hire for adaptability, systems thinking, and problem solving, not just past tool familiarity.• Use AI to explore more product options faster, but do not remove the need to ask whether the feature should exist.Subscribe or follow The Tech Trek for more conversations on how technical teams are building, hiring, and operating as AI changes the work.

Agentic coding is not just making engineers faster. It is changing how teams triage bugs, prototype features, involve product, and think about hiring.Scott Weller, CTO and founder at EnFi, joins The Tech Trek to talk about how his team is building around agentic software development while operating in financial services, where trust, accuracy, and human judgment still matter. EnFi uses AI agents to work through complex financial data rooms, extract knowledge, and support faster analysis in commercial lending.In this episode, Scott breaks down how EnFi moved from simple coding assistance to a broader development harness, why Slack became a central interface for agents, how product and business leaders can now participate earlier in feature creation, and why engineering interviews need to change when AI is part of the actual job.Practical Takeaways• Start with specific productivity goals before trying to rebuild the whole development process.• Agentic tools work better when they connect to the team's real workflow, shared context, and software lifecycle data.• Faster code generation changes the cost model, but it also creates new problems around review, testing, prioritization, and decision fatigue.• Product, sales, and executive teams may be able to prototype ideas faster, but engineering still has to make the work production ready.• Hiring needs to test how people solve problems with AI, not whether they can perform the old interview format without help.Timestamped Highlights00:38, What EnFi is building around financial data, AI agents, and commercial lending02:13, Why software teams may need to forget part of their old development process04:45, How EnFi started with productivity gains before building a broader development harness09:53, Why merge requests went up, and why that alone is not the same as better outcomes10:30, How Slack became the entry point for an agentic development harness14:10, What happens to agile ceremonies when teams can create discovery builds much faster25:08, Scott's view on whether AI reduces engineering headcount or changes the work engineers do31:00, How EnFi is changing technical interviews for an AI assisted engineering environmentOne Line That Stuck“We do not care if you use AI to solve the problems, we just want to know you can solve the problem.”Practical Takeaways For Technical TeamsPut agents close to where work already happens.Keep humans in the loop for review, testing, and production judgment.Treat AI generated code as cheaper to create, not free to maintain.Build stronger test harnesses instead of slowing everything down with excessive process.Update interviews to reflect how engineering work is actually getting done.Subscribe to The Tech Trek for more conversations with technical leaders building, hiring, and operating through the next stage of AI, data, product, and engineering execution.

AI adoption looks very different when mistakes can create legal, financial, and reputational risk.Vijay Gandra, Global CDO at Acrisure, joins The Tech Trek to talk about AI transformation inside a regulated industry, where explainability, data quality, governance, cost, and team readiness matter just as much as model capability.The conversation covers the trust gap in AI, how data teams are shifting from dashboard production to conversational data access, when to buy versus build, and why AI proof of concepts need to be judged by business value, operational efficiency, and customer impact.Practical Takeaways• Regulated industries cannot treat AI as a black box. Decisions need traceability, consistency, and often a human review layer.• Data quality has to be addressed from the start. AI can amplify bad data as easily as it can create value.• Data teams are moving beyond dashboard factories toward conversational data access and generative interfaces.• Most companies can likely use existing AI tools for many needs, but sensitive IP and core business logic may require internal capabilities.• AI cost will become a bigger production question as companies move from experimentation to scaled deployment.Timestamped Highlights00:47, Acrisure's shift from insurance brokerage toward fintech and financial tools.01:44, Why regulated industries face a trust gap with AI and need explainable decisions.04:41, How data teams are evolving from dashboards to conversational data enablement.08:28, The build versus buy question and where internal AI tools may still make sense.10:52, Why AI experimentation can get expensive before companies know what works.16:15, How to evaluate AI proof of concepts based on customer value, efficiency, and business impact.18:14, Why data governance and data quality need to be treated as day one requirements.One Line That Stuck“In an industry like this, a 5 percent deviation is not just a simple glitch. It is actually a legal liability.”Subscribe to The Tech Trek for more conversations with technical leaders building, operating, and adapting modern teams around AI, data, platform, product, and engineering execution.

Leonid Belkind, co founder and CTO at Torq, joins The Tech Trek to talk about what changes when an engineering organization does more than experiment with AI tools. Torq builds agentic security operations, and Leonid shares how his team is using AI across engineering, product, hiring, customer success, and go to market work.This conversation gets past the shallow version of “AI makes coding faster.” Leonid makes a clear distinction between coding and software engineering, and explains why the best teams are using AI to shift cognitive load, not remove judgment.Practical takeaways• AI does not erase software engineering. It changes where engineering judgment shows up.• Strong engineers still produce better AI generated work because they know what to ask, what to test, and what tradeoffs matter.• Hiring processes need to reflect how engineers actually work now, including how they use AI to build, explain, and defend technical decisions.• Productivity should not only be measured by speed. Leonid talks about throughput, maturity of delivery, and whether teams can produce more without lowering quality.• AI adoption becomes more powerful when it moves beyond engineering into product, customer success, revenue operations, and talent.Key moments00:32What Torq means by agentic security operations and why different tasks need different AI approaches.01:49Why building AI native products with AI native methods creates a useful feedback loop for engineering teams.05:28How AI shifts cognitive load so engineers can spend more attention on user experience, architecture, and product value.10:34The difference between software engineering and coding, and why that distinction matters more now.15:13How Torq has changed technical interviews to evaluate AI assisted engineering instead of pretending AI does not exist.21:51How one R&D group measured meaningful delivery gains after adopting AI more deeply.24:25Why AI adoption is moving into product, customer success, revenue operations, and talent teams.One Line That Stuck“Software engineering as a discipline is not going away. It just changes a phase a bit.”Practical moves to stealFor hiring, Leonid suggests giving candidates more complex take home work because AI is now part of the real engineering workflow. The evaluation then shifts to the candidate's ability to explain the architecture, defend decisions, describe how AI was used, and show how they tested and constrained the output.That is a much better signal than asking someone to work as if the tools do not exist.Subscribe or follow The Tech Trek for more conversations with technical leaders building, hiring, and operating through the next shift in software, data, AI, and engineering execution.

AI coding tools are not just changing how software gets written. They are changing how teams work, how engineers are evaluated, and where bottlenecks show up.Scott Breitenother, CEO and cofounder of Kilo, joins The Tech Trek to talk about what engineering looks like when developers are managing multiple agents, work continues overnight, and the real constraint is no longer typing code, but judgment, ownership, and process design.Scott shares how Kilo uses Kilo to build its own product, why AI only creates speed when companies rethink their workflows, and how teams can build trust in agent generated code without creating a new layer of busywork.Practical Takeaways• AI does not automatically make teams faster. If approvals, meetings, and handoffs stay the same, the bottlenecks simply move.• Engineers using coding agents still own the outcome. AI can assist with the work, but accountability for quality does not disappear.• The strongest teams will find a middle ground between blindly accepting AI output and reviewing every line as if nothing changed.• Agentic engineering may feel novel now, but Scott believes it will eventually just be called engineering.• Always on agents are already useful for monitoring, triage, and preparing recommended fixes, even if full autonomy is still selective.Episode Highlights00:38 Scott explains what Kilo is building across AI coding, open source infrastructure, and always on agents.01:16 How Kilo uses its own tools internally, and why developers are shifting from working with one agent to managing many at once.05:34 Why companies often fail to see AI speed gains when they layer new tools onto old processes.08:51 The trust curve with coding agents, from early experimentation to accountability, review, and better judgment.12:39 Why Scott sees agentic coding as a transition phase, not a permanent category.15:32 Two habits he thinks matter most right now, staying curious and trying a wide range of models and tools.18:03 What always on agents can already do today, and how that could expand over the next year.One Line That Stuck“Bringing in AI does not remove accountability from whoever creates the PR.”Pro Tips• Start small with AI assisted workflows, then expand into single agents, multiple agents, and automated review as trust grows.• Match review depth to risk. A mission critical system deserves more scrutiny than a simple cosmetic change.• Use automated review to guide human reviewers toward the areas that deserve the most attention.• Keep experimenting. A tool that fails on Monday may be materially better by Wednesday.Stay ConnectedSubscribe to The Tech Trek for more conversations on how modern technical teams are building, operating, and adapting around AI, data, platform, product, and engineering execution.

Tax is one of the hardest places to earn trust with AI. The work is complex, the stakes are personal, and being mostly right is not good enough.In this episode of The Tech Trek, David Kang, founder and CEO of Keeper, explains how his team is applying AI to tax workflows without pretending humans disappear from the process. He breaks down why tax is such a strong fit for language models, where AI can reduce manual review, how Keeper decides when a case needs human escalation, and why the best products may feel less like autonomous agents and more like systems that make experts sharper.Key Takeaways• AI is most valuable when it removes repetitive work while preserving human judgment where risk is highest.• High trust products need clear escalation logic, especially when edge cases drive most of the anxiety.• Tax is a strong fit for AI because much of the work involves language, rules, validation, and workflow routing.• The smartest AI adoption often starts with bounded operational tasks before moving into more domain specific decisions.• Consumer trust in AI can change quickly, but messaging still matters when the product sits inside sensitive workflows.Highlights00:34 Where Keeper fits for people who have outgrown DIY tax software but do not need a traditional personal accountant.02:27 Why tax may be one of the more practical use cases for AI, even in a high stakes environment.07:15 The accounting talent shortage, what automation may replace, and how roles could shift.10:55 How Keeper uses AI before professional review to flag possible issues and optimization opportunities.13:51 Why the company moved from keeping AI in the background to talking about it more directly.17:58 How Keeper separates the routine parts of a tax return from the parts that need expert attention.21:05 The path from simple customer support automation to more advanced tax focused AI workflows.One Line That Stuck“Across tens of thousands of returns and clients, you can kind of get to the point where you err on the side of safety.”Follow The Tech Trek for more conversations with founders, operators, and technical leaders building through the next wave of AI, data, and engineering change.

What happens after you build a public company, spend nearly three decades at the helm, and then find yourself starting over?Rob Locascio, CEO and founder of Uare.ai, joins The Tech Trek to talk about that exact journey. Rob previously founded LivePerson, helped create web chat for customer service, took the company public, and later scaled it into a major conversational AI business. Now he is back in founder mode, building a new company around individual AI, personal knowledge, and human control over data.This conversation gets into what it takes to return to zero, why strong ideas need more than belief, how Uare.ai evolved from a personal loss into a broader AI platform, and why Rob sees the current AI moment as bigger and more complex than the dot com era.Practical Takeaways• Ideas are not the asset. The ability to turn them into something people understand, join, and buy is what matters.• Starting over after success requires shedding the habits of scale and getting back into a true startup mindset.• The first version of a company may only be an entry point. The deeper opportunity often reveals itself through real users.• Rob believes the future of AI should include individual systems built from a person's own knowledge, voice, and data, not only large aggregated models.• The current AI wave has stronger infrastructure than the dot com era, but also more pressure from incumbents and government involvement.Timestamped Highlights00:33 Rob explains Uare.ai and its approach to building AI around individual human knowledge.01:17 The LivePerson story, from inventing web chat to building a large conversational AI company.03:13 What it felt like to leave the company he spent 28 years building and become a founder again.06:04 The personal and family tradeoffs of starting another company later in life.09:06 Why Rob compares building a company to writing a song, and what it means to manifest an idea.15:52 How the original idea for Uare.ai came from wanting to preserve his father's voice and memory.24:00 Rob compares the dot com boom with the current AI cycle, including where he sees real differences.One Line That Stuck“They may be able to take your company, but they can't take your ideas and they can't take you.”Practical Founder Advice• Find the smallest real entry point for the idea and get moving.• Do not let criticism kill something before the market has a chance to respond.• Pay close attention to who shows up early. The wrong people can distort a young company quickly.• Expect the company to evolve. Staying loyal to the original insight does not mean staying frozen in the original product.Subscribe or follow The Tech Trek for more conversations with founders, technical leaders, and operators building through major shifts in AI, data, product, and engineering.

Snigdha Kumar, CEO and co founder at Bricco, joins The Tech Trek to talk about a part of fintech most people never see, state by state licensing.For any financial company trying to launch in the United States, licensing can be slow, expensive, and operationally painful. Snigdha explains why that barrier limits experimentation, how Bricco is trying to automate the process, and why better compliance infrastructure could help more useful financial products reach the market.Practical takeaways• Financial innovation is not only a product problem. Licensing, compliance, reporting, audits, and exams can shape what gets built before a product ever reaches customers.• Lowering the cost of licensing does not remove regulation. It makes the process more efficient while keeping important protections in place.• The biggest barrier for fintech founders is often not knowing what path is available. Education and clearer process design can keep teams from avoiding licensing or choosing expensive workarounds.• Better financial products still need better distribution and awareness. Easy access is not the same as helping people find the right product for their actual financial life.• Responsible financial behavior may need better product design, better incentives, and a stronger cultural signal, not just more advice.Timestamped highlights00:43, Snigdha explains how Bricco is automating state by state regulatory compliance for financial licensing.02:15, How her career has focused on reducing barriers to financial services across Asia, Africa, and the United States.05:05, The reverse culture shock of finding major access gaps inside the US financial system.06:08, Why licensing costs can run into the millions and shrink the number of fintech experiments.09:58, Why reducing the barrier matters, but eliminating it completely would create real risk.12:21, The difference between making financial products easy and making sure people are using the right product.16:05, Why spending has a social identity, but saving and responsible investing often do not.21:10, How Bricco uses education and content to help founders treat licensing as a strength instead of a blocker.One Line That Stuck“Think about licensing as a strength, think about it as a way to own your destiny.”Practical TakeawaysFor fintech founders and operators, the message is simple. Do not treat licensing as a late stage legal detail. It can affect product timelines, market access, capital needs, and the type of company you are able to build.For technical and product leaders, this is a reminder that infrastructure is not always code. Sometimes the biggest product constraint is the operating system around the business.Subscribe or follow The Tech Trek for more conversations with founders, builders, and operators working through the real decisions behind modern technical companies.

Adam Kirk, CTO and cofounder of Jump, joins The Tech Trek to talk about what it really takes to build AI native products for people who do not want to think like technologists.Jump serves financial advisors, a market where ease of use, trust, workflow fit, and domain context matter as much as the model itself. Adam shares how his team validates product ideas, uses coding agents across engineering, and is rethinking how technical teams build, review, and hire in the AI era.What You'll Take Away• AI native products still win or lose on adoption. If the user feels like they are programming, the product is already too complicated.• The engineering bottleneck is moving. AI can generate code faster, but teams still need humans to review, validate, and understand the tradeoffs.• Product teams can now get closer to the build. PMs using AI to prototype create sharper product definition, even when engineers still rebuild the final version properly.• Technical debt is not disappearing. Code may be cheaper to write, but data models, migrations, architecture, and judgment still carry real risk.• Engineering interviews are breaking. If engineers use AI every day, hiring teams need better ways to assess ownership, judgment, and technical taste.Timestamped Highlights00:38Adam explains how Jump helps financial advisors turn client meetings into notes, CRM updates, and advisor specific workflows02:20Why less technical users force better product validation, and why a flexible interface can still feel like programming.07:00How Jump uses coding agents across the engineering team, and why code review matters more as AI generated code improves.11:15Why PMs vibe coding product ideas can help engineers understand what needs to be built.14:08Where AI is creating real productivity gains, and where human coordination still slows things down.18:00Why some technical debt may get easier to manage, but data modeling and migrations remain hard.20:51How AI is forcing engineering leaders to rethink coding interviews, referrals, and what great engineers should be measured on.One Line That Stuck“Generating code is really not the bottleneck anymore. It is validating the code, reviewing the code, and sharing the context around to the team.”Practical Takeaways• Test product ideas with real users before engineering builds too far.• Treat AI prototypes as product definition, not production architecture.• Use coding agents to speed up the work, but do not skip review.• Assess engineers for judgment, ownership, and decision quality, not just raw syntax.Follow The ShowSubscribe to The Tech Trek for more conversations with technical leaders building the next generation of AI native products, teams, and workflows.

Krishna Sai, CTO at SolarWinds, joins The Tech Trek to talk about one of the biggest shifts happening inside IT and engineering teams: AI is moving people from operators to orchestrators.The conversation goes beyond faster code and automation. Krishna explains why AI is changing how teams think about systems, governance, validation, observability, and the skills technical leaders will need as work moves from manual execution to higher level oversight.Key Takeaways• AI is raising the level of abstraction for IT and engineering teams. The work is shifting from operating systems manually to designing systems that can increasingly run, adapt, and respond on their own.• AI does not automatically reduce workload. In many teams, it changes the type of work by moving effort from execution into validation, judgment, risk management, and governance.• Code generation is only one part of the delivery system. Without testing, security review, observability, and strong engineering process, faster code can create more problems faster.• The best AI outcomes depend on strong foundations. Clean data, connected systems, clear ownership, and resilient architecture matter more as AI becomes part of core workflows.• Technical professionals will need stronger systems thinking, business context, adaptability, and domain understanding as AI changes the shape of day to day work.Timestamped Highlights00:00Krishna Sai joins the show and sets the stage for a conversation about AI, IT responsibility, skill gaps, and the latest SolarWinds IT Trends Report.02:14Why IT is moving from operator to orchestrator, and what that means for teams that used to spend most of their time responding to tickets and manually managing systems.04:54Krishna explains why AI feels different from prior technology shifts. This is not just infrastructure change. It touches individual workflows, jobs, and decision making.08:56The messy middle of AI adoption. Teams are getting faster at some tasks, but the workload has not disappeared. It has moved into validation, review, and oversight.14:46How AI may force teams to rethink the software delivery cycle, sprint structure, feedback loops, and the speed at which customer issues can be resolved24:27Krishna shares how principles from distributed systems, including loose coupling and high cohesion, can help leaders build AI systems that can change without breaking everything around them.Standout Moment“AI is a multiplier. It does not magically fix all your problems. It multiplies your current state.”Pro Tips• Do not measure AI success only by how much faster a team can generate code or complete a task.• Look at the full system around the work, including testing, review, security, observability, and ownership.• Build AI workflows with enough flexibility to swap tools, models, and processes as the technology changes.• Invest in systems thinking and domain knowledge. Those skills become more valuable as execution becomes easier to automate.Call to ActionSubscribe to The Tech Trek for more conversations with technology leaders on how AI, data, engineering, and modern systems are changing the way companies build.

Dan Wald, cofounder and chief AI officer at Sciemo, joins The Tech Trek for a sharp conversation about what AI can and cannot do inside real business workflows.The big question: can AI move beyond quick answers and actually support the messy, context heavy work that still lives in Excel, data teams, and functional expertise?Dan breaks down why consumer style AI has trained people to expect instant answers, why that creates risk inside companies, and why the next wave of AI products needs more than a chat box. It needs context, transparency, guardrails, and humans who understand the work well enough to challenge the output.The conversation also gets into AI agents, coding, entry level talent, narrow workflow specific AI, and why replacing judgment is a much harder problem than replacing repetitive tasks.Key takeaways• AI tools are only useful when they understand the context behind the question, not just the wording of the prompt.• Excel remains powerful because users can see the data, change assumptions, and understand the logic. AI products need to earn that same level of trust.• The best AI workflows are not black boxes. They let users inspect assumptions, challenge outputs, and adjust the answer.• Agents can speed up work, but they still need human judgment, especially when the task requires strategy, constraints, or domain expertise.• AI may change entry level work, but companies still need people who can think critically, solve new problems, and understand why the output is right or wrong.Timestamped highlights00:40 Dan explains how Sciemo helps consumer brands unify messy data and apply AI to inventory, pricing, assortment, and promotion decisions.02:30 Why the single prompt experience has changed what people expect from AI, and why that expectation can break down inside the workplace.04:19 How purpose built AI differs from general AI, especially when the workflow requires context, guardrails, and a clear goal.07:41 Why Excel is still hard to replace, and what AI systems need to learn from the control and transparency users already expect.12:57 Dan compares AI agents to unlimited interns, useful for many tasks, but still limited without expert direction.21:57 The slap chop analogy, and why faster tools do not automatically make someone better at the underlying craft.31:15 Why predictions about technology and work are so hard to get right, even when productivity clearly improves.A line that stuck“Used properly, they're great. Used poorly, it's a very new technology. There will be more mistakes than there are winners.”Practical points worth taking• Do not treat a confident AI answer as a complete answer.• Build AI around real workflows, not generic prompts.• Keep humans close to the assumptions, especially when the decision has business impact.• Use AI to move faster, but make sure someone still understands the logic behind the work.Listen nextFollow The Tech Trek for more conversations with founders, operators, and technical leaders building through the next wave of AI, data, and product change.

Most data teams do not have an AI problem yet. They have an operating model problem.Mike Doll, VP of Data at Guitar Center, joins The Tech Trek to talk about why analytics teams often become reactive ticket factories, and what it takes to turn data into a true business partnership.As companies push harder into AI, automation, and faster decision making, the foundation matters more than ever. If the data team is buried in scattered requests, unclear priorities, and dashboard maintenance, AI will not magically fix the problem. It may only expose it faster.Mike shares how modern data teams can rethink intake, structure analytics partnerships, separate quick BI needs from deeper analytical work, and create a more consultative model that helps the business answer harder questions.Key Takeaways• AI will not fix a broken data operating model. Teams still need clear intake, trusted data, business context, and a better way to prioritize work.• Data teams become ticket factories when every request is treated the same and stakeholders do not understand what happens after they ask for help.• BI and analytics serve different needs. Quick reporting should be fast and reliable, while deeper analytics requires judgment, framing, and business partnership.• Self service only works when the data foundation is strong. Without that foundation, it can create more confusion instead of more speed.• The future of analytics is not just faster answers. It is better questions, stronger context, and data teams that understand how the business actually operates.Timestamped Highlights00:41 Mike explains his role leading Guitar Center's central data organization, including data engineering, analytics, BI, data science, and data strategy.02:09 How data teams become ticket factories, and why unstructured requests can turn analytics into a black box for the business.05:29 Why analytics delivery is different from software delivery, and why data teams need closer alignment with business leaders.07:28 Where self service helps, where it breaks down, and why simple questions need a different model than complex business problems.09:47 Mike explains the consulting model for analytics teams, with dedicated business partners, stronger dialogue, and shared value creation.15:35 How AI is changing quick BI workflows, and why harder analytics questions still require human judgment and problem framing.18:00 How Mike started shifting Guitar Center away from reactive ticket taking by improving intake, visibility, communication, and trust.Line Worth Remembering“The value that analytics teams can bring is answering those hard questions.”Practical MovesFor data leaders trying to move beyond reactive analytics, Mike's advice is to start with the biggest points of friction.That might mean creating a clearer intake process, giving stakeholders visibility into work, assigning dedicated analytics partners to key business areas, or rebuilding trust through fast but meaningful wins.The point is not to add process for the sake of process. The point is to create a data function that can move quickly without losing context, accountability, or connection to business value.Stay ConnectedFollow The Tech Trek for more conversations with technology leaders on data, AI, engineering, platforms, and the operating models behind modern technical teams.

Cybersecurity is no longer just about keeping attackers out. It is about what happens when they get in.Andrew Rubin, CEO and founder of Illumio, joins The Tech Trek to talk about the speed of modern attacks, why AI changes the security equation, and how companies should think about breach containment, micro segmentation, and guardrails for agentic AI.This conversation gets into a practical shift every technology leader needs to understand. As companies move faster with AI, security teams are being asked to protect more systems, more users, more tools, and eventually more agents. The old idea of perfect prevention is not enough. The better question is how quickly teams can detect, contain, and reduce the impact when something goes wrong.Key Takeaways• Cybersecurity is moving at the speed of technology. As AI accelerates product, engineering, and operations, attackers and defenders are both moving faster.• Prevention alone is not a complete strategy. Andrew makes the case for breach containment, where the goal is to stop a bad event from becoming a catastrophic one.• AI gives both sides more leverage. Attackers can move faster with fewer constraints, while defenders can use AI to automate routine security work and improve response time.• Agentic AI will create a new security challenge. Companies need guardrails that let teams use AI at scale without creating uncontrolled risk.• Cyber budgets need to map to risk. The conversation should start with what risk is being reduced, not what a tool can do.Timestamped Highlights00:30 Andrew explains what Illumio does and why micro segmentation is really about breach containment.02:36 Why cyber attacks are accelerating because the rest of the technology world is accelerating too.04:35 Andrew challenges the idea that any security company can promise perfect protection.09:46 How agentic AI could help security teams automate mundane work and monitor continuously.13:28 Why cyber spending often gets misaligned when teams focus on tools instead of risk reduction.16:55 Where human judgment still matters in cybersecurity, especially during moments of crisis.20:10 Why large organizations are struggling to let employees use AI aggressively while still putting meaningful guardrails in place.23:46 The parallel between cloud adoption and AI adoption, and why retrofitting legacy systems is harder than building for AI from the start.A Line That Stuck“Cyber is a math problem. The attackers are going after us, the defenders are trying to prevent it or stop it once it happens, and it becomes a math equation at many levels.”Practical Moves For Tech Leaders• Treat AI as a security and operating model shift, not just another tool rollout.• Start security conversations with risk reduction before product capability.• Look for areas where AI can automate repetitive monitoring and analysis without removing human judgment from high stakes decisions.• Build guardrails early, especially as AI becomes embedded into daily workflows for users and developers.Stay ConnectedFollow The Tech Trek for more conversations with founders, operators, and technology leaders building the next generation of AI, data, infrastructure, and security systems.Subscribe, follow, and share this episode with someone thinking about how AI changes the way modern technology teams build and protect systems.

Kenneth Schwartz, VP of Global Data and Governance at Genmab, joins The Tech Trek to talk about what happens when data teams start applying software engineering discipline to modern data work.As AI raises expectations across the business, the challenge is no longer just building more dashboards or models. It is building data products, governance systems, and engineering cultures that can move from experiment to production in a repeatable way.In this episode, Kenneth shares how data teams can reduce sprawl, create stronger stakeholder alignment, shift governance earlier in the process, and use AI agents to accelerate the data roadmap without simply creating more noise.Key Takeaways• Data sprawl often starts with good intentions. Teams want to move fast, but without alignment they can end up solving the same problem in multiple ways.• Software engineering practices are becoming essential in data. Stable interfaces, data contracts, testing, modular design, and clear ownership help data teams scale with fewer downstream breaks.• Governance works better when it is built into the process early. Kenneth explains why governance should not be treated as a cleanup project after the data already exists.• AI can help data teams move faster, but speed alone is not the goal. The bigger opportunity is using automation to improve quality, reduce manual work, and give teams more time to think.• The future of analytics may depend on better foundations. Catalogs, semantic layers, data marketplaces, and governed metrics can make data more usable across BI, apps, chat interfaces, and agents.Timestamped Highlights00:00Kenneth Schwartz joins the show to discuss data engineering, governance, data products, and the growing role of AI in modern data teams.01:17Why data is still catching up to software engineering, and how low barriers to entry have created sprawl across dashboards, models, and experiments.02:55How stakeholder trust, honest conversations, and change management help reduce duplicated work without slowing the business down.05:23The software engineering ideas data teams should borrow, including stable interfaces, data contracts, tests, modularity, and repeatable frameworks.09:21Why infrastructure, data, and security teams need a more unified engineering culture as AI and data use cases become more complex.14:43What it means to shift governance left, and why governance has to become easier for the people expected to follow it.20:35How unstructured data, semantic layers, catalogs, metrics layers, and data marketplaces could change how analytics gets delivered.24:38Why faster delivery should not automatically mean more dashboards, more models, or more work products.Standout Line“More is not always better.”Pro Tips• Do not treat every new data request as a net new build. Look for overlap, reuse, and shared definitions before creating another dashboard or model.• Build trust before trying to reduce sprawl. People are more willing to standardize when they believe the data team is helping them win, not just saying no.• Move governance earlier in the lifecycle. Capture ownership, quality expectations, access needs, and context when data is ingested, not months later.• Use AI to accelerate the hard parts of the roadmap, but keep the focus on better decisions, not just faster output.Call to ActionSubscribe to The Tech Trek for more conversations with technology leaders building the data, AI, and platform foundations behind modern companies. Follow Amir Bormand on LinkedIn for more clips, takeaways, and episode updates.

Farzan Karimi, Deputy CISO at Moderna, joins Amir Bormand for a sharp conversation on one of the most misunderstood areas in cybersecurity, the ethics of offensive security. From red team rules of engagement to nation state deception and the limits of AI in security testing, this episode gets into what happens when the job requires you to think like an attacker without crossing the line. This is a practical conversation for security leaders, engineers, and operators who want a clearer view into how modern security programs actually work under pressure. Farzan shares hard lessons from his own career, explains why red teaming is really about business risk, and makes the case for storytelling over dashboards when security teams need executive buy in. Key Takeaways• Offensive security is not about finding every weakness. It is about simulating what a real attacker would do to reach the business's worst case scenario. • The gray area is real. Just because you are authorized to test a system does not mean every possible action is justified. • Nation state level threats force teams to think differently. Attackers look across the connective tissue of systems, not just isolated tools or apps. • Good red teaming can make the rest of the business stronger by helping teams see real risk, align on priorities, and justify investment. • AI can speed up security work, but it still misses too much to replace experienced human operators. Timestamped Highlights02:02 What offensive security actually means, and why the best programs are built around business impact, not just technical findings. 03:46 Where the ethical gray area starts, from phishing and social engineering to the personal judgment calls that can end careers. 06:03 A story from Farzan's Microsoft days that shows how a valid finding can still go too far when judgment slips. 11:06 Why security leaders have to explain to executives that attackers do not care about internal process, approvals, or red tape. 14:46 A nation state honeypot turned the red team into the target, and forced a complete shift in approach. 24:14 AI is changing the workflow, but Farzan explains why current tools still fall short of real red team depth. A line worth remembering“Just because you can doesn't mean you should abuse those permissions.” Pro Tips• Tie offensive security work to the business's real doomsday scenario, not a generic list of vulnerabilities. • When you find a serious issue, know exactly where the rules of engagement stop, and stop there. • Use attack stories and patterns to earn trust internally. Raw metrics rarely move people the same way. • Treat AI as an accelerator, not a replacement for experienced security judgment. Listen and followIf this episode gave you a better lens on how modern security teams think, subscribe to The Tech Trek, follow the show, and share this episode with someone building, securing, or scaling technology in the real world.

Spencer Penn, Co founder and CEO of LightSource, joins The Tech Trek for a sharp conversation on AI native procurement, agentic workflows, and what actually happens to knowledge work as automation gets better. This episode is worth your time because it moves past lazy takes about AI replacing jobs and gets into something more useful, how work changes, where human value holds, and why procurement may be more strategic than most companies treat it.This conversation starts with procurement, but it quickly expands into a bigger discussion about role design, change management, and the pace of AI adoption inside real companies. Spencer breaks down why some jobs get redesigned while others disappear, how AI can elevate overlooked functions, and what people should do right now if their company is behind.In this episodeWhy procurement is a strong fit for AI, especially where teams are buried in tedious process workThe difference between job automation and job eliminationSpencer's idea of role plasticity, and why it matters more than most AI debatesWhy procurement teams may become more valuable, not less, as AI improvesPractical ways professionals can start using AI before their company rolls out a formal strategyTimestamped highlights00:37 What LightSource does and why direct material sourcing is a high stakes AI use case01:51 Why procurement teams spend too much time on transactional work06:47 Which jobs get enhanced by AI, which ones get eliminated, and Spencer's framework for role plasticity13:44 What the next few years could look like for procurement professionals26:18 Where to start if your company has not adopted an AI native workflow yet30:07 How to learn more about LightSource and connect with Spencer“AI will not replace your job. Someone who knows how to use AI will.”A practical thread running through this episode is simple. Start using the tools now. Use foundation models for secondary work, reporting, summaries, and internal communication. Build familiarity before the workflow shift gets forced on you.If you are interested in AI, procurement, operations, supply chain, or the future of knowledge work, follow The Tech Trek for more conversations like this.

Michael Fanning, CISO at Splunk, joins The Tech Trek for a grounded conversation on how the security leader role is changing in the AI era. This episode gets into the real tension facing modern CISOs, balancing risk without slowing the business down, hiring for technical depth over narrow credentials, and defining success in a field where perfection is not a realistic metric.This is a practical conversation for security leaders, engineering leaders, founders, and operators trying to make sense of AI adoption inside the enterprise. Mike breaks down why security has to move from fear based messaging to business enablement, why many teams may be overlooking strong security talent hiding in adjacent technical roles, and where AI can either reduce burnout or make it worse.In this episodeWhy the CISO role is becoming more engineering driven and more tightly tied to business outcomesWhere AI creates real leverage for security teams, and where it introduces new operational riskWhy the security talent gap may be as much a hiring mindset problem as a supply problemWhat actually causes burnout in security teams, beyond the usual talking pointsHow to think about success in security when zero incidents is not a serious metricHighlights1:44, The CISO role is shifting from pure protection to business enablement7:11, AI creates leverage for defenders, but it is also accelerating the attacker playbook9:31, The biggest AI security risks, from developer copilots to agent driven decision making14:15, Why security teams need room to experiment with AI or risk falling behind16:58, Only 1 percent of CISOs surveyed prioritized technology to close the skills gap22:16, AI can reduce burnout, but only if it cuts noise instead of creating more of itSecurity is about assessing risk and finding a way to say yes in a way that is responsible.A practical idea worth taking back to your teamLook beyond candidates with formal security titles. Mike makes the case that strong engineers, SREs, and cloud practitioners often already understand the systems, access models, and infrastructure realities that matter most. Security can be taught on top of that foundation.Link to report: https://www.splunk.com/en_us/form/ciso-report.htmlFollow The Tech Trek for more conversations with leaders shaping how technology actually gets built, secured, and scaled.

What does it really take to go from engineer to CEO?In this Tech Trek Brief, Michael White, Co founder and CEO of Multiply, shares a few of the ideas that matter most from a broader conversation on founder growth, leadership, and the shift from building things to building a company.What stood out most is that this is not really a story about title progression. It is a story about learning to operate with more uncertainty, taking on bigger challenges before you feel ready, and realizing that leadership at the highest level starts to look a lot more like influence than execution.What we get into• Why growth often starts before you feel ready• Why strong founders are pulled by a real problem• Why founder timing matters more than people think• Why leadership becomes influence, alignment, and convictionTimestamped highlights00:00 The real shift from engineer to CEO00:18 Growth starts before readiness00:56 Leadership changes when execution is no longer enough01:50 The best founders are pulled by a problem02:35 The three ideas that tie it all togetherFollow The Tech Trek for more conversations on leadership, company building, and the people shaping what comes next. The full Michael White episode is also available.

Raj Koo, CTO at DTEX, joins The Tech Trek for a sharp conversation on insider risk, shadow AI, and why security teams need a more modern way to think about intent. This episode is worth your time if you are trying to understand how AI is changing cyber risk, why non malicious behavior can still create major exposure, and what it takes to protect the business without slowing down innovation. Raj explains why the old approach of blocking known bad behavior is no longer enough. As employees bring personal AI tools into the workplace, security teams are dealing with a new reality, one where productivity gains, agentic workflows, and data exposure are all colliding at once. In this episodeWhy DTEX focuses on inferring intent, not just catching exfiltrationWhy shadow AI is different from shadow IT, and harder to controlHow non malicious employee behavior can become the biggest insider risk categoryWhy agentic AI raises the stakes for visibility and governanceHow mature insider risk programs are shrinking response times even as costs rise Timestamped highlights00:00 Raj Koo on inferring intent in cybersecurity01:59 Why early warning signals matter more than the exfiltration point04:38 The rising cost of insider risk06:25 How shadow AI became a major non malicious risk08:13 Why shadow AI is more complex than shadow IT17:53 Detection times are improving, but the cost problem is getting worse Standout lineSecurity has a chance to stop being seen as the function that blocks productivity and start being seen as the function that helps the business adopt better tools safely. Practical takeawayIf your team is dealing with AI adoption in the wild, start with visibility before judgment. Understand which tools people are using, what they are using them for, and where the real risk sits before defaulting to blanket restrictions. Link to 2026 Cost of Insider Risks Global Report: https://ponemon.dtex.ai/Follow The Tech Trek for more conversations with builders, operators, and technology leaders shaping how modern companies work.

Sumeet Arora, Chief Product Officer at Teradata, joins The Tech Trek for a sharp conversation on the shift from human driven SaaS to agentic software. This episode digs into what changes when software stops just supporting human workflows and starts driving outcomes alongside people, why trust and governance matter more as AI systems take on more responsibility, and what serious companies need to do now to prepare.This is a practical discussion about where the market actually is, what gets overhyped, and what leaders should focus on beneath the noise. Sumeet lays out a clear view of the emerging enterprise stack, from knowledge and context to agents, governance, and outcomes. He also explains why the winners may not be the loudest companies in AI, but the ones that get their data, knowledge, and operating model right.In this episode• Why agentic software is a real shift, but still in its early stages• What trust, governance, and explainability need to look like in an AI first enterprise• How software companies should rethink product strategy for agents as well as humans• Why every employee may need to become a manager of AI agents• Why knowledge infrastructure could matter more than the agent layer itselfTimestamped highlights• 00:45 Teradata's role in helping enterprises become autonomous• 02:34 Where we really are in the agentic AI maturity curve• 10:16 How software shifts from workflow centric to outcome centric• 16:17 Why every employee may need an AI workforce• 21:57 The skill gap between enterprise users and agentic adoption• 24:48 Why knowledge, not just agents, will define the winnersStandout line“The fundamental winners will be ones who get the knowledge fabric correct.”Practical takeawayIf you are building for an AI driven future, do not start with agents alone. Start with trusted knowledge, usable context, clear policies, and systems that can explain decisions. The companies that treat agentic AI as a stack, not a feature, will be in a much stronger position.Follow The Tech Trek for more conversations with leaders shaping the future of technology, product, AI, and enterprise transformation.

Victor Fang, CEO and Founder of Anchain AI, joins The Tech Trek for a timely conversation on crypto crime, AI driven fraud, and what financial institutions need to understand as digital assets move closer to the mainstream. This episode is worth your time if you care about cybersecurity, compliance, crypto risk, anti money laundering, or where agentic AI is starting to reshape investigation work.This conversation goes beyond headlines. Victor breaks down how bad actors are using generative AI for phishing, identity fraud, exploit development, and ransomware, then explains how defenders are using AI, graph intelligence, and agent workflows to fight back. It is a sharp look at the collision of crypto, cybersecurity, regulation, and AI infrastructure.In this episodeWhat crypto crime actually looks like today, from exchange hacks to romance scams and ransomwareWhy crypto risk now extends well beyond crypto native usersHow financial institutions, regulators, and compliance teams are adaptingWhere AI is helping attackers move faster, and where it is giving defenders an edgeWhy agentic workflows and MCP powered investigation tools could change this category fastTimestamped highlights00:00 Victor Fang on crypto crime, AI versus AI, and agentic AML00:53 What Anchain AI does and why blockchain investigation is becoming more important01:56 How generative AI is already being used in crypto crime and phishing06:30 What banks, regulators, and AML teams need to understand about crypto adoption10:44 Why Victor believes AI can give defenders the advantage16:17 How Anchain uses blockchain data, graph intelligence, and agent workflows to investigate faster22:04 Why the company's MCP server could extend beyond crypto into KYC and financial applications25:21 What the next wave of agent driven security and investigation might look likeOne standout idea from the conversation, crypto is much closer to you than you think.Practical takeawaysCrypto risk is no longer a niche issue, it is increasingly tied to broader fraud, ransomware, and financial crimeAI is accelerating both offense and defense, which raises the bar for security and compliance teamsAgentic investigation workflows could dramatically reduce manual work in AML, fraud, and cyber operationsCompanies building in regulated spaces need infrastructure that can handle both speed and scrutinyFollow The Tech Trek for more conversations with builders, operators, and technical leaders shaping what comes next.

Cam Crow, Director of Data and Analytics at Vacatia, joins The Tech Trek to unpack what happens when a startup outgrows informal ways of working. This episode looks at how data teams can introduce project management frameworks without killing speed, how to manage stakeholder demand as complexity rises, and why the right operating model matters even more as AI begins to reshape analytics work.Cam shares a practical view from the middle of real growth, from startup scrappiness to acquisitions, migrations, and a much wider stakeholder base. He explains when process becomes necessary, how to build trust during that shift, and where AI is starting to change both delivery workflows and the future of business insights.In this episode• Why early stage teams should add process cautiously, not by default• The moment speed and quality start breaking under too many competing requests• How public communication and domain based stakeholder channels reduce friction• Why planning routines matter as much for stakeholders as they do for the data team• Where AI fits today, from faster delivery to semantic layers that support better answersHighlights00:00 Cam Crowe joins the show to discuss project management frameworks through the lens of data, startup growth, and stakeholder alignment01:58 Why Cam resisted formal sprint planning in the startup phase and why that made sense at the time05:58 The tipping point where too many priorities start hurting both velocity and quality11:49 How moving conversations out of direct messages and into domain channels changed team operations15:03 Inside the two week development cycle and the planning week that keeps stakeholders engaged21:08 How Cam is thinking about AI, semantic layers, and the future of on demand analyticsA standout idea from this conversation, process should be added conservatively, only when the business truly needs it.Practical takeaways• Do not formalize too early, but do not wait until the system is already breaking• Make prioritization visible once demand exceeds capacity• Use shared channels instead of one to one communication to reduce bottlenecks• Build stakeholder rituals into the operating model, not just team rituals• Treat AI readiness as an infrastructure challenge, not just a tooling decisionFollow The Tech Trek for more conversations with operators, builders, and technology leaders shaping how modern teams work and scale.

Deep Sogani, SVP and Group Data Management Officer at Datasite, joins The Tech Trek to unpack why data governance, lineage, and business process design have become mission critical in the age of AI. This conversation gets past the surface level AI hype and into the operational reality, how companies actually build trustworthy systems, where AI initiatives break down, and why strong data foundations now shape business outcomes in real time.This episode explores the shift from downstream analytics to data that actively drives live decisions, workflows, and automation. Deep explains why many AI projects fail before the model even matters, how business architecture should lead technical design, and why human oversight still matters in high stakes environments.In this episodeWhy AI has made data governance and data lineage far more operationalWhy business process clarity matters before data architecture or tooling decisionsHow real time AI changes the demands on data quality and system designWhere agentic AI fits, from workflow automation to more advanced decision supportWhy human judgment still matters in AI systems shaped by risk, ethics, and securityTimestamped highlights01:47 Why AI raises the stakes for governance, lineage, and trust in data04:57 Why business architecture has to lead before technical design09:11 The progression from predictive models to agentic AI workflows17:55 Why the human in the loop is still essential21:16 What makes an AI project worth prioritizing26:06 What has changed, and what has not, in AI related change managementStandout line“Business architecture and business thinking should dictate the what and the why, and the data architecture is the how part which needs to follow.”Practical takeawayIf you are evaluating AI inside the enterprise, do not start with the tool. Start with the business problem, the workflow, the decision risk, and the quality of the data behind it. Strong models on the wrong problem still fail.Follow The Tech Trek for more conversations with leaders shaping technology, data, AI, and the future of modern business.

Suresh Martha, Head of Data Driven Innovation and Analytics at EMD Serono, joins The Tech Trek for a practical conversation on what leadership looks like when your team is asked to take on new technical capabilities. This episode is about extending team impact, evaluating new tools, building credibility with stakeholders, and leading through change without pretending to be the deepest expert in every domain.For data leaders, analytics managers, technology executives, and operators, this conversation gets into the real work behind capability building. Suresh breaks down how to assess whether a new technology is worth pursuing, when to start with a pilot, how to upskill internal talent, and how to hire for skills your team does not yet have.In this episode• How to evaluate whether a new tool or technology actually adds business value• Why small pilots help leaders build trust before asking for larger investment• What it takes to lead technical work you have not personally done yourself• How to hire for capabilities your team does not yet have• Why business context and data knowledge still matter as much as technical depthTimestamped highlights00:04 Extending technical impact as a leader when new capabilities land on your team03:37 A simple framework for evaluating new tools, investment, and fit05:28 Hiring for skills your team does not yet have07:44 Upskilling as a leader so you can guide the work with confidence12:06 Managing experts whose technical depth goes beyond your own15:21 Making room for learning and experimentation while still deliveringStandout lineAs long as I understand the intricacies and can explain that, that is what matters, especially for a leader.A practical takeawayStart small. Pick a real business problem. Run a focused pilot. Measure the outcome. Earn the right to scale.Follow The Tech Trek for more conversations with leaders building teams, systems, and technical capability inside modern businesses.

Sourish Samanta, Director AI and ML at Advance Auto Parts, joins The Tech Trek for a grounded conversation on where machine learning still creates the most business value, where generative AI fits, and why many teams are chasing the wrong solution. This episode is worth your time if you want a clearer view of how serious operators think about AI strategy, product delivery, and practical use cases that can ship now. This conversation cuts through the noise around AI and gets back to first principles. Sourish explains why machine learning remains the foundation behind today's AI wave, how to choose between deterministic and creative systems, and what it actually takes to build production ready products that solve real business problems.In this episode:Why machine learning is still the core layer behind modern AIWhen to use machine learning, when to use generative AI, and when simple analytics is enoughWhat a real product mindset looks like for AI and ML teamsHow pod based teams can ship faster with better cross functional alignmentWhy AI and ML talent need to spend time continuously reskillingTimestamped highlights:00:00 Why machine learning remains the foundation of today's AI stack01:57 The difference between ML teams, AI teams, and agent focused workflows05:56 Choosing the right solve, from forecasting and inventory to creative content generation10:09 The product mindset required to turn AI ideas into working systems13:51 Why some business problems need analytics, not AI15:52 Why AI teams need to spend part of their time learning, testing, and staying currentStandout line:AI is not the strategy. Solving the right problem is.Practical takeaway:If you are leading an AI initiative, start by classifying the problem. If the outcome needs consistency, prediction, or forecasting, machine learning may be the better path. If the outcome needs creativity or flexible generation, generative AI may be a better fit. And in some cases, the best answer is still a clean dashboard and strong analytics.Follow The Tech Trek for more conversations on AI, data, engineering, and how technology actually gets applied inside real businesses.

Shamoon Siddiqui, CEO and Founder of Human Friendly Robotics, joins The Tech Trek to break down what it really takes to bring robotics into construction. This is not a futuristic thought experiment. It is a grounded conversation about where robots can create value now, why construction has lagged so badly on productivity, and how focused automation could reshape one of the world's biggest industries.At the center of the discussion is Tyler, a tile laying robot built as a practical entry point into construction automation. Shamoon explains why repeatable workflows matter, where human skill still wins, and how robotics can improve speed, safety, and job site economics without needing to look like a science fiction demo.In this episode• Why construction productivity has moved backward while other industries have surged ahead• Why tiling is the right entry point for construction robotics• How Human Friendly Robotics thinks about deployment, rentals, and product iteration• Where robots can reduce hidden job site injuries tied to repetitive strain• Why the long game is much bigger than tile, with plumbing, electrical, and HVAC in sightTimestamped highlights00:35 Why construction is the right market for robotics right now03:56 The bigger shift from humans moving atoms to machines handling more physical work08:29 Why the business model is built around rentals, not one time equipment sales10:24 The wedge strategy today and the larger vision across licensed trades12:12 The overlooked safety problem of repetitive strain in construction20:44 Why useful robots matter more than robots built for flashy demos“Version one is not going to be as good as version five, but if you continue to rent it from us, we can make sure you get version five when it's ready.”Practical takeawayThe smartest automation wedge is not the flashiest one. Start with repetitive, measurable work, prove productivity gains in the real world, and expand from there.Follow The Tech Trek for more conversations on robotics, AI, startups, and the technologies changing how real work gets done.#ConstructionTech #Robotics #Automation #ai #FutureOfWork

Mary Elizabeth Porray, Global Vice Chair Client Technology and COO, Growth and Innovation at EY, joins The Tech Trek for a grounded conversation about what it actually takes to operationalize emerging technologies inside a global enterprise. This episode goes past the AI hype cycle and into the real work of adoption, change management, process redesign, workforce trust, and leadership in ambiguity. A lot of companies are asking what AI can do. Fewer are asking what needs to change for AI to actually work. Mary Elizabeth shares how EY is thinking about experimentation, employee experience, guardrails, internal adoption, and the cultural shifts required to move from curiosity to real impact.In this episodeWhy culture, not technology, is often the biggest blocker to emerging tech adoptionWhy AI is not a magic wand, but can help teams solve problems in a different wayHow leaders can identify the right starting points by listening for real pain pointsWhy productivity gains have to create psychological space, not just more workHow affinity groups, storytelling, and visible leadership help drive adoptionTimestamped highlights01:58 Why cultural norms often slow down emerging technology adoption03:25 AI hype, false expectations, and what the technology can realistically change05:55 The mental load of AI at work, and why EY created Thrive Time11:20 Why AI pilots need to go deeper than surface level experimentation15:19 How AI is creating a shared language between business and technology teams29:29 How storytelling, affinity groups, and positive momentum help people lean inOne line that sticks: AI is not something you dabble in.A practical takeawayThe best place to start is not with the flashiest use case. It is with a real pain point. If a process should take one week and actually takes eight, that is a signal worth following.Follow The Tech Trek for more conversations with leaders building through change, scaling technology, and shaping how modern work actually gets done.