Podcasts about tech trek

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.

Anish Agarwal went from MIT PhD researcher to founding Traversal, an AI company building intelligent site reliability engineering agents for the enterprise. In this episode, he breaks down what it actually takes to lead an AI first company when your entire career was built inside a lab.This is not your typical founder story. Anish never planned to start a company. He was on track to be a professor at Columbia when generative AI hit and rewired his trajectory. Now he is two years into the CEO seat, recruiting top talent away from high paying jobs, and building a product at the intersection of causal machine learning and agentic systems.We get into the mechanics of that transition. How do you go from publishing papers to pitching investors? What does storytelling look like when you are convincing engineers to leave comfortable roles and bet on your vision? And what happens when you start a company without even having an idea?Anish also tackles a question the AI space is wrestling with right now. Is a PhD becoming table stakes for building an AI first company? His answer is more nuanced than you might expect. It is not the degree. It is the training. Reading the landscape, navigating uncertainty, and evaluating models with scientific rigor. Those skills separate builders from everyone else.Key TakeawaysThe best AI founders are not chasing credentials. They are leveraging research instincts to read where models and architectures are heading, and that foresight creates real competitive edges.Starting a company without an idea is not reckless if you have the right co founders. Anish and his team showed up to a WeWork every day and treated idea exploration like a research problem until the right opportunity clicked.Storytelling is the most underrated leadership skill in technical companies. Whether you are recruiting, raising capital, or explaining your product to nontechnical buyers, packaging complexity into a clear narrative is what moves people.Every decision as a founder is a bet, including the decision to do nothing. Viewing inaction as a strategic choice changes how you prioritize and how fast you move.As AI writes more code, someone has to make sure it works in production. That gap between code generation and reliability is where Traversal lives, and it is only getting wider.Timestamped Highlights(00:36) What Traversal does and why AI powered site reliability engineering is a massive unsolved problem in enterprise software(02:00) The moment generative AI changed everything and why Anish walked away from a career he loved(08:43) How Traversal found its problem without starting with an idea, and the co founder dynamic that made it work(14:29) The real advantage of a PhD in AI and why it has nothing to do with the letters after your name(19:49) Advice for PhDs entering the job market on how to position research experience so hiring managers actually get it(20:29) Two years into the CEO role, what Anish wishes he had known and the skills that matter most for early stage foundersWords That Stuck"If AI is writing your code, it has to fix it too. And right now it is only writing the code."Founder PlaybookPick a problem that sustains you for decades. Anish looks for problems that keep getting more complicated because that is where long term value compounds. If the problem has a ceiling, your company does too.Treat recruiting like a core product skill. Painting a compelling picture of the mission is not a nice to have. It is the engine that pulls exceptional talent away from safe, well paying jobs.Think of everything as a series of bets. Fundraising, hiring, product decisions, even waiting. Inaction is a bet too. Once you see it that way, you stop overthinking and start moving with intention.Subscribe to The Tech Trek wherever you listen. If this one hit home, share it with a founder or tech leader navigating their own leap. Follow the show on LinkedIn for more.

Arnie Katz has been running product and engineering under one roof since before most companies even considered combining the roles. As CPTO at GoFundMe, he oversees the teams behind a platform processing over 2.5 donations every second, with more than $40 billion in help facilitated worldwide. Arnie breaks down why the CPTO title keeps gaining traction, how he thinks about the role like a portfolio manager, and where the real trade offs live when one person holds both the product and technology reins.Key TakeawaysThe CPTO role works like a portfolio manager. Arnie manages the company's largest investment center by balancing short term business wins against long term platform bets, knowing when to take on technical debt and when to pay it down.Velocity, coordination, and alignment are the three biggest wins. When product and engineering report to one leader, decisions happen faster, roadmap conflicts get resolved without executive tug of war, and technical investments stay tied to business outcomes.The disadvantages are real. Without separate CPO and CTO voices at the executive table, certain perspectives can get muted. His fix: build a leadership bench strong enough to create the right tension underneath him.AI is changing what small teams can deliver. GoFundMe's eight person team behind Giving Funds is shipping at a pace that would have been impossible five years ago.Timestamped Highlights[00:38] The scale most people don't realize about GoFundMe, including 2.5 donations per second and GoFundMe Pro for nonprofits.[02:02] How Arnie first landed the CPTO title at StubHub seven years ago, and why it clicked.[09:11] The real downside of collapsing two C suite roles into one, and how Arnie designs around it.[13:57] His portfolio approach to technical debt, sequencing re platforming in areas like identity and payments while other teams ship business value.[18:38] AI reshaping engineering velocity, the future of the SDLC, and product teams prototyping without writing code.[23:06] Where the CPTO model is headed as the industry evolves.The Line That Stuck"I often think of myself as a portfolio manager. My job is to invest money where the company gets the best returns, where the mission gets the best return, where the shareholder gets the best returns."Pro TipsSequence your bets instead of spreading them thin. GoFundMe gave their identity and payments teams nine months of runway to re platform with no feature expectations while other squads picked up the pace on near term results.Build leadership that creates productive friction. Without CPO vs. CTO tension at the exec level, let your VPs and SVPs push back against each other. That tension is where the best decisions come from.Think in time horizons, not just priorities. Short term moves for 0.1% to 0.5% metric lifts. Midterm bets for 1% to 5% gains. Long term swings that could transform the business. Allocate across all three.If this conversation changed how you think about product and engineering working together, share it with someone on your team. Subscribe to The Tech Trek so you never miss an episode, and connect with Arnie on LinkedIn to keep the conversation going.GoFundMe is offering listeners of The Tech Trek a chance to open their own Giving Fund. For the first 50 people who open a Giving Fund and add $25 or more to their Giving Fund, GoFundMe will add an additional $25 to that Giving Fund. If you have a Giving Fund but have never contributed into it, you can also participate. The deadline for this incentive is March 13. To get this incentive, click here to start your Giving Fund.

What if the best people on your investing team are still in college? Peter Harris, Partner at University Growth Fund, breaks down how they run a roughly 100 million dollar venture fund with 50 to 60 students doing real diligence, real founder calls, and real deal work.You will hear how their student led model stays disciplined with checks and balances, why repeat games matter in venture and in business, and how this approach creates a flywheel that helps founders, investors, and the next generation of operators win together.Key Takeaways• Student led does not mean unstructured, the process is built around clear stages, data room access, investment memos, student votes, and an advisory style investment committee, with final fiduciary responsibility held by the partners• Real autonomy is the unlock, when interns are trusted with meaningful work, the best ones level up fast and start leading teams, not just supporting them• The goal is win win win outcomes, founders get capital plus a high effort support network, investors get disciplined underwriting, students get experience that compounds into career leverage• Repeat games beat short term incentives, the alumni network becomes a long term advantage, bringing the fund into high quality opportunities years later• Mistakes are inevitable, the difference is containment and systems, avoiding errors big enough to break trust, then building process improvements so they do not repeatTimestamped Highlights00:32 A 100 million dollar fund powered by 50 to 60 students, and what empowered really means01:43 The decision path, from founder screen to student memo to student vote to the advisory investment committee06:44 Why most venture internships underdeliver, and how longer tenures change outcomes10:37 Repeat games and the trust flywheel, how former students now pull the fund into top tier deals13:55 What happens when something goes wrong, damage control, learning loops, and confidentiality as a core discipline24:39 The bigger vision, expanding beyond venture into additional asset classes to create more student opportunitiesA line worth stealingIf you give people real autonomy, they'll surprise you with what they do.Pro Tips• If you are building an internship program, start by deciding what real ownership means, then build guardrails around it, not the other way around• Treat trust like an asset, design your process so every stakeholder wants to work with you againCall to ActionIf you enjoyed this one, follow The Tech Trek and share it with a founder, operator, or student who cares about building real advantage through talent and process.

Yulun Wang, executive chairman and co founder at Sovato Health, joins Amir Bormand to unpack the next wave after telemedicine, procedural care at a distance. If you have ever wondered what it would take for a top surgeon to operate without being in the same room, this conversation gets practical fast, from the real bottlenecks inside operating rooms to the health system changes required to make remote robotics mainstream.Key takeaways• Better care can actually cost less when the right expertise reaches the right patient at the right time• Telemedicine is already normalized, which sets the stage for faster adoption of remote procedures once infrastructure and workflows catch up• Surgical robots already have two sides, the surgeon console and the patient side, today connected by a short cable, the leap is making that connection work reliably across hundreds or thousands of miles• Volume drives proficiency, the outcomes gap between high volume specialists and low volume settings is one of the biggest reasons access matters• Operating rooms spend more than half their time on steps around surgery, which creates room to dramatically increase surgeon throughput when workflows are redesignedTimestamped highlights• 00:42 What Sovato Health is building, bringing procedural expertise to patients without requiring travel• 02:10 The early days of surgical robotics and the transatlantic gallbladder surgery on September 7, 2001• 05:30 The counterintuitive idea, higher quality care can reduce total cost in healthcare• 10:27 What actually changes for patients, local hospitals stay the destination, expertise becomes the thing that travels• 14:57 Why repetition matters, the first question patients ask is still the right one• 17:53 Inside the operating room schedule, where time is really spent and why productivity can jumpA line that sticks“Healthcare is different, higher quality, if done right, costs less.”Practical angles you can steal• If you are building in regulated industries, adoption is rarely about the tech alone, it is about trust, workflows, and incentives• If you sell into health systems, position the value around system level outcomes, access, quality, and margin improvement, not just novelty• If you are designing new workflows, look for the hidden capacity, the biggest gains often sit outside the core taskCall to actionIf you want more conversations like this at the intersection of tech, systems, and real world impact, follow The Tech Trek on Apple Podcasts and Spotify.

Nir Soudry, Head of R&D at 7AI, breaks down how teams can move from early experimentation to real production work fast, without shipping chaos. If you are building AI features or agent workflows, this conversation is a practical look at speed, safety, and what it actually takes to earn customer trust.Nir shares how 7AI ships in tight loops with a real customer in mind, why pushing decisions closer to the engineers removes bottlenecks, and how guardrails and evaluation keep fast releases from turning into security risks. You will also hear a grounded take on human plus AI collaboration, and why “just hook up an LLM” falls apart at scale.Key takeaways• Speed starts with focus, pick one customer and ship something usable in two or three weeks, then iterate every couple of weeks based on real feedback• If you want velocity, remove the meeting chain, get engineers in the room with customers and push decisions downstream• Agent workflows are not automatically testable, you need scoped blast radius, strong input and output guardrails, and an evaluation plan that matches real production complexity• “LLM as a judge” helps, but it is not magic, you still need humans reviewing, labeling, and tuning, especially once you have multi step workflows• In security, trust is earned through side by side proof, run a real pilot against human outcomes, measure accuracy and thoroughness, then improve with tight feedback loopsTimestamped highlights00:28 What 7AI is building, security alert fatigue, and why minutes matter02:03 A fast shipping cadence, one customer, quick prototypes, rapid iterations03:51 The velocity playbook, engineers plus sales in the same meetings, fewer bottlenecks08:08 Shipping agents safely, blast radius, guardrails, and why testing is still hard14:37 Human plus AI in practice, how ideas become working agents with review and monitoring18:04 Why early AI adoption works for some customers, and how pilots build confidence24:12 The startup reality, faster execution, traction, and why hiring still mattersA line worth sharing“When it's wrong, click a button, and next time it will be better.”Pro tips you can steal• Run a two to four week pilot with one real customer and ship weekly, the goal is learning speed, not perfect coverage• Put engineers directly in customer conversations, keep leadership focused on unblocking, not gatekeeping• Treat every agent like a product surface, define strict inputs and outputs, sanitize both, and limit what it can affect• Build evaluation around real workflows, not single prompts, and combine automated checks with human review• Add feedback buttons everywhere, route feedback to both model improvement and the team that tunes production behaviorCall to actionIf you want more conversations like this on building real tech that ships, follow and subscribe to The Tech Trek.

Chandan Lodha, Co-founder at CoinTracker, joins Amir Bormand to unpack the real shift from big tech to building your own company. From Harvard to Google to Y Combinator, Chandan shares what pushed him to take the leap, how he found the right idea, and what he had to unlearn to lead at startup speed.This conversation is for builders and leaders who want to grow faster, ship faster, and build teams that can actually execute.Key Takeaways• The early career advantage is learning velocity, optimize for environments that stretch you fast• Managing the business is rarely the hardest part, people problems scale with headcount• Big company habits can break you at a startup, especially around distribution, speed, and getting your first users• YC helped most through peer proximity, being surrounded by real users and founders who move quickly• Founder growth is a system, use feedback loops like reviews, 360 input, and personal goal trackingTimestamped Highlights00:00 From Harvard and Google to founder mode, what made him leave the safe path00:35 CoinTracker in plain English, crypto taxes and accounting for individuals and businesses03:32 Leap first, think later, the messy six month search for a real idea05:00 Runway reality, setting a 12 to 18 month window to figure it out06:09 Crypto skepticism to conviction, reading the Bitcoin white paper changed his frame10:05 Leadership lessons at 100 people, why people issues become the main work14:43 Y Combinator benefits, users everywhere and a practical playbook for early company building17:55 Personal growth systems, performance feedback and personal OKRs, plus changing your mind on three issues each year21:04 Becoming a new parent, structure, efficiency, and cutting non essentials23:24 The two skills to build before you leap, building and sellingA line worth keepingManaging the business is easy, managing people is hard.Pro Tips• Set a real runway window, then use it to iterate hard with users every week• Expect to unlearn big company instincts, distribution and speed do not come for free• Build a feedback cadence for yourself, not just your team, reviews and 360 input can surface blind spots• Practice building and selling in small side projects now, those skills compound in any startupCall to ActionIf this episode helped you think differently about leadership and the founder path, follow The Tech Trek on Apple Podcasts or Spotify, and share it with one person who is building or thinking about making the leap.

Shahryar Qadri, CTO of OneImaging, joins me to unpack a hard truth about healthcare tech: the goal is not to remove humans, it is to give them more room to be human.We talk about where cost “optimization” actually helps patients, why radiology is a perfect fit for AI but still held back by data access, and how better workflows can improve trust, speed, and outcomes without losing the human touch.OneImaging sits in the radiology benefits space, helping members book imaging in a national network with more transparency and a high touch booking experience, while helping employers cut imaging costs significantly.Key takeaways• The “human touch” in healthcare is not going away, the better play is using tech to increase capacity so caregivers can spend more time being caregivers• Cost optimization is not always about paying less for expertise, it is often about wasting less human time, improving trust, and removing friction around services• Healthcare still runs on outdated plumbing in places you would not expect, including fax based workflows that slow everything down• Radiology is one of the best real world use cases for AI, but the bigger blocker is getting access to imaging data in usable form, not model capability• Your health data is already “there”, but it is not working for you yet. The next wave is tools that scan your longitudinal record and surface what to ask your doctor about, so you can be a stronger advocate for your own careTimestamped highlights• 00:36 What OneImaging actually does, and why “transparent imaging” is more than a pricing story• 02:00 Why healthcare stays personal, and how tech should increase capacity instead of replacing care• 03:36 The real definition of cost optimization, commodity versus service, and where trust matters• 07:01 The surprising reality of imaging ops, why it still feels like 1998, and what gets digitized next• 17:19 AI in radiology is real, but the data access and interoperability gap is the bottleneck• 24:21 Your CDs are full of value, the problem is we do almost nothing with that data todayA line worth replaying“These LLM models are the worst that they'll ever be today. They're only going to get better and better and better.”Call to actionIf this episode sparked a new way of thinking about healthcare tech, follow The Tech Trek on your podcast app, share it with a friend in product or engineering, and connect with me on LinkedIn for more conversations like this.

Swarupa Mahambrey, Vice President of Software Engineering at The College Board, breaks down what tech debt really looks like in a mission critical environment, and how an engineering mindset can prevent it from quietly choking delivery. She shares a practical operating model for paying down debt without stopping the roadmap, and the cultural habits that make it stick.You will hear how College Board carved out durable space for engineering excellence, how they use testing and automation to protect reliability at scale, and how to make the trade offs between features, simplicity, and user experience without slowing the team to a crawl.Key Takeaways• Tech debt behaves like financial debt, delay the payment and the interest compounds until even simple changes become painful• A permanent allocation of capacity can work, dedicating 20 percent of every sprint to tech debt can reduce support load and improve delivery• Shipping more features can slow you down, simplifying workflows and validating with real usage can increase velocity and reduce tickets• Resilience is not about avoiding every failure, it is about designing for graceful degradation so spikes and outages become small blips instead of crises• Automation is not “extra,” it is part of the definition of done, including unit tests as acceptance criteria and clear code coverage expectationsTimestamped Highlights• 00:00 Why tech debt is a mindset problem, not just a backlog problem• 01:00 Tech debt explained with a real example, what happens when a proof of concept becomes production• 03:45 The feature trap, how “powerful” workflows can overwhelm users and explode maintenance costs• 11:03 Engineering Tuesday, one day a week to strengthen foundations, not ship features• 14:39 Stability vs resilience, designing systems that bend instead of shatter• 20:06 Testing and automation at scale, unit tests as a requirement and code coverage guardrailsA line worth keeping“If we don't intentionally carve out space for engineering excellence, the urgent will always crowd out the important.”Practical moves you can steal• Protect a fixed slice of capacity for tech debt, make it part of the operating model, not a one time cleanup• Treat automation as acceptance criteria, no test, no merge, no release• Use pilots and targeted releases to learn early, then iterate based on metrics and real user behavior• Design for graceful degradation with retries, fallback paths, and clear failure visibilityCall to actionIf this episode helped you think differently about tech debt and engineering culture, follow The Tech Trek, leave a quick rating, and share it with one engineer who is fighting fires right now.

Software is still eating the world, and AI is speeding up the clock. In this episode, Amir talks with Tariq Shaukat, co CEO at Sonar, about what it really takes for non tech companies to build like software companies, without breaking trust, security, or quality. Tariq shares how leaders can treat AI like a serious capability, not a shiny add on, and why clean code, governance, and smart pricing models are becoming board level topics. Key Takeaways• “Every company is a software company” does not mean selling SaaS, it means software is now core to differentiation, even in legacy industries. • The hardest shift is not tools, it is mindset: moving from slow, capital style planning to fast iteration, test, learn, and ship. • AI works best when leaders stay educated and involved, outsourcing the whole strategy is a real risk. • “Trust but verify” needs to be a default posture, especially for code generation, security, and compliance. • Pricing will keep moving toward value aligned consumption models, not simple per seat formulas. Timestamped Highlights• 00:56 What Sonar does, and why clean code is really about security, reliability, and maintainability • 05:36 The Tesla lesson: mechanics commoditize, software becomes the experience people buy • 09:11 Culture plus education: why software capability cannot live in one silo • 14:21 Cutting through AI hype with program discipline and a “trust but verify” mindset • 18:23 Boards, governance, and setting an “acceptable use” policy for AI before something goes wrong • 25:18 How software pricing changes in an AI world, and why Sonar prices by lines of code analyzed A line worth saving:“Define acceptable risk as opposed to no risk.” Pro Tips you can steal• Write down what you want AI to achieve, the steps to get there, and the metric you will use to verify outcomes. • For code generation, scan and review before shipping, treat AI output like a draft, not a final answer.• Set clear rules for what is allowed with AI inside the company, then iterate as you learn. Call to ActionIf you want more conversations like this on software leadership, AI governance, and building real impact, follow The Tech Trek and subscribe on your favorite podcast app. If someone on your team is wrestling with AI rollout or developer productivity, share this episode with them.

Ken Ringdahl, CTO at Emburse, joins The Tech Trek to share what it really looks like to grow from engineer to CTO without losing your love for building. He talks about staying close to the code while leading a three hundred person org, how he learned the business side on the job instead of through an MBA, and why curiosity is still his strongest tool. If you are an engineer who cares about leadership, AI, and long term impact, this one will hit close to home. the-tech-trek_copy-of-ken-ringd…Key takeawaysThe best engineering leaders stay technical for as long as they can, then pick their spots to lean in where the business needs them most.You can learn the business side on the job by raising your hand for cross functional work and building real relationships with sales, finance, and product leaders.Curiosity is a career advantage, both in technology and in leadership, because the quality of your questions shapes the quality of your decisions.A practical AI strategy comes from listening to customers, partners, and internal experts, then translating that into focused product bets instead of chasing shiny tools.Do not rush into management just for the title, a deep foundation as an engineer will make every future leadership decision stronger.Timestamped highlights00:38 Ken explains what Emburse does and how modern spend management lives at the intersection of software, data, and finance. the-tech-trek_copy-of-ken-ringd…01:30 How he balances being an engineer at heart with the reality of leading many teams and products as CTO.03:41 Ken reflects on missing his coding days, what he still tinkers with, and why he chose the bridge role between tech and business.08:32 Learning leadership without an MBA, creating your own opportunities, and attaching yourself to people you can learn from across the company.14:58 How he stays smart on AI through office hours, internal experts, cloud partners, customers, and investor networks.21:22 His biggest advice for engineers who want to move into leadership and why he actually went back to a more hands on role before moving up again.One line that stayed with me“Even if you want to be a leader, do not rush it. Do not go so fast that you do not get that foundation.” the-tech-trek_copy-of-ken-ringd…Practical moves for your own careerStay technical as long as you can, then choose a few focus areas such as architecture, AI strategy, or cloud patterns where you can still go deep.Use curiosity as your main tool, ask simple but sharp questions of finance, sales, and customers so you see how technology really creates value.Look for chances to run cross functional projects early in your career so that by the time you step into leadership, you already understand how the wider business works.Treat partners, customers, and internal experts as an extended brain trust, especially when you are trying to shape an AI and platform strategy.Listen and stay connectedIf this episode helped you think differently about your own path from engineer to leader, follow The Tech Trek, leave a rating on your favorite podcast app, and share it with one person on your team. To keep the conversation going, connect with Ken on LinkedIn and find me there as well for more stories from leaders who are building real impact with technology.

Karan Talati, cofounder and CEO at First Resonance, joins me to unpack what modern manufacturing really looks like inside factories that build rockets, drones, reactors, and other complex hardware. We dig into why only a small slice of factories run on real systems today, what a true factory operating system unlocks, and how that connects directly to national security and the AI boom.If you care about where all of this new compute, energy, and defense hardware will actually come from, this conversation gives you a clear view of the stack, the gaps, and the opportunity. Key takeaways• Only a small fraction of factories in the United States use a manufacturing execution system, which leaves a huge gap between legacy on prem tools, paper processes, and generic workflow apps that were never built for hardware work• Cloud infrastructure and open interfaces now make it possible to deploy a purpose built factory operating system at a cost and speed that works for both fast moving startups and long standing suppliers• Reindustrialization does not mean bringing every product back onshore, it means being deliberate about the layers of manufacturing that matter most for national security, chips, optics, and other high value components• The real foundation for modern manufacturing is talent, there is a major chance to re skill people into highly technical, well paid roles in aerospace, semiconductors, energy, and more• AI and agent style workflows will sit across design, manufacturing, and field operations so that hardware teams can close feedback loops, shorten timelines, and make better decisions with the data they already generateTimestamped highlights[00:40] Karan explains what First Resonance does and why he calls it a factory operating system for complex industries like aerospace, defense, energy, and autonomy[01:55] How we ended up with only about fifteen percent of factories running on an MES, and why most hardware work still lives on paper, spreadsheets, and ad hoc tools[06:49] A clear walkthrough of how offshoring looked like a rational path for decades, and why it created hidden risk across chips, optics, and other critical components[11:46] Which parts of manufacturing should come back onshore, why you do not want everything local, and how workforce strategy fits into the new industrial map[16:35] What a horizontal stack across design, factory systems, test, and field data can look like, and how AI agents can keep teams in sync across that stack[23:02] The real timelines of hardware in the age of AI, why software is speeding up physical development, and why examples like SpaceX and TSMC matter for the next decadeA line that stayed with me“Hardware and software are not separate worlds, they are one system that is now converging faster than most people realize.”Practical moves for tech leaders• Map your current manufacturing and hardware workflows, even if you are at a software first company, find the paper, spreadsheets, and disconnected tools that support anything physical you ship• Look for one or two places where a factory operating system or modern MES could remove handoffs, for example design changes that take weeks to reach the line or test data that never feeds back into engineering• Treat manufacturing careers as part of your talent strategy, help your teams see these roles as high skill and high impact, not as a side trackCall to actionIf this episode gave you a clearer view of how hardware, AI, and national security tie together, share it with one other person who should be thinking about the factory side of their roadmap. Follow and subscribe to The Tech Trek so you never miss deep dives like this, and connect with me on LinkedIn if you want more conversations at the edge of data, engineering, and real world impact.