The Tech Trek

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.