The Tech Trek

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.

Mortgage is often the largest financial transaction in a person's life, yet much of the process still runs on outdated workflows, long waits, and paperwork heavy systems.Diane Yu, cofounder and CEO of TidalWave, explains how her team is rethinking mortgage technology with a twenty four seven AI loan assistant built for real work, not just demos. She shares why agentic software in financial services needs auditability, domain knowledge, and a different mindset around human oversight.This episode is a sharp look at what AI adoption looks like inside mature industries where trust cannot be assumed and mistakes carry real consequences.Key Takeaways• AI in regulated industries cannot just be a generic model wrapper. It has to understand the domain, the rules, and the workflow deeply enough to operate safely.• Diane makes a useful distinction between human in the loop and human on the side. The goal is not to remove people, but to let AI do the busywork while humans review, guide, and make judgment calls.• Transparency is the foundation of trust. In mortgage, that means logging interactions, making AI recommendations visible, and giving loan officers enough context to validate the work.• Productivity gains matter most when they free professionals from repetitive tasks and give them more time for borrower relationships, sales, and higher judgment work.• AI native companies also have to use AI internally. Diane explains how TidalWave uses AI to move faster as a small engineering team and release product updates at a pace larger competitors may struggle to match.Timestamped Highlights00:33 Why mortgage is still stuck in outdated technology, and how TidalWave is building an AI loan assistant for the industry01:55 Why incremental tools are not enough to fix a process that takes weeks, hundreds of pages, and still loses money per loan03:46 The shift from workflow software to a system of action where AI does the work and prepares it for human review05:53 Why regulated industries create a high bar for AI, including TidalWave's mortgage compliance benchmark work with Columbia University08:16 How trust is built through transparency, audit logs, human review, and clear boundaries on what AI should and should not answer12:33 How AI can give mortgage professionals more time for human judgment, borrower relationships, and higher value work22:12 How TidalWave monitors new AI models and uses AI internally to accelerate engineering speedA Line That Sticks“Human connection is what AI cannot replace.”Pro Tips• Do not treat AI as a feature button. Build it into the flow of work so it can actually remove friction.• In regulated markets, design for auditability from the start. Every interaction, recommendation, and handoff needs to be visible.• Use AI to improve throughput, but keep humans focused on judgment, trust, and relationship building.• Watch model development closely, but build your platform so you can evaluate and plug in the best models over time.Call to ActionFollow The Tech Trek for more conversations with the builders, operators, and technical leaders shaping how AI is changing real industries. If this episode gave you a useful way to think about agentic AI, share it with someone working through similar questions.

Accounting has a tech problem, and it is not what most people think. In this conversation, Cos Nicolaescu, Co-founder and CEO at Accrual, breaks down why accounting has lagged behind, where AI can actually create leverage, and why the future of the profession is likely more human, not less. This episode gets into the real constraints inside accounting workflows, the difference between deterministic work and judgment based work, and why trust, context, and client knowledge still matter more than most AI narratives admit.What stands out• Accounting has not been slow to adopt tech because accountants resist change. A big reason is that the tools have often been weak, fragmented, and not worth the workflow overhead.• AI can handle more of the mechanical and backward looking work, but the highest value still sits in judgment, context, and forward looking decisions.• In accounting, knowing the tax code is not enough. The hard part is knowing the client, their history, their complexity, and the tradeoffs that shape future outcomes.• The profession is still deeply supply constrained. Firms are understaffed, demand is growing, and better tooling may help accountants do more meaningful work instead of simply shrinking headcount.• Junior talent may benefit more than people expect. As tools improve, newer professionals could ramp faster, though trust and client relationships will still take time to build.Timestamped highlights00:39 What Accrual is building, and why accounting workflows are a major opportunity for AI01:47 Why accounting is more tech conservative than people assume, and why bad software is a big part of the story04:11 The three buckets of accounting work, standardization, firm level process, and personal preference07:29 Where AI fits best in accounting, flexible interfaces, document understanding, and smarter workflow support12:14 The key difference between software engineering automation and accounting automation16:36 Will AI reduce the need for accountants, or make the profession more productive and more valuableA line worth remembering“I would want to see people who spend a lot of time getting CPA degrees and training for decades spending most of their time, not just inputting data from one field to another.”Pro tips• If you are building AI for a regulated or detail heavy workflow, start with where accuracy matters most and do not confuse automation with value• If you work in professional services, context is the moat. The more client history and situational knowledge you can capture, the stronger your systems become• If you are early in your career, tool fluency can compress the learning curve, but trust still has to be earnedStay connectedIf this episode gave you a new lens on AI, accounting, and the future of expertise, follow the show, subscribe for more conversations like this, and share it with someone building at the intersection of software, operations, and professional services.

What does it take to modernize healthcare infrastructure when uptime is not just an SLA, but a patient outcome?In this episode, Amir talks with Jeff Sponaugle, CTO of Surescripts, about building and operating mission critical healthcare systems, navigating the move from on premises infrastructure to the cloud, and figuring out where AI can create real value without compromising reliability. It is a sharp conversation on engineering judgment, modernization, workforce evolution, and why technical leadership still needs real technical depth.What stood outCloud migration in healthcare is not just a cost or architecture decision. It is a reliability decision with real downstream impact on patients.The best reliability strategy is not pretending nothing will ever break. It is designing systems so the customer never feels the break.In regulated industries, structure can be an advantage. Standardized data and consistent formats make AI more useful, especially in healthcare.AI can already improve the patient and clinician experience in practical ways, from transcription to summarizing complex records and surfacing relevant context faster.Technical leaders cannot afford to drift too far from the work. Jeff makes the case that strong CTOs stay close enough to the technology to understand the tradeoffs, guide teams well, and spot what matters next.Timestamped Highlights00:00Jeff Sponaugle joins the show to unpack mission critical technology in healthcare, cloud migration, AI, and workforce upskilling.01:57Why Surescripts sits in a critical layer of healthcare, and why reliability matters when prescriptions need to move in real time.04:02A simple but powerful view of reliability: things will break, but the customer should not know they broke.06:47How to adopt new technology without risky hard cutovers, and why parallel systems matter in high stakes environments.08:53Upskilling legacy teams, preserving tribal knowledge, and why continuous learning matters more than any single technical skill.11:58How regulation can actually help AI in healthcare by creating more consistency in the data.17:33Where AI and agentic systems could create meaningful value in prescribing, diagnostics, and clinical workflows.20:29Why AI has changed executive and boardroom conversations in a way cloud migration never did.A line worth remembering“The customer should not know that something broke.” Pro TipsIf you are modernizing a high stakes platform, avoid the big overnight cutover. Run systems in parallel where possible and learn behind the scenes before customers ever feel the change.If you lead technical teams, do not treat upskilling as a one time event. Give people a path to split time between legacy work and emerging systems so the transition is real and sustainable.If you are evaluating AI in a regulated environment, start with narrow, useful workflows where context, speed, and summarization matter, then expand from there.Stay connectedIf you enjoyed this episode, follow the show, subscribe wherever you listen, and share it with someone building in healthcare, cloud infrastructure, or AI. You can also connect with Amir on LinkedIn for more conversations at the intersection of technology, leadership, and the future of work.

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.

Adi Kuruganti, Chief AI and Development Officer at Automation Anywhere, joins Amir to break down what it actually takes to build agents for the enterprise, not in theory, but in environments where complexity, governance, observability, and real business outcomes matter. This conversation gets into the part of enterprise AI that most people skip. Not just what agents can do, but what changes when you have to deploy them across regulated systems, measure performance in production, manage model drift, and rethink how product and engineering teams ship software. It is a smart look at where enterprise AI is going, and what technical leaders need to understand before the market catches up. What stood out• Enterprise agents are only as strong as their data, context, and deployment model. In large companies, that means dealing with hybrid environments, air gapped systems, privacy controls, and process level context, not just model quality. • AI is changing more than coding. Adi explains how his team is using AI across the full software development lifecycle, from spec creation and test generation to production event triage and release workflows. • The release process is shifting from periodic launches to continuous iteration. That puts more pressure on observability, because teams now have to track model behavior, latency, and runtime performance as features roll out. • Security can no longer sit off to the side. Prompt injection, shared tenant risk, and post production anomaly detection all require security teams to work much closer to AI and product teams. • Mass adoption is not just a technology problem. The tools are improving fast, but enterprises still need change management, clear use cases, internal operating models, and people who know how to make AI part of daily work. Timestamped Highlights00:00 Adi Kuruganti joins the show to unpack what enterprise agent development really looks like today, from deployment models to governance to observability. 02:07 Why enterprise agents are different. Adi explains why context, data control, and environment complexity matter more in large organizations. 04:57 How AI is reshaping the software development lifecycle. From code suggestions to automated tests to incident triage, AI is moving deeper into product delivery. 10:13 The old handoff model is breaking. Product, design, and engineering are starting to work in a much more fluid, AI assisted way. 12:22 What changes in release management when AI writes part of the code and teams ship continuously instead of waiting for big release cycles. 18:17 How enterprises should judge agent performance, from human review and exception handling to evals, runtime benchmarks, and model drift. 27:21 Adi on the real AI adoption curve, job disruption, and why the bigger shift is not replacement, but making AI part of how people actually work every day. A line worth sitting with“AI should be a core element of how they work.” Worth applying• If you are building with AI, evaluate more than accuracy. Cost, latency, and consistency matter too. • If you are leading teams, do not treat observability as a nice to have. Runtime visibility is part of the product now. • If you are thinking about adoption, start with a real business problem and scale from early wins instead of trying to automate everything at once. Follow the show for more conversations with the builders, operators, and technology leaders shaping how modern companies are actually being built.

What happens when software engineers stop thinking like coders and start thinking like orchestrators?In this episode, Amir sits down with Scott Gale, CTO and Founder of Fluency, to unpack one of the biggest shifts happening in engineering right now: the move from writing code by hand to directing AI agents with context, judgment, and intent. Scott shares how his team is already using coding agents in production, what that means for hiring and team design, and why the engineers who adapt fastest will be the ones who gain leverage, not lose relevance.This conversation gets into the real change beneath the AI hype. Not just better tools, but a different shape of engineering work. Less manual syntax, more planning, auditing, collaboration, and system level thinking.Key Takeaways• The value of an engineer is shifting away from typing code and toward directing intent clearly• Teams that give AI better context can get dramatically better output from coding agents• Engineers do not need to become people managers, but they do need to learn how to manage agent driven work• Hiring is starting to favor people who can collaborate, learn the product, and work effectively with AI• Faster software delivery does not mean less to build, it often means companies can finally tackle more of the backlogTimestamped Highlights00:01 Scott Gale, CTO and Founder of Fluency, joins Amir to break down the shift from builder to orchestrator in modern engineering02:36 How Fluency introduced coding agents with a three part approach: safe experimentation, mindset shift, and stronger context04:35 Is this just the next step in software engineering, or does AI fundamentally change the role?08:16 Why some engineers resist AI tools, and what helps people move from skepticism to real adoption11:26 How technical interviews are changing as AI becomes part of everyday engineering work16:59 Scott on whether companies will actually need fewer engineers, and why the demand for meaningful work is not going away21:09 The practical lesson teams miss: better structured systems and better context make coding agents far more effectiveOne line worth remembering“It's not about losing your craft. It's about managing a workforce of junior agents.”Practical edgeScott shares a useful operating principle for teams already experimenting with AI in engineering: if you want better output, do not start with prompts alone. Start with structure. The more clearly a system is organized, and the more context an agent can access, the more useful and reliable the result becomes.That applies to hiring too. Technical skill still matters, but the engineers who stand out now are the ones who can collaborate across product and engineering, understand the business context, and make good decisions with AI in the loop.Call to ActionIf you are thinking through what AI means for engineering careers, team design, or product velocity, follow the show and share this episode with someone building in this new environment. For more conversations with founders and operators shaping where tech is headed, connect with Amir on LinkedIn.

What happens when software engineers stop thinking like coders and start thinking like orchestrators?In this episode, Amir sits down with Scott Gale, CTO and Founder of Fluency, to unpack one of the biggest shifts happening in engineering right now: the move from writing code by hand to directing AI agents with context, judgment, and intent. Scott shares how his team is already using coding agents in production, what that means for hiring and team design, and why the engineers who adapt fastest will be the ones who gain leverage, not lose relevance.This conversation gets into the real change beneath the AI hype. Not just better tools, but a different shape of engineering work. Less manual syntax, more planning, auditing, collaboration, and system level thinking.Key Takeaways• The value of an engineer is shifting away from typing code and toward directing intent clearly• Teams that give AI better context can get dramatically better output from coding agents• Engineers do not need to become people managers, but they do need to learn how to manage agent driven work• Hiring is starting to favor people who can collaborate, learn the product, and work effectively with AI• Faster software delivery does not mean less to build, it often means companies can finally tackle more of the backlogTimestamped Highlights00:01 Scott Gale, CTO and Founder of Fluency, joins Amir to break down the shift from builder to orchestrator in modern engineering02:36 How Fluency introduced coding agents with a three part approach: safe experimentation, mindset shift, and stronger context04:35 Is this just the next step in software engineering, or does AI fundamentally change the role?08:16 Why some engineers resist AI tools, and what helps people move from skepticism to real adoption11:26 How technical interviews are changing as AI becomes part of everyday engineering work16:59 Scott on whether companies will actually need fewer engineers, and why the demand for meaningful work is not going away21:09 The practical lesson teams miss: better structured systems and better context make coding agents far more effectiveOne line worth remembering“It's not about losing your craft. It's about managing a workforce of junior agents.”Practical edgeScott shares a useful operating principle for teams already experimenting with AI in engineering: if you want better output, do not start with prompts alone. Start with structure. The more clearly a system is organized, and the more context an agent can access, the more useful and reliable the result becomes.That applies to hiring too. Technical skill still matters, but the engineers who stand out now are the ones who can collaborate across product and engineering, understand the business context, and make good decisions with AI in the loop.Call to ActionIf you are thinking through what AI means for engineering careers, team design, or product velocity, follow the show and share this episode with someone building in this new environment. For more conversations with founders and operators shaping where tech is headed, connect with Amir on LinkedIn.

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.

Michael White, Co founder and CEO of Multiply, joins the show to talk about the path from engineering leadership to the CEO seat, and what it really takes to build in a high trust, high complexity market. If you are thinking about founder readiness, leadership growth, or where AI creates real value in fintech, this episode gets into the parts that matter.Michael shares how early entrepreneurial instincts showed up long before Multiply, what changed as he moved from builder to company leader, and why some of the most important skills in leadership have less to do with code and more to do with communication, conviction, and influence. He also breaks down how Multiply is using AI to improve the mortgage experience without removing the human element people still need in a major financial decision. In this episode:• The mindset shift from engineer to CEO• Why leadership becomes a form of sales• How founder timing can be an advantage, not a delay• Where AI fits in the mortgage process, and where it does not• Why startups can move faster than legacy players in AI adoption Timestamped highlights00:43 What Multiply is building, and why an AI native mortgage company sees a better path to homeownership01:47 The childhood business story that hinted at an entrepreneurial future06:20 What changed in the move from engineering leadership to founder and CEO08:45 Why so much of leadership comes down to influence, alignment, and selling the vision17:19 Why mortgages are such a strong use case for AI, and why the back office is the real opportunity22:39 The startup advantage in AI, speed, focus, and freedom from legacy systems Follow the show for more conversations with founders, operators, and technology leaders building what comes next.

Susan Liu, Partner at Uncork Capital, joins Amir to break down what actually matters when backing early stage AI companies. From founder market fit to product wedge to the reality of churn, this conversation gets past the hype and into how strong companies separate themselves in a crowded market.If you are building, funding, or evaluating AI startups, this episode gives you a sharper lens on where the market is heading, what Series A investors now expect, and why real ROI is becoming the line between momentum and fallout.What stood out• The best early stage founders usually have earned insight, meaning they have lived the problem before building the solution• In crowded AI markets, the goal is not to be interesting, it is to become one of the few companies that actually wins• AI buyers still care about the same core question, does this drive revenue or cut cost in a measurable way• The Series A bar has moved up fast, and strong growth alone is not enough if retention is weak• Some of today's biggest AI winners may still face painful churn if they are not truly essential to the customerTimestamped Highlights00:37 Susan breaks down how Uncork Capital invests at seed and what it takes to get real conviction early02:00 The three-part framework she uses to evaluate companies, team, market, and product wedge with traction09:42 Why crowded AI markets are not necessarily a red flag, and how winners still pull away from the pack17:04 The ROI test every AI startup has to pass if it wants to survive renewals19:05 Susan's honest take on 2026, cautious optimism, bigger impact, and a likely wave of churn24:33 What founders need now to raise a strong Series A in a market where the bar is higher than everOne line that stuck“If you cannot prove one of these two, it is going to be a tough sell. Companies are not going to renew.”Practical takeaways for operators and founders• If your product cannot clearly tie to revenue growth or cost savings, buyers will eventually cut it• Founder credibility matters more when the market gets noisy, especially in AI• A compelling wedge wins attention, but retention is what keeps the story alive• Happy customers who will speak for you can be one of the strongest assets in a fundraiseStay connectedIf this episode gave you a better lens on AI startups, venture, and what actually drives durable value, follow the show, share it with a founder or operator in your network, and keep up with Amir on LinkedIn for more conversations like this.

What happens to e commerce when AI agents start shopping instead of humans?Maju Kuruvilla, Founder and CEO of Spangle, joins the show to unpack a shift most companies are not prepared for. If AI agents become buyers, the entire digital shopping experience must change. Websites today are designed for human psychology, not machines making decisions.In this conversation, Maju explains why context is becoming the most important layer in commerce. From marketing clicks to storefront visits, most companies lose the context that originally inspired a purchase. The future belongs to systems that can capture, carry, and act on that context across every channel. The discussion explores agent driven shopping, the limits of traditional customer data systems, and how AI can reshape both online and physical retail experiences.Key Takeaways• Context matters more than identity. Knowing what someone is trying to do right now is often more valuable than knowing who they are.• Most e commerce experiences reset the customer journey. When someone clicks from an ad to a site, the original inspiration is usually lost.• AI agents will shop differently than humans. They are not influenced by visual design or marketing psychology the same way people are.• Commerce will not become fully agent driven. Instead, brands must design experiences that work for humans, agents, and hybrid interactions.• Physical retail may benefit the most from AI driven context because stores can blend digital signals with real world behavior.Timestamped Highlights00:00 Why the next generation of e commerce will be built for AI agents, not just human shoppers.02:08 The hidden problem in online shopping today. Most websites lose the context that brought the customer there.06:11 Buyer agents and seller agents. How commerce may evolve into AI systems negotiating purchases.11:38 Why a simple request like “buy a red sweater” is actually a complex problem of interpretation and context.16:30 How AI could transform physical stores through dynamic recommendations and real time shopping guidance.22:30 Why collecting endless customer data might be the wrong approach to personalization.27:59 The future of autonomous shopping and why personal AI agents may eventually handle everyday purchases.A Moment That Sticks“Context is what matters. The fact that I bought a TV before is interesting, but not important. What matters is what I am trying to do right now.”Practical Insight for BuildersIf you are building AI driven commerce tools, start with the product layer.According to Maju, the foundation is making your product catalog intelligent. AI systems need rich product understanding so they can match intent with inventory. Once the catalog becomes machine readable and context aware, everything else becomes easier to automate.Call to ActionIf you enjoyed this conversation, follow the show and share this episode with someone working at the intersection of AI, commerce, or product development.New conversations every week with the builders shaping the future of technology.

Most people never think about the technology behind construction equipment rentals. But behind every crane, excavator, and lift is an industry still running on paper, spreadsheets, and manual workflows.In this episode, Andy Feis, CEO and Co-Founder of Renterra, joins Amir to explain how a hundred billion dollar equipment rental market is finally entering the modern software era. The conversation explores how operational software, telematics data, and AI are reshaping one of the most overlooked parts of the industrial economy. Andy shares how rental companies manage fleets of expensive machines, why legacy workflows still dominate the industry, and how platforms like Renterra are bringing cloud software and automation to a sector that has largely been left behind by the tech revolution.This episode also explores the intersection of operational data, AI automation, and real world infrastructure. From fleet optimization to automated maintenance insights, the future of equipment rental may look very different than it does today.Key Takeaways• The equipment rental industry is a massive but overlooked market where over half of construction equipment is rented rather than owned.• Many rental businesses still run critical operations using pen and paper, manual inspections, and outdated spreadsheets.• Operational software is the first step toward modernization, helping companies manage inventory, dispatch, pricing, and maintenance.• Telematics data from machines unlocks powerful insights around maintenance timing, asset valuation, and fleet utilization.• AI will not replace the physical work in industrial sectors, but it can automate low value operational tasks and dramatically improve decision making.Timestamped Highlights00:00 Introducing the hidden technology opportunity inside the equipment rental industry02:00 Why many rental companies still rely on paper, binders, and manual equipment checks06:20 How Andy Feis discovered a massive opportunity inside industrial operations09:00The low hanging fruit in modernizing equipment rental workflows11:14 What kind of data heavy machines actually generate and how it can be used13:03 Where AI actually helps blue collar industries today20:18 The roadmap for modernizing the industry and what comes nextA Moment That Stuck“The industrial sector is an enormous part of the economy, but it has been one of the last places to feel the impact of the broader tech revolution.” Pro TipsIf you are building technology for legacy industries, start with operational efficiency before advanced analytics.Modernization works best when it removes friction from existing workflows. Once companies see time savings and operational improvements, they become far more open to deeper data and AI driven insights.Call to ActionIf you enjoy conversations about technology transforming real world industries, follow the show and share this episode with someone building in construction, logistics, or industrial software.

Luke Fischer, cofounder and CEO of SkyFi, breaks down how earth intelligence is becoming searchable, and why that changes decision making across defense, energy, logistics, and agriculture.You will hear how his path from Army special operations aviation to Head of Flight Ops at Uber shaped SkyFi's product mindset, plus a practical look at what geospatial imagery and analytics can actually answer today.Key Takeaways• Networks are not nice to have, they are the fastest path to trust, hiring, and deals, especially in government and high stakes markets• SkyFi's core unlock is access, making it possible to task satellites, pull history, and ask questions of the data, not just look at images• Going commercial first can create a faster iteration loop, then government adoption follows once the product is battle tested• The real product future is answers, not imagery, using natural language queries that return decisions grade insight• Privacy is not only about resolution, it is also about who can buy data, screening, and compliance, because access is the real leverage pointTimestamped Highlights00:47 Earth intelligence in plain English, task satellites, pull decades of history, ask questions like vessel detection or soil moisture06:32 Why veteran resumes miss the mark, and how to translate leadership without goofy title inflation10:44 The origin story, a broken buying experience in satellite imagery turns into SkyFi's wedge16:42 Selling into government, people game first, acquisition reality, and why patience is a feature19:46 Use cases you will not expect, livestock behavior, barge counting, palm heights, mineral detection, and more28:10 Where this is headed, ask a question about the world, get an answer, then move toward proactive intelligenceA line worth repeating“Startups are the same thing, you are finding the right people with the right traits to solve these undefined problems in being comfortable with risk.”Practical moves you can stealIf you are hiring, screen for comfort with ambiguity, not just pedigree, undefined problems are the job in high growth workIf you are selling, build your network before you need it, warm paths beat cold volume every timeIf you are building product, shorten the feedback loop, commercial iteration can harden the product before slower cycle buyers adoptCall to ActionIf this episode sparked ideas for how data, defense, or AI driven analytics will reshape markets, follow the show and turn on notifications so you do not miss the next one. Also share it with one operator who makes high stakes decisions and would appreciate a clearer view of what is happening on the ground.

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.

Harry Gestetner built a creator economy platform in college, sold it, and walked away. Then he did the one thing nobody expected. He jumped back in and started building hardware.In this episode, the founder and CEO of Orion (a sleep tech company making smart mattress covers) sits down to talk about what really happens after an exit, why most founders can't stay away from building, and what changes when you go from software to physical products.Harry shares what surprised him about the acquisition process, how he thinks about evaluating new startup ideas, and why he believes hardware is "life on hard mode." He also gets into the mental side of founding, from managing stress to staying sharp when everything feels uncertain.What You'll Walk Away WithGoing through an exit sounds like the finish line, but Harry explains why it's actually a reset. You trade ownership and freedom for financial security, and at some point, most founders start craving the creative control they gave up.Not every idea deserves your time. Harry talks about running new concepts through a "disqualification period" where you actively try to poke holes before committing. The ones that survive that process are worth going all in on.Hardware changes the game. Software lets you pivot fast. Hardware gives you 18 month product cycles, inventory headaches, and supply chain complexity. Conviction has to be higher before you start.The best startup ideas come from problems you and your friends actually have. If enough people share that problem, you've got a market.Knowledge compounds across startups. Harry compares the founder journey to an elastic band. Once you've been stretched, you never go back to your original form. Every challenge you survive makes the next one more manageable.Timestamped Highlights[00:34] What Orion actually does and how it makes six hours of sleep feel like ten[03:01] The emotional arc of an exit that nobody talks about, from relief to restlessness[05:34] How Harry evaluates startup ideas and why he uses a disqualification process[09:30] Why building hardware is "life on hard mode" and what made him take it on anyway[10:39] The elastic band theory of founder growth and why learning compounds over time[15:49] His advice for early career founders: pick one thing and go all inWords That Stuck"As a founder, you're sort of like an elastic band. The more you get stretched, you never go back to the original form."Tactical TakeawaysRun every new idea through a disqualification period. Actively look for reasons it won't work before you commit. The ideas that survive that scrutiny are the ones worth building.Build around problems you personally experience. If your friends share the same frustration, there's a good chance others do too. That's your market signal.If you're going to start something, go all in. Stop hedging across multiple projects. Pick one idea and dedicate yourself to it completely until it works.Keep Up With The ShowIf this episode hit home, share it with a founder or someone thinking about taking the leap. Subscribe wherever you listen so you never miss an episode. And connect with us on LinkedIn for more conversations like this one.

Behnam Bastani, CEO and cofounder of OpenInfer, breaks down why the last two years of AI feel explosive, and why the next wave is not chat, it is action at the edge.We get into always on inference, what actually forces compute to move closer to the data, and the missing layer that makes edge AI scale: the Android like infrastructure that lets devices collaborate instead of living in silos.Key takeaways• The hype spike is real, but the runway is decades, it took compute, sensors, and communication protocols maturing over generations to unlock this moment• AI is shifting from conversational to actionable, which means continuous, always on inference becomes the norm• Edge wins when cost, reliability, and data sovereignty matter, cloud and edge will coexist, but the workload placement changes• The biggest bottleneck is not just silicon, it is the infrastructure layer that makes building and deploying across devices easy, plus a shared fabric so devices can cooperate• Adoption is as much a human story as a technical one, this shift lands faster and broader than previous tech transitions, so anxiety is predictable and needs real attentionTimestamped highlights00:38 OpenInfer's mission, intelligence on every physical surface, and why collaboration matters02:07 Electricity as the earlier revolution, intelligence as the next kind of power, and the control problem05:54 Where we really are on the maturity curve, early products are here, mass adoption and safety take time08:31 When the device boundary disappears, it stops being you versus the agent, it becomes one system11:04 Always on inference, and the three forces pushing compute to the edge: cost, reliability, data sovereignty14:40 The Android moment for edge AI, why the operating system layer unlocks developers, apps, and adoptionA line worth replayingThose are going to be the three pillars that really enforces that edge and cloud are going to live together.Pro tips for builders• If your product needs real time decisions, design for intermittent networks from day one, reliability is not optional• Treat data sovereignty as a product feature, not a compliance afterthought, it is becoming the moat• Push for interoperability early, the fabric that lets devices share the right data is what makes edge feel seamlessCall to actionIf this episode helped you rethink where AI should run and what it takes to ship it in the real world, follow the show and share it with one builder who is working on edge, robotics, devices, or applied AI.

Building data capability from zero is not a tooling problem, it is a trust and prioritization problem. In this episode, Laura Guerin, Head of Data and Data Science at Bevi, breaks down how she goes from blank slate to real business impact, without getting trapped in endless plumbing or endless meetings. Laura shares how she runs an early listening tour, prototypes value before asking for bigger investment, and decides when to hire scrappy generalists versus specialists. We also get practical on AI, where it helps, where it is unnecessary, and why quality data and a clean semantic layer still decide whether anything works.Key takeaways• Start with business priorities, then map data work to the actions and outcomes leaders actually care about• Prototype the end deliverable fast, even if the backend is duct tape at first, then scale after stakeholders see value• Use cases first for AI, most problems do not need AI, but the right problems can see real acceleration• Early teams win with adaptable generalists who can wear multiple hats across data, analytics, and data science• Trust is a shared responsibility, build reliability, then create a culture where users flag weirdness quicklyTimestamped highlights00:44 Bevy explained, smart bottle less dispensers and why the business context matters for data priorities02:01 The listening tour playbook, exec alignment, stakeholder map, and using AI to synthesize themes into a SWOT04:00 The MVP reality, manual prototypes to prove value, then the conversation about scalable pipelines06:33 AI without the hype, use cases, when AI is not needed, and two examples with clear business impact09:22 Hiring from zero, why generalists first, the data analytics data science spectrum, and the personality traits that matter14:21 Self service reimagined, Slack as the interface, semantic layer and permissions, and how to keep a single source of truth20:19 Keeping trust when things break, checks and balances plus a shared responsibility model22:39 Making innovation real, baking it into expectations so the team has time to learn and test new approachesA line worth stealingData on its own is not typically a priority. It is more about the action or the impact that comes out of the data.Pro tips• Run a structured listening tour early, capture themes, then pick two or three priorities you can deliver quickly• Show the business an MVP output first, then use that proof to justify the unglamorous backend work• Treat AI like any other tool, define the problem, validate the use case, then confirm the data quality inputsCall to actionIf you are building analytics, data products, or AI inside a growing company, follow the show and subscribe so you do not miss the next operator level conversation. Share this episode with one leader who is asking for data outcomes but has not funded the foundation yet.

Riya Grover, CEO and co founder of Sequence, breaks down what “good CEO” actually looks like when the job is messy, fast, and high stakes. This is a practical conversation about building excellence through people, clarity, and direction, not through heroics or micromanagement. Riya runs a revenue automation platform for finance teams, helping companies automate order to cash, billing, invoicing, accounts receivable, and revenue recognition. From that seat, she shares a founder level view on leadership that is direct, repeatable, and built for real operating constraints.Key takeaways• The CEO's highest leverage job is building the bench, your company becomes the team you assemble• High performance culture comes from a clear bar, fast decisions when it is not met, and leaders who own outcomes• Great teams do not need more policies, they need context, goals, trade offs, and clarity• Separate reversible decisions from irreversible ones, move fast on two way doors, slow down on one way doors• Hiring signal to watch, motivation and hunger for the stretch challenge often beats the “done it before” resumeTimestamped highlights00:32 What Sequence does, why order to cash is still painfully manual01:48 The CEO role is less about functions, more about direction and execution03:23 Excellence starts with talent density, do not compromise on the bar06:10 Why companies win, direction plus distribution, and the Figma example11:01 Getting real feedback as a leader, how to reduce hierarchy and increase ownership14:39 “They need clarity,” decision frameworks over micromanagement18:01 The hidden damage of the founder weighing in on every micro decision20:53 Hiring underrated talent, motivation, ambiguity tolerance, and the stretch role24:38 Why the CEO should invest time in hiring, the leverage math is obviousA line worth keepingThey do not need policies, they need clarity. Pro tips you can steal• Promote leaders who have done the job and set the pace, it earns trust and improves decision quality• Give teams context and constraints, then treat your input like any other input• Use the door test, reversible decisions get speed and delegation, irreversible ones get more diligence• In hiring, look for motivation plus clear thinking, then bet on aptitude over the perfect backgroundCall to actionIf this one helped you think more clearly about leadership and hiring, follow the show and share the episode with one operator who is building under pressure. New conversations drop with different guests and different problems, so you always have something useful to steal.

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.

Anjali Jameson, Chief Product Officer at Arbiter, says the hard part is not gathering data. It is getting action across patients, providers, and payers without breaking what already works.“Automating something that's broken is not going to necessarily give us better outcomes.”Arbiter is a care orchestration platform built for patients, providers, and payers together, not a single point solution. The operating spine ingests and makes actionable data across the patient journey, including provider directories, EMR integrations, claims, and financial and policy data from health plans, then connects it to highly personalized multi channel agentic outreach. You will hear why cross system context matters, how total cost of care stays in view while each stakeholder chases different leading metrics, and what it looks like to move from automation into optimization, like going from a call center scheduling flow to 60 percent conversion and pushing toward 95 percent conversion.Timeline00:40 Care orchestration platform, operating spine, data across the patient journey04:33 Misaligned incentives, prior authorizations, 12 to 14 hours a week09:42 Total cost of care, star metric, building for different metrics12:25 Long form personalized videos, transportation, education, medication management15:02 Prior authorization from three to six days to almost instantaneous22:07 COVID, provider messaging two, three X, AI responds fasterSubscribe and share it with someone who is building in health tech.

Most data teams do not have a tooling problem. They have a customer service problem.Mo Villagran, Associate Director of Insights, Analytics, and Data at Cambrex, argues that stakeholder expectation management is the difference between being a trusted advisor and being an order taker."In a simple word, it's really just customer service."In this episode, Mo breaks down how to manage stakeholder expectations, define expected delivery value, and keep projects aligned to real business outcomes instead of chasing rebranded tools. She shares why simple solutions often win, how to show progress even when the work is plumbing, and why qualitative stakeholder testimony beats dashboard count KPIs. You will also hear how she thinks about AI as a tool, when it works, when it is just a cool toy, and how to build trust by demoing in real time.00:02:00 Stakeholder expectation management is customer service00:03:00 Why skeleton teams can still deliver value00:06:00 Who defines expected delivery value, and how to shape it00:09:00 Negotiate expectations, do not become an order taker00:18:00 How to show progress when there is nothing visual00:21:00 Stop chasing quantitative KPIs, win with testimonySubscribe and share this episode with anyone who is knee deep in stakeholder management.

Ashok Krishnamurthi, Managing Partner at Great Point Ventures, says the biggest mistake in venture capital is confusing prediction with judgment.Early stage investing is not about perfect stories, it is about first principles and picking the founder who can execute when the story breaks.This episode is for startup founders and investors who want a cleaner filter for what matters.“You have to learn to check your ego at the door because it's a partnership.”Ashok shares his path from engineering into building companies, then into venture capital, and explains how he forms an investment thesis when markets are noisy. We talk about founder evaluation, why picking the jockey matters more than the idea, and how first principles thinking shows up in real domains like healthcare data and cancer. We also get practical about artificial intelligence, why AI is not only a compute race, and how AI inference, energy efficiency, and cost shape what wins.00:00 Why legacy matters more than VC metrics02:28 Engineer to founder to venture capital11:16 How to pick the jockey14:21 First principles, cancer data, and AI constraints23:24 AI is here to stay, keep your mind open30:15 How to reach AshokIf this episode helped, subscribe and share it with a builder or investor who will use it.

Aditya Agarwal did not plan to work in robotics. He got rejected from his first-choice major, joined a student club to keep his parents off his back, and stumbled into one of the fastest-growing fields in tech. Now he is Head of Robotics at Medra, a company building physical AI scientists that let researchers run experiments remotely at speeds a traditional lab cannot touch."Even the companies that have made the most progress haven't deployed at the scale of laptops, cars, or phones. So if you have experience scaling hardware products, that is super valuable at an early-stage robotics company."What we get into: why the PhD requirement is mostly gone, how AI is shrinking the hardware development timeline, and the cheapest way to start building with robotics today if you cannot afford to go back to school or take a step back in your career.Timestamped Highlights01:19 The accidental path into robotics that actually worked03:04 Whether you still need an engineering degree for hardware roles04:48 Master's degree vs. early-stage startup: what gets you there faster10:57 How AI is replacing the guesswork in hardware configuration15:51 How to start learning robotics at home without spending much18:38 Why rigid hiring processes are costing robotics teams good candidatesIf this one lands, subscribe and share it with someone who has been thinking about making a move into the space.

Ronak Desai, Co-founder and CPTO at Payment Labs, breaks down a surprisingly hard problem that sits at the intersection of fintech, sports, and compliance. If you have ever assumed paying winners is just a simple payout flow, this episode will change that view fast.Payment Labs helps tournament organizers, league operators, and modern sports businesses handle payouts plus tax compliance and support, all in one system. Ronak explains why spot payments are high risk, why manual workflows still dominate the space, and how stablecoins and AI are about to reshape fraud, identity, and trust.Key TakeawaysOne time payouts are a fraud magnet, inconsistent winners and risk based rules make verification and compliance much harder than payrollSolving payments without solving tax and forms still leaves the biggest liability sitting with the organizerMany sports and esports operators still run payouts in a surprisingly analog way, checks, cash, and post event cleanupAI is now good enough to pressure identity verification, and stablecoins make recovery harder because transfers are effectively finalProduct adoption depends on meeting users where they are, younger athletes expect texting and simple flows, not tickets and portalsTimestamped Highlights00:29 What Payment Labs actually does, payouts plus tax compliance plus support for sports, esports, and creator economy use cases01:15 The origin story, a real tax problem hit an esports operator and exposed how broken the payout workflow is02:46 Why spot payments raise risk, random recipients, fraud pressure, and why bank partners treat this differently than payroll04:58 The industry reality check, still running on checks and cash, and what digitizing the workflow unlocks next06:58 AI fraud versus AI detection, how identity verification is getting bypassed and why stablecoin rails raise the stakes11:55 The NIL wild west and the product lesson, meet athletes where they already live, including iMessage supportA Line Worth RepeatingNow you have AI committing the fraud and then you have AI detecting the fraud.Pro Tips for Builders and OperatorsIf your users are young and mobile first, build support where they already communicate, texting beats ticketing for adoptionDo not bolt on AI for a storyline, use it where it replaces manual work you already do and frees time for higher leverage decisionsMap your tasks with the Eisenhower quadrant, then automate what is repetitive before you chase shiny featuresCall to ActionIf this episode helped you think differently about fintech, fraud, and modern payout infrastructure, follow the show and share it with a founder or operator who touches payments. For more conversations at the intersection of tech, data, and real world execution, connect with Amir on LinkedIn and subscribe to the Elevano newsletter.

Healey Cypher, CEO of BoomPop and COO at Atomic, breaks down what separates founders who win from founders who stall. You will hear a clear way to judge whether an idea is truly worth building, plus the trust mechanics that get investors, customers, and teammates to actually follow you.This conversation is a practical map for tech builders who want to pick smarter problems, execute faster, and earn credibility without the founder theater.Key TakeawaysFounders matter most, but the idea is still a gate, the same great team can get wildly different outcomes depending on the market and timingVC backed is a specific game, it requires not just big potential, but fast scale, and the incentives are not the same as building a profitable lifestyle businessA quick reality check for market size, if you need more than about five to seven percent penetration to hit meaningful revenue, it is usually a brutal pathPainkillers beat vitamins, solve an urgent problem people feel right now, or you risk getting cut the moment budgets tightenTrust is built through authenticity, logic, and empathy, if one wobbles, people feel it fast, and progress slows everywhereTimestamped Highlights00:00:00 Healey's background, why BoomPop, and what the episode is really about00:02:00 The post pandemic spend shift and the why now behind modern events and group travel00:04:30 Founder versus idea, why execution dominates, but the opportunity still decides the ceiling00:06:40 The VC reality, power law returns, speed, and why some good businesses are still a no for venture00:09:15 A simple market math test, penetration levels that become a growth wall00:19:00 Trust as a founder skill, the three ingredients and how to spot when one is missing00:21:30 Vulnerability as a shortcut to real connection, plus the giver mindset that makes people want you to winA line worth stealingIf everyone wants you to win, it is a lot easier to win.Pro Tips for Tech FoundersAsk yourself what you naturally look forward to doing, that is often your zone of strength, hire around the tasks you dreadLearn the financial basics early, especially cash flow, it is the scoreboard that keeps you alive long enough to winWhen trust is lagging, check the three levers, are you showing the real you, can people follow your reasoning, do they feel you care about their outcomesWhat's next:If you build products, lead teams, or are thinking about starting something, follow the show so you do not miss episodes like this. Also connect with me on LinkedIn for short takeaways and clips from each conversation.

Ty Wang, cofounder and CEO of Angle Health, breaks down what it means to give back through public service, then shows how that same mindset drives his mission to modernize healthcare for small and midsize businesses. We get into why legacy health plans feel opaque and painful, what an AI native health plan actually changes behind the scenes, and how better data and workflows can create real cost stability for employers.Ty shares his path from a federal scholarship and national service work to Palantir, and why he chose one of the most regulated, least glamorous industries to build in. If you have ever wondered why healthcare feels impossible to navigate, or why renewals can blindside a company, this conversation will give you a clear mental model of the problem and a practical view of what modernization looks like when it actually ships. Key TakeawaysHealthcare feels broken because the infrastructure is fragmented, data is siloed, and even basic questions become hard to answer across inconsistent systemsModernizing healthcare is not just about a new app, it is about rebuilding the operational core so workflows, claims, underwriting, and member experience can run on integrated dataSmall and midsize businesses are hit hardest by cost volatility because they lack transparency, predictability, and negotiating leverage, yet health insurance is often a top line item after payrollA strong approach to regulated markets is collaborative, treat regulators as partners in consumer protection, not obstacles to work aroundMission and impact can be a recruiting advantage, especially when the technical problems are genuinely hard and the outcomes touch real people fastTimestamped Highlights00:40 What Angle Health is, and what AI native means in a real health plan02:05 The scholarship path that pulled Ty into public service and set his trajectory04:06 The personal story behind the mission, the American dream, and why access matters09:38 Why healthcare infrastructure is so complex, and how siloed systems create bad experiences11:33 Why SMBs get squeezed, and how manual administration blocks customization at scale13:20 The real pain point for employers, cost volatility and zero predictability before renewal16:55 Why the tech can expand beyond SMBs, but why the SMB market is already massive19:51 Lessons from building in a regulated industry, and why credibility and funding matter22:26 Hiring for high agency, mission driven talent in a world full of AI companiesA line that sticks“Unless you are lucky enough to work for a big company, these modern healthcare services are still largely inaccessible to the vast majority of Americans.”Pro Tips for tech operators and buildersIf you are modernizing a legacy industry, start with the infrastructure layer, fix the data model, integrate the systems, then automate workflowsIn regulated markets, build relationships early, show how your product improves consumer outcomes, and make compliance a design constraint, not a bolt onWhen selling into SMBs, predictability beats perfection, give customers a clear breakdown of what drives costs and what they can controlWhat's next:If this episode helped you see healthcare and legacy modernization more clearly, follow the show on Apple Podcasts or Spotify and subscribe so you do not miss the next conversation. Also, share it with one operator or builder who is trying to modernize a messy industry.

Gabe Ravacci, CTO and co-founder at Internet Backyard, breaks down what the “computer economy” really looks like when you zoom in on data centers, billing, invoicing, and the financial plumbing nobody wants to touch. He shares how a rejected YC application, a finance stint, and a handful of hard lessons pushed him from hardware curiosity to building fintech infrastructure for compute.If you care about where compute is headed, or you are early in your career and trying to find your path without overplanning it, this one will land.Key Takeaways• Startups often happen “by accident” when your competence meets the right problem at the right time• Compute accessibility is not only a chip problem, it is also a finance and operations problem• Rejection can be data, not a verdict, treat it as feedback to sharpen the craft• A real online presence is less about networking and more about being genuinely useful in public• Time blocking and single task focus beats grinding when you are juggling school, work, and a startupTimestamped Highlights00:28 What Internet Backyard is building, fintech infrastructure for data center financial operations01:37 The first startup attempt, cheaper compute via FPGA based prototyping, and why investors passed04:48 The pivot, from hardware tools to a finance informed view of compute and transparency gaps06:55 How Gabe reframed YC rejection, process over outcome, “a tree of failures” that builds skill08:29 Building a digital brand on X, what he posted, how he learned in public, and why it worked13:36 The real balancing act, dropping classes, finishing the degree well, and strict time blocking20:00 Books that shaped his thinking, Siddhartha, The Art of Learning, Finite and Infinite GamesA line worth keeping“The process is really more important than any outcome.”Pro Tips for builders• Treat learning like a skill, ask better questions before you chase better answers• Make focus a system, set blocks, mute distractions, and do one thing at a time• Share what you are learning in public, not to perform, but to be useful and find signalCall to ActionIf this episode sparked an idea, follow or subscribe so you do not miss the next one. Also check out Amir's newsletter for more conversations at the intersection of people, impact, and technology.

Data leaders are being asked to ship real AI outcomes while the foundations are still messy. In this conversation, Dave Shuman, Chief Data Officer at Precisely, breaks down what actually determines whether AI adoption sticks, from hiring “comb shaped” talent to building trusted data products that make AI outputs believable and usable.If you are building in data, AI, or analytics, this episode is a practical map for what needs to be true before AI can move from demos to dependable, repeatable impact.Key TakeawaysComb shaped talent beats narrow specialization, AI work rewards people who can span multiple skills and collaborate wellAdoption is a trust problem, and trust starts with data integrity, lineage, context, and a semantic layer that business users can understandOpen source drives the innovation, commercialization makes it safe and usable at enterprise scale, especially around security and supportData must be fit for purpose, start every AI project by asking what data it needs, who curates it, and what the known warts areHumans are still the last mile, small workflow choices can make adoption jump, even when the model is already accurateTimestamped Highlights00:56 The shift from T shaped to comb shaped talent, what modern AI teams actually need to look like05:36 Hiring for team fit over “world class” niche skills, and when to bring in trusted partners for depth07:37 How open source sparks the ideas, and why enterprises still need hardened, supported versions to scale11:31 Where AI adoption is today, why summarization is only the beginning, and what unlocks “AI 2.0”13:39 The trust stack for AI, clean integrated data, lineage, context, catalog, semantic layer, then agents19:26 A real adoption lesson from machine learning, and why the human experience decides if the system winsA line worth stealing“You do not just take generative AI and throw it at your chaos of data and expect it to make magic out of it.”Pro Tips for data and AI leadersHire and build teams like Tetris, fill skill voids across the group instead of chasing one perfect profileUse partners for the sharp edges, but require knowledge transfer so your team levels up every engagementMake adoption easier by designing for human behavior, sometimes the smallest workflow tweak beats more accuracyBuild governed data products in a catalog, then validate AI outputs side by side with dashboards to earn trust fastCall to ActionIf this helped you think more clearly about AI adoption, talent, and data foundations, follow the show and turn on notifications so you do not miss the next episode. Also, share it with one data or engineering leader who is trying to get AI out of pilots and into real workflows.

Cloud bills are climbing, AI pipelines are exploding, and storage is quietly becoming the bottleneck nobody wants to own. Ugur Tigli, CTO at MinIO, breaks down what actually changes when AI workloads hit your infrastructure, and how teams can keep performance high without letting costs spiral. In this conversation, we get practical about object storage, S3 as the modern standard, what open source really means for security and speed, and why “cloud” is more of an operating model than a place. Key takeaways• AI multiplies data, not just compute, training and inference create more checkpoints, more versions, more storage pressure • Object storage and S3 are simplifying the persistence layer, even as the layers above it get more complex • Open source can improve security feedback loops because the community surfaces regressions fast, the real risk is running unsupported, outdated versions • Public cloud costs are often less about storage and more about variable charges like egress, many teams move data on prem to regain predictability • The bar for infrastructure teams is rising, Kubernetes, modern storage, and AI workflow literacy are becoming table stakes Timestamped highlights00:00 Why cloud and AI workloads force a fresh look at storage, operating models, and cost control 00:00 What MinIO is, and why high performance object storage sits at the center of modern data platforms 01:23 Why MinIO chose open source, and how they balance freedom with commercial reality 04:08 Open source and security, why faster feedback beats the closed source perception, plus the real risk factor 09:44 Cloud cost realities, egress, replication, and why “fixed costs” drive many teams back inside their own walls 15:04 The persistence layer is getting simpler, S3 becomes the standard, while the upper stack gets messier 18:00 Skills gap, why teams need DevOps plus AIOps thinking to run modern storage at scale 20:22 What happens to AI costs next, competition, software ecosystem maturity, and why data growth still wins A line worth keeping“Cloud is not a destination for us, it's more of an operating model.” Pro tips for builders and tech leaders• If your AI initiative is still a pilot, track egress and data movement early, that is where “surprise” costs tend to show up • Standardize around containerized deployment where possible, it reduces the gap between public and private environments, but plan for integration friction like identity and key management • Treat storage as a performance system, not a procurement line item, the right persistence layer can unblock training, inference, and downstream pipelines What's next:If you're building with AI, running data platforms, or trying to get your cloud costs under control, follow the show and subscribe so you do not miss upcoming episodes. Share this one with a teammate who owns infrastructure, data, or platform engineering.

This is an early conversation I am bringing back because it feels even more relevant now, the intersection of AI and art is turning into a real cultural shift.I sit down with Marnie Benney, independent curator at the intersection of contemporary art and technology, and co-founder of AIartists.org, a major community for artists working with AI. We talk about what AI art actually is beyond the headlines, where authorship gets messy, and why artists might be the best people to pressure test the societal impact of machine learning.Key takeaways• AI in art is not a single thing, it is a spectrum of choices, dataset, process, medium, and intent• The most interesting work treats AI as a collaborator, not a shortcut, a back and forth that reshapes the artist's decisions• Authorship is still unsettled, some artists see AI as a tool like an instrument, others treat it as a creative partner• The fear that AI replaces creativity misses the point, artists can use the machine's unexpected output to expand human expression• Access matters, compute, tooling, and collaboration between artists and technologists will shape who gets to experiment at the frontierTimestamped highlights00:04:00 Curating science, climate, and public engagement, the path into tech driven exhibitions00:07:41 What AI art can mean in practice, datasets, iteration loops, and choosing an output medium00:10:48 Who gets credit, tool versus collaborator, and the art world's evolving rules00:13:51 Fear, job displacement, and a healthier frame, human plus machine as a creative partnership00:22:57 The new skill stack, what artists need to learn, and where collaboration beats handoffs00:29:28 The pushback from traditional art circles, philosophy and intention versus novelty00:37:17 Inside the New York exhibition, collaboration between human and machine, visuals, sculpture, and sound00:48:16 The magic of the unknown, why the output can surprise even the artistA line that stuck“Artists are largely showing a mirror to society of what this technology is, for the positive and the negative.”Pro tips for builders and operators• Treat creative communities as an early signal, artists surface second order effects before markets do• If you are building AI products, study authorship debates, they map directly to credit, accountability, and trust• Collaboration beats delegation, when domain experts and technologists iterate together, the work gets sharper fastCall to actionIf this episode hits for you, follow the show so you do not miss the next drop. And if you are building in data, AI, or modern tech teams, follow me on LinkedIn for more conversations that connect technology to real world impact.