The Tech Trek

In this episode, Amir speaks with Ameya Brid, Global Director of Data & Analytics at Invista, about the maturation of GenAI conversations in the enterprise. They dive into the shift from hype to implementation, real-world challenges like data quality and change management, and how composable architecture is helping organizations adapt to rapid innovation cycles.🔑 Key TakeawaysFrom Hype to Value: GenAI conversations are moving beyond experimentation into outcome-driven initiatives—but most companies still struggle to define measurable KPIs.Top Barriers to Scale: Poor data quality, fragmented systems, unclear use cases, and skills gaps continue to stall enterprise GenAI efforts.Composable > Monolith: Modular, API-driven architectures provide agility to swap components as the tech rapidly evolves.Change Management Rebooted: Adoption now means embedding insights directly into workflows—not just “viewing reports.”Upskilling is Social: Peer-driven learning and internal documentation are outperforming formal training in the GenAI era.🕒 Timestamped Highlights00:00 – Introduction to Ameya and Invista’s work in manufacturing and chemicals01:58 – How GenAI conversations have evolved over the past 18 months03:52 – Marrying business outcomes with AI capabilities06:04 – The five biggest barriers to GenAI implementation: use case clarity, data quality, skills gap, governance, and change management11:53 – Managing constant tech evolution with composable architectures15:02 – Data quality’s outsized impact on GenAI success17:46 – Why CFOs must now invest in data quality20:41 – Change management: From “read the dashboard” to “integrate AI into your workflow”24:03 – Upskilling through shared learning and internal knowledge loops💬 Quote of the Episode"The cost of bad data today is far higher than it was 10 or 20 years ago—not just in decision-making, but in the process itself." – Ameya Brid

In this episode of The Tech Trek, Amir sits down with Andy Beam, CTO of Lila Sciences, to explore how AI is transforming the messy, serendipitous nature of scientific discovery into an engineered, scalable process. From automating lab work to accelerating the speed of breakthroughs, Andy explains why the future of science may be less about eureka moments and more about AI-driven iteration.🔑 Key Takeaways:Science as Engineering: AI enables science to move from a lucky break model to a systematic engineering process.Scaling the Scientific Method: Pairing AI with experimentation platforms creates a feedback loop where hypotheses can be tested at unprecedented speed and scale.Productivity Shift: AI copilots are redefining how scientists (and technologists) interact with their work, elevating humans to higher levels of abstraction.Compounding Innovation: Once AI systems start discovering consistently, the rate of breakthroughs could go from decades to weeks—shifting timelines across industries.⏱️ Timestamped Highlights:00:00 – Intro to Andy Beam and Lila Sciences01:00 – Why the scientific literature is a record of debate, not facts03:09 – Science’s reliance on serendipity—and why that’s changing04:55 – The power of scale in AI and what it means for discovery06:15 – Andy’s personal shift in programming with AI copilots08:41 – Will AI cause serendipity instead of waiting for it?09:38 – The fungibility of speed and intelligence in research11:47 – The challenge of change management in scientific communities13:30 – What consumer adoption could look like in a future of constant innovation💬 Quote:“What we’re doing is taking the scientific method and scaling it with AI—so instead of waiting for Einstein, we build a million of them and run them 24/7.” – Andy Beam

In this episode of The Tech Trek, Amir speaks with Alexander Schlager, founder and CEO of AIceberg, about how his company has tackled the AI talent shortage by partnering directly with universities. From building relationships with faculty to onboarding students into real-world R&D roles, Alex shares a unique, cost-effective strategy for hiring early-career tech talent and turning them into long-term contributors. It’s a compelling listen for anyone in emerging tech, hiring, or leadership.🔑 Key TakeawaysFaculty Buy-in Is Crucial: AIceberg’s success hinged on close collaboration with university faculty, ensuring student recruits were well-prepared and supported.Rethink Talent Pipelines: Instead of competing for senior AI engineers, they invested in training early-career talent—gaining loyalty and retention in return.Process Over Pedigree: Success in junior hires wasn’t about academic brilliance alone—it required a willingness to follow processes and grow into professional environments.Retention Through Learning & Ownership: Clear career paths, challenging problems, and the ability to own projects helped retain young talent even with lower initial salaries.⏱️ Timestamped Highlights00:30 – What AIceberg does: AI trust platform for monitoring AI interactions01:39 – The challenges of hiring AI talent in a startup environment03:17 – Why partnering with faculty made their hiring model work05:42 – Managing overhead and coaching needs with junior hires08:02 – Standardizing research and product pipelines with JIRA10:24 – Who to contact when building university partnerships11:50 – Why maturity and teamwork matter more than grades alone14:43 – How AIceberg advises candidates to evaluate offers before accepting16:49 – Documentation and redundancy reduce risks when junior hires leave18:30 – From outreach to onboarding: a 3-4 day ramp-up process20:18 – Fresh perspectives from new grads as a strategic advantage💬 Quote“Don’t underestimate the benefit of a fresh brain—students often approach problems in ways seasoned professionals might never consider.”

Wyatt Smith, CEO of UpSmith, joins Amir to unpack how agentic AI is transforming the skilled trades industry. From dispatch optimization to human-in-the-loop workflows, Wyatt shares a practical and visionary lens on how AI can solve deep productivity challenges, empower call centers, and proactively generate business opportunities. If you think AI only disrupts digital industries, this episode will make you think again.🔑 Key Takeaways:Agentic AI is unlocking productivity by automating repetitive coordination tasks—like technician dispatching—allowing humans to focus on higher-value interactions.Skilled trades businesses already have rich data but need tools to surface and act on it proactively rather than reactively.Selling AI into traditional industries requires proof points, tight business cases, and sensitivity to the human element.AI augments, not replaces—freeing up people to do work they're best suited for, like nuanced customer engagement.💬 Highlight Quote:“Advances in technology automate tasks, not people… Machines do what they're best at so humans can do what they're best at.” – Wyatt Smith⏱️ Timestamped Highlights:00:38 – Intro to Wyatt Smith and UpSmith's mission in the skilled trades.02:51 – Why dispatching the wrong tech to the wrong job is a billion-dollar coordination problem.05:09 – The customer journey in home services—and where productivity breaks down.08:54 – AI adoption challenges in the trades and how business owners evaluate new tech.11:15 – Human-AI dynamics: skepticism, latency, and building trust with agentic systems.13:49 – “AI creates more work”: how automation changes tasks, not headcount.17:19 – How UpSmith trains agents like new hires with workflows and documentation.20:31 – Personalization at scale: how agents remember details from 5 years ago.23:20 – The future of call centers and human-in-the-loop automation.25:49 – Wyatt’s contact info and closing reflections.

What separates a successful founder from the rest? In this episode, Harish Abbott—CEO and co-founder of Augment—breaks down how he repeatedly spots opportunity early, builds products customers actually want, and navigates the fast-moving world of AI without falling into the trap of chasing every shiny benchmark.We explore how Harish’s team shadowed 60 logistics operators before writing a single line of code, why storytelling is a founder's most underutilized superpower, and how to know when it’s time to pivot—even if everything looks good on the surface.Whether you're scaling your first product or figuring out what not to build, this conversation is packed with real-world insights you can apply today.🔑 Key Takeaways:Start with Pain, Not Product: Successful startups begin by deeply understanding real customer pain points, not by jumping into code or chasing tech trends.Shadowing Over Selling: Harish’s team shadowed 60 logistics operators in the early days of Augment—prioritizing observation over assumptions.Strong Opinions, Loosely Held: Founders must balance confidence in their vision with humility to pivot when data points to a better path.AI ≠ The Product: In a world obsessed with benchmarks, remember: AI is a tool. The actual value lies in making things better, cheaper, or faster for users.⏱ Timestamped Highlights:00:32 – What Augment does: AI teammates for the logistics industry02:48 – “Follow one path consistently” – Harish’s approach to serial entrepreneurship05:57 – The importance of shadowing operators before writing code11:21 – When is it time to pivot? Why usage data is often more telling than top-line growth19:23 – Storytelling as a founder’s core job: how to get employees, investors, and customers on board25:02 – The challenge of AI startup building today: chasing stability over shiny new benchmarks30:10 – Avoiding the trap of benchmark chasing in AI product development💬 Quote:“The best founders are always seeking truth. That truth sometimes tells you to let go of the idea you love.”

In this episode of The Tech Trek, Amir speaks with Patrick Leung, CTO of Faro Health, about what it takes to lead an engineering organization through a transformation to become an AI-first company. From redefining the product roadmap to managing cultural and technical shifts, Patrick shares practical insights on team structure, skill development, and delivering AI-enabled features in a regulated domain like clinical trials. This is a must-listen for tech leaders navigating similar transitions.🧠 Key Takeaways:AI-First ≠ Just Using AIBeing AI-first means deeply embedding AI into the core product architecture—not just bolting on an LLM. It requires strategy, structure, and long-term thinking.Build the Right Team EarlyThe biggest shift for engineering orgs is in people—getting the right AI talent onboard early, rather than doing it all yourself, is critical for momentum.Upskilling Is Real—but SelectiveNot every engineer will pivot to AI, but there’s room for involvement across UX, product, and front-end roles. Cultural fit and willingness to contribute matter more than title.Data Engineering is the Unsung HeroMost AI work today isn’t in model building, but in crafting clean, structured datasets. Investment here pays off exponentially.⏱️ Timestamped Highlights:00:00 – What Does It Mean to Be AI-First?Patrick defines the term and outlines Faro Health’s mission to reduce the cost and timeline of clinical trials.04:13 – Defining the AI StrategyHow they started with clinical writing as the first application of LLMs and why it was harder than expected.07:54 – The Role of Change ManagementAI introduces massive shifts; managing sponsor expectations and workflows is as important as the tech.10:28 – Engineering ImpactHow the roadmap changed and what it meant for full-stack vs. data science roles.14:24 – Hiring vs. UpskillingWhy Patrick hired an expert to lead AI efforts and the balance between internal upskilling and external hiring.16:43 – Competing for AI TalentHow startups can win top AI talent despite the lure of FAANG compensation.18:58 – Team Culture and OpportunityCreating space for engineers who want to jump into AI while maintaining alignment on startup needs.21:07 – Realistic Upskilling PathsFrom Coursera to immersive bootcamps—what actually works for engineers wanting to break into AI.23:11 – If He Could Do It AgainThe two things Patrick would do sooner: hire a dedicated AI team and build structured data pipelines earlier.🔖 Featured Quote:“If you're serious about becoming an AI company, you need to find someone amazing who's launched real AI products—and build a team around them.”

In this episode of The Tech Trek, Amir sits down with Sunita Verma, CTO at Character AI and former engineering leader at Google. Sunita shares how she’s transitioned from leading large-scale AI initiatives at Google to building novel experiences in a fast-paced startup environment. She dives into the mindset shift required to prioritize velocity over scale, how to lead AI-native product innovation, and what it means to be a female technical leader in today’s tech ecosystem.🔑 Key Takeaways:Shift in Leadership Mindset: At startups, leaders must prioritize velocity and innovation over scale, focusing on getting frictionless, AI-native products to market quickly.AI Product Loop: Success comes from tightly coupling AI research with product development—shortening the feedback loop to create truly novel user experiences.Female Technical Leadership: Sunita emphasizes the need for more women in senior engineering roles and shares how calculated risk-taking and mentorship shaped her journey.Startup Clarity vs. Corporate Comfort: While startups offer focus and purpose, they also require deep ownership and rapid decision-making without the cushion of big-company resources.💬 Quote:“Focus brings clarity of purpose... but with that comes the pressure of knowing every decision deeply impacts the company.” — Sunita Verma⏱️ Timestamped Highlights:00:00 – Intro: Meet Sunita Verma, CTO at Character AI and former Google engineering leader.01:52 – Google to Startup: Comparing work at Google with her current role at Character AI.03:39 – Leadership Shift: Sunita’s take on building AI-native products from scratch.06:21 – From Scale to Speed: Pivoting from optimization at scale to innovating with velocity.08:12 – Product & Tech Integration: Creating tight feedback loops between AI research and products10:01 – Closer to Engineering: Why Sunita enjoys being hands-on and deeply involved in compute management.12:12 – Focus as a Double-Edged Sword: The simplicity and pressure of startup leadership.14:00 – Female Engineering Leadership: The need for more women in senior tech roles.16:02 – Career Advice: Why calculated risk and building a support network are key to long-term success.19:14 – Leaving Google: Her thought process in taking the leap from a big brand to an emerging category leader.

In this episode of The Tech Trek, Amir sits down with Emily Long, the CEO and co-founder of Edera, a deep tech startup focused on secure infrastructure. Emily shares her unconventional journey from HR leadership into the world of high-performance computing, infrastructure, and cybersecurity. Together, they explore the realities of leading a technical startup as a non-engineer, the underestimated value of soft skills in building scalable companies, and how trust, learning, and risk-taking shape leadership at every stage.💡 Key Takeaways:Soft Skills Scale: Emily challenges the misconception that only hard skills matter in tech leadership, showing how people skills drive team performance and product success.Learning is a Superpower: Her career evolution was fueled by an unapologetic hunger to learn and willingness to step into discomfort and uncertainty.The CEO as Conductor: Emily views the CEO role as orchestrating harmony across functions—ensuring each part of the company plays in sync.Technical ≠ Only Coders: Emily has gained deep technical understanding through proximity, curiosity, and respect—without being an engineer herself.Redefining Career Paths: She encourages others, especially in HR or non-traditional roles, to question labels and stretch into new domains with courage.⏱ Timestamped Highlights:(00:00) Intro to Emily Long and her transition from HR to tech CEO(00:42) What Edera does: security + infrastructure beneath the Linux kernel(02:07) Early career: from public accounting to people operations(03:38) Becoming a founder by learning what others didn’t want to do(06:10) Why she said “yes” to being CEO — and the orchestra analogy(09:36) Relationship with CTO and deep respect for engineering(12:51) The business acumen of HR professionals is underappreciated(14:22) Breaking the “not technical” stigma and respecting both skill sets(20:14) Should founders always scale with the company? A nuanced view(23:25) Would she have jumped into tech sooner? The safety-risk tradeoff(25:45) Where to connect with Emily: LinkedIn and edera.dev💬 Quote to Feature:"Just because you can doesn't mean you should. You’ve got to ask yourself—am I bringing the right energy to the next stage?" – Emily Long

Arlene Watson, a product and engineering leader in the cybersecurity space with experience at CrowdStrike, ServiceNow, and Tenable, joins the show to unpack the critical challenges facing cybersecurity teams today. We dive into breach realities, the need for proactive defenses, how automation is reshaping security operations, and why AI is both a threat and an essential tool. If you’re building, managing, or securing software in today’s threat landscape, this episode is for you.🔑 Key Takeaways:Breaches are a daily reality – Most go unreported, but every breach should raise alarm bells because attackers may be setting the stage for larger, future infiltrations.Automation is critical – Repetitive, manual tasks in cybersecurity can and should be automated to free up teams for higher-value, offensive strategies.AI expands the threat and the solution – Generative AI introduces exponential risk, but it's also becoming a core component of advanced cyber defense strategies.💬 Quote to Highlight:"The moment someone says they know all the adversaries that will show up tomorrow, we know that’s not the fact. Our job is to chase the unknown and prepare for it." — Arlene Watson⏱️ Timestamped Highlights:00:00 – Intro to Arlene Watson and the state of cybersecurity today00:33 – Why breaches are more common than we think02:14 – Breaches must always raise alarm bells05:26 – Understanding the hierarchy of high-value assets08:23 – Automation trends in product engineering for cybersecurity11:35 – Why cybersecurity budgets often lag behind priorities15:04 – How AI is growing the cybersecurity attack surface18:28 – Can AI help defend against adversarial AI?21:22 – Prioritizing cybersecurity product development: foundation, automation, and integration25:10 – Connect with Arlene via LinkedIn

In this episode, Amir sits down with David Marchick, Dean of the Kogod School of Business at American University, to explore how AI is transforming higher education. From early skepticism to full-scale integration, David shares how his faculty is embracing generative AI—not just as a tool, but as a cornerstone of future-ready learning. The conversation dives into what it means to prepare students for an AI-infused workplace, the ethical dilemmas that arise, and how this technology could either widen or bridge existing academic gaps.🔑 Key Takeaways:AI Integration Is No Longer Optional: David emphasizes that resisting AI is like banning calculators—students will use it, so schools must evolve to teach responsible and effective use.Education Must Mirror the Workplace: From proofreading to prototyping, AI skills are becoming table stakes in modern careers. Schools must prepare students accordingly.AI as an Equalizer—or Divider: While AI tutoring tools can democratize learning, lack of access at under-resourced schools could deepen educational inequality.Faculty Need Retraining Too: Teachers are being retrained with help from industry to effectively embed AI into their disciplines—from finance to marketing.🧠 Quote:“You won’t be replaced by AI. But you could be replaced by someone who knows how to use AI.” — David Marchick⏱️ Timestamped Highlights:00:00 – Introduction to David Marchick and American University’s approach to AI in education01:15 – Why early academic response was to ban AI—and why that’s changing03:30 – Shifting from fear to experimentation: How the Kogod faculty embraced AI06:45 – Balancing original student work with AI assistance09:00 – Teaching students to question AI and use it responsibly12:20 – Will AI adoption in education be fast or slow? Marchick predicts years, not decades14:50 – AI exacerbating the education gap: The equity question16:15 – Use case: How AI tutors are built and used in quantitative graduate programs18:45 – Writing, equity, and how AI may lift weaker students without eliminating learning20:45 – Broader career implications: How AI reshapes job boundaries and skillsets22:30 – Marketing example: Cutting down design debates with generative tools24:45 – How to learn more about Kogod’s AI curriculum and initiatives