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On the show
From 25 epsHosts
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Recent episodes
Your Team Is Using AI Anyway
Jun 25, 2026
Unknown duration
What AI Agents Need Before Production
Jun 23, 2026
Unknown duration
Yahoo CTO on AI, Engineering Velocity, and Why the SDLC Has To Change
Jun 18, 2026
Unknown duration
Why AI Founders Need to Say No Faster
Jun 16, 2026
Unknown duration
Why Sovereign AI Matters Now
Jun 11, 2026
29m 04s
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| Date | Episode | Topics | Guests | Brands | Places | Keywords | Sponsor | Length | |
|---|---|---|---|---|---|---|---|---|---|
| 6/25/26 | ![]() Your Team Is Using AI Anyway | AI adoption is no longer just a policy conversation. For many organizations, the bigger question is how to move faster without creating avoidable risk.In this episode of The Tech Trek, Amir Bormand sits down with Aimee Cardwell, CIO and CISO in residence at Transcend, to talk about responsible AI deployment, the tension between speed and control, and how leaders should think about security, compliance, productivity, and customer experience as AI moves through the enterprise.Aimee brings a rare view across the CIO, CISO, and board lens. The conversation gets into why blocking AI often backfires, how prompt redaction can help teams move faster safely, where companies should draw the line on risk, and why some teams may need to rethink old assumptions about tech debt, code ownership, and modernization.Practical Takeaways• Responsible AI depends on the lens. Security, compliance, business, board, and technology teams may all define it differently.• Blocking employee AI usage can create worse outcomes. People may use shadow tools anyway, or teams may fall behind in productivity.• Prompt redaction and enterprise agreements can give teams room to experiment while reducing exposure of sensitive data.• Moving fast is not the same as releasing half finished customer experiences. Bad AI tools can train customers to distrust the entire interaction.• AI may change how teams think about tech debt, refactoring, and whether some legacy systems should be rebuilt instead of patched forever.Timestamped Highlights00:00 Responsible AI deployment and why the definition changes by role02:35 Aimee explains the CIO, CISO, and board perspectives on AI adoption05:14 Why companies that block AI may create shadow usage and slower teams06:52 Prompt redaction as a practical way to let employees experiment safely10:40 How AI risk changes when the data exposure model is different from traditional insider theft15:10 Why releasing poor AI customer experiences can damage trust21:50 Using shared enterprise prompts to raise the quality of AI output across engineering teams26:20 How AI could change the way teams approach security debt and code modernizationOne Line That Stuck“The conversation has flipped, and it is really how can I get the company to go faster.”Pro Tips• Start by identifying what truly makes your business defensible. Not every asset carries the same risk.• Give employees safe paths to use AI instead of pretending they will not use it.• Build shared prompts with engineering standards, approved tools, and company context so teams do not start from scratch every time.• Ask whether old assumptions still hold. Some decisions made sense when changes were expensive, slow, or risky. AI may change that equation.Subscribe to The Tech Trek for more conversations on how modern technical teams are building, hiring, operating, and adapting around AI, data, platform, product, and engineering execution.#ai #agentic #techleadership #engineeringleadership | — | ||||||
| 6/23/26 | ![]() What AI Agents Need Before Production | AI agents are easy to demo. They are much harder to trust, maintain, govern, and put into production.In this episode of The Tech Trek, Amir Bormand talks with Lucas Thelosen, CEO and cofounder at Gravity, about the agent economy, AI analytics, and what changes when analysts move from doing every task themselves to managing AI systems that create more bandwidth.Lucas shares why Gravity built Orion, an AI analyst, after years working in analytics, product, and data teams at companies like Looker and Google. The conversation gets into the messy middle of AI adoption, why so many agent projects struggle to make it into production, and how context may become one of the most valuable assets a company owns.Practical Takeaways• Agent prototypes are easy. Production agents require support, maintenance, accuracy checks, and clear ownership.• Not every company should build every agent internally. If the capability is not core to what you sell, buying may be the faster path.• Context matters because it lets humans critique AI output with business judgment, not just technical review.• Analysts may shift toward data architecture, governed data models, and internal product management for analytics.• AI does not remove human responsibility. It raises the bar for review, delegation, and decision making.Timestamped Highlights00:40, What Gravity is building with Orion, an AI analyst designed around the work analytics teams already know well.02:28, Why mature companies still miss major insights, even when they already have data teams.03:43, The agent economy reality check, easy prototypes, hard production, and the gap between demo and durable system.06:28, Why companies still build agents internally, even when many projects never reach production.09:48, The case for experimenting now instead of waiting for the AI stack to settle.11:40, How AI shifts people from doing the work to managing the work.18:50, What the future analyst role may look like as AI takes on more of the execution layer.One Line That Stuck"Where previously you were the person doing the work, now you're the manager."Subscribe to The Tech Trek for more conversations on how technical teams are building, hiring, operating, and adapting around AI, data, platform, product, and engineering execution. | — | ||||||
| 6/18/26 | ![]() Yahoo CTO on AI, Engineering Velocity, and Why the SDLC Has To Change | Yahoo is not just adding AI on top of existing products. It is using AI across product experiences, internal tools, engineering workflows, and modernization efforts.In this episode of The Tech Trek, Lee Zen, CTO at Yahoo, joins Amir Bormand to talk about modernizing at massive scale, moving from on prem infrastructure to the cloud, rebuilding internal tools with AI, and how engineering organizations need to rethink process when agents can move faster than people.Lee also shares how Yahoo views AI as a coworker, not just a tool, and why the next bottleneck in software delivery may be human judgment.Practical Takeaways• Modernization at scale often means operating in two worlds at once, keeping proven systems running while new cloud based services move faster.• AI can help teams move past legacy tools by reverse engineering requirements and rebuilding modern versions from scratch.• The real unlock is not only code generation. It is connecting agents to documents, chats, emails, production context, and internal knowledge with the right permissions.• As agents speed up execution, engineering teams need to rethink where human approval, judgment, and review should live.• The build versus buy equation is changing because some tools that were too expensive to build before may now be realistic to create internally.Timestamped Highlights00:31, Yahoo’s mission and why the internet still feels hard to navigate02:01, Where AI fits across Yahoo products and engineering work03:30, The challenge of moving from on prem data centers to cloud based infrastructure05:27, How Yahoo has used AI to rebuild internal tools and leave technical debt behind07:25, Why agents need access to engineering context, not just code10:20, AI as a coworker and the shift from human speed to machine speed16:27, Why parts of the SDLC may need to change as AI increases delivery speedOne Line That Stuck“AI as a coworker, not just as a tool.”The Tech Trek is for technical leaders thinking through how teams build, operate, modernize, and adapt as AI changes the work. Subscribe or follow for more conversations with engineering, product, data, and technology leaders. | — | ||||||
| 6/16/26 | ![]() Why AI Founders Need to Say No Faster | Mike Choi wanted to work at Apple for years. Then he got there and had the moment many ambitious builders eventually hit.Is this the thing I was sprinting toward?In this episode of The Tech Trek, Mike Choi, co founder at Koah, shares his path from Korea to the United States, mandatory military service, Apple, Twitter, and eventually building Koah, an AI monetization company helping AI app builders create sponsored experiences.The conversation is less about the glamour of startups and more about what founder work actually demands: making decisions without complete information, learning from Big Tech without copying it, and staying focused when AI moves faster than your team can absorb.Practical Takeaways• Big Tech can teach you strong operating patterns, but startups force you to build your own style.• Founder decisions rarely come with complete data. Moving creates the next data point.• In AI startups, speed can become a distraction if every new tool or feature changes the plan.• Clear vision helps teams make decisions without waiting on the founder.• Knowing when to share an idea matters as much as having the idea.Timestamped Highlights00:38, Mike explains Koah and why AI products need new monetization models.02:25, Mike shares how his father’s Korean Air Force service brought him to the United States as a child.05:01, Mandatory military service, pausing college, and learning to code around strong engineers.07:29, The long term goal of working at Apple and the unexpected feeling after getting there.10:57, Why Mike chose to build from scratch instead of staying on the Big Tech path.14:05, What Big Tech did and did not prepare him for as a founder.17:03, The founder lesson of making decisions before the full picture is clear.19:35, Why AI startups move so fast and how shiny object syndrome drains energy, time, and attention.One Line That Stuck“Just make the decision, produce data points that way through actions, and make a better decision tomorrow.”Subscribe to The Tech Trek for more conversations on how modern technical teams are building, hiring, operating, and adapting around AI, data, platform, product, and engineering execution. | — | ||||||
| 6/11/26 | ![]() Why Sovereign AI Matters Now✨ | sovereign AIshadow AI+4 | Shaun Modi | Capitol AI | — | sovereign AIshadow AI+5 | — | 29m 04s | |
| 6/9/26 | ![]() Healthcare AI Starts With Boring Problems✨ | healthcare AIoperations+4 | Edmund Jackson | Unity AI | — | healthcareAI+6 | — | 28m 00s | |
| 6/4/26 | ![]() AI Coding Agents Are Changing Engineering Teams✨ | AI coding agentsengineering teams+3 | Deepak Bapat | Claude CodeCursor+1 | — | AIcoding agents+5 | — | 26m 44s | |
| 6/2/26 | ![]() Agentic AI Has a Data Layer Problem✨ | Agentic AIdata infrastructure+3 | Karthik Ranganathan | Yugabyte | — | Agentic AIdatabases+6 | — | 29m 42s | |
| 5/29/26 | ![]() When Agentic Coding Changes The Team✨ | agentic codingsoftware development+3 | Scott Weller | EnFiSlack+1 | — | agentic codingAI agents+3 | — | 37m 47s | |
| 5/27/26 | ![]() Data Teams Are Moving Beyond Dashboards✨ | AI adoptiondata governance+4 | Vijay Gandra | Acrisure | — | AIdata teams+5 | — | 23m 27s | |
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| 5/22/26 | ![]() AI Is Changing Coding, Not Engineering✨ | AI in engineeringcoding vs software engineering+4 | Leonid Belkind | TorqElevano | — | AIsoftware engineering+5 | — | 33m 27s | |
| 5/18/26 | ![]() AI Is Changing How Engineers Actually Work✨ | AI in engineeringsoftware development+3 | Scott Breitenother | KiloElevano | — | AI coding toolsengineering+3 | — | 25m 32s | |
| 5/15/26 | ![]() AI Can Handle the Tax Code. What Still Needs a Human?✨ | AI in taxhuman judgment+4 | David Kang | Keeper | — | AItax code+5 | — | 24m 43s | |
| 5/13/26 | ![]() He Built a Public Company. Now He Is Starting Over✨ | entrepreneurshipartificial intelligence+3 | Rob Locascio | Uare.aiLivePerson+1 | — | public companyAI+5 | — | 29m 09s | |
| 5/11/26 | ![]() Why Fintech Products Get Stuck Before Launch | 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. | — | ||||||
| 5/8/26 | ![]() Coding Isn’t the Hard Part Anymore✨ | AI native productsproduct validation+3 | Adam Kirk | JumpElevano+2 | — | AIproduct development+3 | — | 27m 18s | |
| 5/6/26 | ![]() How AI Is Changing the Way Engineering Teams Work✨ | AI in engineeringIT transformation+4 | Krishna Sai | SolarWindsThe Tech Trek | — | AIengineering teams+5 | — | 29m 13s | |
| 5/4/26 | ![]() Why AI Still Needs Human Judgment✨ | AI and human judgmentbusiness workflows+4 | Dan Wald | SciemoExcel+1 | — | AIhuman judgment+5 | — | 37m 06s | |
| 5/1/26 | ![]() Why AI Will Not Fix Broken Data Teams✨ | AIdata teams+5 | Mike Doll | Guitar Center | — | AIdata teams+5 | — | 22m 51s | |
| 4/29/26 | ![]() AI Is Changing Cybersecurity Faster Than Teams Can Keep Up✨ | cybersecurityAI+4 | Andrew Rubin | Illumio | — | cybersecurityAI+5 | — | 27m 54s | |
| 4/27/26 | ![]() Why Data Teams Need Software Engineering Discipline✨ | data teamssoftware engineering+4 | Kenneth Schwartz | Genmab | — | data sprawlsoftware engineering practices+4 | — | 27m 31s | |
| 4/24/26 | ![]() AI Is Rebuilding Mortgage From the Inside Out✨ | AI in mortgagefinancial technology+4 | Diane Yu | TidalWaveElevano | — | mortgageAI assistant+5 | — | 25m 02s | |
| 4/22/26 | ![]() AI Won’t Replace Accountants, It Will Change Where They Create Value✨ | AI in accountingvalue creation+4 | Cos Nicolaescu | Accrual | — | accountingAI+5 | — | 26m 04s | |
| 4/20/26 | ![]() Healthcare Can’t Go Down, Cloud, AI, and Reliability✨ | healthcare infrastructurecloud migration+3 | Jeff Sponaugle | Surescripts | — | healthcarecloud+5 | — | 26m 46s | |
| 4/16/26 | ![]() The Ethics of Offensive Security✨ | offensive securitycybersecurity ethics+4 | Farzan Karimi | Moderna | — | offensive securityred team+5 | — | 27m 35s | |
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