The Tech Trek

Kevin Haggard, Vice President of Engineering at Barracuda, joins The Tech Trek to talk about how AI is showing up across cybersecurity products, engineering workflows, team adoption, and software delivery culture. He shares how Barracuda is approaching AI with guardrails, why adoption varies across teams, and what happened when the company ran protected AI dev days for the engineering organization.

What to take from this episode
* AI adoption inside engineering teams will not be even. Some teams are already orchestrating agents from requirements to deployment, while others are still figuring out where AI fits into their day to day work.
* Guardrails matter more in security sensitive environments. Barracuda uses an AI gateway and control plane so teams can experiment without leaking data or letting agents take uncontrolled actions.
* Protected time changes behavior. Barracuda’s AI dev days gave teams three days with no meetings so they could work with the tools inside real projects instead of treating AI as a side experiment.
* The coding bottleneck may move. AI can create more code faster, but QA, testing, release safety, problem definition, and rollback mechanisms become even more visible.
* The engineer’s role is shifting from operator to orchestrator. Kevin argues that system design, review, context, and crisp instruction will become more valuable as agents take on more execution.

Key moments

00:28, What Barracuda does and how AI pairs with people in cybersecurity
03:35, Why AI adoption varies across engineering teams inside a larger organization
05:06, The need for AI guardrails, gateways, and control planes
06:20, How Barracuda ran AI dev days across the organization
08:42, The light bulb moments from product, design, and engineering teams
13:07, Why AI velocity makes existing delivery bottlenecks harder to ignore
17:00, How engineers may move from writing code to orchestrating agents and reviewing systems

One line that stuck:

“Their role is going to elevate more.”

Practical moves from Kevin’s experience
* Bring trusted partners in for training, but follow that with hands on sessions.
* Give teams protected time to use AI inside actual work, not just demos.
* Share what the advanced teams are learning so adoption does not stay isolated.
* Keep people in the loop, especially when agents are generating code, tests, or workflow changes.
* Work backward from release bottlenecks, not just coding speed.

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#agenticai #ai #techleadership #engineeringleadership #engineering

AI adoption is no longer just a policy conversation. For many organizations, the bigger question is how to move faster without creating avoidable risk.
In this episode of The Tech Trek, Amir Bormand sits down with Aimee Cardwell, CIO and CISO in residence at Transcend, to talk about responsible AI deployment, the tension between speed and control, and how leaders should think about security, compliance, productivity, and customer experience as AI moves through the enterprise.
Aimee brings a rare view across the CIO, CISO, and board lens. The conversation gets into why blocking AI often backfires, how prompt redaction can help teams move faster safely, where companies should draw the line on risk, and why some teams may need to rethink old assumptions about tech debt, code ownership, and modernization.
Practical Takeaways
• Responsible AI depends on the lens. Security, compliance, business, board, and technology teams may all define it differently.
• Blocking employee AI usage can create worse outcomes. People may use shadow tools anyway, or teams may fall behind in productivity.
• Prompt redaction and enterprise agreements can give teams room to experiment while reducing exposure of sensitive data.
• Moving fast is not the same as releasing half finished customer experiences. Bad AI tools can train customers to distrust the entire interaction.
• AI may change how teams think about tech debt, refactoring, and whether some legacy systems should be rebuilt instead of patched forever.
Timestamped Highlights
00:00 Responsible AI deployment and why the definition changes by role
02:35 Aimee explains the CIO, CISO, and board perspectives on AI adoption
05:14 Why companies that block AI may create shadow usage and slower teams
06:52 Prompt redaction as a practical way to let employees experiment safely
10:40 How AI risk changes when the data exposure model is different from traditional insider theft
15:10 Why releasing poor AI customer experiences can damage trust
21:50 Using shared enterprise prompts to raise the quality of AI output across engineering teams
26:20 How AI could change the way teams approach security debt and code modernization
One 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.
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#ai #agentic #techleadership #engineeringleadership

AI agents are easy to demo. They are much harder to trust, maintain, govern, and put into production.

In this episode of The Tech Trek, Amir Bormand talks with Lucas Thelosen, CEO and cofounder at Gravity, about the agent economy, AI analytics, and what changes when analysts move from doing every task themselves to managing AI systems that create more bandwidth.

Lucas shares why Gravity built Orion, an AI analyst, after years working in analytics, product, and data teams at companies like Looker and Google. The conversation gets into the messy middle of AI adoption, why so many agent projects struggle to make it into production, and how context may become one of the most valuable assets a company owns.

Practical Takeaways

• Agent prototypes are easy. Production agents require support, maintenance, accuracy checks, and clear ownership.
• Not every company should build every agent internally. If the capability is not core to what you sell, buying may be the faster path.
• Context matters because it lets humans critique AI output with business judgment, not just technical review.
• Analysts may shift toward data architecture, governed data models, and internal product management for analytics.
• AI does not remove human responsibility. It raises the bar for review, delegation, and decision making.

Timestamped Highlights

00:40, What Gravity is building with Orion, an AI analyst designed around the work analytics teams already know well.
02:28, Why mature companies still miss major insights, even when they already have data teams.
03:43, The agent economy reality check, easy prototypes, hard production, and the gap between demo and durable system.

06:28, Why companies still build agents internally, even when many projects never reach production.

09:48, The case for experimenting now instead of waiting for the AI stack to settle.

11:40, How AI shifts people from doing the work to managing the work.

18:50, What the future analyst role may look like as AI takes on more of the execution layer.

One Line That Stuck

"Where previously you were the person doing the work, now you're the manager."

Subscribe to The Tech Trek for more conversations on how technical teams are building, hiring, operating, and adapting around AI, data, platform, product, and engineering execution.

Yahoo is not just adding AI on top of existing products. It is using AI across product experiences, internal tools, engineering workflows, and modernization efforts.

In this episode of The Tech Trek, Lee Zen, CTO at Yahoo, joins Amir Bormand to talk about modernizing at massive scale, moving from on prem infrastructure to the cloud, rebuilding internal tools with AI, and how engineering organizations need to rethink process when agents can move faster than people.

Lee also shares how Yahoo views AI as a coworker, not just a tool, and why the next bottleneck in software delivery may be human judgment.

Practical Takeaways

• Modernization at scale often means operating in two worlds at once, keeping proven systems running while new cloud based services move faster.
• AI can help teams move past legacy tools by reverse engineering requirements and rebuilding modern versions from scratch.
• The real unlock is not only code generation. It is connecting agents to documents, chats, emails, production context, and internal knowledge with the right permissions.
• As agents speed up execution, engineering teams need to rethink where human approval, judgment, and review should live.
• The build versus buy equation is changing because some tools that were too expensive to build before may now be realistic to create internally.

Timestamped Highlights

00:31, Yahoo’s mission and why the internet still feels hard to navigate
02:01, Where AI fits across Yahoo products and engineering work
03:30, The challenge of moving from on prem data centers to cloud based infrastructure
05:27, How Yahoo has used AI to rebuild internal tools and leave technical debt behind
07:25, Why agents need access to engineering context, not just code
10:20, AI as a coworker and the shift from human speed to machine speed
16:27, Why parts of the SDLC may need to change as AI increases delivery speed

One Line That Stuck

“AI as a coworker, not just as a tool.”

The Tech Trek is for technical leaders thinking through how teams build, operate, modernize, and adapt as AI changes the work. Subscribe or follow for more conversations with engineering, product, data, and technology leaders.

Mike Choi wanted to work at Apple for years. Then he got there and had the moment many ambitious builders eventually hit.

Is this the thing I was sprinting toward?

In this episode of The Tech Trek, Mike Choi, co founder at Koah, shares his path from Korea to the United States, mandatory military service, Apple, Twitter, and eventually building Koah, an AI monetization company helping AI app builders create sponsored experiences.

The conversation is less about the glamour of startups and more about what founder work actually demands: making decisions without complete information, learning from Big Tech without copying it, and staying focused when AI moves faster than your team can absorb.

Practical Takeaways

• Big Tech can teach you strong operating patterns, but startups force you to build your own style.
• Founder decisions rarely come with complete data. Moving creates the next data point.
• In AI startups, speed can become a distraction if every new tool or feature changes the plan.
• Clear vision helps teams make decisions without waiting on the founder.
• Knowing when to share an idea matters as much as having the idea.

Timestamped Highlights

00:38, 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.”

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