The Tech Trek

Enterprise AI is easy to demonstrate. The real test begins when a promising POC meets production costs, security requirements, data movement, latency, and internal adoption.

Shimon Ben-David, CTO at WEKA, joins Amir to discuss the gap between experimenting with generative AI and operating it at scale. They explore how classical AI differs from generative AI, why production exposes problems that demos hide, and how companies with limited AI maturity can start building useful internal capability.

Practical Takeaways

• A successful POC proves that an outcome is possible. It does not prove that the system will be affordable, secure, reliable, or fast at scale.
• Enterprise AI adoption reaches across infrastructure, engineering, data, security, and business teams. It cannot be owned by one group in isolation.
• Adding more GPUs will not fix slow data access, poor utilization, weak pipelines, or an experience users do not want to use.
• External support can help, but the person or firm involved needs to stay through implementation and production, not stop at recommendations.
• Companies that are behind should begin with proven use cases, build internal experience, and quickly stop experiments that fail to show value.

Key Moments

00:00 Why moving enterprise AI into production remains difficult
01:55 The difference between classical AI and generative AI adoption
07:05 How companies can use AI without having a formal AI strategy
11:35 Why successful POCs often struggle when they reach production
17:35 Competitive pressure, AI FOMO, and the need to calculate real ROI
22:00 Why AI adoption requires cross organizational change
33:10 Where a company with limited AI maturity should begin

One Line That Stuck

“The promise is there. It is possible. You just need to do it properly.”

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

AI can generate code faster, but that does not make software delivery simple. It shifts the pressure to requirements, architecture, review, and technical judgment.

Goncalo Silva, CTO at Doist, explains how AI is changing the way teams behind Todoist and Twist build software. He shares why greater individual autonomy has led to more collaboration, why deep expertise still matters, and how faster execution is reshaping product delivery, project planning, and engineering hiring.

What Leaders Can Take From This

• Faster code generation makes strong planning and clear requirements more important, not less important.
• Designers, product leaders, and engineers can work from richer prototypes, but production systems still need experienced technical judgment.
• Engineering capacity does not have to move into other functions. Teams can use it to improve reliability, performance, quality, and the amount of valuable work they ship.
• Token counts are a weak measure of progress. Doist looks at team feedback and whether projects are staying on track.
• Engineering interviews need to test architecture, decision making, curiosity, and depth, not simply whether a candidate can produce working code.

Approximate Highlights

00:00 Meet GonCalo Silva and the products behind Doist
02:00 How broadly AI is being used across Doist
04:15 Why greater autonomy has brought teams closer together
09:45 Where nontechnical coding works, and where it creates risk
17:50 How AI compressed a major refactoring effort by 20 to 30 times
25:05 Measuring AI value without counting tokens
30:20 Why faster execution requires more up front planning
34:50 How Doist changed its engineering interview process

One Line That Stuck

“We are the bottleneck. Our attention span, our ability to memorize, our ability to understand, and deep expertise.”

Follow The Tech Trek for more conversations on how technical teams are changing the way they build, hire, and operate.

AI is not just changing how engineers write code. It is changing who gets close enough to shape the work.

In this episode of The Tech Trek, Robert Stewart, CTO at Arbital Health, joins Amir to talk about how AI is bringing actuarial subject matter experts closer to product and engineering teams, especially in healthcare and risk based contracts. Robert shares how his team is pairing technically minded SMEs with software engineers, using AI tools in development, and rethinking technical hiring now that AI assisted coding is part of the job.

Practical Takeaways

• AI can reduce the distance between domain experts and engineering when the SMEs can clearly describe requirements, acceptance criteria, and edge cases.
• Pairing a subject matter expert with an experienced engineer can be more powerful than traditional pair programming because each person brings a different kind of judgment.
• Better written requirements matter more in an AI assisted workflow because tools can work directly from detailed tickets and context.
• Technical interviews may need to test how candidates use AI, not whether they can avoid it.
• Hiring teams need stronger signals around identity, environment fit, prompting skill, and how candidates respond to AI output.

Timestamped Highlights

00:00 Robert Stewart on Arbital Health, value based care, and the role of actuarial expertise in healthcare infrastructure.
03:06 Why actuarial knowledge is hard to transfer into engineering teams through normal handoffs.
04:40 How AI helps subject matter experts move closer to product and engineering work.
06:08 Why engineering fundamentals still matter, even when AI makes code easier to create.
09:55 How Arbital Health is using Cursor, Claude Code, and human review in a regulated environment.
14:52 Why more detailed Jira tickets are becoming more valuable in AI assisted development.
17:10 How AI is changing technical interviews from “you may use AI” to “you must use AI.”
22:16 What suspicious candidates, remote interviews, and fake profiles are forcing hiring teams to rethink.

One Line That Stuck

“You can judge an expert by the type of questions they ask.”

Pro Tips

• Ask candidates to share their screen during AI assisted technical interviews.
• Watch how they prompt, not just what they produce.
• Look for whether they catch strange or weak AI output.
• Use a rubric, but also evaluate whether the candidate fits the way your team actually works.
• For AI generated code, add stronger human review, especially in regulated environments.

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

Voice AI is moving from simple call routing into work that used to require trained human agents. The harder question is what happens when those conversations involve lending, collections, servicing, compliance, and real customer risk.

In this episode of The Tech Trek, Amir Bormand speaks with Joshua March, founder and CEO of Veritus, about building AI voice agents for regulated financial services. Joshua shares why consumer lending is a demanding test case for voice AI, what makes regulated conversations different, and why the next version of the contact center may be built with much smaller teams overseeing AI systems.

Practical takeaways

AI voice agents only matter if they can actually resolve the issue. Joshua argues that users have been trained to distrust automated phone systems because most IVRs block progress instead of helping.

Regulated communication is not just about what the agent says. It also includes who can be contacted, when they can be contacted, call frequency, TCPA rules, QA, and post call compliance.

Complex voice agents require more than a prompt. Joshua talks about context engineering, state management, specialized background agents, compliance monitoring, KYC workflows, latency, and turn detection.

AI changes startup execution. Small teams with experienced people can build and ship much more than before, but that also raises the pressure to move faster.

Venture backed AI companies face a bigger bar. Joshua makes the case that higher seed valuations and larger funds increase the need for very large outcomes.

Timestamped highlights
00:00, Why Veritus is focused on AI communications for financial services and voice agents in consumer lending
02:10, Joshua’s path from Facebook apps to social customer service, messaging, bots, and now voice AI
05:05, Why AI voice agents may replace a large share of traditional call center work
07:00, Why customers have learned to fight IVRs and what changes when AI can actually solve the problem
10:00, The compliance layers around regulated voice conversations in lending, servicing, origination, and collections
14:00, Why production voice agents need context engineering, state machines, background agents, observability, and monitoring
20:30, How AI has changed startup hiring, management, productivity, and the role of experienced individual contributors

One Line That Stuck

“This isn’t a crappy IVR that’s just trying to get in my way, this is an intelligent system that can actually take actions and actually resolve my issue.”

Practical lens for technical teams

If you are building AI into customer operations, the hard part is not only getting the model to speak well. The harder work is making sure it knows what it can do, when it can act, what rules apply, how it is monitored, and when humans need to step in.

That matters even more in regulated industries, where the conversation itself is only one part of the system.

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

Joanne Chen, VP of Data and AI at SimplePractice, joins The Tech Trek to talk about what it takes to build AI data products in a regulated, sensitive domain where privacy, consistency, monitoring, and customer trust have to be designed from the start.

This conversation gets into why AI product development feels different from traditional software, how teams should think about quality control, and why not every valuable AI solution needs to be GenAI.

Practical Takeaways

• AI products need defense in depth, especially in healthcare, where privacy, confidentiality, and security cannot depend on one layer of protection.
• The core product questions still matter. What customer pain does this solve, who benefits, and what does it take to ship responsibly?
• AI changes the development life cycle because outputs are not always deterministic and quality can degrade after launch.
• Teams need monitoring, validation, and a plan for edge cases before putting AI features in front of customers.
• AI literacy is becoming part of every role involved in building, marketing, supporting, and operating software products.

Timestamped Highlights

00:00 Joanne Chen on AI data products, deterministic outputs, and safely shipping AI features
01:20 What SimplePractice does for mental health practitioners and group practices
02:30 Why healthcare AI needs multiple layers of risk protection
05:00 What makes an AI data product different from a traditional data product
08:15 Why stakeholder expectations around AI have widened so much
10:40 How AI changes the work across engineering, CS, marketing, and support
13:10 Where AI can help reduce tedious administrative work in healthcare
16:45 Why leaders need to keep their hands dirty with new AI tools

One Line That Stuck

“Keeping hands dirty is important.”

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