The Tech Trek

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 backlog
Timestamped Highlights
00:01 Scott Gale, CTO and Founder of Fluency, joins Amir to break down the shift from builder to orchestrator in modern engineering
02:36 How Fluency introduced coding agents with a three part approach: safe experimentation, mindset shift, and stronger context
04:35 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 adoption
11:26 How technical interviews are changing as AI becomes part of everyday engineering work
16:59 Scott on whether companies will actually need fewer engineers, and why the demand for meaningful work is not going away
21:09 The practical lesson teams miss: better structured systems and better context make coding agents far more effective
One line worth remembering
“It’s not about losing your craft. It’s about managing a workforce of junior agents.”
Practical edge
Scott 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 Action
If 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 backlog
Timestamped Highlights
00:01 Scott Gale, CTO and Founder of Fluency, joins Amir to break down the shift from builder to orchestrator in modern engineering
02:36 How Fluency introduced coding agents with a three part approach: safe experimentation, mindset shift, and stronger context
04:35 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 adoption
11:26 How technical interviews are changing as AI becomes part of everyday engineering work
16:59 Scott on whether companies will actually need fewer engineers, and why the demand for meaningful work is not going away
21:09 The practical lesson teams miss: better structured systems and better context make coding agents far more effective
One line worth remembering
“It’s not about losing your craft. It’s about managing a workforce of junior agents.”
Practical edge
Scott 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 Action
If 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 episode
Why procurement is a strong fit for AI, especially where teams are buried in tedious process work
The difference between job automation and job elimination
Spencer’s idea of role plasticity, and why it matters more than most AI debates
Why procurement teams may become more valuable, not less, as AI improves
Practical ways professionals can start using AI before their company rolls out a formal strategy
Timestamped highlights
00:37 What LightSource does and why direct material sourcing is a high stakes AI use case
01:51 Why procurement teams spend too much time on transactional work
06:47 Which jobs get enhanced by AI, which ones get eliminated, and Spencer’s framework for role plasticity
13:44 What the next few years could look like for procurement professionals
26:18 Where to start if your company has not adopted an AI native workflow yet
30:07 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 episode
Why the CISO role is becoming more engineering driven and more tightly tied to business outcomes

Where AI creates real leverage for security teams, and where it introduces new operational risk

Why the security talent gap may be as much a hiring mindset problem as a supply problem

What actually causes burnout in security teams, beyond the usual talking points

How to think about success in security when zero incidents is not a serious metric

Highlights
1:44, The CISO role is shifting from pure protection to business enablement

7:11, AI creates leverage for defenders, but it is also accelerating the attacker playbook

9:31, The biggest AI security risks, from developer copilots to agent driven decision making

14:15, Why security teams need room to experiment with AI or risk falling behind

16:58, Only 1 percent of CISOs surveyed prioritized technology to close the skills gap

22:16, AI can reduce burnout, but only if it cuts noise instead of creating more of it

Security 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 team
Look 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.html
Follow 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 conviction

Timestamped highlights

00:00 The real shift from engineer to CEO
00:18 Growth starts before readiness
00:56 Leadership changes when execution is no longer enough
01:50 The best founders are pulled by a problem
02:35 The three ideas that tie it all together
Follow 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.