GenAI for Engineers, What's Real, What's Not and What's Coming

By the end of this post, you’ll understand:

The new engineering landscape GenAI has created
Practical best practices for leveraging GenAI in your work
Mistakes and misconceptions you absolutely want to avoid
Inspiring tools and frameworks that are already changing the game
What’s on the horizon, both thrilling and a little overwhelming

Let’s get started!

Introduction: Why GenAI Has Every Engineer Talking

You know the feeling—everyone’s suddenly talking about automation, agents, and how AI is about to revolutionize everything. You read about companies “eliminating jobs” thanks to GenAI, and you’re wondering… Is this really happening? Is this a threat, or is it a huge opportunity?

If you’ve ever sat with those questions, you’re in good company! On our latest episode, I spoke with Balki, a seasoned CTO, about how GenAI is reshaping engineering roles, what’s actually working in startups right now, and how you can avoid the typical pitfalls to come out ahead.

Why this blog post matters:

“First time founders focus on product. Successful founders focus on distribution.” — Balki

That means it’s not just about building with AI—it’s about making it useful, scalable, and profitable.

Meet Balki: Everyday Startup Battles, AI Style

Before we jump into the nitty-gritty, let’s meet our expert guest.

Balki's Career: From BigCorp to Fractional CTO

Balki spent his first decade as an engineer at Fiserv—deep in the online banking trenches, working primarily on Microsoft’s tech stack. Eventually, he pivoted to leading his own startup, taking the leap from “comfortable” to “complete accountability.”

Takeaway: The happiness came less from money and more from ownership and learning, even when the risks were high.

“There's nobody else to complain about. I’m accountable for everything.”

How the Leap Happened (and What You Can Learn)

Far from a sudden “drop everything and start a company,” Balki’s transition was informed by education (an EMBA program) and practical testing. The final pivot occurred in an entrepreneurship course—where building a product from scratch “felt like coming home.”

Lesson: Big jumps are rarely one moment. Often, it’s a series of small pivots, each featuring late-night emails, experimental coding, and a few “blind confidence” moments.

CTO Roles: Full-Time, Fractional, and What That Means for Startups

Ever wondered what the difference is between a full-time CTO and a fractional CTO? Balki’s journey is a fascinating example:

Full-Time CTO vs. Fractional CTO

Full-Time CTO: Committed to one company.
Fractional CTO: Provides strategic, high-impact guidance to several startups at once—usually part-time.

Fractional CTOs thrive by:

Bringing deep expertise from multiple sectors
Targeting high-leverage activities (think: 20 hours a month laser-focused on the biggest problems)
Accelerating product clarity and distribution

Big insight: Every minute counts. When you’re dropping in for a few hours a week, priorities become crystal clear!

GenAI for Engineers: From “Just Another Tool” To Business Superpower

GenAI isn’t new—but the way it’s used by engineering teams is evolving fast. Balki says:

“The market is flooded with proof-of-concept tools targeting non-engineers. In my world, there’s a big chasm between building a prototype, getting traction, and scaling.”

You can hack together a quick POC with Lovelace or Bolt in minutes. But scaling that prototype—making it reliable, distributable, and profitable—is a different sport.

Why Scaling Is So Hard (and Where GenAI Can Help)

Most startups stall, even after they get initial traction. Balki specializes in bridging this “chasm”—helping teams go from “scrappy” to “scalable.”

“Engineering Excellence”: Balki’s Five Pillars + GenAI

Let’s get super practical. Any successful engineering team needs engineering excellence. Before GenAI, Balki set out four pillars. With GenAI, a fifth was added. Here’s the breakdown:

The Four Classic Pillars

Automated Testing
- Validates code. Makes sure your software can withstand real-world scenarios.
CI/CD Pipelines
- Enables drama-free code deployments.
- Example: GitHub Actions
Observability
- Let’s engineers be the first to know when something breaks (not your customers).
- Example: Datadog
Modular Architecture
- Flexible, scalable code organization—critical for rapid evolution.

The Fifth Pillar: GenAI Fluency

Today, GenAI fluency is just as critical. In fact, Balki breaks this down further into three sub-pillars (detailed below).

Why GenAI Matters

Thanks to GenAI, startups can rapidly accelerate improvement in these pillars—without blowing the budget or risking bankruptcy. You can stay agile and address technical debt much faster.

GenAI and Job Roles: Are We Really Automating Ourselves Out?

The media loves to declare “X jobs eliminated by GenAI!”—but is this really the story?

What’s Actually Changing in Engineering

Specialization is risky.
Engineers who only focus on one niche (e.g., frontend specialist, TypeScript expert) are seeing those tasks rapidly commoditized.

“For 20 years it was similar—good engineers could rest on their laurels. Now that’s been commoditized. Founder mindset is what matters.”

Full-stack skills are becoming standard. The ability to build, deploy, test, and validate end-to-end is the new norm.

So what tasks are ripe for GenAI automation today?
Balki notes: pure specialization may soon be obsolete, and “mundane tasks” (like repetitive API wrappers, boilerplate code, out-of-date documentation) are prime targets for automation.

Real-World Example: API Wrappers & SDKs

Many engineers are scrambling to build “MCP servers”—wrappers around APIs and OpenAI SDKs. With the release of the OpenAI App SDK, a new “walled garden” effect is emerging. Is this healthy? Are we risking another App Store scenario?

It’s still hard to say if this is a passing trend or a lasting shift. For B2B SaaS, the immediate risk may be low, but keeping up AI fluency is more important than ever.

Engineering AI Fluency: Climbing the Three GenAI Sub-Pillars

What does “AI fluency” really mean for engineering teams? Balki’s framework divides it into three practical levels:

Level 1: Leveraging AI Tools for Engineer Productivity

This is all about using AI tools—like Cursor, Claud Code, Code Rabbit, and GitHub Copilot—to supercharge your coding workflow.

Tip:
Cursor and Cloud Code excel for different personalities—Cursor for leaders, Cloud Code for craftspeople. Setting team-wide Cursor rules keeps best practices consistent.

Level 2: Using AI Tools to Boost Engineering Excellence

Apply AI directly to accelerate the four classic pillars.

Testing & Coverage (e.g., auto-generating tests)
Deployment Pipelines (e.g., AI-powered CI/CD suggestions)
Modularity & Code Review
- Code Rabbit: Cuts review time on pull requests, boosting speed.
- Axel AI: Embeds in legacy code to help modernize architecture.
- Antithesis: Runs containers to catch edge-case bugs in staging.

“Anything that can accelerate engineering excellence lights up my eyes.”

Level 3: Incorporating AI Directly Into Product Features

This is the “cool stuff”—voice agents, automated support, and smarter applications.

But beware:
Before over-engineering with tools like LangChain and fancy agents, start simple. OpenAI API calls often get the job done. As you get closer to product market fit, start worrying about human evals, prompt tuning, and security concerns.

Pro move:
Start with human-in-the-loop. Fully automated AI features rarely work perfectly from Day 1.

Building Distributed Teams: How GenAI Changes Remote Collaboration

If your team is spread around the world—Eastern Europe, Latin America, Hong Kong—you know communication and documentation aren’t easy.

GenAI is a game-changer for:

Communication: Coaching team members to use GenAI for clear, culturally-appropriate communication.
Documentation: Auto-generating code and onboarding docs that are actually useful.

“With proper documentation and plan, new engineers can be effective in two weeks—if not one.”

Challenge your team:
No more 90-day onboarding. With GenAI-assisted docs, you can cut that down to 1-2 weeks.

Real-World Use Case: devIQ—Bridging Technical and Non-Technical Teams

Balki’s tool, devIQ (try it for free!), was born from a common startup pain: the disconnect between technical and non-technical staff.

How devIQ Helps

Assesses engineering teams: Across Balki’s five pillars
Translates complexity: Makes technical progress educational for CEOs and business leads
Gives grades and actionable steps:
- “Your grade is C in automated testing, B in GenAI fluency.”
Offers prescriptive paths: Specific ideas to improve grades

Use-case:
When Balki drops into an engagement, devIQ helps quickly pinpoint engineering gaps and opens authentic conversation between product and engineering.

“If you methodically invest in the right pillars, your org will be 95th percentile or better—no matter your product.”

Practical Tips for Staying Current in GenAI Engineering

Let’s face it—AI moves insanely fast. It’s easy to feel lost, overwhelmed, or permanently behind.

Balki’s Learning Strategy

Focus 80% on problems inside your organization.
Go out and find solutions that fit your actual engineering needs (e.g., legacy code modernization, engineering excellence pillars).
Spend 20% dabbling in shiny new things.
Keeps your creative juices flowing and occasionally unlocks breakthrough opportunities.

Discipline matters.
Without intentional focus, you’ll spend all your time chasing distractions.

“Will AI Replace Us?”: The Real Question to Ask Before Adding GenAI

This is the million-dollar question—and it’s not theoretical. Before shoving AI into your product, be honest:

“Is this something the customers want, and does it make their lives better by using GenAI inside the tool?”

Don’t waste time making your prompts perfect or optimizing the next toolchain before confirming user demand.

Pro advice:

Insert a human eval into early prototypes.
Gradually replace manual involvement as the product matures.
Automation isn’t instant—chip away bit by bit.

The Future: Next-Level Tools and “The Stone Age to Modern Age” Shift

AI moves at a ridiculous pace. Here’s what’s coming—and why you should care.

For New Engineers

Starting today is easier than ever—you’re entering the AI-native world. But those legacy problems will catch up eventually.

Building Team Resilience: Don’t Go It Alone

Here’s a simple tip: get out of your shell. Connect with peer groups, ask for feedback, and share what you’re planning.

“When I see a new tool, I don’t jump in right away. I bring it to my cohorts for feedback, and I shave off weeks and hundreds of hours of research.”

You get richer, higher quality feedback by collaborating—AI or not.

Summary: Key Takeaways for GenAI Engineers

Let’s wrap it all up.

GenAI has transformed engineering—rapid POCs, scalable products, and changed job roles.
“Engineering excellence” depends on five pillars, with GenAI fluency now essential.
Don’t specialize too narrowly—full-stack, founder mindset is becoming the norm.
Use AI for real productivity: code review, documentation, collaboration, and direct product features.
Start simple—and always validate with customer demands before ramping up automation.
Leverage frameworks like devIQ to bridge business/engineering gaps and accelerate progress.
Stay disciplined in your learning, and seek feedback from peers to keep your engineering sharp.