Leverage Stacks for Engineers and Founders: How to Compound Skills in the Age of AI

Five years ago, the path was clear: become really good at one thing. Deep technical expertise in backend systems, ML, frontend, or infrastructure.

AI is changing that equation. Not because technical depth doesn't matter—it does. But because the people winning in the AI age are building leverage stacks: combinations of skills that compound in ways single skills don't.

Let me show you what this looks like in practice and how to build your own leverage stack.

What a Leverage Stack Is

A leverage stack is a combination of 3-4 skills that multiply each other's value.

Traditional skill set:

• Skill A (backend engineering) = Value
• Skill B (ML) = Value
• Total value = A + B (additive)

Leverage stack:

• Skill A (backend engineering) × Skill B (product sense) × Skill C (AI prompting) = Value³
• Total value = A × B × C (multiplicative)

The difference compounds over time.

The Three Layers of a Leverage Stack

Every high-leverage stack I've seen has three layers:

Layer 1: Technical Foundation (Build the Thing)

This is your core engineering skill. You need to be able to build something from scratch to functioning product.

Examples:

• Full-stack development (Next.js, FastAPI, Postgres)
• Mobile development (React Native, Swift, Kotlin)
• ML engineering (Python, PyTorch, model deployment)
• Data engineering (SQL, ETL, data pipelines)

Why it matters: AI makes coding easier, but you still need to understand what to build and how to architect it.

Prompt engineering doesn't replace knowing how to design a database schema, structure an API, or debug a production issue.

The bar: Can you take an idea and ship a functional prototype in a week? If yes, your Layer 1 is solid.

Layer 2: Product/User Sense (Know What to Build)

This is understanding what people actually want and how to deliver it.

Components:

• Talking to users
• Understanding pain points
• Prioritizing features
• Designing UX that doesn't suck
• Knowing when "good enough" is good enough

Why it matters: AI won't tell you what to build. It will help you build it faster, but only if you know what "it" is.

The engineers who struggle most in the AI age are the ones who can build anything but don't know what's worth building.

The bar: Can you talk to 10 users, identify a pattern in their problems, and sketch a solution they'd pay for? If yes, your Layer 2 is solid.

Layer 3: Distribution/Leverage (Get It to People)

This is getting your work in front of users and amplifying your output.

Components:

• Writing (blogs, docs, tweets)
• Building in public
• Understanding growth loops
• Basic marketing/positioning
• Using AI for amplification

Why it matters: The best product that no one knows about is worthless.

In the AI age, distribution matters more than ever because AI has lowered the barrier to building. There are 100 people building the same thing as you. The winner is often the one who's best at distribution, not the one with the best tech.

The bar: Can you explain what you're building clearly enough that strangers understand and want to try it? If yes, your Layer 3 is solid.

Example Leverage Stacks (Real People)

Let me show you leverage stacks from people I know who are winning:

Stack 1: The AI-Powered Creator Tools Builder

Layer 1 (Technical): Full-stack (Python + Next.js) Layer 2 (Product): Deep creator empathy (is a creator themselves) Layer 3 (Distribution): Builds in public on Twitter, engaged creator audience

How it compounds:

• Technical skills let them build tools fast
• Product sense ensures they build what creators need
• Distribution means every tool launch reaches thousands of potential users
• Being a creator means they use their own tools (dogfooding)

Result: Multiple products, $20K+ MRR, built solo.

The multiplication: Without Layer 2: They'd build generic tools that don't solve real problems. Without Layer 3: They'd build great tools that no one discovers. With all three: Every product has built-in PMF and distribution.

Stack 2: The ML Engineer Turned Founder

Layer 1 (Technical): ML engineering + production systems Layer 2 (Product): Worked at Meta, saw what businesses need at scale Layer 3 (Distribution): Technical blog with 50K followers

How it compounds:

• Technical depth lets them build AI products that actually work at scale
• Meta experience gives them pattern recognition for what enterprises need
• Blog audience = built-in early adopters and feedback loop

Result: Raised $2M seed for B2B AI tool with strong technical moat.

The multiplication: Without Layer 2: They'd build impressive tech with no market. Without Layer 3: They'd struggle to get initial customers and feedback. With all three: Launched with waitlist of 200 companies.

Stack 3: The Engineer Who Writes

Layer 1 (Technical): Backend + data engineering Layer 2 (Product): Obsessed with dev tools, deeply understands engineer workflows Layer 3 (Distribution): Writes detailed technical posts, 100K+ blog readers

How it compounds:

• Technical skills let them build tools for engineers
• Product sense ensures they solve real pain points
• Writing attracts other engineers and creates trust
• Writing forces clarity of thought, improves product thinking

Result: Indie developer, $10K+ MRR from dev tools, working 20 hours/week.

The multiplication: Without Layer 2: They'd build tools that solve problems no one has. Without Layer 3: No one would find their tools in the crowded dev tools market. With all three: Every blog post is marketing + product validation + community building.

How AI Changes the Equation

AI is a force multiplier for every layer:

Layer 1: Technical × AI = 10x Speed

What used to take a week now takes a day:

• Boilerplate code → Claude writes it
• Bug fixes → GPT-4 helps debug
• Documentation → AI generates from code

But: You still need to know what code to write and how to architect systems.

Layer 2: Product × AI = Better Research

What used to take hours of user interviews now takes minutes:

• Analyzing feedback → AI finds patterns in comments/reviews
• Competitive research → AI summarizes competitor features
• User segmentation → AI clusters user behavior data

But: You still need to talk to users and develop intuition.

Layer 3: Distribution × AI = More Output

What used to take hours per piece of content now takes minutes:

• Blog outlines → AI structures your thoughts
• Social posts → AI creates variations to test
• Email drafts → AI writes first draft

But: You still need your voice, perspective, and authentic insights.

The pattern: AI accelerates execution in every layer, but doesn't replace judgment, taste, or strategy.

Building Your Leverage Stack

If you're early in your career or pivoting, here's how to build a leverage stack:

Step 1: Audit Your Current Skills

Map yourself on the three layers:

Layer 1 (Technical):

• What can you build from scratch?
• What tools/languages are you comfortable with?
• Can you ship a working prototype in a week?

Layer 2 (Product):

• Do you talk to users regularly?
• Can you identify what to build next?
• Do you understand your domain deeply?

Layer 3 (Distribution):

• How many people see your work?
• Can you explain what you're building?
• Do you have any audience/reputation?

Be honest. Most engineers are:

• Strong on Layer 1
• Weak on Layer 2
• Very weak on Layer 3

Step 2: Pick One Layer to Level Up

Don't try to improve all three at once. Pick the weakest layer that would have the highest impact.

If Layer 1 is weak: Build 10 weekend projects. Ship them publicly. Get comfortable with the full stack.

If Layer 2 is weak: Talk to 50 potential users in the next month. Ask about their problems, not your solution.

If Layer 3 is weak: Start writing. One post per week. Teach what you're learning.

Step 3: Find the Intersection

The magic happens where your layers overlap.

Example: You're good at backend engineering (Layer 1) and understand developer workflows (Layer 2), but have no distribution (Layer 3).

Your leverage move: Write detailed technical posts about what you're building. This:

• Forces you to clarify your thinking (helps Layer 2)
• Attracts other engineers (builds Layer 3)
• Creates feedback loops (improves Layer 1)
• Costs $0

After 20 posts, you'll have:

• An audience
• Better product intuition
• Potential customers/users
• Clear differentiation

Step 4: AI as Accelerant

Use AI to speed up whichever layer you're building:

Building Layer 1:

• Use Claude/GPT to learn new frameworks faster
• Get AI to review your code architecture
• Use AI to explain complex technical concepts

Building Layer 2:

• Use AI to analyze user feedback at scale
• Get AI to generate user interview questions
• Use AI to summarize competitive landscape

Building Layer 3:

• Use AI to generate content outlines
• Get AI to create variations for testing
• Use AI to draft initial versions (then rewrite in your voice)

The Compound Effect

Here's what a leverage stack looks like over time:

Year 1:

• Layer 1: Can build functional prototypes
• Layer 2: Have talked to 100+ users, developed intuition
• Layer 3: Written 50 blog posts, have 1K followers

Year 2:

• Layer 1: Can ship production-ready products in days
• Layer 2: Can smell product-market fit, know what to build
• Layer 3: Have 10K followers, every launch gets 500+ signups

Year 3:

• Layer 1 × Layer 2: Ship products that immediately resonate
• Layer 2 × Layer 3: Validate ideas before building via audience
• Layer 3 × Layer 1: Technical credibility drives distribution
• All three together: Built-in PMF + fast execution + distribution = $50K+ MRR

The stack compounds. Each layer makes the others more valuable.

Common Mistakes

Mistake 1: Going Too Deep on Layer 1 Only

I see engineers spend 5 years becoming world-class at a specific technology (e.g., Kubernetes, ML optimization, etc.) but can't ship a product or get users.

Deep technical expertise is valuable at big tech or specialized roles. But for founders or indie builders, a broad stack is more valuable than extreme depth.

The fix: Once you can ship working products, invest in Layers 2 and 3 before going deeper on Layer 1.

Mistake 2: Ignoring Layer 1 (Tech)

The flip side: people who think they can build businesses without technical skills in the AI age.

AI makes coding accessible, but it doesn't replace understanding system design, debugging, or architectural tradeoffs.

The fix: Get to "can build and ship" proficiency on Layer 1 before optimizing the others.

Mistake 3: Treating Layer 3 as Optional

Most engineers hate marketing, writing, and self-promotion. They think their work should speak for itself.

It doesn't. In the AI age, distribution is the moat.

The fix: Treat Layer 3 like a core skill. Write, build in public, share your work.

The AI-Age Career Path

The traditional path: Junior Engineer → Senior Engineer → Staff Engineer → Principal Engineer

The leverage path:

1. Learn to ship (Layer 1) - 1-2 years
2. Develop product sense (Layer 2) - 1-2 years (can overlap)
3. Build distribution (Layer 3) - Ongoing, start early
4. Compound the stack - Ship products that leverage all three

Where this leads:

• Indie hacker making $50K-500K/year
• Founder with technical credibility + distribution
• Solo engineer who can compete with teams
• Creator-engineer hybrid with multiple income streams

Traditional path leads to:

• High salary but capped upside
• Dependent on one employer
• Limited leverage beyond time

Both are valid. But in the AI age, the leverage path is more accessible than ever.

What to Do This Week

Pick one action for each layer:

Layer 1 (Technical):

• Ship a tiny weekend project using AI to speed up development
• Learn one new framework you've been curious about
• Rebuild something you've built before, but 10x faster

Layer 2 (Product):

• Talk to 3 people who would use something you're building
• Read 10 reviews of products in your space, find patterns
• Use a product you admire and map out why it works

Layer 3 (Distribution):

• Write one post about something you learned this week
• Share something you're building publicly
• Comment thoughtfully on 10 posts in your niche

Do this every week for 6 months. Your leverage stack will be transformed.

The Real Opportunity

AI hasn't made technical skills obsolete. It's made the combination of technical skills + product sense + distribution incredibly powerful.

The engineers who win in the next decade won't be the ones who are 10x better at coding. They'll be the ones who can:

• Build things fast (Layer 1)
• Know what to build (Layer 2)
• Get people to use it (Layer 3)

That's a leverage stack.

Start building yours today.