TECHNOLOGY LEADER · ENGINEER · OPERATOR

From complexity
to execution.

20+ years across design, growth, engineering, product and technical leadership — from hands-on building to CTO-level responsibility, and still close to the work today.

Design → Growth → Engineering → LeadershipEngineering · AI · Product · Growth

THE THROUGH-LINE

The field of view
kept expanding.

Design raised questions about adoption. Growth pulled the work into data and engineering. Engineering expanded into architecture. Technical leadership connected product, teams, economics and responsibility for the outcome.

01Design & web
02Growth & data
03Technical leadership
04Today
05Exploring next

WHERE IT STARTED

Start with the experience.

Make it clear. Make it useful. Make people care enough to use it.

The first problems were visual. Design became web development because shaping the experience meant understanding how it actually worked — not just how it looked.

Then the questions moved underneath the interface. Why did people use one thing and ignore another? Where did they leave? What brought them back? What could the business learn from the behavior?

The interface was only the surface. Performance was underneath it.

TERAPEAK · LATER ACQUIRED BY EBAY

Then make it perform.

Building the experience wasn’t enough. The next question was whether it moved the business.

At Terapeak, design, engineering, experimentation, data and revenue converged around outcomes. The boundaries mattered less than whether the work changed what users did and what the business achieved.

AI prototypes had to prove ROI. Retention work had to move churn. Product decisions had to connect to ARPU and adoption. That operating range continued through Terapeak’s acquisition by eBay.

400%ROI from AI-driven prototypes
−4%annual churn improvement
+10%ARPU year over year

Tie the work to outcomes and the boundaries disappear.

CTO-LEVEL OPERATING SCOPE

Own the whole system.

Eventually, the responsibility stopped ending with the technology.

Architecture still mattered. So did product direction, hiring, operating cost, reliability, delivery speed and whether the technology could support the business it was meant to become.

Across mobility and financial technology, the work moved from early ambiguity into real operating scale. At that scope, engineering decisions were business decisions.

AI · MOBILITY

Scale adoption without letting infrastructure become the ceiling.

+300%adoption
−40%infrastructure cost

FINTECH · AUTOMATION

Turn a manual operating workflow into a scalable decision system.

−60%approval time
operational efficiency

See the whole system. Find the constraint. Move it.

SUKOW VENTURES · SENIOR SOFTWARE ENGINEER / GROWTH & TECHNICAL STRATEGY

Stay close.
Think wide.

The code still matters. So does everything it sets in motion.

Hands-on engineering keeps the constraints real. Broader operating experience keeps the consequences visible — product, infrastructure, economics, adoption and what happens after launch.

The question isn’t only whether something can be built. It’s what deserves to be built, what is actually blocking scale, where AI changes the operating model and which technical choices create leverage.

01Engineering

Build the system, not just the ticket.

02AI & automation

Use new capability where it changes the operating model.

03Product & growth

Connect technical choices to adoption, revenue and user value.

04Strategy

Make decisions with downstream consequences in view.

Depth matters. Range makes the depth more useful.

See the full trajectory

STILL BUILDING

Build into what changes next.

New technology gets interesting when it survives contact with a real problem.

AI is changing what software can do, how products are designed and where the economics of a system live. The useful understanding comes from building with it — turning possibility into working products and testing the assumptions against real constraints.

01

Agentic software systems

How persistent agents coordinate work across tools, infrastructure and business processes — and where human judgment should stay in the loop.

02

AI-native product architecture

What changes when generation, memory, identity, multimodal interaction and model choice become first-class parts of a product system.

03

Economics as architecture

Usage, infrastructure cost, pricing and monetization are increasingly technical design constraints — especially in AI-native products.

Exploration keeps judgment current. Building keeps it honest.

WORKING NOTES

Thinking, made visible.

Ideas on engineering, AI, product, systems and the decisions that connect them.

All notes
01
Systems

Why good systems design starts with the constraint

The strongest architecture decision is often the one that correctly identifies the business, product or operating constraint first.

02
AI Economics

AI systems need an economics layer

As AI moves from assistant to actor, usage, authorization, cost and monetization become part of the product architecture.

03
Leadership

What startup environments teach you about technical judgment

Knowing the ideal architecture is useful. Knowing what the system needs next is what moves the company.

OPEN CHANNEL

Interesting problem?
Start there.

Hard problem. Ambitious product. System that needs to become something more. Those are usually the conversations worth having.