Many individuals have tried AI instruments and walked away unimpressed. I get it — many demos promise magic, however in apply, the outcomes can really feel underwhelming.
That’s why I need to write this not as a futurist prediction, however from lived expertise. Over the previous six months, I turned my engineering group AI-first. I’ve shared before about the system behind that transformation — how we constructed the workflows, the metrics, and the guardrails. At the moment, I need to zoom out from the mechanics and discuss what I’ve realized from that have — about the place our career is heading when software program growth itself turns inside out.
Earlier than I do, a few numbers to illustrate the scale of change. Subjectively, it feels that we are transferring twice as quick. Objectively, right here’s how the throughput developed. Our whole engineering crew headcount floated from 36 at the starting of the yr to 30. So that you get ~170% throughput on ~80% headcount, which matches the subjective ~2x.
Zooming in, I picked a few our senior engineers who began the yr in a extra conventional software program engineering course of and ended it in the AI-first method. [The dips correspond to vacations and off-sites]:
Notice that our PRs are tied to JIRA tickets, and the common scope of these tickets didn’t change a lot via the yr, so it’s pretty much as good a proxy as the information can provide us.
Qualitatively, taking a look at the enterprise worth, I truly see even increased uplift. One cause is that, as we began final yr, our high quality assurance (QA) crew couldn’t sustain with our engineers’ velocity. As the firm chief, I wasn’t pleased with the high quality of a few of our early releases. As we progressed via the yr, and tooled our AI workflows to embrace writing unit and end-to-end assessments, our protection improved, the variety of bugs dropped, customers grew to become followers, and the enterprise worth of engineering work multiplied.
From huge design to fast experimentation
Earlier than AI, we spent weeks perfecting consumer flows before writing code. It made sense when change was costly. Agile helped, however even then, testing a number of product concepts was too pricey.
As soon as we went AI-first, that trade-off disappeared. The price of experimentation collapsed. An thought may go from whiteboard to a working prototype in a day: From thought to AI-generated product necessities doc (PRD), to AI-generated tech spec, to AI-assisted implementation.
It manifested itself in some superb transformations. Our web site—central to our acquisition and inbound demand—is now a product-scale system with lots of of customized elements, all designed, developed, and maintained straight in code by our creative director.
Now, as an alternative of validating with slides or static prototypes, we validate with working merchandise. We take a look at concepts dwell, be taught sooner, and launch main updates each different month, a tempo I couldn’t think about three years in the past.
For instance, Zen CLI was first written in Kotlin, however then we modified our thoughts and moved it to TypeScript with no launch velocity misplaced.
Instead of mocking the options, our UX designers and mission managers vibe code them. And when the release-time crunch hit everybody, they jumped into motion and glued dozens of small details with production-ready PRs to assist us ship an awesome product. This included an in a single day UI structure change.
From coding to validation
The subsequent shift got here the place I least anticipated it: Validation.
In a conventional org, most individuals write code and a smaller group assessments it. However when AI generates a lot of the implementation, the leverage level strikes. The true worth lies in defining what “good” seems to be like — in making correctness express.
We help 70-plus programming languages and numerous integrations. Our QA engineers have developed into system architects. They construct AI brokers that generate and preserve acceptance assessments straight from necessities. And people brokers are embedded into the codified AI workflows that enable us to obtain predictable engineering outcomes through the use of a system.
This is what “shift left” actually means. Validation isn’t a stand-alone operate, it’s an integral a part of the manufacturing course of. If the agent can’t validate it’s work, it might’t be trusted to generate manufacturing code. For QA professionals, this is a second of reinvention, the place, with the proper upskilling, their work turns into a crucial enabler and accelerator of the AI adoption.
Product managers, tech leads, and information engineers now share this duty as properly, as a result of defining correctness has change into a cross-functional ability, not a task confined to QA.
From diamond to double funnel
For many years, software program growth adopted a “diamond” form: A small product crew handed off to a big engineering crew, then narrowed once more via QA.
At the moment, that geometry is flipping. People interact extra deeply at the starting — defining intent, exploring choices — and once more at the finish, validating outcomes. The center, the place AI executes, is sooner and narrower.
It’s not only a new workflow; it’s a structural inversion.
The mannequin seems to be much less like an meeting line and extra like a management tower. People set path and constraints, AI handles execution at pace, and folks step again in to validate outcomes before choices land in manufacturing.
Engineering at the next degree of abstraction
Each main leap in software program raised our degree of abstraction — from punch playing cards to high-level programming languages, from {hardware} to cloud. AI is the subsequent step. Our engineers now work at a meta-layer: Orchestrating AI workflows, tuning agentic directions and abilities, and defining guardrails. The machines construct; the people resolve what and why.
Groups now routinely resolve when AI output is secure to merge with out assessment, how tightly to certain agent autonomy in manufacturing programs, and what indicators truly point out correctness at scale, choices that merely didn’t exist before.
And that’s the paradox of AI-first engineering — it feels much less like coding, and extra like pondering. Welcome to the new period of human intelligence, powered by AI.
Andrew Filev is founder and CEO of Zencoder
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