Generative AI: closing the developer gap and redefining the software moat [Q&A]
Generative AI (GenAI) is reshaping software development, closing the long‑standing gap between surging demand for new applications and the limited supply of skilled developers. But this AI‑driven leap in productivity comes with an unexpected twist: it’s also dissolving the traditional technological edge that once set companies apart.
So where does sustainable advantage come from in this new world? We sat down with Matthias Steiner, senior director of global business innovation at Syntax, to explore how enterprises can redefine their competitive edge in the GenAI era.
BN: Can you explain the software development landscape before GenAI?
MS: For years, organizations faced a stubborn reality: there simply weren’t enough developers to meet the growing appetite for software. This developer shortage became one of the biggest constraints on digital transformation.
Every industry demanded more applications, integrations, and automations than IT teams could deliver. Software engineers were in such short supply that corporate strategy often hinged on their limited availability. The result? Persistent backlogs and stalled innovation.
Companies tried to close the gap with outsourcing, low code/no code platforms, and agile reprioritization. These helped, but none addressed the underlying structural divide between demand and supply.
GenAI changes that. We’ve entered a new chapter.
BN: How is GenAI helping bridge the supply and demand gap?
MS: GenAI coding assistants are breaking the bottleneck -- not by replacing developers, but by dramatically multiplying their productivity. And it’s not just professional developers who benefit. Low‑code platforms integrated with GenAI are empowering ‘citizen developers’ -- business users who can safely build or modify apps under IT governance -- further expanding delivery capacity.
In short: IT can finally deliver more, more consistently, and without burning out its most valuable human resources.
BN: What specific benefits does GenAI bring to development teams?
MS: Tools like GitHub Copilot and OpenAI’s suite enable developers to create clean, functional code in a fraction of the time. The compounding effects are powerful:
- Automating repetitive coding tasks
- Improving overall code quality
- Accelerating testing, refactoring, and documentation
- Reducing bugs and rework
- Tackling backlogs while maintaining capacity for innovation
GenAI moves software engineering from a world of scarcity to an era of possibility. But there’s a catch: the same surge in productivity that clears your backlog can also level the playing field -- eroding your technological edge.
BN: Can you expand on that risk?
MS: Absolutely. Historically, leading software companies built moats -- durable technological advantages that kept competitors at bay. Now, those moats are shrinking rapidly. In an AI‑driven, open‑source‑dominated marketplace, they’re often gone altogether.
Three shifts are driving this:
- Open Source Erodes Technological Moats -- Proprietary codebases once kept rivals years behind. Today, open‑source frameworks, tooling, and libraries let newcomers match incumbents within months.
- AI Removes Development Barriers -- Widely available GenAI models and coding assistants mean the bottleneck is no longer building code -- it’s deploying and refining it.
- Cloud and API‑First Erase Infrastructure Moats -- Massive data centers and custom infrastructure no longer confer exclusivity. Enterprise‑grade environments can be spun up in hours.
When anyone can access the same AI models, cloud resources, and developer tools, technology ownership alone stops being a sustainable advantage.
BN: So, what is the new moat in software engineering?
MS: It’s not in the code anymore -- it’s in the processes surrounding its creation, deployment, and evolution. The winning formula now rests on three interconnected pillars:
- Deep Domain Expertise and Strategic Portfolio Build‑Out -- AI can produce code quickly, but deep industry insight is required to build solutions that truly solve customer problems. Domain expertise filters AI’s productivity into business outcomes.
- State‑of‑the‑Art, GenAI‑Driven Product Engineering -- Integrating AI across people, processes, and pipelines -- from requirements gathering to testing, deployment, documentation, and even support -- creates a modern, automated, agile software lifecycle.
- Go‑to‑Market Excellence -- Rapid delivery, tight feedback loops, iteration based on user data, and synchronized efforts across product, marketing, sales, and customer success teams ensure speed to value.
The old equation -- best engineers equals best software -- is obsolete. Today’s moat is: Deep Domain Expertise × State‑of‑the‑Art Engineering × Go‑to‑Market Excellence. If any factor is missing, the edge disappears.
BN: How do you see this in the long term?
MS: GenAI and related innovations will shrink backlogs across the board -- but technology‑based moats will shrink along with them. Competitive advantage will hinge on speed, quality, and insight.
Winning requires:
- Embedding AI into development processes now
- Building a culture that supports rapid delivery and high quality
- Leveraging proprietary data and unique industry expertise
GenAI in software development isn’t an experiment anymore -- it’s a strategic imperative. Organizations that embed GenAI into their software development life cycle today won’t just clear their backlogs -- they’ll transform them into a source of competitive advantage, delivering higher‑quality software faster, at lower cost, and with greater innovation.
The flip side is equally true: those who delay adoption risk watching competitors outpace them in speed to market, agility, and customer experience. In an environment where digital capabilities often determine market leaders, the decision to act -- or wait -- on GenAI could shape an organization’s position for years to come.
Image credit: BiancoBlue/depositphotos.com
