In a few short years, AI has caused a paradigm shift in software development.
The biggest change is in the role of the individual developer: manual coding is no longer a significant part of the job description. Instead, every developer can now become an architect or a lead, managing a team of AI agents.
This monumental shift carries deep implications for individuals, teams, agencies, and clients alike.
Norrin has fully embraced agent-powered software development
At Norrin, agent-powered development is already a reality. Today, a single developer running an agentic team can achieve what was completely unimaginable five or ten years ago.
In the team, one agent acts as an architect while another generates the code. A trio of critic agents then scrutinizes the end product: One of them focuses on SecOps, the second handles comprehensive testing and QA and the third analyzes the UX.
The developer remains the essential Human-in-the-Loop. They control the end product, they know why it is developed and they are the ultimate authority. The architect-agent comes to them for guidance, and they have the final say on pull requests.
What does the shift mean for the individual developer?
Embracing the new role means learning and utilizing new skills and gaining an understanding of the wider context around client projects. For some, this comes naturally, others might have to work on it. But for all of us, it’s both a level-up and a scale-up. Everyone can do more with less.
However, learning new skills does not make existing ones redundant. A common misconception is that AI makes understanding code unnecessary. While that might be true for a basic personal app, it is wildly untrue in professional software development.
Understanding code, architecture, and infrastructure is more critical than ever when you are responsible for overseeing an entire product. As the owner, you must be able to read the code to recognize when the AI is making the wrong decisions.
The challenges of the paradigm shift
A shift this massive always brings unforeseen consequences. Organizations must actively address four key challenges:
1. Beware the cognitive overload
AI agents process information and generate code at speeds incomprehensible to the human brain. Because agents handle the heavy lifting, developers have more time to think. This can create a temptation (or an expectation) to multitask aggressively.
It becomes a problem when the developer eventually is overwhelmed by the cognitive overload caused by the pace, and burn out. Humans simply cannot work like that for an extended period of time, and they shouldn’t try.
Avoiding this requires a two-fold approach: developers must practice the self-discipline to focus on one task at a time, while tech leaders must protect their teams by nurturing a culture that values quality and wellbeing over raw speed.
2. Say goodbye to laissez-faire token use
The reality of consumption-based token pricing has been a rude awakening for many. For a while, burning tokens was seen as a point of pride, a KPI for the organization’s AI transformation. But now, as token use has skyrocketed due to agentic AI, companies across the world are in near-panic over budgets.
The truth is that it was never sustainable, because it promoted wasteful habits:
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Lazy development: Developers stopped planning ahead, relying on endless re-prompting to fix mistakes.
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Overuse of frontier models: Most defaulted to expensive frontier models when a cheaper, less sophisticated model could have done the job just as well.
Optimizing model selection won’t solve the entire cost problem, but it is an essential first step.
3. Geopolitics and local first
European organizations are increasingly wary of their dependence on US-based AI technology. Combined with volatile token pricing, this dependence has become a strategic risk.
As a result, we are seeing a rise in Local First -thinking regarding both AI models and infrastructure. Thanks to the maturity of open-source AI, many organizations are now running models on local hardware to safeguard both budgets and data security.
4. The future of junior developers
Another legitimate concern is the future of junior developers. If the tasks traditionally entrusted to junior developers are now automated, how do we nurture new talent? Junior developers cannot be expected to jump straight from graduation into architect or lead roles as they lack the experience and maturity those positions require.
However, I believe that while the future now seems uncertain, the industry will adjust. Schools and employers will find ways to prepare young professionals, and AI tools themselves can provide varied, accelerated training methods to shorten the path to seniority.
AI gives junior talent the sandbox to master software architecture and business logic by building real solutions from day one. With AI, we can also create realistic training environments built specifically for learning contextual skills.
The bar for entry is now higher, but with AI handling the bulk of the coding, developers can fast-track the big-picture skills needed in the new era.
What’s next for AI?
Tech-wise, the future will likely bring more of the same: better models handling increasingly complex tasks. The more interesting shifts will be business-related.
Development costs will drop: We will ship production-ready software with smaller teams and smaller budgets. This means more value for clients, which software consultants must leverage into new business models.
The threat of unnecessary software: Because generating code is now cheap and easy, organizations risk using software to solve problems that don't actually require it. Tech leaders must maintain strict focus on what is genuinely relevant to avoid an overflow of useless code.
The human factor: Technology evolves faster than people or organizations. Ultimately, the winners of the AI race won't just have the best tools, they will be the ones who successfully guide their people to apply AI to real-world business issues.
To sum it up: Code is becoming cheap, but human leadership has never been of higher value.
