How GenAI is granting SUPERPOWERS to developers and knowledge workers alike - Part One Vibe Coding

Introduction 

As a CTO and an active practitioner in the space of AI, like many technologists, I do not have the luxury of ignoring the evolution that AI is bringing to our personal and work lives.  My thought process has progressed along two lines: Technology and Business. Below, I have outlined a handful of prioritized themes that I have been tracking based on my own projects at work, exchanges with peers, and plain old research that comes from  countless hours of podcasts. The list is by no means complete, but does represent a good start.

On the technology related side, interest swirls around the following: 

• The new battlefront: Agentic AI Browsers

• Vibe coding for Enterprises: Reimagining the modern programming team

• The Rise of Domain-Specific AI Models

• The “desktop”of the future and the vision for “Next Gen multi-modal Intranets”

• Security, Trust, and Governance in AI

• Accelerating Technical Debt retirement 

On the business front on the other hand, the following thematic front-runners have emerged:

• Strategic investment: Key use cases and industry applications of AI

• AI's transformative impact on the workforce, skills and talent development

• The geopolitics and Infrastructure of AI

• Rapidly changing AI regulatory environment in the US 

• Challenges and prerequisites for AI adoption

This list certainly provides sufficient writing inspiration for multiple articles. The list contains many subjects that enterprises, sooner or later, will have to discuss and address.  I have decided to start this analytic journey by focusing primarily on a combination of two of the listed items:  Specifically I have imagined a short series of articles. The first article, the one you are reading, reflects on the evolving developer experience as impacted by the arrival of ever more powerful and capable tools.  In part two of the series will instead shift to the typical business user. They represent a larger group of workers that constitute the majority of potential users of AI.  For developers and knowledge workers alike, jobs roles will be impacted, perhaps even eliminated. On the brighter side, at least at the onset, roles as currently defined, can surprisingly be supercharged by productivity enhancing tools, the likes of which we have never seen.  I am optimistic that many “coders” will adapt. 

Looking back at my list of business and technology themes, while somewhat organized by categories, I found constructing a true prioritization a bit challenging. The reason: many of the items are dependent on each other. For example, if one were to attempt to imagine the desktop of the future, a theme I have been working on for quite some time, the critical notions of security, trust, delegation across hierarchies of cooperating agentic systems, must be also be addressed in parallel as a foundational set of prerequisites. 

Because of the inherent interconnectedness of these themes, it is not surprising to observe that “all roads lead to AI”.  In a recent discussion with a CIO who leads an insurance firm, I was asked about  perspectives on the topic of low-code, no-code and how such an approach could help retire a core legacy system. Unsurprisingly, within 5 minutes, the conversation pivoted mostly to the topic of GenAI. There was a reason for that.  While the CIO had heard many vendor pitches, this leader wanted to hear a more unbiased, vendor agnostic view that we, as technologists, were seeing in the field, in the real world.  

Generative AI is drastically changing the way we work. It is something that is truly happening. There is a  very perceptible acceleration that makes the evolution and progress in the space  very hard to follow, with changes and refinements surfacing every month. So I decided to consider writing about how GenAI was impacting the way we work.  

What if we looked at this topic from the viewpoint of two personas. The first, through the perspective of a professional programmer, “coders” as they are also known. The second, through the eyes of a typical knowledge worker. For each of these roles, the way work is being performed is inherently driven by the tools that are being made available to them and how those tools are being utilized. These approaches to work are changing as a result of GenAI.

As business users we are very aware of the productivity gains provided by commercial products like MS Copilot in all of its flavors, packaging bundles.  We love the transcription, summarization and most recently the agentic research capabilities that have shown up in our Microsoft 365 subscriptions. But it is much more than that.  

As technical workers, we have come to love capabilities such as code assistants in our favorite integrated development environments (IDEs) or amazing prototyping tools such as Lovable (™), where one can go from idea to prototype in just hours. But, here too, there is so much more that is happening and changing.

So now, let's get started and see how democratized super powers impact our mode and style of work. Let’s start with the programmers and coders. We will leave the business users for another article.

The Changing Role of Developers 

Software development is hard. It has always been a non-trivial, multi-step, multi-discipline exercise. But so was the building of magnificent gothic cathedrals during the middle ages, or, in more recent times, the building of towering suspension bridges that cross the Bosphorus straits. So why be pessimistic? The process by which software is being developed however continues to change. It has happened before. And with this change, so has the role of the programmer had to evolve.   

Code assistants have already proven their utility.  But first generation code assistants are yesterday’s news and productivity enhancement that has already been delivered to the developer community. The current buzz is now around the concept of “Vibe Coding”. Let’s compare the traditional approach to building software versus this emerging new trend.

In traditional software development, developers emphasize manually crafting code, regardless of its intended purpose.  In this model, computer programming languages, mainstream or proprietary are central.  The traditional SDLC places significant effort on requirements gathering, and the creation specifications, implementable and testable. One can say that Agile methodologies ,especially during early stage sprints, can help fill gaps when developing functional requirements, NFRs, and technical specifications, However, within the traditional development paradigm, manual code creation, testing, and revision still remains central to the developer workflow. Individual developers and programming squads go through an highly iterative process of creating, editing, deploying, testing code, and collaboratively documenting progress as work progresses from agile sprint to sprint.  Automation that aims to remove “toil” smooths out the process, increasing frequency of releases, and improving time to market.  

Moreover, the maturing of DevSecOps has introduced a strong level of specialization, and granular breakdown of the overall process, leading to even greater levels of automation across all task categories encountered in DevSecOps. As a result of this specialization and a separation of duties, the adoption of the SRE philosophy has emerged to ensure more frictionless collaboration between those who write code, and those who operate the functionality that the code realizes.  Has this been beneficial? Absolutely? Has this been easy ? It has taken time? Has the art reached a level of operationalized IT nirvana ? Not at all. Is the pure SRE story long term viable? There are signs that it is not , and hence the emphasis on Platform engineering and developer experience portals such as the open source Backstage and commercial products such as Harness. Net net, however, Backlogs of unfulfilled software development projects still remain.  But enough about the old way of doing things. We need faster, better ways to deliver software .

With Vibe Coding instead there is a very fundamental change. Four reasons this is the case.

1. Shift from writing code to prompt based dialog with specialized AI agents, engaged via plain natural language.  English is becoming the language for “programming”.

2. Deep contextual awareness of project artifacts (sourced and generated)

3. Trusted delegation to the agentic system for mundane task execution (polyglot code artifact generation)

4. Agentic comfort with multi-modal inputs (text, images, audio, video, screen captures,  etc) 

The act of creating code is no longer the central activity of the developer workflow.. The ultimate goal is instead the time-optimized delivery of business services, functionality, and positive outcomes. Computer code becomes the byproduct of a revised software development process conducted through a new workflow which relies on iterative engagement with a set of specialized agentic services prompted, interactively guided by a new class of developers. Fulfilling software development tasks via an iterative engagement with AI agents, building software via “prompts” is a very different approach to crafting, emitting code. And in this model, the use of prompt engineering is taken to a very different level.  

When Vibe Coding is performed, developers and teams orchestrate the creation of software systems, through a continuous dialog with specialized agentic services, that are integrated, fully context aware that intelligently work in tandem with the developer.  Who remembers the concept of “pair programming”?  Under a Vibe Coding model, developers effectively have multiple counterparts, virtual agents that specialize in doing different tasks, and not just a single “code wingman”.  Some agents are great at writing well structured requirements documents. Others are masters at generating front end code and wireframe mockups from high quality, AI generated specifications. Others can emit configuration files for specialized tools in  DevOps toolchains.  Do you see where this is going?  Every developer will have a “personal army” of SMEs, virtual assistants that do work.  It gets even better. 

When paired with reasoning models, the behind the scenes orchestration of what needs to be done in order to produce a piece of functioning software is triggered by the initial set of prompts, and is informed by a growing context that builds up as the AI goes to work. This reasoning ability leads to environments that can create plans  that create, perhaps even deploy, full stack software. Consider the new generation of tools such as Replit and Lovable, two popular tools in a market that is quickly becoming crowded and eventually will consolidate.  These products are becoming fully self contained, meaning that they provide environments to build and deploy complete applications.

And then there is the "hyperscaler factor", which can help enterprises derisk their strategy. For example, AWS is poised to release Kiro, an AI IDE that of course will have hooks into their cloud native services.  So with one tool, a single interface and development approach, a new generation of GenAI empowered developers will not only be able to generate applications, but also deploy them with minimal effort across reliable, scalable cloud services.  

Agentic services can be packaged as extensions to an IDE, can be packaged as full IDEs (examples Cusor, Windsurf). They can also be provisioned as standalone CLI tools that open through a developer terminal session or replace the terminal session entirely as Warp 2.0 CLI does.  Regardless of the packaging model, these agentic tools act as smart front ends to the latest LLMs. Examples include Claude Code, a CLI from Anthropic, and personal favorite. This is important as it not only avoids model lock in, but also provides room for specialization as models are quickly specializing in their expertise. It is just like medical doctors pursuing specialization.  

The major breakthrough has in part come from the fact that agents have expanded their understanding of context through LLM session memory expansion, and in part through their ability to “parse”, “understand” and “watch” project artifact repositories as they evolve during the Vibe Coding cycle. Gone are the limitations of first generation code assistants which were primarily aware of single files, as seen through the IDE.  New agentic systems now, not only “see” the code that is present in the IDE, but they can go way beyond that. They see entire projects, version controlled repositories, even specialized cloud drives and file systems (see Warp 2.0) where generated artifacts live.  These agents are specialized and leverage LLMs that are optimized for specific tasks. For example the Claude Code CLI (™) works in provides bindings to models that excel at complex coding tasks such as the powerful Sonet 3.7  and Opus 4.0 reasoning models

We have come a long way from code assistants features present in your favorite IDE.  Today, developers don’t just need a smart IDE. They just don't need a better terminal. They are expecting an agentic development environment that extends their development prowess , making them the custodians of a new collection of superpowers.  These superpowers are unleashed when developers adapt.  Rather than mastering computer languages, developers will need to be masters in articulating requirements, specifications, features , testing scenarios and modalities, rather than experts in computer languages. So if you are a hard-core business analyst that has spent a career in modeling, describing processes and data, then Vibe Coding may perfectly fit and complement your existing skill sets. Exciting times, aren’t they?

Now let's look at the other side of the coin.  The impact on Enterprises.

Enterprise Considerations 

As a practitioner, one needs to be pragmatic, and diligently conservative at the same time.  Here are some questions that must be circulated up and down the organization as a transition to a new paradigm that changes the way software is produced and maintained? 

On the business side, it is important the organizations not react to media hype, but formulate enterprise strategies that consider these guidelines that:

1. Set realistic expectations and goals. Use cases for each organization are prioritized differently based on industry, corporate strategy and long term vision.

2. Implement AI strategically and contextually. AI is a tool, not an end. 

3. Focus on data-driven measurement. Empirical or anecdotal evidence through self-reporting often leads to incorrect insights regarding ROI and true productivity gains.

4. Monitor for negative side effects, such as drops in productivity, introduction of bias, greater cybersecurity risk   Theory vs reality when change is introduced are never aligned.

5. Consider training and usage context. Every organization is unique. Every organization’s talent makeup is different. 

6. Investigate internally first.  Application portfolios and development organizations are structured differently across companies. Therefore tales of external performance won’t necessarily apply. 

When thinking about the business aspects , questions around organizational impact and evolution will be more important than ever.  Vibe Coding inherently requires that resources work and think differently. Therefore it is fair to ask how the human resource element factors in ?  Where does one find this new class of developers?  Will they emerge from existing pools shaped through retraining? Will they be harvested from the ranks of resources that traditionally occupied roles of business analysts,  SMEs in requirements gathering , from pools of practitioners of design thinking? If so, business domain experts will be more important than ever for they will be able to aptly articulate and represent the user perspective, the customer-centric view of what a system experience should be like, and what operational rules should be.  Here too , questions and decision points abound. Lastly, how do human resources departments need to adapt, change, and retrain to ensure that organizational needs for talent are met ? 

On the technical side, AI leaders must also be well guided. Common questions revolve around 

1. What kinds of applications is Vibe Coding appropriate for? Prototypes, proof of concept or complex multi-service, multi-tier applications ? 

2. How does Vibe coding fit in organizations with rich legacy investments, and complex integration points? 

3. How does process and data governance change? 

4. How is auditability and traceability affected ?  When decisions emerge as a result of interactions with agents, prompts, and the extended conversations need to be part of the record. This means that prompt histories, individual and team  will also need to be versioned. 

5. How will system and data versioning need to change? Generated code will also include requirement documents, specifications, architecture diagrams of different kinds, pipeline configuration files, synthetic datasets.

6. What are the appropriate runtime architectures for applications that are generated instead of manually crafted and deployed? This is a very big topic, especially for a person with a background similar to mine.  

7. How will the role of architectural standardization and pattern based deployments change?  How will it be possible to meet functional, non-functional and regulatory requirements, items so critical to operational excellence. 

These are all complex questions and could fill pages. In essence of time, they will remain stated but unanswered, intellectual exercises for the enterprise practitioner of AI. Here too, the list is by no means complete. It represents a few of the items that AI leaders, planners and architects will need to grapple with when embarking on their journeys toward an AI-infused enterprise. The comments section to the article can prove handy to continue the dialog and dig deeper. 

Is Vibe coding the end-all , be all in how software is created?  No, it is just the latest stop along a journey where the infusion of AI within human activity will drive transformation and progress.  The GenAI revolution is moving so fast that it is hard to tell when Vibe Coding will be deemed stale, outdated, ready to be shelved and replaced by an even more capable methodology implemented by ever more intelligent and capable tools.  

In the next installment of this series, I plan on reflecting on the impact of GEN AI on Business users, the good old knowledge workers.

Stay tuned .

mick