Vibe Coding Is Rapidly Reshaping The Software Developer Profession

There is probably no more controversial buzzword in software engineering right now than “vibe coding” — trusting AI to write the code for you. Initially referring to weekend projects, it’s now synonymous with general AI use in coding. People love it, people hate it, they debate it on podcasts, and even teach online courses in it. Parents are questioning whether their children still need to learn computer science.

Beyond the emotions and hype, a fundamental shift is occurring in our industry. I see it in daily conversations with fellow CTOs, in how I build my software business, and in weekly progress reports from our AI lab. As someone who’s spent decades building software companies, part of my role is anticipating future trends while remaining pragmatic. Since the internet boom, we’re approaching the most significant inflection point in software engineering.

The Promiseland

Today, given the right context, AI agents can produce features and fix bugs with minimal supervision. A popular benchmark, SWE-Bench, demonstrates this progress: in 2023, it was state-of-the-art if your AI agent could solve 5% of it. Today, our agents easily solve 60%+ of these challenges, improving with each sprint. Many teams are successfully translating this capability into enhanced productivity.

In the startup ecosystem, Y Combinator founders report generating 90% of their code with AI, regardless of their technical background. Some have established engineering careers, others are learning on the fly, but most blend product management and engineering in their daily work. The traditional separation between product managers defining requirements and engineers implementing them is blurring rapidly. We’re witnessing the rise of “product engineers” — professionals who ideate, implement, and test solutions as individuals, leveraging AI throughout the process.

The reduced implementation cost makes experimentation easier and more affordable, transforming product management. Instead of lengthy customer development cycles, you can prototype products instantly. This will blur the line between “product” and “feature,” enabling innovation at the feature level and replication at the product level.

We’ll see a significant increase in engineering velocity and productivity. Simultaneously, we’ll witness an explosion in software creation. Economic forces will likely create a balancing act: growth initiatives will maintain headcount while benefiting from 2- 10x pace improvements; maintenance initiatives will reduce headcount due to improved productivity; and new products will absorb talent freed from the maintenance side.

The Wasteland 

This transformation creates triple pressure for some legacy vendors:

1. Their products aren’t AI-first compared to the new competition, making them vulnerable in the market.
2. Their codebases aren’t optimized for AI agents, slowing their ability to react and adapt.
3. Their workforce is calibrated to traditional development tempos, exacerbating the above issues.

Meanwhile, new startups are AI-first in value proposition, internal operations, and tempo. This is “Innovator’s Dilemma” on steroids.

I’ve had off-the-record conversations with forward-thinking CEOs, CTOs, and VPs who see this squeeze and start acting.

The Market Premium for AI-First Engineers

The market rewards those who seize new opportunities fastest at the macro and individual levels. As executives accelerate AI agent adoption, I consistently hear about growing demand for engineers who proactively deploy AI in their work — there simply aren’t enough of them. Reuters research estimates an AI talent gap of 50%. This scarcity means professionals demonstrating AI proficiency will enjoy enhanced job security and premium compensation.

Conversely, I’m witnessing growing executive frustration with employees who ignore AI-enabled productivity opportunities. Remember I mentioned above that the current state of the art is solving about 60% of SWE-Bench? Some people see this and think, “With some effort, I can be twice as productive.” In contrast, others exhibit “smartest person in the room” syndrome, focusing on scenarios where AI fails so that they can preserve their sense of intellectual superiority. Most people fall somewhere between, not yet unlocking AI’s benefits for various reasons, but will soon.

AI is a tool, like a hammer. Hammers are excellent for nails, less useful for screws, and terrible for opening wine. AI is significantly more nuanced, requiring practical experience to understand where it works out-of-the-box, needs adjustments, and where traditional methods remain superior.

I’ve seen intellectually curious engineers create remarkable setups connecting AI coding agents with custom MCP servers and prompting instructions to automate substantial portions of their work. I’ve also seen smart but AI-myopic professionals who believe code completion circa 2022 represents state-of-the-art AI coding.

The tech industry always rewards intellectual curiosity and rapid innovation. When considering your next job opportunity, keep these points in mind. Startup interviews increasingly ask candidates to code with AI assistance, and hiring managers frequently inquire about the productivity gains you’ve achieved through AI tools.

Breaking the 4-Minute Mile

This creates opportunities for both experienced professionals and fresh talent. Senior engineers bring depth and breadth of experience that helps them effectively orchestrate AI agents. However, they’ve worked the past decade at a certain pace, making it difficult to reset expectations.

New talent will have the advantage of being AI-native. For them, moving at what we consider a “10x pace” will be normal — they don’t have self-imposed limitations to overcome. This resembles the four-minute mile, once considered impossible but now achieved by top high school athletes.

As we say in the AI industry, “embrace the exponentials.”

The Great Divergence

I’ve highlighted several temporary polarizing effects of AI on our industry: AI-first versus AI-not companies, and AI-first versus AI-not engineers. These are temporary because market economics and competition eventually rebalance industry productivity advantages. Now, let me address a different kind of divergence.

Historically, the software profession has repeatedly branched into specializations (which sometimes later converged). System engineers emerged who understood compiler internals while application developers used high-level APIs. With growing complexity, web development has split into backend, frontend, and design specialties, only to re-emerge in the full-stack engineer role through new cloud and open source technologies.

I foresee a similar divergence happening with full-stack engineers soon:

1. Some will gravitate toward product engineering, creating applications and features at lightspeed. Think of this as Zero-to-One, the process of creation. Product engineers will need strong business acumen and user experience skills.
2. Others will focus on taking applications from initial success to ensuring they’re scalable, reliable, and secure. Think of this as One-to-a-Hundred, the process of scaling. This represents senior engineers and architects who have evolved for AI-first engineering processes.

There’s a skill gap between these specialties, raising concerns about how fresh talent will close it. However, I don’t believe this gap needs closing: using the earlier metaphor, not all application developers want to become system engineers. For those who do, the economy has examples of much harder gaps to bridge. No undergraduate degree prepares you to lead a large company as CEO or perform surgery immediately, yet we have doctors and CEOs.

What This Means for Different Stakeholders

For Software Engineers

You can watch three things forever: fire burning, water falling, and AI agents doing the work for you. Develop skills in prompt engineering and AI collaboration. The best engineers are becoming “AI wranglers” who orchestrate multiple assistants to solve complex problems. With your current tech stack, for many practical use cases, you are more than 1,000 times more productive than an engineer 30 years ago with their stack. It’s time to add another 10x and get closer to the pure form of software creation.

And that speed will put a premium on creativity and a product-oriented mindset. The engineers who will thrive are those who think beyond code to customer needs and business outcomes. As AI handles more routine coding, your value increasingly comes from understanding the “why” of features, not just the “how.”

For Executives

If you’ve been nurturing ideas for new products, now is the ideal time to build AI-first products with AI-first processes using AI-first engineers.

For existing projects, invest in tooling and training with a holistic approach. For example, I recently spoke with the CEO of a large software company with tens of thousands of automated tests. If appropriately leveraged, this asset can enable more proactive AI agent changes by closing the feedback loop and passing test results back to the agents.

Many simple tactical steps are available, from lunch-and-learns to dedicated Slack/Teams channels where teams share successful examples and tips for deploying AI agents. Remember, there are three things you can watch forever.

For Students and Parents

Software engineering remains one of the most promising career paths despite — or rather because of — AI advances. Far from making coding obsolete, these tools offload routine work and free engineers to focus on creative problem-solving.

Think of AI as an autopilot in aviation: It handles routine functions brilliantly but still requires skilled pilots for critical decisions and unexpected situations. As software becomes even more central to every industry, demand for engineers continues to grow.

I would supplement traditional computer science and software engineering fundamentals with product management, UX, deep learning, and LLM foundations. Some colleges may overemphasize traditional “fundamentals” at the expense of breadth and modernity. When I recently saw RISC vs. CISC architecture in a basic computer course curriculum, while the same institution offered no classes on information retrieval (RAG) or LLMs, I questioned whether they were keeping pace with industry developments.

Engineering at the AI Inflection Point

The software engineering profession is evolving and accelerating. For companies, the choice is clear: Embrace AI-augmented development or be outpaced by competitors moving at 10x speed. For individual engineers, now is the time to evolve skill sets and lean into product thinking and AI collaboration.

As someone who has witnessed multiple industry transformations, I believe this shift toward AI-augmented engineering will create numerous opportunities. Engineers who thrive will ride this wave, becoming orchestrators of AI capabilities while applying their uniquely human creativity and judgment to build remarkable products.

The future belongs to those who break their four-minute mile.

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