Leading the Vibe
Leading Within AI-Driven Structures
Hey, fellow Leader 🚀,
I am Artur, and welcome to my weekly newsletter. I am focusing on topics like IT Management, Innovation, and Leadership, with an Entrepreneurial mindset. My goal is to help you navigate the IT corporate landscape. Make better decisions, create awareness, and share real-world stories.
The IT space is undergoing a massive transformation driven by the effects of AI. Leadership is not indifferent to these changes, and it needs to adapt and evolve along with them.
AI ultimately isn’t making engineering teams faster. It’s making them smaller.
While leadership defaults to using automation to slash headcount, they are blindly trading away their operational buffer.
The course is far from steady, which is the number one reason it is so difficult to predict what the future will bring. However, it is important to have a multi-perspective analysis of what the impacts of AI on leadership will be.
AI Is Generally Underperforming
The default modus operandi for management using AI capabilities is to cut costs. Simply because the pressure of reducing operational cost is high, and slashing HC is the easiest way to translate potential AI gains into real numbers.
AI is a tool. Therefore is not empowering companies, but rather empowering individuals. The easiest way to have a winning AI strategy is to have an empowered staff aiming for AI excellence, while maintaining a critical eye on its results.
As Leaders, we should move away from short-sighted AI approaches. We need to build viable arguments, ideas, and strategies to attract capital and budget, aiming to harness the AI disruptive capabilities.
The fundamental aspect is to identify growth opportunities, implement them, and further capitalize on existing solutions.
The pressure to translate AI implementation into tangible success is real. Cost optimization is a straightforward practice to achieve this. However, it risks branding AI solely as a disruptive tool that threatens job security.
A study conducted by MIT, “The GenAI Divide” in mid-2025 concluded that there is a lack of clear results with AI adoption:
The GenAI Divide is most visible when examining industry-level transformation patterns.Despite high-profile investment and widespread pilot activity, only a small fraction of organizations have moved beyond experimentation to achieve meaningful business transformation.
While this study is from June 2025 (which means it risk to become outdated fast given the current pace of AI development), it still highlights the gap between AI pilots and their successful outcomes in companies.
Leveraging AI in products will require creative inspiration from product teams. Unfortunately, current trends lean toward making products and services run cheaper, rather than making them better.
Leaner Teams Focused on Maximizing Impact
For Leaders and from a people management perspective, the future will be easier. Not because people are becoming easier to manage, but because AI will make teams smaller.
Since teams are getting smaller, the value they deliver needs to remain the same, with the caveat that business goals will surely demand ambitious targets with fewer resources.
Below is an article where I dive deeper into this perspective, and where a new opportunity might arise.
There are clear advantages to having leaner teams:
Fewer friction points between colleagues;
Simpler decision-making pathways (assuming that management and leadership layers are also becoming leaner);
Greater ownership of initiatives;
Easier environment to coach and empower team members;
With leaner teams, professionals will become less specialized and more generalists. With AI handling the heavy lifting, there will be less need for pure specialists.
Consequently, leaders will need to become highly intellectual and strategic, connecting the dots across multiple concepts and domains to figure out effective implementation strategies.
For making your own assessment, use this checklist to evaluate or improve your structure:
The Basics: Make sure roadmaps are up to date and reassess progress. Basic capacity planning and goal setting requires a roadmap.
Value Creation: Track initiatives focused on value creation (revenue optimization, upgrading a service or product, etc.).
Risk Management: Track the risk! If teams are getting smaller, is there an increased risk related to turnover? What backups are in place (if any)? Is the quality decreasing, and are the new standards acceptable?
Operational Sustainability: Does the team have standby or on-call operations outside of business hours? How sustainable are the rotations? How many incidents happen after hours? Is this slowly burning out the team’s energy? How stable are the applications and infrastructure? How are teams handling the Unknown?
Strategic Bets: Challenge the structure to have strategic bets using AI as a growth catalyst (not just for cost optimization). What ideas have been discussed lately that is not focused on cost alone? Which existing products or services can we capitalize on with AI? How are those results tracked?
Voice of the Client: Is the company improving its brand and product awareness, as well as its effectiveness with existing clients? How can AI be used to improve client perception of quality or speed? Prepare a plan accordingly.
AI is all about buzz and hype. Leaders need to remain factual and pragmatic.
Deciding to Go “Vibe” or “Old School”
Without sharing too much outside of an NDA, I recently witnessed a platform withstand a massive spike in traffic over a very short period. That website was built in a old-school way: it used some AI assistance, but it was mostly human-designed and implemented.
AI is still not at a stage where we can trust it with highly critical platforms. However, for a day-to-day internal application built around standard CRUD operations, just vibe code it.
AI is a great auxiliary tool, but for highly critical and delicate applications, human agency is mandatory. Especially to assess the quality of the chosen architecture and make changes as Production evolves.
With the current trend of using AI as a cost-cutting tool, development teams have immense pressure to vibe code solely as a means to faster delivery.
For specific projects, leadership may need to find strong arguments to secure and allocate more budget or time to properly design a new system or architecture. AI can facilitate, but it needs Architects or Senior Engineers to oversee, correct, experiment, and set the course of implementation.
AI is the primary tool developers are using to speed up development, but it often comes at the cost of not understanding what is going on under the hood.
It is no longer uncommon to find developers who don’t actually code anymore. They just chat with Claude daily. They are the vibe coders of the modern age.
However, when a system is pushed to its limits (where we need to know exactly what is happening at each step of the workflow and its configuration), vibe coding can be a death sentence.
Don’t get me wrong: AI-assisted development is the way to go. But AI output is, by nature, very average. It requires multiple iterations to achieve a more challenging goal, especially if the user lacks the foundational know-how in the first place.
Critical, high-stress systems will always need architects and senior developers who deeply understand the requirements and how to implement them. To do that, however, they need the budget and the leeway to explore proper design paths.
One of the biggest challenges from leadership in the future will be finding the time and budget to allow for slower, more reliable software engineering.
Again, I am not talking about your everyday CRUD application. I am referring to tactical applications operating in highly specific contexts that require more human intuition than AI generation.
Strangely enough, securing the time and budget for those challenging requirements might become the hardest thing for a Manager to provide.
There is a lot of buzz and hype around AI. As Leaders we need to remain factual and pragmatic.
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Cheers,
Artur