The Dark Side of ‘Try All The Things’

We are constantly encouraged to innovate, to experiment, and to ‘try things out.’ The software development industry has particularly embraced this philosophy. However, what if this continuous quest for novelty is actually harming our development performance and diminishing our software development quality?

It’s essential to ask: Is the obsession with constantly ‘trying things out’ damaging your engineering productivity? Are we so distracted by the latest trends that we’re neglecting essential practices, accumulating technical debt, and ultimately slowing our progress? Innovation is key, but at what cost to long-term project health?

The Allure of Experimentation

The attraction is clear. Experimentation promises breakthroughs, disruptive technologies, and a competitive advantage. We see success stories of companies achieving significant gains through rapid iteration, and we desire similar outcomes. The potential of AI is also appealing, as AI can write code. Despite this, there remains a degree of apprehension surrounding it. Moreover, it can lead to lasting issues if not properly managed.

However, let’s be realistic. Not all experiments yield positive results; many do not. Each experiment, regardless of the result, consumes valuable resources: developer time, infrastructure costs, and mental energy. The cost of failure, or even minimal improvement, can rapidly exceed the potential benefits that it may offer.

A graph illustrating the diminishing returns of constant experimentation, showing a plateau in innovation despite increasing effort.

The Hidden Costs of Constant Change

The problem isn’t with experimentation itself; it’s with the unrestricted, unplanned pursuit of it. Here are some of the hidden costs that can negatively impact engineering efficiency:

Technical Debt Accumulation: Quick prototyping often results in compromises and shortcuts. Code quality suffers, tests are omitted, and documentation is ignored. This technical debt becomes a significant obstacle to productivity, making future development more complex and lengthy. As highlighted in Feature Toggles Without Tech Debt, Strategies for Teams to Avoid Hidden Pitfalls, improperly managed feature toggles can also contribute to this problem.

Context Switching Overload: Frequently switching between different projects, technologies, and codebases divides attention and reduces individual developer efficiency. Every context switch creates a mental burden, slowing down progress and raising the chances of mistakes occurring.

Increased Complexity: Implementing too many new tools, libraries, or frameworks can unnecessarily complicate the development process. The learning curve for each new technology can be steep, and the integration challenges can be significant.

Decreased Morale: When developers feel they are constantly chasing trends without seeing tangible outcomes or contributing to a stable, well-maintained product, their morale can decline. This can lead to reduced engagement, higher turnover, and a fall in overall team performance. Building psychologically safe engineering teams is vital for long-term success, as discussed in 5 Strategies for Building High-Performing, Psychologically Safe Engineering Teams in 2026.

A Data-Driven Approach to Experimentation

So, how can we find the right balance between innovation and efficiency? The answer lies in implementing a more data-driven and strategic method to experimentation.

Define Clear Goals and Metrics

Before starting any experiment, it’s vital to define clear, measurable goals. What problem are you attempting to solve? What specific metrics will be used to evaluate success? Without these parameters, you’re essentially operating blindly, wasting time and resources on experiments that may not align with your overarching business objectives.

Prioritize Experiments Based on Potential Impact

Not all experiments offer the same potential. Some can deliver substantial results, while others are likely to yield only minor improvements. Prioritize experiments based on their potential impact, considering factors like market opportunity, customer needs, and technical feasibility. This ensures your efforts focus on initiatives most likely to produce meaningful outcomes.

A team analyzing data from experiments, highlighting the importance of metrics and informed decision-making.

Implement Robust Tracking and Monitoring

To accurately assess the impact of experiments, you must implement robust tracking and monitoring systems. This involves collecting data on key performance indicators (KPIs), user behavior, and system performance. By carefully analyzing this data, you can identify what’s working, what’s not, and make informed decisions about whether to continue, modify, or stop your experiments. Tools that provide AI-powered code contribution analytics can be invaluable in this process.

Embrace a Culture of Learning and Iteration

Experimentation isn’t just about finding the correct answer; it’s also about learning from errors. Embrace a culture of learning and iteration, where failures are seen as opportunities for growth. Encourage developers to share their findings, both positive and negative, and use this knowledge to improve your experimentation process and enhance overall engineering efficiency.

The Role of AI in Optimizing Development

AI is rapidly changing software development, and it can significantly optimize experimentation. AI-powered tools can automate many tasks associated with experimentation, such as data analysis, code generation, and testing. This allows developers to concentrate on more strategic activities, like defining goals, designing experiments, and interpreting results. As FortyOne shows, AI can be used to manage projects more efficiently.

An AI assistant helping a developer prioritize tasks and identify the most impactful experiments.

Furthermore, AI can help identify patterns and insights that might otherwise go unnoticed, leading to more targeted and effective experiments. For instance, AI can analyze code repositories to identify areas where refactoring or optimization is likely to have the greatest impact on performance or maintainability.

Reclaiming Engineering Efficiency in 2026

The endless pursuit of novelty can be a risky trap. By implementing a more data-driven, strategic, and AI-assisted approach to experimentation, we can recover our engineering efficiency and unlock the true potential of innovation. It’s time to move beyond the obsession with constantly ‘trying things out’ and adopt a more sustainable and effective model for software development.

Remember, the goal isn’t to try everything; it’s to try the right things, in the right way, at the right time. This is how high-performing engineering teams will succeed in 2026 and beyond.