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# Software Development is (not) getting easier

Better tools haven't made software development easier. They’ve merely displaced complexity into your macro-architecture, fueling explosive scopes and driving impossible expectations for today's engineering teams.

- **Speakers:** [Rene Kokott](https://www.wearedevelopers.com/@rene-kokott)
- **Event:** World Congress 2026 Europe - Virtual Stage
- **Published:** July 2, 2026
- **Duration:** 38:19
- **URL:** https://www.wearedevelopers.com/videos/2085-software-development-is-not-getting-easier

## Summary

The core paradox of modern software development is that while tools and resources have vastly improved, the profession has not become inherently easier. Instead, the industry experiences a "rebound effect" where easier, cheaper resources yield exponentially larger scopes and codebases. As demonstrated by the evolution of video game development—scaling from a seven-person team in the 1980s to over 400 specialized contributors today—market competition and feature creep continuously drive organizations to build massive applications. While early hardware limits forced creative hyper-optimization, modern cheap hardware often masks systemic inefficiencies and severely inflates baseline productivity expectations.

To manage this explosive scope, teams adopt structural approaches like microservices and deep framework abstractions (such as the Spring framework) to alleviate mental load. However, these solutions do not eliminate complexity; they merely displace it out of the immediate code and into the macro-architecture. Displaced complexity creates new organizational challenges closely tied to Conway's Law, often resulting in vendor lock-in and ambiguous development practices. Furthermore, practically every "solution" introduces new problems—just as writing asynchronous code to accelerate performance immediately brings the burden of mitigating race conditions.

Ultimately, developers are pressured to fit increasingly unrealistic molds—typified by the elusive "DevDataSecOps" expectation—which forces difficult decisions between deep specialization and broad generalist adaptability. The presentation challenges engineers to think critically about hyped "solutions without a problem," such as blockchain, and to recognize that while baseline knowledge will always shift, real-world constraints of time and budget naturally cap how unmanageable a codebase can actually become.

**Keywords:** software complexity rebound effect, developer productivity paradox, market competition feature creep, microservices complexity displacement, conways law team structure, devdatasecops unrealistic expectations, software specialist vs generalist, asynchronous code race conditions, vendor lock-in challenges, framework abstraction layers, legacy software optimization constraints, reverse conways law, software engineering team scaling, software architecture tradeoffs

## Chapters

1. **Measuring software team growth through video games** (00:01) — How comparing credits from early and modern games reveals the massive exponential increase in team sizes.
1. **The rebound effect in software engineering teams** (05:54) — Why making development faster and cheaper paradoxically leads to larger teams and more complex projects.
1. **How market competition drives application feature bloat** (07:26) — How businesses avoid market cannibalization and free alternative products by continuously cramming more features into single broad applications.
1. **Pioneer phases versus mature gatekept technology markets** (11:02) — The cyclical transition from open experimental hobbyist spaces to strict corporate standards requiring formalized education.
1. **The impact of inflated productivity expectations over time** (15:54) — How continuous incremental improvements eventually establish unsustainable baseline expectations for an individual developer's daily output.
1. **Creative optimization around strict hardware system limits** (19:22) — How historical constraints forced innovative problem-solving mentalities that modern endless computing power often bypasses entirely.
1. **How new technological solutions generate entirely new problems** (21:23) — Why seemingly perfect answers like asynchronous parallel code or decentralized blockchain often lead to distinct new architectural blind spots.
1. **Moving architectural complexity instead of genuinely reducing it** (23:50) — How organizational patterns like microservices merely shift mental loads and necessitate highly specialized internal team hierarchies.
1. **Outsourcing developer mental load to external software frameworks** (31:12) — The trade-offs of heavily relying on third-party abstractions including unavoidable vendor lock-in and navigating deep technology layers.
1. **The real-world constraints capping continuous software complexity** (36:11) — How practical project constraints of budget margins, release cycles, and user comprehension naturally restrict the infinite scaling of technical difficulty.

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