Diasoft explains why junior developers, AI magic, and in-house platforms don't save dev projects

Diasoft and other IT companies have analyzed three persistent myths that cause major clients to lose money and time in development. These are the beliefs that a complex project can be accelerated by mass hiring, that an old system can be almost entirely rewritten using AI, and that a company can solve efficiency problems by launching its own platform. The authors argue that all three ideas seem logical only on paper: in real enterprise projects, they usually add chaos, increase technical debt, and raise the cost of changes.

The first myth is "let's add people and go faster." Discussion participants remind that this approach contradicts Brooks's Law: the more people thrown at a delayed complex project, the higher the communication overhead and the harder synchronization becomes. In practice, this manifests in familiar symptoms: teams build incompatible system components, integration errors surface, UX diverges across products, and scalability and security requirements stop being met uniformly.

This is especially acute where hundreds of teams work simultaneously. According to sources, juniors and cheap developers don't compensate for the lack of architectural thinking. Instead, after six to twelve months, an organization often finds itself with accumulated technical debt and the need to redo a significant portion of the solution.

The second myth relates to generative AI. Here experts take a more nuanced position: they don't dispute that tools like Cursor, internal models, and agent scenarios already genuinely accelerate time-to-market, reduce manual routine, and help close bugs faster. Some teams even track token spending as a separate efficiency metric.

But this doesn't mean an AI can "overnight" rewrite a legacy system that was developed over ten or fifteen years. When dealing with hundreds of thousands or millions of lines of code, what matters isn't only generation, but also validation, architectural constraints, information security requirements, and unified standards across the entire pipeline. Therefore, AI functions as a useful layer within the pipeline, not as an autonomous replacement for engineering.

For mass migration and transpilation, deterministic tools, parsing, AST transformations, and people who understand business context are still needed to verify what the model generated. The third myth is "let's build our own platform and stop depending on the market." The discussion notes that this approach sometimes does work, but only for companies with very long investment horizons, enormous budgets, and business-critical dependence on their own digital platform.

A large bank's case is cited as a reference point: it spent years and tens of billions of rubles on an internal platform, restarting the project several times. For most companies, this strategy means not acceleration but years of retreat into infrastructure building instead of solving applied tasks. At the same time, there's a counterargument: if you gather a small group of strong senior specialists, give them authority and modern AI tools, you can make significant progress faster than before.

But even supporters of this idea don't propose a false dichotomy. Rather, it's about a hybrid model where the product core remains in-house, while external teams or platform vendors cover specific services and project peaks. This leads to a more pragmatic view of the custom development market.

Clients increasingly buy not just programmer hours but expect the contractor to take on the entire result: quickly assemble working architecture, build in quality and security checks, properly apply AI, and reduce team size without sacrificing quality. According to discussion participants, with high automation and a well-built process, a team of four or five people can deliver the volume that previously required twelve to fifteen. But this savings doesn't materialize from thin air or "prompt magic."

It's created by standards, pipeline, static analysis, automated tests, mature leads, and clear separation of what can be handed to the machine and what remains the human responsibility zone. What does this mean for the market: the era of simple answers in enterprise development is ending. Mass hiring without strong architecture no longer looks like a working accelerator, AI without guardrails doesn't become an autonomous code factory, and a proprietary platform without business scale easily becomes an expensive distraction.

Winners will be teams that can combine senior expertise, platform approach, and delivery automation. AI in this scheme isn't a magic wand but an amplifier of an already mature engineering system.