The development tools market exists in a state of continuous release: every week brings new frameworks and AI services promising to accelerate team workflows manifold. For a startup in the hypothesis-testing stage, such drive often works to its advantage, as iteration speed outweighs code longevity. However, the logic of a B2B product is fundamentally different: the client integrates the platform into their own business processes for years to come and expects the system to function with the exact same stability after every update. This is why, as SharksCode CEO Anton Aseyev notes in his column for RBC-Ukraine, choosing a tech stack for an enterprise product should be viewed as a long-term contract with the client, where the cost of a mistake is measured in years rather than sprints.
AI Tools: From Euphoria to Sober Rules
A clear example of accelerated market dynamics is the AI development tools segment, which, according to Aseyev's estimates, has grown faster than almost any other sector of the industry over the past two years. Today, almost every developer has integrated an AI assistant into their daily routine. At the same time, according to the Stack Overflow Developer Survey 2025, nearly half of developers do not trust the accuracy of results generated by AI tools, whereas a year earlier the share of skeptics was noticeably lower. The author of the column interprets this trend as a natural stage of technology adoption: the first few months ride a wave of euphoria over speed, then bugs and limitations emerge in production that were never mentioned in presentations, and teams develop sober working rules. For a B2B product, this very 'post-euphoric' period becomes a comfortable entry point, as the technology's vulnerabilities are already documented and workarounds have been practiced on other projects.
Ecosystem and Talent Pool: The First Two Filters
The first technology selection criterion named by Aseyev is the state of the ecosystem. A live project with regular releases and quality documentation saves a team weeks on onboarding and debugging; ready-made libraries for typical tasks allow everyone to focus on business logic rather than 'reinventing the wheel'. The second filter is the labor market. An exotic stack is easy to spin up with a couple of enthusiasts, but scaling a team to thirty engineers on such a foundation becomes difficult and expensive: vacancies hang open for months, and rare specialists demand conditions that make the product development budget 'melt before your eyes'.
Corporate Security and Integrations
A separate layer of risks involves corporate clients, whose security departments conduct deep audits of every product component even before signing an agreement. A mature technology enters such an audit with a transparent vulnerability history and a clear version support policy; a young tool still has to build that track record, and the deal risks getting stuck at the security review stage. A similar issue arises at the integration level: a B2B product constantly exchanges data with the customer's corporate systems, and each non-standard connector adds about a month to implementation timelines.
TCO and 'Sandboxes' for New Technologies
The final filter, according to Aseyev, is the actual cost of the technology across the product's entire lifecycle. Licenses and cloud resources cover only the starting part of the budget; the main expenses accumulate during the maintenance and regular updates phase. Therefore, TCO should be calculated over a horizon of at least three to five years, and such calculations frequently show that a proven solution is cheaper than a trendy one, even taking all promised accelerations into account. At the same time, the author emphasizes that new technologies must be tested constantly — otherwise, the product becomes bogged down with legacy code that slows development further with every release. The optimal format is isolated sandbox modules whose replacement does not affect the system core: the team gains real hands-on experience with the tool without risking client operations, and a technology that has proven itself in production and gathered a community can gradually migrate into the main architecture. AI tools follow the same path: they noticeably relieve the team from routine tasks, but all generated code goes through rigorous review — an approach confirmed by Stack Overflow data.
The author's ultimate conclusion is this: a B2B client pays for stability over a multi-year horizon, so the stack for such a product is assembled to withstand years of growth without massive code rewrites. New tools can give a product a serious head start, but they should only enter the core architecture after going through a full verification cycle on other projects and establishing a mature community.
