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What is changing in software development?
Three signals stand out: AI is becoming a routine part of developers’ work, coding agents are expanding beyond code completion into more of the development workflow, and internal developer platforms are becoming important infrastructure for delivering software reliably. Each signal has a different kind of evidence behind it: DORA reports survey findings and relationships, GitHub reports activity on its own platform, and Gartner’s statement about how teams may work in 2027 is a forecast.
| Shift | What the evidence says | What it does not establish |
|---|---|---|
| AI-assisted development | DORA’s 2025 report found widespread workplace AI use among respondents and reported relationships between adoption and delivery outcomes. | It does not show that AI alone caused those outcomes or that every team will benefit. |
| Coding agents | Gartner describes agents expanding across planning, code creation, and review. | Gartner’s prediction about IDEs becoming optional by 2027 is not a current adoption rate. |
| Developer platforms | DORA reports broad organizational platform adoption and links high-quality internal platforms with the ability to unlock AI value. | Platform adoption by itself does not establish that a platform is high quality or that it improves every organization’s results. |
The distinction matters: adoption counts and platform activity describe what people or organizations report doing; they are not proof that a tool caused better software or that an industry-wide transition is complete.
How is AI changing software development?
DORA’s 2025 report drew on more than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals around the world. In that study, 90% of respondents reported using AI at work, more than 80% believed it increased their productivity, and 30% reported little or no trust in AI-generated code. These are findings from the study’s respondents, not measurements of every developer or organization.
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DORA reported a positive relationship between AI adoption and software delivery throughput and product performance, alongside a negative relationship with delivery stability. Those findings are associations, not evidence that AI alone produced the results. They suggest that producing changes faster is only part of the job: teams also need to know whether those changes are dependable and useful to users.
Why the surrounding system matters
DORA characterizes AI as an amplifier of organizational strengths and weaknesses. Its findings point to automated testing, mature version control, fast feedback loops, loosely coupled architectures, high-quality internal platforms, clear workflows, and user-centricity as relevant conditions for better outcomes. These are practices DORA identifies in its report, not a universal formula that guarantees success.
For a team introducing AI, that means evaluating the whole workflow rather than counting generated code or measuring only how quickly a task is completed. If validation is weak or feedback arrives late, faster code creation may increase the amount of work that reaches review without making releases more dependable. Generated code still needs review and validation.
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Will AI replace software developers?
The evidence here does not establish that AI will replace software developers. DORA’s 2025 figures describe adoption, reported productivity perceptions, trust, and relationships with delivery outcomes; they are not a labor-market forecast. Gartner’s 2027 statement concerns the possible role of IDEs in teams using agentic coding, not the elimination of developers.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsA more grounded way to think about the change is that AI can alter how software work is carried out while leaving teams responsible for defining the problem, judging proposed changes, validating behavior, and delivering a useful product. The practical question for developers is therefore not whether to compete with a tool, but how to use it while retaining the understanding and judgment needed to make software reliable.
What coding agents may change—and what Gartner is predicting
Gartner’s May 2026 press release describes enterprise coding agents as extending across planning, code creation, and review. That is broader workflow coverage than simply suggesting a line of code. Gartner forecast that over 65% of engineering teams using agentic coding would treat IDEs as optional by 2027. This is a prediction about a specified group of teams and a future date, not a measured share of developers today.
The forecast does not mean that code editors or integrated development environments will disappear. It suggests that, for some teams adopting agents, an IDE may no longer be the required center of every development task. Whether that shift works in practice depends on the team’s workflows, validation controls, governance needs, and the agent’s fit with its environment.
Gartner senior director analyst Philip Walsh said on May 20, 2026: “What began as a race to deliver the most ’magical’ developer experience is now evolving into a contest of operational excellence, commercial maturity, and enterprise readiness.” The emphasis is useful for organizations evaluating agents: product capability alone is not the whole decision. Gartner also names governance, pricing, support, workflows, commercial maturity, and market durability as considerations.
Why internal developer platforms matter
DORA reported that 90% of organizations represented in its 2025 report had adopted at least one platform. That figure describes organizations represented in the report; it should not be read as a universal adoption rate. DORA also associates high-quality internal platforms with organizations’ ability to unlock AI value.
A platform is most useful when it helps teams follow clear, repeatable workflows and get the support they need without making delivery harder. For AI adoption, platform quality belongs in the same conversation as model or agent capability: an organization needs a workable way to integrate tools, validate changes, and support the people using them. Simply having a platform is not the same as having a high-quality one.
What language trends say about the future
GitHub’s Octoverse reported that TypeScript overtook Python and JavaScript on GitHub in August 2025, becoming the platform’s most-used language. The same report describes Python as remaining prominent in AI and data science. These observations show different kinds of activity on GitHub; they do not establish a general ranking of languages across the whole software industry or guarantee career prospects.
The scope is significant. GitHub reported more than 180 million developers on its platform and an average of 43.2 million pull requests merged monthly in 2025. Those figures describe GitHub’s platform, not all developers or software work worldwide. The report also counted more than 1.1 million public repositories using an LLM SDK. GitHub explicitly described its productivity measures as observational and said more work is needed to understand AI’s full impact. Platform activity can reveal a direction worth watching, but it cannot by itself show that AI caused productivity gains.
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For a developer choosing what to learn, the useful takeaway is to match a language to the kind of work they want to do and to treat popularity claims with their date and scope attached. GitHub’s data supports describing TypeScript’s rise on GitHub and Python’s continued prominence in AI and data science—not declaring a universal winner.
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Teams can respond to these shifts by improving the conditions that make software delivery dependable, then assessing tools against actual workflow needs. A measured approach keeps adoption tied to outcomes rather than novelty.
- Start with a user problem. Identify the work that needs improvement and define what a useful product outcome looks like before choosing an AI tool or agent.
- Assess the whole workflow. Consider whether a tool supports planning, implementation, and review as needed, and how it fits the team’s existing processes.
- Strengthen validation. Make automated testing, mature version control, and fast feedback loops part of the adoption plan so generated changes can be checked.
- Check platform and team readiness. Review whether internal platforms, architecture, and workflows help developers use tools without obscuring responsibility or slowing delivery.
- Measure more than volume. Track delivery throughput alongside stability and product outcomes. A rise in completed work alone does not establish that software is more reliable or valuable.
- Evaluate enterprise fit. For agents in particular, consider governance, validation controls, workflow fit, pricing clarity, support, deployment and regulatory requirements, and the provider’s commercial durability.
- Reassess as evidence changes. Treat forecasts as scenarios to plan for, not deadlines that every organization must meet. Compare a tool’s results with the team’s needs and constraints.
What the future is most likely to depend on
AI-assisted coding, broader coding agents, and developer platforms are already visible parts of modern software development, but their presence does not make one future certain. DORA’s findings place organizational conditions alongside tool adoption; GitHub’s figures show activity on GitHub rather than across the entire industry; and Gartner’s IDE prediction remains a forecast. The enduring decision for teams is how to combine new capabilities with sound engineering practices and judge progress by dependable software and useful outcomes—not by output volume alone.
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