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AI coding tools have made it faster to produce working code, and several developer surveys report that engineers notice the difference. Choosing what software is worth building is a different kind of problem, and it has not been solved by the same tools. The evidence below supports the first claim with qualifications. It does not directly measure the second. This article separates the two problems, shows exactly what the cited studies do and do not establish, and offers a practical way to test a project idea before anyone writes code for it.
What “solved” does and does not mean
Calling the “how to code” problem solved overstates things. Writing software still requires understanding a system, reading unfamiliar code, handling edge cases, testing, securing, deploying and maintaining what gets shipped. What has changed is that generative AI tools can now assist with several of these steps: drafting functions, explaining existing code, suggesting tests and filling in boilerplate. GitHub’s 2024 survey article defines AI coding tools as developer tools that use generative AI and large language models to provide engineering assistance throughout the software development cycle, which is a broader scope than autocomplete. The useful reading is that implementation is now assisted at many points, not that it is automatic.
Implementation speed and problem selection are different decisions
Implementation asks whether a defined behavior can be built correctly and at reasonable cost. Problem selection asks whether the behavior is worth building at all, for whom, and whether building it creates more value than it creates ongoing obligations. The two questions have different inputs:
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- Problem selection depends on who has the problem, how often it occurs, what they currently do instead, whether an existing product already solves it, and whether anyone will maintain the result after launch. Errors show up months later as abandoned tools, low adoption or a backlog nobody wants to own.
Faster implementation shortens the cost of testing an idea, which is useful. It does not tell you which idea deserves the test. A team that can generate a prototype in a day may simply reach a poorly chosen product sooner.
What the cited evidence actually measures
The sources below are the main public evidence behind this argument. Each row lists what the source covers and the limit that matters most for interpreting it. Where a source summary did not state the population, the table says so.
| Source | Publisher and date | Population or sample | What it covers | Limit to keep in mind |
|---|---|---|---|---|
| AI coding tools survey article | GitHub, 2024 (article updated April 15, 2025) | Not stated in the summary | Definition of AI coding tools; summary of earlier GitHub research, which reported up to a 55% productivity increase among developers who use GitHub Copilot | The 55% figure is GitHub’s summary of its own earlier work, not a general rate for all developers or AI tools |
| State of AI-assisted Software Development | DORA, 2025 | More than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals | How AI assistance affects software development practice and outcomes | Mixed qualitative and survey method; its “amplifier” framing is DORA’s own characterization |
| Towards Effective AI Support for Developers: A Survey of Desires and Concerns | Microsoft Research, 2024 | 791 Microsoft developers | What developers want from AI support and what worries them | One company’s developer population; prevalence across other organizations is not established |
| 22 AI systems developers want across five task categories | Microsoft Research, 2026 | Not stated in the summary | Desired AI system behaviors across five task categories | Known only from the publication summary, which highlights quality, authority, provenance, uncertainty and access examples |
| Developer survey on collaboration and effort | GitHub, 2023 | Not stated in the summary | 81% expected AI coding tools to increase team collaboration; 87% said Copilot helped preserve mental effort on repetitive tasks | Self-reported expectations and experience, not measured productivity or project-selection outcomes |
| Qualitative article on developer AI use | GitHub, 2024 | 25 developers interviewed | Developer views on AI parsing and synthesizing information, and on seeing source material and adding context | Small interview sample; illustrative, not a population statistic |
DORA’s “amplifier” point
The most useful line for this question comes from DORA’s 2025 report on AI-assisted software development: “The research reveals a critical truth: AI’s primary role in software development is that of an amplifier.” The sentence is a characterization in an official report, not a quote from an individual.
An amplifier makes whatever signal it receives louder. Applied to project choice, the implication is direct: if a team has a clear user problem, faster implementation helps it reach a solution sooner. If the problem is vague, unvalidated or aimed at a workflow nobody uses, the same speed produces more of that uncertainty, faster. DORA’s report does not discuss how teams choose products, so this extension is an interpretation of its framing, not a finding it reports.
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Developers want more than code generation
Several studies suggest that developers’ concerns about AI tools are not limited to whether generated code compiles. They also care about trust, traceability and control.
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What developers said they wanted
Microsoft’s 2024 survey of 791 Microsoft developers, Towards Effective AI Support for Developers: A Survey of Desires and Concerns, examined what developers hoped AI support would do and what worried them. Because it sampled one company’s developers, it shows the range of needs within that population, not how common each need is across the industry.
The quality, authority and provenance questions
Microsoft Research’s 2026 publication on 22 AI systems that developers want, across five task categories, highlights examples such as early quality signals, explicit authority scoping, provenance, uncertainty signaling and least-privilege access. Read as a design agenda, these items ask an AI system to say how reliable its output is, what it is allowed to change, where its information came from, how confident it is, and what access it actually needs. None of these is a coding problem in the narrow sense.
GitHub’s 2024 qualitative article points the same way in a smaller sample of 25 developers. Those interviewed described AI assistance as potentially useful for parsing and synthesizing information and surfacing highlights, but they wanted to see the source material and add their own context. The interviews are illustrative of how developers reason about trust, not a measure of how many developers feel that way.
Why this matters for choosing a project
A product built around an AI feature inherits these questions. If users cannot tell where an answer came from, or whether it is safe to act on, the feature may be technically impressive and still unused. Implementation tools can speed the building. They cannot decide whether a user will trust the result.
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A framework for judging candidate projects
The following is an editorial decision framework. It is not a research-backed ranking, and none of the cited studies tested it. Use it to organize your own evidence before committing time to a build.
1. Evidence of user need
- Can you name specific people who have the problem, and describe how they handle it today?
- Have they paid for a workaround in money, time or tooling?
- Did the problem come up unprompted in conversations, support tickets or forums, rather than only when you asked about it?
2. Feasibility
- Does the core task depend on data you can legally and reliably access?
- Can you describe a version small enough to test within a week?
- Which part is the hard part: the data, the model behavior, the integration, or the user’s willingness to change habits?
3. Maintenance burden
- Who will fix it when a dependency, API or model changes?
- What ongoing costs come with it, such as hosting, monitoring, support requests and content updates?
- If you stopped working on it in six months, would users lose something they depend on?
4. Risk
- What happens when the output is wrong, and who notices?
- Does the product handle personal or sensitive data, and does it need more access than the job requires?
- Would the failure be an inconvenience or a harm to someone outside your team?
An illustrative case
Suppose someone is weighing a tool that summarizes weekly project updates from a team’s chat messages. Using the framework, the first questions would be whether team leads already spend hours writing these summaries, and whether they would trust a summary without reading the source messages. The feasibility answer might look fine, but the risk section would flag the need to show sources and to handle private messages carefully. A build that cannot answer those questions has not earned its first sprint, however quickly the code could be written.
What would show a proposed solution deserves to exist
A framework can organize judgment, but it does not replace evidence from actual users. The strongest signals are behavioral: people using a rough version when nobody is asking them to, returning to it, or replacing an existing workaround with it. Surveys of stated desires, like those cited above, are useful for generating hypotheses, but they are weaker than watching someone choose your tool over what they already use.
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So the test is not whether you can build the idea quickly. It is whether, after you build a small version, the people who have the problem keep using it, and whether removing it would leave them worse off. If that evidence does not exist yet, the faster coding tools have only made it cheaper to find out. What, specifically, would convince you that your next project should exist?
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