AI Coding Tools Fail the Test: Slower Developers, Higher Costs, and Risks for SaaS Startups in Global Tech Hubs

Introduction: The AI Productivity Paradox Every SaaS CEO Must Understand

If you are a pre-seed or seed-stage SaaS CEO, you have almost certainly been pitched the idea that artificial intelligence tools will supercharge your team’s productivity. Investors, advisors, and even your own developers may believe that AI coding assistants like Cursor Pro, Claude, or ChatGPT will make engineers two or three times faster. But what if the opposite is true—what if AI slows your team down?

A groundbreaking study released in mid-2025 tested this assumption in a real-world environment, and the results were eye-opening. Instead of boosting productivity, early-2025 AI tools actually reduced the speed of experienced open-source developers by nearly 20 percent. This finding is especially relevant for CEOs in startup hubs like San Francisco, New York, London, Berlin, and Paris, where AI-driven pitches and products dominate the conversation.

This blog post will break down what the study found, how the methodology was designed, why the results matter for early-stage SaaS CEOs, and what practical steps you should take when deciding how to integrate AI into your product strategy or operations.

Why This Study Matters for Early-Stage CEOs

For a startup founder, speed is everything. The faster you can validate an idea, build an MVP, and iterate toward product-market fit, the better your odds of survival. Venture capitalists in Silicon Valley or London want to see velocity—how quickly you can turn capital into learning and traction.

That is why the promise of AI productivity tools is so seductive. If AI can make every developer on your team twice as fast, it means you could accomplish in six months what used to take a year. But this new research suggests that the productivity boost may not materialize, at least not in the contexts most relevant to experienced teams working on complex products.

The implication is clear: early-stage CEOs cannot simply assume that AI adoption will accelerate engineering. Instead, you must take a nuanced approach, recognizing both the potential and the limitations of AI tools.

Study Overview: What the Researchers Tested

The study, conducted by researchers at METR (Model Evaluation & Threat Research), set out to answer a simple but critical question: how much do state-of-the-art AI tools actually speed up experienced developers in real-world conditions?

The experiment recruited 16 senior open-source developers, each with over a decade of professional experience and an average of five years contributing to large, complex repositories. These were not junior coders or greenfield projects—the participants were highly skilled engineers working on mature codebases with millions of lines of code and tens of thousands of GitHub stars

Over the course of the study, developers completed 246 real tasks, such as fixing bugs and adding features. Each task took about two hours, and tasks were randomly assigned to either allow or forbid AI usage. When AI was allowed, developers could use cutting-edge tools like Cursor Pro or Claude 3.5/3.7 Sonnet. When AI was forbidden, they worked without generative AI, though they could still use standard search engines.

Before starting, developers and outside experts forecasted how much faster AI would make things. Developers predicted a 24 percent speedup. Machine learning and economics experts predicted a 38–39 percent reduction in completion times. After finishing tasks, developers continued to believe AI had saved them time. The reality, however, was the opposite: developers were on average 19 percent slower with AI than without

Methodology: How the Study Was Designed

The strength of this study lies in its careful methodology, which addressed weaknesses in earlier research. Here’s how it worked:

Recruiting Experienced Developers

Participants were drawn from well-known open-source projects. On average, they had contributed 1,500 commits to their repositories. This ensured that the test involved highly skilled developers with deep familiarity with the codebases in question. Unlike prior lab experiments, this was not about toy problems—it was about the real work developers do every day.

Random Assignment of Tasks

Each task was randomly assigned to an AI-allowed or AI-disallowed condition. This randomization removed bias and ensured that differences in outcomes could be attributed to AI usage rather than task selection.

Real Issues, Not Synthetic Problems

The tasks were real issues from live repositories: bug fixes, feature requests, and other contributions. These tasks required context, judgment, and collaboration, unlike synthetic lab tasks that can be solved by writing a few lines of code to satisfy test cases.

Forecasts and Reality Checks

Before beginning, developers forecasted how long each task would take with and without AI. This gave researchers a baseline for expected performance. Experts were also asked to forecast, providing a comparison between perception and reality. After tasks were completed, developers self-reported their estimates of AI’s usefulness. This allowed the study to measure the gap between belief and outcome.

Data Collection

The researchers collected data through self-reported time logs, screen recordings, and analytics from the AI tools themselves. They coded 143 hours of video into detailed activity categories, such as active coding, debugging, prompting AI, or reviewing AI outputs. Post-study interviews added qualitative insights.

Statistical Analysis

The final results were derived using regression analysis, which controlled for task difficulty and other confounding variables. This ensured that the reported 19 percent slowdown reflected a robust effect, not an artifact of uneven task distribution.

Key Findings: Where AI Slowed Developers Down

The most striking result was the slowdown itself: developers using AI tools took longer to complete tasks. But why? The study uncovered several contributing factors:

Developers spent less time coding directly and more time prompting, waiting for, and reviewing AI outputs. In practice, this meant shifting effort from creating solutions to supervising an unreliable assistant.

The acceptance rate of AI-generated code was low. Fewer than 44 percent of AI suggestions were accepted, and when they were, developers almost always had to edit them heavily.

Developers reported that AI lacked the tacit knowledge they had built up over years of working in the repositories. AI often acted like a new contributor unfamiliar with the project’s context, leading to errors or irrelevant suggestions.

Large and complex codebases limited AI’s effectiveness. The bigger the project, the more likely it was that AI would propose changes that did not fit established architecture or coding standards.

Ironically, developers continued to believe AI had saved them time, even after being slowed down. This overconfidence may encourage overuse of AI despite negative effects on productivity

Implications for Early-Stage SaaS CEOs

If you are leading a pre-seed or seed-stage SaaS company, here are the main lessons you should draw from this research:

Do not oversell AI productivity gains to investors. VCs in San Francisco, New York, London, Berlin, and Paris are inundated with AI pitches. Ground your claims in evidence, and emphasize where AI truly helps.

Recognize the contexts where AI adds value. The study shows AI is less effective for senior developers in complex codebases. But AI may still help in greenfield projects, documentation, test generation, or onboarding junior engineers.

Manage your team’s expectations. Developers may believe AI is helping even when it is not. Encourage evidence-based evaluation of AI’s impact on workflow, rather than relying on gut feeling.

Invest in reliability, not hype. AI that produces high-quality, context-aware output will eventually improve productivity. But today’s tools are still limited. Consider whether investing in prompts, scaffolding, or domain-specific finetuning is worth your scarce startup resources.

Frame AI as augmentation, not replacement. Investors and customers will trust you more if you present AI as a tool that helps in specific ways, rather than a silver bullet.

Geographic Context: Why This Matters in Startup Hubs

In San Francisco and New York, early-stage SaaS CEOs face fierce competition for capital. Many founders pitch AI as the differentiator. Having a nuanced, evidence-backed perspective on AI productivity can set you apart when speaking with investors at firms like Andreessen Horowitz or Sequoia.

In London, Berlin, and Paris, where European VCs are both eager and cautious about AI, showing that you understand its limitations will help you build credibility. European regulators are increasingly focused on AI claims, and startups that make realistic promises will earn investor and customer trust.

By tailoring your AI story to local expectations—emphasizing innovation in the U.S. and reliability in Europe—you can maximize your credibility across geographies.

Action Plan for SaaS CEOs

As you think about integrating AI into your product and operations, here is a practical roadmap based on the study’s insights:

Start by mapping where AI could deliver genuine value in your workflows. Look for repetitive, boilerplate-heavy tasks or areas where your team lacks deep expertise.

Run your own internal experiments. Treat AI adoption like a product test: define success metrics, run controlled trials, and measure outcomes.

Educate your investors. Use this study as evidence to show that you are not chasing hype but making disciplined decisions.

Position AI in your pitch as part of a balanced strategy. Emphasize that you are building a sustainable product that will benefit from AI where it works but will not collapse if AI productivity gains fail to materialize.

Keep your strategy flexible. AI tools are evolving rapidly, and what slows you down today may speed you up in a year. Monitor developments and be ready to adapt.

Conclusion: A Realistic Path Forward

For early-stage SaaS CEOs, the message is not that AI is useless—it is that AI is not a magic shortcut to faster development. The study shows that even the best tools in early 2025 can slow down experienced developers working on complex codebases. The real winners will be founders who understand these nuances and incorporate them into their strategy.

If you are building in San Francisco, New York, London, Berlin, or Paris, credibility is your currency. By being realistic about AI, you will stand out in a crowded market full of overpromises. As an early-stage CEO, your job is not to chase every hype cycle but to chart a sustainable path toward product-market fit. This study provides the data you need to make smarter, faster, and more credible decisions about how AI fits into your company’s future.