Most firms get 10% more from AI. Our methodology delivers 2-3x. Here's the system that makes the difference.
AI adoption is nearly universal. But the productivity gains? Minimal for most organizations.
slower initially (METR RCT study found AI made experienced devs slower)
increase in AI usage across 400 companies studied
actual PR throughput increase (DX study across 400 companies)
trust in AI accuracy (all-time low in 2026)
AI generates code faster than humans can review it. Speed in development gets absorbed by bottleneck in approval.
AI-generated code introduces more vulnerabilities without proper governance frameworks and senior review.
Speed gains in coding get absorbed by downstream slowdowns. AI doesn't fix broken processes (it accelerates them).
"AI doesn't fix broken processes. It accelerates them."
We solved the three bottlenecks. Our methodology was designed around AI from day one (not bolted on).
We designed our entire development process around AI from scratch. Every tool, every process, every review point optimizes for AI-human collaboration.
AI handles the typing. Senior engineers handle the thinking. Every AI output is reviewed by experienced professionals who catch issues early.
Follow-the-sun isn't just timezone arbitrage. AI fills the gaps, senior engineers review across timezones, and work never stops.
We don't claim 5x across the board. The multiplier depends on task type. Here's the breakdown.
Project setup, file structure, repetitive patterns, boilerplate code generation
Unit tests, integration tests, API documentation, code comments
System design, algorithm development, novel problem-solving, critical path decisions
Most firms today
Across full project lifecycle
That's up to 2.7x more output from the same team size.
Enter your project parameters and see what 2-3x productivity actually means in hours, time, and cost for your specific situation.
Ship your product months earlier with the same team.
Get more output from fewer billable hours.
One ASP engineer delivers the output of 2-3 traditional engineers.
Typical enterprise project: 2,000 - 20,000 hours
Industry average: $100-$200/hour
Every multiplier in our calculator is grounded in peer-reviewed research and industry data.
DX analyzed data from a random sample of 400 companies between November 2024 through February 2026. AI usage increased by an average of 65%, but PR throughput only increased by 9.97%. A ~10% gain is consistent with what engineering leaders report: most organizations are landing in the 8-12% range.
Source: DX newsletter
16 experienced developers with moderate AI experience completed 246 tasks in mature projects. Surprisingly, AI actually increased completion time by 19%.
Source: METR / arXiv 2507.09089
For the subset of developers who participated in the later study, estimated speedup improved to -18% (CI: -38% to +9%). However, 30-50% of developers refused to submit tasks without AI, creating significant selection bias.
Source: METR blog, Feb 2026
Referenced in MIT Technology Review: A September report described real-world savings as "unremarkable." Data from GitClear shows that most engineers are producing roughly 10% more durable code since 2022.
Source: MIT Technology Review
The 2025 DORA report finds that AI does not automatically improve software delivery performance. Instead, it acts as a multiplier of existing engineering conditions, strengthening high-performing teams while exposing weaknesses in organizations with fragile systems.
Source: Google DORA / InfoQ
Developers with access to the AI pair programmer completed the task 55.8% faster than the control group. The task was implementing an HTTP server in JavaScript — essentially scaffolding work.
Source: arXiv 2302.06590
Preliminary results show developers completed 12.92% to 21.83% more pull requests per week at Microsoft and 7.51% to 8.69% at Accenture.
Source: MIT GenAI pubpub
Study of 500 enterprise development teams found that AI-assisted code generation reduced time spent on routine coding tasks by 42%. Developers report spending 60% less time on boilerplate code, database schemas, and API endpoint creation.
Source: DreamzTech/Forrester
McKinsey assigned developers tasks including refactoring code into microservices, building new application functionality, and documenting code capabilities. AI tools enabled developers to complete coding tasks up to twice as fast, with documentation being one of the strongest areas of gain.
Source: McKinsey Digital
Controlled experiments consistently show significant speed improvements (often 30-55%) for scoped programming tasks such as writing functions, generating tests, or producing boilerplate.
Source: Getpanto.ai
The preregistered experiment involved 758 knowledge workers at Boston Consulting Group. The researchers introduce the concept of a "jagged technology frontier" where AI assistance improves performance for some tasks but worsens it for others, even within the same knowledge workflow.
Source: HBS Working Paper 24-013
Time savings shrank to less than 10 percent on tasks that developers deemed high in complexity due to their lack of familiarity with a necessary programming framework.
Source: McKinsey Digital
Developers on teams with high AI adoption complete 21% more tasks and merge 98% more pull requests, but PR review time increases 91%, revealing a critical bottleneck: human approval. This validates the quality-gate approach (solving the review bottleneck that kills gains for everyone else.
Source: Faros AI
Researchers found that employees worked at a faster pace, took on a broader scope of tasks, and extended work into more hours of the day, often without being asked to do so. Without structure and oversight, AI-driven speed leads to burnout and quality collapse.
Source: Harvard Business Review / UC Berkeley Haas
The cost savings promised by AI-generated code began eroding as teams spent more time debugging and recovering from AI-introduced errors. Organizations started asking not "how much code can AI produce?" but "what is the true cost of code that hasn't been properly validated?"
Source: CodeRabbit
Across 600-plus organizations tracked, more than 60% see at least a 25% productivity improvement from AI. But companies with 80 to 100% developer adoption saw gains of more than 110%. Deep adoption, not surface adoption, is what matters.
Source: McKinsey/Jellyfish
Most organizations get 10%. Our clients get 2-3x. The difference isn't the AI tools (it's the system we built around them).