Contents
- You have a software product you need built.
- Andreessen Horowitz's analysis of AI-first companies has identified a structural shift in team composition: companies building with AI-native workflows are operating with engineering teams 3-5x smaller than their predecessors at equivalent scale.
- The mechanism is not "practice makes perfect." It is accumulated infrastructure.
- If you are currently dependent on contractors or a dev shop for software execution, the question is not whether AI can eventually replace that dependency.
The Setup
You have a software product you need built. You are paying contractors or a dev shop to build it. The work is getting done, but you are dependent — every feature request, every bug fix, every deployment runs through someone else's calendar and invoice cycle. You own the business logic, but you do not control the execution.
The standard advice is to either (a) keep paying for external development indefinitely, (b) hire a full-time engineering team, or (c) learn to code from scratch. Option A creates permanent cost dependency. Option B requires $150,000-$250,000 per engineer annually, plus management overhead, plus the 3-6 month ramp before anyone is productive. Option C is unrealistic for a business operator who needs to ship product, not spend two years in tutorials.
There is a fourth path that the industry is only beginning to document: using AI-assisted development to systematically transfer capability from external contractors to the operator, project by project, until external dependency approaches zero. This is not hypothetical. The trajectory has been measured across a 10-project portfolio, verified against git repositories, and audited against financial records.
What the Data Shows
Andreessen Horowitz's analysis of AI-first companies has identified a structural shift in team composition: companies building with AI-native workflows are operating with engineering teams 3-5x smaller than their predecessors at equivalent scale. Sequoia Capital's "AI in the Enterprise" research points to a related pattern — the companies extracting the most value from AI are not the ones adding AI features to existing workflows, but the ones restructuring their team topology around what AI can now handle. Bain & Company's technology team benchmarks place the median Series A startup's engineering team at 5-8 developers, costing $750,000 to $2,000,000 annually in the US market before benefits, tooling, and management overhead.
The PRJ-02 portfolio provides a measured case of this transition across 10 git-tracked projects built between October 7, 2025, and February 2, 2026. The data is drawn from two source documents — CS07 (The Independence Curve) and the CEM Locked Timeline — and every number is repository-verified.
Here is the operator's share of work over time:
| Month | Operator % | External % | Trend |
|---|---|---|---|
| Oct 2025 | ~30% | ~70% | Team-scaffolded |
| Nov 2025 | ~44% | ~48% | Approaching parity |
| Dec 2025 | ~73% | ~25% | Operator dominant |
| Jan 2026 | ~93% | ~7% | Operator solo |
Four months. From 31% operator execution to 100% solo execution on the final projects. The last two products shipped with $0 external support.
The financial trajectory makes the curve concrete. Contractor spend across the portfolio totaled $65,053.66 (QuickBooks-verified: CON-02 $40,700.00, CON-03 $21,853.66, CON-09 $2,500.00). By Phase 3d (January 2026 onward), contractor spend dropped permanently to $0. The replacement: approximately $105 per month in AI tooling (Anthropic/Claude and OpenAI combined). The total build cost for the entire portfolio — contractors, AI, and software — was $67,894.38.
The transition was not instant, and the early investment was not wasted. The contractor phase was the foundation:
| Project Sequence | External Cost | Operator Share |
|---|---|---|
| Project 1 | $7,995 | 31% |
| Project 2 | $4,080 | 34% |
| Project 3 | $4,005 | 22% |
| Project 4 | $1,680 | 43% — inflection point |
| Project 5 | — | 72% |
| Project 6 | $330 | 86% |
| Project 7 | $90 | 96% |
| Project 8 | $0 | 100% |
Project 4 was the inflection. Below that line, the operator crossed 40% and began absorbing patterns faster than contractors could deliver them. By Project 7, external support was $90 — a single consultation. By Project 8, zero.
The performance data contradicts the assumption that going solo means going slower:
| Metric | With Contractors (Oct 2025) | Solo (Jan 2026) |
|---|---|---|
| Days to ship | 23 days | 5 days |
| External cost | $7,995 | $0 |
| Operator share | 31% | 100% |
The fastest MVP in the portfolio — PRJ-04 — shipped in 5 active days, 62 commits, 100% solo, at the highest complexity rate recorded: 62.5 complexity points per day. The second fastest — PRJ-03 — shipped in 9 days at 91.4% operator execution. Independence did not sacrifice speed. It peaked it.
How It Works
The mechanism is not "practice makes perfect." It is accumulated infrastructure. Each project leaves behind reusable patterns — authentication scaffolding, database architecture, service layer conventions, front-end component libraries. What was invented from scratch in Project 1 deployed instantly in Project 5. The operator's available toolkit expanded with every completed build, and AI tooling amplified the reuse by making pattern application nearly instantaneous.
AI replaced specialists, not judgment. Technical questions that required a $150-per-hour contractor consultation in October got answered by AI in January. The operator's decisions about what to build and why never changed — the support for how to build shifted from expensive human hours to AI tooling at $105 per month. The total transition investment was $34,473. That bought a permanent capability shift — not a one-project shortcut.
The rework data across the portfolio validates the quality trajectory. Portfolio-wide rework was 23.7% (606 of 2,561 raw commits across 10 projects). The actual product defect rate was 12.1% (310 commits) — the remaining 11.6% was design iteration, deployment learning, and integration wiring that is normal execution overhead. The cleanest builds in the portfolio — PRJ-08, PRJ-09, and PRJ-10 — ran at 3.7%, 3.8%, and 3.9% rework respectively. On the largest project (PRJ-01), rework trended from 45.2% down to 27.0% over its lifecycle — a 40% reduction as patterns stabilized.
The cost comparison against external development is stark. CON-02 built 22 database tables across approximately 4 months for $40,700. The operator, solo with AI, built 113 net-new tables (135 total) in 33 active days at approximately $105 in AI tooling — 620x cheaper, 3.6x faster, processing 616,543 leads versus 15,303.
What This Means for Business Operators
If you are currently dependent on contractors or a dev shop for software execution, the question is not whether AI can eventually replace that dependency. The data shows it already has — in a documented, financially audited case. The question is whether the transition investment makes sense for your situation.
The investment phase has a finite cost. In this portfolio, $34,473 bought a permanent capability transformation with a measured ROI of 23.1x to 84.1x. The operator did not become a software engineer. The operator became capable of directing AI tooling to produce production-grade software — 596,903 lines of custom code across 10 projects, including a 194,954-line customer data platform that processes over 616,000 leads and 75,000 transactions. The endgame is not "AI writes all the code." The endgame is operator sovereignty: the ability to build, ship, and iterate without external dependency, at a cost structure that makes traditional team sizing obsolete.
Related: C1_S16, C1_S18
References
- Andreessen Horowitz (2024). AI-first company analysis. Engineering teams 3-5x smaller than predecessors at equivalent scale.
- Sequoia Capital (2024). "AI in the Enterprise." Restructuring team topology around AI capabilities.
- Bain & Company (2024). Technology team sizing benchmarks. Median Series A startup engineering team at 5-8 developers ($750,000-$2,000,000 annually).
- Keating, M.G. (2026). "Case Study: The Independence Curve." Stealth Labz. Read case study