How to Move from Contractor Dependency to In-House AI-Enabled Development

The Setup

Portfolio companies dependent on external contractors face a structural problem that PE operators know well: the knowledge walks out the door every time a statement of work ends. Deloitte's 2024 Global Outsourcing Survey reports that 70% of companies cite "loss of control" as a top outsourcing risk, and McKinsey's 2023 technology operations research found that contractor-dependent organizations spend 30-40% more on maintenance than those with internalized capabilities.

The conventional solution is hiring a full-time engineering team — which means recruiting timelines of 3-6 months, fully-loaded costs of $150K-$250K per developer, and the organizational overhead of managing technical staff. For portfolio companies below $50M in revenue, this is often unfeasible. So the contractor dependency persists, and the knowledge gap widens with every engagement.

There is a third path: a structured transition from contractor-dependent execution to AI-enabled solo operation, where the operator internalizes capability rather than perpetually renting it.

What the Data Shows

A validated dataset covering the October 2025 through January 2026 transition period documents this path in detail. The operator — Michael George Keating — began with approximately 70% of work executed by external contractors (CON-02 and CON-07 primarily). Over four months, that ratio inverted completely.

The trajectory, measured by operator share of work:

• October 2025: 30% operator, 70% contractors. Per-project external support cost: $7,995.
• November 2025: 44% operator, 56% contractors. External support declining.
• December 2025: 73% operator, 27% contractors. Monthly contractor spend: $0 from this point forward.
• January 2026: 93-100% operator. Last two products shipped at 100% solo execution with $0 external support.

The transition was not linear. Projects 1-3 required heavy contractor involvement — the operator was learning while contractors built. Project 4 marked the inflection point where the operator crossed 40%. By Projects 7-8, execution was functionally solo.

Critically, independence did not sacrifice performance. Days to ship fell from 23 days to 5 days. Daily output rate peaked at the point of maximum independence, not maximum contractor support. The fastest build, highest complexity rate, and lowest external cost all occurred at 100% solo execution.

Total investment in the transition: $34,473. Return on that investment, measured against replacement value of the assets produced: 23.1x-84.1x.

How It Works

The transition succeeded because of accumulated infrastructure, not just accumulated skill. Each project left behind reusable patterns — authentication systems, database schemas, admin interfaces, deployment pipelines. The operator did not simply "get better" through repetition. The available toolkit expanded with every completed build.

AI replaced specialists, not judgment. Technical questions that required a $150/hour contractor consultation in October 2025 were answered by AI tooling costing approximately $105/month by January 2026. The operator's decisions about what to build never changed. The support infrastructure for how to build shifted from expensive human specialists to inexpensive AI — permanently.

The contractor phase was the investment, not the problem. The 70% dependency in October was the foundation being built. Contractors created the initial scaffold. The operator absorbed the patterns through collaborative execution. By December, the operator had internalized enough to operate independently. The dependency was finite by design, even if the timeline was discovered empirically.

What This Means for Decision-Makers

For PE operators evaluating technology buildout options, this data reframes the contractor dependency problem. The conventional choice — stay dependent on contractors, or hire an expensive internal team — is a false binary. A structured transition, where contractor engagement deliberately transfers capability rather than simply delivering output, can achieve full independence within four months at a fraction of the cost of a permanent hire.

The $34,473 total investment compares to $150K-$250K for a single mid-level developer's annual cost. The difference: the investment produces a permanent capability shift that compounds across every subsequent project, rather than an ongoing salary obligation. Monthly contractor spend went from $6,486 in September 2025 to $0 in December 2025 — permanently. That is not cost reduction. That is cost elimination.

The question for portfolio operators: is your contractor relationship building a bridge to independence, or a permanent dependency? The data shows the bridge can be crossed in four months.

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Related: [C7_S142 — The Build-vs-Buy Math] | [C7_S146 — Bus Factor Risk in Solo-Built Software] | [C7_S145 — The Compounding Software Portfolio]

References

1. Deloitte (2024). "Global Outsourcing Survey." Analysis of outsourcing risks including loss of control and knowledge retention challenges.
2. McKinsey & Company (2023). "Technology Operations Research." Maintenance cost differentials between contractor-dependent and internalized-capability organizations.
3. Accelerance (2024). "Software Outsourcing Report." Developer cost benchmarks and outsourcing engagement models.
4. Keating, M.G. (2026). "Case Study: The Independence Curve." Stealth Labz. Read case study