Objections & Answers
Every objection, doubt, and risk raised during the strategy formulation — answered directly.
The rule: where a confident answer exists, it is given. Where it does not, the risk is named and accepted openly. A plan that pretends it has no downside is not a plan anyone should trust.
How the concerns break down
Security & code review
Closed-source code, human review, prompt injection, dependency provenance.
Reliability, quality & capability
Hallucination, edge cases, cleanup cost, model plateaus, tech debt.
Cost, vendors & economics
Token-cost blowups, vendor reliability, subscription economics, maintenance burden.
Compliance, data & adoption
The real blocker — DPAs, approval timelines, model restrictions, enterprise readiness.
Competition, market & IP
Vendors building AI natively, replicability, monetization, and the moat question.
Three truths underneath all of it
The blocker is trust, not tech
The single biggest risk is customer compliance and consent. It is the critical path.
Built on today's AI, not tomorrow's
Nothing here depends on models getting better, cheaper, or more reliable. It works at the capability and price that exist right now.
The expert is the gate
Every concern about AI being wrong is answered the same way: a senior engineer reviews and approves before anything ships. That is the product.
The 2026 evidence sharpens this section: METR found experienced developers slower with AI on complex familiar repos, Faros measured quality and review pressure rising with AI adoption, and Gartner found many I&O AI projects stalling before ROI. Those findings are exactly why we sell expert-audited outcomes.
This section is deliberately exhaustive. If a concern is missing, it is an oversight — raise it and it gets added. The goal is that no one walks away with an unspoken doubt.