Human-only vs. AI-only vs. the third way: a cost model

When a client asks, "Why not just hire an agency?" or "Why not let AI build it?", we shift from philosophical debates to practical comparisons. We create three columns to illustrate our approach, which we'll outline here. The numbers provided are representative of typical engagements we encounter, though your specifics may vary. However, the overall comparison usually remains consistent.

01The human-only model

In this traditional delivery method for a mid-sized product, the team typically consists of one tech lead, two to four engineers, and a part-time designer and project manager, totaling around six people over six months. Most of their hours are spent on repetitive tasks such as boilerplate code, test suites, data management, environment setup, and the time-consuming coordination of meetings. You end up paying senior rates for a mix of decision-making and mechanical work, as this model lacks a way to differentiate between the two.

02The AI-only model

Using a fleet of autonomous agents can seem almost free on an hourly basis, but this presents its own issues. Without human judgment involved, you risk encountering problems like confidently incorrect architecture, security vulnerabilities, and codebases that quickly become unmanageable. In our assessments, we frequently meet teams that saved 80% on initial development costs only to spend 300% on fixing issues later. So, the unit cost saved is not the true cost; the outcome ultimately determines the expense.

03The third way

Our approach involves one senior engineer overseeing agents along with one product-focused individual, working for weeks rather than months. Agents handle the repetitive tasks at near-zero additional cost, allowing human experts to focus their valuable hours solely on decision-making aspects such as architecture, domain modeling, code reviews, and trade-off analyses. This ensures delivery quality meets human standards since the sign-off comes from a human, while the speed of delivery is machine-grade due to the efficiency of automated processes.

04When each model is most effective

• —The human-only model excels in situations like novel systems research, highly regulated environments where every decision needs accountability, and organizations that cannot use AI tools on their code.
• —The AI-only model is increasingly favorable for temporary prototypes, one-off scripts, and simple integration tasks where fixing issues later is not costly because the product is not intended for permanence.
• —The third way is most effective for the larger middle ground: products that must be of high quality, delivered within a specific timeline and budget, which traditional methods often fail to meet.

If there's one key takeaway from this analysis, it’s this: stop pricing based on hours worked and start evaluating the division of tasks. The question should never be "humans or AI?" but rather "which specific tasks require which approach?" Getting this distinction correct is crucial to success.