Two engineers using an AI coding assistant can rebuild a tool in roughly one week for about $7,700 in salary costs — a tool their company currently pays $30,000 annually to license, according to a new analysis from The VC Corner. The report examines how artificial intelligence coding tools are reshaping the build-versus-buy decision for software companies, finding that while upfront development expenses have plummeted, the hidden costs of maintaining custom code now represent the larger financial burden. As SaaS spending continues to climb — the typical organization now spends $55.7 million per year on software licenses, an 8% jump over the previous year according to Zylo's 2026 SaaS Management Index — more leadership teams are reconsidering whether to build internal alternatives.

Software carries two distinct expense categories: the initial creation and the ongoing ownership, with AI coding tools slashing the former while leaving the latter largely untouched. Maintenance work consumes roughly 42% of developer time, based on Stripe's Developer Coefficient survey showing engineers spend about 17.3 hours of a 41.1-hour workweek on upkeep tasks. Internal builds appear inexpensive because their continuing maintenance expenses remain buried within engineering salaries rather than appearing as separate line items. Meanwhile, vendors benefit from the same AI productivity gains while distributing maintenance costs across their entire customer base, creating an economic advantage that homegrown solutions can't match.

Research from MIT NANDA in 2025 found that AI tools purchased from specialized vendors succeeded roughly 67% of the time, compared to approximately one-third success rate for internally developed builds. The report also cites an Anthropic trial revealing that developers who relied on AI assistance scored just 50% on comprehension tests about code they had recently written, versus 67% for those who coded manually — evidence of what the report terms "comprehension debt." The analysis recommends building only small, low-risk tools with a documented exit strategy, while purchasing any solution whose failure would prove catastrophic to operations.

The traditional build-or-buy debate once belonged exclusively to large enterprises with substantial engineering resources, but it's now a routine discussion between startup CEOs and CTOs as AI tools advance monthly. The core tension stems from AI's impact being asymmetric: it dramatically reduces the barrier to creating software while doing almost nothing to lighten the long-term burden of supporting it. Vendors possess structural advantages because they can hire specialists focused entirely on a single product, apply efficiency improvements across thousands of clients simultaneously, and absorb the risk of technical obsolescence. Internal teams building custom tools face the opposite dynamic — each homegrown system adds to the maintenance backlog, diverts engineering attention from core product development, and creates knowledge concentration risk when the original builder leaves the company.

The report's guidance centers on risk assessment: companies should reserve internal development for small-scale, non-critical applications where failure carries minimal consequences, and default to purchasing enterprise-grade solutions for systems where reliability, compliance, and continuity matter most. The bottom line is that AI has made starting to build cheaper than ever, but hasn't changed the economics of finishing and maintaining what you've built. The strategic question isn't whether your team can build something quickly — it's whether they should own it permanently. For C-suite leaders evaluating these trade-offs, the decision increasingly hinges on opportunity cost rather than nominal expense, since every hour spent maintaining yesterday's internal tool is an hour not spent building tomorrow's competitive advantage.