In a recent LinkedIn post, Teresa Torres, a prominent voice in product management, challenges the pervasive narrative that Artificial Intelligence has made software delivery “free.” Torres, co-host of the “All Things Product” podcast, along with Petra Wille, argues that while certain aspects of coding might be cheaper, the overall cost and complexity of building and maintaining production-quality products remain significant.
Torres and Wille address the popular misconception head-on, highlighting the dangers of treating AI coding agents as a costless resource. According to Torres, this approach can lead to a cascade of technical debt and operational nightmares.
“Spaghetti code, Frankenstein data models, feature bloat, and the maintenance nightmare waiting three weeks down the road.”
This quote, pulled directly from the post’s description, encapsulates the potential pitfalls Torres identifies. She emphasizes that the perceived cheapness of AI-assisted coding often masks the true cost of integrating these tools into a robust product development lifecycle.
The Crucial Distinction: Build to Learn vs. Build to Earn
A core theme in Torres’s analysis is the vital difference between prototyping for learning and building for revenue. She points out that while creating quick, disposable prototypes for discovery purposes can indeed be inexpensive, this is fundamentally different from developing a market-ready product.
As Torres explains, the latter involves a far more involved process:
“The last 30% of a product is where the real work lives.”
This highlights that the journey from a functional prototype to a polished, reliable product involves substantial effort in areas like performance optimization, security, scalability, and user experience refinement – areas where AI, in its current form, cannot fully replace human expertise and architectural decision-making.
AI Products: A Different Ballgame
Torres further clarifies that building AI-powered products is inherently more complex than working with deterministic code. She notes that the development process for AI products requires significant investment in error analysis, model evaluation, prompt engineering, and continuous iteration.
The Hidden Costs of AI Integration
The narrative that AI makes delivery “free” often conveniently omits the ongoing need for skilled engineering teams to oversee, steer, and debug AI-generated output. Torres argues that outsourcing critical architectural decisions to coding agents is a flawed strategy.
“Why you still need a skilled engineering team observing and steering what AI produces — and why architecture decisions can’t be outsourced to a coding agent.”
This underscores that AI is a powerful tool to augment human capabilities, not replace the fundamental need for skilled product and engineering professionals. The ease with which AI can generate code or content can lead to a “feature spiral,” where the rapid creation of more features masks underlying inefficiencies and quality degradation.
The Reality Check for Product Teams
Torres’s post serves as a crucial reality check for product leaders and teams who may be swayed by the alluring but misleading promise of “free” delivery. She contends that the popular refrain, “Delivery is free, so taste/discovery is all that matters,” is a false dichotomy.
In her view, both delivery and discovery are perpetual and essential components of successful product development. The perceived cheapness of initial AI-assisted output does not negate the enduring costs and complexities associated with building, launching, and maintaining high-quality, valuable products in the long term.
📝 About This Content
This article is based on insights shared by Teresa Torres on LinkedIn.
📅 Originally posted on September 15, 2026 | View original post on LinkedIn →