AI spending caps are training wheels for teams that shouldn’t need them. They create friction. They break flows. They protect pennies while slowing the thing that makes dollars.
Thats why I love Kilocode... Success should not be measured by monetization alone... Undercutting customer growth to increase company revenue might work in the short term but ultimately you are digging a hole to fallback. Totally appreciate Kilocode's vision and a customer centric focus
While many of the arguments here are valid, this post reflects a largely one-sided perspective. To be truly strong, it should engage more deeply with the arguments of the opposition.
Let's dive in a two:
1. Although AI-assisted engineering clearly delivers significant value, as we are all learning, much of what we are doing today still feels like a “playground” phase. This applies not only on the client side, but on the provider side as well: models are evolving extremely rapidly, and the price-to-value ratio varies wildly across models and over time.
Giving unlimited, unbudgeted access to AI tools to a team of 10 developers is similar to giving $100k to 10 teenagers and telling them: “Go buy cars for a delivery business - spend it wisely.”
• Some will optimize for efficiency and spend ~$10k per car.
• Others will think, “Faster delivery requires a faster car,” and spend $30k on a fast one.
• Some may opt for convenience, buying a $45k Tesla with FSD and saying, “It’ll deliver for me.” (but not without them in car, not yet...)
The result is that the $100k is gone long before all 10 are equipped.
There was a good blog post describing how to use more capable (and expensive) models for specs and architecture, and cheaper, faster models for implementation.
But how do you enforce such a wise spending policy in an enterprise environment (not a startup) without tooling, governance, or budget controls?
2. While we all agree that AI can improve quality, speed, and outcomes, businesses are still constrained by short- and mid-term cash flow. Different products, projects, and teams generate very different returns today, and AI spending needs to reflect those realities.
These are just a few thoughts off the top of my head—there are many more arguments that support the opposing view.
Thats why I love Kilocode... Success should not be measured by monetization alone... Undercutting customer growth to increase company revenue might work in the short term but ultimately you are digging a hole to fallback. Totally appreciate Kilocode's vision and a customer centric focus
Agreed!
While many of the arguments here are valid, this post reflects a largely one-sided perspective. To be truly strong, it should engage more deeply with the arguments of the opposition.
Let's dive in a two:
1. Although AI-assisted engineering clearly delivers significant value, as we are all learning, much of what we are doing today still feels like a “playground” phase. This applies not only on the client side, but on the provider side as well: models are evolving extremely rapidly, and the price-to-value ratio varies wildly across models and over time.
Giving unlimited, unbudgeted access to AI tools to a team of 10 developers is similar to giving $100k to 10 teenagers and telling them: “Go buy cars for a delivery business - spend it wisely.”
• Some will optimize for efficiency and spend ~$10k per car.
• Others will think, “Faster delivery requires a faster car,” and spend $30k on a fast one.
• Some may opt for convenience, buying a $45k Tesla with FSD and saying, “It’ll deliver for me.” (but not without them in car, not yet...)
The result is that the $100k is gone long before all 10 are equipped.
There was a good blog post describing how to use more capable (and expensive) models for specs and architecture, and cheaper, faster models for implementation.
But how do you enforce such a wise spending policy in an enterprise environment (not a startup) without tooling, governance, or budget controls?
2. While we all agree that AI can improve quality, speed, and outcomes, businesses are still constrained by short- and mid-term cash flow. Different products, projects, and teams generate very different returns today, and AI spending needs to reflect those realities.
These are just a few thoughts off the top of my head—there are many more arguments that support the opposing view.
Spot on! Glad this resonated!