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.
Outstanding framing of the throughput vs cost tradeoff. The AWS comparison is spot on. I ran infra at a mid-stage company and we had this exact fight about API rate limits vs reliability. What's wild is teams dunno they're optimizing for the wrong metric until they see competitors shipping 2x faster. The hard cap breaking mid-refactor scenario is brutal and probly happens way more than orgs admit.
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.
Outstanding framing of the throughput vs cost tradeoff. The AWS comparison is spot on. I ran infra at a mid-stage company and we had this exact fight about API rate limits vs reliability. What's wild is teams dunno they're optimizing for the wrong metric until they see competitors shipping 2x faster. The hard cap breaking mid-refactor scenario is brutal and probly happens way more than orgs admit.
Spot on! Glad this resonated!
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.