Why am I writing about tokenomics when we are building LumIntelligence, a knowledge platform? It is because we are in the intersection of using AI as a dev partner and embedding it in the knowledge production workflow. Tokenomics affect every fabric of the operation.
1. The Technical Debt at AI Speed
I hear from leadership repeatedly how AI tools are costing them a lot of money while not delivering tangible value. There’s also news about how companies are blowing their AI budget with limited returns. Not to mention, with all that, hallucination is still prevalent.
On the surface, this is a tech problem. Carrying context and tokens around for simple tasks, and worse, poorly thought-out tasks, can be expensive fast. Technical debt is growing at the speed of AI, and the downstream impact is exponential.
2. The Organizational Management & “Blast Radius” Problem
Addressing the AI tech debt is actually an organization management problem.
Some companies start to make up budget limits, but you can’t really use budget as a proxy for behavior and how people think. Bad project management is nothing new, but before, it was limited by the team member as the impact boundary. Now, the radius is your team’s traditional operating scope plus all your AI agents. As much as people say AI can work 24/7, that also means they can produce low impact output 24/7, too, if you let them.
3. The People Problem & The LumIntelligence Difference
So eventually, it is a people problem. What actions do we incentivize? How do we create metrics, measurements, and measures to align expectations, actions, and incentives?
Before we communicate this to teams, leadership needs to understand what is desired.
- Is it to achieve cost zero? Probably not realistic.
- Is it to cut costs? Sure, but at the cost of what? What is the baseline of product quality, especially AI-specific quality control like hallucination, speed, and scalability?
Then we can start to have a slightly more comprehensive perspective on productivity per output.
To quote the Spice Girls,
tell me what you want, what you really really want.
How We Build at LumIntelligence
Native apps at a fraction of the cost: Our own native apps on our own platform cost a fraction of what standard approaches take to run.
Consistent, scalable results: We deliver consistent and scalable results to build confidence in AI embedded systems.
The AI native flywheel: Through our own AI-native development process, we still follow tried-and-true best practices of project management to achieve the flywheel of design, plan, build, and iterate cycle—plus the AI flavor of context management. Instead of amplifying bad PM, we are accelerating purposeful experimentation and development.
It’s partly because we have a transparent budget and it’s easy to evaluate the budget impact. More importantly, performance of the platform is our moat, so we have an anchor to calculate our own cost-to-moat ratio.
For a larger team and organization, maybe it is not already easy, but it is worth considering to understand what development effort should produce what impact, and work out if the tokenomics make sense for this effort in anticipation. That can be the baseline for any retrospect analysis using the same metrics.
Yours in illumination,
Peter @ LumIntelligence