One of the least visible challenges in building closed loop food systems is not hardware. It is chemistry.
Converting captured CO₂ into usable food molecules is not a single reaction. It is a network of possible transformations, intermediates, and constraints. Enzymes play a critical role in many proposed pathways, but identifying which approaches are even worth testing is a major bottleneck.
This is where early stage research often slows down or becomes prohibitively expensive.
The enzyme discovery problem
There are thousands of known enzymes and countless theoretical reaction pathways that could, in principle, contribute to carbon conversion. Testing these possibilities directly in a lab is slow, resource intensive, and often inefficient at the earliest stages.
Before glassware, before reactors, and before scale, there is a more fundamental question that must be answered.
Which paths are even plausible under real world constraints
Early Phase 1 work is not about finding the perfect solution. It is about eliminating the wrong ones.
Why simulation comes first
At Eden Engine, early research focuses on feasibility screening rather than optimization. This means asking structured questions such as:
- Can this class of reactions operate near ambient conditions
- Does the pathway align with closed loop system constraints
- Does it integrate with downstream processing and storage requirements
This kind of filtering is where AI tools are useful.
AI is not being used to invent chemistry. It is being used to explore existing knowledge faster, identify patterns across large bodies of research, and narrow the decision space before any physical experiments are designed.
What AI is actually doing in this phase
In practical terms, AI is helping with:
- Reviewing and organizing known reaction classes
- Identifying constraints that eliminate entire categories of approaches
- Highlighting areas where existing literature overlaps with system requirements
- Reducing the number of candidate paths that require real world testing
This lowers cost and time while improving research discipline.
It also allows early work to remain modular. As new information becomes available, assumptions can be updated without rebuilding the entire system model.
What this is not
This work is not a finished solution.
It is not a production process.
It is not a claim of efficiency, yield, or readiness.
AI does not replace lab validation. It helps decide where lab validation is worth doing.
Why this approach fits Eden Engine Phase 1
Phase 1 of the Eden Engine is explicitly about feasibility and system architecture. The goal is to identify viable routes for converting captured CO₂ into simple sugar inputs that can support later stages of development.
Using AI at this stage allows progress to happen without locking in assumptions too early. It also reduces waste by ensuring physical experiments are informed by structured reasoning rather than trial and error.
Moving forward carefully
As the project advances, insights from simulation will inform physical testing, and physical testing will refine the models. This feedback loop is how complex systems become real infrastructure.
Progress at this stage is quiet by design. The work is about foundations, not announcements.
That is exactly where it should be.
Jack Lawson
Founder, Eden Engine Technologies Inc.
