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Archegon research notes
Short essays from a founder-led research effort exploring geothermal compute, data-centre economics, agentic AI systems, workflow design, and operational governance.
The AI infrastructure conversation usually starts with chips. That is only half the bottleneck. Large-scale inference needs firm capacity, fast interconnection, cooling, land, water, and an operating model that can run continuously.
If grid queues stay slow, the winning sites may be the ones that bring the load to firm energy instead of waiting for the grid to bring energy to the load.
AI inference is a 24/7 load. Geothermal is a firm, high-capacity-factor power source. That alignment matters because it reduces the need to overbuild generation or buy expensive firming for a workload that does not naturally turn off at sunset.
The opportunity is not just to buy clean power. It is to design the data-centre site, power stack, cooling system, and fibre route around the resource from day one.
Archegon is not presenting itself as an operating utility or a 200-person developer. It is a research-stage venture thesis looking for the right co-founders, partners, advisors, and capital to test whether the numbers can become a real project.
The honest next step is not a public offer. It is qualified conversation: technical review, partner discovery, due diligence, and a path to the people who can build.
Agent work should begin by separating decisions into three groups: actions the system can take, recommendations it can prepare, and moments where a human owner must approve the next step.
That boundary makes the build measurable. Once the agent's authority is explicit, the team can test the cases that matter, design the right handoffs, and avoid turning every edge case into a prompt problem.
A compelling demo usually shows the best path through a workflow. A useful pilot shows the ordinary path, the messy path, and the failure path.
Before expanding an agent, build a small evaluation set from real work: typical requests, awkward exceptions, missing information, ambiguous policies, and cases that should trigger escalation.
An agent that cannot hand work back cleanly is not production ready. Handoff design should specify who receives the case, what context travels with it, how urgency is represented, and which decisions have already been made.
The best operating models make escalation predictable: clear queues, ownership, reviewable traces, and enough structure that humans do not have to reconstruct the agent's reasoning from scratch.
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