AI Makes Geothermal Carbon Capture Actually Scalable

A direct air capture (DAC) machine can pull carbon dioxide out of ambient air—but that’s the easy part. The hard part is doing it cheaply, continuously, and with energy you can defend when someone asks, “Why didn’t you use that power to replace fossil fuels instead?”

That’s why Kenya’s Great Rift Valley is drawing serious attention. The region already produces about a quarter of Kenya’s electricity from geothermal and still has heat and capacity that often goes unused. Startups like Octavia Carbon are betting that this “extra” geothermal energy can power DAC and turn the Rift Valley into a “Great Carbon Valley.” It’s a bold bet—and it’s also a useful blueprint for the U.S. energy and digital services market, because the missing ingredient for scale isn’t just geology or renewables.

It’s automation, optimization, and trust. And that’s where AI fits.

The real bottleneck in direct air capture: operations, not chemistry

DAC is already understood at a chemistry level. The bottleneck is operational: heat management, duty cycles, downtime, and verification.

The Kenyan pilots described in the source story (Octavia’s modular units, plus storage via basalt mineralization with partners) highlight an uncomfortable truth: even a good capture method can look bad if it’s run inefficiently. That’s one reason DAC critics keep winning arguments—because early systems have struggled to prove net removals and predictable costs.

Here’s the practical lens I use when evaluating DAC projects:

• Energy intensity is the business model. If your plant isn’t optimized minute-by-minute, your costs explode.
• Uptime is everything. A few percentage points of extra availability can change unit economics.
• Measurement is destiny. If you can’t prove removals with high integrity, buyers disappear.

AI directly targets all three.

Where AI helps first: heat, fans, and cycle timing

Most DAC systems live or die by decisions like:

• How long to run adsorption before regeneration
• What temperature to use for release
• How to balance fan speed vs. pressure drop vs. capture rate
• How to schedule maintenance without losing your best operating windows

These are classic control and optimization problems—ideal for machine learning and advanced control.

In a geothermal-powered setup like Kenya’s, there’s an added twist: you’re not just managing electricity prices or wind variability. You’re managing thermal availability, heat exchanger performance, and the “waste heat” profile of geothermal operations.

AI can continuously tune the DAC operating point—think of it as autopilot for capture efficiency.

Why geothermal + AI is a compelling combo (and why the U.S. should care)

Geothermal is unusually attractive for carbon removal because it offers steady baseload energy and, in volcanic regions, can pair naturally with basalt geology for mineral storage.

Kenya’s advantage is that much of its grid is already renewables-heavy, and geothermal is concentrated in a region with suitable rock formations. The U.S. advantage is different but still strong:

• Geothermal is expanding through enhanced geothermal systems (EGS) and deeper drilling techniques
• The U.S. has mature industrial automation, cloud infrastructure, and AI talent
• U.S. utilities are already investing in AI for grid optimization and predictive maintenance—the same playbook DAC needs

The connection is straightforward: the U.S. doesn’t need to copy Kenya’s geology to learn from Kenya’s operating concept: take a clean energy resource with surplus potential, pair it with smart controls, and build a verifiable climate service.

“AI in Energy & Utilities” reality check: DAC is a grid problem

Within the AI in Energy & Utilities series, most people think about:

• demand forecasting
• grid stability
• renewable integration
• outage prediction

DAC adds a new load type: flexible, schedulable, and measurable.

A well-run DAC facility can behave like a controllable industrial customer that:

• ramps down during grid stress
• ramps up when renewables are abundant
• consumes thermal energy that would otherwise be vented or wasted

AI makes that coordination possible without a room full of operators.

Four AI use cases that make geothermal DAC more efficient

AI shouldn’t be bolted on as a dashboard. It should be built into the plant’s nervous system.

1) Model predictive control for capture cycles

Answer first: AI can reduce energy per ton by optimizing adsorption/regeneration cycles in real time.

DAC units have repeating cycles—capture, release, reset. Small improvements compound across thousands of cycles per year.

A practical approach is model predictive control (MPC) augmented with machine learning models that learn from real operating data:

• Predict the “breakthrough” point when the filter is saturated
• Adjust regeneration temperature (e.g., 80–100°C in Octavia’s approach) based on humidity and inlet CO₂
• Minimize fan energy while maintaining airflow targets

This is the same pattern utilities use for turbine optimization and building energy management—now applied to carbon removal.

2) Predictive maintenance for fans, pumps, valves, and sorbents

Answer first: AI reduces downtime by predicting failures before they become expensive outages.

DAC hardware is industrial: rotating equipment, seals, valves, compressors, vacuum systems. Failures are rarely mysterious; they’re usually preceded by signals—vibration changes, temperature drift, power draw anomalies.

A solid predictive maintenance stack includes:

• sensor baselines per asset (not a one-size-fits-all threshold)
• anomaly detection on vibration and motor current
• maintenance scheduling tied to capture forecasts (don’t service units during peak productivity windows)

This matters more in modular/containerized DAC fleets, where you might operate dozens to hundreds of identical units.

3) Energy forecasting and dispatch (electric + thermal)

Answer first: AI increases net removals by matching DAC load to the cleanest, cheapest energy moments.

Even geothermal systems have operational constraints—maintenance windows, variable steam conditions, grid dispatch realities. Add solar (as many sites do) and you now have a hybrid energy profile.

AI can forecast:

• available thermal energy
• marginal electricity carbon intensity (if grid-tied)
• expected capture per kWh or per unit of thermal input

Then it dispatches capture runs accordingly. If a DAC project can show it systematically schedules around low-carbon energy, it’s easier to defend against the “you stole clean power” critique.

4) MRV you can defend: measurement, reporting, verification

Answer first: AI improves carbon credit integrity by detecting bad data, leaks, and accounting errors.

DAC’s commercial future depends on MRV. Buyers are tired of carbon credits that don’t hold up. The Kenyan story highlights why durable storage in basalt mineralization is attractive—but even then, the project needs airtight accounting.

AI-enabled MRV can:

• flag sensor drift and calibration issues
• reconcile flow meters, pressure readings, and captured mass balance
• detect anomalies suggesting leaks or incomplete injection
• generate audit-ready reports with traceable provenance

This is where U.S. digital services shine: secure data pipelines, tamper-evident logs, and automated compliance reporting are mature capabilities.

The controversy around DAC is real—AI doesn’t magically fix it

DAC has three legitimate criticisms, and any serious plan has to address them head-on.

Criticism 1: “It’s too expensive.”

The source article notes a sharp mismatch between typical carbon market prices (for example, EU allowance prices around the $80s/ton range in late 2025) and DAC credit prices (often several hundred dollars per ton). AI can cut operating costs, but it won’t erase fundamentals overnight.

My take: cost declines will come from reliability, standardization, and learning curves. AI accelerates all three, but only if it’s integrated from day one.

Criticism 2: “It distracts from emissions cuts.”

This is the moral hazard argument: carbon removal becomes an excuse to keep emitting.

My take: the fix is policy and procurement design, not vibes. Tie removals to hard-to-abate sectors, require strong MRV, and stop treating offsets as a PR product.

Criticism 3: “Communities don’t benefit.”

The Kenyan story is explicit about distrust, displacement, and communities living near generation that still lack access to electricity.

My take: if a project can’t show local benefit—jobs beyond security roles, community power access, health and infrastructure commitments—it’s not “climate tech.” It’s extraction with better branding.

AI doesn’t solve community governance. But it can enable transparency: public reporting dashboards, local hiring pipelines tied to real skills needs, and verifiable benefit tracking.

A practical playbook for U.S. energy and climate-tech teams

If you’re building in the U.S. (utility, developer, data center operator, or climate-tech startup), Kenya’s Great Carbon Valley story points to a set of moves that travel well.

Build the stack like an energy service, not a science project

• Treat each DAC unit like a grid asset with telemetry, dispatch logic, and reliability targets
• Design for modular scale (repeatable units beat bespoke mega-plants early on)
• Invest early in MRV automation; don’t “add it later”

Put AI where it touches unit economics

Prioritize AI work that affects:

1. Energy per ton captured (control optimization)
2. Uptime (predictive maintenance)
3. Auditability (MRV and data integrity)

Dashboards are fine. Control loops are better.

Don’t ignore the demand problem—productize the buyer experience

Demand for carbon removal is uneven and sensitive to politics and corporate backtracking. That’s not hypothetical; it’s happening.

A smarter go-to-market approach looks like:

• long-term offtake contracts with clear delivery and penalty terms
• transparent MRV that reduces buyer diligence cost
• integration with corporate carbon accounting systems (APIs matter)

That’s where U.S. digital services and AI platforms can create a real edge.

What the Great Carbon Valley gets right (and what to watch next)

Kenya’s approach is compelling because it combines four ingredients that usually don’t show up together:

• abundant renewable geothermal energy
• potentially durable basalt storage
• a motivated local engineering workforce
• a cluster strategy (multiple companies, shared infrastructure)

The watch-outs are just as clear:

• proof of net carbon removal at scale
• reliable demand for credits over multi-year horizons
• governance that prevents displacement and ensures communities benefit

If the project succeeds, it won’t just validate a Kenyan climate hub. It will validate a broader idea: carbon removal is an energy-and-software industry, not just a hardware industry.

As part of the AI in Energy & Utilities series, that’s the main thread worth pulling. Grid optimization, predictive maintenance, and industrial AI aren’t side quests anymore—they’re how climate infrastructure becomes bankable.

The next few years will show whether geothermal DAC can move from pilots to dependable operations. If it does, AI won’t be the headline—but it’ll be the reason the economics finally pencil out.