AI is pushing investors back toward reliable energy supply. Here’s how Kazakhstan can use AI to boost reliability, cut losses, and modernize oil & gas.
AI Energy Demand Shifts Investors Toward Reliable Supply
Energy investors spent most of the 2010s and early 2020s chasing anything stamped “green.” Then 2025 happened, and priorities started to look… more practical.
The trigger wasn’t a new climate policy or a sudden change in consumer behavior. It was AI—and the uncomfortable reality that large-scale models, data centers, electrified industry, and digital infrastructure pull serious, steady power. When Global Corporate Venturing’s Fernando Moncada Rivera pointed out that energy demand across OECD countries grew only ~1% per year from 1990 to 2020, the subtext was clear: the old “flat demand” world is over. Investors are responding by focusing less on labels and more on supply reliability.
This post is part of our series «Қазақстандағы энергия және мұнай-газ саласын жасанды интеллект қалай түрлендіріп жатыр». The twist is that AI plays two roles at once: it consumes more electricity, and it’s also one of the best tools we have to produce, move, and use energy more efficiently. For Kazakhstan—where oil, gas, power generation, and grids sit at the center of the economy—this is a big strategic opening.
Why 2025 marked a turning point: demand is back
The main shift: AI turned electricity demand into a board-level risk. For years, many markets planned around slow growth. Investors could assume demand would be manageable, and capital could chase “transition narratives” without sweating near-term shortages.
AI changes that because it doesn’t just add load—it adds high-utilization, 24/7 load. Data centers don’t pause because it’s nighttime. Model training runs for days. Inference (the everyday use of AI tools) becomes continuous once businesses integrate it into operations.
What investors are really reacting to
They’re not “anti-green.” They’re reacting to three hard constraints:
- Firm capacity matters again. Intermittent generation can be valuable, but investors are pricing in the need for resources that deliver power when demand spikes.
- Grid bottlenecks are becoming the hidden limiter. Even where generation exists, interconnections, transformers, and transmission capacity can’t always deliver.
- Time-to-power beats PowerPoint. Projects that can realistically add dependable supply and reduce losses in 12–36 months win capital faster than projects with uncertain timelines.
If you work in Kazakhstan’s energy or oil-and-gas sector, this matters because global capital flows influence everything from export markets to equipment supply chains to technology budgets.
The Kazakhstan angle: AI demand is global, but supply is local
Kazakhstan can benefit from the global AI-driven demand surge if it treats energy supply as a competitiveness product. Countries that can provide dependable, affordable power—plus credible decarbonization pathways—will attract industry, data infrastructure, and higher-value processing.
Kazakhstan’s position is unusual: it has deep hydrocarbons expertise, a large industrial base, and real potential to modernize generation and grid operations. But the opportunity isn’t automatic.
What “supply-first” means for Kazakhstan in practice
It means investors and partners will ask pointed questions:
- Can you deliver firm electricity for industrial projects and digital infrastructure?
- Can you reduce outages and technical losses with modern grid operations?
- Can you show measurable progress on methane reduction, efficiency, and carbon intensity—not slogans?
This is where AI stops being a buzzword and becomes operational.
AI as an energy consumer—and why that’s not just a problem
AI increases electricity demand, but it also creates a new reason to modernize energy systems. The most pragmatic path is to treat AI growth as a forcing function: modernize supply, modernize the grid, modernize operations.
Here’s the reality I’ve seen across energy organizations: if you try to “wait out” the AI wave, you end up paying for it anyway—through higher balancing costs, unplanned downtime, equipment failures, and slower project delivery.
Where the new load shows up
AI-driven demand doesn’t only come from hyperscale data centers. It also comes from:
- Electrified oil-and-gas operations (compressors, pumps, processing)
- Digital twins and simulation workloads
- Industrial automation and machine vision
- Cybersecurity monitoring and anomaly detection
- Enterprise AI (document processing, copilots, forecasting)
So even without “big data centers,” Kazakhstan’s energy system will feel the load as domestic industry digitizes.
How energy and oil-and-gas companies use AI to strengthen supply
The best AI projects in energy don’t start with models. They start with constraints: downtime, losses, safety risk, fuel burn, and delayed maintenance. Below are high-ROI use cases that directly support a supply-first investor mindset.
Predictive maintenance that actually reduces outages
Instead of fixed maintenance intervals, AI models use sensor data (vibration, temperature, pressure, acoustic signals) to predict failure.
What changes:
- Fewer forced outages on turbines, compressors, and pumps
- Better spare parts planning
- Higher asset availability (which is what “reliable supply” really means)
Snippet-worthy truth: The cheapest new megawatt is often the one you stop losing to unplanned downtime.
AI-driven production optimization in upstream operations
In oil and gas, AI is increasingly used for:
- Well performance forecasting and decline analysis
- Artificial lift optimization (adjusting settings to reduce energy use per barrel)
- Water cut and sand production prediction to avoid damage and shutdowns
These aren’t just “digital projects.” They reduce energy intensity and stabilize volumes, which affects the whole energy value chain.
Grid analytics: losses, congestion, and faster restoration
If you’re serious about dependable supply, the grid can’t remain a blind spot.
AI helps by:
- Detecting non-technical losses and metering anomalies
- Forecasting load at substation/feeder level
- Optimizing switching and dispatch during faults
- Prioritizing vegetation management and line inspections
For investors, this is attractive because grid efficiency improvements can be faster to deliver than building new generation—and they immediately raise reliability.
Smarter dispatch and fuel management in power generation
AI-based forecasting improves:
- Unit commitment decisions (which plants run and when)
- Heat-rate optimization (less fuel per kWh)
- Emissions performance and reporting accuracy
That combination—better forecasting plus better dispatch—supports both reliability and carbon intensity improvements.
What energy investors now want to see (and how AI helps you prove it)
Investors are rewarding measurable performance, not narratives. If you’re pitching projects, partnerships, or modernization programs in Kazakhstan’s energy sector, you’ll get traction faster when you show operational metrics and the data systems behind them.
The metrics that signal “reliable supply”
Use these as a checklist for your strategy and reporting:
- Availability and forced outage rate (generation)
- SAIDI/SAIFI (distribution reliability)
- Technical loss percentage (transmission/distribution)
- Maintenance backlog and mean time to repair
- Energy intensity (kWh per unit output in processing/upstream)
- Methane leak detection coverage and repair cycle time (oil & gas)
AI supports this by turning raw operational data into consistent measurement—then automating alerting and decision workflows.
A practical modernization sequence (what works)
Most companies get this wrong by starting with an “AI platform” before they fix data quality and operational ownership. A better sequence looks like:
- Instrumentation & data capture (SCADA/historian quality, sensors where failures are costly)
- Data governance (asset hierarchy, naming, time sync, cybersecurity access controls)
- One or two high-value use cases (predictive maintenance + loss detection is a strong pair)
- Operational integration (work orders, dispatch, maintenance planning—no “dashboard-only” projects)
- Scale with standard templates (repeatable models across similar assets)
This approach is also the most “investor-friendly” because it produces near-term reliability gains while building a foundation for deeper transformation.
People also ask: will AI make Kazakhstan’s energy transition harder?
It can, if the response is only “build more.” If you meet AI-driven demand purely with additional fuel burn and no efficiency gains, emissions intensity rises and financing gets harder.
It gets easier if AI is used to cut waste at the same time. The sensible goal isn’t abstract “digital transformation.” It’s a measurable package:
- Increase reliability (fewer outages)
- Reduce losses (more delivered energy without building capacity)
- Reduce fuel burn per kWh (lower operating cost)
- Cut methane leaks and flaring where possible (better environmental performance)
The reality? A supply-first world doesn’t cancel decarbonization. It forces it to become operational—measured in equipment performance, not marketing.
What to do next if you’re leading an energy or O&G team
If your 2026 plan includes AI—either because you want it or because your customers and partners demand it—start with clarity:
- Where are we losing energy today? (losses, downtime, inefficiency)
- Which assets create the most unplanned interruptions? (rank by cost of failure)
- What data do we trust enough to automate decisions? (and what do we need to fix first?)
From there, pick one reliability use case and one efficiency use case, build them end-to-end, and only then scale. That’s how you align with where investors are heading: toward dependable supply backed by provable performance.
AI-driven energy demand is reshaping global priorities. Kazakhstan can either be squeezed by that reality—or use it to justify faster modernization across power and oil-and-gas operations. The next 12–24 months will show which path companies choose.
Forward-looking question: when your stakeholders ask for “AI readiness,” will you show them a pilot— or a reliability and efficiency curve that’s already moving?