The energy transition has a learning problem, not just a technology problem. Shomron Jacob, AI/ML expert and entrepreneur with over a decade of experience deploying production AI systems across enterprise environments, argues that the gap between what artificial intelligence can already do for grids, renewables, and industrial decarbonization and what utilities are actually deploying represents one of the most consequential missed opportunities in climate action today. With three filed patents in AI/ML technology and firsthand experience bridging hardware and intelligent systems at scale, Jacob cuts through the hype to assess where AI is delivering measurable results, where the promises remain unproven, and why the most important application in the entire energy transition may be the least glamorous one.
How do you see AI fundamentally changing the pace and scale of the energy transition compared to previous technological waves?
Every previous technological wave in energy, from steam to electrification to digital controls, changed what we could build. AI is changing how fast we can learn. That’s the fundamental difference. Previous transitions took decades because optimizing complex energy systems required physical experimentation, regulatory cycles, and slow institutional learning. AI compresses that learning curve dramatically.
Consider materials discovery alone. Traditional battery R&D relied on trial-and-error synthesis that could take twenty years from lab to market. AI-driven platforms are now screening millions of candidate compounds in weeks. Researchers at KIT used AI to identify new organic molecules that boosted perovskite solar cell efficiency to 26.2 percent, with only 150 targeted experiments instead of hundreds of thousands. Argonne National Laboratory is training foundation models on billions of known molecules to find next-generation battery electrolytes and electrodes.
But the more immediate impact is operational. AI doesn’t just help us discover better technologies; it helps us run existing infrastructure closer to its physical limits. When 41 percent of North American utilities have already fully integrated AI, beating their own five-year projections according to Itron’s 2025 Resourcefulness Report, that tells you the adoption curve is steeper than anyone planned for. The pace of the energy transition is no longer gated by what we can build. It’s gated by how fast institutions can absorb intelligence.
Which sectors of the energy industry, renewables, grid management, storage, and demand response, are seeing the most meaningful AI impact right now?
Grid management is seeing the most meaningful impact today, followed closely by demand response. Renewables and storage are close behind but are still catching up on data infrastructure.
The reason grid management leads is simple: grids generate enormous volumes of sensor data, the consequences of failure are severe, and the optimization problems are well-defined. National Grid Electricity Transmission used advanced analytics to optimize asset management across 60,000 assets, cutting planning time by 50 percent and avoiding 1,000 outages annually, saving $7.8 million in outage costs alone. Utilities using AI-enhanced predictive maintenance report 60 percent fewer emergency repairs. AI-powered demand forecasting is improving accuracy by up to 20 percent.
Demand response is the next frontier because it turns passive consumers into active grid participants. AI can now orchestrate millions of distributed energy resources, rooftop solar, batteries, EVs, and smart thermostats as a virtual power plant, shifting load in real time to match variable renewable generation. Bidgely’s platform, for example, processes over one terabyte of meter data daily across 38 million meters globally, providing appliance-level intelligence.
Renewables and storage are benefiting significantly from AI in forecasting, siting, and performance optimization, but many renewable assets are still instrumented at a fairly basic level. The real unlock will come when we treat every solar panel, wind turbine, and battery as an intelligent node in a network, not just an isolated generating asset.
Is the energy sector moving fast enough in adopting AI, or are we leaving significant value on the table?
We’re leaving enormous value on the table. The energy sector has historically been conservative about technology adoption, for good reason, since reliability is non-negotiable. But that conservatism is now costing us.
The IBM Institute for Business Value found that 94 percent of utility executives expect AI to contribute significantly to revenue growth within three years, and 88 percent say it will deliver measurable competitive advantage. Yet much of the sector still operates on legacy systems that were designed for a simpler era. Hitachi Energy’s analysis makes this clear: much of the grid’s infrastructure was built decades ago, with analog equipment, transformers, breakers, switchgear, that operate largely independent of modern IT platforms.
The gap between what AI can do and what the energy sector is actually deploying creates a massive opportunity cost. We know predictive maintenance can reduce infrastructure failures by 73 percent. We know AI-driven process optimization in cement plants can cut emissions by 10,000 tons per plant per year. The technology works. The bottleneck is data readiness, institutional inertia, and workforce capability.
The IFS and PwC report found that 86 percent of industrial leaders believe AI will help meet environmental goals. Believing and deploying are different things. The companies and utilities that move fastest will compound their advantages in ways that become very difficult for laggards to close.
How is AI being used to manage the increasing complexity of grids that must balance variable renewable sources like wind and solar?
Modern grids face a problem that didn’t exist twenty years ago: supply is now as variable as demand. When you’re balancing wind that fluctuates minute-by-minute, solar that drops when clouds pass, distributed batteries that charge and discharge independently, and millions of EVs plugging in unpredictably, the optimization problem exceeds what traditional control systems can handle.
AI addresses this at multiple layers. At the transmission level, deep learning models analyze high-frequency sensor data for real-time fault detection, identifying anomalies in milliseconds. In China, utilities are experimenting with reinforcement learning algorithms that give smart grids self-healing capabilities, autonomously rerouting power when faults are detected. In India, predictive models using satellite data and weather analytics help operators plan for solar intermittency while optimizing storage and backup generation.
At the distribution level, Advanced Distribution Management Systems powered by AI can optimize networks in real time, integrating higher levels of renewable energy without compromising stability. Digital twins, virtual replicas of grid infrastructure, allow operators to simulate conditions and forecast outcomes without taking real systems offline.
The critical insight is that AI enables the grid to operate probabilistically rather than deterministically. Instead of maintaining large reserve margins to handle worst-case scenarios, AI allows operators to predict with high confidence what will happen in the next minutes, hours, and days, and allocate resources accordingly. That shift alone can unlock enormous capacity from existing infrastructure.
What role does AI play in predicting and preventing grid failures, and how mature is that capability today?
This capability is genuinely mature and delivering measurable results; it’s not theoretical anymore.
Argonne National Laboratory developed AI-enabled software that predicts when grid components will fail by analyzing sensor data that utilities already collect. Their prognostic models can estimate the remaining useful life of equipment, how many years, months, and weeks a transformer or inverter has left. On solar inverters specifically, they demonstrated 43-56 percent reduction in total maintenance costs and 60-66 percent reduction in unnecessary crew visits.
Across the industry, Deloitte’s research shows predictive maintenance delivering 35-45 percent reduction in downtime, 70-75 percent elimination of unexpected breakdowns, and 25-30 percent reduction in maintenance costs. National Grid’s deployment across 60,000 assets is avoiding roughly 1,000 outages per year.
Where it gets interesting is fire detection and real-time hazard response. Utilities are deploying ultraviolet cameras that can detect corona effects, precursors to arc flashovers, before they ignite. This is the one predictive maintenance use case where edge computing is truly critical, because response time is measured in seconds.
I’d rate this capability at about 7 out of 10 in maturity. The algorithms work well. The limiting factor is data infrastructure; many utilities still have gaps in sensor coverage, inconsistent data quality, and legacy systems that don’t talk to each other. The technology is ahead of the institutional readiness to deploy it at scale.
Can AI meaningfully accelerate the permitting and planning bottlenecks that slow down new energy infrastructure?
This is one of the most underappreciated applications. Permitting and planning delays are arguably the single biggest constraint on the energy transition, not technology, not economics. Goldman Sachs estimates that about $720 billion of grid spending is needed through 2030, and transmission projects can take several years just to permit, then several more to build.
AI can accelerate this in several ways. First, environmental impact assessments involve analyzing enormous volumes of geographic, ecological, and regulatory data. AI can process satellite imagery, biodiversity databases, and regulatory requirements simultaneously to identify optimal sites and flag potential conflicts before applications are filed, compressing months of analysis into weeks.
Second, AI can automate the documentation burden. Permitting involves hundreds of pages of technical documentation, compliance checklists, and regulatory filings across multiple jurisdictions. Natural language processing can draft, review, and cross-reference these documents against applicable regulations, catching inconsistencies early.
Third, AI-driven grid modeling can demonstrate to regulators and communities that proposed infrastructure will be reliable, safe, and beneficial, providing simulation evidence that would otherwise require lengthy study periods.
The caveat is that permitting bottlenecks are fundamentally political and institutional, not just informational. AI can’t fix NIMBY opposition or underfunded regulatory agencies. But it can dramatically reduce the time and cost of the technical work that precedes and supports regulatory decisions. The World Economic Forum’s Energy Transition Index 2025 emphasizes that good governance and clear processes are what unlock private investment. AI can make those processes faster and more transparent.
How significant is AI’s contribution to improving the efficiency of solar, wind, and battery storage assets in the field?
Significant and growing, though the gains vary by asset type.
For wind, AI-driven turbine control systems that adjust blade pitch, yaw, and power output in real time based on predicted wind patterns can increase energy capture by 3-5 percent per turbine. That may sound modest, but across a large wind fleet, it translates to millions in additional revenue annually. Xcel Energy used analytics to predict wind speed variability, saving millions by avoiding unnecessary ramp-ups of coal and gas plants.
For solar, the gains come from predictive maintenance, soiling management, and inverter optimization. Argonne’s work on solar inverters, reducing maintenance costs by 43-56 percent and unnecessary site visits by 60-66 percent, is a good benchmark for what’s achievable with current technology.
Battery storage is where AI arguably has the highest-impact role, because battery degradation is highly sensitive to how assets are operated. AI can optimize charge/discharge cycles based on real-time grid conditions, energy prices, and degradation models, extending battery life by 20-40 percent while maximizing revenue from energy arbitrage and ancillary services. RAND researchers estimate that hybridizing solar and wind with AI-managed storage could unlock up to 30 gigawatts of additional firm capacity in the US between 2025 and 2030.
The common thread across all three is that AI enables operations closer to theoretical maximums. Most renewable assets today operate well below their potential, not because the technology is limited, but because operators lack the real-time intelligence to optimize them continuously.
What does AI-driven demand forecasting mean for utilities trying to integrate more renewables without sacrificing reliability?
It’s transformative because it attacks the core problem of renewable integration: uncertainty. The traditional grid was built on dispatchable generation; you know exactly how much power a gas plant produces because you control the fuel input. Renewables don’t work that way. The wind doesn’t blow on command. The sun doesn’t shine on schedule.
AI-driven demand forecasting has improved accuracy by up to 20 percent compared to traditional statistical methods. That improvement cascades through every operational decision a utility makes: how much reserve capacity to maintain, when to charge or discharge storage, how to schedule maintenance, which peaker plants to keep on standby.
The real breakthrough is combining demand forecasting with supply forecasting. When you can accurately predict both what consumers will need and what your renewable fleet will produce, you can identify the gaps hours or days in advance and fill them intelligently, with storage, demand response, or targeted generation, rather than maintaining expensive always-on backup.
For utilities, this directly translates to cost savings. Every percentage point of improved forecast accuracy reduces the need for expensive spinning reserves and curtailment, situations where renewable energy is literally wasted because the grid can’t absorb it. As grids integrate more renewables, the value of each incremental improvement in forecasting grows exponentially. It’s the enabling technology that makes high-penetration renewable grids economically viable.
How is AI being applied to accelerate materials discovery for next-generation batteries or solar cells?
This is one of the most exciting long-term applications, though it’s important to be honest about where we actually are.
The headline numbers are impressive. Google DeepMind’s GNoME model predicted 2.2 million potential new stable materials, including 52,000 layered compounds and 528 lithium-ion conductors. NJIT researchers used generative AI to discover five entirely new porous transition metal oxide structures promising for next-generation multivalent batteries. Argonne is training one of the largest chemical foundation models on billions of molecules to find better battery electrolytes and electrodes.
For solar, KIT researchers used AI combined with automated high-throughput synthesis to identify new organic molecules that boosted perovskite solar cell efficiency to 26.2 percent, using only 150 targeted experiments instead of hundreds of thousands.
But here’s the reality check, as MIT Technology Review pointed out in a thorough analysis: researchers at UC Santa Barbara found limited evidence of truly novel, credible, and useful compounds among the DeepMind predictions. The bottleneck is not imagining new structures; it’s synthesizing them in the real world and validating that they actually work. That gap between computational prediction and physical reality remains significant.
Having worked with hardware-AI integration throughout my career, I can tell you that the most promising path is closed-loop systems that combine AI prediction with robotic synthesis and automated testing. AI proposes, robots make, instruments test, and the results feed back into the model. That cycle, done fast enough, is what will actually compress the 20-year materials discovery timeline into something meaningful for the energy transition.
Beyond the power sector, where do you see AI having the greatest decarbonization impact: industry, buildings, or transportation?
Buildings, and it’s not close, but not for the reason most people think.
Industry gets more attention because individual facilities have enormous emissions footprints. Transportation gets headlines because EVs are consumer-visible. But buildings account for roughly 30 percent of global energy consumption and a significant share of emissions, and the opportunity is both massive and uniquely suited to AI.
Here’s why: buildings are already full of sensors and control systems, HVAC, lighting, occupancy detection, and building management systems. The data infrastructure exists. What’s missing is intelligence. Most commercial buildings operate on fixed schedules and static setpoints that waste enormous amounts of energy. AI can continuously optimize heating, cooling, ventilation, and lighting based on occupancy patterns, weather forecasts, grid conditions, and energy prices, reducing energy consumption by 15-30 percent without any capital investment in new equipment.
The WEF highlighted AI’s ability to optimize building efficiency and enable flexible demand that adjusts to variable solar and wind output. That’s the dual benefit: buildings become both more efficient and more grid-friendly.
Transportation is transformative, but mostly in the EV-plus-smart-charging domain. AI optimizes charging to minimize grid impact and maximize use of renewable energy, turning EV fleets into distributed storage assets.
Industry has the hardest-to-abate emissions, which I’ll address separately, but AI’s role there is more about process optimization within existing constraints than about wholesale transformation.
How can AI help heavy industries like steel, cement, or chemicals find viable pathways to net zero that are currently considered very hard to abate?
The honest answer is that AI alone doesn’t get heavy industry to net zero. Hydrogen-based steelmaking, carbon capture, and electrification of high-heat processes are still necessary breakthroughs. But AI makes the pathway to those breakthroughs faster and more economically viable, and delivers significant emissions reductions in the meantime.
The immediate opportunity is process optimization. Carbon Re’s AI operating system, deployed in cement plants, reduces emissions on the order of 10,000 tons of CO2 per plant per year. In steel, ABB reports projects avoiding around three kilotons of CO2 annually, with others delivering 10-20 percent energy savings. These aren’t theoretical; they’re running in production at Heidelberg Materials plants and steel mills today.
What makes AI particularly valuable in these industries is its ability to navigate trade-offs that human operators can’t optimize in real time. A cement plant operator is constantly balancing fuel mix, kiln temperature, product quality, and emissions. Carbon Re’s platform discovered that if you compared a good day and a typical day at a cement plant, there was quite a significant difference in fuel consumption; the kiln could burn significantly less if operation was stabilized. Their AI cut coal consumption by up to 15 percent through increased alternative fuel use.
The bigger picture is that cement and steel together account for roughly 14 percent of global CO2 emissions. Eight hard-to-abate sectors, including aviation, shipping, steel, cement, chemicals, and aluminum, represent about 40 percent of total global greenhouse gas emissions. AI-driven process optimization won’t achieve net zero alone, but it can deliver 5-15 percent emissions reductions across these sectors with existing equipment, buying time for breakthrough technologies to mature.
What is AI’s role in carbon accounting, emissions tracking, and making climate data more trustworthy and actionable?
This is where AI addresses one of the most corrosive problems in climate action: trust.
Right now, corporate emissions reporting is a mess. Scope 1 and 2 emissions, direct operations, and purchased energy are manageable, but Scope 3 emissions across supply chains involve estimates built on estimates. Companies use industry averages, outdated emission factors, and incomplete data. The result is that climate commitments are often backed by numbers nobody truly trusts, not investors, not regulators, not even the companies themselves.
AI changes this by enabling continuous, granular, automated emissions tracking rather than periodic manual reporting. The IFS and PwC report specifically highlighted Industrial AI’s unique role in creating traceable, auditable data across operations, addressing the growing demand for measurable, verifiable decarbonization progress from regulators, investors, and customers.
The practical applications include: satellite-based monitoring of methane leaks across oil and gas infrastructure, real-time emissions tracking from industrial processes using sensor data, supply chain carbon footprinting that uses AI to trace emissions through complex global networks, and automated regulatory compliance reporting.
What makes this trustworthy isn’t just the technology, it’s the auditability. When emissions data comes from sensors analyzed by documented AI models rather than spreadsheet estimates, it creates an evidence chain that regulators and investors can verify. As the EU’s Carbon Border Adjustment Mechanism begins taxing carbon-heavy imports, companies that can provide verified, granular emissions data will have a direct competitive advantage over those still relying on estimates.
How are energy investors and utilities evaluating AI-driven companies differently than they did even two years ago?
Two years ago, AI in energy was a PowerPoint slide, a “future capability” bullet point that earned a premium on investor decks but rarely influenced valuation rigorously. Today, investors are asking a fundamentally different set of questions.
The shift is from “Do you use AI?” to “What measurable operational improvement has AI delivered?” Investors want to see specific metrics: percentage improvement in asset availability, reduction in unplanned downtime, improvement in energy yield, and reduction in maintenance costs. The IFS research showing that 86 percent of industrial leaders believe AI will help meet environmental goals has translated into investor expectations that those beliefs be backed by data.
Utilities are evaluating AI-driven companies through three lenses now. First, data moat: does the company have proprietary access to operational data that gives its models a compounding advantage? Second, integration friction: how easily does the AI solution work with existing SCADA, CMMS, and grid management systems? Third, measurable ROI within 12 months, not a three-year horizon.
The broader context matters too. Bloom Energy’s 2025 report found that access to power is now the leading factor in data center site selection. That tells you energy itself has become a strategic bottleneck, and any technology that makes energy systems more efficient, flexible, or reliable commands a structural premium.
The companies getting funded at the highest valuations are those that can demonstrate a clear line from AI deployment to operational improvement to financial return, not those that simply layer AI terminology onto traditional energy services.
Are there business model innovations in energy that simply wouldn’t be viable without AI, and what do they look like?
Several, and they’re already operational, not theoretical.
The first is the virtual power plant. Aggregating thousands or millions of distributed energy resources, rooftop solar, home batteries, EV chargers, and smart thermostats into a coordinated system that can provide grid services requires AI orchestration. No human operator can manage the real-time complexity of dispatching power from thousands of heterogeneous assets with different constraints, states of charge, and owner preferences. AI makes this viable, and companies are already generating meaningful revenue from virtual power plant operations.
The second is predictive maintenance as a service. Instead of selling equipment and maintenance contracts separately, companies can now offer outcomes, guaranteed uptime, and guaranteed efficiency because AI enables them to predict failures and intervene proactively. This shifts the business model from selling hardware to selling performance.
The third is dynamic, real-time energy pricing and trading. AI enables residential and commercial customers to automatically buy and sell energy based on real-time grid conditions, weather forecasts, and price signals. Without AI managing these transactions at sub-second speed, the transaction costs would exceed the value.
The fourth, emerging now, is the AI-optimized microgrid for industrial campuses and remote communities. These systems balance local generation, storage, and load in real time, operating independently or connected to the main grid depending on conditions. The optimization problem is too complex for rule-based systems but well-suited to AI.
Each of these represents billions in market value that simply didn’t exist, and couldn’t exist, before AI made the underlying coordination problems solvable.
Where is there still hype versus where is AI genuinely delivering measurable ROI in energy transition projects?
The hype-to-reality ratio varies dramatically by application.
Genuine, measurable ROI today: predictive maintenance for grid assets and generation equipment, the numbers are clear and reproducible. AI-driven demand forecasting has achieved a 20 percent accuracy improvement across multiple deployments. Process optimization in heavy industry, Carbon Re, ABB, and others have published verified emissions reduction and cost savings. Building energy management, 15-30 percent energy reduction with existing systems.
Promising but still proving out: AI-driven materials discovery, the computational side is impressive, but the gap between predicting a material and manufacturing it commercially remains large. Autonomous grid operation , self-healing grids are being piloted in China and elsewhere, but full autonomy at scale requires regulatory frameworks that don’t yet exist. Carbon capture optimization, AI is being applied, but the underlying technology itself is still maturing.
Mostly hype, so far: claims that AI will single-handedly solve the energy transition. AI is an accelerant, not a substitute for physical infrastructure, policy frameworks, and capital investment. Also overhyped: the idea that every energy company needs to build its own AI models. Most will benefit far more from applying well-validated AI solutions to their specific operational data than from training custom models.
My litmus test: if someone can show you a before-and-after measurement with a control group, it’s real. If they can only show you a projection model, it’s promising but unproven.
AI data centers are themselves enormous energy consumers. How do you think about that tension with the goals of the energy transition?
This is the defining tension of the AI era, and anyone who dismisses it isn’t being serious.
The numbers are stark. The IEA projects global data center electricity consumption will more than double by 2030 to around 945 terawatt-hours, equivalent to Japan’s entire electricity consumption. AI-optimized data centers specifically will see demand quadruple. In the US, data center power consumption is on course to account for almost half of electricity demand growth between now and 2030. Goldman Sachs projects a 165 percent increase in data center power demand by the end of the decade.
And this is already hitting real people. Electricity costs near data center clusters have increased by as much as 267 percent compared to five years ago. Dominion Energy in Virginia proposed its first base-rate increase since 1992, adding $8.51 per month for households in 2026. As one analyst put it, ordinary people are subsidizing the wealthiest industry in the world.
Here’s how I frame the tension: AI’s energy consumption is a real and growing cost. But the question isn’t whether AI uses energy, it’s whether the energy savings AI enables across the entire economy exceed what AI itself consumes. The Grantham Research Institute and Systemiq estimated that AI applications in power, transport, and food could reduce global greenhouse gas emissions by 3.2 to 5.4 billion tonnes of CO2-equivalent annually by 2035. Data center emissions, even at aggressive growth projections, reach about 1-1.4 percent of global CO2 by 2030.
The math suggests the net impact is positive, but only if we’re disciplined about deploying AI where it creates real efficiency gains rather than frivolous applications. And only if data centers are powered by clean energy, not fossil fuels. Right now, roughly 60 percent of data center energy comes from fossil fuels. That has to change.
What are the cybersecurity and resilience risks of making critical energy infrastructure more AI-dependent?
This is something I think about constantly, having worked on hardware-software integration for production systems. The risks are real, growing, and in some cases underappreciated.
The fundamental concern is attack surface expansion. Every sensor, every connected device, every AI model endpoint is a potential entry point. As smart grids integrate more IoT devices and edge computing, the number of potential attack vectors multiplies. A traditional grid could be disrupted by physically damaging infrastructure. An AI-dependent grid can potentially be disrupted by corrupting data feeds, poisoning training data, or exploiting model vulnerabilities, all remotely.
Specific risks include adversarial attacks on AI models, subtly manipulated input data that causes models to make dangerous decisions while appearing to operate normally. If an AI managing grid stability is fed slightly corrupted sensor readings, it might make load-balancing decisions that cascade into blackouts. Data integrity attacks are potentially more damaging than data theft because they erode the trustworthiness of the systems we rely on for critical decisions.
The resilience question is equally important. What happens when AI systems fail? If a utility has optimized operations assuming AI-driven predictive maintenance catches problems early, and that system goes offline during a cyber incident, the utility may not have the manual processes or human expertise to fall back on. We’ve seen this pattern in other industries; automation degrades the very human skills needed when automation fails.
The path forward requires treating cybersecurity as a core design constraint, not an afterthought. That means air-gapped systems for the most critical functions, adversarial testing of AI models, redundancy in decision-making, and maintaining human override capabilities. The energy sector should learn from aviation, where AI augments human decision-making but never fully replaces it for safety-critical functions.
How do we ensure that AI-driven energy optimization doesn’t exacerbate energy inequality or leave vulnerable communities behind?
This is a question that technologists tend to underweight and that policymakers need to lead on. The risk is real and already manifesting.
The most immediate equity concern is cost shifting. When data centers drive up electricity demand and prices in a region, those costs are typically socialized across all ratepayers, including low-income households that derive no benefit from AI services. The CNN and NPR reporting on this has been eye-opening: residential electricity rates were up 5.2 percent year-over-year in areas near data center clusters, with some areas seeing increases of 267 percent over five years.
The second concern is the digital divide in energy access. AI-driven energy optimization, smart thermostats, dynamic pricing, demand response programs, benefit customers who can afford smart devices and have reliable internet connections. Low-income households and rural communities that lack this infrastructure don’t just miss out on savings; they may actually subsidize the grid flexibility that wealthier customers use to reduce their bills.
The third concern is geographic. Emerging and developing economies host much of the world’s cement and steel capacity and often rely on older equipment. AI-driven optimization could disproportionately benefit wealthy nations with better data infrastructure, widening the global gap in industrial efficiency and emissions.
The solutions are primarily policy-driven, not technical. Utilities should be required to ensure that efficiency gains from AI are equitably distributed across rate classes. Smart grid programs should include subsidized access for low-income communities. International development financing should prioritize AI-ready infrastructure in emerging economies. The technology doesn’t inherently create inequality, but deploying it without equity guardrails will absolutely widen existing gaps.
What does the energy system look like in 2035 if AI development continues at its current trajectory, and what has to go right to get there?
In 2035, if things go right, the grid operates more like a living system than a mechanical one. It’s probabilistic, adaptive, and largely self-optimizing.
Here’s what that looks like concretely: renewable penetration exceeds 60 percent in most developed nations, enabled by AI-managed storage and demand flexibility that solves the intermittency problem economically. Predictive maintenance has reduced grid outages by 70+ percent compared to today. Every commercial building operates as an intelligent energy node, consuming, generating, and storing energy based on real-time optimization. Heavy industry has reduced process emissions by 15-25 percent through AI optimization, buying time for hydrogen and carbon capture to reach commercial scale. Permitting timelines for new infrastructure have been cut in half through AI-assisted planning and environmental analysis.
What has to go right? Five things.
First, data infrastructure. Most of the grid still runs on analog equipment disconnected from IT systems. Billions in investment are needed to digitize and connect grid assets.
Second, regulatory modernization. Current regulatory frameworks weren’t designed for AI-managed grids, dynamic pricing, or autonomous energy trading. Regulators need to catch up without stifling innovation.
Third, workforce development. The energy industry needs tens of thousands of people who understand both energy systems and AI. That talent pipeline doesn’t exist at the scale needed.
Fourth, clean energy for AI. Data centers must be powered by renewables, not fossil fuels. If AI’s own energy consumption undermines the transition, the entire value proposition collapses.
Fifth, equity by design. If AI-driven energy optimization only benefits wealthy customers and countries, the political backlash will slow deployment everywhere.
None of these is guaranteed. All of them are achievable with focused policy, investment, and institutional will.
What is the single most underestimated application of AI in the energy transition that you think deserves far more attention?
Industrial process optimization in emerging economies.
Everyone talks about AI for solar forecasting, grid management, and battery optimization in developed markets. Those are important. But the largest untapped opportunity is applying AI to the thousands of cement plants, steel mills, and chemical facilities in India, Southeast Asia, Africa, and Latin America that are operating well below their efficiency potential, and will continue operating for decades.
These facilities account for a disproportionate share of global emissions. They often run on older equipment with limited digital infrastructure. The gap between a “good day” and a “typical day” at these plants, the efficiency opportunity Carbon Re identified in cement production, is typically larger in developing economies than in developed ones, because they’ve had less access to optimization technology.
The math is compelling. If AI can reduce cement kiln fuel consumption by 2 percent per plant globally, that’s millions of tons of carbon reduced, from existing facilities, with no new capital equipment, deployable in months rather than decades. Scale that across steel, glass, ceramics, and chemicals, and you’re talking about gigatons of cumulative emissions reductions.
What makes this underestimated is that it’s not glamorous. It doesn’t involve breakthrough materials, fusion reactors, or autonomous vehicles. It’s about making existing dirty infrastructure less dirty, one percentage point at a time, at a massive scale. But from a climate perspective, emissions reduced from a cement plant in Indonesia are exactly as valuable as emissions reduced from a data center in Virginia. And the ROI, in both financial and carbon terms, may actually be higher.
Having spent over a decade building and deploying AI systems in production environments, I can tell you that the most impactful applications are rarely the most exciting ones. They’re the ones that work reliably, at scale, in challenging real-world conditions. That’s exactly what industrial AI in emerging economies represents.
About Shomron Jacob
Shomron Jacob is an AI/ML expert and entrepreneur with over a decade of experience architecting, building, and deploying production AI systems across enterprise environments. He holds three filed patents in AI/ML technology, has published in VentureBeat, InformationWeek, and other leading outlets, and speaks regularly at industry conferences on AI strategy and implementation. His work spans generative AI, computer vision, and hardware-AI integration.
