about applied computing
Mission
Applied Computing: Cracking the Energy Code
Let’s start with a truth often ignored, yet so essential it underpins the fabric of modern life - every structure we build, every item we wear, every journey we embark on:
Without the energy industry, life as we know it is impossible.
Cement, Steel, Fertiliser, Petrochemicals. These aren’t luxuries - they’re the pillars of modern civilisation. They’re our homes, our hospitals, the roads we travel, the medicines we depend on, and the microchips that connect us. And yet - this same industry, the lifeblood of modern existence - is frozen in time.
Many of the facilities that power our world are nearly half a century old. These installations - out of sight, often out of mind - are responsible for more than 50% of global CO₂ emissions. And to meet our climate goals, those emissions must fall by 93%.
Meanwhile, demand is rising, fast.
In the next two decades, global energy demand will increase by 54%. Steel demand by 8.6%. India’s middle class is set to double, as will its energy needs. Africa’s population will soar to 2.5 billion by 2050.
And just as the world asks more of industry, its expertise is vanishing. The average oil and gas worker is 56 years old. In the last five years, engineering graduates entering the field have dropped by 83%. If nothing changes, the outcome is clear: our infrastructure will grow more dangerous, more polluting, and less capable - until it fails us all.
The Myth of Easy Transition
There is a comforting fiction whispered in policy rooms and news cycles: that we can simply replace the old with the new. That renewables will swiftly take over. But the math - and the physics - say otherwise.
To reach a 50% renewable mix by 2050 would require $110 trillion - more than the combined GDP of Earth. Lithium and cobalt mining would need to rise by 4,200% and 2,100%, respectively. We would need land equal to the size of India and Australia combined, and pour more cement than humanity has used in the past two centuries.
Today’s grids can’t handle the storage, surges, or complexity. And developing nations - producing 90% of the world’s heavy industry output - will keep turning to fossil fuels. Because they are dense, and transportable. Despite remarkable gains in 2023, renewables only account for 30% of electricity. 80% of global energy still comes from fossil fuels.
Let’s be clear: These industries cannot be erased. They must be transformed - urgently.
The Role of Digital Technology
Industrial emissions come from the complex, powerful machines that transform raw material into usable products - through extraction, transportation, refining, and conversion. An average refinery emits 1.2 million tonnes of CO₂ annually - as much as 260,000 cars.
Even modest process improvements at this scale deliver massive financial and environmental gains. The World Economic Forum estimates that digital technology can reduce emissions by at least 20% by 2050.
And yet - 90% of energy production data remains unused.
Why?
Because heavy industry is different. The software is old. Systems are siloed and complex. Upgrades are risky. The people who run these plants are not digital natives - and most are nearing retirement. This is an environment where the cost of error is catastrophic. Change is feared, and rightly so.
Technical barriers run even deeper. Industry data is uniquely complex:
Physics-based simulations are slow and computationally expensive.
Engineering documents are dense, diverse and esoteric.
Time-series data from sensors is vast, noisy, chaotic and must obey the laws of physics and chemistry.
These challenges have made meaningful digital transformation extremely difficult.
Until Now
The Breakthrough: AI That Understands The Energy Industry
In the last 24 months, something extraordinary has happened.
Applied Computing’s breakthroughs in AI have cracked the code. Today’s frontier models - Fourier Neural Operators, vision language models, reasoning engines - are not just good. They are revolutionary.
We now surpass physics-based simulation with AI models millions of times faster.
We can interpret technical schematics, logs, and blueprints with multimodal AI.
We can forecast and detect anomalies in time-series data with >99% accuracy.
And we can now integrate all of these capabilities into unified, intelligent systems - small enough, efficient enough, and powerful enough to run in the facility itself - a new breed of foundational models.
And it shows a path to production environments that are safer. More productive. Far less carbon-intensive. It means emissions reduction that pays for itself - because efficiency is the path that unites economy and ecology. And it lays a path towards using AI to design or upgrade facilities to reduce their carbon footprint.
Why This Matters Now
In 2021, the world’s largest energy companies laid out bold energy transition plans. Biofuels. Hydrogen. Electrification. Carbon capture. But today, most have quietly scaled back.
Why?
Because the margins don’t work. Transformation, as currently conceived, is too expensive. Today, over 21% of global refining capacity - nearly 20 million barrels per day - is at risk of closure due to weak economics.
We cannot build the future on wishful thinking. If we want the transition to happen, we must make it economically inevitable. AI provides a pathway to making this happen, unlocking more revenues from existing facilities and shining a light on profitable investment opportunities that can transform heavy industry.
This is our vision at Applied Computing.
We start with refining because it is the hardest problem - the most complex, one of the most energy-intensive, and among the largest sources of CO₂. Over the past four years, we assembled a team of world-class AI scientists and energy experts. We built new foundation models - models that combine physics, time-series analysis, visual reasoning and domain expertise.
The result? Lower emissions. Safer operations. Reduced maintenance. Higher output.
And not just improved operations - but in future smarter design. Upgrades that cost less, emit less, and scale faster.
Introducing Orbital
Orbital has shown double-digit percentage improvements - translating into tens of millions of dollars in operational gains. In real environments, it’s already enabling new measurements, recommending more optimal set-points, forecasting output under new conditions, and identifying potential failures - and showing 30–50% performance improvements over all existing technologies.
Soon, in the hands of operators it will handle the majority of day-to-day engineering queries - providing the engineering co-pilot that is so desperately needed. It will transform how we run, maintain, and evolve the world’s industrial core.
AI That Works in the Real World
We’re not interested in AI for headlines or hype. We believe in AI that performs where it matters most - in the field, in the factory, on the frontlines of civilisation.
Orbital plugs into existing infrastructure. It doesn’t require massive digital overhauls. It respects the constraints. It works alongside operators. It speaks the language of industry.
There is no need to compromise; You Power the World, We Power You.
Applied Computing