Neural Networks Are Devouring the World: A Report from the Front Lines of Energy Madness

Welcome to the Era Where GPUs Cost More Than Apartments

Do you know what modern neural networks and teenagers have in common? Both are constantly hungry, consume an obscene amount of resources, and are convinced they will change the world. The only difference is that teenagers actually sleep sometimes.

I’ve been in development for over a decade, and I’ve seen my fair share of hype cycles during that time. Blockchain promised a revolution – we got NFTs with monkeys. The Metaverse was supposed to be the new internet – we got an expensive VR toy for corporations. But what is happening right now with artificial intelligence looks different. This isn’t just another bubble from Silicon Valley inflated by venture capitalists. This is a fundamental redistribution of global resources in real time.

And when I say «resources», I don’t mean abstract investments. I’m talking about gigawatts of electricity, millions of square meters of datacenters, and production capacities that used to make chips for your game consoles but are now stamping out hardware for training models capable of writing a mediocre essay about cats. Exponential growth? Guys, we are already on the vertical section of the graph where math starts looking like science fiction.

Numbers That Make Datacenters Sweat

Let’s start with the uncomfortable truth. Training one large language model like GPT-4 consumes roughly as much energy as a hundred American households do in a year. But that’s not even the scariest part. Training is a one-time event. Inference – when millions of people simultaneously ask ChatGPT how to cook an omelet – is a constant load.

OpenAI, by some estimates, spends about seven hundred thousand dollars a day just on electricity to keep ChatGPT running. Per day. That is more than the annual budget of an average startup. And that is just one company with one product.

Google recently admitted that their carbon dioxide emissions have grown by forty-eight percent since 2019. Guess what happened during those years? That’s right, they started shoving AI into everything, whether it fit or not. Microsoft promised to become carbon-negative by 2030, but after integrating AI into their products, their energy consumption spiked so much that this goal now looks like a New Year’s resolution to quit smoking – technically still valid, but everyone knows it won’t work.

Meta is in the game too. They are building datacenter after datacenter, and each one consumes so much electricity that local power grids are starting to get nervous. In some regions of the US, utility companies are already stating they cannot meet the growing demand from AI infrastructure without building new power plants.

The Great Kilowatt Migration

And now for the most interesting part. The world isn’t just ramping up electricity production – it is redistributing it. Datacenters are becoming priority consumers. In Ireland, where the European offices of many tech giants are based, datacenters already consume eighteen percent of the entire country’s electricity. For comparison: all residential homes combined consume twenty-nine percent. That means metal boxes with blinking lights are guzzling more than half of what all humans eat up in their homes.

In the Netherlands, local authorities imposed a moratorium on building new datacenters because the energy system simply cannot cope. In Denmark, half of the new energy consumption comes from datacenters. And this is in a country that invests seriously in renewable energy.

Speaking of renewable energy. All these companies love to brag that their datacenters run on solar panels and wind turbines. Technically, this is true – they buy certificates for «green» energy. Practically, however, their datacenters are plugged into the regular grid, which in most countries is still powered by coal and gas. It’s like ordering a double cheeseburger with bacon but asking for a Diet Coke – technically you are doing something healthy, but the big picture doesn’t change much.

The Chip Famine

Now let’s talk about silicon. NVIDIA didn’t become the most expensive company in the world because their video cards render explosions in games well. Their H100 chips and the new H200s are the foundation of all modern AI infrastructure. One such chip costs anywhere from twenty-five to forty thousand euros. To train a large model, you need thousands of these chips combined into clusters.

The demand is so insane that delivery times have stretched to a year. Companies are ready to pay any amount of money just to get the hardware before their competitors do. This has created a deficit across the entire supply chain. TSMC, the world’s main chip manufacturer, is shifting production capacity from consumer electronics to specialized equipment for AI.

And do you know who suffered? Gamers. Yes, the very people who used to be the primary buyers of top-tier video cards. Now they are competing with corporations that have budgets in the millions of euros. An RTX 4090 costs two thousand euros not because production got more expensive – it’s pure market dynamics. Manufacturers know that if a gamer doesn’t buy it, some startup will buy it for fine-tuning their model.

Intel and AMD are frantically trying to catch up with NVIDIA, but so far unsuccessfully. Developing specialized AI chips takes years of work, and the market is growing so fast that by the time a new generation of hardware is released, the requirements have already tripled.

The Architecture of Madness

Datacenters have stopped being just warehouses with servers. A modern AI datacenter is a high-tech enterprise with infrastructure worthy of a nuclear power plant. Cooling systems, backup power sources, specialized network architectures – all of this costs billions.

Microsoft is building datacenters that cover an area of several football fields. Amazon is expanding AWS so fast that their capital expenditures have grown to seventy-five billion dollars a year – and the lion’s share goes specifically to AI infrastructure. Google announced plans to build datacenters in Spain, and local residents are already worried – not about ecology, but about whether there will be enough electricity for everyone.

Taiwan, where TSMC is located, has become the strategic center of the entire AI industry. If something happens to chip production there tomorrow, half of the global AI development will come to a halt. It’s as if the world’s entire supply of coffee were stored in one warehouse – yes, efficient, but a little scary.

The Exponent Knows No Mercy

And now for the scariest part. All these numbers represent the current state. But the growth is exponential. Every new generation of models requires ten times more computing resources than the previous one. GPT-3 was trained on ten thousand GPUs. GPT-4 – on twenty-five thousand. The next generation? Estimates vary, but we are talking about hundreds of thousands of chips.

Sam Altman, the head of OpenAI, openly says that the future of AI requires trillions of dollars in investment and the construction of entire energy systems for this industry. And he is not joking. OpenAI is in talks to build its own power plants. Not datacenters – power plants.

Elon Musk founded xAI and declared that he would build a supercomputer out of one hundred thousand H100 chips. For reference: that is more than in all existing supercomputers in the world combined. And he needs a separate substation with a capacity of one hundred megawatts for this – that would be enough for a small city.

Real Transformation or a Soap Bubble?

The billion-dollar question: is this a fundamental transformation of the economy or just another overheated bubble that will pop, leaving behind mountains of expensive hardware and burnt investments?

There are arguments for a bubble. First, the return on investment is not yet obvious. Yes, ChatGPT can write code and answer questions. But monetization is limping. OpenAI loses billions of dollars a year. Anthropic, Google, Microsoft – they are all pouring giant sums in, but there is no real profit yet. This is a classic sign of a bubble: when everyone believes in the future, but the present makes no money.

Second, there are physical limits. The laws of physics won’t allow chip efficiency to increase forever. We are already close to the quantum limits of miniaturization. Yet the exponential growth of model requirements isn’t going anywhere.

Third, energy collapse. If the demand for electricity keeps growing at this pace, problems are inevitable. Some regions are already facing deficits. What will happen when every company wants its own cluster of one hundred thousand GPUs?

But there are counterarguments too. AI is already integrated into products used by billions of people. Google Search, Microsoft Office, Photoshop, translators – all of this has been working with AI for a long time. This is not a hypothetical future, it is the present.

Medicine is starting to use AI for diagnostics. Pharma – for drug development. Manufacturing – for process optimization. These aren’t just pretty presentations at conferences, these are real applications that save money and save lives.

The difference between the AI boom and blockchain is that AI solves real tasks right now. Blockchain is still looking for a use case besides speculation. The Metaverse turned out to be useless. But automatic translation, code generation, data analysis – these are things companies are ready to buy.

When It Pops (If It Pops)

If this is a bubble, when will it burst? History teaches us: bubbles collapse when the money runs out or when it becomes obvious that expectations are unrealistic.

The first scenario is financial. If investors get disappointed by the lack of profit, the cash flow will dry up. Startups will begin closing, companies will cut AI investments. This is painful, but not catastrophic. Those who actually create value will remain.

The second scenario is technical. If it turns out that current architectures have reached a limit and new breakthroughs are impossible, the industry will stall. Investments will continue, but the race will slow down.

The third scenario is energy-related. If the growth of electricity consumption becomes a problem, governments will start regulating. Taxes on energy consumption for datacenters, restrictions on construction, environmental requirements – all of this will put the brakes on development.

The fourth scenario is political. Geopolitical risks around Taiwan, trade wars, restrictions on chip exports – any of these factors could destroy the fragile supply chain.

But there is a fifth option – there will be no explosion. The industry will find a balance. Models will become more efficient, hardware – more powerful, and applications – more profitable. Exponential growth will slow down to linear, and AI will become a regular part of the economy, just like the internet once did.

A View from the Trenches

I’m watching this circus with mixed feelings. On the one hand, as a developer, I am thrilled. The tools that have appeared over the last two years are incredible. I save hours of work thanks to AI assistants. My projects have become better.

On the other hand, it looks like a classic mania. Everyone wants a piece of the pie. Every startup adds «AI-powered» to its description. Investors are throwing money around. It reminds me of the dot-com bubble of the early two thousands or the ICO mania of 2017.

The difference is in the scale. Back then, millions burned up. Now, trillions and the planet’s physical infrastructure are at stake. Datacenters won’t disappear, even if people stop using them for AI. Power plants will remain. Production capacities have been reconfigured.

If this is a bubble, the consequences will be more serious than just the disappearance of a few startups. It will be a restructuring of a part of the global economy.

But if it’s not a bubble, we are living in a moment of fundamental transformation. Like the industrial revolution, only faster. And instead of coal and steam – silicon and electricity.

What’s Next

Honestly? I don’t know. And nobody knows. Everyone who claims otherwise is either selling something or engaging in self-deception.

What I see for sure: the industry has reached a point of no return regarding infrastructure. Billions have been invested, datacenters built, production lines rebuilt. Even if the hype subsides, all this infrastructure will remain and find a use.

AI is already integrated too deeply to simply disappear. Maybe there won’t be AGI in five years. Maybe GPT-10 won’t be a thousand times smarter than GPT-4. But the tools that exist now are already useful enough to stay.

Exponential growth will slow down – the laws of physics and economics won’t allow it to continue forever. But the industry won’t vanish. It will mature, become more boring, more predictable. As happened with the internet, mobile phones, cloud computing.

And for now, we are observing the most expensive experiment in human history. Simultaneously exciting and terrifying. An experiment where the stake is the energy budget of entire countries and the future of technological development.

Whether the bubble bursts or not – time will tell. In the meantime, datacenters continue to guzzle gigawatts, NVIDIA stamps out chips faster than people can buy them, and I continue debugging code with the help of that very neural network which consumes more energy than my fridge does in a year.

Welcome to the future. It’s hot, noisy, and very, very hungry. 🔥