{"id":876,"date":"2026-04-28T12:07:15","date_gmt":"2026-04-28T12:07:15","guid":{"rendered":"https:\/\/ilrickyverso.it\/?p=876"},"modified":"2026-04-28T12:16:44","modified_gmt":"2026-04-28T12:16:44","slug":"intelligenza-artificiale-miliardi-al-giorno","status":"publish","type":"post","link":"https:\/\/ilrickyverso.it\/en\/intelligenza-artificiale-miliardi-al-giorno\/","title":{"rendered":"Digital Gold Is Burning. Literally."},"content":{"rendered":"

How the World Is Spending Billions a Day Building a Silicon God \u2014 and Why the Light Might Actually Go Out<\/em><\/em><\/strong><\/em><\/h3>\n\n\n\n

There is one thing that unites the CEOs of Microsoft, Google, Amazon, Meta, and Oracle right now.<\/p>\n\n\n\n

It's not the passion for innovation. It's not the vision of the future. It's the fear of coming second<\/strong>.<\/p>\n\n\n\n

And this fear\u2014this beautiful, expensive, irrational fear\u2014is producing the greatest concentration of private investment in human history. Numbers so large that the human brain struggles to process them. Let's try together.<\/p>\n\n\n\n

How much do you spend? Let's pretend money still exists.<\/h2>\n\n\n\n

Let's start from a concrete number, updated to today: the four big companies that control the global cloud \u2014 Microsoft, Amazon, Google, Meta \u2014 have planned for the 2026 total spending between 635 and 665 billion dollars<\/strong> in artificial intelligence infrastructure. Data centers, chips, fiber networks, cooling systems.<\/p>\n\n\n\n

635 billion. Dollars. In one year.<\/p>\n\n\n\n

Divide that by 365 and you get approximately $1.7 billion per day<\/strong>. Every day. Holidays included. Even Christmas.<\/p>\n\n\n\n

Adding Oracle to the count, it rises to approximately 720 billion<\/strong>, which bring daily spending close to 2 billion dollars a day<\/strong>.<\/p>\n\n\n\n

For context: New Zealand's annual GDP is about $250 billion. These five companies surpass that in 125 days<\/strong> Only in AI investments. Not in total revenue. Only in the money spent to build the physical infrastructure that runs AI.<\/p>\n\n\n\n

By 2024 they had already spent approximately 230 billion<\/strong> (630 million per day). By 2025 they had risen to 320 billion<\/strong> (880 million a day). The spending curve isn't slowing down: it's accelerating like a train whose brakes someone removed overnight.<\/p>\n\n\n\n

When I started writing this post, I was thinking "hundreds of millions a day." But I was overcautious: we're already well beyond that.<\/p>\n\n\n\n

But the money goes Where<\/em>, Exactly?<\/h2>\n\n\n\n

Good question. Because this is where it starts to get interesting.<\/p>\n\n\n\n

Most of those billions end up in silicon and steel<\/strong>: servers, ultra-powerful graphics processors (so-called GPUs, graphics cards redesigned to perform millions of calculations in parallel instead of one at a time), interconnection networks, buildings as large as airports filled with luminescent metal shelving that hum day and night.<\/p>\n\n\n\n

And guess what? All this silicon has to be manufactured by someone.<\/p>\n\n\n\n

The silent winner: Nvidia<\/h2>\n\n\n\n

If there's one company that's watching the world burn billions with a sly smile right now, it's Nvidia<\/strong>.<\/p>\n\n\n\n

In the last quarter alone, Nvidia's data center division \u2014 the one that sells AI processors \u2014 made a turnover of $35.6 billion<\/strong>. In three months. With a gross profit of approximately 75%<\/strong>, which means that for every $100 worth of chips sold, $75 remains almost untouched as profit.<\/p>\n\n\n\n

The most recent estimates speak of a potential of $170 billion in annual revenue from the data center segment alone<\/strong> for fiscal year 2026. Nvidia also said it has over $500 billion in confirmed orders already<\/strong> between current and next-generation chip models. Demand is so much greater than supply that some companies are waiting months to receive the chips they ordered.<\/p>\n\n\n\n

Jensen Huang, the founder of Nvidia, with his iconic black leather jacket, has become the symbol of this historic moment. Not because he built the AI models, but because he sold his shovels and pickaxes during the gold rush.<\/p>\n\n\n\n

The Invisible Lord: TSMC<\/h2>\n\n\n\n

But there is an entity even further upstream from Nvidia, less known to the general public but perhaps more strategically crucial: TSMC<\/strong>, Taiwan Semiconductor Manufacturing Company.<\/p>\n\n\n\n

TSMC is the only company in the world capable of to physically manufacture<\/strong> The most advanced chips on the planet\u2014the ones with components so small (3 and 2 nanometers, or thousandths of a thousandth of a millimeter) that they power processors from Nvidia, Google, Amazon, and everything that powers high-end AI. And it's located on the island of Taiwan.<\/p>\n\n\n\n

Every time a big tech company orders an AI chip, money flows through here\u2014sooner or later. TSMC is investing $30\u201340 billion a year just to expand its facilities and keep up with demand. Controlling this bottleneck is one of the reasons the chip issue has become a geopolitical issue before it becomes a commercial one.<\/p>\n\n\n\n

Who's Writing the Biggest Checks (Besides Big Tech)<\/h2>\n\n\n\n

The big cloud companies are the main capital "burners." But there are other players worth mentioning.<\/p>\n\n\n\n

Gulf Petrodollars<\/h2>\n\n\n\n

The sovereign wealth funds of the Middle East \u2014 Saudi Arabia, United Arab Emirates, Qatar, Kuwait \u2014 have quintupled<\/strong> their investments in AI over the last 12 months. Saudi Public Investment Fund (PIF).<\/strong>, the large state fund that manages oil revenues, controls over $925 billion in assets and is aggressively entering the sector. Abu Dhabi has created MGX<\/strong>, an AI-focused fund that has partnered with BlackRock, Microsoft and other financial giants to raise up to 100 billion dollars<\/strong> intended for data centers and infrastructure.<\/p>\n\n\n\n

The narrative is simple: oil will run out (or it will no longer be burnable). Better to convert the proceeds into "digital oil" before it's too late. OpenAI, Sam Altman's company, is seeking a round of funding from 40 billion dollars<\/strong>, and among the investors are the Saudi fund, SoftBank, Sequoia and Andreessen Horowitz.<\/p>\n\n\n\n

A vaguely dystopian curiosity: funds from countries that build their wealth on fossil fuels are financing companies that consume so much electricity that they destabilize the grid. The circle is (almost) complete.<\/p>\n\n\n\n

Oh, right. Energy.<\/h2>\n\n\n\n

Let's talk about the elephant in the room. Or rather: the mammoth in the room<\/strong>, because it is much bigger.<\/p>\n\n\n\n

All this silicon consumes power. A lot. An amount that, measured on a national scale, is starting to get really scary.<\/p>\n\n\n\n

In 2024, the world's data centers consumed approximately 415 TWh of electricity<\/strong> \u2014 where one TWh (Terawatt-hour) is equivalent to the energy that would power a city of one million inhabitants for an entire year. That total is equivalent to annual electricity consumption of the whole of the UK<\/strong>. In 2025 we have risen to 485 TWh. Projections for the next few years speak of scenarios that reach over 1,000 TWh<\/strong> in the event of massive adoption of generative AI.<\/p>\n\n\n\n

A little data to make everything more concrete: a single question to ChatGPT consumes about 10 times more energy<\/strong> of a Google search. Every time you ask a chatbot to write an email for you, you're powering up a bunch of servers somewhere in the world that consume as much energy as if you were running a refrigerator for a few dozen minutes. Now multiply that by the billions of daily requests globally.<\/p>\n\n\n\n

The result is that electricity grids\u2014designed and engineered in recent decades for a completely different type of consumption\u2014are beginning to buckle under the pressure. Gartner estimates that Power shortage will stall 40% of new AI data centers by 2027<\/strong>. Not because there is a lack of money or chips: because there are not enough megawatts available in local grids.<\/p>\n\n\n\n

Some areas with high concentration of servers have already recorded Energy cost increases until 267%<\/strong>. And projections from the U.S. Department of Energy suggest energy demand could reach enormous levels by 2035\u2014a thirty-fold increase from today.<\/p>\n\n\n\n

Big tech's response? Invest heavily in nuclear energy (including small modular reactors and next-generation miniaturized power plants) and renewables. Microsoft is even trying to restart the Three Mile Island nuclear power plant\u2014yes, the one that famously crashed in 1979. When humanity needs energy, history becomes ironic.<\/p>\n\n\n\n

But why? What are they trying to build?<\/h2>\n\n\n\n

Here we are at the crux of it all. The real reason why national GDP figures are spent every day, electricity grids are emptied, and nuclear power plants are reactivated.<\/p>\n\n\n\n

They want to build the\u2019AGI<\/strong>.<\/p>\n\n\n\n

What is AGI? (The Human Version)<\/h2>\n\n\n\n

Everything we use today \u2014 ChatGPT, Gemini, Claude, Copilot \u2014 is what insiders call Narrow Artificial Intelligence<\/strong>. These systems are remarkably good at doing specific things: write<\/a>, to summarize, generate images<\/a>, translate. But they don't "think." They don't understand. They don't really learn. They are, essentially, highly sophisticated statistical engines that predict which word or pixel comes after another, based on oceans of data they've ingested during training.<\/p>\n\n\n\n

As good as digital slaves with an encyclopedic memory. But lacking any true intellectual generality.<\/p>\n\n\n\n

THE'\u2019AGI (Artificial General Intelligence)<\/strong>, instead, is the final goal of this race: a system capable of learn anything, in any domain, transferring knowledge from one field to another<\/strong>, exactly as an intelligent human being does. Not a surgeon who only knows surgery. A brilliant recent graduate capable of becoming a surgeon, composer, theoretical physicist, criminal lawyer, and Michelin-starred chef\u2014and doing so by learning on his own, with the same exposure to the context a person would have.<\/p>\n\n\n\n

Beyond the AGI there is the\u2019ASI (Superintelligent Artificial Intelligence)<\/strong>: an intelligence not only human, but superior to the greatest mind ever known, in any field. And many researchers believe that once AGI is achieved, the transition to ASI will be so rapid as to be almost instantaneous\u2014because AGI itself will know how to improve itself at speeds we could not even keep up with.<\/p>\n\n\n\n

Why are they burning money for this?<\/h2>\n\n\n\n

Because the value of whoever gets to AGI first is literally incalculable.<\/p>\n\n\n\n

We're not talking about a successful app or a better search engine. We're talking about ownership of intelligence as the planet's primary resource<\/strong>. An AGI could accelerate medical research by decades in a few years, solve physics problems that have occupied laboratories for generations, design future materials and energy systems, and generate new patents in every industrial sector simultaneously.<\/p>\n\n\n\n

Industrial logic is brutal and simple: those with AGI have a self-amplifying competitive advantage. They can use their AI to further improve their own AI, leaving everyone else so far behind that any attempt to catch up is futile.<\/p>\n\n\n\n

In Silicon Valley, they call it \u201cwinner takes all.\u201d Winner takes all. Not the 51%. All.<\/strong><\/p>\n\n\n\n

And that's why none of the big players can afford to slow down, even if the costs are unsustainable, even if energy is lacking, even if the short-term returns on investment are highly uncertain.<\/p>\n\n\n\n

Is it a bubble, or are we facing the greatest technological leap in history?<\/h2>\n\n\n\n

It depends on who you ask. And this, I warn you, is the most uncomfortable part of the article.<\/p>\n\n\n\n

There are two schools of thought.<\/p>\n\n\n\n

The first says: the "bigger is better" logic works. The current approach\u2014giving models more data, more parameters, and more computing power\u2014continues to produce better results. If the trend holds, with enough hardware and enough energy, we'll get to AGI. It's not a question of if<\/em>, but of When<\/em>. And in that case, even spending $3 trillion will have been the best investment ever made by human beings.<\/p>\n\n\n\n

The second one says: we could be in a speculative bubble of historic proportions<\/strong>. Current models may have structural limitations that raw power alone cannot overcome. If a radical conceptual leap is needed to achieve AGI, this entire infrastructure may be, at best, premature. And companies that have invested hundreds of billions would find themselves with enormous servers, astronomical electricity bills, and deficits, without a product revolutionary enough to pay for it all.<\/p>\n\n\n\n

The truth? Probably a bit of both. There's a lot of rational spending and a lot of spending driven by fear, competition, and hype. As with every great moment of technological transformation in history\u2014from railroads to oil, from electricity to the internet\u2014the rush produces excess, local bubbles, and enormous waste. But ultimately, the infrastructure built during the rush often outlasts the bubble itself and becomes the foundation of the future.<\/p>\n\n\n\n

What to think of all this?<\/h2>\n\n\n\n

It depends on what mood you're in this morning.<\/p>\n\n\n\n

If you're a pessimist, you see a group of private companies grabbing control of the planet's future intelligence, burning energy resources in unprecedented quantities, financed by sovereign wealth funds and tech oligarchs, in a race that could destabilize the electricity grids of entire countries and produce a concentration of economic power unprecedented in history.<\/p>\n\n\n\n

If you're an optimist\u2014like me, at my best\u2014you see something different: a time when humanity is building, chaotically, wastefully, and not entirely self-aware, the most powerful tool ever imagined to solve the problems that are wearing us down<\/strong>. Climate change. Disease. Poverty. Inefficient systems. The slowness of science.<\/p>\n\n\n\n

There's no guarantee it will work. It could even create new problems worse than the ones it solves. We don't know if the benefits will be distributed fairly and won't end up in the hands of those who already have everything.<\/p>\n\n\n\n

But the fact that it is happening\u2014in this dimension, at this speed, now\u2014is, for better or worse, one of the most extraordinary moments I have ever experienced being alive.<\/p>\n\n\n\n

Two billion dollars a day, every day, to light the light of a mind that doesn't yet exist.<\/p>\n\n\n\n

Whether it seems silly or magnificent to you, one thing is certain: it's not something you can ignore<\/strong>.<\/p>\n\n\n\n

Sources: IEA, CNBC, Yahoo Finance, Brookings Institution, AIMultiple, EnkiAI, IDC, AInvest (April 2026)<\/em><\/p>\n\n\n\n

<\/p>","protected":false},"excerpt":{"rendered":"

How the world is spending billions a day building a silicon God\u2014and why the light might actually go out. There's one thing the CEOs of Microsoft, Google, Amazon, Meta, and Oracle have in common right now. It's not a passion for innovation. It's not a vision of the future. 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