by Elon Musk has frequently quipped that humans are essentially “low-watt meat computers.” The human brain runs on roughly 20 watts of power, about the same as a dim lightbulb, yet it delivers perception, memory, creativity, emotional judgment, and real-time motor control with extraordinary reliability. By contrast, modern AI systems require megawatts of electricity, acres of silicon, and industrial cooling.
Some big numbers:
Gartner Predicts Global AI spending to top US$2 trillion in 2026.
AI Spending in IT Markets, Worldwide, 2024-2026 (Millions of U.S. Dollars)
| Market | 2024 | 2025 | 2026 |
| AI Services | 259,477 | 282,556 | 324,669 |
| AI Application Software | 83,679 | 172,029 | 269,703 |
| AI Infrastructure Software | 56,904 | 126,177 | 229,825 |
| GenAI Models | 5,719 | 14,200 | 25,766 |
| AI-optimized Servers (GPU and Non-GPU AI Accelerators) | 140,107 | 267,534 | 329,528 |
| AI-optimized IaaS | 7,447 | 18,325 | 37,507 |
| AI Processing Semiconductors | 138,813 | 209,192 | 267,934 |
| AI PCs by ARM and x86 | 51,023 | 90,432 | 144,413 |
| GenAI Smartphones | 244,735 | 298,189 | 393,297 |
| Total AI Spending | 987,904 | 1,478,634 | 2,022,642 |
Morgan Stanley predicts global data center spending to hit $3 trillion by 2029, with half on construction and half on hardware.
The IEA has a base case that global electricity consumption for data centres is projected to double to reach around 945 TWh by 2030. Their lift-off case projects global electricity demand from data centres in 2035 to be around 45% higher than in the Base Case, exceeding the 1,700 TWh mark and reaching around 4.4% of global electricity demand.
The Economist suggested that if OpenAI raises $100bn in its listing, it would be 4x the largest IPO ever.
ExplodingTopics has a top AI statistics page worth a look.
The AI market is expanding at a CAGR of 31.5%.
35.49% of people now use AI tools every day
Some estimates suggest that AI technology could generate $15.7 trillion in revenue by 2030. Boosting the GDP of local economies by an additional 26%.
Bubble Territory
My holiday reading seemed to focus on where exactly we are in the AI cycle and when the bubble will burst.
Veteran Investor Howard Marks of Oaktree posed sensible questions in his latest memo, “Is it a bubble?’
Who will be the winners, and what will they be worth?
Should we worry about so-called “circular deals”?
What will be the useful life of AI assets?
Is exuberance leading to speculative behavior?
What’s the end state?
“Many companies justify their spending because they’re not just building a product, they’re creating something that will change the world: artificial general intelligence, or A.G.I. . . . The rub is that none of them quite know how to do it.”
For deeper and more skeptical analysis, Ed Zitron’s newsletter is a cracking read. Basically, enterprises are trying to adopt generative AI, but it doesn’t work.
How to argue with an AI boster
To be skeptical of AI is to commit yourself to near-constant demands to prove yourself, and endless nags of “but what about?” with each one — no matter how small — presented as a fact that defeats any points you may have.
He kicks off this post with a MIT report by Mit Nanda (it is a little old given it was published in July 2025) which concludes 95% of organizations are getting zero return [on generative AI].” The report says that “adoption is high, but transformation is low,” adding that “…few industries show the deep structural shifts associated with past general-purpose technologies such as new market leaders, disrupted business models, or measurable changes in customer behavior.”
Three Key Things:
Both at work and personally, AI has been both a massive productivity tool and slightly disappointing in a more prescriptive, constrained environment. However, I have high hopes.
I expect that, as with any super cycle, many projects and their hype will never be delivered.
I’m particularly interested in the future of robotics using AI systems. Check out Hannah Fry’s visit to the Google Robotics Lab.
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