In 2023, Sequoia Capital’s David Cahn wrote a post titled “AI’s $200B Question,” where he estimated how much revenue the industry would need to justify the year’s investment. Even after ChatGPT’s blockbuster debut, $200 billion seemed like a lot. But every year Cahn reruns the number, it grows. In 2026, the estimate reached $1.5 trillion—which he says is likely an undercount—and $3 trillion total since ChatGPT.
Those figures may soon look quaint. Another $7.5 trillion will be plowed into chips, data centers, and power in the next five years, John Greenwood, global head of infrastructure and real asset finance at Goldman Sachs, recently told The Information.
The impact of this investment goes well beyond napkin math. Enormous data centers are planned or popping up across the US. A report from the International Data Center Authority estimates data centers now consume 6 percent of the country’s electricity. According to The Economist, AI spending is shaping up to be the biggest investment boom in history, surpassing railway, canal, and dot-com manias.
All this amounts to a historic bet on AI. But it’s actually more specific than that: It’s a bet on a particular AI business model. The wager is that people will demand AI services in droves; most will pay for those services through ads or subscriptions; and their favorite products will be powered by proprietary models in data centers built for a handful of firms. If this proves out, then dollars invested meet up with dollars earned.
This is more or less how things are working out. The most popular AI models are owned and operated by the companies most associated with the data-center bonanza. These include OpenAI, Anthropic, and Google. People are beginning to pay for AI, and the companies behind the models can boast rapidly growing revenue.
But it’s not all smooth sailing. A political backlash to all those resource-hungry data centers is brewing. State and local governments have passed some 275 temporary or permanent data-center bans just this year, with another 75 in the works, according to The Information. And in recent polls, Americans have become much less trusting and more worried about AI and its impact on society, especially young adults.
At the same time, companies are looking to put the brakes on AI spending and better measure value, after some blew their annual budgets in just over a quarter. Others worry AI companies will build competing products using internal data and information acquired by forward-deployed engineers sent to help firms implement AI.
Perhaps none of this would matter if there were no alternatives. But increasingly, there are. Let’s take a look at two of them: Open models and small models.
Open models. The best-known products from Anthropic or OpenAI run on closed models. These companies sell access via subscriptions. In contrast, anyone can download an open model, adjust it for their own purposes, and run it wherever they like. Just how open these models are varies. Most makers of open models release their weights, but few models are truly open source. The performance of open models typically lags closed models, but they tend to be cheaper and more customizable.
Small models. The size of an AI model comes down to how many internal connections, or parameters, it has. The most advanced models are also the biggest. They have a trillion or more parameters and run on thousands of chips. Smaller models aren’t as powerful, but many are open, cheaper, and run on fewer chips. Some can even fit locally on devices billions of people already own.
There’s a lot to like about both options for users worried about privacy, security, or cost. Tech-savvy companies might cut out big AI by downloading an open model, customizing it on data specific to their business, and running it on their own servers. Less savvy firms may simply opt to access cheaper models in the cloud, forcing down prices of closed models. Meanwhile, as the backlash grows, AI that lives on phones instead of in big tech data centers may prove attractive to individuals. This route would require no subscriptions or chips beyond the purchase of a device.
The catch has been that open and small models just aren’t as good as closed ones.
Now, however, large open models appear to be narrowing the gap. In July, Kimi K3, an open model made by Chinese lab Moonshot AI, surprised Silicon Valley by nearly matching the performance of Fable 5 and GPT-5.6 Sol, Anthropic and OpenAI’s best models, released just weeks before. What’s more, Moonshot released K3's weights at the end of the month. At 2.8 trillion parameters, it’s the largest, most powerful open model yet. More new open Chinese models are rolling out too—and China is hardly alone. Thinking Machines, an AI lab founded last year by former OpenAI CTO Mira Murati, has adopted the strategy: Its first release was an open-weights model called Inkling. Other examples include Meta's Llama models and Nvidia's Nemotron series.
Small models are also making progress. A startup called PrismML, for example, aims to compress larger models without sacrificing too much performance. In July, the company said it had compressed Qwen 3.6, a 27-billion-parameter open model, such that it fits on an iPhone. How much the model suffers in practice awaits broad, real-world trials. But Apple, which has declined to join the data-center frenzy and aims to keep as much AI on-device as possible, is reportedly in talks with the startup.
Most people use AI for simple tasks, like searching the internet, summarizing text, or making a PowerPoint presentation. Fewer use it for complex tasks, like writing code or working on obscure mathematical proofs. Five years ago, bleeding-edge AI was just beginning to excel at those simpler tasks, with open models lagging. Now, gains at the frontier are focused on complex work (a minority of power users), while open and small models are nearly as good at simpler services (most mainstream users).
“Imagine a world, maybe three years from now, where 95% of the intelligence that you need is available to you locally, on your phone, on your laptop, on your appliances, and it’s really on the last maybe 5% of high-end stuff that you’ll need to go to the cloud,” PrismML CEO Babak Hassibi recently told The Information. This would “change the economics of AI,” he said.
To be clear, this doesn’t mean AI won’t need data centers. Even if companies favor open models, most won’t host their own, the models have to live somewhere, and when prices go down, demand goes up (though this may eat into profits). Meanwhile, local AI on phones will have to prove its worth with real users, and the competition is stiff. It’s unclear how long it would take to realize Hassibi’s dream of 95%. Still, with Apple tipping the scales, local AI may begin nibbling at the edges sooner than later.
Perhaps all this is why Kimi K3 spooked Silicon Valley. Staring down an infrastructure bet with few historical peers, open models like Kimi narrow the path to profitability. Closed models may keep improving so much that users find them irresistible. But competition from cheaper or local alternatives nipping at their heels will pressure companies to keep prices lower than they’d like and siphon demand from the mass market. This tension between small, open, and closed is sure to increase.
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