Tag: actual intelligence

Green Energy, AI, and Planning

by Arjun Jayadev and J. W. Mason

Last year marked a watershed: For the first time in history, growth in renewable generation exceeded global growth in electricity output, causing fossil fuel power generation to decline for the first time on record. But 2025 also saw the beginning of a historic surge in energy demand from AI. Just four companies — Amazon, Google, Microsoft, and FaceBook – are expected to spend over $1.5 trillion, one quarter of all U.S. fixed investment, on new datacenters in the coming year.

These twin shifts, and the balance between them, will be central to the future of both carbon emissions and energy prices. But they also tell us something fundamental about how the economy works. Because while both these investment booms take place through markets, in the sense that their inputs are purchased and their output are sold, neither is a response to market demand. These two engines of global growth are being directed by something very different from what we traditionally think of as market signals.

The reaction from economists and business commentators to the surge of investment into these sectors has been a mix of exhilaration and fear. On the one hand, these investments promise genuinely revolutionary change, but on the other they violate deep-seated ideas of how the economy is supposed to work.

The expansion of solar-panel production has pushed far past the limits of profitability. Yet despite a drumbeat of warnings of overcapacity and collapsing prices, production keeps growing.

AI investment is, if anything, even more unmoored from profitability. Unit costs for computation have been estimated to be as much as eight times greater than what is being charged for them. Add to this the immense capital expenditures required, and it is far from clear that there is any viable business model for the AI services currently flooding the market. Anthropic’s recent decision to throttle back its less-expensive subscription tiers for Claude code users was, arguably, a recognition of this reality.

Economics teaches us that markets guide resources to their most profitable uses. Resources are allocated based on the relationship between prices and costs. Where the sales price of something is above the cost of production, businesses will add capacity to produce more of it; where the price is below production costs, businesses will scale back or exit. But in both green energy and AI, as in many historical investment booms, that is not what is happening. The resources move first, and the profits come later – or perhaps not at all.

Some experts fear we are fearing another bout of irrational exuberance — a misallocation of resources on a vast scale, this time colored with an appealing green or silicon hue. But from our point of view, the most important question about these investment booms is not whether they are irrational. It is what they reveal about how capitalism has always worked. Decisions about what industries will be built up and which will be abandoned are guided not by the invisible hand of the market, but by the hidden planners of finance.

True, the US AI boom is largely private, while the Chinese solar boom is driven by the country’s public sector. But this is less of a difference than it first appears. Investment in both cases reflects conscious decisions of a small group of people—venture capitalists in one case, government officials in the other. Indeed, given the Chinese model of devolved industrial policy with intense competition between local governments, decisions about investment may be more centralized in the US version.

When investors pour funds into ventures that promise profits years or decades from now, they are not responding to the market. They are making a conscious choice – or a plan – to reorganize the economy. The AI and green-energy industries illustrate this dynamic in especially dramatic form. But any business that operates at a loss (as almost all do in their early years) is doing so in defiance of market signals. How long a business can operate at a loss, and how high profits must be to justify expanding or even remaining in existence, are fundamentally questions about financing.

Finance exists precisely to allow production to depart from market signals. Money organized through finance is a bet on, and a catalyst for, a particular vision of the future.

The extravagant promises and, often, utopian visions, that accompany great investment booms function as coordination mechanisms. They align expectations, justify losses, and stabilize beliefs so that resources can be committed to loss-making industries until they become profitable.

In the language of John Maynard Keynes, such moments are driven less by calculable returns than by “animal spirits”—the fragile, shared confidence that induces investors to act in the face of a fundamentally unknowable future. “Enterprise,” as he wrote, “only pretends to itself to be mainly actuated by the statements in its own prospectus… Only a little more than an expedition to the South Pole, is it based on an exact calculation of benefits to come.” The combination of animal spirits and organized finance is what allows investment to move in advance of profit.

In this environment, money does not operate as a neutral means of exchange. In great waves of investment like we are seeing today, organized money is the instrument through which production is redirected, futures are selected, and value is constructed. All of this happens not in response to the market, but in defiance of it.

Among leading economists, Joseph Schumpeter was among the few to focus on the transformative role of finance. Credit’s essential role in innovation, in his view, has more in common with central planning than with traditional markets. Loans to entrepreneurs, he wrote, are “what corresponds in capitalist society to the order issued by the central bureau in the socialist state.” A loan is, in effect, an order saying: This business has the authority to take whatever labour and resources its project requires up to some certain amount. The hundreds of billions flowing into AI compute and solar manufacturing are precisely the result of such orders. Far from passive reactions to known future profits, they are reshaping the terrain on which future profitability will be assessed.

Finance is planning. To organize money is to select some futures and foreclose others, permitting certain transformations while making others impossible. In green technology, that planning reflects the priorities of the Chinese state, which has channeled both public and private investment into solar manufacturing, battery technology, and electric vehicles. It harnesses markets to do so, but the shift is taking place at a scale and speed that markets alone could never achieve. A similar story is unfolding in AI: The investment boom reflects the convictions of a small number of tech CEOs and venture capital principals. Their commitment to AI reflects their vision of the long-term future of humanity as much as it does expectations of profit.

Once we clear away the idea of monetary neutrality and the role of markets, we can see finance for what it is – what science fiction writer Kim Stanley Robinson called a “Ministry of the Future.” Markets are not an alternative to planning, but the medium through which planning takes place. Which leaves us with the real question: Are the futures being planned for us the ones that we want?

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Arjun and I wrote this op-ed back in May, when Against Money was published. We didn’t manage to do anything with it then, but it feels just as relevant now. 

Also, a note: I am going to try to start posting more regularly on this blog. For the rest of 2026, I am going to aim for one post per week. There’s nothing in particular that you need to do with this information, I am just putting it down here as a marker.

Actual Intelligence

I wanted to put down some thoughts on Large Language Models (LLMs), or so-called artificial intelligence. I apologize that this post is not going to include any links or quotes or data. It’s just an effort to work something out in my own head – something that I don’t feel – tho it’s very likely I’ve overlooked it – has been spelled out in the discussion anywhere else.

It’s a point that is, on one level, obvious, but one that I feel does not get sufficiently foregrounded: LLMs are, as the name says, language models. Given a corpus of text, they create a set of probabilities such that, for any given input, you can calculate the probability that, following a certain input, a certain word should come next. They are, in other words, tools for transforming material that people have put up on the internet.

On one level, again, everyone knows this. It’s what critics mean when they call these programs“stochastic parrots.” It’s what the companies that make them are thinking about when they talk about the problem of training data. But I don’t think we think about it enough when we think about what these things actually are.

We’ve been primed by generations of science fiction stories to imagine machines that think, think well or poorly, helpfully or malevolently. But maybe we would have a better understanding of LLMs — of what they do well as well as what they do poorly or not at all – if we thought of them not as thinking machines but as windows: windows onto the thinking that people have already done.

There is no thinking going on when you enter a query into ChatGPT, in the sense of an abstract model of the world that can be manipulated and then expressed in words. With the LLM, the words are all there are. The reason an LLM can answer a factual question is because someone has posted text on that specific question. The reason they make nice pictures is because there are an immense number of pictures on the web, with descriptive text attached. The reason they are such good coding buddies is because people have posted immense numbers of code snippets (and also because code is so nicely grammatical.)

If you’re impressed that an LLM can give you a stat block for your DnD campaign (one genuinely positive use case I’ve seen) or answers to your economics homework or text for a form letter, what you should really be impressed by is that so many people have posted versions of exactly that over the past 30 years.

People talk about the software and the chips. And sure, it does need a whole lot of chips. But the real secret is that people have posted this immense amount of useful text on the web, for free. That’s where the magic comes from.

OK, they didn’t post it all for free – a lot of it was produced for money. But none of the text that LLMs draw on was produced for sale to LLMs. All of it is free from their point of view. What they are drawing on is the positive externalities of people communicating with each other, for their own reasons, on the web. What LLMs are doing, fundamentally, is reaping the benefits of a vast spontaneous, directly social, decommodified decentralized production of use values.

When we look at the useful stuff that LLMs give us, we should not think, how cool this technology is. We should think, what an amazing range of useful work people are willing to share online, freely, without any monetary compensation. The machine is the least interesting part. It’s just summarizing it for us.

What makes LLMs work as a business is precisely that all this text is decommodified, as far as they’re concerned, it’s free. As they themselves say, they’d have to shut down if they had to pay for their training data. Yet all that data is the product of human labor. This cutting edge of capitalism – the biggest part of new business investment – rests on a substrate of communism.

People who criticize OpenAI and the rest of these companies for not adhering to copyrights are completely correct about their hypocrisy, and about the inconsistent application of the law. But they mostly get the correct resolution backward, in my opinion. Where we want to get to is a world where information is free for everyone, not one where OpenAI and company also respect the gates. You might ask: why does that follow? To which I would say: LLMs themselves demonstrate the value of making content, in the broadest sense, universally available for free.

The lesson we should be taking from LLMs is the immense social value there is in having all kinds of material – all kinds of products of human intellectual labor – freely available online. They should be reminding us of the early utopian promise of the web.

But now we must turn this around. The other side, of course – of course! – is that the companies making LLMs are not doing so with the goal of more easily sharing the material that people have made freely available on the web. They are doing so with the goal of enclosing it, of converting the products of free human activity into commodities.

The problem we have to deal with is that these companies are selling access to the freely shared products of human social activity, as the product of their own particular capitals. (And also that they are encouraging people to use it for dumb or pointless or socially destructive purposes.)

Worse: The project of enclosing and commodifying the world of online communication destroys what made it valuable in the first place. It’s the opposite of the tragedy of the commons – as if the villagers’ animals grazing on the green were what fertilized it and made it valuable in the first place. This case, where joint use of common resources maintains rather than degrades them, is, I suspect, the more usual one in traditional farming and pastoral communities. In any case, it certainly applies to the information commons – private appropriation is incompatible with collective activity that maintains them. Can’t expect people to keep posting on Reddit if all they hear back is AI slop.

Still , I think it’s important – especially for those of us who are deeply skeptical of “AI” as a business – to not lose sight of the genuinely positive and transformative aspect of this technology: the window it gives onto the possibilities of free, decommodified cooperative production.

The great debate going forward is not about this specific technology. (Though it is, to be clear, about its enormous energy demands. The real question is the about the conditions under which people will continue to be able to share the products of intellectual work with each other on the web. The issue is not what “AI” will or will not do. The issue is how we can take advantage for the tremendous opportunities for sharing the products of actual human intelligence, which were opened up by the internet, but have been increasingly closed off by its commercial overlords.