“Free” is a very attractive price when you are trying to break into a new market. Google has used “Free” extensively for Search, Gmail, Docs, and many other consumer services. “Free” has been used by Meta for its Facebook platform, and Cloudflare for its Web Caching services. In a world of constant innovation, “Free” is a way to expose a product or service to the market, hopefully creating dependence that can be monetized later. These days, it’s been used for the introduction of AI tools.
However, “Free” is often a misleading market signal, in that there are very real costs to the service operator that need to be covered, but “Free” provides the illusion that the technology behind the service is provided at a cost that is sufficiently small that it can be met by some other form of indirect subsidy. “Free” also acts as a very effective barrier to competition in that competitors need to match “Free” if they wish to enter or stay in the market.
“Free” is not the complete picture, of course. A consumer service can be offered at a price well below cost to capture market share from competitors (often termed “price leading” in a positive light, or “dumping” in a negative connotation!). Uber posted a $5B (yes, billion) operating loss in three months in 2019, and it took thirteen years for Uber to become revenue positive. SoftBank, the Saudi Arabian Public Investment Fund and Google all played a multi-billion-dollar funding role in subsidizing Uber through its protracted startup phase. Then there was DoorDash, which managed a $1.4B loss in 2022. At the time, every delivery was subsidized by the startup’s financial backers. Of course, that’s not a novel situation, and the expectation is that once the new entrant becomes established, it can turn the business around and drop the subsidization payments. The startup is being funded to essentially buy rapid growth in the market and place pressure on its competitors.
Let’s use this lens of economic analysis to look at the current state of AI. By all accounts, it is a very costly undertaking. It has been made widely available by a small clique of enterprises who are racing each other to achieve a dominant market share, to establish consumer usage habits and ultimately generate consumer dependence.
Expenditure – How Big is the AI Bill?
AI is expensive because of the sheer scale of the computation required to generate the Large Language Models that fuel AI systems. The investment is not necessarily a software investment but a more conventional investment in physical plant and equipment.
The design process for AI Data Centers is quite simple: take everything at the raw bleeding edge of today’s technology and push it together. These days a decent AI data center will need a collection of some 60,000 advanced GPUs, assembled using a packing factor of some 72 GPUs per rack, making a total of about 3,000 to 8,000 racks. The GPUs need to be mesh-connected to both each other and to high-speed storage in a lossless connectivity fabric using 800G optics and high-density switches. And then there is mass storage.
All this technology will need a massive amount of continuous power, capable of delivering some 150Kw to 200Kw per equipment rack. That’s a lot of power, and that means a lot of generated heat. Every rack needs to be equipped with liquid cooling, and the center needs to use a large-scale liquid cooling plant. Using air to cool this hot liquid is not overly efficient, so it makes sense to tap a large source of water to cool this reticulated cooling liquid down. These plant requirements also call for a power supply system capable of sustaining up to a Gigawatt of total power for the plant. That’s a lot of power, and that sustained load profile does not match the varying load profile that has dictated how we’ve built our power infrastructure so far. Our residential and commercial power profiles, which the grid was designed to service, use a profile that varies across the 24-hour cycle, varies across the week and varies season to season. Nor does the AI load profile match the periodic generation profile of solar and wind renewable generators. We can’t just skim off excess production from the existing grid in times of lulls in aggregate demand. These AI Data Centras call for dedicated substations, high-voltage connections, and significant grid-interconnection investment, and call for additional power generation facilities.
It doesn’t stop there. Obviously, all this is not cheap, particularly as we have drained the world’s supply chains for technology items, so you are going to pay a premium price to get your orders filled. That implies that in order to populate this data center you’re going to need substantial financial backing, as well as a massive amount of local community approvals, particularly if you want to be so bold as to try and use modular nuclear generators for power! And once you’ve done all that, then be prepared to build an even bigger one in about 18 months’ time, as most of the centre’s capacity and performance parameters will have doubled!
A representative 200 MW AI training campus currently costs roughly $8.2 billion, about two-thirds of which reflects IT equipment costs and one-third to fund the real estate and associated power infrastructure. If we measure AI Data Centres by their power requirements, then the currently known construction schedule has some 183GW being constructed by 2032 and a further 118GW being planned for after 2032. That equates to a total infrastructure spend in the 7-year period from 2025 to 2032 of $10.3 trillion in the US alone. The average investment would be at a level of almost 4% of US GDP, larger than previous national landmark boom investments in rail, electrification, transportation and telecommunications infrastructure.
This scale of investment surpasses the internal resources of the AI actors. Aggregate capital expenditures by Oracle, Microsoft, Amazon, Meta, and Alphabet rose from about $97 billion in 2020 to more than $400 billion in 2025 and are projected to exceed $800 billion in 2026, surpassing their combined operating cash flow for the first time. This has forced these hyperscalers to broaden their channels of finance, where data center developers, infrastructure investment funds, private equity investors and bond markets are used to provide equity capital, while banks, private credit funds, and securitization vehicles supply debt. This is not used just for the buildings, and power and cooling infrastructure but now complements vendor financing to cover the IT equipment. Investment-grade hyperscaler tenants make these structures financeable by supporting long-duration contractual cash flows.
It’s no coincidence that these arrangements tend to conceal rather than eliminate risk. Moving assets into separately financed vehicles can raise leverage on the underlying infrastructure even when reported hyperscaler leverage remains low. Long-duration debt is then supported by cash flows and collateral values that depend on uncertain AI demand and revenue models, rapid technological change, timely access to power and hardware, and the continued credit quality of a small number of tenants. The resulting capital structure can therefore make exposures more layered, correlated, and difficult to observe.
The issue that comes out of this is that not only is this extensive AI infrastructure investment exposed to technological and operating risks, but that this financing structure can transmit and amplify those risks. Long-duration debt and contractual claims are being written against assets whose future prospects of utilization, technological relevance, and residual value remain highly uncertain. The severity of any adverse shock will therefore depend not only on the economics of AI demand in the market, but also on where leverage resides and how losses are allocated across tenants, asset owners, and creditors.
There is the risk of technical obsolescence. Fuelling this technical evolution over the past six decades or so has been the silicon chip industry. Its inexorable progress to simultaneously increase computational capability while at the same time reducing costs and power requirements of silicon chips has been little short of miraculous. With ongoing access to faster, more capable and lower-power silicon chips, it will always be cheaper in the near future, right? What that implies is a rapid depreciation of the value of the existing IT equipment, and an ongoing need for re-investment to track the current extremes of silicon capability to remain competitive.
Just because the unit cost of manufacturing computational capability falls, it doesn’t mean that AI will be cheaper. The opposite is more likely to happen. Models may be more efficient, chips may be better, and some tasks will therefore cost less per token, but total spending will continue to skyrocket because demand is growing even faster. It is apparent that users want longer contexts, multimodality, agents, search, memory and task execution.
But even that future is not so certain these days. The ability of the silicon industry to continue with incremental evolution to increase the computational power of its chips while simultaneously reducing its unit cost of production on a regular 18-month cycle is heading into a period of profound uncertainty. Maybe next year’s chips won’t be any better or cheaper than this year’s chips. What that scenario means is that any evolution in the AI capabilities that rely on larger and more powerful computational models will only be feasible with larger and more expensive assemblies of computational components and a matching demand for larger data centers with larger power demands.
There is also the burgeoning problem of a growing debt as AI companies need to amortize their investments. Meta, for example, closed 2025 with $72 billion in capex and anticipated between $115 billion and $135 billion for 2026. In February. Anthropic announced $30 billion financing round (valuing it at $380 billion), while Microsoft, NVIDIA and Anthropic have sealed an alliance that will see Anthropic commit to buy $30 billion of Azure capacity and up to an additional gigawatt of compute.
Google has undertaken a $32 billion global bond spree in 2026, tapping the bond markets in the US, Canada, Japan, Europe and Australia to finance its aggressive AI data center infrastructure buildout. Google’s parent company, Alphabet, has indicated capital expenditures could approach roughly $175 billion to $185 billion in 2026, underscoring the scale of computing resources required to support increasingly complex AI systems. Their 100-year bond also signals Alphabet’s push beyond traditional equity investors, tapping long-horizon institutional capital such as pension funds and insurers whose liabilities favor ultra-long assets, reinforcing a broader shift toward infrastructure-style financing as AI investment intensifies.
Chip designers, chip fabricators, cloud operators, model developers, data center operators, and infrastructure investors are interlinked through long-term contracts, strategic investments, and financing arrangements. A shock to one segment, such as weaker demand from model developers or the end of the continuous process of silicon chip refinement, can propagate as a fall in service operator revenues, lease payments, asset values, and creditor recoveries. This circularity makes exposures more correlated than they may appear when each transaction is viewed in isolation. The interdependence can act as an amplifier when any single activity fails.
AI no longer looks anything like an experiment: it looks like a capital-intensive industry that sooner or later will demand profitability, or it will dictate the terms of a rather impressive financial crash of global proportion.
Revenmue – Making Money with AI
Now we’ve looked into the expenditures for AI infrastructure; now let’s turn our attention to revenues. The question now is not whether AI will continue to be free for consumers to use, but who will subsidize it and the nature of the asset being traded in exchange for this subsidy.
Google used a classic two-sided market model to fund its retail services. For example, Google’s search is provided without direct cost to the consumer, but to fund this, Google assembles a profile of each consumer, which it then sells to advertisers. The more users use Google’s search, and the more accurate and comprehensive the assembled profiles, the greater the value of the profile to the advertisers. As Google’s former chief economist, Hal Varian, said in the late ‘90s, “spam is simply a failure of information”. The implication was that the more information you have about a user,, the better you can turn an ad into a helpful suggestion, and the higher the probability of convertinging a presented ad into a transaction. The greater the revenue available to support search, the better the ability to invest in assembling user profiles of search users, and the higher the value of Google as an advertising platform. For Google, the scale of their advertising activity is currently earns some $300 billion per year. So, to put it simply, advertisers are paying for search.
But advertisers did not magically increase their advertising budgets to add online advertising to the portfolio of existing channels to market. In fact, the deal that Google offered was to increase the effectiveness of their advertising programs, and potentially to decrease their total advertising spend. What did advertisers pull back on? The answer is obvious. Newspapers and free-to-air television were the major victims in this change of advertiser behavior. These days the newspaper business is an impoverished shadow of its former dominant self, and it’s reached the point that in some cases governments are forcing these digital giants who usurped their historical advertising revenue to pay back some small percentage of their forgone income (see the Australian News Media Bargaining Code as a good example of this). Free-to-air television had little in the way of comparable bargaining capability, and the content industry turned to the consumer subscription market to fund their production models. Such additional imposts on consumers have to bear the vicissitudes of consumer spending: when times are tougher, when rising interest rates increase household mortgage payments and when petrol costs rise, one of the first items of discretionary expenditure to go is content subscriptions. In most markets, consumers are not lavishly wealthy, and any new spending commitment is made by forgoing some other existing spend.
So now let’s turn our attention to AI and its costs. The capital expenditure component is some $750 billion in 2026 alone. But the cost of that capital is not the only cost of AI. There is the cost of power (and cooling) the cost of continued refinement of the tools and services, and the cost of capital reinvestment. It would not surprise me if the total costs of operation of AI services easily exceed $2 to $4 trillion per year.
Now, in a previous age we could make some sweeping assumptions about Moore’s Law and the continued drive to more efficient silicon. The same level of computational capability would cost us half as much in a couple of years, and this would continue inexorably in the coming years. We could go down the mobile phone path and continue to pack additional functionality or gimmickry into the unit and maintain (roughly) its retail price, in an effort to persuade consumers to replace their perfectly functional phone every couple of years. In an AI world, we could chew through startup funds to gain market share of a free service, in the hope that in a few years, the continuing functioning of Moore’s Law in silicon chips, the operational costs will come down to a level that is sustainable by consumer payments. But what if we are at the end of the silicon road? What if we can’t continue to improve the feature count and reduce the power demands of silicon chips?
Superficially, the answer is simple: the user will pay more for AI subscription services. It may be packaged as part of the services provided by the employer, part of the Office Suite, for example. It’s likely that AI will become an infrastructure cost item that will be packaged, blended, and homologated into other products and services.
And the old advertising-funded models are being adopted. OpenAI recently announced unlimited free access to “Luna”, a lightweight version of its GPT-5.6 model—for all free ChatGPT and low-cost “Go” tier users. They also announced the introduction of advertising for users on the ChatGPT Free and Go subscription tiers to help fund free and low-cost access to its AI tools.
Google has been transforming its search service. In its “AI model,” the search engine no longer returns pointers to resources that look relevant to your query, but uses AI to synthesize the content of these resources to try to generate a direct response to the query. As Google asserts in a promotional blurb, “Just one year after its debut, AI Mode has surpassed one billion monthly users, with queries more than doubling every quarter since launch.” But the real question is whether this change in Google’s search product has managed to generate increases in advertising revenue, or is it a more basic form of last-ditch defense of existing advertising revenue against the incursion into their market from OpenAI and Anthropic? The latter looks like the most likely answer.
No doubt the premium subscription services for AI will persist. These subscriptions will offer better models, faster processing, more capable responses and of course zero advertising. But this is only accessible to those who can afford to pay and only while they have a volume of discretionary income that can afford such a subscription. As with the streaming models, such revenue programs are highly dependent on a broader environment of economic well-being, and, as we’ve already noted, when interest rates rise, or when the costs of petrol, food and clothing rise, or when a greater proportion of an aging population shift into a fixed retirement income, such discretionary subscriptions are often the first to be cut. But the costs of running these AI services are not necessarily based on the volume of queries. The situation of falling subscription revenues without an accompanying fall in costs is a very real prospect.
We inevitably circle back to “Free”. The digital advertising market is valued at approximately $700 to $800 billion per year. The operating cost of AI appears to be easily more than twice that amount, with a very rough estimate of $2 to $4 trillion per year
To fund today’s search engines, we destroyed newspapers and free-to-air television.
How do we fund AI? What part of the spectrum of economic activity are we willing to chop off and forego in order to generate the rest of the money?
We might already be seeing the answer. We are going to find the capital needed by AI by shedding people and the jobs they used to do. Their employers of these laid-off workers appear to be betting they can save enough through a reduced-wage bill to fund premium corporate AI subscriptions that do the same function. Almost every tech company in the past few years has experienced mass layoffs. As a more extreme illustration, Dario Amodei, CEO of Anthropic, has predicted that AI could wipe out half of all white-collar jobs and push unemployment to 20 percent. A labor market upheaval of this magnitude would cause enormous suffering for many households and pose a significant challenge for policymakers, even though it’s a classic example of shifting cost to the public sector while retaining income and value in private hands. We’ve already revised our social definition of “full employment” to equate to an unemployment rate of around 4%. At 20% job loss, we would be leaving the territory of economic “downturn” and entering a full-blown economic “meltdown!”
There is a strong counter voice to this dire prediction of widespread job losses due to AI. The figures so far fall way short of a collapse in the labor market due to AI. While many believe AI will shrink the workforce rather than sustain growth, the available data so far shows no clear signs of significant workforce shrinkage.
Another perspective is that all of this AI expenditure has been an exercise in mass hysteria, and the reality of AI is far more tawdry in terms of its ability to replace people with AI agents. This perspective would see AI being positioned largely within the confines of what can be done within the existing advertising revenues, and those folk who fail to secure a significant proportion of this revenue stream are doomed.
This is a more traditional business story of fear and greed where the incumbents, in this case Google, Meta, and Microsoft, are pushed by fear into investing in AI services as a defensive move to protect their existing revenue streams and associated markets, and the challengers, OpenAI and Anthropic, are aggressive raiders pushed by greed, who have nothing to lose. It’s highly likely that we will be unable to meet the total costs of the construction of all these specialized data centers, power systems and IT plants. It’s just too much money, and we can’t or won’t divert sufficient capital into this sector to sustain everyone’s borrowings. It’s likely that some of these AI entities will fail financially, and the others will pick over the entrails of the failed enterprises to see if there is anything worth salvaging.
So who is more likely to remain standing at the end of this period of AI hype? Do the incumbents have the advantage of existing revenue and a customer base that will sustain them through this? Or are the challengers able to wrest sufficient market share and revenue through innovation and the absence of legacy business models that may weigh down the incumbents?
The current state of play in AI appears to be financially unsustainable, and changes will happen as we head along this journey. But there is a lot to be said for the resilience of our societies and their institutions. Bitcoin was meant to be the finance and banking industry’s Armageddon. AI has been touted as an agent of shattering change in the nature of the way we work and play. But maybe that’s just being overdramatic, and the changes we will be going through in the coming years will occur at a slower pace and with more opportunity to adjust as we go along.