Who paid for the AI boom, and what happens to the businesses paying the toll
1 The Profits Behind the Revolution
Every technological revolution has a financier. The railways were built on the bond markets. The telephone network was built on regulated monopoly rents. The early internet was built on venture capital and an equity bubble. The AI revolution has many financiers – venture funds, sovereign wealth, chipmakers, enterprise software profits – but one of the largest hides in plain sight. Among the companies building the most AI infrastructure, two of the most powerful cash engines were built on advertising. Alphabet and Meta turned the sale of attention and intent into extraordinary profits, and those profits are paying for much of the foundation on which the next technological era is being built.
Add Amazon, the third giant of digital advertising, and the scale becomes clear. In 2023 the three companies earned about $417 billion from advertising between them. By 2025 that had risen to about $560 billion, up 34% in two years. Over the same two years, their capital expenditure – the money spent on data centres, chips, networking and power – rose from $112 billion to $293 billion, up 162%. In 2023, capital spending absorbed 43% of the cash their operations generated. In 2024 it absorbed 52%. In 2025 it absorbed 70%. The figures come from the 2025 annual filings of Alphabet, Meta and Amazon.
The cash engines grew strongly. The capital demands of the next era grew far faster.
The 2026 plans extend the curve sharply. Alphabet has guided to capital expenditure of $195–205 billion, Meta to $135–145 billion, and Amazon to about $220 billion. Together that is roughly $560 billion: almost double 2025, five times 2023, and a third more than all the operating cash the three companies generated last year. Not every dollar is for AI; some serves warehouses, logistics and ordinary computing. By the companies’ own accounts, most of it supports AI and cloud infrastructure, and the direction is unambiguous: Alphabet and Amazon have both said they will spend meaningfully more again in 2027.
Figure 1. Alphabet, Meta and Amazon combined, 2023–2026. Capex is gross cash purchases of property and equipment; 2026 is guidance and includes non-AI spending.
Precision matters about who is paying, because the three companies are not alike.
Meta is the purest case. Advertising is about 98% of its revenue, and its Family of Apps – Facebook, Instagram, WhatsApp and Messenger – earned $102.5 billion of operating income in 2025. Everything else Meta does, from its AI labs to its data centres, is funded by that one engine.
Alphabet is the central case. In 2025, Google Services earned $139.4 billion of operating income against $13.9 billion for Google Cloud. Services includes subscriptions, devices and the Play store, but its core is advertising across Search, YouTube and the partner network. Cloud is growing fast and is profitable, but last year Services earned about ten dollars for every dollar Cloud earned.
Amazon is the different case. AWS earned $45.6 billion of Amazon’s $80 billion operating income in 2025, about 57%. Advertising brought in $68.6 billion of revenue, at margins widely believed to rival AWS, but Amazon does not disclose its advertising profit. Amazon illustrates diversification better than it illustrates advertising dependence.
Figure 2. Where the profits came from: 2025 operating income by segment
One concession belongs at the start. Microsoft, the second-largest cloud provider and one of the largest AI spenders of all, is not an advertising business. Its build-out rests on enterprise software and cloud. So the claim is not that advertising funds all of AI. The claim is that advertising funds the ad-funded AI builders: Alphabet and Meta almost entirely, Amazon in part. Remove Amazon and the pattern sharpens rather than weakens.
The clean case: Alphabet and Meta alone. Capital expenditure absorbed 34% of their combined operating cash flow in 2023, 41% in 2024 and 57% in 2025. Their 2026 guidance of about $340 billion is roughly 120% of the $281 billion of operating cash they generated in 2025.
Two cautions keep the argument straight. Revenue is not profit, and profit is not cash. Advertising revenue measures the toll collected; operating cash flow measures what is available to spend. Measured either way, the conclusion holds: much of the money building the AI economy has come from businesses paying to reach customers. And when a build-out outgrows the engine that funds it, the funding question leaves the income statement and enters the capital markets.
2 From Surplus to Capital Markets
For two decades, the advertising giants financed their futures out of surplus. Search and social generated more cash than even their ambitions could absorb. For most of the past decade the excess went back to shareholders through buybacks, joined in 2024 by dividends. The model was simple: earn from advertising, invest what the business needs, return the rest.
In 2026 that model reached its limit. In the third quarter, Meta’s capital spending of $34.9 billion exceeded the $32.8 billion its operations generated, and its quarterly free cash flow turned negative for the first time since its 2012 listing (results). Meta repurchased no stock in the first nine months of the year, against $26.2 billion a year earlier. Alphabet’s free cash flow turned negative in the second and third quarters, and its trailing twelve-month figure fell 73% to $19.7 billion. Amazon’s trailing twelve-month free cash flow went negative in the middle of the year and had reached an outflow of $42.4 billion by the end of September.
None of these companies has run out of money. Meta’s free cash flow for the first nine months was still positive, at about $11 billion, and all three hold large cash balances. The change is at the margin, and it is structural rather than quarterly: investment has begun to outrun internally generated surplus, and the companies are supplementing operating cash with debt, equity and long-term commitments.
The equity raises need careful reading. In June 2026, Alphabet announced an equity raise with a headline value of $84.75 billion, and its parts served different purposes. About $35 billion of underwritten common and mandatory convertible preferred stock, together with a $10 billion private placement with Berkshire Hathaway, was raised for general corporate purposes, including AI infrastructure. A further $40 billion at-the-market programme, drawn over time, was designed primarily to change how Alphabet meets tax obligations on employee equity awards, and should not be counted as AI funding. In October, Meta priced $37.5 billion of stock: $25 billion of common shares through forward sales, whose proceeds arrive on settlement by November 2027, and $12.5 billion of mandatory convertible preferred. A further programme of up to $60 billion from 2027 is contemplated, not committed.
The cash in hand is therefore smaller than the headlines. But it answers the question that matters: does the market value of these companies now help pay for their AI? At the margin, it does.
For twenty years, advertising paid for the future out of surplus. In 2026, it began to lean on the share price as well.
The mechanism is indirect rather than mechanical. A market capitalisation does not pour concrete or buy chips. But once internal cash runs short, the share price sets how much of the company must be sold to raise each dollar, and the credit markets price their confidence in future cash flows. That confidence still rests on the advertising franchise. On 29 October 2026, the day after Alphabet reported slower Search growth alongside a doubling of Cloud revenue, it lost close to $500 billion of market value, the worst day in its history. Investors value Alphabet as an advertising business building a cloud business, not the reverse.
This creates a loop worth naming. Advertising profits support market value. Market value makes capital available at scale and at a tolerable cost. Capital funds AI infrastructure. AI improves ad targeting and creates new businesses. Better ads support advertising profits.
Figure 3. The reflexive funding loop behind the ad-funded AI builders
George Soros called loops like this reflexive: perception and reality reinforce each other. They compound on the way up. They are tested on the way down, because a weaker advertising engine would lower market value, raise the cost of capital and slow the very investment meant to replace it.
The loop ends only when AI can pay for itself, and that has three stages. Revenue validation asks whether customers pay meaningful sums for AI services; they do. Operating profitability asks whether those services cover their running costs; Google Cloud’s segment profit says yes for one of them, though segment figures leave out shared AI research costs carried at the corporate level. Capital independence asks whether AI generates enough cash to fund its own maintenance, chip replacement and growth without leaning on the old franchise. Nobody has reached that stage. Accelerators wear out within a few years, depreciation compounds, and an AI business can show rising revenue and operating profit while still needing outside capital to grow. Amazon makes the point: AWS earned $17.8 billion of operating income in the third quarter of 2026 alone, while the company as a whole ran a trailing free cash outflow of $42.4 billion. Until capital independence arrives, the advertising engine remains the guarantor.
If advertising is the sponsor, the next question follows. Who pays the advertising bill?
3 The Bill Behind the Profits
Every advertising dollar is a cost line in some other company’s profit and loss account. The sponsor of the AI revolution is, in the end, the world’s brands: retailers, banks, airlines, consumer goods makers, direct-to-consumer start-ups. Most of what they buy is what the invoice says – introductions to strangers, and demand that would not otherwise exist. But part of what they buy is stranger. It is access to people they already know.
Consider how it happens. A customer buys from a retailer. The retailer holds her email address, her mobile number and her purchase history. Over the following months its emails go unopened, its offers blur into the inbox, and its systems eventually mark her as unresponsive. Six months later she searches for the category, clicks a paid listing and buys again. The advertising dashboard records a conversion. The profit and loss account tells a different story: the brand paid a platform to restore a relationship it already possessed.
The pattern runs across the stack: search ads bought on the brand’s own name, retargeting of past visitors, and “new customer” campaigns whose audiences quietly overlap the customer file. The cause is the same each time. Attention in owned channels decays, and the only scalable way back is paid reach.
Not all of this is waste, and the distinction matters. Spend on known customers falls into three tiers. The first is the pool worth investigating: spend that reaches people the brand already knows. Call it potential AdWaste exposure – measurable, probably substantial, and not wasteful in itself. The second is spend that is not incremental, because the customer would have bought anyway. The third is avoidable reacquisition: spend that worked, but that an owned channel kept warm could have matched far more cheaply. Only the second and third are economic waste, and only a controlled test can verify them: a holdout group that receives no contact, compared with the paid route and the owned route.
Figure 4. Exposure is not waste: three tiers of spend on known customers
The platforms have a fair rebuttal. Knowing a customer’s email address is not the same as having an effective, free route to her. A lapsed customer who ignores email may respond to a search ad, and paying for that response can be rational.
The evidence supports the concern without settling its size. A field experiment at eBay, by Thomas Blake, Chris Nosko and Steven Tadelis, found that paid search on eBay’s own brand name produced no measurable short-term benefit, because shoppers simply arrived through the free link below the ad. Returns from other paid search came mainly from infrequent users; frequent users who already knew eBay were barely influenced. On the other side, an analysis of 640 Meta incrementality experiments by the measurement firm Haus found that Meta lifted brands’ primary results by 19% on average, with 65–70% of that impact coming from new customers. That is real acquisition. It also means roughly a third of Meta’s incremental impact came from customers the brands already had – incremental, and therefore not waste, but a measure of how much paid media is spent on people already known.
Nobody knows the size of the wasteful slice, and the reason is structural. Each system measures the job it was built to do. Adtech reports conversions. Martech reports engagement. The agency reports media efficiency. Nobody owns the question that sits between them: was this customer new to the company? The number that should exist is what I call REACQ% – the share of customers that paid channels report as new who were already in the brand’s database before the paid touch. It is a count of people, not a split of spend. It is a warning light, not a verdict: it shows where to look, and the holdout test shows how much of the spend was wasted.
My own estimate, developed across the NeoMarketing essays, is that AdWaste runs to about $500 billion a year globally. That is an estimate of the scale at stake, not a measurement; the real figure will emerge brand by brand, as REACQ% gets counted and tested. Even a modest fraction of it is a material share of the revenue the AI builders collect.
Part of the money building frontier AI is a tax on forgetting: brands paying to rent back customers they already own.
That tax does not sit idle. It is now funding the systems that will reshape how customers are found.
4 The Reinvention Paradox
The paradox of the AI build-out is that it strengthens the advertising engine before it threatens it.
The strengthening comes first. AI improves targeting, creative, bidding and recommendations. Meta’s case for its capital spending rests largely on advertising performance: better models put more relevant ads before more engaged users, and advertisers pay more for them. For brands, the toll becomes more productive, which also makes it harder to escape.
The threat follows. AI answers reduce the clicks that search advertising was built to sell. Agents acting for consumers do not see banners. Discovery shifts from results pages and feeds to conversations and delegated decisions. A question answered is a click not sold, unless the answer itself carries a sponsor.
Three futures follow from this tension.
| Future | What happens |
| 1. Advertising finances the transition | AI lifts the old engine while cloud and AI services scale. The incumbents cross from one profit pool to the next on their own cash and credit. |
| 2. The old engine weakens first | Discovery moves into AI answers and agents faster than it can be monetised. Earnings disappoint, capital gets dearer, and the build-out slows. |
| 3. The toll is reinvented | Agent gateways charge brands for recommendation, visibility and transactions. The old toll fades and a new one replaces it, often collected by the same companies. |
The third future is the most consequential, and on today’s trajectory the most likely – though not inevitable. Gatekeeper rents rarely disappear; they migrate. Classified advertising moved from newspapers to search. Attention moved from portals to social feeds. Each time, whoever controlled the moment of choice found a way to charge for access to it. If agents become the moment of choice, the companies best placed to run them are those that already own the models, the distribution, the identity graphs and the advertiser relationships.
Much depends on whom the agents serve. A platform-controlled agent can monetise recommendations, placement and transactions. A customer-aligned agent, paid for by subscription, could refuse sponsored recommendations and optimise for its user alone. Will agents become the next advertising gatekeepers, or the first effective guardians against them? The answer is open, and few commercial questions this decade matter more.
Either way, two loops are already visible. The first is the incumbent loop: advertising profits fund AI capital, AI produces better products and new businesses, and those strengthen the profit engines. The second is the toll-payer’s loop: brands lose direct attention, re-buy customers through paid advertising, that revenue helps fund AI, and AI builds more powerful gateways to customers – deepening the dependence that started the cycle. Its magnitude remains a hypothesis. Its direction is the point.
Figure 5. Two loops: the incumbent’s and the toll-payer’s
Brands may be helping finance the intelligence that will decide which brands their customers discover, consider and buy from tomorrow.
There is a deeper contest inside the paradox. AI is being put to work in two economies with opposite incentives. In one, platforms use AI to rent attention: sharper targeting, better recommendations, higher ad yield. The objective is to maximise the value of paid access. In the other, brands use AI to earn attention: relevant communication between purchases, better service, retention and recovery through direct relationships. The objective is to minimise the need to buy access again. Both draw on the same improving capabilities. One makes the advertising auction more effective; the other makes some of those auctions unnecessary.
The next great contest in marketing may be between AI that makes advertising more valuable and AI that makes advertising less necessary.
Better AI targeting is therefore not automatically a victory for brands. It can raise short-term returns while leaving the underlying dependence on paid access intact. Which economy a given brand ends up in depends on what it does next.
5 Lessons for the Gatekeepers and the Toll-Payers
Step back from advertising and the story carries lessons for any business with a profitable core and an expensive future.
For every business with a cash engine
Every moat has a payer. Behind every cash cow is a customer paying more than they would in a more contestable market, or paying for something they could have had more cheaply. The first strategic question about any franchise is who funds it, and whether they can stop.
Cross-subsidies are fragile. For decades, newspaper classifieds paid for newsrooms. Readers believed they were paying for journalism; the economics said otherwise. When classifieds moved online, journalism did not become less valuable, but it lost its sponsor, and newsrooms shrank. Any business whose future is funded by a single profit pool should know exactly how that pool could dry up.
Beta can fund Alpha, but Alpha must earn the right to finance itself. Protecting the base business creates the cash and stability to build the next one. Until the new business reaches capital independence, it is an expensive experiment funded by yesterday’s advantage, and its funding is only as secure as that advantage.
Surplus and dilution are different kinds of freedom. A company that funds its future from earned profit controls its own pace; this is the idea behind what I have called the Proficorn. A company that funds its future through its share price depends on a story staying intact. The advertising giants have moved, at the margin, from the first condition towards the second.
For the businesses paying the toll
Never pay twice. The first move is measurement. Count how many of the customers your paid channels call new were already in your database, then use holdout tests to find out how many of those purchases the ads caused. Until both numbers exist, every acquisition budget hides a reacquisition bill.
Owned attention is the asset the toll taxes. An email address is not an audience; it is permission to earn one. Every relationship kept warm in an owned channel is one the brand never has to buy back, and AI now makes daily, individual, relevant engagement affordable in a way it was not three years ago.
Agent-mediated discovery raises the stakes. If agents increasingly decide what customers see, a direct relationship may be the only access to a customer that a brand does not have to rent. A customer who hears from a brand directly is a customer whose choices an intermediary does not fully control.
This is the agenda I have been developing under the name NeoMarketing: measure what you pay to re-buy, earn attention daily rather than renting it back, and pay vendors for the outcomes they deliver. I have set out parts of it in The Profit You Already Own, Every Business Has a Strait and From Rented to Owned. It remains an argument to be proved in practice. But the question it asks has grown larger than marketing efficiency, because the toll brands pay is now financing the gateways they will have to pass through.
What would prove this wrong
- Reacquisition turns out to be a small share of platform revenue once REACQ% is counted and tested brand by brand.
- AI makes paid retargeting so effective that it becomes the cheapest route back to a lapsed customer, not an avoidable one.
- Cloud and AI businesses reach capital independence within two or three years, and advertising’s role in financing the build fades.
- Agents bypass owned channels entirely, which would weaken the case for owned relationships as the remedy.
Each of these is testable, and each will show up in the numbers within a few years. Until then, the pattern in the filings is plain.
The profits of one generation of gatekeepers are financing the next. The open question is who keeps the customer when they are done.
