30.07.2026

Sky-high share prices, but where are the profits?

Playing for highest stakes

Hyperscalers are banking on two colossal assumptions. First, that AI will be profitable - eventually. Second, that there will be widespread demand for AI - and soon. Michael Roberts investigates

Alphabet (Google) announced its earnings results last week. At first sight, the results were spectacular: revenue was up 24%, particularly from its ‘cloud’ business (up 82%). Earnings per share were $9.11, which is very high. But more than two-thirds were based on booking $77 billion from ‘unrealised’ gains from its purchase of shares in Anthropic, a major artificial intelligence model firm. In other words, these ‘gains’ are just ‘on paper’ and depend on Anthropic shares staying up.

Google is one of the so-called ‘hyperscalers’ - the mega tech companies that are ploughing huge amounts of funds into AI, hoping and expecting it to lead to a sharp rise in profits. Google’s investment in AI has pumped so much money into companies like Anthropic and OpenAI, and into data centres to run AI with expensive ‘chips’ made by Nvidia, that its huge cash revenues are no longer covering the cost of investment. ‘Free cash flow’, as it is called, went negative for the first time in Alphabet’s history.

Google is increasing its AI investment massively this year, like its rivals, Meta, Microsoft and Amazon, to build AI infrastructure, with the four hyperscalers combined on track to spend more than $725 billion in 2026. Before those results, Google had been seen as the hyperscaler best placed to withstand the AI arms race, with cash flows from its vast search business expected to cushion the financial pressure. But now, its cash burn to fund AI capex will require raising debt and/or issuing equity shares. Alphabet has already taken on nearly $100 billion in debt, and in June it moved to raise about $85 billion in its first share sale in more than two decades.

All this tells us that the AI stock market ‘bubble’ is getting closer to bursting. The hyperscalers are hugely profitable, based on their existing businesses, with current earnings about 59% above trend. But such is the size of the AI investment boom being conducted by these hyperscalers that even these profits are being sucked into AI like water disappearing into the Sahara desert.

As previously argued,¹ the US economy is one big bet on AI. The stock market value of the hyperscalers now accounts for roughly 40% of total market capitalisation. If there is any sign that, first, they cannot sustain their current investment growth and, second, they are eating away their profits with no return on those AI investments, their stock values could turn south and take the whole stock market with them.

The US capitalist economy is balanced on the apex of AI. All will be well if: first, the AI models become marked by heavy demand and start to be used globally, thus boosting profitability for the hyperscalers and eventually the rest of the corporate sector; second, the generalised use of AI technology in all sectors of the economy also leads to a step change in the level of productivity, which can take the US economy into a new age of prosperity.

Before the advent of the AI boom, the US information technology had already become the driver of relatively faster economic growth in the US, compared to the rest of the G7 economies during the long depression of the 2010s. But then they all saw a slowdown in the growth of real GDP output, investment and productivity. Then, after the pandemic slump, inflation returned in most economies, threatening to introduce a new period of ‘stagflation’² (slow or no growth alongside faster and high inflation) not seen since the 1970s. Now the Iran war is accelerating energy price inflation. Central bank monetary policy failed to get economies going in the 2010s with low interest rates and monetary injections (quantitative easing). And central bank monetary policy is failing to keep inflation from rising since 2020 using higher interest rates, because the only way economies can grow without inflation rising is by increasing growth in the productivity of labour.

Trump-appointed

The new Trump-appointed chair of the Federal Reserve expects a productivity boom from AI. Kevin Warsh says: “AI is perhaps the most significant change in our economy in my adult lifetime. AI will be a significant disinflationary force, increasing productivity and bolstering American competitiveness.” He reckons that productivity improvements from AI would drive “significant increases in real take-home wages. A one-percentage-point increase in annual productivity growth would double standards of living within a single generation.” He is right. Even just a 1% rise in current US productivity growth over 20 years would not only keep inflation down: it would provide income and revenue that could end the deficits in government budgets, without raising taxes or making cuts in spending. Public debt to GDP ratios would fall.

But will AI deliver this step change in productivity? From 2005 to 2019, US productivity growth averaged about 1.5% per year. The pace remained similarly slow during and immediately after the pandemic (2020-22). But US labour productivity has accelerated since 2022. Output per hour grew around 2.5% per year from the end of 2022 to the start of 2026, exceeding its pre-pandemic pace by one percentage point.

So is AI delivering? Most of the acceleration in labour productivity growth comes from faster growth of what mainstream economics calls total factor productivity (TFP). This is not something real that can be measured: it is just a mathematical residual from analysing the drivers of productivity growth. More workers working harder plus more machines working longer is what delivers most productivity from labour. TFP is the residual that is assumed to be from the impact of ‘innovation’ (eg, AI).

But the rise in US TFP is not yet the result of AI application in the economy. According to economists at Barclays, AI adoption has been gradual and steady rather than rapid and transformative, with most households and businesses still reporting limited exposure to the technology.³ The rise in productivity growth since 2022 is really due to more intensive use of existing technology after the end of the pandemic, not the introduction of AI.

How do we know this? Well, Barclays economists looked at the St Louis Fed’s nationwide Real-time Population Survey (RPS) of working-age US adults. The most recent survey shows that, while AI has found its way into work routines for 45% of respondents, its usage is miniscule. Assuming an eight-hour workday, the average AI adopter has gone from roughly 20 minutes of usage in late 2024 to around 30 minutes by mid-2026, the RPS survey shows. And what is not known is whether the workers are using their extra two percent of work time per day to be more generally productive, or to verify and fix whatever the AI has produced, or to slack off!

Indeed, most surveys of AI adoption by companies show only modest progress. The US Census Bureau’s bi-weekly Business Trends and Outlook Survey shows just 21% of businesses were knowingly using AI, 69% reported no use, and 11% were not sure! A Fed Board of Governors discussion paper finds that “productivity trends across all three levels have been relatively consistent over time, suggestive of micro-level productivity gains not adding up in aggregate”. It seems that ‘micro-level’ experiments typically measure task-level productivity, like the speed that a programmer generates code, rather than job-level or firm-level output. A 10% improvement on a task does not necessarily lead to proportional gains for a firm if adjustment costs or other bottlenecks lie elsewhere in the production process and erode the upstream productivity gains.

By running a series of complicated regression analyses on RPS survey data, the US Fed found no evidence of AI doing anything positive: correlations between industry-level adoption rates and improvements in productivity are “not statistically distinguishable from zero”, it says. However, the AI optimists remain just that - optimistic. They refer to the ‘J-curve’ that the productivity impact of new technologies generally follow.⁴ First, there is a slow - even negative - effect on productivity, as companies invest heavily in the technology. And then the boom comes. Such a J-curve was seen in US manufacturing productivity growth, which fell in the mid-1980s and then, after the recession of 1991, accelerated sharply until the mid-2000s.

But even that optimistic view has its caveats. First, it seems that those workers exposed to AI are resisting its use. An April survey of 2,400 knowledge workers by AI firm Writer and Workplace Intelligence found the 29% of employees admit to actively sabotaging their company’s AI strategy.⁵ Among workers under 25, that figure was 44% - up from 41% a year earlier. A separate WalkMe survey of executives and employees across 14 countries, conducted the same month, found that more than 54% of workers had bypassed their company’s AI tools in the past 30 days to do the work manually instead.⁶

In the early 19th century at the start of the industrial revolution in Britain, a group of skilled weavers tried to resist the introduction of machine weaving by various means - including sabotaging and wrecking the machines. They were called Luddites. Now it seems that a form of Luddism has returned over AI. Just as the Luddites had a case about protecting their livelihoods, so do ‘generation Z’ workers now.

The AI optimists, Stanford’s Erik Brynjolfsson and ADP Research, are tracking 4.6 million workers across more than 730 occupations.⁷ They find that jobs for workers aged 22 to 25 in AI-exposed occupations are shrinking more than 4% annually. Goldman Sachs analysis suggests information, professional services, insurance and finance are best positioned for early productivity gains - but these are where the ‘sabotage’ surveys find the highest rates of resistance.

Bottom line

The step change in productivity growth that Fed chair Warsh puts all his hopes in still seems some way off. But what about profits from AI? So far, any revenue that the AI model companies are making is way short of covering the costs of research and development and the building of data centres all over the US. And Goldman Sachs estimates that each dollar of hardware investment requires at least another $1.70 of complementary “intangible” investment - software, data systems and the hardest category to measure: ‘organisational overhaul’. There is now 15 times more data centre capacity than the demand for it.

And debt is building up. Bloomberg estimates that over $500 billion is outstanding in AI data centre debt.⁸ Nikkei Asia reported that Meta, Google, Amazon, Microsoft and Oracle have accrued around $1.65 trillion in outstanding debt in the last five years, with an additional hundreds of billions of dollars’ worth of “off balance sheet” debt, meaning that the corporate structure allows the company to not include it as part of its liabilities.⁹

Now US companies are switching to using ‘open-source’ AI models coming out of China that can nearly match the performance of the US models at a fraction of the cost. First, there was DeepSeek that strikingly hit the industry back in early 2025.¹⁰ Now there is the newest Chinese AI model, Kimi K3, just released by the Beijing-based Moonshot AI. Chinese models are 112 times cheaper than Anthropic per million tokens. One token costs $56 from Anthropic, $26 from OpenAI, $1.50 from Meta, $1 from xAI and Google, and $0.50 from the Chinese models. No wonder the proportion of tokens used by US firms that run through Chinese AI models is up to a record 58%.

But the optimists have not given up. Some suggest that increased efficiency will lead to increased demand, as unit costs of spending on AI falls. That will generate the profitability that AI companies are seeking and the stock market is hoping for. But only two percent of the top-ranked 500 companies mentioned AI productivity during earnings calls in the first quarter of 2026. Among those that did, the focus was overwhelmingly on cost savings rather than revenue growth. This raises serious questions about how the hundreds of billions in investment can be expected to turn into profits, let alone revenue.

The AI bet rests on two big assumptions. The first is that AI will be profitable - eventually - but just because a technology leads to a huge increase in productivity does not mean it will generate strong returns. The second assumption is that there will be widespread demand for AI - and soon. But, as noted above, Goldman Sachs estimates that it could take as long as 15 years (the Organisation for Economic Cooperation and Development says 20 years!). Can the current AI companies survive that long? Can the stock market wait that long before the bubble bursts?

Michael Roberts blogs at thenextrecession.wordpress.com