Optimism surrounds artificial intelligence numbers

Optimism surrounds artificial intelligence numbers

While I go deeper into the aggregated artificial intelligence (AI) Capital Expenditures (Capex) figures and the income and profit that are then needed to earn them back, one thing is striking: a world of AI predictions that are universally optimistic.

That is not common in a hype-inspired tree, but these figures are extraordinary.

Table 1. Gartner’s September 2025 AI expenditure forecast

Global expenditure on AI can reach $ 1.5 trillion alone in 2025, with infrastructure that sucks US $ 500-650 billion, according to Gartner and Deloitte. Those consultants of course secretly cheer from the sidelines. McKinsey and PWC have also become a member of the party and do not surprisingly discover that AI US $ 13-20 trillion will add to annual gross domestic product (GDP) through productivity, innovation and demand multi-fighters by 2030.

They also say that innovation transfer, new markets and policy solutions, such as a Universal Basic Income (UBI), will support the question that will guarantee a return for AI’s Megaspenders.

It is a seductive vision: AI does the grunt work, humanity blooms and economies rise.

It is all so smooth, seamless and … clear!

Transitions are never seamless

The optimistic estimates arise, even if 400-800 million jobs (up to 18 percent of the worldwide population of working age) are expected to be moved by AI-inspired automation.

Do you find it not peculiar that even in the most pessimistic labor scenarios – where hundreds of millions of job seekers are sidelined – these models still make the optimistic GDP growth?

It is essential for investors to consider, because the inflated GDP figures are part of the story that encourages companies in which you invest to pour billions, no, trillions, into the AI ​​-dream (or is it a zinc hole?).

Like Charlie Munger and Warren Buffett often joked:

Do not ask a hairdresser if you need a haircut.

Which self -respecting business consultant will tell you that you should not invest, not spend, not to beat your arch -rival?

For every man with a hammer, the problem is always a nail.

Could AI be nothing more than the newest trend, the newest best hammer that everyone should use to repair any ailment?

An alternative reality

McKinsey’s own task -based simulations and the Organization for Economic Cooperation and Development (OECD) Sercentable General balance frameworks recognize ‘Transitional Drags’ such as inequality and slow reskilling. Nevertheless, they are on net positives (of course!), Often trust in assumptions of seamless work shifts and wage premiums for the AI-Savvy that retain their jobs.

PWC justifies its optimism through its job barometer, which points to historical technical transitions that create more roles than they have destroyed. The problem, however, is that the current data shows that AI-blown jobs grow unevenly, with low skills that the victims are. And what about the negative pressure of automation on wage growth. GDP can only go up in a world of falling wage growth if more volume compensates for the stalled price. But fewer jobs mean that fewer people need, want or be able to pay more things.

The optimistic AI story is hiding over an error: if AI moves hundreds of millions, who exactly buys all these innovative “things” to feed the income that the GDP increase of the GDP? Apparently fewer people will have jobs, but those who do can spend more than enough to compensate those who can no longer buy anything because they have no work.

Productivity

The Bullish proponents of AI call ‘productivity efficiency’ won by AI acceptance. However, efficiency usually comes from lower costs instead of magical boosts to income. To stimulate income, consumers – individual people – ultimately buy more things collectively. For companies to sell more (and therefore stimulate GDP growth), there must be buyers with disposable income.

Rising unemployment threatens the story. Related employees, stripped of income, oblique expenses for everything, from gadgets to groceries that generate a collapse of demand, where even cheaper AI-improved products collect dust on shelves.

The Institute of Labor Economics (Institut Zur Zukunft der Arbeit-Iza), a worldwide research network based in Bonn, Germany, warns that a decreasing labor share of BBP (in some models is decreasing 10-20 percent), because low-income groups cannot be quick enough, so that the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption of the consumption.

Although the B2B sales and global exports can temporarily damping technical giants such as Microsoft or Nvidia, it must be remembered that their electric customers trust consumers with jobs. And companies that are consumer -oriented can be in trouble much earlier if the total purchasing power decreases. That is why Morgan Stanley’s Biljoen-Dollar Genai Revenue Dreams ignore by 2028, which depend on the adoption of companies, how widespread unemployment can wrinkle in B2B treads through reduced business investments.

A voice in the wilderness

In the meantime, Daron Acemoglu is a professor in the MIT institute and a Nobel Prize winner in the economy, known for his work on institutions, growth and the social consequences of technology.

The views of Acemoglu challenges the prevailing story that AI will deliver massive productivity tree, job creation and universal prosperity.

Instead, he argues that the current process of AI Automatisering gives priority over human augmentation, which risks inequality, inflammatory and modest economic profit at its best.

Acemoglu also argues that AI has a limited transformative potential, and that generative AI, such as large language models (LLMS) (eg chatgpt), excel in easy -to -learn tasks but struggling with complex, context -dependent. This, he claims that it will limit broad acceptance. That seems to be certain what Mit’s recent much reported study has discovered.

In his Nber paper from 2024 “The simple macro economy of AI”, Acemoglu Ai estimates profitable automation of only five percent of American labor tasks in the coming decade, which yields a “non -trivial but modest” GDP boost with about one percent and a productivity increase of 0.7 per cent.

This strongly contrasts with Bullish projections, such as the seven percent global GDP rise from Goldman Sachs or the annual addition of US $ 17-25 trillion from McKinsey.

Elsewhere, Daron Acemoglu warns that AI automation focuses a “dual society” with enormous inequalities, especially for low -educated women in servants, by centralizing power, to undermine and undermine democracy. He emphasizes the adjustment costs arising from the mismatch between big-tech and needs to small companies, the myths of inevitable artificial general intelligence (AGI) or social benefits due to insufficient evidence and criticism AI’s massive environmental and potential for “penetrating manipulation” under deregulation.

For investors, the more direct care can be the speculative capital expenditure for a technology that is currently buying an insufficient number of people to justify the investment. And after missing an early investment in Amazon, Acemoglu is one of the few voices that blind optimism challenges the predictions of consultants such as McKinsey, where even high job displacement scenarios (400-800 million jobs) is assumed.

One scenario is that AI does not create enough new tasks or markets to support the demand, which may lead to entry shortages for companies in the midst of falling shares in the labor income.

As an alternative, Acemoglu can be right and AI may not be that transforming, which results in less job losses. Anyway, it points to an AI overbuild that will probably end in tears.

With trillions in AI infrastructure ‘poured by spec’, the industry needs a rapid recovery for profitability. And that is far from guaranteed. Low AI acceptance results in a poor efficiency on investment (Rois), as well as high acceptance, followed by an unemployment dried dried.

Triljins drive on the AI ​​golf, but all waves crash when they meet the reality of the beach.

Read part one here: The Artificial Intelligence Gold Rush & Bubbles Fits.

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Roger Montgomery is the founder and chairman of Montgomery Investment Management. Roger has more than three decades of experience in fund management and related activities, including stock analysis, stock and derivative strategy, trade and effects. Prior to the establishment of Montgomery, Roger positions in Ord Minnett Jardine Fleming, BT (Australia) Limited and Merrill Lynch.

He is also the author of the best -selling investment guide for the stock market, value. Aabel-Hoe to appreciate the best shares and buy them for less than they are worth.

Roger regularly appears on television and radio, and in the press, including ABC Radio and TV, the Australian and Ausbiz. View upcoming media performances.

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