AI stocks are soaring, with companies such as Nvidia and Microsoft driving the market to new highs, according to FX168 Financial News Agency (North America). However, Goldman Sachs has said that if these companies want to maintain their high valuations, they will ultimately need to deliver results after investing huge amounts of capital. #AI craze#

On Friday, July 12th, Goldman Sachs pointed out that over the past four quarters, Amazon, Meta, Microsoft, and Alphabet have invested a total of $357 billion in capital expenditures and research and development. Goldman Sachs said that a "significant portion" of this nine-figure sum has been allocated to artificial intelligence.

Considering this, report authors led by Ryan Hammond are closely monitoring revenue downgrades for signs that AI spending has not brought returns. They predict that the stock of the market leader will show a loss if these results are not achieved.

Analysts wrote: "Today's mega-scale companies will ultimately be required to prove that their investments will generate revenue and returns. Early indications suggest that these revenues and returns may not materialize, measured by negative sales revisions, which could lead to a decrease in valuation."

Goldman Sachs pointed out that concerns have begun to spread in dialogues with market participants.

Analysts wrote: "Investors are unsure about the return on investment for these large tech stocks, but these stocks remain very popular."

They went on to say: "Even investors who are bullish on the potential returns of AI applications seem to have a considerable amount of uncertainty about the timeline."

Goldman Sachs also pointed out that, according to its tracked indicators, only 5% of companies currently use AI to produce goods and services.

However, the disconnect between capital expenditures, actual sales, and profits is not as apparent as it was during the period when oversupply was thought to have occurred in the past.

Analysts wrote: "Adjusted for these companies' profits, the capital expenditure cycle for AI is still far behind the tech bubble."