Thanks to AI, American venture funding reached its highest quarterly total in two years.
Venture capital (VC) investments for the second quarter came to $55.6 billion, Reuters reported Wednesday (July 3), citing data from PitchBook.
That figure marks a 47% increase over the $37.8 billion startups in the U.S. took in during the first quarter, fueled largely by major investments in artificial intelligence (AI) firms, such as the $6 billion raised by Elon Musk’s xAI and $1.1 billion raised by CoreWeave.
The trend, Reuters notes, marks a recovery for VC funding, which hit a record $97.5 billion in the U.S. in the closing quarter of 2021 and a record low of $35.4 billion in the second quarter of 2023, as interest rates climbed and public offerings and acquisitions slowed. Still, the report points out, the initial public offering (IPO) market remains tepid.
“For VC returns to see an increase, large tech companies must begin to list publicly at a higher pace than we have seen through the first half of the year,” Pitchbook analyst Kyle Stanford said.
There’s also been some evidence recently that investors are growing more choosy about AI projects, with a Financial Times report in June showing that most of the stocks that jumped during last year’s AI hype have fallen.
“AI is still a big theme, but if you can’t demonstrate evidence, you’re getting hurt,” said Stuart Kaiser, Citi’s head of equity trading. “Just saying ‘AI’ 15 times is not going to cut it anymore.”
The report said that roughly 60% of stocks in the S&P 500 had climbed so far during 2024, but more than half the stocks included in Citi’s “AI Winners Basket” have fallen. Last year, more than three-quarters of the firms in that group had seen their stocks go up.
The past few months have also seen an uptick in spending on AI infrastructure by Big Tech companies, such as Microsoft’s $3.3 billion investment to establish a data hub in Wisconsin designed to teach employees and manufacturers how to optimize AI use.
“Assuming the infrastructure gets built, companies would have access to more advanced AI capabilities,” tech analyst Vaclav Vincalek told PYMNTS. “For example, more sophisticated machine learning algorithms can help analyze larger and larger datasets in shorter time frames. Or improved natural language processing to better serve customers with AI.”
