The AI industry has experienced a meteoric rise in recent years, with billions of dollars funneled into startups and established firms racing to develop transformative tools. But according to a new report from Bloomberg, global macroeconomic shifts—namely tariffs, rising interest rates, and geopolitical tension—are starting to chill the once-red-hot pace of funding in the artificial intelligence sector.
While headlines about layoffs, startup closures, and cautious investor behavior are increasing, industry experts argue the situation is more of a recalibration than a collapse.
“Smart capital is still flowing, but concern about markets, geopolitics and ethics have caused investors to slow down their moves. This may cause temporary friction, but the long-term fundamentals of growth in AI remain unchanged. The companies building real, scalable value with AI will continue to thrive and AI will continue to grow for the foreseeable future,” says Brian Sathianathan, Co-Founder and CTO of Iterate.ai.
The cooling effect is being felt most by early-stage companies, particularly those focused on experimental or niche applications that haven’t yet demonstrated clear market fit. According to PitchBook data, AI startup funding dropped nearly 30% in Q1 2025 compared to the same period last year. The drop has been especially sharp for companies that rely heavily on cloud infrastructure, chip imports, or international collaborations, all of which are being disrupted by new tariffs and tighter supply chains.
At the same time, larger firms with established customer bases and AI capabilities are still attracting strategic capital. Industry leaders like Microsoft, Alphabet, and Amazon have continued to invest heavily in AI, often expanding internal R&D while also acquiring smaller companies that demonstrate technological breakthroughs in areas like generative modeling, robotics, and compliance automation.
The divergence points to a growing maturity in the market. Investors are now scrutinizing long-term profitability and regulatory readiness, rather than rushing to back the flashiest demos or highest valuations. In that sense, the slowdown may be a sign of stabilization—not retreat.
“There’s a weeding out process happening,” said a senior venture analyst at a major Silicon Valley firm. “The easy money phase is over. What’s left are the startups that can actually build something sustainable, and those are still getting funded—just with more diligence.”
One of the key friction points stems from trade policies that have impacted AI chip access, especially after the U.S. expanded export restrictions on advanced semiconductors to China. In response, several AI startups reliant on high-performance GPUs have faced delays or had to rethink deployment strategies, particularly those working in global markets.
The uncertainty has also prompted investors to think more carefully about where their money goes. Questions about ethical AI deployment, transparency in training data, and the risks of misinformation have become increasingly relevant, particularly as governments around the world start proposing AI regulatory frameworks.
Despite this, the long-term demand for AI remains strong. Across industries—from healthcare and manufacturing to retail and finance—companies are still looking to deploy automation tools, improve customer experience, and analyze data at scale. That demand is expected to drive continued adoption, even if capital flows shift in the short term.
Analysts at McKinsey predict that AI could contribute up to $4.4 trillion annually to the global economy, with the most value derived from enhancements in supply chain operations, personalized marketing, and advanced predictive analytics.
Sathianathan notes that this underlying demand will ultimately outweigh the temporary funding slowdown. “It’s more a question of who will be impacted, but AI is here and will continue its rapid growth trajectory no matter what,” he says.
If anything, the slower funding cycle may push companies to build with more discipline—emphasizing ethical development, regulatory readiness, and tangible user outcomes over moonshot ambitions.
For investors and startups alike, the message is clear: flashy demos may no longer be enough. But for those creating measurable value and scalable AI products, the future still looks bright.