- AI companies face high compute costs and energy demands while developing and deploying models.
- A crop of startups are building solutions to make AI more energy and cost-efficient.
- From data center cooling to chip efficiency, VCs say these startups are making AI cheaper and greener.
DeepSeek sent a clear message to Silicon Valley startups: It’s possible to do more with less.
Now, competitors in the US are scrambling to replicate the Chinese startup’s approach, which appears to rival the performance of top AI models in the US but seemingly at a fraction of the cost.
That’s prompted industry insiders to question the billions of dollars being spent on AI infrastructure. It has also spotlighted startups that are developing solutions to lower the high costs of developing, deploying, and running AI models.
Training AI requires huge processing power, which is fueled by clusters of graphics processing units, or GPUS. They consume a lot of energy, and these power circuits are largely provided by data centers.
“The energy consumption of training a large AI model can produce emissions equivalent to the lifetime emissions of multiple cars,” Andreas Riegler, general partner at APEX Ventures, told Business Insider. “As models grow in size, the demand for energy scales exponentially, raising sustainability concerns for future applications,” he added.
There are many approaches toward making AI greener and cheaper to use, Riegler told BI. Startups can improve software efficiency, develop more energy-efficient chips, and tap into renewable energy sources, he said.
BI spoke to seven investors based in Europe and the US, asking them to put forward startups that are helping make AI cheaper and greener to use.
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