The Download: following DeepSeek’s lead, and OpenAI’s new research agent
DeepSeek might not be such good news for energy after all
In the week or so since DeepSeek became a household name, a dizzying number of narratives have gained steam, including that DeepSeek’s new, more efficient approach means AI might not need to guzzle the massive amounts of energy that it currently does.
The latter notion is misleading, and new numbers shared with MIT Technology Review help show why. These early figures—based on the performance of one of DeepSeek’s smaller models on a small number of prompts—suggest it could be more energy intensive when generating responses than the equivalent-size model from Meta.
The issue might be that the energy it saves in training is offset by its more intensive techniques for answering questions, and by the long answers they produce. Add the fact that other tech firms, inspired by DeepSeek’s approach, may now start building their own similar low-cost reasoning models, and the outlook for energy consumption is already looking a lot less rosy. Read the full story.
—James O’Donnell
What DeepSeek’s breakout success means for AI
If you’re interested in hearing more about DeepSeek, join our news editor Charlotte Jee, senior AI editor Will Douglas Heaven, and China reporter Caiwei Chen for an exclusive subscriber-only Roundtable conversation today at 12pm ET. They’ll be discussing what DeepSeek’s breakout success means for AI and the broader tech industry. Register here.
