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Nokia shipped an ai-native ran in july that already shows 20% better spectral efficiency, and it…

Yethikrishna ROriginal on Threads

nokia shipped an ai-native ran in july that already shows 20% better spectral efficiency, and it targets a 50% gain by 2027 and a full doubling by 2028 on the same spectrum. ericsson’s ai-ran software subscription claims up to 20% higher downlink throughput across 15+ live networks.

the next capacity upgrade for mobile networks is coming from software, not new spectrum.

Context

Verified date: Nokia launched its AI-RAN platform on Jul 15, 2026, built on Nokia's anyRAN software and NVIDIA's Aerial AI-RAN platform, with spectral efficiency gains above 100% as the target. Nokia's Sep 3, 2026 blog says it is committed to delivering twice the spectral efficiency by 2028 from existing spectrum.

Ericsson introduced AI in RAN on Jun 11, 2026, a software subscription; its page lists an AI-native scheduler for link adaptation with 20% higher downlink throughput and up to 10% better spectral efficiency, and DCD reports more than 15 deployments and trials.

How it compares

The doubling by 2028 matches Nokia's blog. The 20% better spectral efficiency shipped in July and the 50% gain by 2027 were not seen in the sources read, so unsupported here, not refuted. Ericsson's 20% is a downlink throughput figure for one scheduler, with up to 10% better spectral efficiency, and its sources describe deployments and trials, not 15 live networks. 'The next capacity upgrade is coming from software, not new spectrum' fits Nokia's framing and is the author's opinion.

Related work

Watch next

  • Look for Nokia's 50% by 2027 target.

Sources

  1. Nokia: defines the next era of radio with the industry's first AI-native RAN platformnokia.com
  2. Nokia blog: the era of AI-native RAN starts nownokia.com
  3. Ericsson: advances AI-native networks through AI in RANericsson.com

Provenance

The note above is reproduced unedited from the original post, first published on Threads on 1 October 2026 at 14:35 IST. Sources are the papers and datasets the note draws on.

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