Using a NeuralOps working benchmark, we estimate that computational carbon footprint could potentially be reduced from around 360 kg CO₂e to 120 kg CO₂e per equivalent workload per year.
That is roughly a 67% reduction.
At a hypothetical scale of around 830 million users, this could represent approximately:
199 million tonnes of CO₂e avoided per year.
This is not a claim that NeuralOps alone can “restore the planet.”
The more accurate point is this:
Reducing computational emissions at scale could lower additional climate pressure and give natural 系统 more room to stabilise over time.
潜在 benefits include lower electricity demand from AI infrastructure, reduced pressure on power grids, less cooling demand, slower growth in data-centre infrastructure requirements, lower associated emissions, and more efficient use of renewable energy capacity.
It could also contribute indirectly to reducing long-term pressure on forests, oceans, biodiversity and other natural carbon sinks.
The core idea behind NeuralOps is simple:
Not every task needs a 大 语言 模型.
Some workloads can be handled by:
• 智能路由
• Specialised Parsers
• 分离式系统
• Smaller local models
• 确定性 processing
• 主权 local inference
The 目标 is not to stop people from using AI.
The 目标 is to allow more people to use AI while requiring less computation to achieve the same outcome.
My belief is that the next phase of AI sustainability will not be solved only by greener data centres or more renewable electricity.
It will also be solved at the architecture level.
Because the most sustainable computation may be the computation we never needed to perform in the first place.
NeuralOps
架构 Before 计算.
智能 Without Computational 废弃物.
规模 AI. Not Its 碳足迹.
#NeuralOps #Artificial智能 #可持续AI #GreenAI #AI基础设施 #ESG #DigitalTransformation #AgenticAI #可持续发展 #ClimateTech #能源效率 #主权AI
Ruang pembaca
Apa pendapat anda?
Komen disemak sebelum diterbitkan. 电子邮件 diperlukan untuk moderasi dan tidak dipaparkan kepada pembaca.
如果有更多data-centre的数据和结果会更完整。
这段关于1的说明帮我把之前的问题连起来了。
不太同意1那里，不过整体还是站得住。
关于roughly a 67% 67%的风险和限制还可以再展开，不过基础说明已经很好。
关于reducing computational emissions at scale的例子很实用，适合团队继续讨论。
文章对my belief的结论比较平衡，不只是强调好处。