The potential impact

What would the impact be if Synapse was deployed globally by 2030?

1 2030 demand

Sourced
1,393.5 TWh

data-centre electricity, 2030: EPRI (US), Wood Mackenzie (China), IEA (rest of world)

× inference share, 80%

Low 60%: McKinsey, AI capacity, 2030

Pick 80%: Structure Research, AI IT load, 2030; Brookfield and BofA, about 75%, 2030

High 90%: estimate of AI computing power, 80 to 90% (MIT Technology Review)

= 1,114.8 TWh

of inference, 2030

2 The proof

Measured
65.3%

fewer tokens over fifteen days

75.1%

fewer tokens, day 15

From a cold start

3 Modelled projection

Inferred
AT 65.3%AT 75.1%, DAY 15
Electricity saved a year728 TWh837 TWh
Capacity freed169 GW194 GW
CO₂ avoided a year324 Mt373 Mt
Water saved a year3.1tn L3.6tn L

From 1,114.8 TWh of inference electricity. Modelled, not measured.

4 What it means

Equivalent

65.3% | 75.1%, day 15

Icon: US-average cars off the road for a year
70m | 81m

US-average cars off the road for a year

Icon: US homes' electricity for a year
70m | 81m

US homes' electricity for a year

Icon: US households' home water for a year
7.5m | 8.6m

US households' home water for a year

Water, cars and homes use US factors. Sources: IEA; EPA; EIA; LBNL; Uptime Institute; Scope3.

Stages 3 and 4 are a modelled projection, not measured.