A16 is 200 sq mm of silicon while an H100 is about 800. That means you get about 100-120 A16's on a wafer, while you only get ~30 H100's (see https://www.silicon-edge.co.uk/j/index.php/resources/die-per...).
Let's assume yield is 100% to make things easier. The rated max power of the A16 is about 250W, while the H100 is quoted at 700W. Thus, a wafer of A16's is about 25-30 kW of power, while a wafer of H100's is about 21 kW.
Edit: Just clarifying, this is not about the Apple A16, but the Nvidia A16. The mobile process used by the Apple chips is built for much lower performance and power, so I can't imagine the two chips being anywhere near comparable - they fill two completely different roles.
I think if you had said "AMD Epyc" rather than a mobile chip, that would be a much more apt comparison. The AI chips are somewhat more power intensive per box, but fairly similar on power/area. It turns out that these silicon processes are fairly uniform in terms of the power/area that they can sustain for any kind of workload.
Mobile chips are designed for <10% utilization and "rush-to-idle" workloads, and they are not remotely comparable to datacenter silicon (of any kind).
However, that discounts the waste on the edges of the circular wafer, as well as the chip yield, which will both likely be worse for the larger chip [3]. But, assuming a generous 70% yield by area [4], one wafer's worth of H100s all packaged into GPUs and running full blast will use maybe 20 kilowatts, while the same wafer of A16s might use 3.6 kilowatts. Although in practice, the A16s will spend most of their time conserving battery power in your pocket, and even the H100s will spend some of their time idle.
TSMC is now producing over 14 million wafers per year. At most 1.2 million of those are on the 3nm node, and not all of that production goes to GPUs. But as an upper bound, if we imagine that all of TSMC's wafers could be filled up with nothing but H100 chips, and if all of those H100 chips were immediately put to use running AI 24/7, how much additional load could it put on the power grid every year?
The answer is, around 280 gigawatts, or if they were running 24/7 for a year, about 2500 terawatt-hours. That's about 10% of current world electricity consumption! So it's not completely implausible to imagine that a huge ramp-up in AI usage might have an effect on the electric grid.
*edit: This assumes we're talking about the Apple A16 (ie. the difference between phone chips and GPU chips). If we're talking about the Nvidia A16 (ie. the difference between current GPU chips and last node's GPU chips) see pclmulqdq's comment. ⠀
[1] https://nanoreview.net/en/soc/apple-a16-bionic
[2] https://www.techpowerup.com/gpu-specs/h100-pcie-80-gb.c3899
[3] https://news.ycombinator.com/item?id=24185108
[4] https://www.extremetech.com/computing/analyst-tsmc-hitting-5...
[5] https://www.tsmc.com/english/dedicatedFoundry/manufacturing/...
[6] https://www.wolframalpha.com/input?i=%2814+million%29+*+%282...*
Edit: Oh, you are talking about the Apple A16. Those chips are completely different in function, so sure.
A few mobile phone chips had a higher TDP, up to 10 W, but those were notorious for overheating and for low battery life.
1.2 x 30 x 30000($/board) ~ 1 trillion $$$. Time for NVDA call.