4 ms·

Cost of electricity isn’t a long term advantage in my opinion. Private companies will figure it out. What matters most is $/completed task. It does seem like O

Cost of electricity isn’t a long term advantage in my opinion. Private companies will figure it out. What matters most is $/completed task. It does seem like OpenAI and Anthropic are winning here even with worse electricity rates. Perhaps it is made up by the efficiency of Nvidia and Broadcom chips, which China can’t get in mass. I do think that OpenAI and Anthropic are moving up in stickiness. My company has rallied around Claude. We are customizing Claude Code, adding knowledge bases for non technical people, writing skills for them, using Claude features company wide. It’s hard to move. Meanwhile, I personally use ChatGPT outside of work. The memory, ease of use, habit keeps my subscribed.

golem14 3mo ago
I'd really love to see the evidence on this!
culi 3mo ago
> Private companies will figure it out. Across sectors, China added 543 GW of energy in 2025. Next year, USA is expected to add between 70 and 80 GW of energy
- anonzzzies 3mo ago
  They are not stopping either.
- metalspot 3mo ago
  You really have to look at energy/capita and how much energy is embedded in exports. The gross numbers are misleading. The US wasn't building new electrical generation capacity because it didn't need it and there was no market for it (caveats apply, but in a broad sense this is the major reason). Now that the market exists the question is how much can the US actually bring online and how rapidly, which is a real challenge after decades of degrowth politics used to justify slash and burn consumption of the industrial base. AI is really all about electricity. AI could be completely fake and yield zero value whatsoever and the US would do exactly what it is doing now because the AI bubble is what creates the market for building new electrical generation capacity, which is needed for re-industrialization. Also why our friends in UK/Europe/China are so busy pushing anti-AI propaganda to try to undermine this.
  - 21asdffdsa12 3mo ago
    Oh, the UK and Europe castrated themselves and want others to follow the example. They still don't have made the connection between "I give up ability" and i get attacked by a proxxy opponent by those i gave ability up too. They do not want to life in the world that is and thats going to be, but in the past and the world they green ideology promised. Reality denial be a addictive poison.
  - flir 3mo ago
    That last para is a novel idea. I wonder if there's any evidence to support/undermine it though?
    - metalspot 3mo ago
      The concepts of industrial reserve capacity and using dual-use consumer goods to subsidize military production capacity are well known and widely practiced historically in the US. China adopted this strategy from the US, and the US conveniently forgot about it for a few decades in order to justify selling off the industrial base to China, but at least based on public documents like the published U.S. National Security Strategy, I would assess with high probability that this is explicitly recognized and being followed now. AI is not fake and it does work, but what I am saying is that from a pure systemic analysis perspective, you can do the numbers, and even if AI was complete fugazi, the benefits you get from the electrical generation capacity, and the ability to fund it through private markets, which bypasses Congress, and locks in commercial contracts (often with foreign governments) which will be almost impossible politically to reverse, would still make it optimal from a strategic perspective. That is my calculation, and to the extent that it is correct, I would assume that the US Military's strategic planning apparatus would arrive at the same conclusion. AI compute has some unique characteristics that make it especially useful for grid management. Moving consumer compute to the cloud means that the electrical use of that compute can be centrally managed. In an emergency, you can cut electrical use for consumer AI by 50% or more, because chips run more efficiently at lower power, and you can shift workloads onto quantized models, reduce resolution for video output, etc, to reduce compute, which leads to minor service degradation but not interruption. AI datacenters are also adding massive amounts of battery storage capacity, which is an additional grid buffer. For every GW in capacity added by hyperscalers that is creating a dispatchable reserve capacity of 50% under completely normal circumstances (hyperscalers do this internally to optimize their own costs) and then that number goes up depending on the scale and duration of the emergency.
wbadart 3mo ago
Seems like most popular harnesses, including codex and Claude code, support Agent Skills (an open spec for skill formatting/ organization): https://agentskills.io/clients https://agentskills.io/clients Which is to say, this isn't really a lock-in/ stickiness vector (unless maybe the wording itself of a skill is hyper-optimized for a specific model)
- littlecranky67 3mo ago
  you can simply switch to z.ai/GLM-5.2 inside Claude Code by settings env variables in .claude/settings.json
blensor 3mo ago
I'm a model nomad, using whatever solved my last problem the best and where it makes the most sense to start my next work in. However with the latest models Fable, Kimi K3, 5.6, it's getting to a point where I sometimes forget what model I am on without noticing a difference. And once I realize it because something may not be exactly like I expected it I won't switch for that work either because I don't want to invalidate the cache. For the next work I will do there is maybe a 50/50 chance to remember to switch the model before I start. That's not what I would call stickiness towards a certain provider.
- amelius 3mo ago
  You didn't say what kind of problems you solve with AI. It matters a lot if you are doing HTML versus C++, for example.
  - blensor 3mo ago
    In no order of importance: - Refactoring a 13 year old in-house vacation rental booking system ( python/turbogears ) - Backend development for our VR fitness game ( flask/python ) - Unity development on our VR fitness game ( C#/Unity ) - VR game development experiments ( Godot/GDScript ) - Standalone SLAM localization service ( C++ ) - Audio analysis ( python/pytorch ) - Virtual display with Viture display glasses ( C ) - Reverse engineering a library I am using for another project ( ghidra -> C - no MCP yet, that's something I am looking forward to ) - Public facing website rebuilding for the booking system above ( PHP/JS ) - Generative 3D environments for our VR fitness game ( python ) - Wireless camera/IMU based tracker for the SLAM system ( C ) Once I've dug in with a specific model into a problem I tend to stick to that because I have a feeling what it will do and how well it works, but when I start a new thing I usually use whatever the model was last set to.
    - amelius 3mo ago
      Wow, now we're talking :)

golem14 3mo ago

I'd really love to see the evidence on this!

culi 3mo ago

> Private companies will figure it out. Across sectors, China added 543 GW of energy in 2025. Next year, USA is expected to add between 70 and 80 GW of energy

anonzzzies 3mo ago
They are not stopping either.
metalspot 3mo ago
You really have to look at energy/capita and how much energy is embedded in exports. The gross numbers are misleading. The US wasn't building new electrical generation capacity because it didn't need it and there was no market for it (caveats apply, but in a broad sense this is the major reason). Now that the market exists the question is how much can the US actually bring online and how rapidly, which is a real challenge after decades of degrowth politics used to justify slash and burn consumption of the industrial base. AI is really all about electricity. AI could be completely fake and yield zero value whatsoever and the US would do exactly what it is doing now because the AI bubble is what creates the market for building new electrical generation capacity, which is needed for re-industrialization. Also why our friends in UK/Europe/China are so busy pushing anti-AI propaganda to try to undermine this.
- 21asdffdsa12 3mo ago
  Oh, the UK and Europe castrated themselves and want others to follow the example. They still don't have made the connection between "I give up ability" and i get attacked by a proxxy opponent by those i gave ability up too. They do not want to life in the world that is and thats going to be, but in the past and the world they green ideology promised. Reality denial be a addictive poison.
- flir 3mo ago
  That last para is a novel idea. I wonder if there's any evidence to support/undermine it though?
  - metalspot 3mo ago
    The concepts of industrial reserve capacity and using dual-use consumer goods to subsidize military production capacity are well known and widely practiced historically in the US. China adopted this strategy from the US, and the US conveniently forgot about it for a few decades in order to justify selling off the industrial base to China, but at least based on public documents like the published U.S. National Security Strategy, I would assess with high probability that this is explicitly recognized and being followed now. AI is not fake and it does work, but what I am saying is that from a pure systemic analysis perspective, you can do the numbers, and even if AI was complete fugazi, the benefits you get from the electrical generation capacity, and the ability to fund it through private markets, which bypasses Congress, and locks in commercial contracts (often with foreign governments) which will be almost impossible politically to reverse, would still make it optimal from a strategic perspective. That is my calculation, and to the extent that it is correct, I would assume that the US Military's strategic planning apparatus would arrive at the same conclusion. AI compute has some unique characteristics that make it especially useful for grid management. Moving consumer compute to the cloud means that the electrical use of that compute can be centrally managed. In an emergency, you can cut electrical use for consumer AI by 50% or more, because chips run more efficiently at lower power, and you can shift workloads onto quantized models, reduce resolution for video output, etc, to reduce compute, which leads to minor service degradation but not interruption. AI datacenters are also adding massive amounts of battery storage capacity, which is an additional grid buffer. For every GW in capacity added by hyperscalers that is creating a dispatchable reserve capacity of 50% under completely normal circumstances (hyperscalers do this internally to optimize their own costs) and then that number goes up depending on the scale and duration of the emergency.

wbadart 3mo ago

Seems like most popular harnesses, including codex and Claude code, support Agent Skills (an open spec for skill formatting/ organization): https://agentskills.io/clients https://agentskills.io/clients Which is to say, this isn't really a lock-in/ stickiness vector (unless maybe the wording itself of a skill is hyper-optimized for a specific model)

littlecranky67 3mo ago
you can simply switch to z.ai/GLM-5.2 inside Claude Code by settings env variables in .claude/settings.json

blensor 3mo ago

I'm a model nomad, using whatever solved my last problem the best and where it makes the most sense to start my next work in. However with the latest models Fable, Kimi K3, 5.6, it's getting to a point where I sometimes forget what model I am on without noticing a difference. And once I realize it because something may not be exactly like I expected it I won't switch for that work either because I don't want to invalidate the cache. For the next work I will do there is maybe a 50/50 chance to remember to switch the model before I start. That's not what I would call stickiness towards a certain provider.

amelius 3mo ago
You didn't say what kind of problems you solve with AI. It matters a lot if you are doing HTML versus C++, for example.
- blensor 3mo ago
  In no order of importance: - Refactoring a 13 year old in-house vacation rental booking system ( python/turbogears ) - Backend development for our VR fitness game ( flask/python ) - Unity development on our VR fitness game ( C#/Unity ) - VR game development experiments ( Godot/GDScript ) - Standalone SLAM localization service ( C++ ) - Audio analysis ( python/pytorch ) - Virtual display with Viture display glasses ( C ) - Reverse engineering a library I am using for another project ( ghidra -> C - no MCP yet, that's something I am looking forward to ) - Public facing website rebuilding for the booking system above ( PHP/JS ) - Generative 3D environments for our VR fitness game ( python ) - Wireless camera/IMU based tracker for the SLAM system ( C ) Once I've dug in with a specific model into a problem I tend to stick to that because I have a feeling what it will do and how well it works, but when I start a new thing I usually use whatever the model was last set to.
  - amelius 3mo ago
    Wow, now we're talking :)