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Ben's article "distills" down to 2 reasons that US frontier labs shouldn't be "afraid": 1. US frontier lab unit economics are better 2. US frontier labs are mo
by larrysalibra 2mo ago
Ben's article "distills" down to 2 reasons that US frontier labs shouldn't be "afraid":
1. US frontier lab unit economics are better
2. US frontier labs are moving up the stack making tools that are "stickiness" and will prevent users from switching.
For 1...he doesn't provide any evidence for US lab unit economics being better...the major input to unit economics is electricity...which is cheaper in China. And building data centers and connecting them to electricity is both cheaper and an order of magnitude faster in China. The main input that US labs might have an advantage in is in cost/access to chips, but that given the level of chip investment in China it seems unlikely to hold.
For 2...there's little evidence these tools are sticky. At least in programming, the trend seems to be tools like opencode that support multiple models and providers.
And even when they are sort of sticky, as we know on hacker news, people figure out how to point the tools they like to competing models even when the app doesn't official support it.
And every improvement in model capability makes it increasingly easier to make your own tools.
Wrote more on this in a blog post that has an earlier HN discussion: https://news.ycombinator.com/item?id=48982061 https://news.ycombinator.com/item?id=48982061
Direct link: https://larrysalibra.com/ben-thompson-is-wrong-us-frontier-labs-are-right-to-be-panicking/ https://larrysalibra.com/ben-thompson-is-wrong-us-frontier-l...
- aurareturn 2mo agoCost 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 2mo agoI'd really love to see the evidence on this!
- culi 2mo 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 2mo agoThey are not stopping either.
- metalspot 2mo agoYou 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 2mo agoOh, 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 2mo agoThat last para is a novel idea. I wonder if there's any evidence to support/undermine it though?
- 2mo ago
- wbadart 2mo agoSeems 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 2mo agoyou can simply switch to z.ai/GLM-5.2 inside Claude Code by settings env variables in .claude/settings.json
- blensor 2mo agoI'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 2mo agoYou 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 2mo agoIn 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 2mo agoWow, now we're talking :)
- pishpash 2mo agoMore basically, production cost matters only if inference is priced at commodity prices. That's not what VC's signed up for, which is rent-seeking.
- Computer0 2mo agoIn a corporate setting yes Opencode all the way. However in a non corporate setting I am getting $3000 of api usage a month for $100 at Anthropic and only use open code for the smallest cheapest tasks
- gmerc 2mo agoHe’s glossing over the reason they are not: 90% profit margin of Nvidia. Power is only a small part, single digit, it will eventually matter but does not really today. What is the cost of AI? The single largest ingredient is Nvidia profit margin. Huawei accelerators are not as efficiency yet, but they don’t nearly extract as much margin. Why would future revenue stay with the labs given this situation? This whole thing had an airline industry sized red flag on it that makes investing into frontier lab about as sexy as investing in United. Maybe the token economy is some kind of reverberation of the airline reward miles economy, the emergency hatch to be able to survive under maximal supplier extraction (Nvidia is just the top of a monopoly stack here, even if they replace those chips, the HBM, ASML, Foundry layer can get their dues)
- michaelt 2mo ago> Power is only a small part, single digit, it will eventually matter but does not really today. Sorta yes, sorta no. A single 5090 consumes 450W - at Californian energy prices of $0.38 per kWh that's $0.17 per hour. And the card itself costs $4100 on amazon. So after 2.75 years running at full power 24/7 you'll have spent more on electricity than on the card. I would have thought most data centres being built today would have a design life longer than 3 years. Of course you can throttle the cards to ~300W without losing too much performance. But also you need more than a single 24GB card to run most modern LLMs.
- verall 2mo agoDC has wider margins than the 5090. by someone elses rough numbers (https://www.spheron.network/blog/gb300-nvl72-vs-gb200-nvl72-pricing-availability-2026/ https://www.spheron.network/blog/gb300-nvl72-vs-gb200-nvl72-...) for GB300 rack: > 132kW > ~$3.7-4M So about 300x the 5090's power but 1000x the price. Roughly 9 years for electricity to exceed price at $0.38 and datacenters will show up in areas with cheaper power than CA.
- Omniusaspirer 2mo agoAnyone seriously building out AI infrastructure I presume is paying nowhere near $.38/kWh which is extortionate. Utility scale solar is closer to $.02-.03/kWh, then maybe around ~$.10/kWh for natural gas peaker plants.
- tvink 2mo agoI think calling opencode the trend is naive. This not what is being run on company time.
- hack1312 2mo agoOpenCode is absolutely used on company time.
- sciencejerk 2mo agoShhhhhh...! OpenCode only runs on authorized machines by responsible employees following company policy ;)
- HarHarVeryFunny 2mo ago> 1. US frontier lab unit economics are better That's not generally true, since there is generally still much reliance on NVIDIA. The true low cost providers are Google with their TPU and vertically optimized stack, and Amazon with Trainium. However, Google does not have their own frontier model, and Anthropic (who are partially served by Amazon) are also paying a premium for extra NVIDIA-based capacity from SpaceX, maybe soon from Meta too. I don't know how the economics of domestic Chinese Huawei-based clouds (no NVIDIA) compares to the west, but since serving cost is mostly hardware depreciation and to a lesser extent electricity, they are not necessarily at a disadvantage (Ascend 950 costs roughly 50% of an NVIDIA H100), and more to the point it is irrelevant when considering US commercial use that is more likely to be using Chinese open weights models from US providers served on NVIDIA based hardware. I think the real significance of Chinese frontier models being open weight is that it takes development cost amortization out of the US-based serving cost, while the US AI labs can't afford to do this. The US labs therefore need to reduce development spending to remain price competitive. The Chinese companies are of course still making money from the Chinese market, whether by selling API access or by other business models such as Ziphu making 75% of it's total revenue by selling services to Chinese customers who are running their models on-prem due to the Chinese apparently being very concerned about data privacy.