bg24

You run a business and have millions of high paying customers. Someone happens to use your brand to market things and create harm for the society. Legally, you may be clean. But, your first line of defense is not "it is not my problem".

Leading companies need to be proactive in reducing these surface areas. For example, if anyone can submit an agent skill, plugin, or MCP server with Anthropic or OpenAI's name into a marketplace, it is an attack vector.

Nice research and structuring into 4-tier layer. For providers like Anthropic and OpenAI, subscription is the entry point for all these, right? Besides the measures proposed in the article, can token usage % determine these clusters of accounts?

I believe your statement. Labs do not publish subscription vs. api revenue and difficult to guess with no priors.

Subscription is to drive adoption - fixed cost, can adjust the usage eg. give resets, increase quota based on capacity available. We subscribers tend to take it as a mandatory benefit :-) For labs, it is not letting the capacity go waste.

api is the $$ driver - pay per use, enterprises.

Right now, Kimi needs to first hit the subscribers at the level of OpenAI and Anthropic. With the api usage skyrocketing due to K3, it will be clear in a few months on the actual subscription benefits.

> The US has invested trillions in AI that the companies involved are never going to make back. Even without competition from open models, but definitely not with it. And if those trillions turn out to be worthless, that's going to have a massive impact on the market and cause a lot of bankruptcies.

This is the point. And what nobody seems to bring up that inference is a massively profitable margin. The labs can still grow at 80% margin as opposed to 99% margin (I do not have the numbers). But that will hurt the valuation and investors.

With protection from Govt, you can compete at 1.5 or 2x of the equivalent open weights price. But it is not sustainable to keep the prices high, just a matter of time.

Follow the money, eg. investors and their connections to the Govt and media.

I think this goes to show that the newer models will be capable of. I won't be surprised if the Governments across the board come together to put a size limit on open weights model or ship them with guardrails in place. That will be really a sad day if that happens. It is difficult to imagine the state of the Internet if models of this capability are left open.

AI (llm) will be a commodity market => I am not sure it was obvious. As of last month, folks thought open weight models are lagging by 6+ months. Once K3 is taken for a deep run across many use cases, it will be clear where it stands. But yes, I agree that now that intelligence is commodity, everything changes.

I think it is deeper than that. "LLM => peak of productivity" takes way less time and effort than "Linux kernel => any productive work". Compare Dec 2025 vs July 2026 models in terms of capabilities.

Nobody can predict 5 year out. However, the country that can be ultra efficient by making their governance, health, manufacturing, military, etc AI-native will be far ahead in the game.

I think in general rest of the world needs to take notice (not saying afraid), starting with the US. It cannot be taken for granted that China's frontier labs will be a few months behind. They might be at par or exceed.

The lessons from steel, solar and EV needs to be learned by all lawmakers. You have to respect and learn from how China Government puts the system in place for complete industry takeover and they have been very good at it. The problem with AI is that democracies will be inherently slow in adopting AI, unless something changes in the system.

At minimum, every democratic Government (US, Europe, India) need to build long-term AI vision and execute that no matter which party comes to power. Additionally, be ruthless about protecting domestic labs. It can only be possible if the intelligence pricing by domestic labs per productive task is in the similar range as open-weights models. Right now, it is not the case, even if the article gives the example of Sol vs K3.

Protecting domestic labs means not bailout, but fast track to cheapest energy, fast track approval for data centers, enforce some guardrails so customers get to use the open weights models only hosted in the country by US (or Europe) businesses. Without these protections, it might be a slow death.

Correct. I used AI to refine the text. Thanks for the heads-up.

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There is no coming back. After all, open-weight is NOT open-source. It is basically free model. And you pay to host it.

The value proposition is that 100's of providers and host and sell it. 1000s of businesses (eg. Microsoft, Databricks, Palantir to small startups) can run it, finetune it and own the IP and pay only for hosting.

On the other hand, you have OpenAI and Anthropic, who need to charge at 90%+ inference margin. It is because of 1) sunk cost, 2) sky-high salaries that they paid to keep the talent. Companies like Meta screwed things up badly by paying billions of $ for chief engineers.

Chinese labs are doing a favor to the world. But I can also say with 100% certainty that if US labs were to close shops next year, Chinese labs would immediately start charging $$. In fact, I think it might happen with open weights model soon. But still these fees will be one-fifth or one-tenth per token. Also it does not come with all the guardrails.

Solution: US labs need to reduce their costs, cut the salaries across the board and compete. AI and robotics are the last hope of US to get back to industrialization and continue being the superpower.

There is a time window when it will flip. When Internet came along, we had a number of businesses that did not survive over the next years.

This time, it is different with AI. The rate of change is significant.