kimi3

I just saw some news: someone in the AI circle has also bowed their head today. Kimi issued an apology statement about the quota issue for the K3.1 Pro subscription plan 😂

Here’s what happened: previously, many users found that their quota was being consumed for no apparent reason, and usage came with all kinds of restrictions. This time Kimi directly admitted that it was all a platform issue and should have been fixed earlier. The compensation plan now is: reset all subscription users’ quota once, restoring 25% of the monthly quota, roughly one week’s worth of usage. If your usage hadn’t reached 25% before, then it gets zeroed out and recalculated from scratch. It’s expected that by the end of today Pacific Time, the progress bar will show the adjustment.

Besides apologizing and giving quota, Kimi also changed its tune, saying it will reduce unnecessary “sub-agent calls” and “output length” to lower quota consumption. In plain language, it means the backend model was shuffling things around too much and spitting out too much fluff, so the compute costs really couldn’t be sustained; now they’re starting to simplify backend operations.

My personal view: this is the aftereffect of the big-model race to the bottom. In the early stage, to grab users and boost retention, they opened permissions wide and gave away quota like crazy; once real user volume came in and inference costs were calculated, they realized the numbers just didn’t add up, so they could only start tightening back up. This kind of move—first throttling usage, then apologizing and resetting after getting criticized—though it gives users a bit of relief, also exposes that the platform still isn’t very mature in allocating compute resources.

For regular users, when using various big models now, you really have to be mentally prepared for them to get “shrunk” at any time. The wool you got in the early days will eventually have to be paid back through quota limits.

Everyone, do you use Kimi a lot in daily life? Do you think the quotas of the various AI large models are still enough to use? Let’s chat in the comments ~#KIMI3

China's Kimi K3 ranks first in the front-end code competition arena, surpassing Claude Fable 5. Although its overall capabilities are not as strong as GPT 5.6 Sol and Fable 5.

It’s expected that competition will intensify → the entire AI ecosystem will expand (the most core logic): improved model capabilities + lower prices will lead more enterprises and developers to actually deploy and use them (from big companies to mid-sized businesses and individual developers).

Front-end code generation is a high-frequency, must-have scenario. Once cost-effectiveness becomes sufficient, it will massively replace/assist human development, causing token consumption to skyrocket. So even if a single model is cheaper, total compute demand still increases (more people use it, use it more frequently, and employ more complex agent workflows).

No matter how strong or cheap the model is, running it still requires GPUs/HBM/servers/optical modules/power/data centers. In the US stock market’s AI sector, pure model/software companies (whose high valuations depend on a model moat) may face long-term pressure.

Minor negative: If the market interprets it as the US model moat being weakened, it could create short-term pressure on some high-valuation AI software/application stocks.

Let me estimate the deployment cost for K3. Previously, a 1T model would require about 4 Mac Studio clusters; to use it smoothly, it would need roughly 6–8. Now it’s 2.8T, handling more context and memory. It would likely require at least 21–32 units.

It should reach a scale of 64+ GPUs: total hardware cost $2 million–$10 million+ (and GPUs alone could be several million USD).
#KIMI3

#KIMI3 60,000 market cap—I bought a bit, not bad (personal record only, don’t follow)

Reasons for buying

1. The concept is interesting. In the AI concept, China’s Kimi K3 ranks #1 in the frontend code competition arena, surpassing Claude Fable 5, and Musk even interacted with it twice.

2. Lower market cap. It launched on July 16, topping out at 74,000. On July 17, it hit a new high with a 176,000 market cap. It then dropped to a low of 42,000, climbed back up to 60,000, and I promptly bought a little. The chips are concentrated: the top 100 account for 69%. The average entry price is around 67,000 market cap. There are a few key holders/influencers at the front, like Dongji, MANA, etc.

3. The community is decent—500+ holders, 300+ in the community, mainly text-based promotions.

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