DongDing

Ding Dong

[DongDing @ioBwhoMe]

@j69084 @Ross__Hendricks And of course the market is always correct. Guess investing in the future is dumb now

[DongDing @ioBwhoMe]

@BourbonCap 1.2 billion in interest to Google is like me going out to buy a burger for dinner

[DongDing @ioBwhoMe]

@haranguetan_ @aleabitoreddit coming from the person that has a orangutan in their profile pic 🤡

[DongDing @ioBwhoMe]

@BobEUnlimited Or they could be left behind or let their market share erode from competitors if they turn out to be wrong

[DongDing @ioBwhoMe]

@tengyanAI Mega bullish for Kioxia

[DongDing @ioBwhoMe]

@KobeissiLetter More money transferring from HS to semis.

[DongDing @ioBwhoMe]

@haranguetan_ @aleabitoreddit Primates don’t understand that capex going up is bad for hyperscalers like Google and good for semis and ai trade.

[DongDing @ioBwhoMe]

@InTheAssembly Semis goes burrrrrrr

[DongDing @ioBwhoMe]

@jukan05 @bubbleboi Too soon

[DongDing @ioBwhoMe]

@zerohedge How about sticking to reporting and less bias?

[DongDing @ioBwhoMe]

@bubbleboi @RJCcapital Both y’all squabbling like babies. How about some actual usefulness?

[DongDing @ioBwhoMe]

@bubbleboi @ScottLe34203798 @institLPGP Has you even considered where all the HBM is suppose to be coming from? Until that issue is resolved NAND usage will still grow exponentially

[DongDing @ioBwhoMe]

@JamieMcullough You comparing a product that actual has real world use to crap crypto just tells people you are a complete dipshit 🤡

[DongDing @ioBwhoMe]

@zephyr_z9 @bubbleboi Got lil bro’d

[DongDing @ioBwhoMe]

@bubbleboi Oh man it could be bad or good

[DongDing @ioBwhoMe]

@MadmanDotMoney @jbulltard1 There’s literal proof that they made thousands of accounts to distill.

[DongDing @ioBwhoMe]

@bubbleboi This is mega bullish for NAND

[Gavin Baker @GavinSBaker]

Kimi K3 may be an important inflection point for AI. Potentially negative for Anthropic and OpenAI while being net positive for essentially every other company in the world. I mean that very literally. Although the real “Sputnik moment” would be an open-source frontier model that was also token efficient unlike Kimi K3 which is 50-70% more expensive to run than GPT 5.6 per Artificial Analysis.

Rationale:

A world where there are only 2-3 dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those 2-3 labs. Those labs would become monopsonies for power, data centers, semiconductors and hyperscalers and would obviously vertically integrate over time into all those layers while also completely subsuming the application/software layers. 

Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software.

This is why Jensen is so supportive of open-source. An open-source model requires the *exact* same amount of compute to run as a closed frontier model of similar size and architecture. Kimi K3 is roughly the same price as GPT 5.6 Terra on a per token basis, which actually suggests that it is less computationally efficient as I am sure that GPT 5.6 is priced to a higher margin than K3. And given that K3 is a token wastrel, i.e. token inefficient, it is significantly more expensive per task than GPT 5.6 and Grok 4.5, which are much more token efficient. Cost per token and token efficiency (i.e. intelligence density per token) are the drivers of intelligence per unit of cost. The winning AI companies will be those that offer the most intelligence per $ over time.

Lower margin % at the model layer = more margin $ at every part of the infrastructure layer and is a godsend for software. This can happen either through open-source models like K3 at the frontier *or* having a vertically integrated model company like Meta, SpaceX or Google at the frontier. Both outcomes result in a lower margin % at the model layer as vertically integrated model companies don’t really care where the margin $ come from. This is why it was so painful for OpenAI and Anthropic when Google was right there with them from a model competitiveness perspective and why Grok 4.5 and Muse 1.1 were just as important as Kimi K3.

The reason Kimi K3 is only *potentially* negative for Anthropic and OpenAI is 1) the @ericvishria point that the Claude and ChatGPT products and harnesses may be more important than their models today and 2) the hypothesis that they have much more advanced model checkpoints internally that are already being used for RSI. In the latter scenario, reaching RSI even a few months ahead of other labs might be enough to cement a permanent lead.

Time will tell on both points. And likely fairly quickly.

Caveat would be that since Kimi K3 is not token efficient and thereby actually more expensive than ChatGPT 5.6, we may need to see a more token efficient open-source model at the frontier or see Grok 5/Composer 4/Muse 2 at multiple points on the Pareto frontier for this potential risk to Anthropic and OpenAI to play out. And I am sure they will both vertically integrate as quickly as possible while continuing the product/harness strength they have shown over the last 8 months.

[DongDing @ioBwhoMe]

@jiahanjimliu Appreciate the analysis

[Jim Liu @jiahanjimliu]

Neoclouds: The Kimi K3 Scare

Kimi K3 caused a large scare in the AI trade as this Chinese open source model matched frontier models on benchmarks. Let me unpack what's actually going on.

Chinese Labs have much less GPUs than American Labs and yet are able to train "just as good" of a model. This implies that Chinese Labs have huge efficiencies that allow them to use much less GPUs in training. This is would imply less HBM, less datacenters, less cloud bills - the whole capex heavy buildout that the AI trade is predicated upon.

Now here's the big hole in all this logic. MoonshotAI, the Lab that made Kimi K3, is supposedly a magnitude more efficient in training than American Labs yet their inference compute consumption is the same or less efficient! Kimi K3 cost exactly the same as GPT 5.5 and slightly less than Claude 4.8 Opus High.

Some people are misunderstanding what expensive tokens mean. Yes the cost of the open source weights/topology is 0 but the amount of the compute/GPUs that you need to run the model is a metric of a efficient your inference is. Compute/GPU time is very expensive and cost of open source inference is very not free.

Now, it makes absolutely zero sense that MoonshotAI Kimi is so much more efficient in training but slightly less efficient in inference. Why? Training is a the forward pass plus backward pass and inference is the forward pass. This means that training efficiency improvements lead to inference efficiency improvements.

You know why MoonshotAI training and inference efficiencies are asymmetric? Because their "training efficiencies" come from distilling American models. If MoonshotAI had true training efficiencies they would also show inference efficiencies but they have no advantage in inference efficiencies!

AI Capex will still continue because:
1. If American Labs stop training capex, then Chinese models will also stop improving. AI progress will have stopped. American companies have never given up just because Chinese are trying to copy them.
2. Chinese model still consume alot of compute/GPUs for inference. Inference demand will outstrip training demand anyways.