MARKET BUBBLE 003 series for the best crypto podcast In the last episode Ansem launches the Z500 and takes a victory lap for calling the Bitcoin bottom 41 days early. Tonight we'll see more!

Rational Dissent Episode 003. Valuations in An Earnings Explosion Available on YT, Spotify, Apple, Overcast, etc. ---- Is the juice worth the squeeze? Earnings keep rising, valuations are questionable, and investors are uncertain. What should you make of earnings these days? Chapters 00:00 Hot Open & Intro 00:51 Why Earnings Confuse 03:54 The Anthropic Effect 07:10 Include Other Income 09:36 OpenAI Next Wave 12:32 Valuations vs Earnings 18:05 Data Sources Diverge 22:42 Quant Models in Trouble 26:05 Vendor Financing History 29:15 CapEx & Bubble Risks 37:10 Lightning Round Fed or Earnings 38:53 Wrap Up & Subscribe

ZERO Market Flash #003# China's Second AI Shockwave: Is the Market Misreading It Again? Why Cheaper AI May Mean More Infrastructure—Not Less In January 2025, DeepSeek triggered one of the biggest debates in AI investing. The market quickly concluded that if powerful models could be trained with fewer GPUs, future demand for AI infrastructure must decline. NVIDIA lost nearly 17% in a single day, and AI-related stocks sold off across the board. More than a year later, history appears to be repeating itself. Kimi K3 has once again demonstrated that Chinese companies can build highly competitive large language models at significantly lower cost. The market immediately returned to the same question: If AI keeps getting cheaper, will we need fewer GPUs? Ironically, Kimi itself may have provided the opposite answer. Shortly after launch, the company suspended new subscriptions—not because the model had reached its limits, but because user demand had pushed GPU capacity close to its deployment limit. That may be the most important signal from Kimi's release. The first bottleneck wasn't model capability. It was deployment capacity. For the past several years, AI competition has largely been defined by training. Whoever trained larger models with more GPUs was assumed to have the strongest competitive advantage. Under that framework, lower training costs naturally imply lower infrastructure demand. But that assumption depends on one premise: that AI's value is created primarily during training. The more important question is: What happens if cheaper AI leads to dramatically more adoption? A foundation model may be trained once. It may perform billions of inference requests afterward. Over the long run, infrastructure consumption is driven less by training than by continuous deployment. Lower cost reduces the price of each interaction. Growing adoption increases the number of interactions. If usage grows faster than cost declines, total infrastructure demand can continue to expand. That is why Kimi's GPU capacity announcement may matter more than the model itself. The market focused on lower training costs. Reality exposed growing deployment demand. This also gives new context to SK Group Chairman Chey Tae-won's observation that the memory industry may gradually shift from a Price-driven cycle to a Volume-driven one. If AI deployment continues to expand, future industry growth may depend less on rising prices and more on rising deployment volumes. Kimi's capacity constraints do not prove that transition has already happened. They do suggest that AI competition is beginning to extend beyond training and into deployment. One year ago, DeepSeek forced investors to rethink training costs. Today, Kimi may be forcing investors to rethink deployment demand. Training creates models. Deployment creates industries. — This article reflects personal research and opinions only and should not be considered investment advice. Please conduct your own research before making investment decisions. ZERO Good is not good enough for conviction.
Only the best deserves concentration. Scientist · Doctor · A9 Investor Search Tags #AI# #ArtificialIntelligence# #GenerativeAI# #LLM# #KimiK3# #MoonshotAI# #DeepSeek# #Inference# #Deployment# #Training# #AIAgents# #GPU# #NVIDIA# #Memory# #HBM# #Semiconductors# #AIInfrastructure# #SKHynix# #Micron# #SNDK# #TechInvesting# #ZERO#

PopLab Beta Test Notes 003 🤖 Over the past two weeks, PopDEX has continued moving fast. Last week, we entered the mainnet Beta Test phase. More early traders have started testing the product, placing trades, and sharing valuable feedback with the team. During WebX in Japan, we were excited to hear from many traders who showed strong interest in PopDEX, our trading experience, and our value return model. More regional community meetups are now being planned as more traders join the road to public mainnet. On the product side, the recent focus has been on refining the core systems that support a better trading venue: invitation and referral rebates, VIP mechanisms, message and notification flows, user activity records, liquidity improvement and trading page optimization for public mainnet. The goal is simple: Serve real traders better. Improve liquidity and execution. Prepare the full trading experience for public access. PopLab Beta Test continues.