#OpenAIInferenceCostTest

#OpenAIQ2LossWidens Fast-growing companies don't always become great businesses. OpenAI keeps growing, but losses are growing too. Anthropic is taking a different path by showing early signs of profitability. Revenue wins headlines. Sustainable economics usually decide who wins the marathon. Which matters more to you today, growth or profitability?

JUST IN: Odds of Anthropic IPOing above SpaceX's $SPCX $1.77 trillion valuation surge.

OpenAI says its new Jalapeño AI chip could significantly lower the cost of running artificial intelligence, but Jim Cramer (@jimcramer) remains skeptical that it poses a serious threat to Nvidia ($NVDA). Jalapeño is OpenAI’s first custom inference chip and was developed with Broadcom ($AVGO). The company plans to begin deploying it internally by the end of 2026, with production expected to ramp further in 2027. OpenAI CEO Sam Altman (@sama) described the chip simply by saying, “We made a chip and it is fast.” The company is already working on second- and third-generation versions. OpenAI says Jalapeño can deliver both higher throughput and lower latency, a combination that is difficult to achieve. Its internal benchmarks showed better performance per watt than Nvidia’s GB200 and GB300 systems across several large AI models. Depending on the workload, OpenAI claims Jalapeño delivered roughly 1.5x to 1.9x more performance per watt and significantly lower latency. The chip is designed specifically for inference rather than AI training. OpenAI hardware executive Richard Ho said those efficiency gains could eventually translate into lower token prices for customers as the chip scales into production. Still, OpenAI does not plan to replace Nvidia entirely. The company said it will continue using Nvidia accelerators and hardware from other partners for both training and inference. Cramer dismissed the idea that every new AI chip represents a meaningful Nvidia challenger. He said he regularly hears claims about superior chips but continues to see “no real competitors” to Nvidia at scale.

OpenAI’s Jalapeno is a signal that the AI industry is entering the “own the stack” era. The playbook use to be stay in your lane and focus. Model companies trained models. Chip companies made chips. Data companies collected data. That era is over. OpenAI started as a pure model lab. Now they’re designing custom silicon and optimizing the full systems around their actual workloads. This week we also saw @Figure_robot moving into the data layer and launching Index, their own data collection efforts that spans 108 countries. Everyone is moving up and down the stack. Some of it is probably to deepen thin moats, some of it is to cut costs, some of it is to justify valuations.

OpenAI built a custom inference chip (Jalapeño) and went from first RTL to tapeout in 9 months. When I designed and taped out complex SoCs, 18–24 months from RTL to tapeout was normal. Verification and physical design consumed most of that schedule. AI writing Verilog/VHDL is kinda expected with all the coding agents. Impressive part was... @OpenAI team used an internal model with fast QoR feedback and robust verification to optimize the RTL AI is really compressing the design cycle.. good for everyone. We'd see more silicon & more innovation. Now differentiation moves to securing TSMC capacity and HBM allocation.. More chips can be designed.. but fewer can be produced at scale.

OpenAI's early Jalapeño tests point to a potentially meaningful shift in inference economics: 1.5x-1.9x more throughput per watt and 1.7x-3.6x lower end-to-end latency on open models. GPT-5.6 Sol also reportedly used 54% fewer output tokens than a leading rival on coding tasks. The measured judgment is that efficiency gains could improve unit economics, but company-tested benchmarks are not yet proof of lower aggregate costs. With $6.7B in Q2 revenue against a $12.3B operating loss, deployment at scale and workload growth will matter more than headline performance. Not advice, just analysis. #OpenAIInferenceCostTest

Anthropic flipped the AI race upside down. OpenAI: $6.7B Q2 revenue, +18% Anthropic: $11.6B, more than 2x YoY And the wild part is Anthropic is already profitable on an adjusted basis. Enterprise is becoming the real AI battlefield. If this trend continues, the valuation debate is about to get VERY interesting. OpenAI may still have the bigger name. But if Anthropic keeps growing revenue this fast, investors may start asking a very uncomfortable question Why should the market value the slower-growing company higher?

🔥$OPENAI just released a performance report that made the market nervous. Q2 revenue was $6.7 billion, up 18% from $5.7 billion in Q1. Sounds decent, right? But the problem is—the quarter-over-quarter growth rate was cut in half, down from 35.7% in Q1. Even more painful, operating losses increased from $9.3 billion to $12.3 billion. Slower earnings growth, faster losses.#BTCRallyOrSqueeze #AnthropicIPONears #PopMartEarningsWatch