The Usage Limits with Grok 4.7 are fantastic. I've been running it on high for about 2 hours on my Alt account 20$ sub and it used like 7% of my weekly, honestly it feels the same as grok 4.6 usage limits literally no difference. More intelligence same amount of fun.

Another usage case for robot dog. It's used for firefighting! Can withstand 500 C temperature for 2 min & work in 300 C for 40 min. Has already entered firefighting team. There are certainly many applications for robots that's a safety hazard for humans.

Token usage just went vertical, and the power grid is not ready for it (Save this). This chart shows that agentic token usage on OpenRouter rose from roughly 0.51 trillion tokens on February to about 7.3 trillion by Augustwhich is a fourteenfold increase in six months. Human usage grew only about 2.8 times during that period, reaching approximately 1.4 trillion tokens and agents now consume roughly five times more than humans. This matters because agents use roughly fifteen times more tokens per request, since a single employee action can trigger dozens of model calls, tool invocations, and retries before a person sees the result. This is Jevons paradox in practice because every improvement in inference efficiency lowers the cost per token and unlocks new behaviors rather than reducing total spending. The scale is already substantial, as OpenRouter's weekly token consumption grew roughly nine thousand times since early 2024 to more than 90 trillion, with agents accounting for approximately 71% of usage. Goldman Sachs estimates that agentic AI could increase 2030 token consumption by twenty four times. The honest counterargument is that roughly 85% of agentic tokens come from cached prompts billed at lower rates, so revenue is growing more slowly than raw token counts suggest. However, cached tokens still require servers, memory, networking and electricity, which means the physical demand remains real. The infrastructure math is the most important part because US data center power demand climbing from 31 gigawatts in 2025 to 66 gigawatts in 2027. That requires capacity additions of 36.3 gigawatts in 2027, compared with actual additions of only 8.5 gigawatts in 2025, which represents more than a fourfold acceleration. The announced pipeline points toward 135 gigawatts, but only about 60% of scheduled capacity is expected to arrive on time. The capital commitments are enormous, since combined spending from Amazon, Microsoft, Alphabet, Meta, Oracle, and SpaceX is projected to exceed $1.3 trillion by 2027, while total global AI data center investment could reach $7 trillion by 2030. This is why the bottleneck is shifting from chips to electricity, and some estimates project that 40% of AI data centers will face power shortages by 2027. The beneficiaries therefore extend beyond GPUs into memory, networking, power generation, transmission, cooling, and electrical equipment. If you want to see how Milk Road is positioning around this next wave of AI infrastructure spending, from power and cooling to memory and networking, check out the link below. We’re already making trades around the companies we think benefit most as agentic AI pushes demand even higher.