ModelsAug 27, 2026watch
The global AI race is often described as a contest for the most advanced chips. Z.AI's work with Chinese hardware points to a different pressure: what happens when teams have to make strong models run well on the hardware they can actually get.
Why it matters: The real test is production performance. Benchmarks can create attention, but latency, stability, cost, and developer adoption decide whether an alternative stack matters. AI competition will increasingly reward teams that can do more with less.
InfrastructureAug 26, 2026watch
NVIDIA's latest numbers make the AI boom look less like a software story and more like an infrastructure race measured in chips, power, and capital commitments. The company is still turning model demand into data-center demand, and every forecast now becomes a readout on how much compute the industry believes it can absorb.
Why it matters: This is why NVIDIA earnings belong on Pagish: they are one of the clearest signals for the pace of AI deployment. Watch customer concentration, financing arrangements, export rules, and whether inference demand grows fast enough to justify the next wave of buildout.
InfrastructureAug 25, 2026watch
Jalapeno remains important because it points at the pressure underneath every AI product: serving prompts quickly, cheaply, and reliably. Model intelligence gets the headline, but inference economics decide how often users can actually use that intelligence.
Why it matters: The key is independent evidence. Pagish will track whether Jalapeno produces durable latency and cost advantages in real workloads, because that would affect pricing, product design, and the balance of power between model labs and infrastructure providers.