AGI Is Old News

Key Takeaways

• Blackwell took ~20 months from announcement to consumer models.
• Claude Mythos deemed too dangerous, prompting emergency central‑bank meetings.
• Vera Rubin chip delivers five‑times lower inference cost than Blackwell.
• Nvidia’s software optimizations yielded 2.8× inference speedup on Blackwell.
• Feynman, slated for 2028, will use die‑stacked memory architecture.

Pulse Analysis

The lag between a chip’s public reveal and its deployment in production AI systems has become a critical metric for investors and technologists alike. Nvidia’s Blackwell, unveiled in March 2024, required almost two years before its first models reached end‑users, a timeline that set the stage for a wave of high‑impact applications. Early adopters such as Anthropic and OpenAI leveraged Blackwell to push the boundaries of language models, prompting regulators to convene emergency sessions as the technology’s societal implications surfaced.

Nvidia’s roadmap now accelerates that cadence dramatically. The Vera Rubin processor, announced at CES, promises a five‑fold reduction in inference costs, turning dollar‑per‑token calculations into dime‑per‑token economics. This efficiency gain translates into faster iteration cycles and lower barriers for startups seeking to fine‑tune large models. Looking further ahead, the Feynman architecture, expected in 2028, will employ die‑stacked memory and custom silicon, delivering another order of magnitude in performance without waiting for a new transistor node. The compounding effect of hardware upgrades and aggressive software optimization creates a super‑exponential growth pattern that outpaces traditional forecasting models.

For the broader economy, these rapid advancements mean AI capabilities will become ubiquitous across sectors—from finance, where real‑time risk analytics could reshape trading, to cybersecurity, where adaptive defenses must keep pace with ever‑more sophisticated threats. Companies that can align their product pipelines with the evolving hardware timeline will capture disproportionate market share, while regulators must grapple with the speed at which potentially hazardous models can be deployed. The convergence of faster chips, smarter software, and relentless optimization signals that the next wave of AI-driven disruption is already in the fab, awaiting activation.