Unstoppable Acceleration: 8 Years of LLM Deployment Visualized

What becomes clear when the past eight years are collapsed into a single view is not just the pace of releases, but the transformation of AI into a self-sustaining system. Competition, capital, and curiosity have created a cycle where each launch accelerates the next, collapsing the distance between breakthrough and deployment. The effect is less a story of individual models than of a whole industry spiraling outward with unprecedented velocity.

Takeaways

1. The center of gravity has shifted from a handful of labs to a crowded ecosystem. OpenAI and Google may have set the tempo, but the sheer diversity of entrants—from boutique startups to state-backed giants—shows how porous the frontier has become.
2. Specialization now matters as much as scale. Where early models competed on raw size, newer entrants stake claims on safety, coding, multimodality, or efficiency—signaling a field that is branching rather than converging.
3. No single actor controls the feedback loop. Releases are now both competitive and collaborative, each model triggering benchmarks, fine-tuning, and rival launches elsewhere; the system itself is what drives acceleration.

Data & Caveats

• Dates reflect public releases, not internal prototypes—meaning some breakthroughs appeared later on this timeline than they were first achieved.
• Model naming conventions vary across labs (e.g., families like Phi, Claude, Llama), and many intermediate versions or private deployments are not shown.
• The visualization emphasizes model announcements; adoption and real-world impact often lagged months behind.
• Several labs publish openly (e.g., Hugging Face, Mistral) while others release selectively, creating uneven visibility into activity.