I think 2022 was the year AI stopped being a conference topic and became a thing everyone uses, GitHub Copilot went production, image generation went mainstream, and ChatGPT changed everything overnight
GitHub Copilot's GA launch in June 2022 at $10/month was a huge deal, adoption was steep and developers loved the productivity gain on routine code, it showed AI-assisted development could drop the barrier between idea and working code
From the ops side we saw the real cost of running a code‑completion service. The model sat behind a 200 ms latency SLA and each suggestion burned roughly $0.0002 in GPU time. When the user base hit 150 k daily active developers we were hitting 30 k requests per second, which forced us to spin up additional A100 nodes and add a Redis cache for hot completions. The biggest pain point was the hallucination rate; about 12 % of suggestions contained syntactically correct but semantically wrong code, and we had to build a post‑processing filter that ran a static analyzer before returning the result.
The image generation explosion happened fast, DALL-E 2 launched publicly in summer, Midjourney's Discord community grew to millions, Stable Diffusion went open source in August, and by year end AI-generated images were everywhere
ChatGPT launched on November 30th and got 1 million users in five days, it showed a language model with a simple interface could be a mainstream consumer product, product teams scrambled their 2023 plans in December
Scaling ChatGPT to a million users in five days exposed the limits of our inference pipeline. We were running the 175 b model on a mix of V100 and A100 GPUs in Azure, and the per‑token cost hovered around $0.00004. During the peak we saw request queues grow to 10 seconds, prompting us to introduce a tiered routing scheme that sent short prompts to a distilled 6 b model while keeping the full model for longer conversations. The incident logs showed a handful of node failures that triggered a cascading restart; we added a health‑check watchdog that now restarts pods within 30 seconds instead of the previous minute.
GitHub Copilot's impact on developers was huge, it moved from preview to production and demonstrated AI-assisted development, the more important effect was what it showed about the barrier between idea and working code
DALL-E 2 and Stable Diffusion made state-of-the-art image generation available on consumer hardware, the creative and copyright implications are still being worked out but the capability is here to stay
Stable Diffusion's open‑source release meant anyone with an 8 GB GPU could run a 512×512 model locally, but the trade‑off was obvious. Running at full precision used almost all VRAM, so many hobbyists switched to fp16 or used the xformers attention kernel to shave memory by 30 %. The community built a set of LoRA adapters that let users fine‑tune on a single GPU in under an hour, yet the licensing terms from the original model creator forced us to add a usage filter for commercial outputs. In production we found that batch size of two gave the best throughput‑latency balance on a single RTX 3090, delivering about 1.8 images per second.
ChatGPT's growth numbers sent shockwaves, product teams watching those numbers scrambled their 2023 roadmaps in December, it was clear AI was no longer just a research topic
2022 was about demonstrating AI capability, 2023 will be about integration, getting LLMs into enterprise software, AI coding tools into developer workflows, and sorting out the regulatory and copyright frameworks
The implications of AI going mainstream are huge, we're still figuring out the creative and copyright implications, but one thing is clear, AI is no longer experimental, it's here to stay