The underlying spirit in many debates about the pace of AI progress—that we need to take safety very seriously and proceed with caution—is key to our mission. We spent more than 6 months testing GPT-4 and making it even safer, and built it on years of alignment research that we pursued in anticipation of models like GPT-4. We expect to continue to ramp our safety precautions more proactively than many of our users would like. Our general goal is for each model we ship to be our most aligned one yet, and it’s been true so far from GPT-3 (initially deployed without any special alignment), GPT-3.5 (aligned enough to be deployed in ChatGPT), and now GPT-4 (performs much better on all of our safety metrics than GPT-3.5). We believe (and have been saying in policy discussions with governments) that powerful training runs should be reported to governments, be accompanied by increasingly-sophisticated predictions of their capability and impact, and require best practices such as dangerous capability testing. We think governance of large-scale compute usage, safety standards, and regulation of/lesson-sharing from deployment are good ideas, but the details really matter and should adapt over time as the technology evolves. It’s also important to address the whole spectrum of risks from present-day issues (e.g. preventing misuse or self-harm, mitigating bias) to longer-term existential ones. Perhaps the most common theme from the long history of AI has been incorrect confident predictions from experts. One way to avoid unspotted prediction errors is for the technology in its current state to have early and frequent contact with reality as it is iteratively developed, tested, deployed, and all the while improved. And there are creative ideas people don’t often discuss which can improve the safety landscape in surprising ways — for example, it’s easy to create a continuum of incrementally-better AIs (such as by deploying subsequent checkpoints of a given training run), which presents a safety opportunity very unlike our historical approach of infrequent major model upgrades. The upcoming transformative technological change of AI is something that is simultaneously cause for optimism and concern — the whole range of emotions is justified and is shared by people within OpenAI, too. It’s a special opportunity and obligation for us all to be alive at this time, to have a chance to design the future together.