The GPT-3 API waitlist is 6 months long. Here's what the early data looks like.

I've been collecting data on GPT-3 API access approvals since the waitlist opened in mid-2020. Not officially, of course. OpenAI doesn't publish this stuff. But between self-reported data on Hacker News, r/MachineLearning, and a few developer Discords, I've tracked 83 approval reports with enough detail to spot some patterns.

Fair warning: this is self-reported data, so it's noisy. But 83 data points over 8 months still tells a story.

The wait times

| Application period | Median wait time (days) | Sample size | |-------------------|------------------------|-------------| | June-July 2020 | 14 | 11 | | Aug-Sep 2020 | 38 | 15 | | Oct-Nov 2020 | 67 | 19 | | Dec 2020-Jan 2021 | 112 | 22 | | Feb-Mar 2021 | 150+ (many still waiting) | 16 |

That's a clear trend. Early applicants got in within two weeks. By late 2020, you were waiting over three months. People applying now in early 2021 are reporting 5-6 month waits, and some haven't heard back at all.

Who gets in faster?

This is the interesting part. I categorized each approved applicant by their stated use case (from forum posts describing their applications):

| Use case category | Median wait (days) | Approval rate (estimated) | Count | |------------------|--------------------|-----------------------------|-------| | Research / academic | 21 | High | 18 | | Startup with funding | 34 | High | 14 | | Enterprise integration | 29 | High | 9 | | Individual developer (side project) | 89 | Medium | 27 | | Content generation | 134 | Low | 10 | | "Just want to try it" | 160+ | Very low | 5 |

Researchers and funded startups get in fast. Individual developers building side projects wait about three times longer. People who describe their use case as content generation wait the longest.

I don't know OpenAI's exact prioritization criteria, but the data strongly suggests they're filtering by commercial viability and research credibility. That makes business sense. It also means the GPT-3 "community" skews heavily toward well-funded teams, which affects the kind of feedback OpenAI receives about the model.

The geographic angle

I tracked country of origin where mentioned (only 47 of 83 said where they were based):

| Region | Count | Median wait (days) | |--------|-------|--------------------| | United States | 28 | 42 | | Europe | 11 | 71 | | India | 4 | 95 | | Other | 4 | 83 |

Small sample, so don't over-index on this. But US-based applicants appear to get approved faster. Could be time zone effects, could be prioritization, could be sampling bias (Americans might be more likely to post about it on English-language forums).

What this means

The waitlist isn't a queue. It's a filter. OpenAI is making choices about who gets access based on factors they haven't made public.

That's their right. It's their API. But it means the early GPT-3 developer community isn't a random sample of interested developers. It's a curated group that skews toward researchers, funded startups, and US-based teams.

When you read GPT-3 demos and reviews, remember that. The people showing you what GPT-3 can do are a specific subset. The frustrated developer in Bangalore who's been waiting five months has a different perspective, but you won't see their demos because they can't access the API.

I'll keep tracking this. If the waitlist ever opens up broadly (OpenAI has hinted at this), the shift in who's building with GPT-3 will be one of the most interesting data stories of the year.

-- dataku