Tony Wang7 min readIndia, Not the US, Has the Most AI Developers on GitHub
We re-sliced our 973K GitHub census around the ml-ai tag: India leads AI/ML developer geography, and this group is far more reachable than GitHub overall.
Our census of 972,576 GitHub developer profiles found the US narrowly ahead of India in raw developer count. Filter that same dataset down to the 28,603 profiles tagged ml-ai — Crawlora's inferred interest-domain label for AI/ML-focused developers — and the ranking flips: India has more geo-resolved AI/ML developers than the United States.
The geography flips: India leads AI/ML, not the US
Across the whole GitHub census, the US leads India in raw developer count. Filtered to ml-ai, India pulls ahead:
By city, Bengaluru tops the list at 518 — consistent with it also leading the overall GitHub census — but the rest of the top cities show the same India-heavy pattern the country totals do: Hyderabad (289), New Delhi (254), Pune (235) and Chennai (218) all place in the global top 10, alongside San Francisco (337), New York (305) and London (304).
Show the top 25 cities by ml-ai-tagged developer count
| City | Developers |
|---|---|
| Bengaluru | 518 |
| San Francisco | 337 |
| New York | 305 |
| London | 304 |
| Hyderabad | 289 |
| New Delhi | 254 |
| Pune | 235 |
| Chennai | 218 |
| Paris | 192 |
| Seoul | 178 |
| Dhaka | 164 |
| Mumbai | 161 |
| Toronto | 155 |
| Berlin | 148 |
| Beijing | 143 |
| Kolkata | 138 |
| Seattle | 128 |
| Lahore | 127 |
| Karachi | 104 |
| Boston | 101 |
| Noida | 99 |
| Coimbatore | 97 |
| Los Angeles | 95 |
| Nairobi | 91 |
| Islamabad | 87 |
Academia shows up here in a way it doesn't for GitHub overall
Our earlier study found Red Hat and Microsoft topping the employer mentions for GitHub overall, with zero universities in the top 10. Filtered to ml-ai, universities crack the list outright:
Microsoft, NVIDIA and Red Hat lead — the same pattern of open-source-forward companies from our earlier study — but Northeastern University (16 mentions) alone outranks SAP, Deloitte, ByteDance and Huawei among named affiliations, and Stanford, CMU, Tsinghua, Peking and Zhejiang all appear further down the list. AI/ML research still runs substantially through universities, and their researchers' GitHub bios reflect it in a way the general developer population's don't.
This population is dramatically easier to reach and recruit from
The starkest difference between ml-ai-tagged developers and the general GitHub population isn't geography — it's reachability:
56.5% of ml-ai-tagged developers expose some public contact channel, versus 21.2% of the general population — and 21.1% mark themselves open to hire, versus 7.5% overall. Both figures run roughly 2.7-2.8x higher than the GitHub-wide baseline. Activity levels don't explain the gap (93.9% of ml-ai-tagged developers pushed, opened a PR, or reviewed one in the last 90 days, essentially matching the overall population's 93.6%) — this looks like a hiring-market effect: developers building visibly in a hot field have more reason to make themselves findable.
Follower distribution shifts too, though more modestly. 90.5% of ml-ai-tagged developers sit in the nano tier (under 100 followers), versus 95.3% of GitHub overall; the micro tier (100-999) holds 8.3% versus 4.2% overall. The single most-followed developer carrying the ml-ai tag in this dataset — Sebastian Raschka (@rasbt, author of several ML textbooks) at 38,746 followers — doesn't reach GitHub's overall macro-tier leaders, a reminder that broad platform fame and deep specialist reputation are correlated but distinct.
What this means if you're hiring, sponsoring, or building AI tooling
For technical recruiting in AI/ML specifically, India isn't a secondary sourcing market — by this measure it's the largest single geography, ahead of the US. For OSS sponsorship and DevRel aimed at the AI/ML community, the 2.7x reachability gap means outreach lists built from this population will convert on contactability far better than a generic GitHub-wide list would. And for academic partnership or research-collaboration tooling, university affiliations are a real, measurable signal in this specific population in a way they simply aren't for GitHub developers generally.
Query the GitHub Users dataset by interest domain
Filter 972,576 enriched developer profiles by domain (ml-ai, web, devops, security and more), geography, company or influence tier — over one REST API.
Frequently asked questions
Does India or the US have more AI/ML developers on GitHub?
India, by this measure. Filtering Crawlora's 972,576-profile GitHub Users census to the 28,603 profiles tagged ml-ai (an interest-domain tag inferred from repo topics and bio text), India has 4,810 geo-resolved developers versus 3,536 for the United States - a reversal of the overall GitHub census, where the US leads India 43,996 to 32,915.
How many GitHub developers work in AI/ML?
28,603 of the 972,576 profiles in Crawlora's GitHub Users dataset (2.9%) carry the ml-ai interest-domain tag. This is inferred from repo topics and bio text, not a self-declared label, so it should be read as a lower-bound signal rather than an exact headcount.
Are AI/ML developers easier to reach and recruit than other GitHub developers?
Yes, substantially. 56.5% of ml-ai-tagged developers expose a public contact channel, versus 21.2% of GitHub overall - and 21.1% mark themselves open to hire, versus 7.5% overall. Both figures run roughly 2.7-2.8x higher than the GitHub-wide baseline, while activity levels (93.9% active in the last 90 days) barely differ from the overall population's 93.6%.
Do universities show up in AI/ML developers' GitHub profiles?
Yes, in a way they don't for GitHub overall. Northeastern University, Stanford, Carnegie Mellon, Tsinghua and Peking University all appear among the top named affiliations for ml-ai-tagged developers, alongside companies like Microsoft, NVIDIA, Red Hat and Google. The overall GitHub census's top 10 employer mentions include zero universities.