Nathaniel Whittemore

OpenAI's launch of the Sora app a week later could also be boosting Meta's platform as an alternative.

Sora still requires an invite code while Meta's platform is freely available.

Now obviously these numbers in aggregate are still quite low relative to the billions of users that mainstream social apps have, but the growth is notable nonetheless.

Next up, some fundraising news.

OpenEvidence, the AI assistant for doctors, has raised $200 million at a $6 billion valuation.

This is the second large fundraising round for the company this year.

They raised $210 million at a $3.5 billion valuation back in July.

And with the level of growth they've displayed recently, it's not hard to see why the valuation has almost doubled.

Open Evidence now supports around 15 million clinical consultations a month, up from 8.5 million in July.

The product is free to use for registered medical professionals and monetized through advertising rather than subscription.

That unconventional approach for a professional tool has allowed Open Evidence to expand into 10,000 medical centers.

OpenEvidence only began commercializing their app three months ago and is already halfway to their target of $100 million in advertising revenue for next year.

The assistant is trained on leading medical journals like the New England Journal of Medicine and is designed to help doctors quickly access the literature for diagnosis and treatment options.

The system is also designed to reject low-confidence outputs, reducing hallucination risk.

Alongside medical journals, the model is also being fine-tuned on the 100 million clinical consultations assisted by the tool.

Co-founder Daniel Nadler said that this is one of the company's largest moats, adding, no one else in the world has that data.

Speaking to Adoption Among Doctors, Zangin Zeb of Google Ventures, the lead investor in the round, said it's reaching verb-like status.

Now, this data type of moat, where companies in verticals have access to actual real-world data based on the usage of their tool, is one of the most interesting themes and questions.

So far in the history of LLMs, we've seen that the bitter lesson applies.

In other words, that mass access to data beats out specialized data when it comes to pre-training.