>let's say you work at uber. uber probably has a database with a bunch of rides data in it. it probably has a rides_fulfilled table, a users table, a drivers table, and a whole lot more tables but just imagine it was that simple for now.
What I don't get from the landing page clearly is what data will be sent to your app. Will users give you db credentials so that you have full access to schema and underlying data? And later these data will be sent to openai?
yes, users have to provide db credentials with read-only access to the data.
no, db credentials are never shared with OpenAI. our API handles user authentication and credential storage and calls out to OpenAI for the AI function calls
We work with lots of companies that are comfortable with it but have met others that say "No Way in Hell". For some reason, they tend to be more comfortable using OpenAI APIs on Azure than OpenAI directly though.
From financial perspective, Sequoia had a very insightful statement: "For every $1 spent on a GPU, roughly $1 needs to be spent on energy costs to run the GPU in a data center. So if Nvidia sells $50B in run-rate GPU revenue by the end of the year (a conservative estimate based on analyst forecasts), that implies approximately $100B in data center expenditures. The end user of the GPU—for example, Starbucks, X, Tesla, Github Copilot or a new startup—needs to earn a margin too. Let’s assume they need to earn a 50% margin. This implies that for each year of current GPU CapEx, $200B of lifetime revenue would need to be generated by these GPUs to pay back the upfront capital investment."
The power/environment cost is usually largely ignored and it can be as much as the hardware cost (this is insane!). And if this cost won't decrease over time, it'll add additional risks to all end consumer companies being profitable.
>“We see how Google is degrading in quality. We see how AI content mills are digesting our stories and gaming the system to get more views than our original reporting they’re ripping off,” cofounder Emanuel Maiberg told me an email.
Putting content behind a wall doesn't solve the problem, at least not fundamentally. As I'm not seeing them having completely unique stories, there will always be AI content to compete with.
Current generation AI cannot compete with investigative journalists. An AI currently cannot maintain a network of sources, follow up on leads, double check statements of different sources to see if one is lying and so on.
The primary currency of an investigative journalist is trust. Trust from the sources that the journalist will publish their story correctly. And also trust from the public that what they write is true.
Of course an AI can help you write the story. But at the end of the day you have to build the whole structure on trusting that there is a human I can trust at the foundation.
Why would sources and whistleblowers trust current generation AIs? How do they know openAI and co is not listening? Why would the audience trust the current models? They have shown that they can hallucinate things regularly.
>As I'm not seeing them having completely unique stories
Not sure if I'm reading this as you intended, but 404 broke multiple interesting stories first including the one about the Russian with no ticket or passport who ended up in LA.
I was also under impression that this was a new biomed llm initially. But it's a perfect example to illustrate context matters most in many cases.
If I understand correctly, this is like a custom GPT with 2 external APIs access. With the right context/data provided, it outperforms llm + bing search (not surprised).
What I don't get from the landing page clearly is what data will be sent to your app. Will users give you db credentials so that you have full access to schema and underlying data? And later these data will be sent to openai?