Grammarly certainly comes to mind, for being essentially a free feature of most chat AIs now.
Interestingly this time around I could see the 'fire' affecting mid-large corporations (or at least some divisions of them) if they don't adapt. Adobe, being heavily focused on graphic design seems like it could be under pressure. Low-end consulting / outsourcing is largely doing the same work AI is good at. Similarly with technical gig-work (like Upwork).
People don't seem to realize that Oracle is deep in the AI play, taking on a bunch of debt to make speculative leases and buildout of datacenters to rent to other players.
It's been great for them so far, but if there's an AI winter, Oracle will be the first to freeze.
It could make it worse. IP from companies that got chopped up and sold for parts can be a nightmare. You may have to do deals with multiple parties, and it can be unclear who owns what (even to the potential owners themselves).
There is debate as to whether the FreeZFS license (CDDL) is compatible with the GPL, which is why FreeZFS is not part of the Linux Kernel. Some distros are baking it in, but there has long been concern about if merging it violates the license or not.
Even if Oracle evaporated and their contemporary ZFS source became unencumbered, I doubt OpenZFS would want to try and merge significantly parts. They already have their own encryption implementation for example.
Not many people buy Windows, they buy laptops that happen to have Windows installed. IMO this is a worthwhile distinction because most people don’t really care about operating systems anyway, and would happily (I suspect, at least) use an Open Source one if it came installed and configured on a device that they got in a store.
Installing an OS is seen as a hard/technical task still. Installing a local program, not so much. I suspect people install LLM programs from app stores without knowing if they are calling out to the internet or running locally.
Broad estimates I'm seeing on the cost of a 1GW AI datacenter are $30-60B. So by your own revenue projection, you could see why people are thinking it looks like a pretty good investment.
Note that if we're including GPU prices in the top-line capex, the margin on that $70-150B is very healthy. From above, at 0.4J/T, I'm getting 9MT/kWh, or about $0.01/MT in electricity cost at $0.1/kWh. So if you can sell those MT for $1-5, you're printing money.
> So if you can sell those MT for $1-5, you're printing money.
The IF is doing a lot of heavy lifting there.
I understood the OP in the context of "human history has not produced sufficiently many tokens to be sent into the machines to make the return of investment possible mathematically".
Maybe the "token production" accelerates, and the need for so much compute realizes, who knows.
My interpretation of that moment was that they had already decided to give away protein sequences as charity, it was just a decision of all as a bundle vs fielding individual requests (a 'service').
Still great of them to do, and as can be seen it's worth it as a marketing move.
(as an aside, this is a common thing that comes up when you have a good model: do you make a server that allows people to do one-off or small-scale predictions, or do you take a whole query set and run it in batch and save the results in a database; this comes up a lot)
I think most people (namely high school seniors) go to college for neither. They go because that was the expectation, and was assumed to be at least approximately productive path.
While arguably that's indirectly 'for the piece of paper', I'd argue the pleasant experience is a factor too, even if not quoted as such. i.e. if it was a purely rational, economic choice (my interpretation of going to college just for the degree) we'd see higher enrollment in high-ROI majors.
Interestingly this time around I could see the 'fire' affecting mid-large corporations (or at least some divisions of them) if they don't adapt. Adobe, being heavily focused on graphic design seems like it could be under pressure. Low-end consulting / outsourcing is largely doing the same work AI is good at. Similarly with technical gig-work (like Upwork).