Wouldn’t this make the $200k drop below EBIT and thus increase accrual accounting profitability? Sure it could be less cash efficient but generally capitalizing expenses is net favorable.
Think the massive increase in demand is due to mass inference of open source LLMs? Or is the transformer architecture driving mass inference of other models too?
“Own” is likely a very simplistic term for the complex agreement in place between SpaceX and NASA. I’m sure SpaceX retains all of the developed IP, but NASA can “do whatever they want” with the actual ship.
I’m a Googler who doesn’t do anything remotely close to setting capital return policy. Just remember Alphabet has been buying back 10s of billions for a while. Dividends are just a different capital return mechanism.
Let’s compare. I’ve interviewed for and been offered both high paying tech jobs and investment banking jobs. My interviews for tech really required me to have a deep understanding of the technology and how to solve problems with it. They tested my critical thinking skills. For banking, sure there were some technical questions but a lot of it came down to what school I went to, how I acted, who I knew. I suppose either one could be a “nightmare” depending on who you are. I do think the tech interview process was much better at hiring generally competent people. There’s also consulting which I have not directly interviewed for but the interview style is more similar to tech with more of a focus on mental math, those can be very difficult if you don’t have that skill.
I’m not completely read in to all the details but she told me prior to this drug her diagnoses was a “death sentence.” The lung cancer had metastasized throughout her bones. The indication was sudden lower body paralysis caused by a spinal tumor. She had spinal fusion surgery as well. I understand because it’s stage 4 she will never be in “remission” but it has regressed significantly.
Why is RAG a horrible hack? LLMs can draw from only 2 sources of data: their parametric knowledge and the prompt. The prompt seems like a pretty good place to put new information they need to reason with.
When I prototype RAG systems I don’t use a “vector database.” I just use a pandas dataframe and I do an apply() with a cosine distance function that is one line of code. I’ve done it with up to 1k rows and it still takes less than a second.
Our network is completely software defined, so we just fake it to the VM and make it look like it's talking right to the service, and do all the routing via magic.
In your cost comparison are you including headcount to maintain all of that infrastructure? Security? And the equivalent of multi zone redundancy? Scale headroom?
What's really shocking is that this number is post provision for future losses, which is a non cash accounting charge that the media often uses to make banks look worse. These are real losses.