HN user

_sword

596 karma
Posts4
Comments131
View on HN

I was modeling configurations purpose-built for running specific models in specific workloads. I was trying to figure out how much of a gross margin drag some software companies could have if they hosted their own models and served them up as APIs or as integrated copilots with their other offerings

I've done the modeling on this a few times and I always get to a place where inference can run at 50%+ gross margins, depending mostly on GPU depreciation and how good the host is at optimizing utilization. The challenge for the margins is whether or not you consider model training costs as part of the calculation. If model training isn't capitalized + amortized, margins are great. If they are amortized and need to be considered... yikes

GPT-5 12 months ago

Neat, more scalable intelligence for me to tell "plz fix" over my code

Even before LLMs were popularized, the shift to remote work made hiring awful in my experience. In finance roles, I had candidates who aced their tests and projects but then showed up to the job unable to competently use excel or write coherent sentences in English. Phone / zoom interviews all went fine, but clearly there was rampant cheating during remote projects.

Makes sense and was only a matter of time considering it has essentially no revenue growth to date this year and Non-GAAP margins in the low-to-mid 30's %. With near-0% revenue growth, investors will expect a SaaS company to post 40%+ margins.

At virtually all banks, equities analysts are banned from trading in their coverage. Banned as in, if you, your spouse, your dependents, etc. have an interest you didn't proactively disclose and dispense with you're fired on the spot.

FINRA regulation states that registered equities analysts (i.e. the ones working at banks) at a minimum cannot trade against their ratings [0]. At most / all banks there are further restrictions that ban trading in coverage.

[0] https://www.finra.org/rules-guidance/rulebooks/finra-rules/2...

I took undergrad quantum physics with Prof Greene and it was a great time. I definitely could have chosen a better professor for the math, but the theory and his banter was fun.

The tech could be really cool if e.g. classifiers could be represented within the probability space modeled on their hardware. However their shaman-speak isn't confidence inducing.

At this point I wonder how much of the GPT-4 advantage has been OpenAI's pre-training data advantage vs. fundamental advancements in theory or engineering. Has OpenAI mastered deep nuances others are missing? Or is their data set large enough that most test-cases are already a sub-set of their pre-training data?

The White House’s AI regulatory order requires big cloud providers to monitor for and report foreign clients (ie Chinese) for using their services to train AI fyi. Monitoring for malicious activity isn’t nearly as invasive as what the government is requiring