Spyke

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Shoulda listened

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Python is great for standalone scripts/CLIs with standard system dependencies. For actual apps you want venvs, probably managed by uv these days.

I've written 1000s of python CLIs/scripts over the years, typically wrapping APIs for otherwise shitty enterprise and SaaS software (also many one-off scripts for ETL).

Nowadays, AI writes these scripts in a fraction of the time (and reads the shitty API docs / otherwise figures it out) and my CLI toolkit is better than ever (with $0 ongoing cost). Calling MCPs from CLIs is also interesting, as some vendors never had usable APIs before now.

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Companies Are Realizing They’re Spending a Fortune on AI Just So Their Employees Can Generate Error-Riddled Slop, and Suddenly Cutting Back Budgets.

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72b of that 80b is an increase of the estimated fair value of some debt instruments, funnily enough because Anthropic's valuation has increased.

IMHO, it serves as an incentive for creditors to keep the ship afloat, rather than pulling the plug. No guarantees, but interests are better aligned.

Revenue-wise, it's on the right trajectory.. However, infra investments will eventually have to match the revenue they generate.

They might also start pulling rabbits out of their hat with meaningful economic value beyond writing code and sorting emails ... solve male pattern baldness in rich white men and you're golden 🤣

fuck_ai

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My GF uses AI

Your passion for the well being of your girlfriend is admirable.

Are you in a position to collaborate on a project with her, to challenge yourselves and increase your understanding?

I'm sure that the lecturers/teachers will be tailoring their subjects in light of AI already. So I wouldn't focus there, but on collaboration and real world problem solving ... something that won't kill the relationship either!

My own experience MANY years ago showed me that around 2/3rds of my classmates had only superficial understanding of the topics at the time (note: I was a geek, and was often well ahead of the course material, and I was bored in most classes as a result). Some really struggled but persisted (much respect to them!), while others left or switched.

I'm sure most of my class ended up in gainful employment and benefited from their studies regardless. I suspect that those who enjoyed a good working life are those who could collaborate well, who could talk with their colleagues and explain their viewpoints and positions, and listen and understand others viewpoints as they worked toward shared solutions to real world problems.

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Companies Are Realizing They’re Spending a Fortune on AI Just So Their Employees Can Generate Error-Riddled Slop, and Suddenly Cutting Back Budgets.

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Both Anthropic & OpenAI have debt repayments coming up in 2027 that:

  • cannot be restructured
  • cannot be postponed
  • they absolutely cannot pay back without even more VC money, and that faucet is being cut off right now.

Well then. I stand corrected. The random Internet person with unprecedented access into the books & private financial dealings of two major competitors has an air-tight argument.

I suggest we come back to this thread in a year's time and see how it is playing out.

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Companies Are Realizing They’re Spending a Fortune on AI Just So Their Employees Can Generate Error-Riddled Slop, and Suddenly Cutting Back Budgets.

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Some large companies do it ... but I think you greatly overestimate the willingness of large enterprises to build and maintain their own infra. Once a company has lost the competency (and many have), it's hard to get it back. There are always pockets of people with the right skillset in a large org, but they mostly don't want to do DC work anymore. Team/personal servers and beefy laptops, otoh...

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Companies Are Realizing They’re Spending a Fortune on AI Just So Their Employees Can Generate Error-Riddled Slop, and Suddenly Cutting Back Budgets.

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Of course they want to turn the corner and ideally keep all their infra, but they might need to restructure their debts if/when the creditors come knocking.

BUT: they have a money printing machine with inference... Enterprise is where most of the money is made, as they pay (closer to) API prices... consumer & small business plans are heavily discounted in comparison, with mostly underutilized use-it-or-lose it plans. They'd be stupid if they were making an opex loss on inference.

As for why API prices are dropping? Competition is a bitch... but they seem willing and able to play the game for now.

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Companies Are Realizing They’re Spending a Fortune on AI Just So Their Employees Can Generate Error-Riddled Slop, and Suddenly Cutting Back Budgets.

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And we haven't even gotten to the crazy price hikes per token that are coming.

Why do you think that?

IMHO, API prices are likely to drop over time as we scale up and efficiencies are found, combined with general pricing pressure from the competition... SOTA models will continue to have SOTA prices, of course, but reasonable priced distilled and open weights models are already common.

A consequence of API prices dropping is that "heavily discounted" consumer plans will also adjust and won't be so generous relative to API prices.