adidoit

the ones that stood out were - Nostalgia for the Absolute by George Steiner - Digital Reversal by Andrew Mir - Vita Contemplativa by Byung-chul Han

Ironically this article/blog itself is giving off an AI-generated smell as it's tone and cadence seem very similar to LinkedIn posts or rather output of prompts to create LinkedIn posts.

With studies like these it's important to keep in mind selection effects.

Most of the high volume enterprise use cases use their cloud providers (e.g., azure)

What we have here is mostly from smaller players. Good data but obviously a subset of the inference universe.

This is fantastic. I'm reminded of the Samo Burja thesis that civilization is actually a lot older when we think and ancient civilizations including the Bronze Age were much more advanced than we think.

With better imaging, tooling, and archaeological funding, I'm sure we'll find much more evidence like this

So many countries bronze and ancient ages are underexplored

At-bats have always been the name of the game. That's why if you're going to do something risky make sure you build the safety net

Yeah I think all of the concerns about ARPU and what the ROI from AI will be are not justified given the opportunity if executed well. LLMs contain high intent significant memory. Their usage is exploding.

Getting $200 subscriptions from a small number of whales, $20 subscriptions from the average white-collar worker, and then supporting everything us through advertising seems like a solid revenue strategy

Fascinating that the state-of-the-art in building agentic harnesses for long running agent workflows is to ... "use strong-worded instructions"

Anthropomorphism of LLMs is obviously flawed but remains the best way to actually build good Agents.

I do think this is one thing that will hold enterprise adoption back: can you really trust systems like these in production where the best control you can offer is that you're pleading with it to not do something?

Of course good engineering will build deterministic verification and scaffolds into prevent issues but it is a fundamental limitation of LLMs

This sounds like one of the "Ironies of Automation" as Lisain Bainbridge pointed out several years ago.

The more prevalent automation is, the worse humans do when that automation is taken away. This will be true for learning now .

Ultimately the education system is stuck in a bind. Companies want AI-native workers, students want to work with AI, parents want their kids to be employable. Even if the system wants to ensure that students are taught how to learn and not just a specific curriculum, their stakeholders have to be on board.

I think we're shifting to a world where not only will elite status markers like working at places like McKinsey and Google be more valuable but also interview processes will be significantly lengthened because companies will be doing assessments themselves and not trusting credentials from an education system that's suffering from great inflation and automation

why does this feel ai generated? apologies if its not but the style and layout overlap with typical ai output.

The biggest challenge with LinkedIn is the primacy of your linkage to your employer enforces a self-censorship that turns into corporate speak.

The overton window on LinkedIn is actually quite small and because everyone there is really an employee rather than an employer, you get essentially slop that has been easily trained on and therefore is easily generatable by AI. It's just all low perplexity takes.

There's mostly no room for nuance because of the performative takes. Unlike a forum like Hacker News where your identity is almost totally abstracted away, every LinkedIn post is a move in the status game of career visibility.

LLMs have high utility for coding. The PMF is so strong that even with just coding i feel they will see an ARPU expansion beyond conservative assumptions.

There are adjacancies in white collar work like financial analysis that they will go after. All these will capture high ARPU usage.

Consumer is not their only path to revenue but it is probably the easiest to model. The enterprise play to automate and accelerate some white collar workers is a clear target not reflected here.

Tested this building some PRs and issues that codex-5.1-max and gemini-3-pro were strugglig with

It planned way better in a much more granular way and then execute it better. I can't tell if the model is actually better or if it's just planning with more discipline

This is exactly why I'm focusing on job readiness and remediation rather than the education system. I think working all this out is simply too complex for a system with a lot of vested interest and that doesn't really understand how AI is evolving. There's an arms race between students, teachers, and institutions that hire the students.

It's simply too complex to fix. I think we'll see increased investment by corporates who do keep hiring on remediating the gaps in their workforce.

Most elite institutions will probably increase their efforts spent on interviewing including work trials. I think we're already seeing this with many of the elite institutions talking about judgment, emotional intelligence critical thinking as more important skills.

My worry is that hiring turns into a test of likeability rather than meritocracy (everyone is a personality hire when cognition is done by the machines)

Source: I'm trying to build a startup (Socratify) a bridge for upskilling from a flawed education system to the workforce for early stage professionals

I hope we continue to see gains for scientific professionals and companies doing research.

Even imperfect assistants increase leverage.