At its peak, Sun Microsystems was valued at 205B (394B if inflation adjusted). Sold software in enterprise servers. Got disrupted by Linux, x86, and commodity hardware. Ended up selling to Oracle for 7.4B, losing 96% of its value.
Open source models running on local hardware can have a similar impact given what’s going on.
Estos 2 tienen 41 años y la diferencia no es solo genética, son los hábitos.
Estos son los hábitos que te mantendrán joven.
1. Entrenamiento de fuerza pesado
"I find it ironic that the status quo is to then turn around and impose restrictive terms on distillation, and to reserve the right to learn from customer usage and interaction data. If learning flows in only one direction, economic value converges toward the owners of the learning infrastructure rather than the creators of the knowledge itself. Therefore, it's imperative that we distribute the learning infrastructure to every firm so that they can control their own learning loop."
Well said.
this is f*cking gold
Andrej Karpathy joined Anthropic five weeks ago.
A friend on his team just showed me the exact Claude.md file he actually uses.
I dropped it into my setup. The very first response was different.
Not slightly different. Completely different.
Claude stopped giving generic answers and started working exactly the way I think.
Bookmark it before it gets lost in your feed.
Read it now, then check the article below.
Google CEO, Sundar Pichai:
"If you don't learn to how to orchestrate agents now, you'll spend 2027 catching up to people who started today"
In 30 minutes he explains why the best engineers stopped writing code and started running agents.
Watch the interview, then save the exact setup below 👇
Satya’s post is worth reading closely because it gets at the real AI question for companies.
Who captures the learning?
His argument is that companies are becoming a new kind of learning system.
People bring judgment, taste, relationships, context and ambition. AI brings scale, memory, reasoning and execution. The value comes from building a loop where the company gets smarter every time work happens.
The important asset is the learning system around the model.
That system is built from the record of how work actually gets done. Workflow traces show the path people take. Corrections reveal judgment. Accepted outputs show what good looks like. Rejected approaches sharpen the standard.
Private evaluations, domain-specific context and institutional memory give that learning structure.
Over time, the company starts to retain more of what used to disappear inside meetings, edits, comments, decisions and individual experience.
That is the learning loop Satya is pointing at.
The judgment that once lived in a few people’s heads can become part of how the company operates.