So excited to begin exploring the new frontier of web3 and its pending impacts on education, the future of work, and technology. Join me via wordpress or mirror.xyz. edtechmpeck.wordpress.com/20…

Ben Horowitz says instead of banning AI, educators should set problems students can't solve without it: "I was talking to Dan Boneh, who is a great professor of computer science and cryptography at Stanford. His take on AI was: You have two choices. You can ban it, and by the way, that won't work. Or you can make the problems so hard that you can't solve them without AI." "What he's seeing is, 'I've got students solving things that no student in history could have ever solved.' That's what's possible. You want to have that orientation: What can you solve with the tools? What can you do that's a breakthrough? Now everybody has an opportunity to create a breakthrough." "Then you're a main character, not an NPC. That's what the academy is about. Do you want to be a main character? If you want to be a main character, we help you do that. If you want to be a cog in a machine, this is not going to be the best place for you." @bhorowitz

is this too much

Anyone else find it real interesting that certain models tend to get lazy? My Hermes Agent really struggles with tool calling and executing multiple steps. Kinda funny how GLM 5.2 is like "yeah Mimo is hallucinating again"

This has been obvious to anyone who studies history and previous technological revolutions. We abstract lower level problems away and move up the stack to new problems. That creates new complexity and a wider variety of jobs. Complexity breeds more complexity. The work is infinite because the problems are infinite. No machine, human, or man and machine hybrid can solve them all. AI is not magic and we need to stop thinking about it that way. The stack of problems is infinite and never ending.

real leadership

love the feed being all US Soccer takes feels refreshing

MALIK TILLMAN IS AN AMERICAN HERO 🇺🇸 The USMNT double the lead, thanks to the German-born midfielder's free kick 🔥

One thing I've been doing is having my Hermes agent make a workflow chart every 30 days to track changes in use case and enhancements. Would be great to see other and how they use theres. I think I really need to create subagents...

MOA is seriously impressive. The outputs were excellent and Heremes worked for about 17 minutes on the prompt despite channeling everything through a single custom endpoint. Using open sources models via @AskVenice I was sub $1.

The next cypherpunk era

Interesting..

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Never imagined I would be able to run my own AI locally. Usin @NousResearch Hermes made the process of setting up seemless. Next step is to work on fine tuning.

beware absolutes and absolutists: > ai will take all the jobs > there will always be a human in the loop > acceleration is out of our hands > we need to shut it all down the more productive stance is “here are a set of probable sober realities, what incredible things can we build with these new affordances and constraints”

⚡️Satya is describing the new balance sheet of the firm. The old firm owned people, processes, software, customer relationships, brand, data, and IP. The new firm will own a compounding cognition loop. Every workflow becomes a training surface. Every decision becomes a trace. Every expert judgment becomes reusable signal. Every internal correction becomes model improvement. Every model run becomes a chance to turn human judgment into institutional intelligence. That is what “token capital” really means. It is accumulated machine-operable cognition. A company’s expertise becomes executable, queryable, evaluable, improvable, and portable across models. That is a massive shift. The most important line is the one about switching out the generalist model without losing the company veteran expertise. That is the entire enterprise AI war. Model providers want the firm’s knowledge to flow into the model layer. Enterprises need that knowledge to stay inside their own loop. Whoever owns the loop owns the future economic rent. Satya is laying out Microsoft’s answer to the frontier-model monopoly problem. If all company knowledge flows upward into a few foundation models, the foundation model labs become landlords of the entire economy. They absorb everyone’s expertise, commoditize every workflow, and capture the value created by every firm’s learning process. That equilibrium will trigger political backlash, customer resistance, regulatory pressure, and corporate revolt. So Microsoft’s doctrine is: every company should build its own AI learning system on top of frontier models, while Microsoft owns the infrastructure where that happens. That is elegant and self-serving. Microsoft does not need to own the single best frontier model forever. It needs to own the enterprise control plane: identity, security, permissions, data, workflow, evals, agents, memory, developer tools, cloud, compliance, and model routing. If the model becomes swappable, the platform underneath the firm’s learning loop becomes the durable asset. Satya is quietly saying the frontier model alone is unstable. A world of a few models eating every company’s expertise breaks the political economy. A world where every company builds firm-specific AI capital on top of models is more stable, more defensible, and much better for Microsoft. The “human capital gets more valuable” line is partly true and partly corporate diplomacy. High-agency humans become more valuable. People with taste, judgment, relationships, domain intuition, ambition, and the ability to direct agentic systems become much more valuable. Routine cognitive labor loses bargaining power. The future firm does not need every human equally. It needs humans who can generate high-quality signal for the loop. The human becomes a trainer, judge, strategist, relationship node, taste layer, and goal-setter. The work that cannot feed the loop or direct the loop gets compressed. This also connects directly to the Anthropic crisis. If frontier model access can be restricted, pulled, nationality-gated, or subordinated to state power, then enterprises cannot allow their intelligence layer to live entirely inside one external model. They need portability. They need private evals. They need internal memory. They need their own traces. They need model-agnostic learning systems. The model can change. The firm’s cognition loop has to survive. That is the new sovereignty test. A company that only buys AI access is a renter. A company that turns its workflows, judgments, corrections, and outcomes into a private learning loop is building capital. The deeper implication: the future economy splits between firms that compound cognition and firms that leak cognition. Firms that compound cognition will get stronger every time they operate.

Models are getting smaller and faster in a way that is hard to comprehend. The implications we're only beginning to understand, though a safe bet is we're moving towards a world of ambient AI.

Check out the latest article in my newsletter: What If the Best Preparation for AI Isn't More Technology? linkedin.com/pulse/what-best… via @LinkedIn