@jamescham But they sort of are, aren’t they?
Isn’t the actual argument that the tasks are more myriad and nuanced than most appreciate so automation is farther off than most believe?
But still, automation is probably on the horizon?
“Hiring competent people doesn't solve the slop problem at scale. Individual taste compounds only against a company's philosophy of business.”
This is why most PE backed software companies are toast. They are run by managers without a real opinion on the “why” of the business.
Core argument is that AI commoditizes yesterday's competence, which creates sameness, which creates demand for difference. A few loose thoughts:
1. Dan says the value moves to the "framer," the human who supplies live judgment that the model can't generate. How do you keep the "frame" consistent across an organization?
2. The frame problem is a narrative problem before it's a talent problem. A company without a worldview has no basis for distinguishing its AI-assisted output from anyone else's.
3. This is why I'm less excited about "10x employees" or whatever the new meta is. Hiring competent people doesn't solve the slop problem at scale. Individual taste compounds only against a company's philosophy of business.
4. Who is building the narrative infrastructure to frame every output against what they actually believe?
"If products can be copied, categories can be renamed, and technical advantages can collapse in months, then the enduring question is what kind of organization you build around the people capable of building it. The shape of the company itself is becoming the moat."
Love this: "If products can be copied, categories can be renamed, and technical advantages can collapse in months, then the enduring question is what kind of organization you build around the people capable of building it. The shape of the company itself is becoming the moat."
New Anthropic research: Natural Language Autoencoders.
Models like Claude talk in words but think in numbers. The numbers—called activations—encode Claude’s thoughts, but not in a language we can read.
Here, we train Claude to translate its activations into human-readable text.
@FirmBrain@michelletomkim At the limit, this is cope.
Not sure we reach the limit. But on an AGI or ASI world, all of what you are describing can be trivially recreated.
Jerry Seinfeld on why chasing your "passion" is embarrassing, and what to do instead:
Seinfeld pushes back against the popular advice to find your one great passion in life.
In his view, it's not just unnecessary, it's a little ridiculous.
"Let go of this idea that you have to find this one great thing that is my passion. My great passion with your shirt torn open and your heaving pec muscles. It's embarrassing."
Instead of chasing something dramatic, he offers a quieter alternative:
"Find fascination. Fascination is way better than passion. It's not so sweaty."
He explains why the heavy-breathing version of passion is actually counterproductive:
"Just be willing to do your work as hard as you can with the ability you have. We don't need the heavy breathing and the outstretched arm from your passion. It makes co-workers uncomfortable in the cubicle next to you."
Then Seinfeld offers what he calls his three real keys to life, no jokes:
"Number one, bust your ass. Number two, pay attention. Number three, fall in love."
@JerrySeinfeld elaborates on the first one:
"You obviously already know whatever you're doing, I don't care if it's your job, your hobby, a relationship, getting a reservation at M Sushi, make an effort. Just pure stupid… effort."
And here's the part worth sitting with:
"Effort always yields a positive value even if the outcome of the effort is absolute failure of the desired result. This is a rule of life. Just swing the bat and pray is not a bad approach to a lot of things."
Obligatory reference to this alternate explanation (which, even if totally inaccurate, is also an extremely normal type of thing for a technology to say about itself)
https://t.co/m9rUWZMH50
"If our world survives, the next great challenge to watch out for will come--you heard it here first--when the curves of research and development in artificial intelligence, molecular biology and robotics all converge."
--Thomas Pynchon, 28 Oct 1984.
https://t.co/IF6F7QoSLC
A question I am thinking about a lot recently:
“How do you not lose your soul as a leader in the AI era?”
I won’t lie to you. I see a lot of people operating in a way that, when they look back on their life, will fill them with disgust. It fills me with disgust.
People ask why I have become so negative about AI and I simply say “I am watching people in the workplace lose their humanity.”
So how do you lead with dignity? The first three things that are very clear to me:
1. You owe it to your people to think very deeply about what every single role will look like 1-2 years from now. - What parts of this job are different
- Where is human judgement still required?
- Do I even need this job any more?
- If I need a different job, can I train the people I have today to start doing the new job?
2. Clarity is kindness. Anxiety is at an all time high. Every single person is already asking themselves that question. You owe them clarity. “I don’t know” is still clarity. Perhaps they will show you the future.
3. Try to show up with kindness. At the end of the day you will be fucking dead and nobody will remember your name. Nobody will remember the work you did. It doesn’t matter. It’s not worth it to be a piece of shit, sven if you’re scared or stressed. Just try to be kind.
the Claude Code vs Codex battle is a peek into the future of software. There are no laurels to rest on. You’re going to have to prove value every minute of every day on every agent action
A common dynamic I observe with AI: it feels most impressive when you don’t know much about the subject, don’t care or don’t have a clear idea of what the you want.
This applies across design, code, legal, and more. If I don’t know code very well, every piece of code it writes feels very impressive.
Once you know what something should feel or look like, it becomes almost impossible to guide AI there. And you definitely can’t one-shot it.
Built clawsweeper, which runs 50 codex in parallel around the clock, scans issues/prs deep and closes what is already implemented or what makes no sense.
Closed around 4000 issues today, a few thousand are in the pipeline. (rate limits are rough) https://t.co/AiNNDcvGke
a masterclass in coding agents from the head of anthropic.
there’s still a tonne of leverage in knowing how to use these systems optimally and this is the best i’ve seen.
make sure to bookmark so you can watch again and again chat
🧵 My tips for getting the best results out of Claude Design! I’m on the verticals team at Anthropic which means I serve 7 different products. Claude Design makes it possible!
1. Set up your design system and your core screens. An hour of setup and refinement here is worth it
We use OpenClaws to do all of our work at @every.
We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack.
I chatted with @every COO Brandon (@bran_don_gell) and @every head of platform Willie (@bigwilliestyle) to share what we’ve learned.
We get into:
- Why agents become mirrors of their owners, and how that influences how other people on the team interact with them
- How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in
- The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human
- Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do
- This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent
Watch below!
Timestamps
Introduction:
How Brandon built Zosia, an AI agent to run his household:
Brandon’s “aha” moment:
What happened when everyone on the team got their own agent:
How agents take on their owners' personalities, and why that matters inside an org:
Why it’s important for agents to work in public:
What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem:
How we built Plus One, our hosted OpenClaw product:
The cultural shift required to make agents work at scale: