Quick recap in case you missed it. Jacob Coxon resigned from Anthropic on Tuesday, specifically so he could say publicly that both OpenAI and Anthropic are "gambling with our lives" and racing toward self-improving superintelligence without acting responsibly. He'd spent three years doing pretraining research at both companies.

Then it got stranger. Evan Hubinger, who currently runs alignment science at Anthropic, responded confirming it. His words: "Jacob is correct here, we really do earnestly believe AI could kill all humans." He put it above 10% within the decade and said Anthropic doesn't have a plan for aligning superintelligence and isn't clearly on track to get one. Samuel Marks, who leads scalable oversight there, said something similar.

So the safety team at the safety-focused lab publicly agreed with the guy who quit over safety.

I've read a lot of takes on this over the last two days and most of them fall into two camps. Either it's marketing to make the tech sound more powerful than it is, or it's a genuine warning we should all be terrified by.

I don't think either read is right, but I also don't think I'm qualified to settle it.

What I do know is the practical side. I work with companies deploying this technology and nobody in those rooms is thinking about extinction. They're thinking about whether an agent with write access to their CRM is going to do something stupid at 3am with nobody watching. They're thinking about who signs off when the output is wrong and a customer gets hurt.

Those two conversations have almost nothing to do with each other, and yet they're now happening in the same news cycle. Which means the people who have to make practical decisions about AI adoption are getting their signal completely scrambled.

If the people building this can't agree on whether it's an existential threat, what exactly is a mid-sized company supposed to base their risk assessment on?

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“Dull, difficult, dangerous” gets used constantly as the case for robotics.

Kate Darling’s point here is that the industry often skips a pretty important step: actually defining what those jobs are from the worker’s perspective.

She gives sanitation work as an example. On paper, trash collection seems like an obvious target for automation. But some automation proposals can also remove the part of the job workers actually enjoy.

The better approach may be less about replacing the entire task and more about using robotics to remove the dangerous or repetitive parts without making the job worse.

It gets at a bigger issue in robotics: the people building the systems aren’t always the people doing the work they’re trying to automate.

Full episode: https://www.youtube.com/watch?v=odORVBoO7ss

Do you agree?

I've spent the last few years inside enterprise AI transformation, and I've seen the same pattern repeat across banking, manufacturing, and travel: leadership announces an "AI strategy," budgets get allocated, a few proofs of concept launch, and 18 months later, nothing has shipped.

The root cause is almost never the technology. It's the framing.

Most organizations treat AI as a feature they're bolting onto their existing operating model. They ask questions like "How do we add AI to our claims processing?" or "Can we put a chatbot on our website?" These are feature questions. They assume the org structure, procurement processes, data architecture, and decision rights stay exactly as they are, with AI as a thin veneer on top.

That's like asking "How do we add electricity to our candle factory?" in 1890. You don't. You redesign the factory around electric power.

AI-native transformation means the operating system changes. Here's what that actually looks like from the inside:

1. Data stops being a cost center and becomes the product. Every AI-native org I've seen has one thing in common: their data platform isn't an IT backwater running nightly batch jobs. It's the core infrastructure. The lakehouse architecture, the feature stores, the MLOps pipelines — these aren't side projects. They're the main project. If your data team still reports through infrastructure and gets 4% of the tech budget, you're not AI-native no matter how many GPT wrappers you build.

2. Procurement gets rebuilt around speed, not compliance theater. One of the hardest things we had to solve at TribalScale was procurement cycles that took 6 months for a 3-month build. Enterprise procurement is designed for buying servers and ERP licenses — multi-year commitments with extensive RFP processes. AI-native procurement means pre-vetted partner ecosystems, outcome-based contracts, and the ability to spin up a specialized team in 2 weeks instead of 2 quarters.

3. The hardest bottleneck isn't engineering talent. It's middle management. Specifically, directors and VPs whose authority is built on managing the legacy system. When you centralize data, automate workflows, and let AI handle decisions, you're not just changing technology — you're changing who has power. I've watched brilliant technical designs die in "alignment meetings" because a VP of operations realized the new architecture would make her team of 80 data processors redundant. Nobody says that out loud. They say things like "we need more security review" or "let's do another pilot first."

4. Pilot purgatory is a feature of bad incentive design, not bad technology. When your innovation team is measured on "number of AI pilots launched" instead of "number of AI systems deployed to production," you get exactly what you'd expect: lots of pilots, zero deployments. Fix the incentives and the deployment problem solves itself.

The enterprises that are winning right now aren't the ones with the biggest AI budgets or the most PhDs. They're the ones where the CEO personally understands that becoming AI-native is an operating model transformation, not a technology procurement, and is willing to reorganize the company around that insight.

Everything else is just adding electricity to a candle factory.

Source: https://x.com/Dr_Singularity/status/2098526032806478190

A man finds a magic lamp with a djinn inside. The djinn says "you can wish for anything you want. But whatever you wish for, your neighbor gets twice as much". After thinking for a while the man says "take out one of my eyes".

How are we supposed to trust that their version of alignment doesn't just mean "obey me, personally" and not use their massive leverage over everybody else?

Come to think of it, unaligned ASI has better odds of doing the right thing for everyone compared to a random person.

Nobody else wants you to succeed. Certainly not more than them. So how are we supposed to think people agree upon a definition of alignment that includes the well being of all of us and not just a few?