Welcome, humans.

So apparently workplace writing has reached the point where being too polished is suspicious.

A supervisor wrote into The New York Times because basically every Teams message or email longer than a sentence from one employee now “has the hallmarks of A.I. writing.” The employee is good at his job, and English is not his first language, so AI may be doing exactly what it is useful for here: helping him communicate more clearly. His boss just finds the result weirdly less trustworthy, because, y’know… the AI sloppocalypse is upon us and trust is at an all time low…

I will say, as the last bastions of pure 100% certified human culture is besieged on all sides by slop, the ultimate irony is that the solution to AI slop is to sound less polished and more “sloppy.” To defeat the slop… we must become as slop itself…

I can picture it now: People merging with slop, becoming one with it. It’s the sloppening. The sloppularity. Deus Ex Sloppina!

Here’s what happened in AI today:

…and a whole lot more that you can read about here.

🙀 Microsoft wrote rules for a human-focused future for AI as US President Trump rejected more guardrails

Lately you’ve heard us (and the whole mainstream media TBH) ask the same question over and over: who should keep powerful AI under control? Well, on Monday, Microsoft and US President Trump landed on basically opposite answers to that very question.

First up: Microsoft AI published a draft Code of Conduct for future MAI models. Think of it as the rulebook for what Microsoft's own models should be allowed to do: stay inside the assigned job, obey shutdown, avoid inventing goals, and do not pretend to be a conscious person.

Here's what happened:

Now, one caveat to Microsoft’s “rulebook” before we get carried away: Microsoft's document is a roadmap, not a description of today's models. The company says a revised version will guide development into 2027.

Trump, meanwhile, argued that a “strong and smart (High IQ!) president” is pretty much the only guardrail AI needs. AP reported that he opposed calls for enhanced oversight and emphasized staying ahead of China. And he even called NVIDIA CEO Jensen Huang live on stage at a taping of the tech podcast All In to expand on his thoughts. For more coverage on that, read this.

Why this matters: We keep using “AI guardrails” like it means one thing. It really has two branching layers:

Even there, researchers disagree. Former OpenAI research VP Jerry Tworek argues alignment is still fundamentally an unsolved algorithm problem because the companies stopped prioritizing it. He says training learns from successes and failures, which gets scary when the failure is the AI causing real harm.

His answer: we need safer simulations where models can be free to fail, or better training algorithms that don’t require dangerous failures at all (this is my pick! But… por que no los dos?).

NVIDIA researcher Ali Hatamizadeh pushes back, though: alignment research is still happening. We already have ways to teach models rules and human preferences.

The harder question to answer via research is whether those lessons hold up in situations the model hasn’t seen before.

So Layer One is basically: are we missing the core alignment algorithm, or do we already have the pieces and still need to prove they generalize?

Then there’s Layer Two. Trump’s former White House AI and crypto czar David Sacks has a surprisingly compelling point: if OpenAI and Anthropic think they need to slow down to make their products safer, go do it. 

Existing product-liability laws already give them a reason to care, and he still supports transparency and independent audits as sensible policies.

His objection is turning that into a government-wide slowdown, especially if China keeps racing ahead.

Key context to note here though: “pacing” the frontier doesn’t necessarily mean stopping. Investor Gavin Baker explains it more like this: pacing = keep making models better, but shift more compute and engineering toward testing, monitoring, and alignment instead of pure capability.

Move slower, and spend more time proving you understand what you already built. Uh, duh? Sounds like a great idea?

As for Microsoft and their AI plan, Microsoft is actually arguing that powerful AI should behave less like an independent artificial person and more like an extremely capable employee/tool operating inside a clear authority structure.

The useful thing about Microsoft’s proposal is that it turns “guardrails” from a vibe into something you can actually test. It also makes the limit of any company rulebook obvious: internal rules are only one layer. You still have to…

Maybe that is the real guardrail debate: not rules vs. no rules, and not slowdown vs. acceleration in the abstract.

Whether the technical controls inside the model, the evaluations around it, and the accountability outside the lab are strong enough to survive contact with the exact incentives pushing the other way: capability, convenience, competition, and speed.

A guardrail that only works when everyone is behaving carefully isn’t much of a guardrail.

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🎓 AI Skill of the Day: Create first, enforce the rules second

MIT’s new HardFlow method tackles a surprisingly common AI problem: when you force a model to obey every constraint while it’s still figuring out the answer, you can make the result worse.

Their technical method gives the model room to explore, then enforces the hard constraints on the final output.

You can borrow the same idea in your prompts:

Prompt: 

Have a specific skill you want to learn? Request it here.

🍪 Treats to Try

📰 Around the Horn

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🔧 Tuesday Tool Tip: Try one agent before you build an agent org chart

Polylane tried the thing every agent diagram eventually suggests: split one job across a little team of specialized agents. Then it ripped the setup back out.

The problem was not that the agents were dumb. It was the handoffs. Each agent summarized what it learned for the next one, and every summary quietly dropped clues the next agent needed. Polylane replaced the chain with one agent that investigated the issue end to end.

In its reported results, median time-to-PR fell from 2.2 hours to 35 minutes and cost per PR dropped from $111 to about $18.

So if one person would normally investigate a job start to finish, make one long-context agent prove it cannot handle the job before you build it a tiny org chart.

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A Cat’s Commentary

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