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Hey Friends,
Everyone is yelling “AGI” again. Cool story. The label is cheap.
What matters (and what’s actually happening) is way more concrete: models are moving from talking about work to doing work — and the world is starting to treat their outputs like something that needs verification, governance, and (in some cases) legal permission.
This week’s signal beats the hype:
Math: AI-assisted proofs are getting formalized and checked in public, on real open-problem forums.
Healthcare: a U.S. state is piloting AI participation in prescription renewals inside a regulatory sandbox.
Sales: one of the biggest SaaS communities is openly saying the classic SDR org chart is about to get nuked.
Science: sleep data just got “foundation-model-ified” into disease-risk prediction at serious scale.
Let’s get into it.
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Math: the workflow is going unattended
The shift isn’t “AI can do math.” It’s end-to-end: pick a problem → produce an argument → formalize in Lean → post → humans review.
Takeaway: verification is the moat. And problem framing is now a competitive skill.
Links:
(hype context) https://x.com/tyler_m_john/status/2008487701775863934
(hype context) https://x.com/nasqret/status/2008672809094905970
(hype context) https://x.com/acerfur/status/2008608669047644398
Healthcare: Utah made it real
Utah’s Department of Commerce announced a partnership with Doctronic to evaluate autonomous AI for prescription renewals for chronic conditions inside a regulatory sandbox. Translation: this is autonomy with guardrails, metrics, and accountability — not vibes.
Link:
Sales: the org chart is the product now
SaaStr’s take is blunt: fewer humans, more agents, and a new job title emerging — Agent Manager (tooling, QA, escalation, iteration).
Whether you buy every number or not, the direction is obvious: the middle layer of “polite persistence + routing” is getting crushed.
Link:
Science: SleepFM turns sleep into an embedding
Nature Medicine published “SleepFM,” a multimodal sleep foundation model trained on ~585,000 hours of PSG data (~65,000 participants). It predicts a broad set of future disease risks from sleep signals.
Bull case: better screening/triage.
Caution: risk prediction without clinical integration becomes anxiety at scale.
Link:
The point
“AGI” is a word people use when they don’t have a better one.
What’s actually happening: work is being converted into pipelines models can run.
If you want a practical response:
Pick a workflow that’s mostly “humans shuffling information.”
Build an agent around it.
Put a human in charge of QA + escalation.
Measure outcomes like you mean it.
Stop predicting. Start rebuilding.
That's all for this week!
Happy Building!
🙇Martin
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