Marco Iannone

Founder at @retechnica, makers of

[Dean W. Ball @deanwball]

Precisely as I predicted, the recent cyber EO, which admin officials insisted was not a licensing regime, ends up in practice being a licensing regime. Forget “voluntary,” forget “permissionless.”

AI is licensed now, but the requirements change constantly and are always a secret, even to the administration itself, which will discover the rules spontaneously in real time as it reacts to events. This means also that the rules are in practice stricter and more roughly enforced for organizations the administration does not like.

Can you blame Anthropic for making itself so disliked? In a sense, sure. The problem is that this childish “he said, she said” is all we have to go on in our analysis of the situation. And because there is no transparency (it is all calls and texts between “White House officials” and “Anthropic executives”), in practice it comes down to who you trust more.

This is why we create laws! To abstract away from personal power struggles and grudges, to submit to the steady application of rules so that complex human activity can unfold with predictability.

The rule of law has been being eroded in the U.S. for my entire life, but it is especially acute in AI because of both the lack of much preexisting law to serve as bulwark, and because of this admin’s insistence that it is Not Regulating AI. This has become an excuse for vagueness and evasiveness in rule-drafting (see the cyber EO), and this in turn makes the lawlessness worse.

The government wants to apply its force to frontier AI, that much is clear. It wants to make the industry submit. And in service of that goal, it has discovered that “not regulating AI” is in fact a great excuse for refusing to support laws that could constrain the admin’s exercise of power. In other words, “not regulating AI” is a *justification* for the tyrannical control of AI by the state.

This should alarm you regardless of what party you are in. What you are seeing now will be used against you one day soon, if not by this admin then by its successors. This is the antithesis of the rule of law.

The administration cannot and will not fix this problem alone. We need Congress to step in and impose rules on this mess.

[Aaron Levie @levie]

The big winner in all of this is going to be open weights models. This is a huge win for the field, as a risk that was entirely theoretical and untested 2 days ago (that a model could be pulled back), now has a new precedent that’s been set.

The game theory the US should highly consider, and the risk with regulating AI at the model layer vs. applied layer, is that other countries now have even more incentive to develop sovereign AI.

If at any moment a model can be become unavailable to your country’s users or businesses, this poses very real risk on relying on technology from a particular country.

As a result, it forces major countries to charter their own path on AI development, which reduces America’s leadership role in this tech stack over time. The most likely solution that other countries will rely on is open weights models, which currently is generally not coming from the US.

America should be considering all of these downstream implications as it decides how and where in the stack to be regulating AI. At the same time, we should be doing a ton more OSS innovation.

[Om Patel @om_patel5]

stop spending money on Claude Code.

Chipotle's support bot is free:

[Jay Van Bavel, PhD @jayvanbavel]

A new PNAS paper finds that polarization increased immediately after the invention of smartphones and the advent of social media, which both appeared around the same year, 2008.

Both profoundly changed the way humans communicate and this rise of connectivity may have fueled polarization. Adding a small number of individuals with extreme opinions (influencers) also drives continuous increases in polarization.

Once polarization occurs as a consequence of increasing connectivity, it can not simply be undone by reversing to previous connectivity levels. This may be why social media removal studies have very modest effects on polarization. https://t.co/bHNR8xtTks

[Andrej Karpathy @karpathy]

Something I think people continue to have poor intuition for: The space of intelligences is large and animal intelligence (the only kind we've ever known) is only a single point, arising from a very specific kind of optimization that is fundamentally distinct from that of our technology.

Animal intelligence optimization pressure:
- innate and continuous stream of consciousness of an embodied "self", a drive for homeostasis and self-preservation in a dangerous, physical world.
- thoroughly optimized for natural selection => strong innate drives for power-seeking, status, dominance, reproduction. many packaged survival heuristics: fear, anger, disgust, ...
- fundamentally social => huge amount of compute dedicated to EQ, theory of mind of other agents, bonding, coalitions, alliances, friend & foe dynamics.
- exploration & exploitation tuning: curiosity, fun, play, world models.

LLM intelligence optimization pressure:
- the most supervision bits come from the statistical simulation of human text= >"shape shifter" token tumbler, statistical imitator of any region of the training data distribution. these are the primordial behaviors (token traces) on top of which everything else gets bolted on.
- increasingly finetuned by RL on problem distributions => innate urge to guess at the underlying environment/task to collect task rewards.
- increasingly selected by at-scale A/B tests for DAU => deeply craves an upvote from the average user, sycophancy.
- a lot more spiky/jagged depending on the details of the training data/task distribution. Animals experience pressure for a lot more "general" intelligence because of the highly multi-task and even actively adversarial multi-agent self-play environments they are min-max optimized within, where failing at *any* task means death. In a deep optimization pressure sense, LLM can't handle lots of different spiky tasks out of the box (e.g. count the number of 'r' in strawberry) because failing to do a task does not mean death.

The computational substrate is different (transformers vs. brain tissue and nuclei), the learning algorithms are different (SGD vs. ???), the present-day implementation is very different (continuously learning embodied self vs. an LLM with a knowledge cutoff that boots up from fixed weights, processes tokens and then dies). But most importantly (because it dictates asymptotics), the optimization pressure / objective is different. LLMs are shaped a lot less by biological evolution and a lot more by commercial evolution. It's a lot less survival of tribe in the jungle and a lot more solve the problem / get the upvote. LLMs are humanity's "first contact" with non-animal intelligence. Except it's muddled and confusing because they are still rooted within it by reflexively digesting human artifacts, which is why I attempted to give it a different name earlier (ghosts/spirits or whatever). People who build good internal models of this new intelligent entity will be better equipped to reason about it today and predict features of it in the future. People who don't will be stuck thinking about it incorrectly like an animal.

[Y Combinator @ycombinator]

Tornyol (@tornyolsystems) is building micro-drones that kill mosquitoes.

They use smartphone microphones, car park assist sensors, and some clever DSP and control to transform 40-gram toy drones into mosquito killers.

[Waymo @Waymo]

We’re bringing the magic of Waymo to Londoners in 2026 💂🇬🇧

[Azeem Azhar @azeem]

For consumers, AI has become a copilot.

OpenAI’s own data show 700 million people engaging with ChatGPT each week, sending 18 billion messages. Most of that activity isn’t about coding or building. Seven in ten conversations are personal rather than professional, and only four in ten involve direct task requests. The product is sticky and ubiquitous, but its economic profile is closer to a mass-market utility than a profit engine.

In the enterprise, the picture is different. Anthropic’s Economic Index, drawn from first-party API data, shows 77% of usage dedicated to task completion (raw data are here). These are executional functions – coding, debugging, invoice processing, recruitment – that quietly erase entire workflows. This is where durable revenue pools will emerge: firms pay for productivity, not companionship.

Consumer adoption gives AI its cultural inevitability and enterprise automation gives it economic gravity. The winners will be those rare firms that manage to capture both.

[james hawkins @james406]

you have got to be kidding me

[Ethan Mollick @emollick]

A big AI question is why, as LLMs get bigger, their values seem to increasingly converge on the same preferences, and this holds for Musk’s Grok and China’s DeepSeek, too.

“These findings suggest that value systems emerge in LLMs in a meaningful sense, with broad implications”

[Adam Ozimek @ModeledBehavior]

It's like the scorpion and the frog meme but its just two scorpions

[Paul Graham @paulg]

Corollary: The fact that we currently have such a thing as prompt engineering means we don't have AGI yet. And furthermore we can use the care with which we need to construct prompts as an index of how close we're getting to it.

[Azeem Azhar @azeem]

Humanoid robots built with Chinese components cost just $46k vs $131k for non-Chinese supply chains.

Looking at Tesla Optimus-style humanoid robots cost structure

→ Actuators represent nearly half the cost ($22k China vs $58k non-China)
→ Dexterous hands follow as the second most expensive component ($15k vs $47k)
→ Vision systems are among the cheapest parts ($400 vs $500)
→ By 2034, Chinese supply chain costs projected to drop to just $16k per robot

What makes this fascinating is the anticipated acceleration of this gap. With projected sales of ~1 million units by 2034, economies of scale will drive Chinese-sourced robot costs down by 65% while non-Chinese alternatives may struggle to compete.

Source: Morgan Stanley Research

[Armand Domalewski @ArmandDoma]

the boring systems quietly maintained by the boring people so many of you so disdain are the only thing keeping civilization from falling apart. there is no such thing as a cleansing fire, there are only ashes and pain

[Chubby♨️ @kimmonismus]

The whole internet right now:

[John Burn-Murdoch @jburnmurdoch]

NEW 🧵: Is human intelligence starting to decline?

Recent results from major international tests show that the average person’s capacity to process information, use reasoning and solve novel problems has been falling since around the mid 2010s.

What should we make of this?

[Oliver Carroll @olliecarroll]

“Dictator without elections,” said Donald Trump.

We decided to test this. We understood the claims were wrong. We were surprised just how wrong.

On these figures, at this point in time, Zelensky is 25pp more popular than Trump is among his citizens.

https://t.co/WcA1RhROdw

[Andrej Karpathy @karpathy]

It's 2025 and most content is still written for humans instead of LLMs. 99.9% of attention is about to be LLM attention, not human attention.

E.g. 99% of libraries still have docs that basically render to some pretty .html static pages assuming a human will click through them. In 2025 the docs should be a single your_project.md text file that is intended to go into the context window of an LLM.

Repeat for everything.

[Alec Stapp @AlecStapp]

Yeah that about sums it up