Notable links: July 24, 2026

Most Fridays, I share a handful of pieces that caught my eye at the intersection of technology, media, and society.

Did someone forward this to you? Subscribe for free.China delivers a one-two punch to America’s AI dominance

AI models, as a product in themselves, have very little moat beyond what amounts to brand loyalty and superficial switching costs. Instead, the moat is in the enterprise services that sit around them: the deals and contracts, connectivity with enterprise systems, and quality of life features in an enterprise context.

If we consider the models themselves, it’s easy to switch between them: someone could be using ChatGPT today and Claude tomorrow, with very little impact on their workflows. This is particularly true in the engineering world, where models are accessed via API: you can swap out the API and use the same prompt.

Those companies can make deals to lock their customers in, but in practice there’s very little long-term technical incentive to use one vendor over another. You pick the best model for your needs and change models and vendors if another one becomes better.

The US government has placed export controls on GPUs. There are also strong regulations that (reasonably) prevent sharing certain kinds of data with Chinese servers. The result is that while Chinese companies have enough compute to train models, they can’t really provide the kinds of global-scale centralized services that we see from OpenAI and Anthropic — at least, not in the same way.

And open almost always wins when it comes to infrastructure adoption. Open technologies can be used permissionlessly and therefore can be at the center of more innovation. You can host them where you want, experiment with them, alter them, and tweak to fit your use case. Open weights models are not open source, but they are portable and permissionless.

With all this in mind, it makes sense for China to release its AI models openly. It turns a US-created compute disadvantage into a distribution advantage; it commoditizes the layer where American companies make money; and it creates a far more effective global ecosystem than could be established through locked-in, centralized services. It’s obvious to me that there are ecosystem benefits throughout China, from manufacturing to scientific research; every sector can just plug in these models.

The saving grace for American companies has been that US frontier models have outperformed open ones. That gap is now closing:

“Moonshot and Alibaba unveiled models they claim can go toe-to-toe with the best from OpenAI and Anthropic at a fraction of the cost. The rapid-fire releases suggest America’s lead at the AI frontier is increasingly tight, just as the technology is becoming central to national security, economic power, and geopolitical influence.”

Even without these new capabilities, the strategy has already been working. a16z partner Martin Casado noted in the Economist that there’s an 80% chance that any given startup is using Chinese models, and Chinese models are poised to take the lead.

It’s worth taking a step back and considering the surprising underlying dynamics. We think of China as being a locked-down society — and it is in many ways. I have serious concerns about how these models might reflect Chinese government perspectives ( try asking them about Tiananmen Square ). But it’s American companies that are keeping tight control of their technology rather than releasing it as openly as possible. This is in stark contrast to the strategy behind US government support for the open internet, for example.

Locked-down business practices for a technology with no real moat but significant potential ecosystem benefits is an obviously losing strategy; permissively releasing it with an open, collaborative approach is obviously a winning one. But the incentives in the US aren’t there: instead, these companies are forced to chase first-order profits rather than ecosystem benefits, and the government tries to put its finger on the scale through forcible measures like tight export controls. We should consider what would need to change to make those incentives more aligned. That’s particularly important given how much of the US economy is currently driven by AI spending. If the bottom falls out of that spending — and I think it clearly will, given the dynamics — the outcome could be severe.

I care about having open technology that can be run in the public interest, aligned with the public’s values. Threads like public AI, federated services, and open research have traction but need backing. Getting there in the US needs more nuanced strategy and support than we’re seeing today.This Conversation Is Being Recorded. They All Are.

I’ve been thinking about this story for days.

“A Zoom call isn’t complete without an artificial-intelligence note taker. Phones are out at meetings, capturing every word. During impromptu conversations with co-workers, someone might turn on the Granola transcription app, which can turn the interactions into one-page summaries or a list of action items. Even at bars and on dates, people are using AI-infused listening apps to analyze conversations later on.”

The story goes on to talk to a woman who uses Granola to record her dates, then pours the transcripts into Claude to give her feedback about how she could have done better. And there’s account after account of people using it in meetings without asking for consent or revealing that they’re recording.

Certainly in Silicon Valley, a societal shift seems to be underway. It’s likely much more widespread than that. I’ve been present in meetings outside the tech industry where Granola’s watermarking was visible but I wasn’t asked to consent. The watermarking is optional; I have to assume I’ve been in meetings where I’ve been recorded without my knowledge.

Pair this trend with the story that the Trump Administration actively sought the phone records of journalists — and their families — who reported on the new Qatari-gifted Air Force One. Subpoenas were issued to the phone carriers, and the Department of Justice notified the newsroom a week later. In some cases, subpoenas can be issued to carriers and service providers privately, allowing the data to be retrieved without the newsroom’s knowledge; in this case, the DoJ did try to gag the phone company from alerting the newsroom.

A world in which every conversation is recorded and transcribed is one where every conversation can be subpoenaed or surveilled. Here, the surveillance is decentralized through people who actively want to conduct it for their own benefit, but the data is still stored centrally and available for authorities to subpoena or someone else to mine. Granola’s security page makes clear that the data is accessible to them — and therefore to a third party that compels them to hand it over — and notes that:

“Granola trains on your anonymized data so we can keep making Granola better. You can opt out of this in your Settings.”

Granola makes a point of saying that audio is not stored, but given that transcriptions are, this seems moot: the words in a conversation carry its meaning. Subpoenas for your conversations go to it, not to you, and you may never know they were served. If you record someone’s conversation without letting them know, you’re putting them at risk.

Don’t get me wrong: I would love to have an automatic summary of meetings I’ve taken part in. I have also run meetings on non-sensitive topics where I’ve asked for consent before starting transcription. It’s the ubiquity and covert nature of the transcription that bothers me, paired with its central storage in what amounts to a honeypot for subpoenas and hackers.

Recording a conversation with someone without their consent is illegal in many states and countries, so this behavior may be forced to change. California is one of them, and Granola appears to be thriving there, so there is a world where the law changes to meet t…