Marthe Lübbers is secretly a geek.
And you - as the intellectually curious person that you are - should read a few of her blog posts.
And you - as the intellectually curious person that you are - should read a few of her blog posts.
Yesterday Anthropic released a new privacy policy, effective 8 July 2026, with a change of clause that sparked debates. The relevant clause is in Section 3 of Anthropic's Privacy Policy. It says Anthropic may share personal data (meaning your conversations) with government authorities, law enforcement, or other third parties when it has a good-faith belief that disclosure is reasonably necessary to do one of four things: comply with law, regulation, legal process, or an enforceable government request; prevent serious harm to a person or property; detect, prevent, or address fraud or other illegal activity; or enforce its terms and protect the rights, property, security, or safety of Anthropic, its users, or others.
What caused this change? The likeliest answer is liability ahead of Anthropic’s IPO. Across late 2025 and into 2026, rival OpenAI was sued repeatedly for not reporting concerning user engagements with their LLM platform ChatGBT. In the Tumbler Ridge school shooting, OpenAI's systems had flagged the shooter's account months earlier for gun-violence planning; an internal safety team reportedly pushed to call the authorities, and the company deactivated the account instead of reporting it. Families sued for negligence. A parallel suit followed over the Florida State University shooting, alongside a state criminal investigation, and more than twenty cases now allege OpenAI failed to act on users moving toward violence or suicide. A clause that explicitly lets a company disclose to law enforcement to prevent serious harm is the direct legal answer to that pattern. It is a cover-your-ass move.
Discretionary disclosure is easy to defend when the alternative is a body count. The trouble is that "good-faith belief" and "reasonably necessary" are standards the company applies to itself, and the categories stretch. "Serious harm," "illegal activity," and "the rights of others" mean one thing under independent courts and something else under a government that has criminalized protest, dissent, or being gay.
That gap matters more in 2026 than it would have a decade ago. Speech protections are weakening in places that used to feel safe, and "illegal activity" is precisely the phrase that grows to fit whoever holds power. A disclosure pipe built for school shooters is the same pipe a future government reaches for to obtain a journalist's sources, an activist's plans, or a user's reproductive decisions in a jurisdiction that has outlawed them. The pipe does not care what runs through it.
The failure mode isn't only authoritarian; it’s also error: Disclosure decisions begin with automated classifiers, and classifiers are context-blind. A novelist drafting a murder, a tabletop player narrating a raid, someone working through dark intrusive thoughts in what they believed was a private space. All trip the same detectors as a real threat. The typical argument by people that don’t understand the important of privacy, “I have nothing to hide”, assumes the system reads you correctly. It often doesn't, and a false positive routed to the police is not a minor inconvenience. Refusing privacy because you have nothing to hide is like refusing free speech because you have nothing to say.
For Anthropic, the damage is mostly reputational. It sold itself as the safety-first lab, the one you could trust with what you'd never type into a search bar. A standard police-disclosure clause doesn't make it worse than rivals, but "no worse than OpenAI" is not the brand Anthropic built. And there is a gap worth naming. In a separate, friendlier document on handling government requests, Anthropic says it requires a warrant or subpoena except in imminent-harm emergencies. That promise is stronger than the policy. Promises relax toward the contract when pressure arrives; the binding text is the one that governs.
Which is why privacy-minded users keep looking past the cloud. A model running on your own machine has no terms of service, no good-faith clause, and nothing to hand over, because no second party ever sees the conversation. Open-weight models such as Llama, Mistral, Qwen, DeepSeek, Gemma now run locally through tools like Ollama or LM Studio on a strong laptop or a single GPU. The trade-offs are honest: the best local models still trail frontier systems like Claude on the hardest reasoning and coding, you supply your own hardware, and you give up the cloud conveniences such as web access, large-scale context, and agentic connectors.
The argument isn't that everyone should self-host. It's that the only conversation a company can't be compelled, or persuaded by its own good faith, to disclose is the one it never receives.
The distinguishing factor for the biggest AI breakthroughs so far have not been the models themselves, but the amount of data they were trained on. A well-known example from 2013 is ImageNet, the huge dataset of categorised images that dramatically improved the accuracy and consequent usefulness of image-recognition models that evolved due to it. The same was true for large language models. OpenAI’s ChatGPT was trained on hundreds of billions of words, 60–80% of the data coming from the Common Crawl dataset. Until now, the most disruptive models were the ones with access to the most amount of data. What do we do with this insight? We should be thinking about applications of AI that are most beneficial to society and curate databases in those areas.
Mass unemployment, information control, and potential new bioweapons aside (for now), utilising AI for scientific breakthroughs will yield immense benefits and advance society at an unprecedented rate. Reducing CO₂ emissions or early detection of, and innovative treatments for diseases, stands to eliminate an enormous amount of suffering in this world. Deep-learning algorithms can already detect lung or breast cancer better than radiologists and years before regular screening would pick up cancerous cells. New drugs developed with AI to treat rare diseases are reaching late-stage clinical trials. Multimodal models for medical imaging provide reports of chest x-rays and detect tumours with 15% more accuracy than previous models could. However, there is one constraint on large-scale medical breakthroughs that is significantly reducing the rate of discovery: data privacy.
Medical data is protected by laws like HIPAA (US), GDPR (Europe), and similar frameworks globally, for good reason. Researchers can’t just scrape patient records the way you might scrape the web. Access requires consent, institutional review, and often years of bureaucracy. Data privacy laws are non-negotiable in safeguarding individual rights, but they unnecessarily hinder life-saving medical research. Is it possible to scale medical breakthroughs through AI without breaching data-preserving laws? I stumbled upon the answer in November 2025 and it left me in awe.
As I was researching privacy-preserving architecture for my side-project FlowBud, I read about federated learning and discussed it with my former professor who pointed me towards the non-profit organisation OpenMined. Federated learning is a machine learning approach where models are trained across multiple devices or servers holding local data, so the data stays decentralised while only model updates are shared and aggregated. OpenMined built the technical infrastructure to apply federated learning in real-world, privacy-sensitive domains such as healthcare, finance, and mobile applications. Here’s how:
The Foundation: PySyft
PySyft is the core library that decouples private data from model training, using Federated Learning, Differential Privacy, and Encrypted Computation techniques like Multi-Party Computation and Homomorphic Encryption, working within mainstream frameworks like PyTorch and TensorFlow. PySyft lets you write software that computes over data you do not own, on machines you do not control. The sensitive data never moves.
SyftBox — The Protocol Layer
SyftBox is an open-source protocol that enables developers and organizations to build, deploy, and federate privacy-preserving computations seamlessly across a network, running computations on distributed datasets without ever centralizing the data. It turns isolated local folders (called “datasites”) into nodes in a secure, distributed file and compute network. Data stays local but can still participate in global-scale AI. Governance is programmable and enforced by protocol, not trust.
Syft Hub
Syft Hub is essentially the discovery and marketplace layer sitting on top of SyftBox. Users can download the SyftBox client, connect to the decentralized network, explore it to find datasets and privacy-preserving APIs offered by various data owners, and ask a data owner to run any API on their data. Once approved, they can perform computations and extract insights without compromising privacy.
syft-flwr — Federated Learning at Scale
syft-flwr is the integration between SyftBox and Flower (flwr), one of the most popular federated learning frameworks. It allows multiple organizations — say, ten hospitals across five countries — to jointly train a single AI model without any of them ever sharing their raw patient data. Each hospital trains locally, shares only model weight updates (not data), and those updates are aggregated into a global model. This is the architecture that makes cross-border medical AI feasible under GDPR and other data-protection laws.
Attribution-Based Control — The Big Picture
Syft introduces Attribution-Based Control as a new paradigm: data owners can decide which AI predictions are informed by their data while preserving attribution and the value of their contribution. AI developers can access high-quality, licensed content legally and transparently. End users can choose which trusted sources power their answers and understand where the information came from. Every interaction is logged and attributed, while data owners earn royalties automatically. When you control your data instead of surrendering it to AI intermediaries through scraping or copying, you decide every use and know when it’s used. This solves AI’s core architectural flaw: the lack of traceable data usage, which currently precludes attribution and measurable compensation.
The story of AI’s greatest breakthroughs has always been a story about data at scale. But applied to its most important frontier, human health, we slam into a wall: the data that could save millions of lives is locked behind consent frameworks, institutional review boards, and privacy laws that exist for very good reason. Federated learning is the framework to overcome what felt like an unsolvable contradiction: either you protect people’s privacy or you unlock medical breakthroughs at scale. The non-profit organisation OpenMined provides the infrastructure for scientists to run models on sensitive data, data owners to retain control over their contributions, or even competing companies to train jointly without sacrificing user data. Mass adoption of privacy-preserving technologies through federated learning may be one of the most consequential quiet revolutions of our time because it lets the most effective technologies access the data that matters most for societal progress.
