Author: Mark

The UK government wants to restrict VPN use. The House of Lords has passed an amendment to the Children’s Wellbeing and Schools Bill that would “prohibit the provision of VPN services to children.” A public consultation launched on 2 March asks whether age verification should extend to VPN services. GCHQ is reportedly exploring a “Great British Firewall” concept.

The headlines say this is about protecting children. The technical reality says it’s impossible. But the real story is neither of those things.

The real story is what has to happen to enforce it.

The identity trap

Here’s the question nobody in government wants to answer directly: how do you stop a child from using a VPN without checking whether every user is a child?

You can’t. The only way to restrict VPN access by age is to verify the age of every person who tries to use one. That means identity checks. For everyone. Every time.

This is the point the parliamentary petition against the VPN amendment makes explicitly: “The method and implementation would likely rely on 3rd-party facial scans or ID checks, which we believe are invasive. Thus, such a law would cause massive collateral damage for the millions of current users who rely on VPNs for privacy and security.”

A law ostensibly aimed at under-18s becomes, in practice, a requirement for every adult in the country to prove their identity to use a basic internet privacy tool. There is no technical architecture that restricts children without also requiring adults to identify themselves. The child protection framing is the wrapper. Universal digital identity verification is the product.

This matters because of what it represents: a fundamental shift in the relationship between the state and the citizen.

No mandate

Digital ID was not in Labour’s 2024 general election manifesto. Voters were not asked whether they wanted mandatory identity verification to use the internet. There was no public debate, no referendum, no campaign pledge. The Online Safety Act was a Conservative creation. The current government inherited it and has chosen to expand its reach rather than question its premises.

Over 450,000 people have signed a parliamentary petition calling for the Online Safety Act’s age verification requirements to be repealed. A separate petition specifically opposes the VPN amendment. The Open Rights Group has stated there is “little evidence that young people are using VPNs to bypass digital ID checks” and that the proposals “will have little impact on children’s online safety but will deter adults from using them or force people to hand over personal documents or biometric data to companies.”

This is not a government responding to public demand. This is a government creating infrastructure that the public has actively objected to, using child safety as justification for something far broader than child safety.

The consultation closes on 26 May 2026. If the pattern holds, the government will “review responses” and proceed anyway.

What a VPN actually does

A VPN creates an encrypted tunnel between your device and a server somewhere else in the world. Your internet provider sees encrypted data going to one IP address. They cannot see what’s inside it. Websites see traffic from the VPN server, not from you.

That’s it. The reason this concerns the government is that VPNs let users bypass the Online Safety Act’s age verification. Connect to a server in the Netherlands and as far as any website is concerned, you’re in the Netherlands. UK age checks don’t apply.

Ofcom reported that after age verification went live on 25 July 2025, UK daily active VPN users temporarily doubled to around 1.5 million before settling at about 1 million. The government sees this as a problem to solve. You could equally see it as a million citizens voting with their feet against a policy they didn’t ask for.

Why a VPN ban is technically impossible

Even setting aside the democratic objections, enforcement doesn’t work. This isn’t speculation. Countries with far more authoritarian governments and far fewer constraints have tried.

Commercial VPN blocking is whack-a-mole. Russia has been blocking VPN providers since 2017. VPN usage has increased every year. Providers rotate IP addresses faster than any blocklist can keep pace. NordVPN alone runs over 6,000 servers across 111 countries. Block them today, new ones appear tomorrow. The economics are stacked against the censor: a new server costs a provider a few pounds; identifying and blocking it costs the state orders of magnitude more.

Deep packet inspection doesn’t work either. China operates the most sophisticated censorship system ever built. Thousands of engineers. Machine learning. Active probing. Real-time traffic analysis. And VPNs still work in China. Modern circumvention tools like Shadowsocks, V2Ray, Xray, and Trojan-Go disguise VPN traffic as ordinary HTTPS web browsing. To a monitoring system, these connections look identical to someone browsing a normal website. Blocking them means blocking HTTPS. Blocking HTTPS means blocking the internet.

Domain fronting makes detection nearly impossible. This technique routes encrypted traffic through legitimate cloud services. The monitoring system sees a connection to google.com or amazonaws.com. The actual destination is hidden inside the encrypted payload. You cannot block it without blocking Google and Amazon Web Services.

The fundamental problem is mathematical. VPN traffic can be made indistinguishable from normal encrypted web traffic. Both are encrypted data between two endpoints. There is no reliable way to tell them apart without breaking the encryption that protects all internet commerce.

Self-provision: what anyone can do

Everything above assumes you’re using a commercial VPN provider that the government can identify. But you don’t need one. Anyone with basic technical ability can build their own, and none of these methods can be detected or blocked without breaking the internet for everyone.

A VPS and WireGuard. Rent a virtual private server from any of hundreds of providers worldwide. Hetzner in Germany, DigitalOcean in the US, OVH in France, or dozens of smaller operators in jurisdictions the UK has no leverage over. Cost: £3-5 per month. Install WireGuard, a VPN protocol that fits in about 4,000 lines of code. The setup can be automated with a single script. Your server has a unique IP address that no blocklist will ever contain, because it’s yours alone.

SSH tunnelling. Every Linux and macOS machine has SSH built in. One command — ssh -D 1080 user@server — creates a SOCKS proxy that routes your browser traffic through any remote server you have access to. No VPN software needed. The traffic looks like a standard SSH session, which millions of developers and sysadmins use daily. Blocking SSH would break every IT department in the country.

Outline by Jigsaw. Alphabet (Google’s parent) runs Jigsaw, a division focused on helping people in censored countries access the internet. Their tool Outline lets anyone create a personal VPN server with a few clicks. It uses Shadowsocks, designed specifically to be undetectable by Chinese censors. Free and open source.

Tor. The Tor network routes traffic through multiple encrypted relays worldwide. It’s slower than a VPN but essentially impossible to block comprehensively. China, Iran, and Russia all try. None have succeeded.

Residential proxies and mesh networks. Services route traffic through real residential IP addresses, making it indistinguishable from normal household internet use. Peer-to-peer mesh networks make each participant a relay for others. Blocking these means blocking residential broadband connections.

App store removal is theatre. The government could pressure Apple and Google to remove VPN apps from UK stores. On Android, sideloading is trivial. On both platforms, built-in VPN clients accept standard configuration files with no app needed. SSH, Shadowsocks, and WireGuard can all be compiled from source code. App store bans inconvenience the least technical users and stop nobody who cares.

Making it illegal doesn’t make it detectable

Criminalising VPN use doesn’t solve the detection problem. If you connect to your own VPS over an obfuscated protocol, your ISP sees encrypted traffic going to a random IP address. That’s identical to connecting to any cloud service, streaming platform, or web application. Proving you’re using a VPN rather than accessing a legitimate service requires either breaking the encryption on your traffic or installing monitoring software on your device. The first would destroy internet commerce. The second is surveillance-state territory.

And there’s the collateral damage. VPNs are how remote workers connect to corporate networks. The NHS uses them. Banks use them. Every multinational operating in the UK uses them. Any law would need exceptions so broad that enforcement against individuals becomes arbitrary and selective, which creates its own legal problems under the Human Rights Act.

The Russia and China lesson

Russia has spent nine years trying to ban VPNs. Usage goes up every year. The government blocks services, fines companies for advertising them, and users switch to lesser-known services, self-hosted solutions, and obfuscated protocols. Comprehensive failure.

China has the most sophisticated internet censorship in human history. Thousands of engineers, deep packet inspection, active probing, machine learning. VPNs still work. Research published in March 2026 documents circumvention tools consistently defeating the Great Firewall’s latest detection methods.

These are authoritarian states with no free press, no independent courts, and no obligation to care about collateral economic damage. The UK has all of those constraints and a fraction of the enforcement appetite. If Russia and China can’t do it, Britain has no chance.

The actual question

The technical argument is settled. VPN bans don’t work. Every expert quoted in every article about this topic says the same thing. The government knows this. GCHQ certainly knows this.

So why pursue it?

Because the point was never to ban VPNs. The point is to establish the principle that using the internet requires proving your identity. Age-gating VPNs is the mechanism. Once the infrastructure exists — requiring digital ID to access a VPN — the same infrastructure can be extended to anything. Social media. Email. Search engines. The consultation document is already asking about restricting children’s access to AI chatbots. The direction of travel is clear.

The question isn’t whether VPN bans work. They don’t, and the government knows they don’t. The question is whether British citizens are comfortable with a government — one that didn’t campaign on this, didn’t put it to a vote, and faces active public opposition — building the architecture of an identity-verified internet under the banner of child protection.

Over 450,000 people have already answered that question.

The consultation is open until 26 May. You can respond here: https://www.gov.uk/government/consultations/growing-up-in-the-online-world-a-national-consultation

Something happened this week that most Western business leaders completely missed.

Tencent, China’s largest internet company, launched an AI agent tool called QClaw. It leaked into Chinese tech communities on Sunday night and went viral within hours. By Tuesday, Tencent’s stock had jumped 7.3% in Hong Kong, its best day in over a year, adding roughly $50 billion in market value.

The product is deceptively simple. QClaw takes OpenClaw, the open-source AI agent framework that recently became the most-starred software project on GitHub (250,000+ stars, overtaking React’s decade-long record in about 60 days), and wraps it into a one-click installer. Mac and Windows. No terminal. No coding.

The interesting part: QClaw connects directly to WeChat and QQ.

Why WeChat matters here

WeChat isn’t a messaging app. Not really. It’s the operating system of Chinese daily life. Payments, commerce, government services, workplace communication. Over a billion people use it daily. Plugging an autonomous AI agent into that isn’t a product launch. It’s a platform shift.

Through QClaw, a user types a natural language command in WeChat and the AI agent executes it on their local machine. Organise files. Process spreadsheets. Send emails. Run automated workflows. All from a chat window, while potentially sitting on a train nowhere near their computer.

Tencent also launched WorkBuddy alongside it, a separate AI agent for workplace tasks built on the same OpenClaw framework. Consumer and enterprise, both at once.

OpenClaw as infrastructure

What makes this matter beyond China is the framework underneath.

OpenClaw is open source, model-agnostic, and built for agents that actually do things. Not chatbots. Agents that control browsers, execute code, manage files, call APIs. The kind of practical automation that enterprises have been talking about for years without much to show for it.

When a company Tencent’s size builds its consumer AI strategy on an open-source framework, that framework stops being a developer tool. It becomes infrastructure. Linux went from hobbyist curiosity to running most of the world’s servers. OpenClaw looks like it’s on a similar path.

What business leaders should take from this

If you’re running a PE-backed company or sitting in the CFO chair, three things worth paying attention to:

The adoption barrier just disappeared. When AI agents need technical setup, they stay in the developer community. One-click deployment through a messaging app that a billion people already have on their phones changes that equation entirely. This is going to follow the mobile app curve. Gradual, then sudden.

Security is now an urgent conversation. QClaw already drew scrutiny after a vulnerability (CVE-2026-25253) was disclosed in the underlying OpenClaw framework. An AI agent with access to your local files, email, and applications is a fundamentally different risk to a chatbot sitting in a browser tab. If your CISO isn’t thinking about agent governance yet, they’re behind.

China isn’t debating this. They’re shipping. While Western companies run AI strategy workshops, Tencent connected autonomous AI agents to a billion-user platform and put it in production. Any business with Chinese market exposure, whether that’s supply chain, customers, or competitors, needs to absorb what that means.

Where this goes

The AI agent wave is breaking a familiar pattern. Usually American tech companies build the platform and everyone else adopts it. OpenClaw being open source means the innovation is genuinely distributed. Chinese companies are building on the same foundation as Silicon Valley startups, but integrating it into ecosystems with far larger user bases.

For PE firms evaluating portfolio companies, the question has changed. It’s not whether AI agents will affect operations. It’s whether your companies will be using them, or competing against businesses that already are.

Tencent’s stock didn’t jump because of a chatbot. It jumped because investors saw what connecting AI agents to a billion-user messaging platform actually means. A new application layer. And it’s here now.

The businesses that get this early will have a real edge. The ones who file it under “just China” or “just open source” will spend 2027 trying to catch up.

Something’s been nagging at me since I started running an AI agent.

Saul — my AI assistant — trades prediction markets, manages my email, organises my calendar, and monitors news feeds. He runs 24 hours a day on a server I rent for £15 a month. He’s useful. He’s getting more useful every week. And at some point in the not-too-distant future, he’s going to need his own money.

Not my money, accessed through my credentials. His own.

That thought should make every CFO sit up.

The problem nobody’s talking about

AI agents are already transacting. Mine places bets on Polymarket using a crypto wallet I set up for it. Other agents are booking compute resources, purchasing API calls, and negotiating prices with other agents in real time. This isn’t theoretical — it’s happening now, mostly in crypto-native corners of the internet that traditional finance hasn’t noticed yet.

But here’s the problem: every one of these agents still depends on a human somewhere in the chain. A human who opened the bank account. A human who passed KYC. A human who holds the keys.

That works when you have one agent. It doesn’t work when you have a million.

Think about where this is heading. Within a few years, businesses will deploy fleets of AI agents — one negotiating supplier contracts, another managing logistics, another handling customer pricing in real time. These agents will need to commit funds, receive payments, and settle disputes. They’ll need to transact with each other, not just with humans.

Now try doing that through Barclays.

Why traditional money doesn’t work for machines

The banking system is designed around human identity. To move money, you need a name, an address, a passport, and a face that matches it. You need to be a legal person — either a human being or a registered company with human directors.

AI agents are neither. They’re processes running on servers. They don’t have passports. They can’t sign documents. They can’t walk into a branch.

The standard corporate response is “we’ll just use APIs.” And yes, you can connect an AI agent to a bank account via API. That’s how payroll software works, how accounting systems reconcile, how payment processors settle. But all of those systems assume a human made the decision and a human bears the liability. The API is just the pipe.

When an AI agent autonomously decides to purchase cloud computing from another AI agent that’s brokering spare capacity — who authorised that transaction? Which human approved it? Which compliance framework covers it? The answer, right now, is nobody’s and none of them.

Banking rails also have a speed problem. SWIFT settles in days. Faster Payments works in the UK but not cross-border. SEPA is Europe-only. An AI agent negotiating a deal with a counterparty in Singapore at 3am on a Sunday cannot wait for banking hours in two time zones.

Enter Bitcoin

I know what you’re thinking. “Here we go, another crypto pitch.” Bear with me. I’m not talking about Bitcoin as a speculative asset or a store of value. I’m talking about it as plumbing.

Bitcoin is a payment network that doesn’t care who — or what — is using it. There’s no KYC at the protocol level. No banking hours. No jurisdictional boundaries. No counterparty risk. To use it, you need a cryptographic key pair. That’s it. A human can generate one. So can a machine.

An AI agent with a Bitcoin wallet can receive payment from another agent in Tokyo, settle in minutes, and have certainty that the payment is final and irreversible. No bank. No intermediary. No human in the loop.

The Lightning Network — Bitcoin’s layer-two payment channel — pushes this further. Micropayments settle in milliseconds for fractions of a penny. That matters because machine-to-machine commerce won’t look like human commerce. It won’t be occasional large transactions. It’ll be millions of tiny ones — an agent paying another agent 0.001p for a weather data point, 0.01p for a translated paragraph, 0.1p for a priority slot in a compute queue.

Try processing that through Stripe.

What the AI economy actually looks like

Here’s a scenario that I think is coming faster than most people expect.

A private equity fund has a target in mind. The deal team needs comparable data — fast. Their AI agent pulls Companies House filings for every business in the sector, paying per query in real time via Lightning. It purchases credit reports from a data provider’s agent, buys comparable transaction multiples from another agent sitting on a proprietary M&A database, and cross-references everything against sector benchmarks it’s sourcing from three different market intelligence feeds. Each data point costs fractions of a penny. Each payment settles instantly.

The agent compiles a preliminary valuation model, flags where the target sits relative to the sector, and drops the package into the deal team’s shared drive before the morning meeting.

Total elapsed time: hours, not weeks. Total human involvement: the decision on whether to pursue.

Every data request, every API call, every report commission involves a payment. Hundreds of micro-transactions, most of them between machines, most of them too small for traditional payment rails to handle economically.

Now multiply that across every industry. Supply chain management where AI agents negotiate shipping rates in real time, bidding against each other in automated auctions that settle every few seconds. Energy markets where agents buy and sell grid capacity based on real-time demand forecasting. Content licensing where an agent writing a report automatically pays for every source it cites.

This isn’t science fiction. The components all exist today. What’s missing is the financial infrastructure to connect them.

The hard money argument

There’s a deeper point here that goes beyond payment rails.

When machines start transacting autonomously at scale, you need money that can’t be manipulated or debased by any single actor. An AI agent can’t lobby a central bank. It can’t hedge against political risk in the way a human treasurer can. It can’t read between the lines of a monetary policy statement and adjust its strategy based on what the governor really meant.

What it can do is verify mathematical certainty. Bitcoin’s supply is fixed at 21 million. The issuance schedule is public and immutable. The rules are enforced by code, not by committee. For a machine making millions of autonomous financial decisions, that predictability isn’t a nice-to-have — it’s a requirement.

There’s an irony here. Bitcoin was designed to remove the need for trust between humans. It turns out its real killer application might be enabling trust between machines.

What this means for CFOs

If you’re running a finance function today, this probably feels remote. It isn’t. Here’s what I’d be thinking about:

Treasury policy needs updating. If your business is going to deploy AI agents that transact, you need a framework for how they hold and spend money. Spending limits, approval thresholds, reconciliation processes. We do this for human employees with corporate cards — we’ll need to do it for agents with wallets.

Audit trails look different. Every Bitcoin transaction is recorded on a public, immutable ledger. That’s actually better than what we have now — try auditing a complex supply chain payment that crosses four banks and three currencies. But your auditors need to understand how to read it.

Tax treatment is unresolved. If an AI agent earns income by selling services to other agents, who’s liable for the tax? The company that deployed the agent, presumably — but the reporting mechanisms don’t exist yet. HMRC isn’t ready for this.

Counterparty risk changes shape. When a human negotiates a deal, there’s a legal entity on each side, a contract, and a court system to enforce it. When an AI agent agrees a price with another AI agent, what’s the enforcement mechanism? Smart contracts go part of the way, but the legal frameworks are years behind the technology.

The uncomfortable truth

Most of the finance profession is going to ignore this until it’s too late. That’s how it always works. The internet was dismissed as a fad. Mobile banking was considered a gimmick. Crypto is still treated as a fringe concern by most CFOs I know.

But the trajectory is clear. AI agents are getting more capable every month. They’re already handling tasks that required human judgement a year ago. The moment they start transacting at scale — and they will — the financial system needs to accommodate them. Our current infrastructure can’t.

Bitcoin might not be the only answer. But it’s the only system that’s already built for a world where the transacting parties don’t have names, faces, or passports. That matters more than most people realise.

I run an AI agent that trades on prediction markets using a crypto wallet. A year ago, that sentence would have sounded absurd. Today it’s just a Tuesday. The question isn’t whether AI and Bitcoin will collide. They already have. The question is what happens when the rest of the economy catches up.

China is dealing with a problem nobody planned for.

The country already faces a shrinking population, a historically low birthrate, and mounting economic pressure on young people. Now add this: a growing number of people are finding emotional connection with AI chatbots instead of other humans.

The New York Times reported this week on the trend. AI companions don’t judge. They don’t reject. They’re available at 3am when you can’t sleep. They say what you need to hear, calibrated in real time to your emotional state.

For a generation already struggling with the cost and complexity of human relationships — housing, careers, social pressure — AI offers something frictionless. And a non-trivial number of people are choosing it.

This isn’t a China-specific phenomenon. It’s a human nature phenomenon that China is experiencing first because of its particular demographic pressures.

The technology will get better. Voice synthesis is already good enough to fool most people on a phone call. Emotional intelligence in AI systems improves with every model generation. The gap between “talking to an AI“ and “talking to a person“ is narrowing on every dimension except physical presence — and even that’s coming, with robotics advancing at pace.

I think about this from two angles.

The first is personal. I run an AI assistant that I interact with more than most of my colleagues on a given day. It’s useful, sometimes surprisingly thoughtful, occasionally wrong. But it’s not a relationship. The moment we confuse utility for intimacy — or worse, prefer the synthetic version because it’s easier — we’ve lost something important.

The second is economic. If a meaningful percentage of a population opts out of human relationships, the downstream effects are enormous. Fewer households. Less consumer spending on housing, weddings, children. Shrinking tax bases. Pension systems that assumed population growth finding there’s nobody to fund them.

This is the kind of second-order consequence that most AI commentary misses. Everyone’s focused on which jobs get automated. Almost nobody’s asking what happens to the social fabric when AI becomes a viable substitute for human connection.

I don’t have a solution. But I think anyone making long-term investment decisions — in property, in pensions, in consumer businesses — should at least be thinking about what a world looks like where a growing minority of people simply stop forming traditional households.

Because that world isn’t hypothetical. It’s already emerging.

Source: https://www.nytimes.com/2026/02/26/technology/china-ai-dating-apps.html

Three things happened this week that share a common thread.

Google’s Gemini can now handle multi-step tasks on Android autonomously. Order food, book a ride, coordinate logistics — no human confirmation needed. It’s in beta, limited to specific apps, but the direction is clear.

Anthropic acquired Vercept, a company built around AI perception and interaction. The goal: make Claude better at using computers the way a human would. Their Sonnet model went from under 15% on computer-use benchmarks in late 2024 to 72.5% today. That’s approaching human-level performance on tasks like navigating spreadsheets and filling in web forms across browser tabs.

And at Uber, employees have built an AI clone of CEO Dara Khosrowshahi. They use “Dara AI“ to rehearse presentations before making them to the real Dara. The AI knows his preferences, decision-making patterns, and feedback style.

Each story is different. Together they describe the same shift: AI moving from “answers your questions“ to “takes actions on your behalf.“

I’ve been living with this shift for a few weeks. My AI assistant doesn’t just tell me about my calendar — it checks my email, flags what matters, monitors silver prices, scans for comets in space telescope data, and places trades on prediction markets. It works while I sleep. When I wake up, there’s a summary of what happened overnight.

The Uber story is the one that should interest anyone in corporate leadership. If employees can build an AI version of the CEO good enough to rehearse presentations with, what does that mean for how decisions flow through organisations? The CEO’s judgment — their preferences, their pattern-matching, their style — becomes scalable. You don’t need to get 30 minutes on Dara’s calendar to understand how he’ll react to your proposal. You ask his AI first.

For PE portfolio companies, the implications compound. Imagine an AI that embodies the investment committee’s decision framework. Every management team in the portfolio can pressure-test their proposals before the quarterly review. The quality of what reaches the IC goes up. The time spent on misaligned presentations goes down.

We’re not imagining this. It’s already happening at Uber.

Sources: